A network map of apelin-mediated signaling

The apelin receptor (APLNR) is a class A (rhodopsin-like) G-protein coupled receptor with a wide distribution throughout the human body. Activation of the apelin/APLNR system regulates AMPK/PI3K/AKT/mTOR and RAF/ERK1/2 mediated signaling pathways. APLNR activation orchestrates several downstream signaling cascades, which play diverse roles in physiological effects, including effects upon vasoconstriction, heart muscle contractility, energy metabolism regulation, and fluid homeostasis angiogenesis. We consolidated a network map of the APLNR signaling map owing to its biomedical importance. The curation of literature data pertaining to the APLNR system was performed manually by the NetPath criteria. The described apelin receptor signaling map comprises 35 activation/inhibition events, 38 catalysis events, 4 molecular associations, 62 gene regulation events, 113 protein expression types, and 4 protein translocation events. The APLNR signaling pathway map data is made freely accessible through the WikiPathways Database (https://www.wikipathways.org/index.php/Pathway:WP5067).Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-021-00614-6.     

The network map of Elabela signaling pathway in physiological and pathological conditions

Elabela (ELA; also called Apela and Toddler) is one of the recently discovered ligand among the two endogenous peptide ligands (Apelin and Elabela) of the apelin receptor (APLNR, also known as APJ). Elabela-induced signaling plays a crucial role in diverse biological processes, including formation of the embryonic cardiovascular system and early placental development by reducing the chances of occurrence of preeclampsia during pregnancy. It also plays the major role in the renoprotection by reducing kidney injury and the inflammatory response and regulation of gene expression associated with heart failure and fibrosis. Elabela may be processed into different active peptides, each of which binds to APLNR and predominantly activates the signals through PI3K/AKT pathway. Owing to its biomedical importance, we developed a consolidated signaling map of Elabela, in accordance with the NetPath criteria. The presented Elabela signaling map comprises 12 activation/inhibition events, 15 catalysis events, 1 molecular association, 34 gene regulation events and 32 protein expression events. The Elabela signaling pathway map is freely made available through the WikiPathways Database (https://www.wikipathways.org/index.php/Pathway:WP5100).Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-021-00640-4.     

SARS-CoV-2 signaling pathway map: A functional landscape of molecular mechanisms in COVID-19

Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 has been declared a pandemic by WHO. The clinical manifestation and disease progression in COVID-19 patients varies from minimal symptoms to severe respiratory issues with multiple organ failure. Understanding the mechanism of SARS-CoV-2 interaction with host cells will provide key insights into the effective molecular targets for the development of novel therapeutics. Recent studies have identified virus-mediated phosphorylation or activation of some major signaling pathways, such as ERK1/2, JNK, p38, PI3K/AKT and NF-κB signaling, that potentially elicit the cytokine storm that serves as a major cause of tissue injuries. Several studies highlight the aggressive inflammatory response particularly ‘cytokine storm’ in SARS-CoV-2 patients. A depiction of host molecular dynamics triggered by SARS-CoV-2 in the form of a network of signaling molecules will be helpful for COVID-19 research. Therefore, we developed the signaling pathway map of SARS-CoV-2 infection using data mined from the recently published literature. This integrated signaling pathway map of SARS-CoV-2 consists of 326 proteins and 73 reactions. These include information pertaining to 1,629 molecular association events, 30 enzyme catalysis events, 43 activation/inhibition events, and 8,531 gene regulation events. The pathway map is publicly available through WikiPathways: https://www.wikipathways.org/index.php/Pathway:WP5115.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-021-00632-4.     

A modular map of Bradykinin-mediated inflammatory signaling network

Bradykinin, a member of the kallikrein-kinin system (KKS), is associated with an inflammatory response pathway with diverse vascular permeability functions, including thrombosis and blood coagulation. In majority, bradykinin signals through Bradykinin Receptor B2 (B2R). B2R is a G protein-coupled receptor (GPCR) coupled to G protein family such as Gα_qs, Gα_q/Gα_11, Gα_i1, and Gβ1_γ2_. B2R stimulation leads to the activation of a signaling cascade of downstream molecules such as phospholipases, protein kinase C, Ras/Raf-1/MAPK, and PI3K/AKT and secondary messengers such as inositol-1,4,5-trisphosphate, diacylglycerol and Ca²⁺ ions. These secondary messengers modulate the production of nitric oxide or prostaglandins. Bradykinin-mediated signaling is implicated in inflammation, chronic pain, vasculopathy, neuropathy, obesity, diabetes, and cancer. Despite the biomedical importance of bradykinin, a resource of bradykinin-mediated signaling pathway is currently not available. Here, we developed a pathway resource of signaling events mediated by bradykinin. By employing data mining strategies in the published literature, we describe an integrated pathway reaction map of bradykinin consisting of 233 reactions. Bradykinin signaling pathway events included 25 enzyme catalysis reactions, 12 translocations, 83 activation/inhibition reactions, 11 molecular associations, 45 protein expression and 57 gene regulation events. The pathway map is made publicly available on the WikiPathways Database with the ID URL: https://www.wikipathways.org/index.php/Pathway:WP5132. The bradykinin-mediated signaling pathway map will facilitate the identification of novel candidates as therapeutic targets for diseases associated with dysregulated bradykinin signaling.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-021-00652-0.     

A comprehensive network map of IL-17A signaling pathway

Interleukin-17A (IL-17A) is one of the member of IL-17 family consisting of other five members (IL-17B to IL-17F). The Gamma delta (γδ) T cells and T helper 17 (Th17) cells are the major producers of IL-17A. Aberrant signaling by IL-17A has been implicated in the pathogenesis of several autoimmune diseases including idiopathic pulmonary fibrosis, acute lung injury, chronic airway diseases, and cancer. Activation of the IL-17A/IL-17 receptor A (IL-17RA) system regulates phosphoinositide 3-kinase/AKT serine/threonine kinase/mammalian target of rapamycin (PI3K/AKT/mTOR), mitogen-activated protein kinases (MAPKs) and activation of nuclear factor-κB (NF-κB) mediated signaling pathways. The IL-17RA activation orchestrates multiple downstream signaling cascades resulting in the release of pro-inflammatory cytokines such as interleukins (IL)-1β, IL-6, and IL-8, chemokines (C-X-C motif) and promotes neutrophil-mediated immune response. Considering the biomedical importance of IL-17A, we developed a pathway resource of signaling events mediated by IL-17A/IL-17RA in this study. The curation of literature data pertaining to the IL-17A system was performed manually by the NetPath criteria. Using data mined from the published literature, we describe an integrated pathway reaction map of IL-17A/IL-17RA consisting of 114 proteins and 68 reactions. That includes detailed information on IL-17A/IL-17RA mediated signaling events of 9 activation/inhibition events, 17 catalysis events, 3 molecular association events, 68 gene regulation events, 109 protein expression events, and 6 protein translocation events. The IL-17A signaling pathway map data is made freely accessible through the WikiPathways Database (https://www.wikipathways.org/index.php/Pathway: WP5242).Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-022-00686-y.     

A complete map of the Calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) signaling pathway

Calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) is a serine/threonine-protein kinase belonging to the Ca²⁺/calmodulin-dependent protein kinase subfamily. CAMKK2 has an autocatalytic site, which gets exposed when Ca²⁺/calmodulin (CAM) binds to it. This results in autophosphorylation and complete activation of CAMKK2. The three major known downstream targets of CAMKK2 are 5′-adenosine monophosphate (AMP)-activated protein kinase (AMPKα), calcium/calmodulin-dependent protein kinase 1 (CAMK1) and calcium/calmodulin-dependent protein kinase 4 (CAMK4). Activation of these targets by CAMKK2 is important for the maintenance of different cellular and physiological processes within the cell. CAMKK2 is found to be important in neuronal development, bone remodeling, adipogenesis, and systemic glucose homeostasis, osteoclastgensis and postnatal myogensis. CAMKK2 is reported to be involved in pathologies like Duchenne muscular dystrophy, inflammation, osteoporosis and bone remodeling and is also reported to be overexpressed in prostate cancer, hepatic cancer, ovarian and gastric cancer. CAMKK2 is involved in increased cell proliferation and migration through CAMKK2/AMPK pathway in prostate cancer and activation of AKT in ovarian cancer. Although CAMKK2 is a molecule of great importance, a public resource of the CAMKK2 signaling pathway is currently lacking. Therefore, we carried out detailed data mining and documentation of the signaling events associated with CAMKK2 from published literature and developed an integrated reaction map of CAMKK2 signaling. This resulted in the cataloging of 285 reactions belonging to the CAMKK2 signaling pathway, which includes 33 protein–protein interactions, 74 post-translational modifications, 7 protein translocation events, and 22 activation/inhibition events. Besides, 124 gene regulation events and 25 activator/inhibitors involved in CAMKK2 activation were also cataloged. The CAMKK2 signaling pathway map data is made freely accessible through WikiPathway database (https://www.wikipathways.org/index.php/Pathway:WP4874). We expect that data on a signaling map of CAMKK2 will provide the scientific community with an improved platform to facilitate further molecular as well as biomedical investigations on CAMKK2 and its utility in the development of biomarkers and therapeutic targets.Electronic supplementary materialThe online version of this article (10.1007/s12079-020-00592-1) contains supplementary material, which is available to authorized users.     

Accelerating the Original Profile Kernel

One of the most accurate multi-class protein classification systems continues to be the profile-based SVM kernel introduced by the Leslie group. Unfortunately, its CPU requirements render it too slow for practical applications of large-scale classification tasks. Here, we introduce several software improvements that enable significant acceleration. Using various non-redundant data sets, we demonstrate that our new implementation reaches a maximal speed-up as high as 14-fold for calculating the same kernel matrix. Some predictions are over 200 times faster and render the kernel as possibly the top contender in a low ratio of speed/performance. Additionally, we explain how to parallelize various computations and provide an integrative program that reduces creating a production-quality classifier to a single program call. The new implementation is available as a Debian package under a free academic license and does not depend on commercial software. For non-Debian based distributions, the source package ships with a traditional Makefile-based installer. Download and installation instructions can be found at https://rostlab.org/owiki/index.php/Fast_Profile_Kernel. Bugs and other issues may be reported at https://rostlab.org/bugzilla3/enter_bug.cgi?product=fastprofkernel.     

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.     

Approximate Bayesian Computation

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

This is a “Topic Page” article for PLOS Computational Biology.

     

Flow Cytometry Bioinformatics

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

This is a “Topic Page” article for PLOS Computational Biology.

     

Multi-state Modeling of Biomolecules

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1], BioNetGen [2]–[5], the Allosteric Network Compiler [6], and others [7], [8]. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm [9], [10]. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim [11], DYNSTOC [12], RuleMonkey [9], [13], and the Network-Free Stochastic Simulator (NFSim) [14], and spatial simulators, including Meredys [15], SRSim [16], [17], and MCell [18]–[20]. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.

This is a “Topic Page” article for PLOS Computational Biology.

     

Evolving Digital Ecological Networks

“It is hard to realize that the living world as we know it is just one among many possibilities” [1]. Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs (i.e., digital organisms) that experience the same major ecological interactions as biological organisms (e.g., competition, predation, parasitism, and mutualism). Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations (hereafter tasks) that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology (e.g., the extent to which the architecture of multispecies networks shape coevolutionary outcomes, and the processes involved).

This is a “Topic Page” article for PLOS Computational Biology.

     

Scale Insects,edition 2, a tool for the identification of potential pest scales at U.S.A. ports-of-entry (Hemiptera,Sternorrhyncha, Coccoidea)

We provide a general overview of features and technical specifications of an online, interactive tool for the identification of scale insects of concern to the U.S.A. ports-of-entry. Full lists of terminal taxa included in the keys (of which there are four), a list of features used in them, and a discussion of the structure of the tool are provided. We also briefly discuss the advantages of interactive keys for the identification of potential scale insect pests. The interactive key is freely accessible on http://idtools.org/id/scales/index.php     

Viral Phylodynamics

Viral phylodynamics is defined as the study of how epidemiological, immunological, and evolutionary processes act and potentially interact to shape viral phylogenies. Since the coining of the term in 2004, research on viral phylodynamics has focused on transmission dynamics in an effort to shed light on how these dynamics impact viral genetic variation. Transmission dynamics can be considered at the level of cells within an infected host, individual hosts within a population, or entire populations of hosts. Many viruses, especially RNA viruses, rapidly accumulate genetic variation because of short generation times and high mutation rates. Patterns of viral genetic variation are therefore heavily influenced by how quickly transmission occurs and by which entities transmit to one another. Patterns of viral genetic variation will also be affected by selection acting on viral phenotypes. Although viruses can differ with respect to many phenotypes, phylodynamic studies have to date tended to focus on a limited number of viral phenotypes. These include virulence phenotypes, phenotypes associated with viral transmissibility, cell or tissue tropism phenotypes, and antigenic phenotypes that can facilitate escape from host immunity. Due to the impact that transmission dynamics and selection can have on viral genetic variation, viral phylogenies can therefore be used to investigate important epidemiological, immunological, and evolutionary processes, such as epidemic spread [2], spatio-temporal dynamics including metapopulation dynamics [3], zoonotic transmission, tissue tropism [4], and antigenic drift [5]. The quantitative investigation of these processes through the consideration of viral phylogenies is the central aim of viral phylodynamics.

This is a “Topic Page” article for PLOS Computational Biology.

     

Fast Association Tests for Genes with FAST

Gene-based tests of association can increase the power of a genome-wide association study by aggregating multiple independent effects across a gene or locus into a single stronger signal. Recent gene-based tests have distinct approaches to selecting which variants to aggregate within a locus, modeling the effects of linkage disequilibrium, representing fractional allele counts from imputation, and managing permutation tests for p-values. Implementing these tests in a single, efficient framework has great practical value. Fast ASsociation Tests (Fast) addresses this need by implementing leading gene-based association tests together with conventional SNP-based univariate tests and providing a consolidated, easily interpreted report. Fast scales readily to genome-wide SNP data with millions of SNPs and tens of thousands of individuals, provides implementations that are orders of magnitude faster than original literature reports, and provides a unified framework for performing several gene based association tests concurrently and efficiently on the same data. Availability: https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz, with documentation at https://bitbucket.org/baderlab/fast/wiki/Home     

BepiPred‐3.0: Improved B‐cell epitope prediction using protein language models

B‐cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development and disease diagnostics. The introduction of protein language models (LMs), trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred‐3.0, a sequence‐based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy, performance was further improved, thus achieving unprecedented predictive power. Our tool can predict epitopes across hundreds of sequences in minutes. It is freely available as a web server and a standalone package at https://services.healthtech.dtu.dk/service.php?BepiPred-3.0 with a user‐friendly interface to navigate the results.