Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data

Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.

Methods

Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.

Results

Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.

Discussion

The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Coracle-a machine learning framework to identify bacteria associated with continuous variables

SUMMARY

We present Coracle, an artificial intelligence (AI) framework that can identify associations between bacterial communities and continuous variables. Coracle uses an ensemble approach of prominent feature selection methods and machine learning (ML) models to identify features, i.e. bacteria, associated with a continuous variable, e.g. host thermal tolerance. The results are aggregated into a score that incorporates the performances of the different ML models and the respective feature importance, while also considering the robustness of feature selection. Additionally, regression coefficients provide first insights into the direction of the association. We show the utility of Coracle by analyzing associations between bacterial composition data (i.e. 16S rRNA Amplicon Sequence Variants, ASVs) and coral thermal tolerance (i.e. standardized short-term heat stress-derived diagnostics). This analysis identified high-scoring bacterial taxa that were previously found associated with coral thermal tolerance. Coracle scales with feature number and performs well with hundreds to thousands of features, corresponding to the typical size of current datasets. Coracle performs best if run at a higher taxonomic level first (e.g. order or family) to identify groups of interest that can subsequently be run at the ASV level.

AVAILABILITY AND IMPLEMENTATION

Coracle can be accessed via a dedicated web server that allows free and simple access: http://www.micportal.org/coracle/index. The underlying code is open-source and available via GitHub https://github.com/SebastianStaab/coracle.git.

2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualisations in Virtual Reality

Relational information between different types of entities is often modelled by a multilayer network (MLN) - a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied this impact for several commonly occurring tasks related to MLN analysis. Additionally, layer arrangements with a dimensionality beyond 2D, which are common in this scenario, motivate the use of stereoscopic displays. We ran a human subject study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer arrangements. The study employs six analysis tasks that cover the spectrum of an MLN task taxonomy, from path finding and pattern identification to comparisons between and across layers. We found no clear overall winner. However, we explore the task-to-arrangement space and derive empirical-based recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for MLNs.

Conception and pilot testing of a self-management health application for patients with pollen-related allergic rhinitis and allergic asthma-the APOLLO app

It has been shown that pollen information services are an important self-management tool for patients with pollen-related allergic rhinitis (AR) and allergic asthma (AA). This study aimed to design an online application for patients with AR and AA, which supports patients to better manage their disease as well as to evaluate the app and present the first results of the pilot study. The pollen data were obtained from the electronic pollen information network of Bavaria, Germany. Participants were asked to fill in their allergy-related complaints in the app over a 60-day period. Subsequently, the app was evaluated. Indices and diagrams visualized the participants' individual complaints as well as the daily pollen concentration in the air. In order to motivate participants to complete the app on a daily basis, we used elements of gamification. Two thirds of the participants (N = 46) reported feeling better informed about pollen counts and their allergy when using the app. The app's simple and comprehensible design was rated positively. More than 80% of the participants would recommend the app to their family and friends. The app can be a tool for patients with AR and AA to better understand their disease.

Beyond the horizon: immersive developments for animal ecology research

More diverse data on animal ecology are now available. This "data deluge" presents challenges for both biologists and computer scientists; however, it also creates opportunities to improve analysis and answer more holistic research questions. We aim to increase awareness of the current opportunity for interdisciplinary research between animal ecology researchers and computer scientists. Immersive analytics (IA) is an emerging research field in which investigations are performed into how immersive technologies, such as large display walls and virtual reality and augmented reality devices, can be used to improve data analysis, outcomes, and communication. These investigations have the potential to reduce the analysis effort and widen the range of questions that can be addressed. We propose that biologists and computer scientists combine their efforts to lay the foundation for IA in animal ecology research. We discuss the potential and the challenges and outline a path toward a structured approach. We imagine that a joint effort would combine the strengths and expertise of both communities, leading to a well-defined research agenda and design space, practical guidelines, robust and reusable software frameworks, reduced analysis effort, and better comparability of results.

Specifications of standards in systems and synthetic biology: status and developments in 2022 and the COMBINE meeting 2022

This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2022 special issue presents three updates to the standards: CellML 2.0.1, SBML Level 3 Package: Spatial Processes, Version 1, Release 1, and Synthetic Biology Open Language (SBOL) Version 3.1.0. This document can also be used to identify the latest specifications for all COMBINE standards. In addition, this editorial provides a brief overview of the COMBINE 2022 meeting in Berlin.

APL@voro-interactive visualization and analysis of cell membrane simulations

SUMMARY

Molecular dynamics (MD) simulations of cell membranes allow for a better understanding of complex processes such as changing membrane dynamics, lipid rafts and the incorporation/passing of macromolecules into/through membranes. To explore and understand cell membrane compositions, dynamics and processes, visual analytics can help to interpret MD simulation data. APL@Voro is a software for the interactive visualization and analysis of cell membrane simulations. Here, we present the new APL@Voro, which has been continuously developed since its initial release in 2013. We discuss newly implemented algorithms, methodologies and features, such as the interactive comparison of related simulations and methods to assign lipids to either the upper or lower leaflet.

AVAILABILITY AND IMPLEMENTATION

The current open-source version of APL@Voro can be downloaded from http://aplvoro.com.

Towards a hybrid user interface for the visual exploration of large biomolecular networks using virtual reality

Biomolecular networks, including genome-scale metabolic models (GSMMs), assemble the knowledge regarding the biological processes that happen inside specific organisms in a way that allows for analysis, simulation, and exploration. With the increasing availability of genome annotations and the development of powerful reconstruction tools, biomolecular networks continue to grow ever larger. While visual exploration can facilitate the understanding of such networks, the network sizes represent a major challenge for current visualisation systems. Building on promising results from the area of immersive analytics, which among others deals with the potential of immersive visualisation for data analysis, we present a concept for a hybrid user interface that combines a classical desktop environment with a virtual reality environment for the visual exploration of large biomolecular networks and corresponding data. We present system requirements and design considerations, describe a resulting concept, an envisioned technical realisation, and a systems biology usage scenario. Finally, we discuss remaining challenges.

Visual Comparison of Networks in VR

Networks are an important means for the representation and analysis of data in a variety of research and application areas. While there are many efficient methods to create layouts for networks to support their visual analysis, approaches for the comparison of networks are still underexplored. Especially when it comes to the comparison of weighted networks, which is an important task in several areas, such as biology and biomedicine, there is a lack of efficient visualization approaches. With the availability of affordable high-quality virtual reality (VR) devices, such as head-mounted displays (HMDs), the research field of immersive analytics emerged and showed great potential for using the new technology for visual data exploration. However, the use of immersive technology for the comparison of networks is still underexplored. With this work, we explore how weighted networks can be visually compared in an immersive VR environment and investigate how visual representations can benefit from the extended 3D design space. For this purpose, we develop different encodings for 3D node-link diagrams supporting the visualization of two networks within a single representation and evaluate them in a pilot user study. We incorporate the results into a more extensive user study comparing node-link representations with matrix representations encoding two networks simultaneously. The data and tasks designed for our experiments are similar to those occurring in real-world scenarios. Our evaluation shows significantly better results for the node-link representations, which is contrary to comparable 2D experiments and indicates a high potential for using VR for the visual comparison of networks.

An Intelligent Strategy with All-Atom Molecular Dynamics Simulations for the Design of Lipopeptides against Multidrug-Resistant Pseudomonas aeruginosa

Multidrug-resistant Gram-negative bacteria seriously threaten modern medicine due to the lack of efficacious therapeutic options. Their outer membrane (OM) is an essential protective fortress to exclude many antibiotics. Unfortunately, current structural biology methods are not able to resolve the membrane structure and it is difficult to examine the specific interaction between the OM and small molecules. These limitations hinder mechanistic understanding of antibiotic penetration through the OM and antibiotic discovery. Here, we developed biologically relevant OM models by quantitatively determining membrane lipidomics of Pseudomonas aeruginosa and elucidated how lipopolysaccharide modifications and OM vesicles mediated resistance to polymyxins. Supported by chemical biology and pharmacological assays, our multiscale molecular dynamics simulations provide an intelligent platform to quantify the membrane-penetrating thermodynamics of peptides and predict their antimicrobial activity. Through experimental validations with our in-house polymyxin analogue library, our computational strategy may have significant potential in accelerating the discovery of lipopeptides against bacterial "superbugs".

Design considerations for representing systems biology information with the Systems Biology Graphical Notation

Visual representations are commonly used to explore, analyse, and communicate information and knowledge in systems biology and beyond. Such visualisations not only need to be accurate but should also be aesthetically pleasing and informative. Using the example of the Systems Biology Graphical Notation (SBGN) we will investigate design considerations for graphically presenting information from systems biology, in particular regarding the use of glyphs for types of information, the style of graph layout for network representation, and the concept of bricks for visual network creation.

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

Erratum: Lattice gas study of thin-film growth scenarios and transitions between them: Role of substrate [Phys. Rev. E 103, 023302 (2021)]

This corrects the article DOI: 10.1103/PhysRevE.103.023302.

Spatially resolved transcriptomics in immersive environments

Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomics is presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.

Investigation of microcystin conformation and binding towards PPP1 by molecular dynamics simulation

Microcystins (MC) are a group of structurally similar cyanotoxins with currently 279 described structural variants. Human exposure is frequent by consumption of contaminated water, food or food supplements. MC can result in serious intoxications, commensurate with ensuing pathology in various organs or in rare cases even mortality. The current WHO risk assessment primarily considers MC-LR, while all other structural variants are treated as equivalent to MC-LR, despite that current data strongly suggest that MC-LR is not the most toxic MC, and toxicity can be very different for MC congeners. To investigate and analyse binding and conformation of different MC congeners, we applied for the first time Molecular Dynamics (MD) simulation to four MC congeners (MC-LR, MC-LF, [Enantio-Adda5]MC-LF, [β-D-Asp3,Dhb7]MC-RR). We could show that ser/thr protein phosphatase 1 is stable in all MD simulations and that MC-LR backbone adopts to a second conformation in solvent MD simulation, which was previously unknown. We could also show that MC congeners can adopt to different backbone conformation when simulated in solvent or in complex with ser/thr protein phosphatase 1 and differ in their binding behaviour. Our findings suggest that MD Simulation of different MC congeners aid in understanding structural differences and binding of this group of structurally similar cyanotoxins.

Specifications of standards in systems and synthetic biology: status and developments in 2021

This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2021 special issue presents four updates of standards: Synthetic Biology Open Language Visual Version 2.3, Synthetic Biology Open Language Visual Version 3.0, Simulation Experiment Description Markup Language Level 1 Version 4, and OMEX Metadata specification Version 1.2. This document can also be consulted to identify the latest specifications of all COMBINE standards.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

QSDB-a graphical Quorum Sensing Database

The human microbiome is largely shaped by the chemical interactions of its microbial members, which includes cross-talk via shared signals or quenching of the signalling of other species. Quorum sensing is a process that allows microbes to coordinate their behaviour in dependence of their population density and to adjust gene expression accordingly. We present the Quorum Sensing Database (QSDB), a comprehensive database of all published sensing and quenching relations between organisms and signalling molecules of the human microbiome, as well as an interactive web interface that allows browsing the database, provides graphical depictions of sensing mechanisms as Systems Biology Graphical Notation diagrams and links to other databases. Database URL: QSDB (Quorum Sensing DataBase) is freely available via an interactive web interface and as a downloadable csv file at http://qsdb.org.

A novel chemical biology and computational approach to expedite the discovery of new-generation polymyxins against life-threatening Acinetobacter baumannii

Multidrug-resistant Gram-negative bacteria represent a major medical challenge worldwide. New antibiotics are desperately required with 'old' polymyxins often being the only available therapeutic option. Here, we systematically investigated the structure-activity relationship (SAR) of polymyxins using a quantitative lipidomics-informed outer membrane (OM) model of Acinetobacter baumannii and a series of chemically synthesized polymyxin analogs. By integrating chemical biology and all-atom molecular dynamics simulations, we deciphered how each residue of the polymyxin molecule modulated its conformational folding and specific interactions with the bacterial OM. Importantly, a novel designed polymyxin analog FADDI-287 with predicted stronger OM penetration showed improved in vitro antibacterial activity. Collectively, our study provides a novel chemical biology and computational strategy to expedite the discovery of new-generation polymyxins against life-threatening Gram-negative 'superbugs'.

The Value of Immersive Visualization

In recent years, research on immersive environments has experienced a new wave of interest, and immersive analytics has been established as a new research field. Every year, a vast amount of different techniques, applications, and user studies are published that focus on employing immersive environments for visualizing and analyzing data. Nevertheless, immersive analytics is still a relatively unexplored field that needs more basic research in many aspects and is still viewed with skepticism. Rightly so, because in our opinion, many researchers do not fully exploit the possibilities offered by immersive environments and, on the contrary, sometimes even overestimate the power of immersive visualizations. Although a growing body of papers has demonstrated individual advantages of immersive analytics for specific tasks and problems, the general benefit of using immersive environments for effective analytic tasks remains controversial. In this article, we reflect on when and how immersion may be appropriate for the analysis and present four guiding scenarios. We report on our experiences, discuss the landscape of assessment strategies, and point out the directions where we believe immersive visualizations have the greatest potential.

Molecular dynamics simulations informed by membrane lipidomics reveal the structure-interaction relationship of polymyxins with the lipid A-based outer membrane of Acinetobacter baumannii

BACKGROUND

MDR bacteria represent an urgent threat to human health globally. Polymyxins are a last-line therapy against life-threatening Gram-negative 'superbugs', including Acinetobacter baumannii. Polymyxins exert antimicrobial activity primarily via permeabilizing the bacterial outer membrane (OM); however, the mechanism of interaction between polymyxins and the OM remains unclear at the atomic level.

METHODS

We constructed a lipid A-based OM model of A. baumannii using quantitative membrane lipidomics data and employed all-atom molecular dynamics simulations with umbrella sampling techniques to elucidate the structure-interaction relationship and thermodynamics governing the penetration of polymyxins [B1 and E1 (i.e. colistin A) representing the two clinically used polymyxins] into the OM.

RESULTS

Polymyxin B1 and colistin A bound to the A. baumannii OM by the initial electrostatic interactions between the Dab residues of polymyxins and the phosphates of lipid A, competitively displacing the cations from the headgroup region of the OM. Both polymyxin B1 and colistin A formed a unique folded conformation upon approaching the hydrophobic centre of the OM, consistent with previous experimental observations. Polymyxin penetration induced reorientation of the headgroups of the OM lipids near the penetration site and caused local membrane disorganization, thereby significantly increasing membrane permeability and promoting the subsequent penetration of polymyxin molecules into the OM and periplasmic space.

CONCLUSIONS

The thermodynamics governing the penetration of polymyxins through the outer leaflet of the A. baumannii OM were examined and novel structure-interaction relationship information was obtained at the atomic and membrane level. Our findings will facilitate the discovery of novel polymyxins against MDR Gram-negative pathogens.

Visual Exploration of Large Metabolic Models

MOTIVATION

Large metabolic models, including genome-scale metabolic models (GSMMs), are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualisation and interactive exploration can facilitate a better understanding of these models.

RESULTS

We developed a novel method for the visual exploration of large metabolic models and implemented it in LMME (Large Metabolic Model Explorer), an add-on for the biological network analysis tool VANTED. The underlying idea of our method is to analyse a large model as follows. Starting from a decomposition into several subsystems, relationships between these subsystems are identified and an overview is computed and visualised. From this overview, detailed subviews may be constructed and visualised in order to explore subsystems and relationships in greater detail. Decompositions may either be predefined or computed, using built-in or self-implemented methods. Realised as add-on for VANTED, LMME is embedded in a domain-specific environment, allowing for further related analysis at any stage during the exploration. We describe the method, provide a use case, and discuss the strengths and weaknesses of different decomposition methods.

AVAILABILITY

The methods and algorithms presented here are implemented in LMME, an open-source add-on for VANTED. LMME can be downloaded from www.cls.uni-konstanz.de/software/lmme and VANTED can be downloaded from www.vanted.org. The source code of LMME is available from GitHub, at https://github.com/LSI-UniKonstanz/lmme.

Lattice gas study of thin-film growth scenarios and transitions between them: Role of substrate

Thin-film growth is investigated in two types of lattice gas models where substrate and film particles are different, expressed by unequal interaction energy parameters. The first is of solid-on-solid type, whereas the second additionally incorporates desorption, diffusion in the gas phase above the film and readsorption at the film (appropriate for growth in colloidal systems). In both models, the difference between particle-substrate and particle-particle interactions plays a central role for the evolution of the film morphology at intermediate times. The models exhibit a dynamic layering transition which occurs at generally lower substrate attraction strengths than the equilibrium layering transition. A second, flattening transition is found where initial island growth transforms to layer-by-layer growth at intermediate deposition times. Combined with the known roughening behavior in such models for very large deposition times, we present four global growth scenarios, charting out the possible types of roughness evolution.

Roughness evolution in strongly interacting donor:acceptor mixtures of molecular semiconductors. An in situ, real-time growth study using x-ray reflectivity

The evolution of surface roughness in binary mixtures of the two molecular organic semiconductors (OSCs) diindenoperylene (DIP) as electron-donor and 1, 3, 4, 5, 7, 8-hexafluoro-tetracyano naphthoquinodimethane (F6TCNNQ) as electron-acceptor is studied. We co-deposit DIP and F6TCNNQ in vacuum with varying relative molar content while keeping a molar excess of DIP in order to produce phase-heterogeneous mixtures. The excess DIP phase segregates in pristine crystallites, whereas the remaining mixed phase is constituted by DIP:F6TCNNQ co-crystallites. We calculate the surface roughness as function of film thickness by modelling x-ray reflectivity data acquired in situ and in real-time during film growth. To model the experimental data, two distinct approaches, namely the kinematic approximation and the Parratt formalism, are applied. A comparative study of surface roughness evolution as function of DIP:F6TCNNQ mixing ratio is carried out implementing the Trofimov growth model within the kinematic approximation. Depending on the thickness regime, mixing ratio-specific trends are identified and discussed. To explain them, a growth mechanism for binary heterogeneous mixtures of strongly interacting OSCs is proposed.

Genome-Scale Metabolic Modeling Reveals Metabolic Alterations of Multidrug-Resistant Acinetobacter baumannii in a Murine Bloodstream Infection Model

Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat to human health globally. We constructed a genome-scale metabolic model iAB5075 for the hypervirulent, MDR A. baumannii strain AB5075. Predictions of nutrient utilization and gene essentiality were validated using Biolog assay and a transposon mutant library. In vivo transcriptomics data were integrated with iAB5075 to elucidate bacterial metabolic responses to the host environment. iAB5075 contains 1530 metabolites, 2229 reactions, and 1015 genes, and demonstrated high accuracies in predicting nutrient utilization and gene essentiality. At 4 h post-infection, a total of 146 metabolic fluxes were increased and 52 were decreased compared to 2 h post-infection; these included enhanced fluxes through peptidoglycan and lipopolysaccharide biosynthesis, tricarboxylic cycle, gluconeogenesis, nucleotide and fatty acid biosynthesis, and altered fluxes in amino acid metabolism. These flux changes indicate that the induced central metabolism, energy production, and cell membrane biogenesis played key roles in establishing and enhancing A. baumannii bloodstream infection. This study is the first to employ genome-scale metabolic modeling to investigate A. baumannii infection in vivo. Our findings provide important mechanistic insights into the adaption of A. baumannii to the host environment and thus will contribute to the development of new therapeutic agents against this problematic pathogen.

Outer Membranes of Polymyxin-Resistant Acinetobacter baumannii with Phosphoethanolamine-Modified Lipid A and Lipopolysaccharide Loss Display Different Atomic-Scale Interactions with Polymyxins

Resistance to the last-line polymyxins is increasingly reported in multidrug-resistant Gram-negative pathogens, including Acinetobacter baumannii, which develops resistance via either lipid A modification (e.g., with phosphoethanolamine [pEtN]) or even lipopolysaccharide (LPS) loss in the outer membrane (OM). Considering these two different mechanisms, quantitative membrane lipidomics data were utilized to develop three OM models representing polymyxin-susceptible and -resistant A. baumannii strains. Through all-atom molecular simulations with enhanced sampling techniques, the effect of lipid A-pEtN modification and LPS loss on the action of colistin (i.e., polymyxin E) was examined for the first time, with a focus on the dynamics and energetics of colistin penetration into these OMs. Lipid A-pEtN modification improved the OM stability, impeding the penetration of colistin into the OM; this differed from the current literature that lipid A-pEtN modification confers resistance by diminishing the initial interaction with polymyxins. In contrast, the LPS deficiency significantly reduced the negative charges on the OM surface, diminishing the binding of colistin. Moreover, both lipid A-pEtN modification and LPS loss also constituted colistin resistance through disturbing the conformational transitions of the colistin molecule. Collectively, atomic-scale interactions between polymyxins and different bacterial OMs are very different and the findings may facilitate the discovery of new-generation polymyxins against Gram-negative 'superbugs'.

Simulations of octapeptin-outer membrane interactions reveal conformational flexibility is linked to antimicrobial potency

The octapeptins are lipopeptide antibiotics that are structurally similar to polymyxins yet retain activity against polymyxin-resistant Gram-negative pathogens, suggesting they might be used to treat recalcitrant infections. However, the basis of their unique activity is unclear because of the difficulty in generating high-resolution experimental data of the interaction of antimicrobial peptides with lipid membranes. To elucidate these structure-activity relationships, we employed all-atom molecular dynamics simulations with umbrella sampling to investigate the conformational and energetic landscape of octapeptins interacting with bacterial outer membrane (OM). Specifically, we examined the interaction of octapeptin C4 and FADDI-115, lacking a single hydroxyl group compared with octapeptin C4, with the lipid A-phosphoethanolamine modified OM of Acinetobacter baumannii Octapeptin C4 and FADDI-115 both penetrated into the OM hydrophobic center but experienced different conformational transitions from an unfolded to a folded state that was highly dependent on the structural flexibility of their respective N-terminal fatty acyl groups. The additional hydroxyl group present in the fatty acyl group of octapeptin C4 resulted in the molecule becoming trapped in a semifolded state, leading to a higher free energy barrier for OM penetration. The free energy barrier for the translocation through the OM hydrophobic layer was ∼72 kcal/mol for octapeptin C4 and 62 kcal/mol for FADDI-115. Our results help to explain the lower antimicrobial activity previously observed for octapeptin C4 compared with FADDI-115 and more broadly improve our understanding of the structure-function relationships of octapeptins. These findings may facilitate the discovery of next-generation octapeptins against polymyxin-resistant Gram-negative 'superbugs.'

Polymyxins Bind to the Cell Surface of Unculturable Acinetobacter baumannii and Cause Unique Dependent Resistance

Multidrug-resistant Acinetobacter baumannii is a top-priority pathogen globally and polymyxins are a last-line therapy. Polymyxin dependence in A. baumannii (i.e., nonculturable on agar without polymyxins) is a unique and highly-resistant phenotype with a significant potential to cause treatment failure in patients. The present study discovers that a polymyxin-dependent A. baumannii strain possesses mutations in both lpxC (lipopolysaccharide biosynthesis) and katG (reactive oxygen species scavenging) genes. Correlative multiomics analyses show a significantly remodeled cell envelope and remarkably abundant phosphatidylglycerol in the outer membrane (OM). Molecular dynamics simulations and quantitative membrane lipidomics reveal that polymyxin-dependent growth emerges only when the lipopolysaccharide-deficient OM distinctively remodels with ≥ 35% phosphatidylglycerol, and with "patch" binding on the OM by the rigid polymyxin molecules containing strong intramolecular hydrogen bonding. Rather than damaging the OM, polymyxins bind to the phosphatidylglycerol-rich OM and strengthen the membrane integrity, thereby protecting bacteria from external reactive oxygen species. Dependent growth is observed exclusively with polymyxin analogues, indicating a critical role of the specific amino acid sequence of polymyxins in forming unique structures for patch-binding to bacterial OM. Polymyxin dependence is a novel antibiotic resistance mechanism and the current findings highlight the risk of 'invisible' polymyxin-dependent isolates in the evolution of resistance.

Specifications of standards in systems and synthetic biology: status and developments in 2020

This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.

The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.

Selection of resistance by antimicrobial coatings in the healthcare setting

Antimicrobial touch surfaces have been introduced in healthcare settings with the aim of supporting existing hygiene procedures, and to help combat the increasing threat of antimicrobial resistance. However, concerns have been raised over the potential selection pressure exerted by such surfaces, which may drive the evolution and spread of antimicrobial resistance. This review highlights studies that indicate risks associated with resistance on antimicrobial surfaces by different processes, including evolution by de-novo mutation and horizontal gene transfer, and species sorting of inherently resistant bacteria dispersed on to antimicrobial surfaces. The review focuses on antimicrobial surfaces made of copper, silver and antimicrobial peptides because of the practical application of copper and silver, and the promising characteristics of antimicrobial peptides. The available data point to a potential for resistance selection and a subsequent increase in resistant strains via cross-resistance and co-resistance conferred by metal and antibiotic resistance traits. However, translational studies describing the development of resistance to antimicrobial touch surfaces in healthcare-related environments are rare, and will be needed to assess whether and how antimicrobial surfaces lead to resistance selection in these settings. Such studies will need to consider numerous variables, including the antimicrobial concentrations present in coatings, the occurrence of biofilms on surfaces, and the humidity relevant to dry-surface environments. On-site tests on the efficacy of antimicrobial coatings should routinely evaluate the risk of selection associated with their use.

COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms

Researchers around the world join forces to reconstruct the molecular processes of the virus-host interactions aiming to combat the cause of the ongoing pandemic.

Erratum to Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans

In this manuscript, which appeared in ALTEX (2020), 37(1), 24-36, doi:10.14573/altex.1904031 , there were errors in Tables 1 and 3.

JIB.tools 2.0 - A Bioinformatics Registry for Journal Published Tools with Interoperability to bio.tools

JIB.tools 2.0 is a new approach to more closely embed the curation process in the publication process. This website hosts the tools, software applications, databases and workflow systems published in the Journal of Integrative Bioinformatics (JIB). As soon as a new tool-related publication is published in JIB, the tool is posted to JIB.tools and can afterwards be easily transferred to bio.tools, a large information repository of software tools, databases and services for bioinformatics and the life sciences. In this way, an easily-accessible list of tools is provided which were published in JIB a well as status information regarding the underlying service. With newer registries like bio.tools providing these information on a bigger scale, JIB.tools 2.0 closes the gap between journal publications and registry publication. (Reference: https://jib.tools).

Induction chemotherapy (IC) followed by radiotherapy (RT) versus cetuximab plus IC and RT in advanced laryngeal/hypopharyngeal cancer resectable only by total laryngectomy-final results of the larynx organ preservation trial DeLOS-II

Background

The German multicenter randomized phase II larynx organ preservation (LOP) trial DeLOS-II was carried out to prove the hypothesis that cetuximab (E) added to induction chemotherapy (IC) and radiotherapy improves laryngectomy-free survival (LFS; survival with preserved larynx) in locally advanced laryngeal/hypopharyngeal cancer (LHSCC).

Patients and methods

Treatment-naïve patients with stage III/IV LHSCC amenable to total laryngectomy (TL) were randomized to three cycles IC with TPF [docetaxel (T) and cisplatin (P) 75 mg/m2/day 1, 5-FU (F) 750 mg/m2/day days 1-5] followed by radiotherapy (69.6 Gy) without (A) or with (B) standard dose cetuximab for 16 weeks throughout IC and radiotherapy (TPFE). Response to first IC-cycle (IC-1) with ≥30% endoscopically estimated tumor surface shrinkage (ETSS) was used to define early responders; early salvage TL was recommended to non-responders. The primary objective was 24 months LFS above 35% in arm B.

Results

Of 180 patients randomized (July 2007 to September 2012), 173 fulfilled eligibility criteria (A/B: larynx 44/42, hypopharynx 41/46). Because of 4 therapy-related deaths among the first 64 randomized patients, 5-FU was omitted from IC in the subsequent 112 patients reducing further fatal toxicities. Thus, IC was TPF in 61 patients and TP in 112 patients, respectively. The primary objective (24 months LFS above 35%) was equally met by arms A (40/85, 47.1%) as well as B (41/88, 46.6%). One hundred and twenty-three early responders completed IC+RT; their overall response rates (TPF/TP) were 94.7%/87.2% in A versus 80%/86.0% in B. The 24 months overall survival (OS) rates were 68.2% and 69.3%.

Conclusions

Despite being accompanied by an elevated frequency in adverse events, the IC with TPF/TP plus cetuximab was feasible but showed no superiority to IC with TPF/TP regarding LFS and OS at 24 months. Both early response and 24 months LFS compare very well to previous LOP trials and recommend effective treatment selection and stratification by ETSS.

Clinical trial information

NCT00508664.

Invited Review: The spectrum of age-related small vessel diseases: potential overlap and interactions of amyloid and nonamyloid vasculopathies

Deep perforator arteriopathy (DPA) and cerebral amyloid angiopathy (CAA) are the commonest known cerebral small vessel diseases (CSVD), which cause ischaemic stroke, intracebral haemorrhage (ICH) and vascular cognitive impairment (VCI). While thus far mainly considered as separate entities, we here propose that DPA and CAA share similarities, overlap and interact, so that 'pure' DPA or CAA are extremes along a continuum of age-related small vessel pathologies. We suggest blood-brain barrier (BBB) breakdown, endothelial damage and impaired perivascular β-amyloid (Aβ) drainage are hallmark common mechanisms connecting DPA and CAA. We also suggest a need for new biomarkers (e.g. high-resolution imaging) to deepen understanding of the complex relationships between DPA and CAA.

Corrigendum to A high-throughput approach to identify specific neurotoxicants / developmental toxicants in human neuronal cell function assays

In this manuscript, which appeared in ALTEX 35 , 235-253 ( doi:10.14573/altex.1712182 ), the Acknowledgements should read: This work was supported by the Land BW, the Doerenkamp-Zbinden Foundation, the DFG (RTG1331, KoRS-CB), the BMBF (NeuriTox), and it has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 681002 (EU-ToxRisk).

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2019

This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.

Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans

Microcystins (MC) represent a family of cyclic peptides with approx. 250 congeners presumed harmful to human health due to their ability to inhibit ser/thr-proteinphosphatases (PPP), albeit all hazard and risk assessments (RA) are based on data of one MC-congener (MC-LR) only. MC congener structural diversity is a challenge for the risk assessment of these toxins, especially as several different PPPs have to be included in the RA. Consequently, the inhibition of PPP1, PPP2A and PPP5 was determined with 18 structurally different MC and demonstrated MC congener dependent inhibition activity and a lower susceptibility of PPP5 to inhibition than PPP1 and PPP2A. The latter data were employed to train a machine learning algorithm that should allow prediction of PPP inhibition (toxicity) based on MCs 2D chemical structure. IC50 values were classified in toxicity classes and three machine learning models were used to predict the toxicity class, resulting in 80-90% correct predictions.

Mental-Map Preserving Visualisation of Partitioned Networks in Vanted

Biological networks can be large and complex, often consisting of different sub-networks or parts. Separation of networks into parts, network partitioning and layouts of overview and sub-graphs are of importance for understandable visualisations of those networks. This article presents NetPartVis to visualise non-overlapping clusters or partitions of graphs in the Vanted framework based on a method for laying out overview graph and several sub-graphs (partitions) in a coordinated, mental-map preserving way.

Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0

The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).

Fly with the flock: immersive solutions for animal movement visualization and analytics

Understanding the movement of animals is important for a wide range of scientific interests including migration, disease spread, collective movement behaviour and analysing motion in relation to dynamic changes of the environment such as wind and thermal lifts. Particularly, the three-dimensional (3D) spatial-temporal nature of bird movement data, which is widely available with high temporal and spatial resolution at large volumes, presents a natural option to explore the potential of immersive analytics (IA). We investigate the requirements and benefits of a wide range of immersive environments for explorative visualization and analytics of 3D movement data, in particular regarding design considerations for such 3D immersive environments, and present prototypes for IA solutions. Tailored to biologists studying bird movement data, the immersive solutions enable geo-locational time-series data to be investigated interactively, thus enabling experts to visually explore interesting angles of a flock and its behaviour in the context of the environment. The 3D virtual world presents the audience with engaging and interactive content, allowing users to 'fly with the flock', with the potential to ascertain an intuitive overview of often complex datasets, and to provide the opportunity thereby to formulate and at least qualitatively assess hypotheses. This work also contributes to ongoing research efforts to promote better understanding of bird migration and the associated environmental factors at the global scale, thereby providing a visual vehicle for driving public awareness of environmental issues and bird migration patterns.

Metabolic Responses to Polymyxin Treatment in Acinetobacter baumannii ATCC 19606: Integrating Transcriptomics and Metabolomics with Genome-Scale Metabolic Modeling

Multidrug-resistant (MDR) Acinetobacter baumannii has emerged as a very problematic pathogen over the past decades, with a high incidence in nosocomial infections. Discovered in the late 1940s but abandoned in the 1970s, polymyxins (i.e., polymyxin B and colistin) have been revived as the last-line therapy against Gram-negative "superbugs," including MDR A. baumannii. Worryingly, resistance to polymyxins in A. baumannii has been increasingly reported, urging the development of novel antimicrobial therapies to rescue this last-line class of antibiotics. In the present study, we integrated genome-scale metabolic modeling with multiomics data to elucidate the mechanisms of cellular responses to colistin treatment in A. baumannii. A genome-scale metabolic model, iATCC19606, was constructed for strain ATCC 19606 based on the literature and genome annotation, containing 897 genes, 1,270 reactions, and 1,180 metabolites. After extensive curation, prediction of growth on 190 carbon sources using iATCC19606 achieved an overall accuracy of 84.3% compared to Biolog experimental results. Prediction of gene essentiality reached a high accuracy of 86.1% and 82.7% compared to two transposon mutant libraries of AB5075 and ATCC 17978, respectively. Further integrative modeling with our correlative transcriptomics and metabolomics data deciphered the complex regulation on metabolic responses to colistin treatment, including (i) upregulated fluxes through gluconeogenesis, the pentose phosphate pathway, and amino acid and nucleotide biosynthesis; (ii) downregulated TCA cycle and peptidoglycan and lipopolysaccharide biogenesis; and (iii) altered fluxes over respiratory chain. Our results elucidated the interplay of multiple metabolic pathways under colistin treatment in A. baumannii and provide key mechanistic insights into optimizing polymyxin combination therapy. IMPORTANCE Combating antimicrobial resistance has been highlighted as a critical global health priority. Due to the drying drug discovery pipeline, polymyxins have been employed as the last-line therapy against Gram-negative "superbugs"; however, the detailed mechanisms of antibacterial killing remain largely unclear, hampering the improvement of polymyxin therapy. Our integrative modeling using the constructed genome-scale metabolic model iATCC19606 and the correlative multiomics data provide the fundamental understanding of the complex metabolic responses to polymyxin treatment in A. baumannii at the systems level. The model iATCC19606 may have a significant potential in antimicrobial systems pharmacology research in A. baumannii.

Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa

Background

Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level.

Findings

A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover.

Conclusions

Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.

Two time scales for self and collective diffusion near the critical point in a simple patchy model for proteins with floating bonds

Using dynamic Monte Carlo and Brownian dynamics, we investigate a floating bond model in which particles can bind through mobile bonds. The maximum number of bonds (here fixed to 4) can be tuned by appropriately choosing the repulsive, nonadditive interactions among bonds and particles. We compute the static and dynamic structure factor (intermediate scattering function) in the vicinity of the gas-liquid critical point. The static structure exhibits a weak tetrahedral network character. The intermediate scattering function shows a temporal decay deviating from a single exponential, which can be described by a double exponential decay where the two time scales differ approximately by one order of magnitude. This time scale separation is robust over a range of wave numbers. The analysis of clusters in real space indicates the formation of noncompact clusters and shows a considerable stretch in the instantaneous size distribution when approaching the critical point. The average time evolution of the largest subcluster of given initial clusters with 10 or more particles also shows a double exponential decay. The observation of two time scales in the intermediate scattering function at low packing fractions is consistent with similar findings in globular protein solutions with trivalent metal ions that act as bonds between proteins.

Correction to: Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

[This corrects the article DOI: 10.4056/sigs.5279417.].