Revealing the hierarchical structure of microbial communities.

Measuring the dynamics of microbial communities results in high-dimensional measurements of taxa abundances over time and space, which is difficult to analyze due to complex changes in taxonomic compositions. This paper presents a new method to investigate and visualize the intrinsic hierarchical community structure implied by the measurements. The basic idea is to identify significant intersection sets, which can be seen as sub-communities making up the measured communities. Using the subset relationship, the intersection sets together with the measurements form a hierarchical structure visualized as a Hasse diagram. Chemical organization theory (COT) is used to relate the hierarchy of the sets of taxa to potential taxa interactions and to their potential dynamical persistence. The approach is demonstrated on a data set of community data obtained from bacterial 16S rRNA gene sequencing for samples collected monthly from four groundwater wells over a nearly 3-year period (n = 114) along a hillslope area. The significance of the hierarchies derived from the data is evaluated by showing that they significantly deviate from a random model. Furthermore, it is demonstrated how the hierarchy is related to temporal and spatial factors; and how the idea of a core microbiome can be extended to a set of interrelated core microbiomes. Together the results suggest that the approach can support developing models of taxa interactions in the future.

Computing all persistent subspaces of a reaction-diffusion system.

An algorithm is presented for computing a reaction-diffusion partial differential equation (PDE) system for all possible subspaces that can hold a persistent solution of the equation. For this, all possible sub-networks of the underlying reaction network that are distributed organizations (DOs) are identified. Recently it has been shown that a persistent subspace must be a DO. The algorithm computes the hierarchy of DOs starting from the largest by a linear programming approach using integer cuts. The underlying constraints use elementary reaction closures as minimal building blocks to guarantee local closedness and global self-maintenance, required for a DO. Additionally, the algorithm delivers for each subspace an affiliated set of organizational reactions and minimal compartmentalization that is necessary for this subspace to persist. It is proved that all sets of organizational reactions of a reaction network, as already DOs, form a lattice. This lattice contains all potentially persistent sets of reactions of all constrained solutions of reaction-diffusion PDEs. This provides a hierarchical structure of all persistent subspaces with regard to the species and also to the reactions of the reaction-diffusion PDE system. Here, the algorithm is described and the corresponding Python source code is provided. Furthermore, an analysis of its run time is performed and all models from the BioModels database as well as further examples are examined. Apart from the practical implications of the algorithm the results also give insights into the complexity of solving reaction-diffusion PDEs.

COVID-19-associated pulmonary aspergillosis (CAPA): identification of Aspergillus species and determination of antifungal susceptibility profiles.

Among the co-infectious agents in COVID-19 patients, Aspergillus species cause invasive pulmonary aspergillosis (IPA). IPA is difficult to diagnose and is associated with high morbidity and mortality. This study is aimed at identifying Aspergillus spp. from sputum and tracheal aspirate (TA) samples of COVID-19 patients and at determining their antifungal susceptibility profiles. A total of 50 patients with COVID-19 hospitalized in their intensive care units (ICU) were included in the study. Identification of Aspergillus isolates was performed by phenotypic and molecular methods. ECMM/ISHAM consensus criteria were used for IPA case definitions. The antifungal susceptibility profiles of isolates were determined by the microdilution method. Aspergillus spp. was detected in 35 (70%) of the clinical samples. Among the Aspergillus spp., 20 (57.1%) A. fumigatus, six (17.1%) A. flavus, four (11.4%) A. niger, three (8.6%) A. terreus, and two (5.7%) A. welwitschiae were identified. In general, Aspergillus isolates were susceptible to the tested antifungal agents. In the study, nine patients were diagnosed with possible IPA, 11 patients were diagnosed with probable IPA, and 15 patients were diagnosed with Aspergillus colonization according to the used algorithms. Serum galactomannan antigen positivity was found in 11 of the patients diagnosed with IPA. Our results provide data on the incidence of IPA, identification of Aspergillus spp., and its susceptibility profiles in critically ill COVID-19 patients. Prospective studies are needed for a faster diagnosis or antifungal prophylaxis to manage the poor prognosis of IPA and reduce the risk of mortality.

Evaluation of amino acid profile in serum of patients with Covid-19 for providing a new treatment strategy.

Background: Amino acids have an important role in metabolism and may affect COVID-19-related outcomes. In our study, the amino acid serum level of hospitalized COVID19 patients was evaluated to determine a new treatment strategy. Methods: The amino acid profile covering 43 amino acids in 68 subjects, comprising 30 (14 men and 16 women) controls and 38 (16 men and 22 women) COVID-19 patients, were examined. The amino acid profiles of the participants were screened by LC-MS/MS. Results: Compared with the control group, serum levels of 27 amino acids increased in the patient group. Alpha-aminopimelic acid, sarcosine, and hydroxyproline amino acids were considerably higher in the control group than in the patient group (p<0.0001). There was no notable difference among control group and the case group for 13 amino acids (p≥0.05). A significant positive correlation was seen among the control and the patient groups in the mean amino acid values (r=0.937; p<0.0001). Conclusions: These results postulated a clear picture on the serum levels of amino acid in the COVID-19 patients. Serum amino acids measured in hospitalized COVID-19 patients can explain the patient's metabolic status during the disease.

Evaluation of the Relationship Between Aquaporin-1, Hepcidin, Zinc, Copper, and Iron Levels and Oxidative Stress in the Serum of Critically Ill Patients with COVID-19.

Our study aims to determine the relationship between hepcidin, aquaporin (AQP-1), copper (Cu), zinc (Zn), iron (Fe) levels, and oxidative stress in the sera of seriously ill COVID-19 patients with invasive mechanical ventilation. Ninety persons with and without COVID-19 were taken up and separated into two groups. The first group included seriously COVID-19 inpatients having endotracheal intubation in the intensive care unit (n = 45). The second group included individuals who had negative PCR tests and had no chronic disease (the healthy control group n = 45). AQP-1, hepcidin, Zn, Cu, Fe, total antioxidant status (TAS), and total oxidant status (TOS) were studied in the sera of both groups, and the relations of these levels with oxidative stress were determined. When the COVID-19 patient and the control groups were compared, all studied parameters were found to be statistically significant (p < 0.01). Total oxidant status (TOS), oxidative stress index (OSI), and AQP-1, hepcidin, and Cu levels were increased in patients with COVID-19 compared to healthy people. Serum TAC, Zn, and Fe levels were found to be lower in the patient group than in the control group. Significant correlations were detected between the studied parameters in COVID-19 patients. Results indicated that oxidative stress may play an important role in viral infection due to SARS-CoV-2. We think that oxidative stress parameters as well as some trace elements at the onset of COVID-19 disease will provide a better triage in terms of disease severity.

Bioinformatics Analysis of the Periodicity in Proteins with Coiled-Coil Structure-Enumerating All Decompositions of Sequence Periods.

A coiled coil is a structural motif in proteins that consists of at least two α-helices wound around each other. For structural stabilization, these α-helices form interhelical contacts via their amino acid side chains. However, there are restrictions as to the distances along the amino acid sequence at which those contacts occur. As the spatial period of the α-helix is 3.6, the most frequent distances between hydrophobic contacts are 3, 4, and 7. Up to now, the multitude of possible decompositions of α-helices participating in coiled coils at these distances has not been explored systematically. Here, we present an algorithm that computes all non-redundant decompositions of sequence periods of hydrophobic amino acids into distances of 3, 4, and 7. Further, we examine which decompositions can be found in nature by analyzing the available data and taking a closer look at correlations between the properties of the coiled coil and its decomposition. We find that the availability of decompositions allowing for coiled-coil formation without putting too much strain on the α-helix geometry follows an oscillatory pattern in respect of period length. Our algorithm supplies the basis for exploring the possible decompositions of coiled coils of any period length.

Coating indwelling urinary catheters with moxifloxacin prevents biofilm formation by Burkholderia cepacia.

BACKGROUND: Burkholderia cepacia adhesion and biofilm formation onto abiotic surfaces is an important feature of clinically relevant isolates. The in vitro biofilm formation of B. cepacia onto coated indwelling urinary catheters (IDCs) with moxifloxacin has not been previously investigated. OBJECTIVES: To examine the ability of B. cepacia to form biofilms on IDCs and the effect of coating IDCs with moxifloxacin on biofilm formation by B. cepacia in vitro. MATERIAL AND METHODS: The adhesion of B. cepacia to coated and uncoated IDCs with moxifloxacin was evaluated. Pieces of IDCs were coated with moxifloxacin (adsorption method). The spectrophotometric method was used to check moxifloxacin leaching into tubes. Coated and uncoated tubes were incubated with 107 colony forming units (cfu)/mL of B. cepacia. The viable bacterial count was used to count the number of bacteria adhered to coated and uncoated IDC pieces. RESULTS: A significant adhesion of B. cepacia to uncoated IDC pieces started 15 min after the incubation in a bacterial suspension (107 cfu/mL). A maximum adhesion was observed at 48 h. The pretreatment of IDCs with 100 µg/mL of moxifloxacin produced the best adsorption of antibiotic onto the IDCs. Coating IDC pieces with moxifloxacin significantly reduced the adhesion and biofilm formation of B. cepacia (p < 0.05) at various time intervals (1 h, 4 h and 24 h). CONCLUSIONS: The present study has demonstrated for the first time that coated IDCs with moxifloxacin reduce B. cepacia adhesion and biofilm formation. This finding has opened the door to the production of the new generation IDCs that prevent bacteria from attaching and forming biofilms.

Agent-based modelling of iron cycling bacteria provides a framework for testing alternative environmental conditions and modes of action.

Iron-reducing and iron-oxidizing bacteria are of interest in a variety of environmental and industrial applications. Such bacteria often co-occur at oxic-anoxic gradients in aquatic and terrestrial habitats. In this paper, we present the first computational agent-based model of microbial iron cycling, between the anaerobic ferric iron (Fe3+)-reducing bacteria Shewanella spp. and the microaerophilic ferrous iron (Fe2+)-oxidizing bacteria Sideroxydans spp. By including the key processes of reduction/oxidation, movement, adhesion, Fe2+-equilibration and nanoparticle formation, we derive a core model which enables hypothesis testing and prediction for different environmental conditions including temporal cycles of oxic and anoxic conditions. We compared (i) combinations of different Fe3+-reducing/Fe2+-oxidizing modes of action of the bacteria and (ii) system behaviour for different pH values. We predicted that the beneficial effect of a high number of iron-nanoparticles on the total Fe3+ reduction rate of the system is not only due to the faster reduction of these iron-nanoparticles, but also to the nanoparticles' additional capacity to bind Fe2+ on their surfaces. Efficient iron-nanoparticle reduction is confined to pH around 6, being twice as high than at pH 7, whereas at pH 5 negligible reduction takes place. Furthermore, in accordance with experimental evidence our model showed that shorter oxic/anoxic periods exhibit a faster increase of total Fe3+ reduction rate than longer periods.

Correction: Measuring attitudes towards biology major and non-major: Effect of students' gender, group composition, and learning environment.

[This corrects the article DOI: 10.1371/journal.pone.0251453.].

Measuring attitudes towards biology major and non-major: Effect of students' gender, group composition, and learning environment.

This study examined the effect of collaborative learning (CL) versus traditional lecture-based learning (TL) pedagogies and gender group composition in effecting positive or negative attitudes of biology major and nonmajor men and women students. The experimental research method was administered in experimental and control groups to test the hypotheses. Students' attitudes refer to their positive or negative feelings and inclinations to learn biology. A nine-factor attitude scale was administered in (1) single-gender nonmajor biology, (2) mixed-gender nonmajor biology, (3) single-gender major biology, and (4) mixed-gender biology major groups. Men (221) and women (219) were randomly assigned into single and mixed-gender classes without groups and single-gender groups (4M) or (4W) and mix-gender (2M+2W) groups. In CL nonmajor and major single-gender groups, women demonstrated significantly higher positive attitudes than men. In contrast, men's attitudes were significantly improved in mixed-gender CL groups for major and nonmajor sections, and the effect size was larger in mix-gender classes. Women feel less anxious in single-gender groups but more anxious in mixed-gender groups. In mixed-gender groups, men's self-efficacy, general interest, and motivation enhanced significantly; overall, men experienced greater satisfaction and triggered their desire to collaborate better, affecting all nine attitudinal factors. There was an interaction effect demonstrating the teaching pedagogy's impact on improving students' attitudes toward biology; students' gender and gender-specific group composition have been the most influential factor for nonmajor students. These findings suggest that there is a need for developing gender-specific and context-specific learning pedagogies, and instructors carefully select gender grouping in teaching undergraduate science subjects.

Structure and Hierarchy of SARS-CoV-2 Infection Dynamics Models Revealed by Reaction Network Analysis.

This work provides a mathematical technique for analyzing and comparing infection dynamics models with respect to their potential long-term behavior, resulting in a hierarchy integrating all models. We apply our technique to coupled ordinary and partial differential equation models of SARS-CoV-2 infection dynamics operating on different scales, that is, within a single organism and between several hosts. The structure of a model is assessed by the theory of chemical organizations, not requiring quantitative kinetic information. We present the Hasse diagrams of organizations for the twelve virus models analyzed within this study. For comparing models, each organization is characterized by the types of species it contains. For this, each species is mapped to one out of four types, representing uninfected, infected, immune system, and bacterial species, respectively. Subsequently, we can integrate these results with those of our former work on Influenza-A virus resulting in a single joint hierarchy of 24 models. It appears that the SARS-CoV-2 models are simpler with respect to their long term behavior and thus display a simpler hierarchy with little dependencies compared to the Influenza-A models. Our results can support further development towards more complex SARS-CoV-2 models targeting the higher levels of the hierarchy.

The International Virus Bioinformatics Meeting 2020.

The International Virus Bioinformatics Meeting 2020 was originally planned to take place in Bern, Switzerland, in March 2020. However, the COVID-19 pandemic put a spoke in the wheel of almost all conferences to be held in 2020. After moving the conference to 8-9 October 2020, we got hit by the second wave and finally decided at short notice to go fully online. On the other hand, the pandemic has made us even more aware of the importance of accelerating research in viral bioinformatics. Advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks. The International Virus Bioinformatics Meeting 2020 has attracted approximately 120 experts in virology and bioinformatics from all over the world to join the two-day virtual meeting. Despite concerns being raised that virtual meetings lack possibilities for face-to-face discussion, the participants from this small community created a highly interactive scientific environment, engaging in lively and inspiring discussions and suggesting new research directions and questions. The meeting featured five invited and twelve contributed talks, on the four main topics: (1) proteome and RNAome of RNA viruses, (2) viral metagenomics and ecology, (3) virus evolution and classification and (4) viral infections and immunology. Further, the meeting featured 20 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.

A novel method for achieving an optimal classification of the proteinogenic amino acids.

The classification of proteinogenic amino acids is crucial for understanding their commonalities as well as their differences to provide a hint for why life settled on the usage of precisely those amino acids. It is also crucial for predicting electrostatic, hydrophobic, stacking and other interactions, for assessing conservation in multiple alignments and many other applications. While several methods have been proposed to find "the" optimal classification, they have several shortcomings, such as the lack of efficiency and interpretability or an unnecessarily high number of discriminating features. In this study, we propose a novel method involving a repeated binary separation via a minimum amount of five features (such as hydrophobicity or volume) expressed by numerical values for amino acid characteristics. The features are extracted from the AAindex database. By simple separation at the medians, we successfully derive the five properties volume, electron-ion-interaction potential, hydrophobicity, α-helix propensity, and π-helix propensity. We extend our analysis to separations other than by the median. We further score our combinations based on how natural the separations are.

Differential equation-based minimal model describing metabolic oscillations in Bacillus subtilis biofilms.

Biofilms offer an excellent example of ecological interaction among bacteria. Temporal and spatial oscillations in biofilms are an emerging topic. In this paper, we describe the metabolic oscillations in Bacillus subtilis biofilms by applying the smallest theoretical chemical reaction system showing Hopf bifurcation proposed by Wilhelm and Heinrich in 1995. The system involves three differential equations and a single bilinear term. We specifically select parameters that are suitable for the biological scenario of biofilm oscillations. We perform computer simulations and a detailed analysis of the system including bifurcation analysis and quasi-steady-state approximation. We also discuss the feedback structure of the system and the correspondence of the simulations to biological observations. Our theoretical work suggests potential scenarios about the oscillatory behaviour of biofilms and also serves as an application of a previously described chemical oscillator to a biological system.

Extending the minimal model of metabolic oscillations in Bacillus subtilis biofilms.

Biofilms are composed of microorganisms attached to a solid surface or floating on top of a liquid surface. They pose challenges in the field of medicine but can also have useful applications in industry. Regulation of biofilm growth is complex and still largely elusive. Oscillations are thought to be advantageous for biofilms to cope with nutrient starvation and chemical attacks. Recently, a minimal mathematical model has been employed to describe the oscillations in Bacillus subtilis biofilms. In this paper, we investigate four different modifications to that minimal model in order to better understand the oscillations in biofilms. Our first modification is towards making a gradient of metabolites from the center of the biofilm to the periphery. We find that it does not improve the model and is therefore, unnecessary. We then use realistic Michaelis-Menten kinetics to replace the highly simple mass-action kinetics for one of the reactions. Further, we use reversible reactions to mimic the diffusion in biofilms. As the final modification, we check the combined effect of using Michaelis-Menten kinetics and reversible reactions on the model behavior. We find that these two modifications alone or in combination improve the description of the biological scenario.

Trends in mathematical modeling of host-pathogen interactions.

Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host-pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.

Combining evolution and self-organization to find natural Boolean representations in unconventional computational media.

Designing novel unconventional computing systems often requires the selection of the computational structure as well as choosing the right symbol encoding. Several approaches apply heuristic search and evolutionary algorithms to find both computational structure and symbol encoding, which is time consuming because they depend on each other. Here, we present a novel approach that combines evolution with self-organization, in particular we evolve the computational structure but let the symbol encoding emerge through self-organization. This should not only be more efficient but should also lead to a more "natural" symbol encoding. We successfully demonstrate the potential of the technique, using an evolutionary algorithm to optimize the parameters of two non-linear media to perform as NAND-gates: a continuous-time recurrent neural network (CTRNN) and a computational model of BZ-droplet-based computing (DropSim). In both cases, the technique identified representations for TRUE and FALSE, and system configurations that performed successfully as NAND-gates. The effectiveness of the evolved NAND gates was further evaluated by their performance in half-adder networks, where again, both evolved systems performed correctly, producing the correct output for all possible inputs and for all possible transitions between inputs. We conclude that beyond the specific applications demonstrated here, combining evolution with self-organization could be a promising strategy widely applicable.

Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis.

Influenza A virus is recognized today as one of the most challenging viruses that threatens both human and animal health worldwide. Understanding the control mechanisms of influenza infection and dynamics is crucial and could result in effective future treatment strategies. Many kinetic models based on differential equations have been developed in recent decades to capture viral dynamics within a host. These models differ in their complexity in terms of number of species elements and number of reactions. Here, we present a new approach to understanding the overall structure of twelve influenza A virus infection models and their relationship to each other. To this end, we apply chemical organization theory to obtain a hierarchical decomposition of the models into chemical organizations. The decomposition is based on the model structure (reaction rules) but is independent of kinetic details such as rate constants. We found different types of model structures ranging from two to eight organizations. Furthermore, the model's organizations imply a partial order among models entailing a hierarchy of model, revealing a high model diversity with respect to their long-term behavior. Our methods and results can be helpful in model development and model integration, also beyond the influenza area.

The Third Annual Meeting of the European Virus Bioinformatics Center.

The Third Annual Meeting of the European Virus Bioinformatics Center (EVBC) took place in Glasgow, United Kingdom, 28-29 March 2019. Virus bioinformatics has become central to virology research, and advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks, being successfully used to detect, control, and treat infections of humans and animals. This active field of research has attracted approximately 110 experts in virology and bioinformatics/computational biology from Europe and other parts of the world to attend the two-day meeting in Glasgow to increase scientific exchange between laboratory- and computer-based researchers. The meeting was held at the McIntyre Building of the University of Glasgow; a perfect location, as it was originally built to be a place for "rubbing your brains with those of other people", as Rector Stanley Baldwin described it. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The meeting featured eight invited and twelve contributed talks, on the four main topics: (1) systems virology, (2) virus-host interactions and the virome, (3) virus classification and evolution and (4) epidemiology, surveillance and evolution. Further, the meeting featured 34 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.

Spatial Rule-Based Simulations: The SRSim Software.

SRSim combines rule-based reaction network models with spatial particle simulations allowing to simulate the dynamics of large molecular complexes changing according to a set of chemical reaction rules. As the rule can contain patterns of molecular complexes and specific states of certain binding sites, a combinatorially complex or even infinitely sized reaction network can be defined. Particles move in a three-dimensional space according to molecular dynamics implemented by LAMMPS, while the BioNetGen language is used to formulate reaction rules. Geometric information is added in a specific XML format. The simulation protocol is exemplified by two different variants of polymerization as well as a toy model of DNA helix formation. SRSim is open source and available for download.