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Hydrogenotrophic methanogens of the mammalian gut: functionally similar, thermodynamically different. A modelling approach

Methanogenic archaea occupy a functionally important niche in the gut microbial ecosystem of mammals. Our purpose was to quantitatively characterize the dynamics of methanogenesis by integrating microbiology, thermodynamics and mathematical modelling. For that, in vitro growth experiments were performed with pure cultures of key methanogens from the human and ruminant gut, namely Methanobrevibacter smithii, Methanobrevibacter ruminantium and Methanobacterium formicium. Microcalorimetric experiments were performed to quantify the methanogenesis heat flux. We constructed an energetic-based mathematical model of methanogenesis. Our model captured efficiently the dynamics of methanogenesis with concordance correlation coefficients of 0.94 for CO2, 0.99 for H2 and 0.97 for CH4. Together, experimental data and model enabled us to quantify metabolism kinetics and energetic patterns that were specific and distinct for each species despite their use of analogous methane-producing pathways. Then, we tested in silico the interactions between these methanogens under an in vivo simulation scenario using a theoretical modelling exercise. In silico simulations suggest that the classical competitive exclusion principle is inapplicable to gut ecosystems and that kinetic information alone cannot explain gut ecological aspects such as microbial coexistence. We suggest that ecological models of gut ecosystems require the integration of microbial kinetics with nonlinear behaviours related to spatial and temporal variations taking place in mammalian guts. Our work provides novel information on the thermodynamics and dynamics of methanogens. This understanding will be useful to construct new gut models with enhanced prediction capabilities and could have practical applications for promoting gut health in mammals and mitigating ruminant methane emissions.

microbiology

Characterizing and comparing phylogenies from their Laplacian spectrum

Phylogenetic trees are central to many areas of biology, ranging from population genetics and epidemiology to microbiology, ecology, and macroevolution. The ability to summarize properties of trees, compare different trees, and identify distinct modes of division within trees is essential to all these research areas. But despite wide-ranging applications, there currently exists no common, comprehensive framework for such analyses. Here we present a graph-theoretical approach that provides such a framework. We show how to construct the spectral density profiles of phylogenetic trees from their Laplacian graphs. Using ultrametric simulated trees as well as non-ultrametric empirical trees, we demonstrate that the spectral density successfully identifies various properties of the trees and clusters them into meaningful groups. Finally, we illustrate how the eigengap can identify modes of division within a given tree. As phylogenetic data continue to accumulate and to be integrated into various areas of the life sciences, we expect that this spectral graph-theoretical framework to phylogenetics will have powerful and long-lasting applications.

Systems Biology

Role of autoregulation and relative synthesis of operon partners in alternative sigma factor networks

Despite the central role of alternative sigma factors in bacterial stress response and virulence their regulation remains incompletely understood. Here we investigate one of the best-studied examples of alternative sigma factors: the {sigma}B network that controls the general stress response of Bacillus subtilis to uncover widely relevant general design principles that describe the structure-function relationship of alternative sigma factor regulatory networks. We show that the relative stoichiometry of the synthesis rates of {sigma}B, its anti-sigma factor RsbW and the anti-anti-sigma factor RsbV plays a critical role in shaping the network behavior by forcing the {sigma}B network to function as an ultrasensitive negative feedback loop. We further demonstrate how this negative feedback regulation insulates alternative sigma factor activity from competition with the housekeeping sigma factor for RNA polymerase and allows multiple stress sigma factors to function simultaneously with little competitive interference.\n\nMajor Subject Areas: Computational and systems biology, Microbiology & Infectious disease\n\nResearch Organism: Bacillus subtilis

Systems Biology

Next-generation sequencing in neuropathological diagnosis of infections of the nervous system

ObjectiveTo determine the feasibility of next-generation sequencing (NGS) microbiome approaches in the diagnosis of infectious disorders in brain or spinal cord biopsies in patients with suspected central nervous system (CNS) infections.\n\nMethodsIn a prospective-pilot study, we applied NGS in combination with a new computational analysis pipeline to detect the presence of pathogenic microbes in brain or spinal cord biopsies from ten patients with neurological problems indicating possible infection but for whom conventional clinical and microbiology studies yielded negative or inconclusive results.\n\nResultsDirect DNA and RNA sequencing of brain tissue biopsies generated 8.3 million to 29.1 million sequence reads per sample, which successfully identified with high confidence the infectious agent in three patients, identified possible pathogens in two more, and helped to understand neuropathological processes in three others, demonstrating the power of large-scale unbiased sequencing as a novel diagnostic tool. Validation techniques confirmed the pathogens identified by NGS in each of the three positive cases. Clinical outcomes were consistent with the findings yielded by NGS on the presence or absence of an infectious pathogenic process in eight of ten cases, and were non-contributory in the remaining two.\n\nConclusionsNGS-guided metagenomic studies of brain, spinal cord or meningeal biopsies offer the possibility for dramatic improvements in our ability to detect (or rule out) a wide range of CNS pathogens, with potential benefits in speed, sensitivity, and cost. NGS-based microbiome approaches present a major new opportunity to investigate the potential role of infectious pathogens in the pathogenesis of neuroinflammatory disorders.

Genomics

Computational Pan-Genomics: Status, Promises and Challenges

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic datasets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this paper, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies, and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

Genomics

NGMASTER: in silico Multi-Antigen Sequence Typing for Neisseria gonorrhoeae

Whole-genome sequencing (WGS) provides the highest resolution analysis for comparison of bacterial isolates in public health microbiology. However, although increasingly being used routinely for some pathogens such as Listeria monocytogenes and Salmonella enterica, the use of WGS is still limited for other organisms, such as Neisseria gonorrhoeae. Multi-antigen sequence typing (NG-MAST) is the most widely performed typing method for epidemiologic surveillance of gonorrhoea. Here, we present NGMASTER - a command-line software tool for performing in silico NG-MAST on assembled genome data. NGMASTER rapidly and accurately determined the NG-MAST of 630 assembled genomes, facilitating comparisons between WGS and previously published gonorrhoea epidemiological studies. The source code and user documentation are available at https://github.com/MDU-PHL/ngmaster.

Genomics

Preliminary investigation of Incorporation and Viability of Crystal Violet in Aspergillus flavus

The abusive use of antimicrobials have caused resistance in the microorganisms. Therefore, inovations in research of modern microbiology developed the photodynamic inactivation that is non-toxic and do not promote microbian resistance since it has multiples action sites. When a photoactive molecule is activated by light, it becomes more toxic for the microorganism than for the human cells, increasing the phototoxic effect against such cells. We tested the incorporation of the photosensitizer crystal violet by Aspergillus flavus and its viability in different periods of time of incubation (6 and 18 hours), concentrations of crystal violet (4M, 10M e 15 M) and different conditions (resting and agitation). We also tested the photoinactivation of Aspergillus flavus containing crystal violet. The incorporation rate does not depend on the concentration of CV neither on the condition of incubation. It depends exclusively on the medium, since it presented higher values in lack ingredients medium. The assays of viability of fungi after exposure to crystal violet show that there is fungic growth when samples are maintained in the dark. However, when the culture were submitted to excitation via laser, there was a decrease in the biomass growth, which indicates that there was photodynamic inactivation of the fungi.

biochemistry

A new improved and extended version of the multicell bacterial simulator gro

gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use and provide new functionalities. The new version of gro is between one and two orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 20 minutes. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

synthetic biology

Refractive index as an intrinsic imaging contrast for 3-D label-free live cell imaging

Live cell imaging provides essential information in the investigation of cell biology and related pathophysiology. Refractive index (RI) can serve as intrinsic optical imaging contrast for 3-D label-free and quantitative live cell imaging, and provide invaluable information to understand various dynamics of cells and tissues for the study of numerous fields. Recently significant advances have been made in imaging methods and analysis approaches utilizing RI, which are now being transferred to biological and medical research fields, providing novel approaches to investigate the pathophysiology of cells. To provide insight how RI can be used as an imaging contrast for imaging of biological specimens, here we provide the basic principle of RI-based imaging techniques and summarize recent progress on applications, ranging from microbiology, hematology, infectious diseases, hematology, and histopathology.

cell biology

Key Issues Review: Evolution on rugged adaptive landscapes

Adaptive landscapes represent a mapping between genotype and fitness. Rugged adaptive landscapes contain two or more adaptive peaks: allele combinations that differ in two or more genes and confer higher fitness than intermediate combinations. How would a population evolve on such rugged landscapes? Evolutionary biologists have struggled with this question since it was first introduced in the 1930s by Sewall Wright.\n\nDiscoveries in the fields of genetics and biochemistry inspired various mathematical models of adaptive landscapes. The development of landscape models led to numerous theoretical studies analyzing evolution on rugged landscapes under different biological conditions. The large body of theoretical work suggests that adaptive landscapes are major determinants of the progress and outcome of evolutionary processes.\n\nRecent technological advances in molecular biology and microbiology allow experimenters to measure adaptive values of large sets of allele combinations and construct empirical adaptive landscapes for the first time. Such empirical landscapes have already been generated in bacteria, yeast, viruses, and fungi, and are contributing to new insights about evolution on adaptive landscapes.\n\nIn this Key Issues Review we will: (i) introduce the concept of adaptive landscapes; (ii) review the major theoretical studies of evolution on rugged landscapes; (iii) review some of the recently obtained empirical adaptive landscapes; (iv) discuss recent mathematical and statistical analyses motivated by empirical adaptive landscapes, as well as provide the reader with source code and instructions to implement simulations of adaptive landscapes; and (v) discuss possible future directions for this exciting field.

evolutionary biology

Optimal Growth Of Microbes On Mixed Carbon Sources

A classic problem in microbiology is that bacteria display two types of growth behavior when cultured on a mixture of two carbon sources: in certain mixtures the bacteria consume the two carbon sources sequentially (diauxie) and in other mixtures the bacteria consume both sources simultaneously (co-utilization). The search for the molecular mechanism of diauxie led to the discovery of the lac operon and gene regulation in general. However, why microbes would bother to have different strategies of taking up nutrients remained a mystery. Here we show that diauxie versus co-utilization can be understood from the topological features of the metabolic network. A model of optimal allocation of protein resources to achieve maximum growth quantitatively explains why and how the cell makes the choice when facing multiple carbon sources. Our work solves a long-standing puzzle and sheds light on microbes optimal growth in different nutrient conditions.

systems biology

Plasmid Profiler: Comparative Analysis Of Plasmid Content In WGS Data

SummaryComparative analysis of bacterial plasmids from whole genome sequence (WGS) data generated from short read sequencing is challenging. This is due to the difficulty in identifying contigs harbouring plasmid sequence data, and further difficulty in assembling such contigs into a full plasmid. As such, few software programs and bioinformatics pipelines exist to perform comprehensive comparative analyses of plasmids within and amongst sequenced isolates. To address this gap, we have developed Plasmid Profiler, a pipeline to perform comparative plasmid content analysis without the need for de novo assembly. The pipeline is designed to rapidly identify plasmid sequences by mapping reads to a plasmid reference sequence database. Predicted plasmid sequences are then annotated with their incompatibility group, if known. The pipeline allows users to query plasmids for genes or regions of interest and visualize results as an interactive heat map.\n\nAvailability and ImplementationPlasmid Profiler is freely available software released under the Apache 2.0 open source software license. A stand-alone version of the entire Plasmid Profiler pipeline is available as a Docker container at https://hub.docker.com/r/phacnml/plasmidprofiler_0_1_6/.\n\nThe conda recipe for the Plasmid R package is available at: https://anaconda.org/bioconda/r-plasmidprofiler\n\nThe custom Plasmid Profiler R package is also available as a CRAN package at https://cran.r-project.org/web/packages/Plasmidprofiler/index.html\n\nGalaxy tools associated with the pipeline are available as a Galaxy tool suite at https://toolshed.g2.bx.psu.edu/repository?repository_id=55e082200d16a504\n\nThe source code is available at: https://github.com/phac-nml/plasmidprofiler\n\nThe Galaxy implementation is available at: https://github.com/phac-nml/plasmidprofiler-galaxy\n\nContactEmail: gary.vandomselaar@canada.ca\n\nAddress: National Microbiology Laboratory, Public Health Agency of Canada, 1015 Arlington Street, Winnipeg, Manitoba, Canada\n\nSupplementary informationDocumentation: http://plasmid-profiler.readthedocs.io/en/latest/

bioinformatics

Exploring the Diversity of Bacillus whole genome sequencing projects using Peasant, the Prokaryotic Assembly and Annotation Tool

BackgroundThe persistent decrease in cost and difficulty of whole genome sequencing of microbial organisms has led to a dramatic increase in the number of species and strains characterized from a wide variety of environments. Microbial genome sequencing can now be conducted by small laboratories and as part of undergraduate curriculum. While sequencing is routine in microbiology, assembly, annotation and downstream analyses still require computational resources and expertise, often necessitating familiarity with programming languages. To address this problem, we have created a light-weight, user-friendly tool for the assembly and annotation of microbial sequencing projects.\n\nResultsThe Prokaryotic Assembly and Annotation Tool, Peasant, automates the processes of read quality control, genome assembly, and annotation for microbial sequencing projects. High-quality assemblies and annotations can be generated by Peasant without the need of programming expertise or high-performance computing resources. Furthermore, statistics are calculated so that users can evaluate their sequencing project. To illustrate the computational speed and accuracy of Peasant, the SRA records of 322 Illumina platform whole genome sequencing assays for Bacillus species were retrieved from NCBI, assembled and annotated on a single desktop computer. From the assemblies and annotations produced, a comprehensive analysis of the diversity of over 200 high-quality samples was conducted, looking at both the 16S rRNA phylogenetic marker as well as the Bacillus core genome.\n\nConclusionsPeasant provides an intuitive solution for high-quality whole genome sequence assembly and annotation for users with limited programing experience and/or computational resources. The analysis of the Bacillus whole genome sequencing projects exemplifies the utility of this tool. Furthermore, the study conducted here provides insight into the diversity of the species, the largest such comparison conducted to date.

bioinformatics

Predicting Drug Resistance Evolution: Antimicrobial Peptides Vs. Antibiotics

Antibiotic resistance constitutes one of the most pressing public health concerns. Antimicrobial peptides of multicellular organisms are considered part of a solution to this problem, and AMPs produced by bacteria such as colistin are last resort drugs. Importantly, antimicrobial peptides differ from many antibiotics in their pharmacodynamic characteristics. Here we implement these differences within a theoretical framework to predict the evolution of resistance against antimicrobial peptides and compare it to antibiotic resistance. Our analysis of resistance evolution finds that pharmacodynamic differences all combine to produce a much lower probability that resistance will evolve against antimicrobial peptides. The finding can be generalized to all drugs with pharmacodynamics similar to AMPs. Pharmacodynamic concepts are familiar to most practitioners of medical microbiology, and data can be easily obtained for any drug or drug combination. Our theoretical and conceptual framework is therefore widely applicable and can help avoid resistance evolution if implemented in antibiotic stewardship schemes or the rational choice of new drug candidates.

evolutionary biology

The SMuSh pathway is essential for survival during growth-induced compressive mechanical stress

Cells that proliferate within a confined environment build up mechanical compressive stress. For example, mechanical pressure emerges in the naturally space-limited tumor environment. However, little is known about how cells sense and respond to mechanical compression. We developed microfluidic bioreactors to enable the investigation of the effects of compressive stress on the growth of the genetically tractable model organism Saccharomyces cerevisiae. We used this system to determine that compressive stress is partly partly sensed through a module consisting of the mucin Msb2, and the cell wall protein Sho1, which act together as a sensor module in one of the two major osmosensing pathways in budding yeast. This signal is transmitted via the MAPKKK kinase Ste11. Thus, we term this mechanosensitive pathway the SMuSh pathway, for Ste11 through Mucin / Sho1 pathway. The SMuSh pathway delays cells in the G1 phase of the cell cycle and improves cell survival in response to growth-induced pressure. We also found that the Cell Wall Integrity (CWI) pathway contributes to the response to mechanical compressive stress. These latter results are confirmed in complimentary experiments in the accompanying manuscript from Mishra et al. When both the SMuSh and the CWI pathways are deleted, cells fail to adapt to compressive stress and all cells lyse at relatively low pressure when grown in confinement. Thus, we define a network that is essential for cell survival during growth under pressure. We term this new mechanosensory system the SCWISh (Survival through the CWI and SMuSh) network.\n\nSignificance StatementGrowth in confined environments leads to the build up of compressive mechanical stresses, which are relevant to diverse fields, from cancer to microbiology. In contrast to tensile stress, little is known about the molecular integration of compressive stresses. In this study, we elucidate the SMuSh pathway, which, together with the Cell Wall Integrity pathway, is essential for viability of the budding yeast S. cerevisiae when growing under mechanical pressure. Pressure-sensing requires the transmembrane mucin, Msb2, which is linked to the actin cortex. Our result raises the intriguing question of whether mucins, widely conserved in eukaryotes and frequently misregulated in cancers, might sense compressive stresses in other organisms, including humans.

cell biology

Genome reconstruction and characterisation of extensively drug-resistant bacterial pathogens through direct metagenomic sequencing of human faeces

Whole-genome sequencing of microbial pathogens is revolutionising modern approaches to outbreaks of infectious diseases and is reliant upon organism culture. Culture-independent methods have shown promise in identifying pathogens, but high level reconstruction of microbial genomes from microbiologically complex samples for more in-depth analyses remains a challenge. Here, using metagenomic sequencing of a human faecal sample and analysis by tetranucleotide frequency profiling projected onto emergent self-organising maps, we were able to reconstruct the underlying populations of two extensively-drug resistant pathogens, Klebsiella pneumoniae carbapenemase (KPC)-producing Klebsiella pneumoniae and vancomycin-resistant Enterococcus faecium. From these genomes, we were able to ascertain molecular typing results, such as MLST, and identify highly discriminatory mutations in the metagenome to distinguish closely related strains. These proof-of-principle results demonstrate the utility of clinical sample metagenomics to recover sequences of important drug-resistant bacteria and application of the approach in outbreak investigations, independent of the need to culture the organisms.

genomics

The CURE for Culturing Fastidious Microbes

Course-based Undergraduate Research Experiences (CUREs) expand the scientific educational benefits of research to large groups of students in a course setting. As part of an ongoing effort to integrate CUREs into first-year biology labs, we developed a microbiology CURE (mCURE) that uses a modified dilution-to-extinction high throughput culturing protocol for isolating abundant yet fastidious aquatic bacterioplankton during one semester. Students learn common molecular biology techniques like nucleic acid extraction, PCR, and molecular characterization; read and evaluate scientific literature; and receive training in scientific communication through written and oral exercises that incorporate social media elements. In the first three semesters, the mCUREs achieved similar cultivability success as implementation of the protocol in a standard laboratory setting. Our modular framework facilitates customization of the curriculum for use in multiple settings and we provide classroom exercises, assignments, assessment tools, and examples of student output to assist with implementation.

scientific communication and education

A plasmid-based Escherichia coli gene expression system with cell-to-cell variation below the extrinsic noise limit

Experiments in synthetic biology and microbiology can benefit from protein expression systems with low cell-to-cell variability (noise) and expression levels precisely tunable across a useful dynamic range. Despite advances in understanding the molecular biology of microbial gene regulation, many experiments employ protein-expression systems exhibiting high noise and nearly all-or-none responses to induction. I present an expression system that incorporates elements known to reduce gene expression noise: negative autoregulation and bicistronic transcription. I show by stochastic simulation that while negative autoregulation can produce a more gradual response to induction, bicistronic expression of a repressor and gene of interest can be necessary to reduce noise below the extrinsic limit. I synthesized a plasmid-based system incorporating these principles and studied its properties in Escherichia coli cells, using flow cytometry and fluorescence microscopy to characterize induction dose-response, induction/repression kinetics and gene expression noise. By varying ribosome binding site strengths, expression levels from 55-- 10,740 molecules/cell were achieved with noise below the extrinsic limit. Individual strains are inducible across a dynamic range greater than 20-fold. Experimental comparison of different regulatory networks confirmed that bicistronic autoregulation reduces noise, and revealed unexpectedly high noise for a conventional expression system with a constitutively expressed transcriptional repressor. I suggest a hybrid, low-noise expression system to increase the dynamic range.

synthetic biology