METABOLIC MODELING OF MULTISPECIES MICROBIAL BIOFILMS

Biofilms are ubiquitous in medical, environmental, and engineered microbial systems. The majority of naturally occurring microbes grow as mixed species biofilms. These complicated biofilm consortia are comprised of many cell phenotypes with complex interactions and self-organized into three-dimensional structures. Approximately 2% of the US population suffers from non-healing chronic wounds infected by a combination of commensal and pathogenic bacteria whereas about 500,000 cases of Clostridium difficile infections (CDI) are reported annually. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich environments and species interactions that promote community stability and robustness. This thesis focusses on developing metabolic modeling framework to study the interactions and the spatial/temporal organizations in the biofilms. The modeling framework is based on integrating genome scale metabolic reconstructions of considered species in this work, with the nutrient uptake kinetics to predict the species abundances, growth rates and byproduct secretions. The spatiotemporal modeling framework accounts for the nutrient concentration gradients in the biofilm system. Spatiotemporal biofilm metabolic models were formulated by combining genome scale metabolic reconstructions of considered species with uptake kinetics for available nutrients and reaction-diffusion type equations for species biomass, supplied substrates and synthesized metabolic byproducts. The resulting partial differential equations embedded with linear programs were discretized in the space and integrated using a dynamic flux balance method. This framework was used to calculate the spatial and temporal variations in the species, nutrient and byproduct concentrations in biofilms. This framework was used to study the species organization and dynamics in chronic wound infections, CDI and environmental biofilms. The chronic wound biofilm model was comprising of two most dominant species, Pseudomonas aeruginosa and Staphylococcus aureus. The CDI biofilm model was comprising of representative species from three most common phyla in gut Bacteroidetes thetaiotaomicron, Faecalibacterium prausnitzii, Escherichia coli and pathogen C. difficile. The simulation results were used to study the interspecies interactions, the spatial partitioning in the biofilms and important crossfeeding relationships within the community. These predictions would be useful in devising effective antibiotic treatment strategies to cure the biofilm infections associated with chronic wounds and C. difficile. The environmental biofilm model for cyanobacteria and heterotrophs was developed and validated with the experimental results, this model was used to evaluate the community dynamics under extreme environmental conditions The second modeling framework considered biofilm as a well-mixed homogenous system at steady state. Steady state in silico community models were formulated by combining genome scale metabolic reconstructions of the considered species. The community models were solved using SteadyCom method. This method uses community flux balance analysis to calculate the relative abundance of each species with an objective of maximizing the community growth rate. A 12 species chronic wound community metabolic model covering 74% of 16S rDNA pyrosequencing reads of dominant genera from 2,963 chronic wound patients was developed. The community model was used to predict species abundances averaged across this large patient population. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy. The frameworks developed in this thesis would be useful in developing patient/disease specific therapeutic treatments

Spatiotemporal Metabolic Modeling of Pseudomonas aeruginosa Biofilm Expansion

Spatiotemporal metabolic modeling of microbial metabolism is a step closer to achieving higher dimensionalities in numerical studies (in silico) of biofilm maturation. Dynamic Flux Balance Analysis (DFBA) is an advanced modeling technique because this method incorporates Genome Scale Metabolic Modeling (GSMM) to compute the biomass growth rate and metabolite fluxes. Biofilm thickness is pertinent because this variable of biofilm maturation can be measured in a laboratory (in vitro). Pseudomonas aeruginosa (P. aeruginosa) is the model bacterium used in this computational model based on previous research conducted by Dr. Michael Henson, available GSMMs, and the societal significance of patients suffering from P. aeruginosa airway infections. Spatiotemporal Flux Balance Analysis (SFBA) will be the computational method used in this thesis to simulate biofilm growth. Another level of accuracy will be introduced to SFBA which is a dynamic finite difference grid that will vary relative to the biofilm’s velocity of expansion/contraction. This novel idea is governed by a differential equation that defines the biofilm’s velocity and updates the spatial dependency of the finite difference grid which has never been done while utilizing GSMM. Environmental conditions (bulk concentrations of metabolites) are altered to investigate how varying nutrients (glucose, oxygen, lactate, nitrate) affected biofilm maturation

Spatiotemporal Metabolic Network Models Reveal Complex Autotroph-Heterotroph Biofilm Interactions Governed by Photon Incidences

Autotroph-heterotroph interactions are ubiquitous in natural environment and play a key role in controlling various essential ecosystem functions, such as production and utilization of organic matter, cycling of nitrogen, sulfur, and other chemical elements. Understanding how these biofilm metabolic interactions are constrained in space and time remains challenging because fully predictive models designed for this purpose are currently limited. Toward filling this gap, here we developed community metabolic network models for two autotroph-heterotroph biofilm consortia (termed UCC-A and UCC-O), which share a suite of common heterotrophic members but have a single distinct photoautotrophic cyanobacterium (Phormidesmis priestleyi str. ANA and Phormidium sp. OSCR) that provides organic carbon and nitrogen sources to support the growth of heterotrophic partners. After determining model parameters by data fitting using the spatiotemporal distributions of microbial abundances, we comparatively analyzed the resulting biofilm models to examine any fundamental differences in microbial interactions between the two consortia under the variation of key environmental variables: CO2 and photon levels. The UCC-A model predicted generally expected responses, i.e., the autotroph population increased in response to elevated levels of CO2 and photon, followed by increase in the heterotroph population. In contrast, the UCC-O model showed somewhat complicated dynamics, e.g., higher photon incidence rates resulted in the increase in autotroph population but decrease in heterotroph population due to the lowered provision of glucose from the autotroph. A further analysis showed that species coexistence was governed by the photon incidences rather than the carbon availability for UCC-O, which was the opposite for UCC-A

Spatiotemporal Metabolic Network Models Reveal Complex Autotroph-Heterotroph Biofilm Interactions Governed by Photon Incidences

Autotroph-heterotroph interactions are ubiquitous in natural environment and play a key role in controlling various essential ecosystem functions, such as production and utilization of organic matter, cycling of nitrogen, sulfur, and other chemical elements. Understanding how these biofilm metabolic interactions are constrained in space and time remains challenging because fully predictive models designed for this purpose are currently limited. Toward filling this gap, here we developed community metabolic network models for two autotroph-heterotroph biofilm consortia (termed UCC-A and UCC-O), which share a suite of common heterotrophic members but have a single distinct photoautotrophic cyanobacterium (Phormidesmis priestleyi str. ANA and Phormidium sp. OSCR) that provides organic carbon and nitrogen sources to support the growth of heterotrophic partners. After determining model parameters by data fitting using the spatiotemporal distributions of microbial abundances, we comparatively analyzed the resulting biofilm models to examine any fundamental differences in microbial interactions between the two consortia under the variation of key environmental variables: CO2 and photon levels. The UCC-A model predicted generally expected responses, i.e., the autotroph population increased in response to elevated levels of CO2 and photon, followed by increase in the heterotroph population. In contrast, the UCC-O model showed somewhat complicated dynamics, e.g., higher photon incidence rates resulted in the increase in autotroph population but decrease in heterotroph population due to the lowered provision of glucose from the autotroph. A further analysis showed that species coexistence was governed by the photon incidences rather than the carbon availability for UCC-O, which was the opposite for UCC-A

\u3cem\u3ePseudomonas aeruginosa\u3c/em\u3e reverse diauxie is a multidimensionl, optimized, resource utilization strategy

Pseudomonas aeruginosa is a globally-distributed bacterium often found in medical infections. The opportunistic pathogen uses a different, carbon catabolite repression (CCR) strategy than many, model microorganisms. It does not utilize a classic diauxie phenotype, nor does it follow common systems biology assumptions including preferential consumption of glucose with an ‘overflow’ metabolism. Despite these contradictions, P. aeruginosa is competitive in many, disparate environments underscoring knowledge gaps in microbial ecology and systems biology. Physiological, omics, and in silico analyses were used to quantify the P. aeruginosa CCR strategy known as ‘reverse diauxie’. An ecological basis of reverse diauxie was identified using a genome-scale, metabolic model interrogated with in vitro omics data. Reverse diauxie preference for lower energy, nonfermentable carbon sources, such as acetate or succinate over glucose, was predicted using a multidimensional strategy which minimized resource investment into central metabolism while completely oxidizing substrates. Application of a common, in silico optimization criterion, which maximizes growth rate, did not predict the reverse diauxie phenotypes. This study quantifies P. aeruginosa metabolic strategies foundational to its wide distribution and virulence including its potentially, mutualistic interactions with microorganisms found commonly in the environment and in medical infections

Challenges of biofilm control and utilization : lessons from mathematical modelling

Funding This work was supported by a scholarship grant from the School of Natural and Computing Sciences at the University of Aberdeen and the Faculty of Health Sciences at Curtin University.Peer reviewedPostprin

Perspectives and Challenges in Microbial Communities Metabolic Modeling

Bacteria have evolved to efficiently interact each other, forming complex entities known as microbial communities. These “super-organisms” play a central role in maintaining the health of their eukaryotic hosts and in the cycling of elements like carbon and nitrogen. However, despite their crucial importance, the mechanisms that influence the functioning of microbial communities and their relationship with environmental perturbations are obscure. The study of microbial communities was boosted by tremendous advances in sequencing technologies, and in particular by the possibility to determine genomic sequences of bacteria directly from environmental samples. Indeed, with the advent of metagenomics, it has become possible to investigate, on a previously unparalleled scale, the taxonomical composition and the functional genetic elements present in a specific community. Notwithstanding, the metagenomic approach per se suffers some limitations, among which the impossibility of modeling molecular-level (e.g., metabolic) interactions occurring between community members, as well as their effects on the overall stability of the entire system. The family of constraint-based methods, such as flux balance analysis, has been fruitfully used to translate genome sequences in predictive, genome-scale modeling platforms. Although these techniques have been initially developed for analyzing single, well-known model organisms, their recent improvements allowed engaging in multi-organism in silico analyses characterized by a considerable predictive capability. In the face of these advances, here we focus on providing an overview of the possibilities and challenges related to the modeling of metabolic interactions within a bacterial community, discussing the feasibility and the perspectives of this kind of analysis in the (near) future

Novel Insights into Skin Bacterial and Viral Communities in Health and Acute Wounding

Human skin is colonized by diverse microbial communities that have broad impacts on health and disease. Bacterial communities have been associated with dermatological diseases including Atopic Dermatitis and Psoriasis, and while roles of virus communities (viromes) in cutaneous health are poorly characterized, virome dysbiosis has been implicated in other human diseases and individual viruses are known to impact cutaneous health. Here we present a comprehensive research program aimed at broadly understanding the roles of bacteria and viruses in human dermatological health and perturbation by wounding. In the first section, we characterize the healthy human skin virome and investigate potential interactions between virus and bacterial communities. Samples were collected from sixteen subjects at eight body sites over one month. Virome diversity and composition varied by natural skin occlusion and the microenvironment substrates. Viruses were enriched for temperate replication-cycle genes, and maintained genes encoding potential antibiotic resistance and virulence factors. We also highlighted potential interactions between the virus (phage) and bacterial communities, including CRISPR targeting and significant ecological associations by co-occurrence modeling. This work provides a greater ecological context for our individualized understanding of cutaneous viruses, and provides a foundation for future studies of the skin virome upon perturbation and disease. In the second section, we characterize the microbial communities associated with skin perturbation in the form of acute, open fracture wounding. Thirty subjects presenting to the Hospital of the University of Pennsylvania for acute care of open fractures were enrolled in a prospective cohort study. Microbiota were collected from wound center and adjacent skin upon presentation to the ER and during follow up visits. Bacterial communities were studied using 16S rRNA amplicon sequencing. Microbiome composition and diversity colonizing open fracture wounds became increasingly similar to adjacent skin microbiota with healing. Clinical factors were associated with various aspects of microbiota diversity and composition. We also developed an analysis tool patPRO to facilitate analysis of this longitudinal dataset, and to aid others in analyses of similar data. The results of this pilot study demonstrate the diversity and dynamism of the open fracture microbiota, and their relationship to clinical variables

The microbiome of diabetic foot ulcers and the role of biofilms

Diabetic Foot Ulcers are a common precursor to the development of infection and amputations. A breach in the protective skin barrier represents a portal of entry for invading microorganisms, where infective episodes frequently pursue. Three key areas that may augment clinical care are one. understanding what microorganisms are present in Diabetic Foot Ulcers, two. differentiating if microorganisms are planktonic microbial cells or slow growing microbial biofilms and three. treating Diabetic Foot Ulcers complicated by microorganisms with effective topical agents. As part of this thesis, 16S rDNA next generation sequencing was utilised to profile the microbiota of infected Diabetic Foot Ulcers (DFUs). Clinical / laboratory data and treatment outcomes were collected and correlated against microbiota data. Thirty-nine patients with infected DFUs were recruited over twelve-months. Shorter duration DFUs (less than six weeks) all had one dominant bacterial species (n= five of five, 100%, p <⋅001), S. aureus in three cases and S. agalactiae in two. Longer duration DFUs (≥six weeks) were diversely polymicrobial (p = .01) with an average of 63 (range 19-125) bacterial species. Severe Diabetic Foot Infections (DFIs) had complex microbiota’s and were distinctly dissimilar to less severe infections (p = .02), characterised by the presence of low frequency microorganisms. Our results confirm that short DFUs have a simpler microbiota’s consisting of pyogenic cocci but chronic DFUs have a highly polymicrobial microbiota. The duration of a DFU may be useful as a guide to directing antimicrobial therapy. Secondly, we utilised Scanning electron microscopy (SEM) and Fluorescent in situ Hybridisation (FISH) techniques to determine if DFUs were complicated by sessile, slow growing bacteria referred to as biofilms. 65 DFU specimens were obtained from subjects with infected chronic ulcers. Of the 65 DFU specimens evaluated by microscopy, all were characterized as containing biofilm (100%, p < .001). Molecular analyses of DFU specimens revealed diverse polymicrobial communities. No clinical visual cues were identified in aiding clinicians identify wound biofilm. Microscopy visualization when combined with molecular approaches, confirms biofilms are ubiquitous in DFUs and a paradigm shift of managing these complicated wounds needs to consider anti-biofilm strategies. Lastly, the effectiveness of various topical antimicrobials commonly used in woundcare were tested in two separate studies by employing in vitro models, ex vivo porcine skin explant models and in vivo human studies. In the first study, 17 participants with chronic non-healing DFUs due to suspected biofilm involvement were recruited to receive one-week application of Cadexomer Iodine ointment. Real-time qPCR was used to determine the microbial load with 11 participants exhibiting one-two Log10 reductions in microbial load after treatment, in comparison to six patients who experienced less than one log10 reduction (p =.04). Scanning electron microscopy (SEM) and/or fluorescent in situ hybridisation (FISH) confirmed the presence or absence of biofilm in all 17 participants. 16SrDNAnextgenerationsequencing provided useful insights that these wounds support complex polymicrobial communities and demonstrated that Cadexomer Iodine had a broad level of antimicrobial activity in reducing both facultative anaerobes such as Staphylococcus spp., Serratia spp., aerobes including Pseudomonas spp., and obligate anaerobes including Clostridiales family XI. In the second study, a range of topical antimicrobial wound solutions were tested under three different conditions; (in vitro) 4 % w/v melaleuca oil, polyhexamethylene biguanide, chlorhexidine, povidone iodine and hypochlorous acid were tested at short duration exposure times for 15-minutes against three-day mature biofilms of S. aureus and P. aeruginosa. (ex vivo) Hypochlorous acid was tested in a porcine skin explant model with twelve cycles of tenminute exposure, over 24 hours, against three-day mature P. aeruginosa biofilms. (in vivo) 4 % w/v Melaleuca Oil was applied for 15-minutes exposure, daily, for seven days, in ten patients with chronic non-healing Diabetic Foot Ulcers (DFUs) complicated by biofilm. In vitro assessment demonstrated variable efficacy in reducing biofilms ranging between 0.5 log10 reductions to full eradication. Repeated instillation of hypochlorous acid in a porcine model achieved less than one log10 reduction (0.77 log10, p < 0.1). Application of 4 % w/v melaleuca oil in vivo, resulted in no change to the total microbial load of DFUs complicated by biofilm (median log10 microbial load pre-treatment = 4.9 log10 versus 4.8 log10 (p = .43). In conclusion, to the best of our knowledge, the in vivo human studies testing the performances of topical antimicrobials represents the first in vivo evidence employing a range of molecular and microscopy techniques. These demonstrate the ability of Cadexomer Iodine (sustained release over 48-72 hours) to reduce the microbial load of chronic non-healing DFUs complicated by biofilm. In contrast, short durations of exposure to topical antimicrobial wound solutions commonly utilised by clinicians are ineffective against microbial biofilms, particularly when used in vivo

The Colorful World of Extracellular Electron Shuttles

Descriptions of the changeable, striking colors associated with secreted natural products date back well over a century. These molecules can serve as extracellular electron shuttles (EESs) that permit microbes to access substrates at a distance. In this review, we argue that the colorful world of EESs has been too long neglected. Rather than simply serving as a diagnostic attribute of a particular microbial strain, redox-active natural products likely play fundamental, underappreciated roles in the biology of their producers, particularly those that inhabit biofilms. Here, we describe the chemical diversity and potential distribution of EES producers and users, discuss the costs associated with their biosynthesis, and critically evaluate strategies for their economical usage. We hope this review will inspire efforts to identify and explore the importance of EES cycling by a wide range of microorganisms so that their contributions to shaping microbial communities can be better assessed and exploited