Antibiotic resistance (AR) is a critical and looming threat to human health that requires action across the One Health continuum (humans, animals, environment). Coordinated surveillance within the environmental sector is largely underdeveloped in current National Action Plans to combat the spread of AR, and a lack of effective study approaches and standard analytical methods have led to a dearth of impactful environmental monitoring data on the prevalence and risk of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in aquatic environments. In this dissertation, integrated surveillance approaches of surface water and wastewater systems are demonstrated, and efforts are made towards standardizing both metagenomic- and culture-based techniques for globally comparable environmental monitoring. A field study of differentially-impacted watersheds on the island of Puerto Rico post-Hurricane Maria demonstrated the effectiveness of metagenomics in defining direct impact of anthropogenic stress and human fecal contamination on the proliferation of ARGs in riverine systems. The contribution of treated wastewater effluents to the dissemination of highly mobile and clinically-relevant ARGs and their connection to local clinical settings was also revealed. At the international scale, a transect of conventional activated sludge wastewater treatment plants (WWTPs), representing both US/European and Asian regions, were found to significantly attenuate ARG abundance through the removal of total bacterial load and human fecal indicators, regardless of influent ARG compositions. Strong structural symmetry between microbiome and ARG compositions through successional treatment stages suggested that horizontal gene transfer plays a relatively minor role in actively shaping resistomes during treatment. Risk assessment models, however, indicated high-priority plasmid-borne ARGs in final treated effluents discharged around the world, indicating potentially increased transmission risks in downstream environments. Advancements were also made toward standardizing methods for the generation of globally representative and comparable metagenomic- and culture-based AR monitoring data via two comprehensive and critical literature reviews. The first review provides guidance in next-generation sequencing (NGS) studies of environmental AR, proposing a framework for experimental controls, adequate sequencing depths, appropriate use of public databases, and the derivation of datatypes that are conducive for risk assessment. The second review focuses on antibiotic-resistant Enterococcus spp. as robust monitoring targets and an attractive alternative to more widely adopted Gram-negative organisms, while proposing workflows that generate universally equivalent datatypes. Finally, quantitative metagenomic (qMeta) techniques were benchmarked using internal reference standards for high-throughput quantification of ARGs with statistical reproducibility. / Doctor of Philosophy / Antimicrobials have contributed to the reduction of infectious diseases in humans and animals since the early 20th century, increasing productivity and saving countless lives. However, their industrial-scale application across human, animal, and agricultural sectors over the last several decades, especially the use of antibiotics, have engendered the proliferation of antibiotic resistance (AR). AR occurs when changes in bacteria cause the drugs used to treat infections to become less effective and has become one of the leading public health threats of the 21st century. The global spread of AR through the transmission and evolution of antibiotic resistant bacteria (ARB; known colloquially as "superbugs") and antibiotic resistance genes (ARGs) across the One Health continuum (i.e., humans, animals, and the environment) is resulting in increased hospitalization, length of hospital stays, suffering, death, and overall health-care associated costs globally. This dissertation demonstrates the use of metagenomics, the sequencing of all genetic material (e.g., DNA) recovered from a microbial community, for the comprehensive monitoring of ARB and ARGs in aquatic environments, a key pathway for the dissemination of AR into and out of human populations. In order to impede the proliferation of AR, surveillance systems are currently in place to track the spread and evolution of ARB and ARGs in humans and livestock, as well as agri-food sectors. However, the surveillance in natural and built environments (i.e., rivers and domestic sewage) has significantly lagged due to the lack of standard monitoring targets and methodologies. It is also a goal of this dissertation to suggest guidance for the collection of metagenomic- and culture-based AR monitoring data to generate universally comparable results that can be included in centralized databases. Riverine systems are ideal models for tracking input of antibiotic resistance to the natural environment by human activity. After Hurricane-Maria, many of Puerto Rico's wastewater treatment plants (WWTPs) went offline, discharging raw sewage to local surface waters. In a cross-sectional study of watersheds impacted by WWTPs, the abundance of ARGs was directly correlated to increases in local population density. Also, highly mobile and clinically-relevant ARGs were found directly downstream of WWTPs across the island. We found that many of these ARGs corresponded well to forms AR endemic to the region. WWTPs are the primary engineering controls put in place to curb the spread of human and animal waste streams and can help to reduce AR. An international transect of conventional activated sludge WWTPs representing US/Europe and Asia were sampled to garner a mechanistic understanding of the fate or ARGs through treatment. Although WWTPs remove total bacteria, human fecal indicators, and much of the abundance of ARGs, mobile and clinically-relevant ARGs are discharged around the world in large quantities. Consideration is needed in certain regions of iv the world where the managing of human waste streams is the first line of defense against the dissemination of resistance to local communities. Two comprehensive critical literature reviews were conducted to evaluate the various methodologies for generating and analyzing metagenomic- and culture-based AR monitoring data. These reviews address the need for experimental rigor and disclosure of extensive metadata for inclusion in future, centralized databases. The articles further provide guidance with respect to universally comparable datatypes and efficient workflows that will aid in the scale-up of the collection of environmental monitoring data within a global surveillance framework. Finally, a study was conducted to benchmark the use of internal DNA reference standards for the absolute quantification of ARGs (i.e., on a ARG copy per volume of sample basis). The statistical framework for ARG detection and its implications for wastewater-based surveillance systems of AR are also discussed.

BACKGROUND: Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α- & β-diversity. Feature subset selection - a sub-field of machine learning - can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the human gut microbiome. RESULTS: We have developed a new Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. We demonstrate the software tools capabilities on publicly available datasets. CONCLUSIONS: We have made the software implementation of Fizzy available to the public under the GNU GPL license. The standalone implementation can be found at http://github.com/EESI/Fizzy.

BACKGROUND:Metagenomics has a great potential to discover previously unattainable information about microbial communities. An important prerequisite for such discoveries is to accurately estimate the composition of microbial communities. Most of prevalent homology-based approaches utilize solely the results of an alignment tool such as BLAST, limiting their estimation accuracy to high ranks of the taxonomy tree.RESULTS:We developed a new homology-based approach called Taxonomic Analysis by Elimination and Correction (TAEC), which utilizes the similarity in the genomic sequence in addition to the result of an alignment tool. The proposed method is comprehensively tested on various simulated benchmark datasets of diverse complexity of microbial structure. Compared with other available methods designed for estimating taxonomic composition at a relatively low taxonomic rank, TAEC demonstrates greater accuracy in quantification of genomes in a given microbial sample. We also applied TAEC on two real metagenomic datasets, oral cavity dataset and Crohn's disease dataset. Our results, while agreeing with previous findings at higher ranks of the taxonomy tree, provide accurate estimation of taxonomic compositions at the species/strain level, narrowing down which species/strains need more attention in the study of oral cavity and the Crohn's disease.CONCLUSIONS:By taking account of the similarity in the genomic sequence TAEC outperforms other available tools in estimating taxonomic composition at a very low rank, especially when closely related species/strains exist in a metagenomic sample.

Were he alive today, would Louis Pasteur still champion culture methods he pioneered over 150 years ago for identifying bacterial pathogens? Or, might he suggest that new molecular techniques may prove a better way forward for quickly detecting the true microbial diversity of wounds? As modern clinicians faced with treating complex patients with diabetic foot infections (DFI), should we still request venerated and familiar culture and sensitivity methods, or is it time to ask for newer molecular tests, such as 16S rRNA gene sequencing? Or, are molecular techniques as yet too experimental, non-specific and expensive for current clinical use? While molecular techniques help us to identify more microorganisms from a DFI, can they tell us ‘who done it?', that is, which are the causative pathogens and which are merely colonizers? Furthermore, can molecular techniques provide clinically relevant, rapid information on the virulence of wound isolates and their antibiotic sensitivities? We herein review current knowledge on the microbiology of DFI, from standard culture methods to the current era of rapid and comprehensive ‘crime scene investigation' (CSI) techniques.

Microorganisms play central roles in planet Earth’s geochemical cycles, in food production, and in health and disease of humans and livestock. In spite of this, most microbial life forms remain unknown and unnamed, their ecological importance and potential technological applications beyond the realm of speculation. This is due both to the magnitude of microbial diversity and to technological limitations. Of the many advances that have enabled microbiology to reach new depth and breadth in the past decade, one of the most important is affordable high-throughput DNA sequencing. This technology plays a central role in each paper in this thesis. Papers I and II are focused on developing methods to survey microbial diversity based on marker gene amplification and sequencing. In Paper I we proposed a computational strategy to design primers with the highest coverage among a given set of sequences and applied it to drastically improve one of the most commonly used primer pairs for ecological surveys of prokaryotes. In Paper II this strategy was applied to an eukaryotic marker gene. Despite their importance in the food chain, eukaryotic microbes are much more seldom surveyed than bacteria. Paper II aimed at making this domain of life more amenable to high-throughput surveys. In Paper III, the primers designed in papers I and II were applied to water samples collected up to twice weekly from 2011 to 2013 at an offshore station in the Baltic proper, the Linnaeus Microbial Observatory. In addition to tracking microbial communities over these three years, we created predictive models for hundreds of microbial populations, based on their co-occurrence with other populations and environmental factors. In paper IV we explored the entire metagenomic diversity in the Linnaeus Microbial Observatory. We used computational tools developed in our group to construct draft genomes of abundant bacteria and archaea and described their phylogeny, seasonal dynamics and potential physiology. We were also able to establish that, rather than being a mixture of genomes from fresh and saline water, the Baltic Sea plankton community is composed of brackish specialists which diverged from other aquatic microorganisms thousands of years before the formation of the Baltic itself. / <p>QC 20150505</p>

Thesis (PhD)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: The grape and wine ecosystem contains fungi, bacteria and yeasts whose interactions contribute to the final wine product. While the non-Saccharomyces yeasts are dominant in the early stage of alcoholic fermentation, the later stage is always dominated by Saccharomyces cerevisiae. Although their presence in wine fermentation is often short-lived, the non-Saccharomyces yeasts are known to produce an array of extracellular hydrolytic enzymes which facilitate the extraction and release of aroma compounds, but might also play a role in microbial interactions. The present study aimed to investigate the microbial diversity of grape juice and to evaluate the potential of non-Saccharomyces yeasts to produce hydrolytic enzymes and display anti-fungal properties. To capture the microbial diversity, culture-dependent (plating) and –independent (Automated Ribosomal Intergenic Spacer Analysis (ARISA)) techniques were used in parallel. The fungal and bacterial ARISA displayed a wider range of operational taxonomic units (OTUs) in comparison to cultivation-based technique, demonstrating that ARISA is a powerful culture-independent technique applicable to ecological studies in wine. Some of the uncommon yeast isolates derived from our cultivation-based study were subjected to an enzymatic screening process. Hydrolases, such as chitinases, β-1,4-cellulases, β-1,3-1,6-glucanases, β-glucosidases, pectinases and acid proteases were specifically sought. Most of the yeast isolates exhibited chitinase, β-1,4-cellulase as well as β-1,3-1,6-glucanase activities. Only Metschnikowia chrysoperlae exhibited β-glucosidase activity. We also retrieved the partial chitinase gene sequences from M. chrysoperlae, Pichia burtonii, Hyphopichia pseudoburtonii that exhibited chitinase activity. Among the isolates, Pseudozyma fusiformata exhibited a strong antagonistic activity against the wine spoilage yeasts B. bruxellensis AWRI 1499 and B. anomalus IWBT Y105. Furthermore, we showed that the killer phenotype of P. fusiformata cannot be attributed to a viral encoded dsRNA. Finally, two metagenomic approaches were employed in an attempt to explore the indigenous microbiome in a more holistic manner, where we adopted whole metagenome Roche GS-FLX 454-pyrosequencing and construction of a fosmid library. The whole metagenome sequencing revealed a wide range of hydrolytic enzymes that showed homology to enzymes from different fungal and non-Saccharomyces yeast species. Moreover, the metagenomic library screening resulted in the retrieval of 22 chitinase and 11 β-glucosidase positive fosmid clones originating from yeasts. Two clones of interest, BgluFos-G10 and ChiFos-C21, were subjected to next generation sequencing. BgluFos-G10 revealed 2 ORFs exhibiting homology to glycosyl hydrolase family 16 proteins whereas no ORFs encoding chitinase enzymes could be identified in the ChiFos-C21 clone. However, all the potential ORFs identified exhibited homology to a gene cluster from Clavispora lusitaniae ATCC 42720, suggesting that the cloned DNA fragments belonged to a yeast species closely related to C. lusitaniae or members of the family Metschnikowiaceae. Overall, our study identified a variety of novel hydrolytic enzymes. However, retrieving the full gene sequences of these identified enzymes would be the immediate follow-up of our study. Moreover, the hydrolytic and antifungal activities exhibited by the yeast isolate could be of major interest in evaluating their potential as biocontrol agents against grapevine fungal pathogens and subsequently the wine spoilage yeasts. It would be interesting to evaluate as well the potential impact of these enzymes under wine making condition and could be our next step of investigation. / AFRIKAANSE OPSOMMING: Die druif en wyn ekosisteme bevat swamme, bakterië en giste en die interaksies van hierdie organismes dra by tot die finale wyn produk. Die nie-Saccharomyces giste is dominant in die vroeë stadium van die alkoholiese fermentasie, maar die latere fase word altyd gedomineer deur Saccharomyces cerevisiae. Alhoewel hulle teenwoordigheid in wyngistings gewoonlik kortstondig is, is die nie-Saccharomyces giste bekend vir die produksie van ‘n verskeidenheid ekstrasellulêre hidrolitiese ensieme wat die ekstraksie en vrylating van aroma komponente fasiliteer, en ook moontlik ‘n rol kan speel in mikrobiese interaksie. Hierdie studie beoog om die mikrobiese diversiteit van druiwesap te bestudeer en die potensiaal van nie-Saccharomyces giste te evalueer ten opsigte van die produksie van hidrolitiese ensieme, asook die demonstrasie van anti-swam eienskappe. Kweking-afhanklike (uitplating), asook –onafhanklike (Automatiese Ribosomale Intergeniese Spasieerder Analise (ARISA)) tegnieke is in parallel gebruik om die mikrobiese diversiteit te bepaal. Die swam en bakteriële ARISA het ‘n groter verskeidenheid van operasionele taksinomiese eenhede (OTUe) vertoon in vergelyking met die kweking-gebasseerde tegniek en dit demonstreer dat ARISA ‘n kragtige kweking-onafhanklike tegniek is, wat toepasbaar is in ekologiese studies van wyn . Sommige van die skaarser gisisolate, uit ons kweking -gebasseerde studie was vir ensiemaktiwiteite geskandeer. Daar is spesifiek gesoek vir hidrolases soos chitinases,β-1,4-sellulases, β-1,3-1,6-glukunases, β-glukosidases, pektinases en suur proteases. Die meeste gisisolate het chitinase,β-1,4-sellulase asook β-1,3-1,6-glukunase aktiwiteit vertoon. Slegs Metschinikowia chrysoperlae het β-glukosidase aktiwiteit vertoon. Ons het verder die gedeeltelike chitinase geensekwensies van M. chrysoperlae, Pichia burtonii en Hyphopichia pseudoburtonii wat chitinase aktiwiteit vertoon het, bepaal. Een isolaat, Pseudozyma fusiformata, het ‘n sterk antagonistiese aktiwiteit teenoor die wyn bederfgiste, Bretanomyces bruxellensis AWRI 1499 en B. anomalus IWBT Y105 vertoon. Verder het ons gewys dat die killer fenotipe van P. fusiformata nie gekoppel kan word aan’n viraal gekodeerde dsRNA nie. Ten laaste is twee metagenomiese benaderings, naamlik die volledige metagenoom Roche GS-FLX 454-pirovolgordebepaling en konstruksie van ‘n fosmied biblioteek, gebruik om die inheemse mikrobioom op ‘n meer holistiese wyse te bestudeer. Die volgordebepaling van die volledige metagenoom het ‘n wye verskeidenheid hidrolitiese ensieme aan die lig gebring wat homologie met ensieme van verskillende swamme en nie-Saccharomyces gisspesies getoon het. Verder het die skandering van die metagenomiese biblioteek die isolasie van fosmiedklone van gisoorsprong wat positief is vir chitinase aktiwiteit (22 klone) en β-glukosidase aktiwiteit (11 klone) tot gevolg gehad. Twee van hierdie klone, BgluFos-G10 en ChiFos-C21, is met volgende generasie volgordebepaling ontleed. BgluFos-G10 het twee oopleesrame (OLRe) wat homologie met glikosiel hidrolase familie 16 proteïene het, vertoon maar geen OLRe wat chitinase ensieme enkodeer kon in die ChiFos-C21 kloon geïdentifiseer word nie. Al die potensiële OLRe wat geïdentifiseer is, het homologie aan ‘n genepoel van Clavispora lusitaniae ATCC 42720 vertoon, wat daarop dui dat die gekloneerde DNS fragmente aan ‘n gisspesie behoort wat naverwant aan C. lusitaniae of lede van die Metschinikowiaceae familie is. In geheel gesien het ons studie ‘n verskeidenheid van nuwe hidrolitiese ensieme geïdentifiseer. Die bepaling van die volledige geenvolgordes van hierdie geïdentifiseerde ensieme sal die onmiddelike opvolg aksie van hierdie studie wees. Verder is die hidrolitiese en anti-swam aktiwiteite wat deur die gisisolate gedemonstreer is, van hoof belang, asook die evaluering van hulle potensiaal as biokontrole agente teen wingerd swampatogene en wyn bederfgiste. Dit sal ook interessant wees om die potensiële impak van hierdie ensieme onder wynmaakkondisies te bepaal, en dit kan dus ons volgende ondersoek stap wees.

Motivation: Network interpretation using correlations has several known difficulties. Firstly, the data structure has discrete counts with an excess of zeros creating non-normal non-continuous data. Secondly, correlations, often used as similarity measures in network inference, are not causal. Thirdly, there is a masking effect of mutualism on commensalism and competition on amensalism in ecological networks that interfere with interpretation (Faust and Raes, 2012). More explicitly, the symmetric nature of correlations (cor(X,Y)=cor(Y,X)) can mask the affect of the asymmetric ecology relationship (commensalism and amensalism). We aim to solve the third issue which may speed up targeted drug therapies or disease diagnosis based on specific relationships in gut microbiomes. Methods: We apply a non-symmetric correlation method, Gini Correlations which should serve as a better classifier of ecological relationships revealing a fuller picture of microbiomes. First, create simulated correlated and independent Zero-Inflated Negative Binomial data. Second, validate Gini correlations by comparing Gini with Pearson Spearman and Kendall correlations; calculate false positive rate, true positive rate, accuracy, ROC, AUC after applying Benjamini-Hochberg (1995) multiple testing correction. Simulation Result: Gini is consistent and out performs other methods for small sample sizes of 10 and 25 producing consistently low false positive rates across 64+ simulation settings as well as consistently high accuracy rates. When sample size is increased to 50 Gini performs as well as other methods. Real Data Result: For well-defined microbial communities Gini correlations found novel biologically and medically relevant relationships. However, Gini's ability to unmask non-symmetric ecological relationships is yet to be determined.

No description available.

A Profile Hidden Markov Model (HMM) is a statistical model for representing a multiple sequence alignment (MSA). Profile HMMs are important tools for sequence homology detection and have been used in wide a range of bioinformatics applications including protein structure prediction, remote homology detection, and sequence alignment. Profile HMM methods result in accurate alignments on datasets with evolutionarily similar sequences; however, I will show that on datasets with evolutionarily divergent sequences, the accuracy of HMM-based methods degrade. My dissertation presents a new statistical model for representing an MSA by using a set of HMMs. The family of HMM (fHMM) approach uses multiple HMMs instead of a single HMM to represent an MSA. I present a new algorithm for sequence alignment using the fHMM technique. I show that using the fHMM technique for sequence alignment results in more accurate alignments than the single HMM approach. As sequence alignment is a fundamental step in many bioinformatics pipelines, improvements to sequence alignment result in improvements across many different fields. I show the applicability of fHMM to three specific problems: phylogenetic placement, taxonomic profiling and identification, and MSA estimation. In phylogenetic placement, the problem addressed is how to insert a query sequence into an existing tree. In taxonomic identification and profiling, the problems addressed are how to taxonomically classify a query sequence, and how to estimate a taxonomic profile on a set of sequences. Finally, both profile HMM and fHMM require a backbone MSA as input in order to align the query sequences. In MSA estimation, the problem addressed is how to estimate a ``de novo'' MSA without the use of an existing backbone alignment. For each problem, I present a software pipeline that implements the fHMM specifically for that domain: SEPP for phylogenetic placement, TIPP for taxonomic profiling and identification, and UPP for MSA estimation. I show that SEPP has improved accuracy compared to the single HMM approach. I also show that SEPP results in more accurate phylogenetic placements compared to existing placement methods, and SEPP is more computationally efficient, both in peak memory usage and running time. I show that TIPP more accurately classifies novel sequences compared to the single HMM approach, and TIPP estimates more accurate taxonomic profiles than leading methods on simulated metagenomic datasets. I show how UPP can estimate ``de novo'' alignments using fHMM. I present results that show UPP is more accurate and efficient than existing alignment methods, and estimates accurate alignments and trees on datasets containing both full-length and fragmentary sequences. Finally, I show that UPP can estimate a very accurate alignment on a dataset with 1,000,000 sequences in less than 2 days without the need of a supercomputer. / Computer Sciences / text

Microbial communities are recognized as major drivers of global biogeochemical processes. However, the genetic diversity and composition, as well as processes leading to the origin and diversification of these communities in space and time, are poorly understood. Character- ization of microbial communities using high-throughput sequencing of 16S tags shows that Operational Taxonomic Unit (OTU) abundances can be approximated by a gamma distribu- tion, which suggests structuring around small numbers of highly abundant OTUs and a large proportion of rare OTUs. The current methods used to characterize how communities are structured rely on multivariate statistics, which operate on pair-wise distance matrices. My analyses demonstrate that use of these methods, by reducing a highly-dimensional data set (tens of samples, thousands of OTUs), results in a significant loss of information. I demon- strate that, in some cases, up to 80% of the least abundant OTUs may be removed while still recovering the same community relationships; this indicates these metrics are biased toward the highly abundant OTUs. I also demonstrate that the observed patterns of OTU abundance detected from microbial communities can be robustly modeled using techniques similar to those used to model the presence and absence of genes in genome evolution. Using simulation studies, I show that general Markov models in a Bayesian inference framework out- perform traditional, multivariate ecological methods in recovering true community structure. Applying this new methodology to Atlantic Ocean communities uncovered a distance-decay effect which was not revealed by the traditional methods; applying to communities discov- ered on Hog Island point toward mechanisms of thicket establishment. Although the ocean data set operated on a much larger, continental scale, characterization of the sequence data generated from the nutrient-poor soil on Hog Island, a barrier island off the Virginia coast, allows for a better characterization of the processes affecting these communities on a much smaller scale. Finally, using 16S data from the Human Vaginal Microbiome Project, gener- ated here at VCU under the umbrella of the overall NIH HMP initiative, I give examples of the quality control, analysis and visualization pipeline that I developed to support the efforts of this project. In conclusion, my analyses of the metagenomic sequence data from bacterial communities sampled from different environments demonstrate that the proper identification of the biological processes influencing these communities requires the development and im- plementation of new statistical and computational methodologies that take advantage of the extensive amount of information generated in next-generation, high-throughput sequencing projects.