We would like to congratulate Michael Mills and Tao Wang on receiving the Outstanding Teaching Assistant Award for 2020-2021! The Center for Teaching and Learning administers the Outstanding Teaching Assistant (OTA) award, sponsored by the Office of the Vice President for Instruction. This award recognizes teaching assistants who demonstrate superior teaching skills while serving in the classroom or laboratory. Way to go Michael and Tao!!

Abstract:

Single-stranded DNA phages of the family Microviridae have fundamentally different evolutionary origins and dynamics than their more frequently studied double-stranded DNA counterparts. Despite their small size (generally <5kb), which imposes extreme constraints on genomic innovation, microviruses have adapted to become prominent components of viromes in numerous ecosystems and hold a dominant position among viruses in the human gut. Yet until recently, they were known almost exclusively from metagenomic sequence data. By in-vitro synthesizing and transforming their miniature genomes into an E. coli host, I develop an experimentally tractable host-virus system that allows their study in the laboratory. Through building microviruses with combinations of genomic segments from different phages (mimicking diversity observed in natural populations), their interactions with each other and with their hosts can be recreated. Using this approach, I discover the role of a hypervariable genomic region in prophages that defends their hosts against infection by other members of the microvirus population. By detecting microviruses in metagenomic and genomic datasets, I show that this hypervariable region has evolved multiple times independently in response to the preceding evolution of lysogenic ability. These results provide a rare insight into the biology of these elusive phages and emphasize the importance of virus-virus interactions in viral evolution in general. The establishment of a microviral model system also paves the way for gaining a more thorough understanding of the roles of microviruses in the larger ecosystem of the human gut and elsewhere.

Abstract:

The paradigm has been that reactive oxygen species are toxic to bacteria. Indeed, an important strategy by which immune cells like neutrophils control and eliminate invading microbes is through synthesizing bleach (HOCl), the same chemical used as a household disinfectant. However, bacteria that colonize animals are not passive players and have evolved their own counter strategies to manipulate and repurpose reactive oxygen species in surprising ways. My research focuses on pro-inflammatory bacteria that have acquired robust antioxidant defenses sufficient to colonize inflamed tissue and tolerate millimolar concentrations of bleach. I will discuss my discovery that some pathogenic bacteria are attracted to bleach, which is potentially a mechanism by which they seek and acquire nutrients from damaged host tissue. 

Abstract:

Chronic infections place a significant burden on healthcare systems, requiring over $20 billion in treatment annually in the United States alone. Notably, chronic infections are frequently polymicrobial and are often recalcitrant to antibiotic treatment, through a process termed “synergy”. Despite the clinical importance, many key features of bacterial physiology in chronic infections, including the molecular mechanisms and impacts of microbe-microbe interactions, remain understudied, in part due to the challenge of assessing bacterial physiology in the human host. My work aims to address this challenge, focused on the human pathogen Staphylococcus aureus, a leading cause of human infection worldwide and a significant cause of both morbidity and mortality. Specifically, I ask foundational questions about how S. aureus, causes disease and persists in human infection, both by using validated experimental models of infection and by directly analyzing human clinical samples. In my previous work, I found co-infection altered the requirement for ~6% (192 genes) of the S. aureus genome when compared to mono-infection in three different infection models, indicating global changes in S. aureus metabolism in response to another microbe, highlighting the importance of microbe-microbe interactions in mediating bacterial physiology in vivo. In addition, I recently performed the first large scale assessment of S. aureus physiology in situ in chronic human infection and found remarkable conservation of S. aureus gene expression in the cystic fibrosis (CF) lung across patients using RNA-seq, despite numerous epidemiological differences. With this data, I inferred the metabolic state and nutritional environment of S. aureus in CF sputum including iron scarcity, carbohydrate use, and virulence factor production. Further, through a machine learning framework, I defined a ‘human CF lung transcriptome signature’ – 32 genes whose transcription distinguishes human CF sputum from other in vivo and in vitro environments, primarily consisting of genes involved in metabolism and virulence – and was able to apply these findings to make an in vitro model more similar to CF infection. Ongoing work further explores mechanisms that allow S. aureus to establish and persist in chronic polymicrobial infections, including CF, osteomyelitis, and chronic wounds, centered on the role of microbe-microbe interactions.

Abstract:

Protein glycosylation, one of the most complex post-translational modifications, plays central roles in a variety of cellular processes in prokaryotes. Elucidating its involvement in biofilm formation is crucial for a detailed understanding of antibiotic resistance and pathogenicity mechanisms. Yet the complexity and variability of glycoproteins in prokaryotes have made their system-wide analysis, thus far, virtually unachievable. Here, I will present an interdisciplinary approach that combines bioinformatics, comprehensive glycoproteomics and phenotypic characterizations for the functional analysis of prokaryotic glycosylation. This includes the development of universally applicable bioinformatic tools that are suitable to analyze the plethora of glycan compositions existing in prokaryotes, as well as the initiation of the Archaeal Proteome Project, a community-effort that combines proteomics datasets across a broad range of experimental conditions to harvest the wealth of information buried within them. Together with an in-depth proteomic analysis of glycosylation pathway mutants from the model archaeon Haloferax volcanii, this has led to the identification of the largest archaeal glycoproteome described so far. It also revealed the concurrence of two independent N-glycosylation pathways that can modify the same glycosylation sites. A variety of phenotypic assays of mutants defective in N-glycosylation pathways or glycosylated proteins were further used to show the involvement of glycosylation in crucial cellular processes such as biofilm formation and cell shape. The establishment of these approaches for H. volcanii not only provided new insights into the extent, complexity, and roles of glycosylation in archaea, but their applicability to a multitude of prokaryotes also paves the way for functional glycoproteomics in biofilm forming pathogens such as Pseudomonas aeruginosa.

Abstract:

Microbial populations can adapt rapidly due to their vast supply of mutations.  To understand what makes these mutations adaptive, we need a systems-level approach that accounts for the wide range of biological traits that mutations can affect.  Using a combination of high-throughput experimental methods and computational modeling, I will discuss two systems-level approaches to microbial evolution.  First, I will show that both genetic and non-genetic mechanisms lead to substantial covariation in microbial life-history traits, such as the maximum growth rate in an environment and the lag time when transitioning between environments.  In particular, I will demonstrate the potential for tradeoffs in these traits and how such tradeoffs produce rich evolutionary and ecological phenomena, including stable coexistence of multiple strains and higher-order ecological effects.  Second, I will describe our development of a high-diversity DNA barcode library to track adaptation at high-resolution in bacterial populations with large numbers of simultaneously segregating mutations.  We apply this method to study adaptation to ultra-low concentrations of antibiotics, which are believed to be common in both clinical and natural environments.  We show that these ultra-low concentrations have distinct effects on evolution even without detectable resistance, and that adaptation to these conditions is highly predictable at the population level.  Furthermore, the fate of individual lineages can also be highly predictable in some conditions, depending on the relative contributions of standing genetic variation and de novo mutations to adaptation.  Altogether these examples demonstrate the power of systems-level approaches to elucidating the evolution of microbes.

Abstract:

Bacterial gene expression is highly heterogeneous even in isogenic bacteria grown in the same conditions; bacteria differentiate into subpopulations that may assume different roles for the survival of community. Population-level gene expression measurements are insufficient to resolve such phenotypic states which have been only discovered through single-cell methods. Using quantitative single-cell time-lapse microscopy, we discovered a novel microbial gene regulatory strategy in the model organism Bacillus subtilis accomplished through chromosomal arrangement of key genes regulating the heterogeneous sporulation cell fate. Methods such as fluorescence microscopy, however, are typically based on reporters allowing to measure only a limited set of genes at a time and requiring tractable model organisms. To address these limitations, I developed microSPLiT, a scalable single-cell RNA sequencing method tailored for bacteria. MicroSPLiT revealed a plethora of gene expression states in >25,000 single B. subtilis cells, including rare and unexpected cell states that remained hidden at a population level. With high scalability and resolution, microSPLiT is an emergent technology for single-cell gene expression studies of complex natural and engineered microbial communities.

Please email mibcoord@uga.edu for meeting info.