Autism spectrum disorder is a neurodevelopmental disorder marked by social deficits, obsessive behavior, and repetitive actions. It has been shown that there is a communication pathway between the brain and gut called the gut-brain axis. Communication is thought to occur between the bacterial collections known as the microbiota in the gut and the resident immune cells in the brain, microglia. It has been postulated that bacteria in the gut are capable of secreting signaling molecules that can induce increases in pro-inflammatory cytokines. Specific cytokines such as IL-1B and IL-17 will elicit microglia activation and will likely result in alterations in neurotransmission in the brain. The activation or prohibition of maturation of microglia can lead to severe developmental delays. Four animal models will be used for this experiment. C57 will be the control or wildtype model; valproic acid is an anti-seizure medication that will be given to pregnant mice to see effects on offspring. BTBR is the third model, which has been genetically bred to have a thinned corpus callosum. The last model is poly IC, which is a virus that will be injected into mothers. Brain tissue, blood, and fecal samples were collected from animals for each model 21 days after birth. An intense brain developmental procedure known as pruning is occurring at postnatal day 21 that would correlate with the pruning age of a young human child. Pruning is thought to be greatly influenced by immune activation. Immunohistochemistry for the microglial marker IBA-1 (N=4) and peripheral blood analysis for six cytokines (N=5) has been performed in male animals from the four groups. It is hypothesized that there will be an increase in pro-inflammatory cytokines in the blood and microglial activation in the brain. These studies are instrumental in the creation of future mechanistic strategies that may illuminate treatable signaling pathways for ASD.

The microbiota-gut-brain axis is a multidirectional communication chain between the central and enteric nervous systems that relates brain function to peripheral intestinal functions. Gut microbiome composition is influential within the axis because different bacteria produce different shortchain fatty acid markers. Short-chain fatty acids can cross the blood-brain barrier to induce neuroinflammation and likely affect neural development. Autism spectrum disorder (ASD) is a neurodevelopmental disorder that has no defined etiology, cure, or therapeutic treatment. Neuroinflammation, microbiome alterations, and social deficits have been demonstrated in ASD. It is tempting to speculate that neuroinflammation caused by peripheral inflammation or microbiome products can induce abnormal brain development that results in social behavior deficits. However, to contribute to the previous statement a suitable animal model must be used. The current study uses three popular animal models that demonstrate social behavior deficits to determine if short-chain fatty acid profiles are different between the two models as well as a wild-type control strain. Fecal samples were collected from the following mouse strains between 90 and 120 days of development: C57BL/6J control mice, BTBR genetic knockout mice, C57BL/6J injected with valproic acid, and C57BL/6J injected with polycytidylic acid. The last two models were pregnant dams injected during day 11 of gestation. Short-chain fatty acid profiles were obtained from fecal samples to determine differences between the models. Percentages were obtained for the following short-chain fatty acids: acetic, propionic, isobutyric, butyric, isovaleric, and valeric acids. With this research, developmental cues that attribute to autism spectrum disorders may be better understood and, in the future, new preventative treatments may be advanced.

The gastrointestinal (GI) tract is a diverse habitat for multiple microorganisms. Disturbances in the microbiome of the GI tract have been associated with psychiatric disorders including autism spectrum disorder (ASD). Individuals with ASD, when compared to neurotypical individuals, have demonstrated differing gut species. Also, it has been shown that microbial transplant therapies impact ASD symptoms in patients. Animal models of behaviors associated with ASD might offer insight for the actual role these microbial differences may occupy regarding symptoms. Unfortunately, ASD does not have an accepted animal model where the GI alterations have been thoroughly explored. In this study, we sought to determine if the microbiome and other GI alterations were observed in two potential mouse models of social behavior deficits, the genetic BTBR T+Itpr3tf/J (BTBR) mouse strain and an environmental mouse strain consisting of offspring of valproic acid (VA) treated pregnant controls. Both mouse models have been shown to exhibit social and repetitive behaviors that are found in human ASD. Using the Illumina MiSeq, we were able to identify taxonomy associated with 16S ribosomal DNA sequences extracted from fecal matter. We were able to compare the sequencing results from the two affected strains and a control C5BL/6J mouse strain for both female and male animals using the Qiagen CLC Genomics Workbench software. Overall, microbiome composition was found to be significantly different between the male control animals (N=6) when compared to the VA (N=5; p-value=.00216; F-score 11.20904) or the BTBR (N=7; p-value=.00216; F-score 18.47839) males using a PERMANOVA analysis. This was replicated in female groups where composition significantly differed between the control (N=14) and VA (N=14; p-value=.00001; F-score 3.53307) or BTBR (N=14; p-value=.00001; F-score 11.23443) females. Additionally, short-chain fatty acid analysis using gas capillary-based chromatography was used to examine acetate, butyrate, propionate, and valerate levels in feces. Only valerate levels were significantly lower (p

The gastrointestinal (GI) tract is a diverse habitat for multiple microorganisms. Disturbances in the microbiome of the GI tract have been associated with psychiatric disorders including autism spectrum disorder (ASD). Individuals with ASD, when compared to neurotypical individuals, have demonstrated differing gut species. Also, it has been shown that microbial transplant therapies impact ASD symptoms in patients. Animal models of behaviors associated with ASD might offer insight into the actual role these microbial differences may occupy regarding symptoms. Unfortunately, ASD does not have an accepted animal model where the GI alterations have been thoroughly explored. In this study, we sought to determine if the microbiome and other GI alterations were observed in two potential mouse models of social behavior deficits, the genetic BTBR T+Itpr3tf/J (BTBR) mouse strain and an environmental mouse strain consisting of offspring of valproic acid (VA) treated pregnant controls. Both mouse models have been shown to exhibit social and repetitive behaviors that are found in human ASD. Using the Illumina MiSeq, we were able to identify taxonomy associated with 16S ribosomal DNA sequences extracted from fecal matter. We were able to compare the sequencing results from the two affected strains and a control C5BL/6J mouse strain for both female and male animals using the Qiagen CLC Genomics Workbench software. Overall, microbiome composition was found to be significantly different between the male control animals (N=13) when compared to the VA (N=14; p-value=.00003) or the BTBR (N=15; p-value=.0001) males using a PERMANOVA analysis. This was replicated in female groups where composition significantly differed between the control (N=14) and VA (N=14; p-value=.00003) or BTBR (N=14; p-value=.00001) females. Additionally, short-chain fatty acid analysis using gas capillary-based chromatography was used to examine acetate, butyrate, propionate, and valerate levels in feces. Only valerate levels were significantly lower (p

This thesis reports the development of BioMe, a co-culture microplate platform that enables high-throughput, real-time quantitative growth dynamics measurements of interacting microbial batch cultures. The primary BioMe components can be 3D-printed, allowing ease of fabrication and DIY accessibility in the microbiome community. A pairwise 3D-printed iteration of the BioMe device was used in diffusion and co-culture experiments. Genetically engineered Escherichia Coli lysine and isoleucine auxotroph strains were used to characterize the diffusion of amino acids across the porous membranes. Results demonstrated a nonlinear relationship between growth rate and pore size and also distinct diffusion behavior for lysine and isoleucine. Pairwise syntrophic co-culture experiments demonstrated synergistic but repressed interaction between these two paired auxotrophs. Investigation of the effect of varying initial amino acid conditions on growth dynamics demonstrated that small changes in initial media condition can consistently affect patterns of yield and growth rate of constituent microbial species. / 2020-08-30T00:00:00Z

The human microbiome, the complex and dynamic ecosystem that populates our body, performs essential functions such as aiding digestion and protecting us from harmful pathogens. An increasing number of diseases are found to be associated with a shift of balance - or dysbiosis - of the microbiome. However, we still know little about this delicate balance and how it depends on different microbial functions. In this thesis project, I used metagenomic sequencing data to study the variability of microbes and their functions in different areas of the human body. First, in an attempt to characterize the dysbiosis associated with periodontitis, I examined the microbial community of the oral cavity in presence and absence of this chronic inflammatory disease. Specifically, I catalogued the phylogenetic signatures composed of tetramer nucleotide frequencies and observed that the disease state occupies a much narrower region than the healthy one. This result suggests that upon onset of the disease, through host cell invasion, pathogenic bacteria may find a more consistent environment for their parasitic lifestyle. Motivated by these findings, I sought further evidence of an environment-specific use of metabolic functions in the oral and gut communities. Rather than focusing on the abundance of individual metabolic functions, I evaluated their diversity, i.e., the extent to which these functions are performed by different classes of organisms. My hypothesis was that such diversity may confer increased robustness to taxonomic variability. Using metagenomic sequencing data and NCBI's Protein Clusters database, I characterized the multiplicity of gene families associated with a given metabolic function. I found that different human body sites display different degrees of metabolic functional diversity, as assessed by Shannon entropy. For some well-studied gene functions, such as those involved in glycolytic pathways, I found entropy signatures consistent with the known degree of oxygen availability of different environmental niches. Conversely, in an unsupervised analysis, I identified functions with nontrivial entropy signatures. These results pave the way for a new way to inspect human microbiome activity, and could help understand its functional resilience and suggest ways to shift its balance towards healthy configurations.

Indiana University-Purdue University Indianapolis (IUPUI) / Idiopathic urethritis (IU) comprises up to 50% of symptomatic cases of male urethritis in clinical settings. The syndrome is of an unknown etiology but may be due to an as yet unidentified bacterial pathogen(s). We were interested in identifying pathogens that could cause IU using multiple methods. Shotgun metagenomic sequencing or 16S rRNA sequencing methods can provide rich datasets but are limited by the completeness of the corresponding sequence reference databases. We generated metagenomic and 16S datasets from DNA extracted from urethral swabs of men with IU to determine the composition of their urethral microbiome. In order to enrich the corresponding reference databases used to identify the reads in the sequence datasets, I cultivated bacteria from the first void urine (FVU) of men with IU. My goal was to grow and whole genome sequence bacterial isolates that are not currently represented in the reference databases. Of the 216 men we enrolled at the Bell Flower STD clinic in Indianapolis, IN, 59 men had IU. I grew a total of 802 isolates from the FVU of the IU patients and identified those isolates using colony-based 16S rRNA PCR. Based on % sequence similarity to the nearest type strain, I sorted the 16S alleles into four categories: Species (≥98 % identity) (N=264), Genus (≥95 % identity) (N=407), Closest Match (<95 % identity) (N=95), and No Hit (0 % identity) (N=22). There were 24 genera represented in the isolate collection. Of these, the six most abundant genera were Streptococcus, Staphylococcus, Corynebacterium, Haemophilus, Gardnerella, and Prevotella. These six genera composed nearly 80% of all IU-associated isolates. All sequences below 98% sequence similarity represent potentially novel strains of bacteria. We will proceed with whole genome sequencing of bacterial isolates with the goal of improving genome database coverage of bacterial diversity in the male urethra.

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Since the invention of high-throughput sequencing, the majority of experiments studying bacterial microbiomes have relied on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiomic sample. Several computational methods exist for analyzing 16S amplicon based metagenomics, but the most commonly used bioinformatics tools are unable to produce quality genus-level or species-level taxonomic calls and may underestimate the degree to which such calls are possible. In this thesis, I have used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, with a focus on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. With the efficacy of these tools established, I then applied them in the analysis of data from two studies into human microbiomes. I evaluated the metagenomics analysis tools Qiime 2, Mothur, PathoScope 2, and Kraken 2, in conjunction with reference libraries from GreenGenes, Silva, Kraken, and RefSeq, using publicly available mock community data from several sources, comprising 137 samples with varied species richness and evenness, several different amplified regions within the 16S gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole genome metagenomics, outperformed Qiime 2 and Mothur, which are theoretically specialized in 16S analyses. I used PathoScope 2 to analyze longitudinal 16S data from infants in Zambia, exploring the maturation of nasopharyngeal microbiomes in healthy infants, establishing a range of typical healthy taxonomic profiles, and identifying dysbiotic patterns which are associated with the development of severe lower respiratory tract infections in early childhood. I used Qiime 2 to analyze 16S data from human subjects in a controlled dietary intervention study with a focus on dietary carbohydrate quality. I correlated alterations in the gut microbiome with various cardiometabolic risk factors, and identified increases in some butyrate-producing bacteria in response to complex carbohydrates. I also constructed a metatranscriptomics pipeline to analyze paired rRNA-depleted RNAseq data. My evaluation of 16S methods should improve 16S amplicon analyses by advocating for the modernization of computational tools; my analysis of infant nasopharyngeal microbiomes lays groundwork for future predictive models for childhood disease and longitudinal microbiomic studies; my analysis of gut microbes illuminates the mechanisms through which bacteria can mediate cardiovascular health. Taken together, the research I present here represents a significant contribution to 16S metagenomics and its application to epidemiology, clinical nutritional science.