The Pennsylvania State University

The Graduate School

THE OPTIMIZATION OF BUTYRATE PRODUCTION BY HUMAN GUT

MICROBIOMES IN THE PRESENCE OF RESISTANT STARCHES

A Thesis in

Food Science

by

June Y. Teichmann

© 2019 June Y. Teichmann

Submitted in Partial Fulfillment of the Requirements for the Degree of

Master of Science

August 2019

The thesis of June Y. Teichmann was reviewed and approved* by the following:

Darrell W. Cockburn Assistant Professor of Food Science Thesis Adviser

Gregory R. Ziegler Professor of Food Science

Ryan J Elias Professor of Food Science

Robert F. Roberts Professor of Food Science Head of the Department of Food Science

*Signatures are on file in the Graduate School

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ABSTRACT

A healthy gut microbiome has been linked to prevention of multiple inflammatory related diseases like inflammatory bowel disease, obesity, and colorectal cancer. One of the mechanisms at work is thought to be bacterial production of butyrate, a short chain fatty acid

(SCFA) known to provide energy to colon cells and promote apoptosis in cancerous cells in addition to preventing inflammation. Resistant starch (RS) is an emerging fiber that has been shown to modify the SCFA profile in favor of butyrate production in the large intestine.

This study uses a human fecal community in a batch fermentation system to determine how different RS and addition of RS degrading organisms (primary degraders, or PD) affect butyrate production. The RSs tested were a type II potato starch, high amylose maize starch, green banana flour, and tiger nut starch; a type IV tapioca starch and high amylose maize starch; and a type III potato starch in a whole food and an extracted form. PD tested were

Ruminococcus bromii and Bifidobacterium adolescentis. Briefly, samples containing a fecal inoculum and one of the above RS or PD treatments were incubated at 37°C for 24 h in an anaerobic chamber. All RS were predigested with pancreatin and amyloglucosidase and ethanol sterilized before use. Inoculums from ten different people were tested against all treatments in triplicate. Samples were analyzed for organic acid production (butyrate, acetate, propionate, lactate, formate, and succinate) via HPLC and UV-vis and their final community via 16S rRNA sequencing.

Results showed that each RS produced a unique fermentation profile from each microbiome. While there was variation among the inoculums, they could be broadly classified as low, medium, or high butyrate producing communities (LBC, MBC, and HBC, respectively).

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HBC produced the highest levels of butyrate (21.7mM) on average, but values were relatively uniform across treatments. LBC were the opposite, where each community could only use one or two starches to significantly increase their butyrate production, but those that did, produced large increases. Due to this, on average, butyrate production among the LBC was only 7.26mM.

MBC were in between and produced 10.67mM butyrate, on average. Addition of primary degraders R. bromii and B. adolescentis did not have consistent significant effects on butyrate production. Results indicate that butyrate production seems to be activated at the expense of lactate production, and vice versa. Linear discriminant analysis effect size (LEfSe) on sequencing data showed that the PD R. bromii was consistently associated with high butyrate production, and that butyrate producers Fecalibacterium prausnitzii and Coprococcus eutactus were enriched in the inoculums and final communities of microbiomes that could produce significant amounts of butyrate from supplementation with type IV high amylose maize starch. These butyrate producers might be especially adept at converting carbohydrates to butyrate, while other butyrate producers, like Intestinimonas butyriciproducens, were often associated with low butyrate production. The PD B. adolescentis was also associated with low butyrate production. Our research on specific RS and their specific effects on bacterial metabolomes brings us one step closer to using personalized approaches to modify the gut microbiome.

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TABLE OF CONTENTS LIST OF TABLES ...... viii LIST OF FIGURES ...... ix LIST OF ABBREVIATIONS ...... x ACKNOWLEDGEMENTS ...... xii CHAPTER 1: LITERATURE REVIEW ...... 1 1.1 The Gut Microbiome ...... 1 1.1.1 Energy sources ...... 2 1.1.2 Measurement via DNA sequencing ...... 7 1.2 Fermentative byproducts ...... 16 1.2.1 Absorption by the host ...... 18 1.2.3 Benefits ...... 19 1.2.2 Measurement ...... 20 1.3 Butyrate ...... 22 1.3.1 Benefits ...... 24 1.3.2 Production ...... 26 1.4 Resistant Starch ...... 27 1.4.1 Fiber ...... 27 1.4.1 Structure of Resistant Starch ...... 29 1.4.2 Measurement ...... 31 1.4.3 Benefits ...... 32 1.4.4 Breakdown ...... 35 1.5 Resistant Starch and the Gut Microbiome ...... 36 1.5.1 In-vivo vs in-vitro experiments ...... 36 1. 6 Significance, Hypothesis, and Objectives ...... 38 1.6.1 Significance ...... 38 1.6.2 Hypothesis and objectives ...... 40 CHAPTER 2: MATERIALS AND METHODS ...... 42 2.1 General study design ...... 42 2.1 In-vitro Batch Culture Set Up ...... 42 2.1.1 Parameter development ...... 42

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2.1.2 Media ...... 43 2.1.3 Anaerobic chamber ...... 45 2.1.4 Inoculum ...... 45 2.1.5 Preparation of resistant starches ...... 47 2.2 Experimental Measurements ...... 48 2.2.1 Measurement of SCFA ...... 48 2.2.2 Measurement of OD and pH ...... 49 2.5 DNA Extraction, PCR, Gel Electrophoresis, and Sequencing ...... 49 2.5.1 DNA extraction ...... 49 2.5.2 PCR and gel electrophoresis ...... 50 2.5.3 16S rRNA sequencing ...... 51 2.6 Sequence Processing ...... 51 2.7 Statistical analysis ...... 52 2.7.1 Fermentation profile analysis ...... 52 2.7.2 Diversity analysis ...... 52 2.7.3 Heatmap generation ...... 53 2.7.4 Linear discriminant analysis effect size ...... 53 2.8 Study1: Feeding Purified Resistant Starch to 11 Microbiomes ...... 54 2.8.1 Study design...... 55 2.8.2 Fermentation profile analysis ...... 55 2.8.3 LEfSe analysis ...... 56 2.8.4 Follow-up experiments ...... 57 2.9 Study 2: Addition of Primary Degraders to 11 Microbiomes ...... 57 2.9.1 Preparation of primary degrader experiments ...... 57 2.9.2 Final study design ...... 58 2.9.3 Analysis ...... 58 2.9.3 Follow-up experiments ...... 59 2.10 Study 3: Feeding Resistant Starch in Mashed Potatoes to 3 Microbiomes ...... 60 2.10.1 Preparation of whole food RS ...... 60 2.10.2 Study design...... 61 2.10.3 Analsysis ...... 61 CHAPTER 3: RESULTS AND DISCUSSION ...... 62 3.1 Study 1: Feeding Purified Resistant Starch to 11 Microbiomes ...... 62

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3.1.1 Effect of different resistant starches on butyrate production ...... 62 3.1.2 Butyrate and lactate levels were inversely related ...... 66 3.1.3 Effect of different resistant starches on the microbiome diversity ...... 69 3.1.4 Effect of different resistant starches on butyrate producer and primary degrader populations ...... 73 3.1.5 Follow-up experiments ...... 86 3.2 Study 2: Addition of Primary Degraders to 11 Microbiomes ...... 86 3.2.1 Effect of primary degrader addition on butyrate production ...... 86 3.2.2 Effect of primary degrader addition on the microbial community ...... 88 3.2.3 Discussion ...... 90 3.3 Study 3: Feeding Resistant Starch in Mashed Potatoes to 3 Microbiomes ...... 92 3.3.1 Effect of whole-food resistant starch on butyrate production ...... 92 3.3.2 Effect of whole-food resistant starch on the microbial community ...... 93 3.2.3 Discussion ...... 95 CHAPTER 4: CONCLUSION AND FUTURE STUDIES ...... 97 4.1 Conclusions ...... 97 4.2 Future Studies and Moving Forward ...... 98 4.2.1 Future studies ...... 98 4.2.2 Moving forward ...... 100 APPENDIX A: FERMENTATION DATA ...... 102 APPENDIX B: HEATMAPS OF EACH MICROBIOME ...... 107 APPENDIX C: LEFSE ANALYSIS OF HIGH VS LOW BUTYRATE-PRODUCING SAMPLES FOR EACH RS AND THEIR INOCULUMS ...... 111 APPENDIX D: R SCRIPTS ...... 116 REFERENCES ...... 130

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LIST OF TABLES

Table 1. Method development experiments...... 43

Table 2. Resistant starches used in this experiment...... 47

Table 3. Pearson correlation between butyrate and lactate production...... 67

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LIST OF FIGURES

Figure 1. Bubble plots of butyrate production by butyrate-production status and treatment....63

Figure 2. Inoculation fermentation profile...... 64

Figure 3. RS treatment fermentation profile...... 66

Figure 4. Shannon diversity among treatments and inoculums...... 70

Figure 5. Shannon diversity of final communities fed RS...... 72

Figure 6. LEfSe of LBC, MBC, and HBC...... 75

Figure 7. LEfSe classifying all samples by their original inoculum...... 76

Figure 8. Beta diversity of all samples colored by production level...... 83

Figure 9. PD butyrate production...... 87

Figure 10. Shannon diversity of PD treatments and inoculums...... 89

Figure 11. LEfSe categorizing R. bromii and B. adolescentis samples as such...... 90

Figure 12. Heatmap of butyrate producers and primary degraders from PD experiment...... 90

Figure 13. Type III wholefood/ extracted potato starch fermentation profile...... 93

Figure 14. Shannon diversity of type III RS, whole food and extracted...... 94

Figure 15. LEfSe on type III PS: Samples categorized by their treatments...... 95

Figure 16. Heatmap of butyrate producers and primary degraders from RSIII experiment...... 96

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LIST OF ABBREVIATIONS

AMPK AMP-activated protein kinase Amy Amylopectin BAM Binary alignment map BCFA Branched chain fatty acid Bn Banana flour buk Butyrate kinase C.comes Coprococcus comes CA Correspondence analysis CAZy Carbohydrate active enzymes CBM Carbohydrate binding modules CCA Canonical correspondence analysis CDI Clostridium difficile infection CE Carbohydrate esterases CS Corn starch ERS Type 3 resistant starch extracted from potato FFA2/3 Free fatty acid receptor 2/3 GC Gas chromatography GH Glycoside hydrolases GI Gastrointestinal tract GPCR G-coupled protein receptor HAM2 HI-MAIZE 260 HAM4 Versafibe 2470 High butyrate producing community, as determined by baseline butyrate HBC production HDAC Histone deacylatase HPLC High performance liquid chromatography IBD Inflammatory bowel disease ITS Internal transcribed spacer Low butyrate producing community, as determined by baseline butyrate LBC production LDA Linear discriminat analysis LEfSe Linear discriminat analysis effect size LPS Lipopolysaccharide LSA Local similarity analysis Mid-level butyrate producing community, as determined by baseline butyrate MBC production MCT Monocarboxylate transporters mM Milimolar MUC-2 Mucin 2 NFKB Nuclear factor kappa B NGS Next generation sequencing

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NMDS Non-metric multidimensional scaling NSP Non-starch polysaccharide OA Organic acid Olfr Olfactory receptor OTU Operational taxonomic unit PCA Principal component analysis PCoA Principal coordinate analysis PCR Polymerase chain reactions PD Primary degraders PL Polysaccharide lyase PS Potato starch ptb Butyryl-CoA:aceatate-CoA transferase PUL Polysaccharide utilization loci R.bromii Ruminococcus bromii RDA Redundancy analysis RLE Relative log expression method RMT Random matrix theory rRNA ribosomal ribonucleic acid RS Resistant starch RSI-RSV Resistant starch type 1 - 5 RUM Ruminant media SAM Sequence alignement map Sca Scaffoldin protein SCFA Short chain fatty acid SMCT Sodium-dependent monocarboxylate transporter SMRT Single-molecule real-time sequencing Sus Starch utilization structure SV Sequence variants SYBR Synergy Brands, Inc. T1-T11 Inoculum 1 - 11 TEER Trans-epithelium resistance TN Tigernut starch TNF-α Tumor necrosis factor α Treg Regulatory T cells TRS Tapioca starch WF Type 3 resistant starch in whole potato WGS Whole genome sequencing

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ACKNOWLEDGEMENTS

This study was made possible by the American Heart Association grant number 17SDG32770001, as well as the Department of Food Science in the College of Agriculture at Penn State University.

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CHAPTER 1: LITERATURE REVIEW

1.1 The Gut Microbiome

The human microbiome consists of the genomes of all microorganisms— bacteria, bacteriophage, fungi, protozoa and viruses—that live inside and on the human body [1]. The gut microbiome, then, refers to the genomes specifically found in the human gut. Within this system studies focus specifically on the colon, where the majority of the microbes in the gut reside [2]. Additionally, while this definition includes genetic material from all organisms (bacteria, viruses, eukaryotes, archaea), the bacteria are the most prevalent and the best studied, so further discussion here of the gut microbiome focus only on bacteria in the colon. Formation of the gut community begins during birth, from ingestion of vaginal and fecal bacteria from the mother, then quickly develops as dietary and host selection modifies bacterial populations. By three years of age, the microbiomes of children have stabilized to largely look like their parents, or adults’ microbiomes in the same geographic region [3].

Most human microbiomes consist of 5 main bacterial phyla: Bacteroidetes,

Firmicutes, Actinobacteria, Proteobacteria, and Veruccomicrobia. The first two constitute approximately 60-80% of all species, with Firmicutes representing a large majority of this percentage [4,5]. The Bacteroidetes are widely characterized as generalists, while Firmicutes are specialists. Generalist species are characterized by their ability to utilize a wider variety of nutrients. In the gut, this largely refers only to the ability to utilize a variety of carbohydrate sources, as gut microbes are restricted by host

1 conditions and the nutrients available to them. Bacteroides thetaiotaomicron is one such example in the human intestine that boasts a wide variety of carbohydrate degrading enzymes and can utilize both diet and host-derived carbohydrates [6].

From the bacteria’s perspective, a gut is a great place to live and provides consistent food sources and stable environmental conditions, but humans benefit greatly as well. The colonization of these “good bacteria” prevent pathogens or foreign organisms from causing GI damage, increase nutrient break down and absorption, and regulate metabolism [7]. The gut microbiome is increasingly being linked to more benefits, however, ranging from local beneficial effects, like decreasing inflammation in the colon [8], to affecting central nervous disorders like autism and anxiety [9]. Not all bacteria living in the gut are beneficial, however. There are some bacteria that are known as opportunistic pathogens, who only cause disease if an opportunity arises. An opportunity could be another illness or stress or other factor that weakens the immune system. It could also be something that disrupts the typical gut community like an antibiotic. Clostridium perfringens is one such bacteria that is commonly found in the environment that causes disease in these compromised people. Another example is

Bacteroides fragilis, which is often found at some level in healthy people’s guts, however generally does not cause problems unless there is some other issue [10].

1.1.1 Energy sources

1.1.1.1 Diet derived: By the time food ingested by the host reaches the colon, most of the nutrients have been absorbed. What is left is mainly tough fibers that human enzymes were not able to digest. Bacteria have harnessed a variety of methods

2 for breaking down these kinds of carbohydrates. Some well-studied examples for starch digestion are found in Bacteroides thetaiotaomicron (B. theta) and Eubacterium rectale.

B. theta uses a “sequestration” system whereby proteins on the outer membrane grab and transport degraded starch into the cell, which can then be further used. Known as the starch-utilization-structures (Sus) [11] gene cluster, this system in B. theta has 7 genes (susA through susG) working in tandem, as described by Reeves et al. [12], to code for outer-membrane proteins involved with starch binding. susA and B are periplasmic, while D, E, and F are binding proteins. susG is the actual amylase on the cell surface, and C transports breakdown products inside the cell. Once the starch is bound, it can be transported into the periplasmic space where hydrolyzing enzymes can digest it. B. theta and other members of the Bacteroidetes have many such gene cluster systems each devoted to a particular polysaccharide and are known as polysaccharide utilization loci (PUL). The large number of such PULs are why the Bacteroidetes are generalists for carbohydrates. E. rectale has a much simpler mechanism that relies on a large amylase that is attached to its cell wall along with a number of transporters for import of mono, di, and oligosaccharides. As most products that reach the large intestine are in the polysaccharide form, this bacterium largely relies on other bacteria to initiate the breakdown of these compounds. E. rectale can use the smaller carbohydrate fragments for energy in a process known as cross feeding [13].

Glycoside hydrolases (GHs) are a common class of enzymes that bacteria use to degrade starches and other polysaccharides and are grouped into 130 families [6,14].

The 13th GH family (GH13) is known for its strong ability to degrade starch, and includes

3 both amylases and pullulanases, which digest α-1,4 linkages and α-1,6 linkages, respectively [7,15]. The highly present Firmicutes and Bacteroidetes phyla have around

100 or 250 GH enzymes on average, respectively [13], reflecting their respective niches as specialists and generalists. Other important carbohydrate active enzymes (CAZy) are the polysaccharide lyases (PL) and carbohydrate esterases (CE). PLs digest polysaccharide chains with uronic acid sequences through beta-elimination, including pectin, xanthan, and alginates [16]. CE catalyze the breakdown of esters or amides where sugars are the alcohol or amine group. Substrates include acetyl/methyl-ated pectin, acetylated xylan and acetylated chitin [17]. The CAZy website (cazy.org) describes the actions of these enzymes in-depth.

Before degradation, attachment is an important factor in carbohydrate breakdown. If the bacteria cannot anchor itself or its enzyme to the substrate, then it cannot access its nutrients. Within many CAZy sequences, carbohydrate binding modules (CBMs) have often been associated which help the CAZy adhere to a carbohydrate [15]. 64 families of CBMs have been identified [6]. So, while most gut bacteria utilize the GH13 enzymes to some extent [18], not all are able to grow on starch because the enzymes are intracellular for processing imported degradation products or for using their own storage polymer glycogen. These enzymes would then not have access to large carbohydrate molecules and would not have a need for CBMs.

Because cellulose and other structural plant polysaccharides are so difficult to dissolve, some bacteria have developed multi-enzyme complexes known as cellulosomes. The enzymes within can bind and digest these non-starch polysaccharides

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[19]. A structural, non-catalytic protein called a scaffoldin (Sca) [20] provides the backbone for the enzymes to attach to sites known as cohesin domains. Other cellulolytic enzymes can then bind to the cohesin domains with their own dockerin domains. The presence of these cohesin-dockerin interactions mark the cellulosome apart from other complexes. The Sca proteins also mediate binding of the cellulosome complex to the substrate CBM [21,22]. The carbohydrate-degrading enzymes that contain dockerins, and can therefore be included in a cellulosome, are glycoside hydrolases (GH), carbohydrate esterases (CE), and polysaccharide lyases [6,23]. In intestinal systems, only the bacteria Ruminococcus flavefaciens and R. champenellensis have been shown to use cellulosomes [6].

Another multi-enzyme complex has recently been suggested in Ruminococcus bromii that degrades starch and was coined the amylosome by Ze et al. in 2015 [13].

Like the cellulosome, it contains a number of carbohydrate-degrading enzymes that have cohesin-dockerin domains to form a protein complex, and a Sca protein with a similar structure to the cellulosome, that can bind to the cell surface. The amylases coded by this bacterium not only contain dockerins for binding the Sca protein, but also cohesins which suggest binding other amylases. This bacteria has already been noted for its fantastic starch-degrading capability, so it seems likely that it would need such a complex structure to do its work [13].

1.1.1.2 Host-derived mucin: Besides dietary sources, the large intestine itself creates carbohydrates that some bacteria can utilize, in the form of a thick layer of mucus designed to protect the colon from bacteria and other microbes or toxins in the

5 lumen. Mucins, largely MUC2, are gel-forming glycoproteins that constitute the main component of this mucus and are secreted by goblet cells in the small and large intestine. N-acetylgalactosamine, N-acetylglucosamine and O-linked-glycans make up the majority of the mucus, and these structures can be used by some bacteria for energy [24]. Only the outer layer of mucin can usually be accessed by microorganisms.

The inner layer of mucin in a typical subject supports almost no bacterial life, and it is much more firmly anchored to the intestinal wall. In these healthy subjects, it is expected that bacteria will be living off the loosely bound outer layer of mucus, but the inner layer should stay bacteria-free and provide a buffer between microbe and host

[25].

There are several phyla of bacteria that are known to freely use mucin-derived- glycans for energy. The largest studied group are the Bacteroidetes, especially B. theta, which were discovered to have a number of carbohydrate degrading enzymes, including glycoside hydrolases encoded in polysaccharide utilizing loci (discussed earlier) [26].

Also of note in this genus are B. fragilis and B. vulgatus, who have been shown to grow on mucin as a sole carbon source and can degrade porcine mucin, respectively [27].

Species in the phylum Firmicutes that can degrade mucin include Ruminococcus torques and R. gnavus, which are known to grow even if mucin is their only carbon source [28].

Among the Actinobacteria, Biffidobacterium bifidum, B.breve and B. longum subsp. infantis and longum have the ability to degrade mucin, although they do not seem to grow as well as some of the other species listed thus far [29]. The phylum

Verrucomicrobia has one mucin degrader of note, Akkermansia muciniphila. This species

6 can use mucin as a sole carbon and nitrogen source, and utilizes a number of mucinolytic proteins that are not yet well characterized [30]. Some groups of organisms have been enriched when grown in mucin-rich environments, but it is possible that only one or two species within the group are able to degrade mucins. Examples include the sulfate-reducing and acetogenic bacteria and methanogenic archaea [31,32].

1.1.2 Measurement via DNA sequencing

Identification of the composition of microbiomes has become possible with the advent of next generation sequencing (NGS). Also called second generation or massively parallel sequencing, it refers to technology capable of sequencing multiple samples at once to produce billions of data points at a reduced monetary and time cost compared to first generation sequencing.

Broadly speaking, there are two types of genetic sequencing used in microbiome analysis: amplicon and whole genome sequencing (WGS). Amplicon sequencing targets a specific region of DNA. PCR is used to capture and amplify the region (called an amplicon) which is then sequenced. Regions of interest can be accurately identified in complex samples, allowing for identification of specific bacteria on the species level. On the other hand, amplicons that are common across a genus or even a domain can be used, which allows for identification of most of groups of organisms in a sample. This usually limits detection of rare species and depending on the depth (coverage of sequencing), could prevent species-level identification. The 16S rRNA region is commonly used to identify all bacteria in a sample, as this region is conserved across all bacteria, while the 18S or ITS (internal transcribed spacer) region is conserved across

7 fungi. In more complex samples, one can expect to find multiple organisms, and PCR would capture and amplify all of them. However, since NGS can sequence multiple samples at once, it becomes important to tag amplicons so it is known what sample they originated from. This is done using DNA labels called barcodes. Now, amplicons from different samples can be mixed into what is called a sample library [33]. After sequencing, the sequences are demultiplexed, or separated by their original sample, using the barcode.

On the other hand, WGS captures all the genetic information in an organism.

Genetic and plasmid DNA can all be sequenced at once. In terms of bacterial sequencing and identification, this method is often more accurate in identifying lower level taxonomies and capturing rare species [34]. It also allows identification of all of the genetic material in a sample, which can be used to identify all microbes as well as their genomes. A specific type of WGS is shotgun metagenomic sequencing. Once DNA is extracted from a sample, it is broken up, usually by physical means, into short, random fragments. These fragments are then barcoded and amplified like in amplicon sequencing, except it’s not just the one amplicon from each bacteria, it’s all the DNA

[33].

1.1.2.1 Hardware and output: There are a number of methods and machines that are capable of sequencing both amplicons and entire genomes. Illumina/Solexa’s sequencing by synthesis method [33] is probably the most widely used today, but Life

Technologies Ion Torrent (semiconductor sequencing) [35], Pacific Biosciences (Pac Bio) single-molecule real-time (SMRT) sequencing [36], and Oxford Nanopore’s nanopore

8 sequencing [37] are all of note as well. Illumina’s machines dominate the market because, historically, they have been at the forefront of NGS tech in terms of output

(sequencing reads per run) and cost reductions. In Illumina’s sequencing by synthesis method, sequences are read on a glass flow cell covered in adaptor-ligated DNA.

Samples must have the compliments of these adapters bound to them before sequencing; so that when they are placed on the flow cell, they can bind to them. Then, the rest of the bound DNA is sequenced one nucleotide at a time in a cyclic process of washing, flooding the cell with the next nucleotide, imaging, and cleaving. The machine is able to see which base is added because the nucleotides are fluorescently labeled.

After the image is captured, the fluorescent label is cleaved and the cycle restarted.

Illumina boasts error rates below 1% and can sequence fragments from 250-300 bp, depending on the machine [38].

Sequencing outputs come in a few flavors: FASTA, FASTQ and SAM files. All are text files that contain raw sequencing information and associated metadata. FASTA files are the most basic. They can store a number of sequence reads containing the base sequence and an ID tag. The files look something like this:

>M00946:65:000000000-C2PMN:1:1101:15246:1641 2:N:0:56

TGACTACTCTTGTTTCTAATCCTGTTTGCTCCCCACCCTT...

Where the ID is marked by the “>” character and followed on the next line by the actual sequence (which has been shortened to save space). This data format was created by

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Pearson and Lipman in 1988 [39]. Because there is no quality information that can be saved with sequences in this format, FASTA files are often used just to store reference sequences [40].

FASTQ files are an upgrade to the FASTA file format proposed by the Wellcome

Trust Sanger Institute in the early 2000s. Along with the sequence and ID, there is also quality information (Phred score) associated with each read. As there are a number of sequencing technologies on the market, the Phred quality score measures how confident the machine is in identifying a nucleotide. FASTQ files look like this:

@M00946:65:000000000-C2PMN:1:1101:14481:1732 1:N:0:39

GTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGT...

+

3>3AAFFBFFFB2AEE2EEGGGHFG2A2EECEACHEGGFGBFA0?BE...

The identifier is marked by the “@” sign, which is followed by the sequence. A new line starting with the “+” character follows after which the identifier might be repeated, followed by the quality scores [40].

SAM files are another upgrade from FASTQ files, and are fully described by the

SAM/BAM Format Specification Working Group pdf [41]. Briefly, SAM stands for sequence alignment/map format. As its name suggests, in addition to the information

FASTQ files provide, SAM files also contain alignment information. That is, where the sequence falls onto its reference. It is tab-delimited, so each read fills only one line.

There are 11 fields that help explain the mapping position of the sequence and an

10 optional header field that would come before the alignment (marked with an ‘@’). Here is an example from the SAM pdf:

@HD VN:1.6 SO:coordinate

@SQ SN:ref LN:45

r001 99 ref 7 30 8M2I4M1D3M = 37 39 TTAGATAAAGGATACTG *

r002 0 ref 9 30 3S6M1P1I4M * 0 0 AAAAGATAAGGATA *

SAM files are often stored as BAM files, which are binary versions that are efficiently stored and easy to access information from [40].

1.1.2.2 Processing and analysis of sequencing data: Once the sequences are obtained, they must be processed and analyzed. There are a multitude of tools available to do this on amplicon and WGS data, but in this paper, techniques will only be looked at in terms of amplicon data. That is, while they might be useful in WGS analysis, the focus will be on their amplicon analysis functions. The most commonly used tools for targeted amplicon based 16S rRNA analysis are Mothur, QIIME (Quantitative Insights

Into Microbial Ecology, versions I and II), and MG-RAST (Metagenomics - Rapid

Annotation using Subsystems Technology). While there are many other tools, these are the most commonly used and historically accepted, and they can take sequences from raw sequencing data to a finished OTU (operational taxonomic unit) table. An OTU is an arbitrary taxonomic categorization for a sequence based on sequence similarity. Each analysis of a data set will generate a new OTU table based on the parameters and data it is given. Depending of the number of reads and the parameters given to the program,

11 the classification can be all the way up at the phylum level, down to the species level.

Traditionally, sequences are clustered into an OTU if they are 97% similar to a reference sequence, which correlates to a genus-level identification. The final OTU table is a list of

OTUs and how many sequences fell into each OTU for each sample. A separate taxonomy table must be made to match OTUs to taxonomic identifications. A larger number of programs exist that carry out one or several functions along the way from sequence to OTU, but not the whole process.

Papers comparing the various 16S analysis programs have been written is greater detail [42-44]. The broad steps are common across programs, but this paper will only discuss the Mother pipeline. Mothur was developed in Pat Schloss’s lab in 2013, and a thorough, beginner friendly SOP can be found on Wikipedia [45]. It is a free command line program built on Mac OS X, but compatible versions exist on Windows and Linux operating systems. Analysis is broken up into six steps: getting started

(uploading your data into Mothur), reducing sequencing and PCR errors (filtering), processing improved sequences, assessing error rates, and preparing for analysis.

Mother process FASTQ files into a *.files file, which it can then turn into contigs (a consensus DNA strand made from two compliment, overlapping strands). Filtering involves removing sequences with ambiguous bases and removing sequences that are not the expected length. Processing sequences saves only those sequences that are unique and aligns them to a reference database. These databases are massive compilations of sequences from multiple sources around the world. Common databases include the SILVA, GreenGenes, and Ribosomal Database Project (RDP). There are some

12 differences among the databases, such as the type of sequencing data (bacterial, eukaryotic, archaea) or the lowest taxonomic level (genus or species) they provide [46].

After alignment, sequences are re-filtered to conserve only those that are unique, are not chimeras (deviant pairings of sequences), and are the correct type of DNA (eg, only bacterial—no mitochondrial or chloroplast DNA). Assessing error rates is only possible if there is a mock community that was sequenced along with the actual samples. It compares the error rate in the mock to the actual community. Lastly, Mothur generates an OTU table, with a user-set cutoff level. Mothur is also capable of running analysis on the finished OTU table, but this is usually saved for other programs that can also graph or visualize the data [47].

1.1.2.3 Sequencing analysis: Like the upstream steps just discussed, there are a huge variety of programs that can analyze and visualize data. They include graphical user interface programs like Microsoft Excel, command line programs like R and online programs like Galaxy [48]. Many analysis and graphs can be done in multiple platforms and are chosen based on personal preference and experience. Basic population analysis common to microbiome studies like correlation analysis and alpha and beta diversity, for example, can be computed in most programs.

Correlation analysis measures the relationship between two continuous variables. The most common way to do this is by finding the Pearson product moment correlation coefficient, usually shortened to Pearson correlation or Pearson coefficient.

The coefficient is marked by the variable r and has values ranging from -1 to +1. The closer the value is to |1|, the stronger the association between the two variables.

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Positive values would indicate a positive relationship, while negative values indicate a negative relationship. A value of zero indicates no relationship. Another common correlation index is the Spearman correlation coefficient, which ranks relationships before associations are found. Statistical significance can be found for correlation values, but it is important to note that correlations, even strong ones, do not indicate causation.

Other basic types of analysis are alpha and beta diversity measures. Alpha diversity measures the diversity of species within a sample. The simplest measures include richness, the number of different OTUs in a sample, and evenness, how evenly the population is distributed among these OTUs. The Chao1 index, Shannon’s diversity index, Simpson’s index, and Berger-Parker index take these measures into account to measure alpha diversity. The Chao1 index estimates diversity from richness data and corrects for low-abundance species. The Shannon index includes evenness in its estimation. Simpson’s index computes similarity of two communities based on richness and evenness. The inverse can be taken to get the dissimilarity. The Berger-Parker index only gives the proportional abundance of individuals belonging to the most common species [49].

Beta diversity measures the difference in diversity between two samples.

Common measures include the Bray-Curtis and Jaccard methods. Bray-Curtis measures

(dis)similarity based on taxonomic abundance and presence/absence, while Jaccard only takes into account presence/absence. Beta-diversity is helpful because it allows for comparison of microbiomes across different samples.

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Ordination refers to any technique that organizes multivariate data along a gradient. Gradient simply means gradual changes in a variable over space or time, and includes changes in pH or temperature, nutrient availability, oxidation levels, etc. While gradients reduce the number of variables in the analysis, they still capture a majority of the variability in the dataset. Generally, ordination methods are quite robust, and do not depend on data normality or independence. Because of the complexity of microbiome data sets, reductionist models like this are commonly used [50,51]. There are many types of ordination, including Principal Coordinates Analysis (PCoA, used in this study),

Principal Components Analysis (PCA), Nonmetric Multidimensional Scaling (nMDS),

Correspondence Analysis (CA), Canonical Correspondence Analysis (CCA), and

Redundancy Analysis (RDA). PCA, PCoA, nMDS, and CA are used for exploratory data analysis while CCA and RDA are hypothesis driven.

More sophisticated analysis can answer deeper questions. LEfSe, PICRUSt, and random forest analysis are commonly used in microbiome analysis. Linear discriminant analysis effect size, or LEfSe, uses a series of statistical significance, biological relevance, and effect size tests to determine what factors most contribute to variability among classes in a dataset and was used in this study. In terms of microbiome analysis, factors can mean OTUs, genes, functions, or other genomic features; and classes are generally different samples, communities, sub-communities or other biological condition. For example, in a microbiome study feeding people different RS and tracking their microbiomes, the bacterial species most associated with different RS treatments could be determined using LEfSe. The technique was developed in 2011 by the Huttenhower

15 group and can run in the Galaxy framework [48,52]. LEfSe analysis can be visualized in two outputs, an LDA score plot or a cladogram. The plot is helpful to visualized ranked effect sizes, while the cladogram shows how factors are related to taxonomic classes

[53].

1.2 Fermentative byproducts

From digesting carbohydrates like RS, bacteria produce a variety of compounds through fermentation. Fermentation is the decomposition of organic matter, usually carbohydrates, by microbes. Fermentation pathways are usually only used in the absence of oxygen, but some yeasts are known to use them under certain low glucose conditions. Unlike aerobic respiration, which produces only CO2, water, and energy, fermentation has a number of pathways that produce a number of by-products.

Bacteria will selectively choose these pathways based on their (among other things) energy needs, redox state, and nutrient availability [54]. Byproducts of fermentation include gasses (e.g methane, hydrogen, hydrogen sulfide, carbon dioxide), short chain fatty acids (e.g acetate, propionate, butyrate, and valerate), and other organic acids (e.g lactate, succinate, and formate). Other compounds of interests such as branched SCFAs

(e.g isobutyrate and isovalerate) and alcohols (e.g methanol and ethanol) are made as well [7].

The short chain fatty acids (SCFA) are the main products of bacterial gut carbohydrate fermentation and account for about 10% of the total caloric energy usage by humans [8,55]. As their name suggests, SCFA are short, 2-5 carbon fatty acids. Also known as volatile fatty acids, they have low molecular weights and easily cross the

16 blood brain barrier. Physiological concentrations in the human colon are around 80-130 mM, but this varies along the length of the colon. SCFA are acids, and can lower the pH of the lumen [56], which means they can influence microbial composition and nutrient uptake [57]. Studies have shown that increasing the SCFA concentration will help cells to a point but will eventually become deleterious because of this pH lowering [58,59].

Considering these factors, fibers that allow slow, steady production of butyrate would have more positive effects on the colon than rapidly fermenting fibers that could raise the SCFA concentration to dangerous levels [58].

Acetate, propionate, and butyrate make up the main SCFAs produced from hind gut fermentation of carbohydrates. Depending on the type of carbohydrate and bacterial composition, different levels of each are produced. However, generally they are produced in a ratio of 60:20:10 (acetate: propionate: butyrate) [60]. SCFAs can also be produced by some bacteria from amino acids. Certain amino acids will elicit different reactions from different bacteria, and besides the SCFA, branched chain fatty acids

(BCFA) are also formed [61]. These are any fatty acid with carbon branches on the main carbon chain, but the main BCFA in microbial contexts are isobutyrate and isovalerate.

These are formed at especially high levels when the branched chain amino acids leucine, isoleucine, or valine are present [62]. These are important energy sources for the brain and muscle, especially during states of fasting or starvation [63]. The BCFA’s function is less understood, but there is evidence that they effect electrolyte absorption and secretion [62,64]. The SCFA can also be produced at low levels from amino acids:

17 butyrate and acetate are produced from glutamate, lysine, and threonine; acetate from glycine, alanine, and aspartate; and propionate from alanine and threonine [61,64,65].

Certain carbohydrates can also favor production of specific SCFA. Starch and bran seem to be strongly butyrogenic, while other polysaccharides like pectin and oligofructose favor the production of acetate and propionate [66,67]. Acetate and butyrate can be used as an energy source by colonocytes, but also have other intracellular targets and effects. Acetate is also utilized in the muscles, while propionate has functions in the liver [64,66]. Butyrate, however, is of special interest as it is the preferred energy source for colonocytes and has been linked many functions that keep the gut healthy, including decreasing gut inflammation and regulating transcription of tumor-suppressant genes [68,69].

1.2.1 Absorption by the host

Once the SCFAs are produced, they are absorbed by the endothelial cells.

As acids, they have both an undissociated and dissociated form. The former can passively flow across cell membranes, but as the pH of the lumen is generally above the pKa of the SCFAs (~6.5), it is likely most of them are dissociated and must be actively transported across the membrane. As increased SFCA concentration lowers luminal pH, higher levels of undissociated SCFA can passively cross the membrane [57].

Three types of transports are thought to exist on the apical and basolateral sides of the enterocyte to bring SCFA into the cell. The monocarboxylate transporters (MCT) are located on both the apical and basolateral side of the membrane, and cotransport

SCFA ions with H+ ions [70]. Sodium-dependent monocarboxylate transporter (SMCT) is

18 located only on the apical membrane, and cotransports two molecules of sodium ions in with one SCFA molecule [71]. The third transporter is of unknown identity but is thought to exist on both the apical and basolateral membranes. This transport exchanges bicarbonate in the blood for SCFAs. The MCT and unknown transporters on the apical and basolateral sides of the cell are distinct from each other [72]. Some studies have shown that MCT transporters are more actively used than the SMCT transport, as the latter has a poor affinity for butyrate [73].

1.2.3 Benefits

Inside the body, the SCFAs not only act as energy sources, but signaling molecules as well. They can be detected by G-protein coupled receptors (GPCR) in almost any tissue in the body, including the gut, starting a variety of signaling cascades.

In this way, microbial by-products can have a direct effect on host physiology, namely regulating inflammation. This SCFA-mediated attenuation of inflammation makes SCFAs an important area of study, as one of the biggest issues with gut health is inflammation.

This condition has been observed to exacerbate or possibly cause inflammatory bowel disease (IBD) [74], alcoholic liver disease [75], colorectal cancer, HIV [23], obesity [76], diabetes [77], and Parkinson’s disease [58]. Inflammation is largely a result of exogeneous toxins from bacteria in the lumen of the gut (commensal and otherwise) as well as the body’s own defense mechanisms attacking (gut) endothelial cells.

GPCRs are large transmembrane proteins expressed in virtually every tissue of the body. The GPCRs associated with SCFA are the free fatty acid receptors (FFAR) [78], specifically FFA2 and FFA3. In addition to these, olfactory receptor 78 (Olfr78) and

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GPR109a are known to interact with SCFA, although they are not currently classified as

FFAR. FFA2 and Olfr78 preferentially bind acetate and propionate, while FFA3 and

GPR109a bind butyrate (GPR109a only responds to butyrate). These receptors are not confined to the large intestine, and are also found in the small intestine, other parts of the digestive tract, adipose tissue, and lymph tissues, among others. The final downstream effect that occurs is specific both to the location of the receptor and its ligand. For example, FFA2 can inhibit insulin signaling or deactivate NFkB, a proinflammatory transcription factor, depending on if it’s located in adipose or lymph tissue, or if acetate or butyrate binds it [79,80].

In the gut, FFA2 is thought to have a number of functions that promote gut homeostasis through regulation of immune responses. It both helps in mounting the initial neutrophil response to an insult and then suppresses the immune response after the insult has been neutralized by recruiting regulatory T cells (Treg). FFA3 seems to be more limited in its pathways and confers protection only through increased expression of cytokines and chemokines. Like FFA2, GPR109a acts to suppress the immune response through recruitment of Treg cells. The function of Olfr78 is largely unclear, but it is observed at elevated levels in colon cancers [79,80].

1.2.2 Measurement

SCFAs are largely measured using gas chromatography (GC) or high performance liquid chromatography (HPLC). These are currently the most widely used methods of measuring organic acids and many other types of compounds as well. Chromatography is a powerful separation technique that can handle isolating many different compounds

20 of interest, or analytes, at once from a single sample. While other techniques exist, like nuclear magnetic resonance and capillary electrophoresis, GC and HPLC have become the standards for organic acid (OA) analysis. Due to this, the available information and validation of methods for using these techniques are much more extensive, and this equipment is now found in most biochemical laboratories [81].

Both GC and HPLC are great separation methods. They work using basically the same mechanism, where a chemical mobile phase pushes a sample through a thin column, and chemicals lining the column, or stationary phase, bind and slow the different analytes within the sample. Different analytes in the compound are attracted to the mobile or stationary phase more strongly, and separation occurs as they move at different speeds. Polarity, charge, size, hydrophobicity, and many other aspects of compounds can be used to separate the compounds using a variety of combinations of mobile and stationary phases. The main differences between GC and HPLC are the phases of the sample and mobile phase, which must be gaseous for GC and liquid for

HPLC. The columns used in either system also differ, and GC columns are much longer, generally 25-30 m, compared to the 150-300 mm HPLC columns. This allows for greater resolution of compounds. However, HPLC systems are more flexible in the composition of their mobile phase, which allows for more targeted separation techniques, and can run samples over a wider temperature range [82]. Namely, HPLC can run at lower temperatures than GC, since it does not require samples to be volatilized. This is important for fatty acid analysis, since these compounds are susceptible to degradation at high temperatures. In addition, while the SCFA can be volatilized, this is not true for

21 all organic acids. Volatile derivatives can be created for most compounds of interest, but this is time consuming, expensive, and can result in loss of analyte through volatilization or degradation during the process [81,83]. HPLC systems and columns are also less expensive; so for these reasons, HPLC was used in this study.

Besides resolution, detection is an important aspect of GC and HPLC analysis and allows accurate determination and quantification of analytes. Mass spectrometry, or

MS, is the most powerful of these detection techniques. While it is applicable with both

GC and HPLC, this detection method is very expensive and detects OA concentrations far below that which would be found in a fecal sample. Instead, UV-vis detection was used in this study, which is the most common alternative to MS. This method uses light in the

UV (190-400nm) or visible (400-700 nm) range to detect analytes. As analytes exit the column (hopefully one at a time), they enter a flow cell where a specific wavelength is used to detect their presence or absence. Currently, most UV-vis detectors are variable wavelength detectors, meaning the wavelength they can use for detection can be changed. Originally, only single wavelength detectors were available, permanently set at

254, 214, 280, or 365 nm. However, because the foundations of research using UV-vis were built around these wavelengths, many protocols today still use them [84]. A major advantage to HPLC UV-vis over MS is that samples are not destroyed during detection, meaning collection systems can be set up that retrieve the separated analytes [85].

1.3 Butyrate

As mentioned earlier, Butyrate is of special interest among the SCFA as it is the preferred energy source for colonocytes and will be used before the other SCFAs and

22 endogenous glucose [68,69]. It has also been linked many functions that keep the gut healthy, including decreasing gut inflammation, increasing proliferation of healthy enterocytes, and regulating transcription of tumor-suppressant genes [67].

The Firmicutes consistently produce butyrate, especially the bacterial families

Eubacteriaceae and Lachnospiraceae. Within these families, Eubacterium and Roseburia stand out as butyrate producers [4,86,87], as well as Faecalibacterium prausnitzii in the

Clostridia family [87,88]. Notably, in diseased patients, these populations fall drastically.

Antharam et al. [4] found that in patients with Clostridium difficile infections (CDI), 10 genus within the Clostridia class were the most significantly depleted. The many butyrate producers in this bacterial class were also significantly depleted, as well as many acetogens (bacteria that produce acetate from fermentation).

Butyrate and butyrate producing populations have been shown to increase through fiber-rich diets that feed the gut microbiome. Starches that are particularly resistant to human digestion, known as resistant starch, have been shown in many experiments to increase both absolute and relative levels of butyrate. While other non- digestible starches and non-starch carbohydrates have been studied for their butyrate- producing capabilities, they often only increased acetate formation or increased production of all three major SCFA [69]. However, it has been shown that raising absolute levels of butyrate without also increasing the ratio of butyrate to acetate and propionate can mask the benefits typically associated with the compound because of excessive calories gained from the increase in acetate and propionate [104].

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1.3.1 Benefits

In addition to regulating the immune response of the gut, butyrate also keeps the physical structures of the gut intact, also limiting inflammation. The gut epithelia, or the monolayer of cells lining the small and large intestines, separates and protects the rest of the body from components in the lumen. Lining the epithelial cells is mucin, the thick mucus (glycoprotein) layer that protects the epithelial cells from physical, chemical, and microbial insult [32]. However, the barrier needs openings to allow intracellular communication as well nutrient and gas flux in and out of the cell.

Therefore, tight junctions exist between cells as gateways that control its permeability.

Trans-epithelium resistance (TEER) is a common measure of the health of this barrier. It is a very sensitive and reliable method to confirm the integrity and permeability of the monolayer by measurement of electrical resistance across it. SCFA, especially butyrate, have demonstrated a positive effect on both mucin production and tight junction activity [89,90].

Several in-vitro studies have exposed the cancer cell line LS174T to butyrate and assessed the resulting growth and mucin production. Hatayama et al. [91] examined mucin production in terms of actual mucin production and MUC-2 mRNA levels (MUC-2 being the main mucin forming protein). Both protein and mRNA levels were increased in a dose-depended manner by butyrate supplementation. Concomitantly, a down- regulation in histone deacetylase production was observed, which arrested LS174T growth [91]. Inhibition of histone deacetylase (HDAC) is a major mechanism through which butyrate provides its anti-inflammatory/anti-cancer benefits [32,92,93]. HDAC is

24 an enzyme that removes acetyl groups from histones, allowing chromatin to uncoil and transcription to occur. Butyrate stops this, effectively stopping transcription. The genes affected by butyrate’s HDAC inhibition are thought to be Sp1 and Sp3, which mediate cell cycle arrest in mutated cells, and genes affecting expression of interleukin-8 and free radical scavengers [93,94].

On the tight-junction side, Chen et al. [58] found that SCFAs have both a protective and reparative effect on a Caco-2 cell model of the intestinal epithelia from both LPS and TNF, with butyrate serving as the main functioning factor [89]. Mechanistic studies showed that butyrate increases AMPK (AMP-activated protein kinase) expression, a kinase that facilitates assembly of tight junction proteins. While levels of major tight junction proteins themselves did not increase with butyrate treatment; since assembly of the structures is aided by increased AMPK, the total number of healthy tight junctions is increased [95].

Some research suggests that increasing butyrate concentrations over a certain point will cease to benefit the cell and might cause harm instead, especially in babies

[96,97]. Butyrate inhibits cancer cell growth, but some of the cells in the GI tract divide at similar rates and are likewise inhibited by butyrate. However, in the large intestine, these cells are located at the very bottom of the crypts of Lieberkuhn. The crypts are pits on the apical side of colonocytes found along the length of the colon. At the deepest points of these structures are rapidly dividing stem cells and progenitor cells.

Like the LS174T cancer cells, these cells also exhibited stunted growth when exposed to butyrate. However, the high rate of butyrate uptake in healthy, fully-formed enterocytes

25 at the entrance of the crypt structure protects them from butyrate exposure. The only time these rapidly dividing cells are in danger of high levels of butyrate exposure is when damage occurs to the crypts. In this case, to prevent lesions and/or carcinogenesis, it is beneficial that butyrate would halt the growth of these cells [98].

Most of the information on butyrate production comes from animal studies or from levels in the feces of humans. However, this method does not take into account total butyrate production, as a percentage of it would be taken up by the colonocytes. A study by Jiminez et al. [99] found lower levels of SFCA in the proximal colon than the rate of fermentation they found would suggest. They proposed that this is caused by faster absorption rates of SCFAs in this part of the colon. Production (microbial and endogenous), rate of uptake, and removal of butyrate all affect how much butyrate is available to the host, and fecal samples will only give a clear picture of the removal of butyrate from the body. The ratios of these factors, however, might change in a human from different stimuli. The studies examining the other factors in butyrate availability are scarce, mainly because it is difficult to conduct them in humans and because of lack of precise knowledge of the mechanisms through which butyrate is produced and absorbed by the microbiota and colonocytes, respectively.

1.3.2 Production

Butyrate is produced by colonic bacteria through the phosphotransbutyrylase

(ptb)/ butyrate kinase (buk) pathway or the butyryl-CoA:acetate-CoA pathway. Both start out with acetyl-CoA from glycolysis being converted to acetoacetyl-CoA, then B- hydroxybutyrate-CoA, then crotonyl-coA. This intermediate can be formed from

26 succinate and some amino acids as well. Crotonyl-coA is then transformed to butyryl-

CoA via butyryl-CoA transferase. In the ptb/buk pathway, ptb exchanges the CoA from butyryl-CoA for a phosphate group. Buk then removes this phosphate grouped to produce butyrate. Using butyryl-CoA:acetate-CoA, butyryl-CoA is converted directly to butyrate [57]. by detaching the CoA and putting it on acetate. This means that this pathway requires acetate to be present. The ptb/buk pathway seems to be the preferred pathway of human butyrate-producing bacteria, and is expressed in a majority of butyrate-producing bacteria using acetyl-CoA [65,67,88]. Lactic acid has also been shown as a substrate for butyrate production if it is first converted to acetyl-CoA [57].

1.4 Resistant Starch

1.4.1 Fiber

The carbohydrates that reach the gut are largely plant-derived polysaccharides like starch, cellulose, B-glucan, xylan, xyloglucan, mannan, pectin, lignin [6,19]. These can broadly be divided into the starch and non-starch [19] polysaccharides. Starch is made up of amylose and amylopectin and is used as an energy storage molecule in plants. Starch is made solely of glucose monosaccharides connected by either alpha 1,4

(amylose) or alpha 1,4 and alpha 1,6 (amylopectin) bonds. The NSPs are largely structural components in the cell wall, and are made from a variety of monosaccharides and a variety of structures. Cellulose, for example, forms a tightly packed, linear crystalline structure composed solely of glucose monomers bound by beta 1,4 bonds, while pectin adopts a helical shape and is made mainly from galacturonic acid units

27 connected by alpha 1,4 bonds with side chains of other sugars such as xylose or galactose.

Some of these polysaccharides can be categorized as dietary fiber (DF) or resistant starch (RS). The American Association of Cereal Chemists defines DF as “the edible part of plant foods or analogous carbohydrates that are resistant to digestion and absorption in the human small intestine with complete or partial fermentation in the small intestine” [100] and contains carbohydrates like fructooligosaccharides, arabinoxylan, and beta-glucan [58]. RS is defined as “the starch fraction that escapes digestion in the small intestine of healthy humans” [100].

The Institute of Medicine determined that people under the age of 18 should be consuming about 19-38 g of total fiber per day, while those aged 19-70 are advised to have 21-38 g/day [101]. Looking just at RS, Murphy et al. [102] estimate that adults should be consuming about 18 g of RS/day but are only getting about 3-8 g, mostly coming from breads and cooked cereals/pasta [102]. Most of the RS in people’s diets is naturally occurring, but several varieties of RS have been commercialized to use as functional fibers: Hi-Maize®, a type of RS extracted from high amylose maize starch produced through natural breeding program over the past 30 years; Novelose®, a retrograded high amylose maize starch, and Fibersym®, a chemically modified wheat starch [103]. Others are in development, but RS may confer stiffness and other off textures to food products so is difficult to incorporate into products. Sharma et al. [104] found that while added RS to cookies and noodles did not significantly affect their flavor

28 or overall acceptability, the substitution in muffins significantly dropped overall acceptance.

1.4.1 Structure of Resistant Starch

Resistant starch is the total amount of starch and products of starch degradation that cannot be digested by the small intestine and passes on to the colon to be fermented by microbiota. This starch makes up an important food source for the gut microbiome. There are five types of RSs, classified by the manner in which they resist digestion [105].

Type I RS (RSI) have physical barriers to digestion, like protein or cell wall barriers. These barriers prevent moisture from entering the starch matrix, which in turn keeps the starch from swelling and exposing itself to hydrolytic enzymes. The barriers themselves also keep out these enzymes. Seeds or kernels fall into this category, as well as whole grain breads or pastas [7,102].

Type II RS (RSII) resistance is based on their crystal structure. Starch is made up of glucose in one of two forms, amylose and amylopectin. Amylose is the smaller of the two, and forms mostly linear chains of glucose connected through alpha-1,4 bonds.

Amylopectin is the larger, branched macromolecule and also the more abundant form in starches (~70% in maize). These amylose and amylopectin chains form into semi- crystalline structures called granules. Depending on how tightly packed the crystal structure is, or how much water it can bind, these crystal structures are categorized into

A or B polymorphs. Starch crystals are categorized as type A, B, or C starch if it has A, B, or both crystal polymorphs, respectively [106,107]. Starches with higher amylopectin

29 content generally crystalizes into the A form. High amylose starches generally crystalize in the B form and pack together tightly because of their long linear sections. Therefore, high amylose starches are generally more resistant to digestion than low amylose starches. The B and C type starches are particularly resistant to digestion, and are categorized as RSII. However, regardless of its crystal structure, if a starch can pack tightly enough, there are studies that show it will be resistant to digestion and categorized as an RSII [108]. Some food examples include uncooked potato starch, green banana starch, gingko starch, some legumes, and high-amylose maize starch.

High-amylose maize starch is the most widely studied resistant starch [102]. Many of these products will lose this structure upon cooking, however, as a result of gelatinization [7].

Type III (RSIII) is starch that has been heated and cooled, so gelatinization then retrogradation occurs. Retrogradation is the reformation of the starch’s crystalline structure, and is accelerated at refrigeration temperatures and in the presence of water.

This is what causes bread or chips to stale. Once retrograded, only very high temperatures (much higher than those found in a typical kitchen) can cause gelatinization again [7]. Some examples of RSIII starch are cooked and cooled potatoes, rice, bread, and cornflakes [102,109].

Type IV (RSIV) is formed via a chemical change to the starch structure, such as cross linking or addition of a chemical. Cross linking inhibits gelatinization, which prevents digestion by gut enzymes and most bacteria. Cross linking can be induced by chemicals such as phosphoryl chloride (POCl3) or sodium trimetaphosphate (STMP)

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[110]. Substrates like octenyl succinic groups or acyl groups [111] can also be directly bound to starch to prevent breakdown as well, but areas the chemical does not bind will still be susceptible to digestion [7].

Type V (RSV) involves binding of starch to lipids. In a spontaneous reaction, amylose will bind with fatty acids or fatty alcohols. In this state, amylase cannot break down the amylose bonds. These complexes can reform even after cooking [7]. This type of RS might not be fully resistant to digestion so is not fully accepted as a RS.

There are many other interconnected factors that affect starch digestibility.

Other nutrients in a meal, how the starch itself is prepared, and its structure will affect the final solubility of the starch. For example, starch interaction with proteins, lipids, calcium, potassium, and soluble sugars has been shown to increase its solubility

[20,112]. Time is also a huge factor, and given enough time, even human enzymes will break down RS. However, the rate at which food passes through the small intestine prevents this digestion from occurring. This is important when considering measurement of RS, which is done by digesting the RS and measuring the resulting release of glucose.

1.4.2 Measurement

Quantification of DF or RS in a food sample is determined by isolating the DF or

RS from the other compounds in the sample and quantifying the remaining carbohydrates. DF can be determined by the McCleary Method (AOAC 2009.01) and RS by AOAC method 2002.02 [113]. If the food matrix contains proteins, a proteolytic step might be used to expose the carbohydrates [7,114]. Currently, not all RS are considered

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DF, but in 2016 the FDA created a definition of dietary fiber to include all fibers proven to provide a health benefit. High amylose corn starch and cross-linked starches were judged to fit this criteria and are on the FDA’s list of proposed fibers to include as a DF.

The other RS are still considered fiber, but would fall under the ‘total fiber’ instead of the ‘dietary fiber’ label [115].

1.4.3 Benefits

Ingesting RS has many benefits for the host. Physiologically, RS’s low glycemic index (low release of glucose into the blood stream) does not cause blood sugar to spike after meals, which prevents insulin resistance. As other fibers do, RS will increase the mass moving through the GI tract, since it is not digested. In a study by Phillips et al.

[116], humans fed increased levels of RS had both increased frequency and size of their bowel movements. Subjects also reported significantly greater ease of defecation.

Bowel movement occurrence and mass are important in preventing and relieving constipation, diverticulosis and anorectal disorders. It has also been hypothesized that increased bulking dilutes carcinogens in the diet; and at the very least, decreases exposure they would have with the lower bowel [117]. Increased RS in the diet has also been shown to increase satiety in mammals [118,119].

While these benefits might encourage a fiber-heavy diet, there is a drawback to a single-food diet. Many studies have shown that diseased patients, especially those with GI disorders, have decreased microbial diversity in their gut. Therefore, eating a single food or single type of food, like RS, can be counterproductive. Too much can create a homogeneous microbiome [120], which is generally considered a risky state to

32 be in. This indicates that personalized diets are important for achieving the full benefit from RS. Moreover, there is no consensus on what a ‘healthy’ microbiome should look like. More work must be done to determine how different types of RS influence the microbiome and how the microbiome in turn affects its host.

1.4.3.1 RS as a prebiotic: RS has also been proposed as a prebiotic, or a supplement designed to feed the microbiome. With an increasing number of benefits being tied to a productive microbiome, and commercialization of products that can do this, prebiotics are of increasing interest to people, scientists, and companies [121]. If RS attained such a label it might be used more in food products. The three criteria for prebiotic labeling are resistance to digestion in the upper GI tract; fermentation and utilization by the gut microbiota; and selective stimulation and/or growth of one or limited number of specific gut bacteria [103]. Current approved prebiotics include inulin, fructooligosaccharide [122], galacto-oligosaccharide [123], mannan-oligosaccharides

(MOS), and xylos-oligosaccharides (XOS), and these have all undergone rigorous studies proving their safety and effectiveness [103,121]. Isomalto-oligosaccharide is currently the most widely used prebiotic, which is imported from Japan at a total of 69,000 tons/year [124].

RS does meet the first two requirements, and current studies suggest strongly that RS affects specific bacterial populations but does so in unpredictable ways

[5,125,126]. In Martinez et al.’s study [5], subjects were fed either RSII or RSIV carbohydrates, and their microbiome was tracked before, during and after this feeding period. RSII and IV produced different end communities, but trends were never seen

33 across all the subjects in the study. To be labeled a prebiotic, specifically Bifidobacterium and Lactobacillus populations must increase. However, these genera dominate the gut only in human infant stages. As adults, humans have a much wider variety of microbes in the Bacteroidetes and Firmicutes phyla. In particular, increasing populations of the

Firmicute Ruminococcus bromii are looked for to determine effective RS degradation and utilization. R. bromii has been strongly linked to keeping the gut microbiome healthy as a whole because of its ability to initiate the breakdown of digestion-resistant starch. While it is never present in high populations, this ability allows cross feeding to occur [18,127,128]. This cross feeding in turn allows other bacteria to proliferate. Some of these bacteria include Bifidobacterium and Lactobacillus, both of which do not have the enzymes necessary for RS degradation (other than one species called B. adolescentis). Therefore, it is important to measure not just a few bacterial populations when considering the effectiveness of a prebiotic.

Another way to measure the impact of prebiotics is the measure of fermentative products the gut bacteria are producing, mainly SCFAs. These compounds, especially butyrate, are generally agreed to be beneficial to the GI tract, so an RS that increases

SCFA production could be considered an effective one. In a study by McFarlane et al.

[129], among four polysaccharides tested, starch and non-starch, only starch was consistently shown to induce butyrate production at significant levels by in-vitro fermentation. This angle examines RS through functional shifts it causes in the metabolism of the microbiome rather than a population shift. It might be outside the realm of a prebiotic but is still relevant to using RS to prevent or mitigate disease.

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1.4.4 Breakdown

Only a handful of bacteria have been mentioned here as being particularly successful in breaking down the complex carbohydrates that reach the colon. In fact, all of microbes in the gut rely on metabolic byproducts from their neighbors for energy.

These byproducts are a natural end product in fermentation, where often times the electron acceptors at the end of energy-generation are not inorganic as in aerobic fermentation. As a result, many of them can be used by a variety of bacteria in their own fermentation pathways. In addition, breakdown of RS (or other complex carbohydrates) is accomplished through external enzymes which release smaller, easier to digest carbohydrates or other products into the environment for other bacteria to use. This is known as cross-feeding, and it allows a much wider diversity of microorganisms to thrive [8,127].

R. bromii and B. adolescentis are two such organisms. Known as primary degraders, they are currently the only known human gut organisms to break down RS.

R. bromii accomplishes this with its amylosome (described in section 1.1.1), while B. adolescentis seems to simply have a huge variety of external GH enzymes along with

CBMs and glycosyltransferases [13,130,131]. The exact break down products produced by each of these PD is still being explored. For example, butyrate producers

Fecalibacterium prausnitzii and Eubacterium halii were found to grow only in coculture with B. adolescentis when supplemented with RS [132,133]. Using tagged carbon, the butyrate producers were shown to use acetate and lactate produced by B. adolescentis to produce butyrate. A similar phenomenon is seen with Eubacterium rectale and R.

35 bromii, where E. rectale can only grown on RS when R. bromii is present [127]. In this case, oligosaccharides were used by E. rectale to produce butyrate. So, it is known that both PD can break down RS into shorter oligosaccharides (carbohydrate chains about 3

– 8 units in length, in this case made of glucose) that other bacteria can use, but the exact length of the oligosaccharide products is not known. In addition, B. adolescentis is producing the organic acids acetate and lactate, and R. bromii is producing acetate and formate. However, how these end products specifically help increase butyrate production or butyrate producers is not entirely clear.

1.5 Resistant Starch and the Gut Microbiome

1.5.1 In-vivo vs in-vitro experiments

In studying the microbiome as a whole, the issue of in-vitro vs in-vivo studies becomes a big one. The human gut is such a complex system that it is hard to replicate in a test tube. In addition, in-vivo human studies are difficult to orchestrate and have many bureaucratic barriers to ensure the safety of the subjects. Animal models are often used instead of humans, but this has its own set of issues. While this model has a fully functional GI tract, the physiology and microbial populations in such models greatly diverge at the species level from humans [134]. Another method to replicate the human gut are in-vitro, gut-simulation devices. Open systems have been designed that supply fresh medium/nutrients to the system, while removing waste and dead cells. Probes in this system can measure things like pH or temperature or even OD in real time without disturbing the community, which is very helpful. These systems often have multiple

36 chambers that replicate the entire GI tract, including the stomach, small, and large intestine. However, these systems are still lacking because they lack an immune system, and removal of metabolites cannot be regulated (say by absorption by enterocytes)

[135]. In addition, while in a single chamber, the environment is very similar to a simple batch system.

The simplest of these in-vitro systems are closed systems, and can be as simple as a test tube with media and an inoculum that represent the colon. However, the simplicity of the system allows for great control over the variables of interest, although not as many variables can be tested at once. Their simplicity also offers a lower cost, easier troubleshooting and/or repair of the system, and results are generally easier to interpret. These simple, streamlined systems are great for initial experiments and mechanistic experiments. In 1989, Weaver et all. [136] used a simple closed system to model how different microbiomes utilized glucose and maize starch over a 3.5 year period, and was one of the first studies to find that microbiomes are quite stable within subjects over time. Twenty years later, Ze et al. [127] used a similar closed system to determine the mechanisms behind Ruminococcus bromii’s complex starch degrading system.

Moving up in complexity, organ-on-a-chip and body-on-a-chip systems are being engineered to overcome this barrier. These systems would contain microfluidic channels that are populated with living cells to recreate the environment found in the human colon [89]. Like all cell culture models, scientists would have a large amount of control

37 over it, unlike in a true human body. However, right now, the chips are a bit too simple, and represent more of a typical flask monoculture [137].

1. 6 Significance, Hypothesis, and Objectives

1.6.1 Significance

Due to the benefits being associated with butyrate, many studies, in-vitro and in- vivo, in humans and animals, have been conducted looking for a butyrate increase with

RS supplementation, and they’ve largely been successful [126,128,138,139]. This is important, as the benefits associated with butyrate production, like decreasing inflammation, do not have great medical treatments either because of their low efficacy or side effects. However, there was never complete consensus in these studies. There were always a few people or microbiomes that did not seem to respond to the RS treatment, and did not have an increase in butyrate. This could be because these studies only looked at a few types and sources of RS, namely type 2 starches from maize or potato. However, there are four types of RS from a wide variety of food sources. Martin et al. [140] found that there was a difference in the butyrate production and uptake in pigs fed a type II potato starch, type II high amylose maize starch, and a type IV high amylose maize starch. It stands to reason that in humans, the same effect would be seen and some RS would induce different levels of butyrate production (and other organic acids) in the same person. For this reason, we are testing the effect of 6 different RS on the same microbiome, including potato (RSII), tapioca (RSIV), high amylose maize (RSII), high amylose maize (RSIV), banana (RSII), and tiger nut (RSII)

38 starches. Potato, tapioca, and banana starches are common in many peoples’ diets across the world. The high amylose maize starches are among the first RS to be recognized by the FDA as dietary fibers, or fiber that has a proven health benefit. Tiger nut starch comes from the tiger nut tuber, which today is largely used to produce a milk.

However, in humanity’s hunter gatherer phase, these would have been eaten raw and provided high levels of RS. Considering our possible evolution with this fiber, and the growing movement towards paleolithic diets, this fiber is also of interest in its current health benefits.

In addition to different RS inducing different fermentation profiles, there are studies that show the underlying communities are altered in specific ways by specific RS.

Martinez et al.’s study [6], subjects were fed either RSII or RSIV carbohydrates, and their microbiomes were tracked before, during and after this feeding period. RSII and IV produced different end communities, with R. bromii and E. rectale populations increasing with the former and B. adolescentis and Parabacteroides distasonis increasing with the latter. However, there was still a high level of variation among the communities, and there were no universal patterns.

Due to this wide diversity among microbiomes, personalized medical approaches are championed as the best way to create an effective approach to beneficially modulate the gut including increasing butyrate production. However, personalized approaches are often not practical for many people or their physicians. Instead, it is more efficient to find population-level treatments. For example, being able to recommend RS a if this person has microbiome 1, or RS b if they have microbiome 2.

39

Understanding broader phenotypic and compositional patterns that arise from various

RS supplementations can both pave the way for personalized treatments and also population-level treatments.

1.6.2 Hypothesis and objectives

My hypothesis is that the SCFA profiles produced by supplementing human fecal communities with different types and sources of RS will differ from each other and depend on the composition of the starting community.

Essentially, different RS will induce different fermentation profiles, including butyrate formation, in the same microbiome, so that one RS will be the best at inducing butyrate production. The underlying communities the, will also change differently with each RS, and these changes will be specific to the RS. These specific community changes can then be used to recommend which RS is best for a person or group of people in terms of increasing their butyrate production.

To test this hypothesis, I will do the following:

1. Determine the optimal in-vitro batch fermentation conditions for modeling butyrate production in a human gut.

2. Determine the results of a fecal community fermentation of a variety of different RS types and sources by measuring its organic acid production (butyrate, acetate, propionate, lactate, formate, succinate) and identifying its community composition before and after this fermentation.

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3a. Use fecal samples from multiple people to address the above aims to determine global patterns.

3b. Find bacterial signatures across the communities that can be used to indicate a person’s ability to use a certain RS to produce butyrate

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CHAPTER 2: MATERIALS AND METHODS

2.1 General study design

Fecal samples were used to obtain a representative sample of the donor’s microbiome and were procured from lab members of the Cockburn Lab for preliminary experiments. For studies 1, 2, and 3, described later, fecal samples were from a collaborative project between the Cockburn and Kris Etherton Labs. Diluted fecal slurries were inoculated into fermentation vessels with equal volumes of synthetic nutrient media (called RUM media here) and a carbohydrate, for a final volume of 4 mL.

All media and fermentation vessels were stored in an anaerobic chamber until use, at least 24 h beforehand. Samples were allowed to ferment for 24 h at 37°C. Samples were then removed from anaerobic conditions, and their OD, pH, fermentation profile, and final community were measured.

2.1 In-vitro Batch Culture Set Up

2.1.1 Parameter development

Experiments done to optimize butyrate production within this system are listed in Table 1 below. Experimental conditions were chosen based on a literature review and final values were chosen based on which value maximized butyrate production. In these preliminary experiments, the fecal inoculum came from a single person. In the media development experiments, potato starch was used as an energy source.

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Experiment Treatment Final value Inoculum dilution 1:1 1:3 1:3 1:5 1:10 Inoculum volume 1:5 1:20 1:10 1:20 1:50 Filtration no filtration no filtration .2 µm filtration Agitation no agitation no agitation shaking on vortexer [Acetate] 1x 0.5x 0.5x 0.25x 0.1x 0.05x Buffer( [NaCO3]) 1x 1x 2x pH 5.5 6.5 6.5 7 Mucin addition 0 0 (%) 0.1 0.5 1 Taurine addition 0 0 (%) 0.1 Table 1: Media and methods tested to optimize butyrate production. Several values were tested, and the final value was used in proceeding experiments.

2.1.2 Media

A synthetic medium was developed for this system based on ruminant fluid described by Ze et al. [13], as it is known to provide the necessary nutrients and growth

43 conditions for human gut bacteria. The media contains micronutrients and amino acids in abundance, and small quantities of carbohydrates and lipids. A separate carbohydrate source was added during the experiments, generally a type of resistant starch (RS). A 2x stock solution of the media, called RUM in this paper, was prepared. Before use, it was filter sterilized at 0.2 µm and allowed to sit in an anaerobic chamber for 24 h to remove oxygen. In the fermentation vessel, it is diluted with an equal volume of a carbohydrate solution to 1x. 2x RUM was newly prepared once a week and stored at room temperature in an anaerobic chamber.

The 2x RUM contained (per 100 mL of filter-sterilized water) 2 g tryptone, 0.8 g sodium bicarbonate, 0.5 g yeast extract, 0.2 g cysteine, 0.18 g ammonium sulfate, 186

µL acetic acid, 200 µL of tween 80, 0.1 mg resazurin (an oxygen indicator that turns pink/purple in aerobic conditions and is clear under anaerobic conditions); 10 mL of

+ + Ka /Na salts, 1 mL of CaCl2 solution, and 1 mL of MgSO4 solution; and 1 mL of vitamin mix, 1 mL of RUM vitamin mix, and 1.6 mL of hematin/L-histidine solution. The stock mineral mixes were stored at room temperature. The “K/Na salts” was a 20x stock that consisted of 4.5 g K2HPO4, 4.5 g KH2PO4, and 9 g of NaCl filter-sterilized in 500 mL of water. The “CaCl2 solution” and “MgSO4 solution” were 200x stocks, the former consisting of 1.8 g of CaCl2, the latter 0.88 g of anhydrous MgSO4. The stock vitamin mixes and hematin solution were refrigerated and protected from light. The “vitamin mix” contained 2 mg biotin, 2 mg cobalamin, 6 mg p-amino benzoic acid, 10 mg folic acid, 30 mg pyridoxamine, 10 mg thiamine, and 10 mg riboflavin. The “RUM vitamin mix” contained 20 mg pantothenate, 20 mg nicotinamine, and 2 mg folinic acid. All four

44 solutions were dissolved in 100 mL of water and filter sterilized at 0.2 µm. The

“hematin/L-histidine solution” contained 1.24 g of L-histidine in 39 mL of water and 48 mg of hematin. The hematin was dissolved in 0.5 mL of 1 M NaOH then neutralized by an equal volume of 1 M HCl. The L-histidine and hematin solutions were mixed and brought to pH 8.

2.1.3 Anaerobic chamber

An anaerobic chamber (Coy Laboratory, Ann Arbor, MI) housed all of the experiments and media. Within, oxygen levels were kept around 0 ppm and 1.5-4.5% hydrogen. Two catalytic converters (Coy Laboratory, Ann Arbor, MI) were used to purge any oxygen that entered the chamber. They were recharged by baking at 160°C for 2 h biweekly. Pure nitrogen gas and a gas mix of nitrogen, carbon dioxide, and hydrogen were used to flush the air lock and the chamber when necessary. Most of the chamber was at room temperature, except for an incubator located within the chamber, which was kept at 37°C.

2.1.4 Inoculum

Fecal samples were obtained through a concurrent study at the Kris-Etherton lab at Penn State University, which looked at the impact of potato and wheat dishes on cardiovascular markers and the gut microbiome (STUDY00007854). This protocol allowed for using leftover fecal samples for other experiments. For our study, we used untreated baseline samples from the Etherton experiments. Subjects were between the ages of 25-75 years with BMI between 20 and 40 kg/m2. Subjects were non-smokers,

45 and were free from metabolic and inflammatory diseases such as diabetes, hypertension, colitis, etc. Samples were collected in sterile containers and immediately frozen at the subject’s home. Samples were transported to Penn State on ice within 24 h of collection and further stored at -80°C until needed. Samples were defrosted in the anaerobic chamber as they were needed.

After defrosting, inoculums were diluted in phosphate buffered saline in a 1:3 dilution and thoroughly mixed in a blender for 1 min. The resulting slurry was aliquoted into 15 mL falcon tubes (Corning, Corning, NY) and frozen at -80°C for later use. Before use, aliquots were defrosted and inoculated into the fermentation vessels.

After preliminary experiments, 18 inoculums were screened for their baseline butyrate production. This was the amount of butyrate the community could produce without carbohydrates. Inoculums were grown in the RUM media with water instead of a carbohydrate source. Samples that produced less than 12 mM butyrate were classified as low butyrate producing communities (LBC). If they produced greater than 26 mM butyrate, they were considered high butyrate producing communities (HBC).

Communities in between were called medium butyrate producing communities (MBC).

These cut-offs were determined by the data, with 12 mM and 26 mM being the first and third quartiles of the butyrate production from all the inoculums.

Endogenous butyrate levels in each fecal sample were also determined, however these levels are not true indicators of SCFA production because of differential host absorption of these compounds. To remove the host to host bias and just test the SCFA

46

capacity of each microbiome, butyrate levels were measured under standardized

fermentation conditions.

2.1.5 Preparation of resistant starches

Various resistant starches (RS) were tested from different food sources and of

different RS types (see Table 2).

RS ABBREVIATION COMMERCIAL NAME FOOD SOURCE TYPE SUPPLIER PS Potato starch Potato 2 Bob's Red Mill, Milwauki, OR TRS ActiStar Tapioca 4 Cargill, Wayzata, MN HAM2 HI-MAIZE 260 Corn 2 Ingredion, Westchester, IL HAM4 Versafibe2470 Corn 4 Ingredion, Westchester, IL BN Banana flour Green banana 2 Blue Lily Organics, Pheonix, AZ TN Tiger nut starch Tiger nut 2 Organic Gemini, Brooklyn, NY Table 2 Starches: These resistant starches were used in the final fermentation experiments.

Starches were prepared by predigesting with pancreatin and amyloglucosidase.

Briefly, 2 g of sample were incubated at 37°C for 16 h in a shaking water bath with 800

mg porcine pancreatin and 900 µL amyloglucosidase. Samples were rinsed 10 times with

~30 mL DI water. Supernatant was removed by centrifugation at 3000 g for 5 min.

Samples were ethanol (EtOH) sterilized overnight for ~16 h with 70% EtOH. Samples

were then rinsed 10 times with ~30 mL of sterilized water. After the last rinse, samples

were diluted to 2% (w/v) starch/sterile water suspensions. RS samples were let to sit

overnight in anaerobic chamber before use.

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RS content was determined in the digested samples with Megazyme’s Resistant

Starch assay kit. Briefly, samples were incubated with pancreatic α-amylase and amyloglucosidase for 16 h to remove non-resistant starches. This time frame mimics a typical digestion time in the human digestive tract. After terminating the reaction with ethanol, samples were digested in KOH to dissolve the RS. The solution was then neutralized with an acetate buffer and hydrolyzed to glucose with amyloglucosidase. A glucose oxidase/peroxidase reagent was used to measure the released glucose via spectroscopy. Absorbance at 510 nm was measured against a blank and glucose reference sample to determine RS content.

2.2 Experimental Measurements

2.2.1 Measurement of SCFA

To determine the fermentation profile produced from each experiment, samples were analyzed via high performance liquid chromatography (HPLC). After the 24 h incubation in the anaerobic chamber, samples were brought onto the bench. One mL was aliquoted out and spun down at 16010 x g for 10 min. The pellet was kept for DNA sequencing. The supernatant was filtered at 0.2 µm then diluted 1:1 in 10 mM H2SO4 to prep it for the HPLC (final concentration of 5 mM H2SO4). Thermo Fischer’s Dionex

5000+ series HPLC was used in this experiment, with a 50 mm guard column (Micro-

Guard Cation H Cartridge, Bio Rad, Hercules, CA) and a 300 mm ion exclusion column

(Aminex HPX-87H, Bio Rad, Hercules, CA). Runs were 60 min long at 50°C, 4.5 mL/min flow rate, with no temperature or flow gradients. Quantification was done with a

48 standard curve. Standards were made for the SCFA acetate, propionate, and butyrate, as well as the organic acids (OA) lactate, formate, and succinate. Branch chain fatty acids were run through to determine their retention times on the column, but standard curves were not created.

2.2.2 Measurement of OD and pH

To measure OD, samples right out of the anaerobic chamber were centrifuged briefly at 200 g for 2 min. The supernatant was diluted 1:10 in phosphate buffered saline before taking OD at 650 nm (V-1200 spectrophotometer, VWR, Radnor, PA). The remainder of the supernatant was further centrifuged at 3000 g for 20 min. Supernatant was removed and used to measure pH.

2.5 DNA Extraction, PCR, Gel Electrophoresis, and Sequencing

2.5.1 DNA extraction

DNA was extracted from one replicate of each treatment from each experiment.

Qiagen’s Power Fecal DNA Kit (QIAmp, Hilden, Germany) was used and protocol followed exactly. Extracted DNA was diluted 1:10 in sterile water and frozen at -20°C.

Five µl of Bacillus toyonensis was added to each sample before extraction as an internal control to allow for quantification. B. toyonensis was grown overnight in brain heart infusion broth the day before extraction. Inoculation volume was measured at OD650 and normalized to a value of 1.5 and volume of 50 µl. So, if a sample’s OD650 = 1.5, then

50 µl were inoculated, and if OD650 = 2, then 67 µl were inoculated. Kit-control samples

49 were run, which were MilliQ-water-only samples run through the extraction kit. This was to determine bench contamination or bacteria inherently present in the kit itself.

2.5.2 PCR and gel electrophoresis

PCR and gel electrophoresis: 20 µl polymerase chain reactions (PCR) were carried out on all extracted DNA samples using the following recipe:

15.85 µl water 2 µl buffer 0.5 µl forward primer 0.5 µl reverse primer 0.15 µl DNA polymerase

The 16S rRNA V4 region was amplified using the forward primer 505F (5’-TCG TCG GCA

GCG TCA GAT GTG TAT AAG AGA CAG GTG YCA GCM GCC GCG GA A-3’) and reverse primer 806R (5’-GTC TCG TGG GCT CGG AGA TCT GRA TAA GAG ACA GGG ACT ACN VGG

GTW TCT AAT-3’)

All PCR reactants were purchased from Invitrogen (Carlsbad, CA). PCR run conditions were as follows:

94°C 2 min 94°C 20 sec 56°C 30 sec 68°C 40 sec 30 cycles (step 2-4) 72°C 5 min

To confirm PCR product purity, products were run through gel electrophoresis.

Gels were made from 1.5% agar in agarose buffer (3.85 g borate in DI water). SYBR green (Lonza, Basel, Switzerland) was used as the gel dye. 5 µl of sample was mixed with

1 µl of loading dye (10x BlueJuice, Invitrogen, Carlsbad, CA) and loaded into the gel. Gels

50 ran at 100 mV for 45 min (power source-Power Ease 90 W, Life technologies, Carlsbad,

CA; gel box, combs, and tray-VWR, Radnor, PA). Images were taken in the Fast Gene B/G

GelPic Box (Nippon Genetics Europe, Dueren, Germany).

2.5.3 16S rRNA sequencing

PCR products were sent to Penn State’s Core Genomics Facility for sequencing on an Illumina MiSeq. Before running, they conducted a second round of PCR to add on the Illumina adapters and reconfirmed product size by BioAnalyzer. Confirmed PCR products were normalized by concentration and purified before running on the MiSeq using v2 reagent kits and 250 x 250 nucleotide paired-end sequencing.

2.6 Sequence Processing

Sequencing results were returned from the Genomics Facility as demultiplexed

FASTQ files. Primers were still attached and removed with the oligos function in

Mothur’s make.contigs command. A final OTU table and taxonomy table was generated using Pat Schloss’s MiSeq SOP [141]. OTUs were assigned taxonomy via BLAST+, a command line tool provided by NCBI to perform large volume searches on their databases. OTUs were identified to the species level. Further analysis was done in R studio [142]. Sequencing controls were examined and Escherichia marmota was determined to be external contaminant (found at high levels in kit-control samples) and discarded from analysis. Internal standard B. toyonensis was determined to not be in all samples at sufficient levels, so samples were normalized using the relative log expression method (RLE) using the edgeR package [143-145]. R was also used to

51 determine alpha [146] and beta (Bray-Curtis) diversity indices, as well as to generate heat maps of the butyrate producers and PD in each community using the R packages vegan, phyloseq, ade4, pheatmap, microbiome, dplyr, and ggplot2 [147-152]. A full description of the analysis follows, and all R scripts are in Appendix D.

2.7 Statistical analysis

2.7.1 Fermentation profile analysis

There were levels of acetate, lactate, and propionate in the basal media that were subtracted out from all sample values. Means for OA were calculated for each treatment after the basal media subtraction. Means for OD and pH were also calculated.

Raw OD values were multiplied by 10 and OA values by 2 to account for dilution factors.

Means of each attribute were compared across treatments with the same inoculum using a one-way ANOVA. Tukey’s Honest Significant Difference was used to calculate significant differences. Statistics were calculated using SAS software (version 9.4, SAS

Institute, Cary, NC USA). All graphs were generated in R studio.

2.7.2 Diversity analysis

After processing the raw sequences as described above, the final OTU table, tax table, and metadata were combined into one phyloseq object in R. The phyloseq package in R is a convenient and powerful tool to import and analyze the various forms of data that microbiomes are analyzed with. The phyloseq object was subset into inoculum samples or treatment samples. All were pruned to remove OTUs not found in any sample. Plot_richness was used to then determine the Shannon diversity indices of

52 these communities. The alpha diversities were found for the inoculums and treatments of each study (1, 2, 3).

The beta diversity of all the samples was determined using the Bray-Curtis method in the vegdist command from the vegan package. The results were plotted on a

PCoA plot using the pcoa command from the ape package. Samples were colored by butyrate-production category (LBC, MBC, and HBC).

2.7.3 Heatmap generation

Heatmaps of the butyrate producers and PD were determined for each inoculum, as well as the communities generated by the PD treatments and whole food treatments. All heatmaps were generated the same way. After subsets of the samples were made, the top 50 OTUs in each community were determined. Each list of OTUs was examined and butyrate producers and primary degraders were identified. Butyrate producers were determined from the literature [153]. Primary degraders were defined as either Ruminococcus bromii or Bifidobacterium adolescentis. Heatmaps shown here

(Appendix B) were generated only displaying these functional bacteria using the pheatmap command and package. Rows (species) were organized by their similar appearances in each sample. Relative abundances were scaled by row.

2.7.4 Linear discriminant analysis effect size

LEfSe was also used to test OTUs that were statistically more abundant in several situations: in each treatment, each microbiome, high and low butyrate production within each treatment, and high and low butyrate production associated with the

53 inoculum [52]. The Galaxy module [48] created by the Huttenhower Lab was used for this analysis using the default parameters. Using R studio, a subset of the samples of interest were created. Since multiple OTUs shared the same species identification, the sum of the counts of identical species was calculated so only unique species were present. Samples were categorized to determine OTUs that drove differences among each category. For example, to determine which OTUs caused the difference between community T9 vs T2, or between samples that produced high levels or low levels of butyrate in the presence of PS. OTU tables were exported as tab-delimited files and formatted for LEfSe through the Huttonhower application on Galaxy. After formatting, the next module in the application is called LDA Effect Size, which uses the Kruskall-

Wallis test to analyze the different OTUs and determining if they are differentially abundant among the categories or classes. The resulting subset is used to build a linear discriminant analysis model which allows ranking of the differential OTUs according to the effect they had on differentiating the classes. The final LDA scores were plotted using the Plot LEfSe Results module.

2.8 Study1: Feeding Purified Resistant Starch to 11 Microbiomes

RS feeding has consistently been demonstrated to increase butyrate production in gut microbe communities. However, there has never been a consistent increase in butyrate shown, and only a limited number of RS have been tested for this effect. The

RS that have been tested have created both different levels of butyrate production and different underlying community changes. This study aims to determine how the same microbiome changes in response to different RS. From this experiment, we hope to find

54 a consistent increase in butyrate by RS, so that every community has at least one RS that can increase its butyrate production, and perhaps find a RS that is better than others at this. In addition, this experiment aims to shed more light on the underlying changes in community that each RS causes that drives the increase in butyrate.

2.8.1 Study design

Eleven microbiomes (T1-T11) were tested with various RS (Table 2) to determine the effect on the OA profile and community composition. Nine of the microbiomes were from unique subjects, while two (T5 and T11) were from the same person at different time points. Six were LBC, 3 were MBC, and 2 were HBC. Both T5 and T11 were LBC. A

2% amylopectin solution, a 2% corn starch solution, and water (true negative control) replaced the RS in samples that served as controls. All treatments were run in triplicate.

2.8.2 Fermentation profile analysis

In addition to the analysis listed above, several analyses were completed only for these experiments. Bubble plots were created showing the butyrate production by each microbiome when fed each RS and non-RS. Butyrate values displayed here were the butyrate production for each treatment with the amount produced by the control for that microbiome subtracted. Pearson’s correlation coefficient was calculated between each OA and butyrate for each inoculum to determine relationships between butyrate production and the other OAs. Student’s t-tests were used to determine if the correlation was significant at p < 0.05. P-values were corrected using multiple t-tests using Benjamini-Yekutieli’s correction (FDR-adjustment) [154].

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For the individual RS fermentation profiles, the amount of each organic acid produced was compared to the control using the Dunnett test using R studio.

2.8.3 LEfSe analysis

Two LEfSe analysis were run for each RS tested here, determining differentially abundant species in high or low butyrate producing samples. High and low butyrate production was determined using the first and third quartiles of butyrate production across samples for the RS. While the groupings were the same, either the final fermentation community or the initial inoculum communities were used to run the

LEfSe. For example, for the HAM4 treatments, inoculums T1, T3, T6, T7, and T9 were considered high, and T2, T4, T5, T8, T10, and T11 were low. Each of these had their initial inoculum as well as a sample that was grown on HAM4 that were sequenced and used for LEfSe analysis. The exceptions were amylopectin and banana flour samples, which had only one community and all communities, respectively, produce high levels of butyrate. Instead, only the final fermentation communities were run in LEfSe. For the amylopectin, the LEfSe compared the two T2 amylopectin samples to all other amylopectin fed samples. For banana flour, all banana flour-fed samples were fed to non-banana flour samples.

A LEfSe was also run comparing samples that produced high lactate or high butyrate for each inoculum. All species were compiled into a single list.

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2.8.4 Follow-up experiments

Low pH has been shown to be correlated to increased BCFA formation instead of

SCFA production. In addition, HBC in this experiment were thought to be better at using amino acids for OA production because of their high butyrate production in the absence of carbohydrates. Since, BCFA are produced during amino acid fermentation, an HBC was grown in RUM media with its pH lowered to 6.0 to test if BCFA production increased. PS was used as the carbohydrate source. The resulting fermentation profiles were examined for OA production, particularly the branched chain amino acids. Pellets were saved for sequencing.

2.9 Study 2: Addition of Primary Degraders to 11 Microbiomes

While butyrate producing organisms directly affect butyrate production, in this system where RS is the primary energy source, it must be the bacteria who can access these nutrients, the PD, that are actually increasing butyrate production. For this reason, one of the PD (B. adolescentis or R. bromii) were spiked into samples in addition to the fecal inoculum, ensuring high populations of the PD. With these higher PD levels, we hypothesized that the RS would be more completely broken down, releasing more energy to especially the butyrate producing organisms and allowing higher butyrate production.

2.9.1 Preparation of primary degrader experiments

In primary degrader (PD) supplementation experiments, Ruminococcus bromii and Bifidobacterium adolescentis were spiked into the fermentation vessels in addition

57 to the inoculum. The PD were first grown overnight in monoculture on amylopectin.

Two inoculums were tested, 50 µL and 100, from a 0, 10-2, and 10-4 dilution. The final inoculation volume was normalized to an optical density (OD) 650 of 1.5. This experiment was repeated three times for each primary degrader. B. adolescentis was also tested at 50 µL of the 10-5 dilution. Controls had no primary degrader added but still had the fecal inoculum. The carbohydrate solution used in this experiment contained potato starch. The resulting fermentation profiles were examined for organic acid (OA) production. Pellets were saved for sequencing. It was determined that future experiments with PD would add in 50 µL of the culture at OD 650 of 1.5 without further dilution

2.9.2 Final study design

Ten different microbiomes (11 total) were tested addition of the primary degraders (PD) Ruminococcus bromii and Bifidobacterium adolescentis (ATCC 27255 and

15703d-5, respectively), to determine the effect on the OA profile and community composition. PD were grown in monoculture overnight and 50 µL were inoculated into samples with a fecal sample and PS as a carbohydrate source. A sample with only PS, no

PDs, was used as a control.

2.9.3 Analysis

Analysis in the form of butyrate production comparisons among the treatments, alpha diversity, and a heatmap were run as described previously. In the LEfSe analysis for this experiment, the final fermentation communities were classified as either B.

58 adolescentis, R. bromii or PS treatments to determine species enriched by each treatment.

2.9.3 Follow-up experiments

From preliminary experiments, we found that lactate levels in samples with B. adolescentis spiked in were much higher than average without butyrate levels rising. We hypothesized that there were not many lactate to butyrate fermenting organisms, so we designed a separate experiment that spiked in a lactate to butyrate fermenter

(Eubacterium hallii) to see if we could decrease the lactate and increase the butyrate in these B. adolescentis samples. In addition to a fecal community, 50 µl of E. hallii (ATCC

27751) and B. adolescentis were spiked into the fermentation vessel at all combination of a 0, 10-1 and 10-2 dilutions. Volumes were normalized to an OD650 of 1.5. PS was used as the carbohydrate source. The resulting fermentation profiles were examined for OA production. Pellets were saved for sequencing.

In addition to the PD, butyrate producers are the main organisms affecting butyrate production. It is known that E. rectale is a strong butyrate producer in the presence of RS [128]. To test whether increased abundance of a butyrate producer will increase butyrate production in a LBC in the presence of RS, an LBC was grown with additional E. rectale spiked in. Fifty µl of an undiluted culture was added in, normalized to OD650 = 1.5. PS was used as the carbohydrate source. The resulting fermentation profiles were examined for OA production. Pellets were saved for sequencing.

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2.10 Study 3: Feeding Resistant Starch in Mashed Potatoes to 3 Microbiomes

While the starches in this study are commercially available and gaining in popularity, many people are not getting their RS from these purified starches. Instead, they are coming from products that have RS in them, largely from starchy carbohydrate sources like bread, pasta, cereal, or potatoes. It is well documented that ingredients isolated from their food sources are not metabolized the same way as those same ingredients in their whole food form. RS is no exception, and non-RS carbohydrates, vitamins, minerals, and phenolic compounds are associated with many plant-based, RS- rich foods. Almost no studies exist looking at the effect of RS in whole food on butyrate production in the gut. For this reason, this study looks at a type III RS in mashed potatoes for its effect on the gut. Mashed potatoes were chosen to avoid any variation caused by variable particle sizes and because people would naturally consume this type of RS.

2.10.1 Preparation of whole food RS

RS formation was induced in mashed potatoes (RSIII) and examined as is (whole food form) and as an extracted, purified type III RS. Russet potatoes purchased from

Walmart were washed and peeled. Half the potatoes went to whole food RSIII [155] production and half went to extracted RSIII (ERS) production. The WF potatoes were cut into ~1.5” cubes and boiled for 15 min, then mashed with a mortar and pestle until no clumps remained. RS formation was induced by cooling in the refrigerator (4°C) and reheating in a 110 W microwave oven twice for 1 min at 100% power (power level 10)

(model number JES1351WB, GE, Boston, MA). The other potatoes had the starch 60 extracted out of them, following a protocol by Krunic et al. [156]. Briefly, the potatoes were ground up in a food processor (Mini-prep PLUS, Cuisinart, Stamford, CT). Sample was reduced in 4 mM sodium sulfate and rinsed using 10 volumes of 0.05 mM sodium phosphate buffer (pH 6.5) and DI water. After centrifuging, the starch (sediment) was left to dry at room temperature. The ERS was then treated to the same boiling, cooling, and reheating scheme as the WF samples. Both WF and ERS were both predigested with the pancreatin and amyloglucosidase, prepared at 2% solutions, and RS contents measured, as described above.

2.10.2 Study design

Three different microbiomes (LBC-T3, MBC-T8, and HBC-T9, chosen at random) were examined in these experiments. The starting community had the RUM nutrients as well as a type III potato RS: in whole potato [155] or extracted from potato (ERS) starch.

In addition, PS was used as another control. This PS was pure type II RS, and was the same PS used in the previous experiments. The resulting fermentation profiles, OD and pH were measured as described above. Pellets were saved for sequencing.

2.10.3 Analsysis

Analysis in the form of organic acid production comparisons among the treatments, alpha diversity, and a heatmap were run as described previously. For this experiment, the final fermentation communities were classified as either WF, ERS or PS treatments and run through LEfSe to determine species enriched by each treatment.

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CHAPTER 3: RESULTS AND DISCUSSION

3.1 Study 1: Feeding Purified Resistant Starch to 11 Microbiomes

3.1.1 Effect of different resistant starches on butyrate production

3.1.1.1 Results: Ten different fecal communities were inoculated into

fermentation vessels with one of six RS (listed in Table 2). The resulting fermentation

profiles varied widely from treatment to treatment across the different donors. A full

table of the averaged results as well as their significance can be seen in Appendix A.

The banana flour (Bn) was the only RS to consistently increase butyrate

production across all donors, while tiger nut starch (TN) increased butyrate the least

frequently. The various RS boosted butyrate production by at least 10% in half of the

donors, by 50% in about one third of the donors, and 100% in at least one donor (see Fig

1).

A LBC B MBC C HBC

Figure 1: Bubble plots of butyrate production by butyrate-production status and treatment. Larger bubbles with lighter colors indicate higher butyrate production, and smaller, darker bubbles indicate less butyrate. Values here had butyrate levels produced by the control subtracted out. A) Butyrate production by LBC, T1 -T5 and T11, B) Butyrate production by MBC, T6, T8 and T10, C) Butyrate production by HBC, T7 and T9

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The LBC were more selective in which RS they could use for increasing butyrate production and included T1 - T5 and T11. When butyrate production was increased, however, it was often a significantly larger (>50%) increases in butyrate compared to the

HBC. The HBC could use more of the different RS to increase their butyrate production, but this same increase was seen across most of the RS. The maximum amount of butyrate produced was still highest in the HBC compared to the LBC. In terms of significance, banana starch was able to significantly increase butyrate production across all 11 donors, while HAM2 and HAM4 did this across 5, and PS, TRS, and TN significantly increased butyrate in 2-3 communities.

Figure 2 Inoculation fermentation profile: Relative levels of the organic acids butyrate, acetate, propionate, and lactate produced by the treatments were averaged for each inoculum and are displayed in mM, organized by inoculum production level (low, mid, high) and chronological order. Values represent the absolute amount of each organic acid minus the value found in the base RUM media. Lines on the plot represent averages (solid line) and means (dashed line) of the inoculums within each production level.

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Concerning all of the organic acids (OA), or the entire fermentation profile, there were also differences among the different communities and RS treatments. The differences between the total OA profile differed from individual to individual and strong patterns did not appear among the LBCs, MBCs, or HBCs (Fig 2). Categorizing the inoculums by their endogenous levels of butyrate did not change analysis much to the current LBC/MBC/HBC grouping. T7 produced an especially high amount of OA and T8 an especially low, but generally the LBC produced the least OA and butyrate, and the

HBC produced the most. Lactate levels were largely driving the individual differences among the production levels. For example, the total OA difference between T2 and T5 or T8 and T10 or T9 and T7 were largely due to increases in lactate. T4, T5, and T10 produced the highest amounts of lactate. T7 produced moderate levels of lactate but higher levels of acetate and propionate compared to all other microbiomes.

The fermentation profiles of the RS, seen in Fig 3, show that all the RS have slightly different profiles. Noticeably, the profiles of the non-RS controls amylopectin and CS were very different from the RS treatments, having higher lactate and lower

SCFA production, and much higher OA production overall. Banana starch had the highest average production of butyrate. Zero values are present in some treatments for lactate because the amount of OA in the basal RUM media were subtracted.

64

Figure 3 RS treatment fermentation profile: Relative levels of the organic acids butyrate, acetate, propionate, and lactate were averaged over each inoculum for every treatment and displayed in mM. Values represent the absolute amount of each organic acid minus the value found in the base RUM media. Red stars represent significance difference compared to values produced by the control.

3.1.1.2 Discussion: The fact that HBC could not be induced to produce more butyrate has been seen in in vivo experiments. Venkataraman et al. [128] fed type II PS to individuals and measured the butyrate content in their feces before and after. This paper also found people could be grouped by their initial butyrate production, with the highest natural butyrate producing communities not able to further increase their butyrate production. However, their low and enhanced (mid) communities both saw increases. Thus, it is likely that communities eventually reach a plateau at which their butyrate production can no longer be increased by dietary supplementation. It is unclear what the exact value of this plateau is, and it would almost certainly change in vivo. Regardless, this plateau did not appear to rise in these experiments by addition of

PD or RS. While butyrate levels did increase significantly in HBC by RS supplementation,

65 the increases were not as dramatic as in the LBC, indicating the inability of RS to increase butyrate production past a certain point in a given system.

3.1.2 Butyrate and lactate levels were inversely related

3.1.2.1 Results: Butyrate was the main product of interest, so the production of other OAs in relationship to the butyrate production was also examined. Acetate, propionate, formate, and succinate seemed to have no consistent relationship with butyrate. In all donors except T1, T2, and T4, lactate significantly negatively correlated with butyrate (see table 3). That is, the treatments that induced higher levels of

Table 3 Pearson correlation between butyrate and lactate production: Correlation between butyrate and lactate production for each treatment for each microbiome was determined (in the Pearson column). Student’s T-tests determined if the correlation was significant. P-values were compared to FDR corrected q-values, alpha = 0.05. If p < q, the relationship was considered significant. butyrate, also had lower levels of lactate and vice versa by these communities. This is most apparent in the control samples amylopectin and corn starch, where a twenty-fold

66 increase in lactate and tenfold decrease in butyrate could be seen compared to banana starch.

3.1.2.2 Discussion: Since communities that produced higher levels of butyrate didn’t necessarily produce more OA in total, it appears that the RS nudged the communities toward butyrate-based pathways at the expense of other fermentation products, mainly lactate. This compound can be used by a number of organisms for energy, including those that convert it to butyrate. However, in this experiment, the higher amounts of lactate did not increase the abundance or activity of lactate fermenting organisms. The follow-up experiment co-culturing E. hallii with B. adolescentis was meant to determine if a lactate to butyrate producer could use the large amount of lactate being produced by the PD. However, no significant change in butyrate or lactate was observed. This may suggest that other strains of lactate consuming bacteria need to be tested, or there are other mechanisms within the community that prevented E. hallii from using this pathway. The latter pathway seems likely, as other experiments have demonstrated butyrate production by E. hallii in the presence of lactate on its own, and when co-cultured with B. adolescentis with fructooligosaccharides (which E. hallii cannot grow on by itself) [130].

It is interesting that higher production of lactate was associated with lower butyrate production. Most prebiotics aim to increase the populations of Bifidobacterium and Lactobacillus populations, which are lactate producers. These bacteria are currently associated with good gut health and are especially present in infant gut communities, where lactate producing bacteria are among the first colonizers of the gut. The results

67 presented here suggest that this approach might not be helpful to all people, including infants. Lactate producing bacteria invariably promote lactate utilizing bacteria, which produce gas end products like H2 and H2S, both of which are associated with colic in infants [157]. While lactate utilizing bacteria didn’t seem to be abundant in these experiments, the lactate producers were in treatments that resulted in high lactate and low butyrate production. Thus, the lactate production induced by current probiotics could be disadvantageous, especially to those who want to increase their butyrate levels in their gut. Including substrates like RS that increase butyrate production or butyrate producing bacteria in gut health products would coincide with the idea behind prebiotics [132] and provide benefits to a wider variety of people.

This high lactate production was unexpected. Even considering the levels in the raw inoculums, lactate was high for what one might expect. An average of 12.6 mM was detected in the original fecal samples used, while in typical individuals fecal lactate is less than 5 mM [158]. Some studies used pretreatment methods (SCFA extraction

[139,159]) on samples before running through the HPLC, to remove coeluting compounds and achieve more accurate SCFA determination in samples. However, this is largely done for GC measurements. In this case, since we did not see high numbers of lactate utilizing bacteria, their absence might have caused the higher amounts of lactate to build up rather than over-production of lactate or mis-measurement of lactate.

High(er) numbers of lactate producers might have also been present but not correctly identified because 16S sequencing cannot reliably identify bacteria at the species level.

Around 1000 OTUs were identified as in the genus Eubacterium, but only 15 unique

68 species. This suggests that some of these species were incorrectly identified. As this genus has a number of lactate-to-butyrate producers, it is just one example where this type of bacteria (and others) might be under-identified.

3.1.3 Effect of different resistant starches on the microbiome diversity

Ctrl

Figure 4 Shannon diversity among treatments and inoculums: All end-fermentation communities are plotted as individual points for each treatment and were used to create the box plots. Blue points are the diversity of the initial inoculum.

3.1.3.1 Results: The 11 original inoculums (T1-T11) along with the end fermentation communities from the pure RS, whole food RS, and PD experiments were sent out sequencing (170 samples total) of the V4 region of the 16S rRNA. Sequences were trimmed following Pat Schloss’s MiSeq SOP, resulting in a total of 7,659,675 sequences. There were 36,407 ± 13,536 reads per sample with a mean length of 253 bp.

OTUs identified as contaminants (found at high quantities in water control samples)

69 were removed, as well as OTUs with zero reads. The final OTU count was 18,691. OTUs were matched to species level identifications using BLAST+, and are talked about by their species name from this point forward.

The initial inoculums varied in their diversities (see Fig 4). Diversity varied in similar patterns to total organic acid production, with similar diversity between the LBC and MBC (3.45 and 3.51, respectively) and highest in the HBC (4.39). T10, T4, and T5 had the lowest diversities (2.42, 2.46, 2.68, respectively) of their category and in total, and

T7, 1, 3, and 8 were the most diverse (4.56, 4.45, 4.31, 4.44, respectively). T2 had an extremely low diversity at 1.41.

Averaging the results from each inoculum and looking at the impact of treatment showed that the RS treatments had effects on diversity, although they did not vary as drastically as the inoculums, ranging from HAM4 at 2.02 to Bn at 2.95 (Fig 5). Samples taken after RS fermentation had a lower diversity than the inoculums (average of 2.55 vs

3.63), and the controls had the lowest (1.95). Bn, TN and control had the highest diversities of the non-inoculum samples at 2.96, 2.95, and 2.95, respectively.

70

Figure 5 Shannon diversity of final communities fed RS: Bar heights represent the average diversity of each treatment averaged across the inoculums. Error bars represent standard deviation.

3.1.3.2 Discussion: The diversity of microbiomes fed a single substrate often decreases [120]. We expected the water control to have the highest diversity, as amino acids were the limiting nutrient, which more bacteria can utilize compared to RS.

However, two treatments, Bn and TN, that had similar diversities to the water control.

TN was virtually unusable by most of the microbiomes, so diversity similar to the control is expected. However, the Bn treatment produced surprising results. It was somehow able to maintain diversity in the communities while also increasing butyrate production in not several, but all of the communities. It is not immediately clear why this occurs.

While banana flour or starch has been shown in other studies to increase butyrate production, comparisons to other starches and microbial data has not been looked at in other studies [7,160,161]. The Bn treatment in this experiment was banana flour, and not just starch, so it might have contained a more diverse set of nutrients for the

71 communities to utilize. The nutrition facts panel states that the flour was about 90% carbohydrates, while only 10% of this was dietary fiber. Of course, this label did not account for RS, and much non-RS carbohydrate would be lost in the predigestion treatment, but it still most likely would have had more non-RS components in it than the other starches. However, the WF treatment (type III RS in mashed potato) should have also then increased butyrate production, as it would have had other non-RS components as well. But the WF did not increase butyrate production significantly over the ERS (type III RS purified from potato), except by the HBC (full results discussed in section 3.3). So, it is unclear if the extra nutrients had a positive or negative impact on butyrate production. In the Bn samples across inoculums, Clostridium punense was the only butyrate producers present at differentially abundant levels. However, there have been very few studies on this bacterium, and it has not yet been fully sequenced.

It was not surprising that T2 ended up with very little diversity in some samples, as its fermentation profile was very different from the other communities as well. C. perfringens largely took over this community. There are both commensal and pathogenic versions of these strains, the latter of which are often associated with opportunistic gastrointestinal illness. This species also has a number of glycoside hydrolases and carbohydrate binding modules. This suggests it can degrade more complex carbohydrates. Closer studies of these enzymes point to mucin degradation rather than starch degradation, but in some strains, there is most likely starch degrading activity because of the multitude of GH and CBMs present [162]. It is interesting to note that this bacteria might be able to produce butyrate from amino acids [65], which could

72 explain why T2 produced so much butyrate despite its lack of common butyrate producers and diversity.

On the other hand, it is maybe expected, but still interesting that the HBCs had among the highest diversities among the microbiomes. Both high butyrate production and high diversity are general markers of a healthy microbiome. While we hypothesized that the PD and the butyrate producers (to an extent) were affecting butyrate formation, the extra diversity in the HBCs could be contributing to the larger number of

RS they were able to utilize. Supporting this, T1, T3, and T11 also had high diversities, and were able to utilize the most RS among the LBC. However, T6 was able to use all the

RS, and while by a small margin had the highest diversity among the MBC, it was lower than that of T1, T3, T11.

3.1.4 Effect of different resistant starches on butyrate producer and primary degrader populations

3.1.4.1 Results: LEfSe was used to determine if any species in the original inoculums might be associated with LBC, MBC, or HBC in the final fermentation communities (see Fig 6). Anaerostipes caccae (a lactate to butyrate fermenter) and

Ruminococcus torques were the butyrate producers associated with HBC inoculums.

However, these species did not survive well during fermentation, as they were not found in the final communities at high levels. Low butyrate producing inoculums were

73

LBC MBC HBC

HBC

MBC

LBC

Figure 6 LEfSe of LBC, MBC, and HBC: LEfSe-identified species enriched in LBC, MBC, and HBC communities across all treatments. associated with amino acid to butyrate fermenter Anaerotignum lactatifermentans.

Inoculums that, overall, produced neither very high nor very low levels of butyrate were associated with two butyrate producers, Eubacterium rectale and Anaerobutyricum hallii.

74

T9

T8

T7

T6

T5

T4

T3 T2

T11

T10

T1

Figure 7: LEfSe of original inoculums: Species enriched by each inoculum across treatments.

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A LEfSe was also run comparing the inoculums (T1-T11) and what species were enriched for each across all RS and control treatments (Fig 7). Most communities had multiple species associated with them, but many were not clearly related to butyrate production. T2 stood out with only one species, Clostridium perfringens, associated.

Most microbiomes had a couple of butyrate producers present, with T1 having the most with R. faecis, R. intestinalis, R. inulinivorans and C. eutactus.

From running LEfSe analysis on RS-fed samples, grouped as either high or low butyrate production, a number of species were found to be related to high butyrate production in two or more treatments: Alistipes shahii, Alistipes finegoldii, Alistipes putredinis, Bacteroides nordii, Bacteroides xylanolyticus, Blautia hydrogenotrophica,

Eubacterium ramulus, Flintibacter butyricus, Herbinix hemicellulosilytica, and

Ruminococcus bromii (Appendix C). Looking at individual treatments, they each have a few unique bacteria associated with them. For example, across all inoculums, when PS induced a high level of butyrate, Coprococcus catus was a butyrate producer determined to be differentially abundant. HAM2 and HAM4 had a number of butyrate producers associated with them. For HAM2, E. ramulus and Butyricimonas faecihominis were increasingly abundant. In addition, R. bromii was present. HAM4 had higher abundances of Anaerostipes hadrus; Coporococcus etactus and C. comes; Roseburia faecis, and

Fecalibacterium prausnitzii. Ruminococus bromii and cellulose-degrader R. champanellenis were also present. Some organisms known to have butyrate producing pathways were enriched low butyrate producing samples, like Intestimonas

76 butyriciproducens, and a number of butyrate producers were found in amylopectin-fed samples that produced low levels of butyrate.

Positive controls amylopectin and CS created high levels of butyrate in inoculums

T2 and T9, but in these samples, no butyrate producers were associated. Instead, in samples where butyrate production was low, amylopectin and CS were associated with butyrate producers and B. adolescentis. A number of lactate producers were also differentially abundant, although there were not associated with the corresponding inoculums. Bn samples could not be classified as high or low butyrate, as all samples had high butyrate. When differentially abundant species were looked for in Bn versus other carbohydrates, a number of Clostridium species came up, some of which were butyrate producers. However, these species did not appear again or at high abundances in any samples. In general, the microbiomes fed TN starch always had a similar organic acid and community profile to the negative control (water) community, HAM4 was similar to the amylopectin samples in most of the communities, and PS was similar to CS in a number of communities.

A similar LEfSe analysis was repeated for each treatment, but categorizing samples by high butyrate or high lactate production. There were no species that appeared across treatments. However, R. bromii, F. prausnitzii, C. comes, R. intestinalis, and R. inulinivorans appeared at differential levels again in some of the high butyrate producing treatments. Among the high lactate producing treatments, lactate producers

Streptococcus pasteurianus, Blautia spp., and Enterococcus saigonensis were promoted

(data not shown).

77

Heatmaps: Looking at the heatmaps (Appendix B), there was not a lot of consensus between the species LEfSe identified as differential in the inoculums with what was prominent in their resulting communities. For example, Anaerostipes caccae and Ruminococcus torques should have been associated with T7 and T9, which produced the highest overall levels of butyrate, but neither were detected consistently in the final fermentations or inoculums. Differentially abundant species did appear in multiple communities, namely R. bromii and F. prausnitzii, which often appeared together in high butyrate producing treatments across communities.

Except for T9, the butyrate producers in most of the microbiomes were outcompeted in the RS and starch supplemented communities. T9 had only three out of thirteen butyrate producing organisms fall below the levels detected in the original inoculum in the new RS-supplemented environment and retained high levels of R. bromii in many of its treatments. B. adolescentis was not detected in any of T9’s samples

Greater decreases of butyrate producers were most noticeable in T2 and T8. T2’s community started with an average diversity and had nine butyrate producers and B. adolescentis present. However, only three of these bacteria survived at high abundances when supplemented with any of the treatments or controls, and one of them (F. prausnitzii) was only present in the Bn samples (which maintained the highest diversity for this microbiome in addition to creating significant amounts of butyrate).

T4 and T5 stood out for their low butyrate production. Looking at their communities, T4 and T5 had very low diversities as well and had low numbers of

78 butyrate producers. In both, R. bromii was either below the limits of detection or missing entirely, while B. adolescentis was consistently present. In T4, except for Bn, no

RS was able to boost butyrate production above the amount produced by the negative control. Butyrate producers Anaerotignum aminivorns and Roseburia intestinalis were present at highest levels in Bn and negative control samples. These species were also present at high levels in the inoculum. T5 could use PS and HAM2 to somewhat boost butyrate production above baseline, but Bn was really the only starch to do so. This inoculum was very depleted of butyrate producers and PD. Again, A. aminivorans was present at high levels only in these treatments and the control. C. comes was also present in only these treatments, but at lower levels.

Looking across microbiomes, the HAM4 treatment’s communities often looked very similar to the amylopectin treatment. The main differences between the two were generally higher levels of butyrate producers and lower populations of Blautia. In addition, the TN communities looked like the negative controls, and PS were similar to

CS communities. No butyrate producer (or other species) was present at higher levels in all the high-butyrate treatments. Bacteroides uniformis, a butyrate producer, comes the closest, and appears at moderate levels in all treatments except HAM4 and the initial inoculum.

As stated earlier, T1, T3, T6, and T9 stood out for their ability to degrade a variety of starches (for their production-level category) to produce butyrate (Fig 1).

Except for T6, all also had high diversities. T1 and T3 could each use four RS (TRS, HAM2,

79

HAM4, Bn and PS, TRS, HAM4, Bn, respectively) and CS, T6 could use all RS, and T9 could use all RS and CS. Akkermansia muciniphilia was found in all of these inoculums and in their final communities at variable levels, and was found differentially abundant in LEfSe analysis. T2, T8, and 11, also had decent levels of success in degrading multiple RS. T2 could use TRS and Bn as well as PS and TN to a limited extent. This microbiome was also able to use both amylopectin and CS to produce huge amounts of butyrate. T8 utilized

Bn to produce significantly higher levels of butyrate but could also use PS, TRS, HAM2, and TN to produce varying levels of butyrate. T11 could use TRS, HAM2, and TN. In 2 and 8, A. muciniphilia did not carry over to any of the treatments, and in 11, it was not in the inoculum. Also associated with these communities were a number of butyrate producers and R. bromii while the other microbiomes less adaptable in their ability to use RS were more associated with lactate producing species (see heatmaps in Appendix

B).

3.1.4.2 Discussion: It was surprising that the amino acid to butyrate fermenter A. lactatifermentans was associated via LEfSe with LBC. As the major energy source in the baseline media was amino acids, we thought that we might be identifying communities with high levels of these bacteria instead of carbohydrate-to-butyrate producing communities. If this were true, then the HBC communities would have the highest abundances of these bacteria, because these communities could produce high levels of butyrate from the media alone. However, it appears that these types of butyrate producers were most important for butyrate production in LBC. In T4 and 5 (LBC), the species most common in high butyrate producing treatments was A. aminivorans,

80 another amino acid to butyrate fermenter. Alistipes putredinis was the only of these types of butyrate producers to be associated with HBC, but it did not appear to be differentially abundant in any consistent pattern with resistant starch treatment. HBC also did not produce higher levels of BCFA or organic acids in general at lower pH levels as determined by the experiment outlined in section 2.8.4. This indicates not much protein fermentation is occurring, even under conditions that promote protein fermentation. Initially, we thought this might be because we did not lower the pH enough, or that this pH-amino-acid-fermentation relationship simply did not exist, but it might be the case that we simply do not have a lot of amino acid fermenting bacteria in the HBC.

There was also concern about the classification into the LBC/MBC/HBC system as it might be driven by carbohydrates carried over from the initial fecal sample. However, because of the initial processing and the small inoculation volume, the initial sample was diluted by a factor of 60. At such a low level, the initial nutrient content in the fecal sample probably did not transfer to a significant extent.

While there was a lot of variation, from LEfSe analysis and observation of abundant species, it appears that R. bromii, F. prausnitzii, C. comes, and A. muciniphilia stood out as important bacteria for increasing butyrate production. F. prausnitzii especially but also C. comes were correlated with high butyrate production for many of the inoculums, with R. bromii following a similar pattern. F. prausnitzii was also

81 associated with the high butyrate producing samples in the beta diversity analysis (Fig

8). For the most part, it appeared that high levels of the butyrate producer were

LBC

HBC

MBC

Figure 8: Beta diversity of all samples colored by production level (low-LBC, medium- MBC, or high-HBC). Bray-Curtis dissimilarity was used. Plot explains 20.6% of variance along the first dimension and 11.6% on the second dimension. Vectors indicate species most strongly associated with the samples and the driving forces behind the explained variance more important in increasing butyrate production than the PD. Previous research shows that R. bromii usually exists at lower populations in cocultures, but its cross feeding affects are still very present [127]. Usually, there was at least one butyrate producer present at higher levels in the treatments. F. prausnitzii and C. comes were the most important for creating significant amounts of butyrate, particularly when R. bromii was present as the PD. Both of these species are known butyrate producers and are associated with healthy, disease-free guts [163-165]. F. prausnitzii strains are also known to degrade a variety of oligosaccharides and more complex carbohydrates,

82 including pectin [6]. A. muciniphilia is not a PD or butyrate producer, instead producing acetate and propionate. It is also unable to degrade starch but is very good at using mucin as a substrate. While A. muciniphilia might not be associated with butyrate production, several studies have looked at this bacteria and F. prausnitzii as candidates for new probiotics due to their association with disease-free guts [166-168]. It is not clear what the association of A. muciniphilia is here in our treatments that don’t contain mucin, however, it is possible that either some small amount of mucin was carried over from the inoculum and/or that it participates in other cross-feeding networks linked to butyrate production.

The presence of other non-butyrate producers was interesting. These species might be related to optimizing butyrate producing conditions in ways that are not completely understood. For example, Blautia hydrogenotrophica was enriched in several high-butyrate producing treatments. This species can use H2 from the environment and producing acetate. H2 is a byproduct formed by many butyrate producers during butyrate production, so clearing it could allow continued butyrate production, and acetate can be used as a substrate for butyrate production as well. The other species identified by LEfSe as enriched in one RS or another do not all have clear links to butyrate production, however.

It is interesting that butyrate producers were also associated with the amylopectin and CS controls but without the increase in butyrate production as well.

Scott et al. also found that amylopectin from potato starch increased the relative abundance of a number of butyrate producers [132]. However, our work shows that this

83 does not ensure that butyrate production will actually increase, and Scott et al. did not track metabolic data. It has generally been assumed amylopectin and CS are not butyrogenic due to the fact that they do not survive to the large intestine to be fermented. These results suggest that even if they make it to the large intestine, they may promote lactate formation instead of butyrate, as seen in this experiment. This is important as various strategies looking to alleviate obesity or type 2 diabetes seek to inhibit the human amylolytic enzymes to limit starch utilization in the small intestine.

Besides limiting glucose release in the large intestine, it is assumed that these would be fermented in the colon for health benefits [169,170]. If, however, they are fermented to things other than butyrate, perhaps lactate as we see here, the benefits may not be realized or it could even be harmful.

In our studies, amylopectin fermentation in particular led to a sharp decrease in pH. While butyrate producing organisms are generally tolerant to low pH, this may stress them beyond their capacity to cope. Alternatively, most butyrate producers are capable of producing lactate, and low pH conditions may select for the lactate pathway over the butyrate pathway. This may be linked to the lower pKa of lactate (3.86) compared to butyrate (4.82). Organic acids at pH close to or less than their pKa exist in the undissociated, uncharged form which can readily penetrate bacterial membranes and then dissociate in the near neutral pH of the cytoplasm, which they can acidify and/or interfere with various metabolic processes. Indeed, acetate, propionate and lactate are often used as antimicrobial agents and food preservatives [171]. At low pH, the bacteria may favor lactate production to avoid these effects.

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It is not surprising that TN fed communities resembled the media only controls, as the microbiomes in this experiment appeared unable to utilize TN. However, it was surprising that the PS communities were similar to CS communities, and HAM4 communities to amylopectin communities. CS, while not resistant, still has the full granular starch structure, so was used as a control for the impact of granular structure.

It should have been more difficult to degrade than amylopectin, but not as difficult as

RS. As such, when looking at the SCFA production, CS was most similar to the amylopectin profile, but moving towards what some of the RS profiles looked like. In fact, CS increased butyrate production in 4 communities versus the one community with increased butyrate by amylopectin. PS increased butyrate levels to varying degrees in 7 microbiomes, and HAM4 by high amounts in 5 communities. The main difference was that there were generally more butyrate producers and/or R. bromii present at higher levels in the treatment versus control samples, further demonstrating these organisms’ importance in butyrate production. However, it is likely that there are many other important relationships among these organisms and all the other organisms in the gut.

For example, A. muciniphilia and B. hydrogenotrophica are two bacteria in this study that seem to be linked to but not directly affecting butyrate production. This is highlighted by the fact that high diversity inoculums were able to use either higher numbers of RS to produce butyrate or produce larger amounts of butyrate (both, in the case of the HBCs).

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3.1.5 Follow-up experiments

The follow up experiments did not generate anything upon preliminary inspection (data not shown here). Branched chain fatty acid production, specifically isoleucine and iso valerate, was very slightly but not significantly increased under low pH conditions.

3.2 Study 2: Addition of Primary Degraders to 11 Microbiomes

3.2.1 Effect of primary degrader addition on butyrate production

Figure 9 PD butyrate production: Butyrate production induced by addition of either B. adolescentis (blue) or R. bromii (green) is displayed compared to a control (gray). Error bars represent standard deviation. Each cluster of three bars was inoculated by the same microbiome. Samples are ordered by production level (low, mid, high) and by chronological order. Clusters with a star (*) denote significant differences in butyrate production between one or either treatment compared to the control of the same inoculum.

3.2.1.1 Results: Currently, only Ruminococcus bromii and Bifidobacterium adolescentis are confirmed primary degraders, who are able to break down RS.

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Considering the system used here is supplemented mainly with RS as an energy source, it was hypothesized that the increased amounts of these PD would increase butyrate production. However, the PD’s ability to induce butyrate formation by spike in was determined to be largely non-significant. In inoculum T2 – T11, the control induced higher or significantly higher levels of butyrate than the treatments. While R. bromii induced significantly higher levels of butyrate than the control in inoculum T1 (Fig 9), this was the only community able to do so. In T5 and T10, R. bromii addition significantly decreased butyrate compared to the control. B. adolescentis never produced significantly higher levels of butyrate compared to the control, and in T5, T8, and T10, butyrate production was significantly less in the B. adolescentis treatment.

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3.2.2 Effect of primary degrader addition on the microbial community

Figure 10: Shannon diversity of PD treatments and inoculums

The PD treatments had similar Shannon diversities with B. adolescentis at 2.49 and R. bromii at 2.83 (Fig 10), which was similar to the PS control (2.50) and RS average

(2.65) (Fig 5).

LEfSe analysis showed that, as expected, B. adolescentis and R. bromii were, overall, significantly associated with their respective treatments where they were added to the fermentation (Fig 11). However, from the heatmap (Fig 12), the samples’ final compositions were much more influenced by the inoculum than the treatment. In general, the PD and control samples from the same inoculum look similar. Other species

88 associated with the treatments via LEfSe were not clearly related to butyrate production or primary-degrading activities.

Figure 11: LEfSe categorizing R. bromii and B. adolescentis samples as such

Figure 12: Heatmap of butyrate producers and primary degraders in samples with additional PD spiked in: Samples (columns) are organized by what microbiome they were inoculated with, whether those inoculums were classified as LBC, MBC, or HBC, and what treatment (PD or control) they were given. Rows represent OTUs, shown as the species they are most closely associated with. Species were organized by how often the species were found together. Rows are labeled in green if they represent a species that is a butyrate producer and red if they are a primary degrader.

One sample stands out, T1 with B. adolescentis added in. This sample, unlike its

R. bromii and PS counterparts from the same inoculum, experienced a large increase in the amount of butyrate producers present. However, while the measured amount of 89 butyrate was higher than in the control, it did not reach statistical significance. In addition, neither of the PDs are at particularly high levels in this sample.

T1 with R. bromii spiked in was the only treatment-inoculum combination to produce significantly higher levels of butyrate than the control, however this sample did not have many distinguishing species. It had higher levels of R. bromii, and a variety of butyrate producers were present but none at particularly high levels. This community might have had just the right conditions for increased R. bromii levels to have an impact on butyrate, with the right collection of butyrate producers and other bacteria adapted to working with R. bromii. Specific bacteria were also enriched with B. adolescentis addition. This could mean there exists a community composition that supports butyrate production with this PD as well. We hypothesized that a lactate to butyrate producer such as Eubacterium halii may fulfill this role, but the combination of B. adolescentis and

E. halii did not increase butyrate production.

3.2.3 Discussion

The PD treatments did not deviate much in their OA production compared to their respective controls, and their final diversities were similar. The B. adolescentis treatment did favor the growth of a number of butyrate producers but without a significant increase in butyrate production highlights the potential complexity in the relationship between the PD and butyrate producing organisms.

B. adolescentis and R. bromii use different RS-degrading systems, which might explain why they have different effects on the population. The former produces lactate as an end product of fermentation, and some amounts of acetate as well as smaller

90 oligosaccharides from initial breakdown of the RS [130,172]. R. bromii largely releases oligosaccharides and some acetate. However, R. bromii is not able to utilize glucose and best uses oligosaccharides longer than maltose [127]. Therefore, shorter oligo and monosaccharides are readily available for other bacteria to utilize. As this study indicates, high lactate production seems to be linked to low butyrate production. From its buildup, it appears that these microbiomes were not able to ferment lactate to a high degree. Breaking down the main energy source (RS here) to more lactate would not have helped these communities. In fact, LEfSe analysis identified a number of lactate producers associated with low butyrate producing treatments. Associated with high butyrate producing samples were a number of species known to utilize starch very well.

Among these are the Lachnospiracaea, Eubacteriacae, and Ruminococcaceae families, who all harbor butyrate producers. It makes sense then, that these communities would produce more butyrate in the presence of R. bromii but not B. adolescentis.

There was no observable effect on PD addition to microbiomes on butyrate production. While R. bromii especially seemed to be involved with increased butyrate production in conjunction with butyrate producers, its artificial addition to communities did not yield the same effect. In addition to this, the overall decrease in butyrate in many of the communities with PD addition indicates that we do not yet understand all the relationships between RS-degrading bacteria and butyrate production. Likewise, adding the butyrate producer Eubacterium hallii did not increase butyrate production by itself or when co-cultured with B. adolescentis. This research was important in taking

91 these first steps at observing the relationship between PD and butyrate producers on entire microbiomes.

3.3 Study 3: Feeding Resistant Starch in Mashed Potatoes to 3 Microbiomes

3.3.1 Effect of whole-food resistant starch on butyrate production

Figure 13: Type III wholefood/ extracted potato starch fermentation profile: Relative levels of the organic acids butyrate, acetate, propionate, and lactate produced by the treatments and are displayed in mM, organized by inoculum production level (low, mid, high). Values represent the absolute amount of each organic acid minus the value found in the base RUM media. Lines on the plot represent averages (solid line) and means (dashed line) of the inoculums within each production level. Letters represent significant differences of the organic acid produced within the production level.

In addition to the six, pure-RS starches that were tested, a type III RS in the form of mashed potatoes was also tested for its effect on an individual’s microbiome. In this form, there would be other nutrients besides the RS that the bacteria would have access

92 to, including non-starch carbohydrates and small amounts of lipid, amino acids, and compounds like polyphenols that could have escaped digestion that might improve butyrate production. The RS was added to the fermentation vessels as mashed potato

(whole food, WF) or extracted starch (ERS). Only the HBC could use WF to induce more butyrate production than the ERS. This reversed in the LBC and MBC, where the ERS had higher butyrate levels. Lactate levels were always higher in the WF supplemented communities (see Fig 13). The type III PS consistently produced higher butyrate levels than the type II PS. In terms of total organic acid production, the LBC produced the highest OA levels, followed by the HBC and MBC.

3.3.2 Effect of whole-food resistant starch on the microbial community

Figure 14: Shannon diversity of type III RS, whole food and extracted. Plotted here are the diversities of the initial inoculums and the resulting diversities after the inoculums were supplemented with WF, ERS, or type II PS.

The treatments across the board had much lower diversities than their inoculums (Fig 14). The low butyrate producing community fed WF had an extremely

93 low diversity, at 1.1. In general, across all the production levels, the diversity stayed around 2.6.

LEfSe analysis did not correlate butyrate producers or PDs to the treatments, but there were some species of interest (Fig 15). The ERS was associated with

Figure 15: LEfSe on type III PS: Samples categorized by their treatments

Bacteroides thetaiotaomicron, an excellent degrader of many carbohydrates (but not

RS). Lachnospira pectinoschiza was also associated with ERS, a bacterium known to digest pectin, and it is possible some pectin was co-isolated with the starch. The lactate producer Lactobacillus rogasae was enriched as well, although this treatment consistently produced very little lactate. PS samples were associated with Flavonifractor plautii, which can use flavonoids for energy. Flavonoids are plant polyphenolic compounds found in potato flesh and skin, especially in red or purple potatoes. No species were specifically associated with the WF starch treatment.

The heatmap (Fig 16) showed that both individual differences (same as production-driven differences in this case) and treatment affected the final community.

Within inoculums, the WF and PS treatments were more similar to each other than the

ERS. In the MBC, the ERS has a higher diversity and a higher number and relative

94 abundance of butyrate producers. ERS also has a lower abundance of B. adolescentis. R. bromii is present in the LBC-inoculated ERS treatment and in all the treatments inoculated with the HBC.

Figure 16: Heatmap of butyrate producers and primary degraders from RSIII experiment: Columns were organized by production level LBC, MBC, HBC, or the inoculum) and treatment (inoculum, WF, ERS, or PS). Rows were organized as in the PD heatmap.

3.2.3 Discussion

It seems that potato starches in general were average in their ability to produce butyrate, and their RS content drives this butyrate production. Again, it was observed that R. bromii and F. prausnitzii are associated with higher butyrate producing treatments.

The extra components present in whole potatoes do not seem to have a positive effect on butyrate production. However, we did not examine the final nutrient contents of the potatoes, only the final RS content. While we controlled the final amount of RS added to the treatment, it is unknown how much of the other nutrients were added, or

95 what these nutrients might be. Most likely, the non-RS carbohydrates would play the biggest part in influencing butyrate production. A number of generalist, carbohydrate- degrading Bacteroides species were notably prevalent in many of the RS treatments, and significantly so in the ERS samples compared to the WF and PS samples. However, there are several studies that suggest that starch is the best carbohydrate source for butyrate production [132,173,174]. From this lens, it would make sense that the additional nutrients did not increase butyrate production. They would only serve to help non-PD or non-butyrate producing species. However, due to the different effects seen for each RS, it is still important to continue looking at RS in whole foods. For instance, the banana flour treatment had a very positive butyrate-production effect, and this RS most likely had higher non-RS components in it than the other RS treatments. Perhaps the different whole food components have different effects on overall butyrate production.

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CHAPTER 4: CONCLUSION AND FUTURE STUDIES

4.1 Conclusions

The data here shows that butyrate production can be increased across different microbiomes as a result of RS supplementation, with banana flour being the most consistent to do so. One of the first patterns in butyrate production we found was that people’s microbiomes could be grouped as low, medium, or high butyrate-producing based on fermentation in the absence of RS. Moving forward, these categories became somewhat limited in their predictive power. In general, the data showed that the production capabilities and final community structures of each of the groups were similar, but there were outliers in each category. In addition, despite the butyrate boost seen in most of the communities, the butyrate capacity did not change, as seen in the smaller change in butyrate production in the HBC compared to the LBC. Whatever limits the community or environment has on the total amount of butyrate produced, RS seems unable to increase these. That being said, the increases in butyrate seen, especially in the LBC, were still significant.

Several studies have looked for enterotypes that could be linked to raising butyrate with specific RS supplementation but with little luck so far. ‘Enterotypes’ refer to groups of bacteria associated with a specific phenotype. For example, Arumugam et al. proposed that human gut microbiomes could be split into three enterotypes based on the enrichment of one of three genus, Bacteroides, Prevotella, and Ruminococcus

[175]. In this study, it was found that microbiomes with high levels of Coprococcus

97 comes and Faecalibacterium prausnitzii were able to utilize HAM4 to produce significantly more butyrate. These bacteria were also found at differentially abundant levels in the final fermentation communities of HAM4-fed inoculums, suggesting they were important to utilizing HAM4 and in the increase in butyrate that was seen. R. bromii, F. prausnitzii and C. comes were also significantly linked to high butyrate production in a number of treatments, and other bacteria were linked to specific treatments. However, it not always clear why these bacteria were increasing butyrate production in these treatments compared to others. This suggests we do not yet fully understand RS utilization networks in microbiomes. For example, considering the vast number of species in the gut, it is likely there are more than the current two RS degraders. In addition, the exact mechanism of RS degradation and how this specifically increases butyrate production is not clearly understood. Both discovery of novel bacteria and novel degradation pathways could reveal more RS-butyrate relationships.

Increasing butyrate is a promising way to mitigate and prevent the effects of a number of GI and metabolic diseases. The research here paves the way for RS to be used as an effective and consistent way to increase butyrate production by the gut.

4.2 Future Studies and Moving Forward

4.2.1 Future studies

4.2.1.1 Nutrient analysis of substrates: As noted in the WF discussion, there was a lack of information here on the nutrient composition of the substrates used, especially outside of the RS content. While RS should be the major energy source, banana flour’s

98 high and consistent ability to increase butyrate production compared to the other starches indicates that other nutrients might have a synergistic effect on butyrate production. In addition, while the nutrients in the WF seemed to decrease butyrate production, these compounds would be important to discover as well.

4.2.1.2 Nutrient analysis of final RS breakdown products: The PD are known to break down RS, but their exact carbohydrate breakdown products are not known yet.

Determining these could provide more insight as to how RS seems to be increasing butyrate production specifically.

4.2.1.3 Testing the predictive power of the determined bacterial signatures:

Multiple bacteria were found to be associated with high butyrate production, high lactate production, and high butyrate production from specific RS. Feeding RS to a wider diversity of microbiomes and seeing these same patterns would help solidify enterotypes for each starch so that eventually predictive models can be made for microbiomes and the RS that will best increase its butyrate production.

4.2.1.4 In-Vivo studies: These experiments must be conducted in-vivo to get the most accurate data on how microbiomes react to different RS. This can be done with a cross over experiment, where each participant is fed each RS for a period of time, with washout periods in-between starches. Fecal samples would be collected at base line and during periods of RS consumption and analyzed for their SCFA content and microbial community. The larger the subject pool and the more diverse, the more rigorous this study. Similar analysis used in this study can be applied to the in-vivo version to determine SCFA profiles and bacterial communities associated with eating certain RS.

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4.2.2 Moving forward

These proposed studies would further research towards using RS as an effective and consistent way to increase butyrate production in the gut. The current study’s findings revealed a link between non-RS nutrients in RS products and butyrate production. Determining what those nutrients are could make the difference between a starch being highly butyrogenic (like the Bn treatment) and weakly butyrogenic (like the

WF treatment). The final nutrient content of any RS is also important for determining the exact mechanism of PDs. While this study and many others found a relationship between R. bromii and increased butyrate production, this benefit generally did not increase with larger populations of R. bromii present. In addition, on the contrary to other studies, this study found the other PD, B. adolescentis, was related to lower butyrate production. Determining the nutrients released by each of these PD can give more insight as to how these bacteria shape their environments.

Bacterial signatures linked to RS that produced high levels of butyrate were also determined in this study. Combing through microbiomes for patterns can be tedious and it is almost never clear if a pattern is true or biased by your study design, equipment, or analysis. With repetition, however, and in larger trials or human feeding trials, one can be more and more certain that a pattern does exist. In addition, with a priori knowledge, more directed experiments and analysis can be done to prove a hypothesis. This study laid the ground work for such hypothesis driven experiments like

‘In communities where HAM4 increases butyrate production, F. prausnitzii and C. catus

100 populations will increase’. Such experiments will further allow humans to harness the gut in specific ways to mitigate disease.

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APPENDIX A: FERMENTATION DATA MF Compliation Trial.Inoculum Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 1.1 PS 5.27ab 5.47bc 9.56a 67.02cd 16.2ab 96.3abc 73.07a 2.91b Low butyate TRS 5.14b 5.55bc 9.1a 55.89cd 18.26a 95.12abc 73.45a 6.19ab WF 7.25a 5.5bc 6.68ab 46.71d 15.64a 89.52bc 71.38a 17.69a ERS 5.79ab 5.22cd 2.28c 72.37bc 6.63cd 88.33bc 69.02a 8.34ab B.ado 3.72b 5.28cd 8.87a 68.54cd 12.52abc 102.2ab 71.14a 4.33b R.bro 4.57b 5.65b 3.63c 56.8cd 7.81bcd 104.67ab 64.25a 11.72ab Amy 5.22ab 5.06d 2c 93.36ab 4.49d 74.75c 66.71a 6.19ab CS 4.74ab 4.9d 5.84a 100.71a 6.68cd 111.83a 67.99a 3.85b Water 4.08b 6.9a 3.59bc 18.62e 14.14a 69.55c 74.35a 4.02ab 1.2 HAM2 4.24b 6.26b 3.45a 31.63d 23.8a 64.77c 75.43a 7.87b HAM4 5.51a 5.8c 3.36a 43.26c 10.11cd 72.89cb 75.06a 14.89a Bn 5.35a 5.97c 6.15a 17.25e 7.88de 85.45b 69.92ab 15.67a TN 3.73b 6.76a 4.5a 17.26e 16.01b 56.38c 70.74ab 3.14cb Amy 4.24b 4.11e 3.95a 139.71a 13.38cb 111.5a 57.46d 1.5c CS 4.07b 4.82d 4.13a 98.57b 6.3e 106.23a 63.45cd 3.66cb Water 3.34b 6.85a 4.57a 14.45e 13.15cb 62.42c 67.11bc 4.65cb Low butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 2.1 PS 1.62de 6.73ab 5.25a 27.64c 21.69c 67.91de 75.99a 6.33d TRS 2.82bc 5.02de 3.89a 56.12a 33.24c 114.45a 70.08abc 21.08b WF 2.46bcd 6.45b 5.1a 24c 19.36c 73.7cd 71.7abc 10.64cd ERS 4.02a 5.3cd 3.33a 32.84bc 80.55b 92.64b 63.66c 16.57bc B.ado 1.94cde 5.68c 3.87a 49.29a 20.27c 94.21b 72.1ab 5.8d R.bro 2.52bcd 5.57c 4.02a 45.81ab 18.39c 92.01b 73.46a 10.63cd Amy 3.17ab 5de 5.77a 33.54bc 133.05a 84.37bc 70.16abc 31.85a CS 3.2ab 4.73e 5.55a 49.72a 104.28ab 95.12b 64.54bc 32.96a Water 1.45e 7.01a 4.99a 25.18c 19.67c 59.9e 72.99a 5.35d 2.2 HAM2 1.5d 6.84a 5.77a 27.03c 17.87c 64.08e 74.37a 7cd HAM4 1.65cd 6.7a 5.74a 25.03c 16.9c 68.25de 75.18a 5.26d Bn 2.19bc 6.55a 6.34a 15.11d 15.46c 90.27ab 72ab 16.3b TN 1.73cd 6.84a 5.38a 16.96d 14.02c 78.62cd 69.25ab 10.09cd Amy 2.58ab 5b 5.76a 36.3b 127.27a 81.77bc 68.38ab 33.56a CS 2.84a 4.42b 6.93a 48.44a 114.88b 96.49a 64.37b 33.64a Water 1.46d 6.95a 3.41b 24.11c 20.03c 66.61e 71.14ab 7.64cd Low butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 3.1 PS 4.98c 6.41bc 5.04ab 13.32c 15.98ab 73.04edf 78.99ab 5.55bc TRS 4.83c 6.61ab 3.91b 11.64c 16.67a 67.92ef 80a 5.14bc WF 7.57a 5.24d 8.23a 39.78bc 10.12dc 96.17ab 71.6b 13.02a ERS 6.41b 5.04d 5.87ab 69.38b 5.61e 90.86abc 63.5c 7.2b B.ado 4.45c 6.43bc 3.67b 10.14c 14.95ab 79.87cde 81.45a 6.98bc R.bro 4.56c 6.36bc 3.83b 11.34c 15.2ab 79.58cde 81.53a 6.55bc 102

Amy 6.03b 4.46e 7.05a 121.12a 7.88de 102.84a 59.27c 0d CS 5.01c 6.19c 6.19ab 8.53c 12.74bc 84.93bcd 82a 8.23b Water 4.56c 6.79a 3.55b 11.69c 13.62abc 64.45f 77.37ab 3.34cd 3.2 HAM2 4.87dc 6.67b 2.72c 12.01c 18.57a 74.62c 94.92a 5.33b HAM4 5.68b 6.32c 1.8c 5.4c 15b 95.01b 82.26ab 15.94a Bn 5.82ab 6.08d 8.38a 6.63c 11c 96.47b 72.5ab 16.03a TN 4.77dc 6.88a 3.5c 11.25c 14.24b 71.76c 80.2ab 6.14b Amy 6.54a 4.64f 5.82b 111.71a 9.2c 99.91b 65.13b 0.44c CS 5.26bc 5e 7.97ab 84.13b 10.37c 114.29a 74.87ab 2.63bc Water 4.49d 6.87ab 3.61c 11.96c 14.69b 72.74c 81.37ab 5.77b Low butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 4.1 PS 2.22cd 6.15ab 7.24a 44.29ef 29.26cb 66.09bcd 63.51ab 1.54a TRS 2.61cbd 5.43bc 6.58a 80.7c 46.64a 56.95cd 58.36ab 0.93a WF 4.86a 5.57bc 8.26a 60.98dc 4.64d 97.33a 70.21a 2.95a ERS 4.85a 4.89cde 7.12a 118.95b 13.93cd 74.45bc 64.38ab 1.94a B.ado 2.59cbd 5.37cd 8a 52.39de 19.57bc 89.99ab 59.63ab 1.05a R.bro 2.24cd 5.6bc 7.57a 54.09cde 25.69bc 67.17cd 60.5ab 0.97a Amy 3.47b 4.17e 6.44a 191.2a 16.52bcd 47.95d 54.31b 0a CS 2.96bc 4.67de 7.51a 132.38b 29.12b 58.7cd 57.78ab 0.72a Water 1.71d 6.73a 6.57a 21.95f 18.03bcd 71.58bc 62.48ab 1.73a 4.2 HAM2 2.57cd 6.43b 6.55c 37.25d 11.34b 75.77abc 69.97ab 4.86b HAM4 2.76c 6.13c 5.28c 62.86c 11.08b 68.71bc 66.97abc 4.73b Bn 2.49cde 6.38b 6.36c 12.24e 6.24b 83.1a 72.18a 14.16a TN 2.1e 6.6a 6.09c 18.51e 14.97ab 73.35abc 57.87bc 3.98b Amy 3.69a 4.1e 13.31a 191.85a 23.05a 54.8d 57.22c 0c CS 3.28b 4.62d 8.39b 139.11b 23.34a 63.4cd 58.81bc 1.02c Water 2.21de 6.68a 6.18c 16.2e 8.71b 80.81ab 66.45abc 5.41b Low butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 5.1 PS 3.08cd 6.05b 7a 50.25fe 17.18ab 81ab 74.28a 6.97a TRS 2.92cd 5.2cd 4.94b 96.77c 22.88ab 57.87c 67.73a 3bc WF 5a 5.24c 7.72a 86.79c 16.01ab 88.65ab 80.71a 3.61bc ERS 3.87b 4.98d 8.3a 128.05b 9.32b 72.33bc 74.67a 1.38c B.ado 2.72d 5.32c 6.89a 57.45de 20.23ab 101.16a 70.53a 4.03abc R.bro 2.75d 5.45c 7.12a 82.74cd 20.38ab 75.58bc 76.82a 3.67bc Amy 3.57bc 4.24e 4.59b 206.55a 26.4ab 70.23bc 67.57a 2.82bc CS 3.31bcd 4.33e 4.09b 192.36a 29.24a 60.08c 67.81a 4abc Water 2.02e 6.74a 7.42a 11.14f 18.6ab 74.94bc 73.86a 6.49ab 5.2 HAM2 2.48bc 6.66a 7.41ab 17.68d 18.47a 81.05b 75.59ab 8.7b HAM4 3.15ab 5.79b 5.87c 62.79c 21.88a 68.53b 82.94a 6.1cb Bn 2.8b 6.6a 6.79abc 10.68d 8.33b 97.46a 74.15abc 15.49a TN 2.07c 6.69a 7.82a 12.87d 19.94a 79.41b 74.14abc 6.87cb Amy 3.52a 4.29d 6.63bc 203.12a 18.74a 47.3c 62.94c 2.56c CS 2.85ab 4.92c 6.71abc 132.76b 14.72ab 53.3c 68.62bc 2.76c Water 2.07c 6.69a 7.51ab 14.09d 19.46a 73.07b 72.54abc 7.39cb

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Mid Butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 6.1 PS 5.23a 5.28b 1.73b 36.69c 3.43abc 86.06a 88.57a 13.95a TRS 3.33dc 5.3b 2.21b 38.13c 2.94bc 74.14ab 82.27a 13.53a WF 3.65bcd 4.49c 23.68a 97.3b 3.4abc 72.86ab 63.19b 1.96c ERS 3.82bcd 4.32c 21.48a 131.56ab 3.42abc 71.04ab 64.79b 0.32c B.ado 3.1dc 5.33b 5.43b 31.3c 4.31ab 92.82a 82.24a 10.7ab R.bro 4.44abc 5.54b 1.91b 32.53c 2.19c 89.51ab 85.07a 13.21a Amy 4.68ab 4.15c 29.44a 166.22a 5.02a 73.4ab 71.38b 0c CS 3.24d 4.37c 23.89a 130.87ab 3.39abc 67.73b 68.34b 1.14c Water 2.82d 6.83a 7.03b 8.87c 3.09bc 80.03ab 92.17a 8.34b 6.2 HAM2 3.34d 6.54b 8.12c 7.7d 10.2a 92.88ab 91.43a 12.83d HAM4 4.21b 5.42c 4.25e 34.54c 5.36ab 71.57c 79.81bc 19.24b Bn 3.87c 6.48b 7.53cd 5.18d 6.9ab 84.9b 78.4bc 26.25a TN 3.06de 6.95a 5.32de 5.14d 4.96ab 103.41a 69.52c 17.61c Amy 4.54a 4.2e 24.43a 167.19a 4.9ab 73.16c 68.96c 0f CS 3.22d 4.43d 21.14b 136.65b 3.34b 71.68c 70.05c 1.11f Water 2.77e 6.88a 7.24cd 7.05d 6.08ab 81.96bc 89.47ab 8.63e High Butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 7.1 PS 6.23abc 5.4cb 3.39cde 33.94cd 13.94ab 131.14a 98.5ab 14.59bcd TRS 5.74bc 5.7b 3.07ed 15.02de 15.42a 122.03a 100.91a 15.7bc WF 7.74a 5.66b 4.83bcd 55.97c 7.09bc 84.24b 93.11ab 23.94a ERS 4.84c 4.39d 4.92bcd 178.54a 4.09c 54.21c 64.29d 0f B.ado 6.11abc 5.45cb 5.39bc 44.66c 12.16ab 134.09a 96.25ab 11.59d R.bro 6.88ab 5.47cb 4.41bcd 38.58cd 9.57abc 127.58a 95.18ab 12.51cd Amy 5.72bc 4.35d 10.08a 195.06a 4.82c 61.99bc 73.07cd 0f CS 5.68bc 5.06c 6.08b 85.32b 7.56bc 135.92a 85.33bc 7.57e Water 4.82c 6.79a 2.01e 7.49e 11.6ab 77.78b 100.76a 16.79b 7.2 HAM2 6.51a 6.25c 2.9cd 6.19c 26.68a 88.61c 103.03a 21.68b HAM4 4.75c 5.92d 3.79c 8.99c 8.46bc 79.44de 93.41ab 24.6a Bn 6ab 6.47b 2.06d 7.26c 2.07e 109.62b 93.6ab 20.84b TN 4.83c 6.9a 1.97d 7.01c 9.79bc 87.29c 95.37ab 15.33c Amy 5.77ab 4.24f 9.56a 192.48a 5.05d 72.3e 72.32c 0e CS 5.75b 5.06e 6.98b 79.86b 7.61c 134.82a 87.91b 7.97d Water 4.88c 6.91a 1.98d 6.96c 10.58b 84.48dc 97.02ab 15.14c Mid Butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 8.1 PS 3.37ab 5.48b 4.67c 7.31f 22.38bc 81.56bc 79.16a 9.3ab TRS 3.54a 5.59b 4.77c 4.61f 22.58bc 82.76bc 76.77a 10.73a WF 2.8bcd 4.41d 31.5b 80.65c 23.72bc 52.99d 61.06bc 0.76de ERS 2.21de 4.2e 5.87c 155.66a 2.36e 49.31d 57.52c 0e B.ado 2.68cd 5.22c 3.52c 30.5d 10.84d 92.8a 74.81a 4.01cd R.bro 3.1abc 5.49b 3.99c 7.18f 27.27a 81.9bc 78.07a 8.22ab Amy 1.87e 4.31de 35.59a 93.93b 24.1b 52.47d 66.17b 0e CS 2.29de 5.58b 3.78c 22.01e 20.82c 77.03c 74.64a 5.54bc Water 2.59cd 6.64a 5.89c 5.06f 8.37d 87.49ab 79.82a 7.49abc

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8.2 HAM2 3.26ab 5.66b 4.05d 4.66d 20.34b 69.25b 77.55ab 11.44b HAM4 2.53e 4.61d 33.64a 66.82b 24.48a 50.89c 64.18b 0.75c Bn 3.58a 5.52c 3.6d 4.48d 17.93c 68.14b 74.76ab 18.58a TN 3.18abc 6.53a 6.33c 5.3d 10.24d 90.06a 88.12a 9.92b Amy 2.79cde 4.3e 31.87b 93.42a 23.97a 50.51c 65.01b 0c CS 2.73de 5.53c 3.23d 24.53c 19.04bc 74.31b 75.49ab 4.57c Water 3.06bcd 6.63a 6.28c 5.17d 7.8e 92.91a 65.65b 9.61b High Butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 9.1 PS 4.76bcd 6.33ab 2.14a 5.51c 21.53bc 93.02bc 94.92a 26.41b TRS 4.27dc 6.38ab 1.84a 5.7c 17.91cd 100.42b 97.49a 24.64bc WF 7.29a 4.77d 4.24a 86.9b 31.09a 67.3de 71.87ab 16.59c ERS 3.26d 4.39d 3.98a 184.06a 3.26e 46.58f 42.3b 0d B.ado 5.36bc 5.54c 1.49a 7.61c 13.2d 123.1a 85.65a 23.77bc R.bro 5.21bc 5.95bc 1.53a 5.66c 21.61bc 96.24b 80.81ab 27.8b Amy 6.1ab 4.33d 4.87a 153.08a 30.7a 54.52ef 74.76ab 3.91d CS 5.63abc 5.9bc 1.83a 5.39c 23.05b 80.82cd 84.53a 37.99a Water 3.5d 7.03a 5.39a 5.97c 4.19e 92.51bc 85.51a 16.88c 9.2 HAM2 5.29abc 6.07c 2.13c 5.34c 22.83c 87.18b 90.74a 27.45b HAM4 4.96bc 5.62e 6.55a 8.7b 38.75a 71c 83.65a 27.5b Bn 4.42dc 6.6b 1.68cd 5.13c 13.21d 109.06a 52.16a 30.57b TN 3.44d 7.02a 1.59cd 5.61c 3.82e 109.51a 63.88a 21.7c Amy 6.45a 4.37f 5.26b 137.89a 31.58b 54.63d 72.21a 5.03d CS 5.75ab 5.88d 1.87cd 5.21c 23.23c 80.33bc 77.43a 38.63a Water 3.33d 7.02a 1.33d 5.79c 3.83e 91.94b 77.62a 17.41c Mid Butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 10.1 PS 2.68a 5.69b 3.42bc 31.31f 12.89b 89.72b 69.59a 21.42a TRS 2.32b 5.06c 5.01bc 107.42c 6.6e 55.09d 64.07a 4.99c B.ado 2.74a 5.06c 5.51b 61.92e 11.15bc 107.34a 63.73a 2.3c R.bro 2.57ab 5.21c 3.47bc 82.8d 10.15cd 76.17c 66.88a 7.3bc Amy 2.66a 4.14e 11.14a 179.4a 6.73e 38.35e 56.04a 1.12c CS 2.72a 4.44d 3.54bc 145.38b 7.81ed 41.28e 61.76a 2.33c Water 1.58c 6.72a 2.11c 8.43g 17.33a 89.67b 53.69a 13.51b 10.2 HAM2 1.75c 6.4c 2.33c 25.37d 18.37b 75.97b 68.89a 4.7c HAM4 2.97a 4.96e 6.38b 115.39c 17.9b 66.8c 63.06b 1.91d Bn 2.36b 6.24d 2.54c 20.38e 11e 82.74a 67.23ab 14.84a TN 1.68cd 6.65b 2.22c 10.72f 15.86c 79.29ab 69.39a 7.9b Amy 2.95a 4.22f 6.79ab 191.8a 17.15bc 53.17d 56.01c 1.72d CS 3.09a 4.28f 7.49a 184.3b 20.63a 54.5d 57.45c 1.39d Water 1.39d 6.74a 2.02c 11.11f 14.32d 77.31ab 69.42a 7.51b Low Butyrate Trt OD pH Succinate Lactate Formate Acetate Propionate Butyrate 11.1 PS 3.63a 5.3bc 9.08b 66.57c 10.72bc 102.03a 84.14a 4.29abc TRS 3.53ab 5.38b 9.31b 46.31d 12.52a 109.44a 82.31a 5.51a B.ado 3.63a 5.2de 8.27b 63.94c 11.57ab 104.72a 79.32a 2.24de R.bro 3.43ab 5.27cd 8.69b 67.85c 9.6cd 104.64a 83.57a 3.94bc

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Amy 3.52ab 4.35f 15.22a 166.53a 5.4e 38.01c 71.61b 1.63e CS 3.47ab 5.17e 8.62b 79.57b 9.95cd 106.86a 83.42a 3.11cd Water 2.77b 6.74a 9.47b 5.88e 8.6d 78.74b 84.56a 5.08ab 11.2 HAM2 3.73a 5.82c 9.23bc 9.06e 12.81a 99b 90.73a 8.21b HAM4 2.97bc 5.39d 2.68d 93.45b 6.17cd 46.65e 69.27bc 2.2c Bn 2.8c 5.77c 5.69cd 33.97d 13.39a 46.55e 78.72abc 12.19a TN 2.85bc 6.56b 9.15bc 8.53e 7.1cd 87.62c 85.47a 13.75a Amy 3.32ab 4.31f 15.34a 156.93a 5.22d 35.54f 67.18c 0d CS 3.05bc 5.13e 8.49bc 78.55c 9.48b 105.05a 80.79ab 2.73c Water 2.71c 6.67a 10.42b 7.22e 7.96bc 81.57d 82.79a 3.37c

106

APPENDIX B: HEATMAPS OF EACH MICROBIOME LBC T1

T2

T3

107

T4

T5

T11

108

MBC T6

T8

T10

109

HBC T7

T9

110

APPENDIX C: LEFSE ANALYSIS OF HIGH VS LOW BUTYRATE- PRODUCING SAMPLES FOR EACH RS AND THEIR INOCULUMS

Resistant starch Inoculum LEfSe Final fermentation LEfSe Potato starch

Tapioca starch

111

High amylose maize type 2

High amylose maize type 4

112

Banana flour (this LEfSe compares Bn and non- Bn treatments)

Tiger nut starch

113

Amylopectin (final fermentation LEfSe only)

114

Corn Starch

115

APPENDIX D: R SCRIPTS

Bubble plots of butyrate production by pure RS library(tidyverse) library(ggplot2) library(forcats) library(tidyr) setwd("~/Microbiome/R stuff/RSdotplot") data<-read.csv("dp_data.csv", fileEncoding = "UTF-8-BOM") names(data)<-c("Trt","T1","T2","T3","T4","T5","T11","T6","T8","T10","T7","T9")

####new#### #melt stacks data #https://stackoverflow.com/questions/53177306/how-to-add-two-different- magnitudes-of-point-size-in-a-ggplot-bubbles-chart #legends: http://www.cookbook-r.com/Graphs/Legends_(ggplot2)/ #id=independent variable, needs to be in original df #variable and value name are just names for the new df data$Trt<-factor(data$Trt, levels=data$Trt) data2<-reshape2::melt(data,id.vars="Trt", variable.name="Donor",value.name="Butyrate")

#low butrate producers lbp<-data2 [(1:48),] bp1<-ggplot(lbp, aes(x=Donor, y=fct_rev(Trt)))+ geom_point(aes(size=Butyrate, color=Butyrate))+ scale_color_continuous(guide=FALSE)+ scale_size(range=range(0,30),breaks=c(0,5,10,20), guide=guide_legend())+ labs(title="Butyrate production by RS from low butyrate producers", x="Fecal donor", y="RS", size="mM Butyrate")+ theme(legend.title = element_text(size=12, face="bold"), legend.text= element_text(size=12), title = element_text(size=14,face="bold"), axis.text = element_text(size=12)) windows() #trying to change color of size-legend shapes bp1+

116

scale_color_manual(guide=guide_legend(override.aes=list(colour="black", "deepskyblue4","dodgerblue4")))

#mid butyrate producers mbp<-data2 [(49:72),] bp2<-ggplot(mbp, aes(x=Donor, y=fct_rev(Trt)))+ geom_point(aes(size=Butyrate, color=Butyrate))+ scale_color_continuous(guide=FALSE)+ scale_size(range=range(0,30),breaks=c(0,5,10,20), #highest value only goes to 17, so "20" bubble is eliminated--can I override this? guide=guide_legend())+ labs(title="Butyrate production by RS from medium butyrate producers", x="Fecal donor", y="RS", color="mM Butyrate")+ theme(legend.title = element_text(size=12, face="bold"), legend.text= element_text(size=12), title = element_text(size=14,face="bold"), axis.text = element_text(size=12)) windows() bp2

#high butyrate producers hbp<-data2 [(73:88),] bp3<-ggplot(hbp, aes(x=Donor, y=fct_rev(Trt)))+ geom_point(aes(size=Butyrate, color=Butyrate))+ scale_color_continuous(guide=FALSE)+ scale_size(range=range(0,30),breaks=c(0,5,10,20), guide=guide_legend())+ labs(title="Butyrate production by RS from high butyrate producers", x="Fecal donor", y="RS", color="mM Butyrate")+ theme(legend.title = element_text(size=12, face="bold"), legend.text= element_text(size=12), title = element_text(size=14,face="bold"), axis.text = element_text(size=12)) windows() bp3

RS treatment bar plot of organic acid profile data<-read.csv("RUM_sub.csv",fileEncoding = "UTF-8-BOM") rs<-subset(data, Trt!= "B.ado" & Trt!= "R.bro") rs.data1<-aggregate(rs [,6:11], list(rs$Trt), mean) rs.data<-stack(rs.data1 [,c(2:7)]) rs.data$Trt<-rep(rs.data1$Group.1,6)

117

#subset out formate and succinate rs.data2<-subset(rs.data, ind!= "Succinate" & ind!= "Formate") rs.data2$Trt<-factor(rs.data2$Trt, ordered = TRUE, levels = c("PS","TRS","HAM2","HAM4", "Bn","TN","Amy","CS","Ctrl")) rs.data2$ind<-factor(rs.data2$ind, ordered=TRUE, levels= c("Butyrate","Acetate","Propionate","Lactate")) rs.data2$values [rs.data2$values<0]<-0

#Dunnet test library(DescTools) #butyrate DunnettTest(rs$Butyrate,rs$Trt, control = "Water") #bn and amyp different #acetate DunnettTest(rs$Acetate,rs$Trt, control = "Water") #bn and PS different #propionate DunnettTest(rs$Propionate,rs$Trt, control = "Water") #CS, HAM2 and amyp different #lactate DunnettTest(rs$Lactate,rs$Trt, control = "Water") #Amy, CS, HAM4, PS, TRS

#plot windows() ggplot(rs.data2, aes(x=Trt, y=values,fill=ind, label = round(values,1)))+ geom_bar(stat="identity",position="stack")+ geom_text(size=5, colour = "White", fontface="bold", position=position_stack(vjust=.5))+ labs(title="RS Fermentation Profile",x="Treatment",y="",fill="Organic Acid")+ theme(axis.title.x=element_text(face="bold"), title = element_text(face="bold"), legend.title = element_text(face="bold"))+ scale_fill_manual(values = c("#330066","#0066cc","#33cc66","#999999"))

Average organic acid profiles produced by each microbiome data<-read.csv("RUM_sub.csv",fileEncoding = "UTF-8-BOM") rs<-subset(data, Trt!= "B.ado" & Trt!= "R.bro") rsIno<-aggregate(rs [,6:11], list(rs$Inoculum), mean) in.data<-stack(rsIno) in.data$Inoculum<-rep(rsIno$Group.1,6) Production<-c("Low","Mid",rep("Low",5),"Mid","High","Mid","High")

118 in.data$Production<-Production in.data2<-subset(in.data, ind != "Succinate" & ind != "Formate" ) in.data2$Inoculum<-factor(in.data2$Inoculum, ordered = TRUE, levels =c("T1","T2","T3","T4","T5","T11","T6", "T8", "T10","T7","T9")) in.data2$Production<-factor(in.data2$Production, ordered=TRUE, levels= c("Low","Mid","High")) in.data2$ind<-factor(in.data2$ind, ordered=TRUE, levels= c("Butyrate","Acetate","Propionate","Lactate"))

#make df for mean and median values of each facet sum<-in.data2 %>% group_by(Inoculum) %>% dplyr::summarize(sum=sum(values)) sum$Production<-c(rep("Low",6),rep("Mid",3),rep("High",2)) line<-sum %>% group_by(Production) %>% dplyr::summarize(mean_val=mean(sum)) line.2<-sum %>% group_by(Production) %>% dplyr::summarize(median_val=median(sum)) line<-plyr::join(line,line.2,by="Production") line$Production<-factor(line$Production, ordered=TRUE, levels= c("Low","Mid","High"))

#plot windows() ggplot(in.data2, aes(x=Inoculum, y=values,fill=ind, label=round(values,1)))+ geom_bar(stat="identity",position="stack")+ labs(title="Inoculum Fermentation Profile",x="Inoculum",y="", fill="Organic Acid")+ theme(axis.title.x=element_text(face="bold"), title = element_text(face="bold"), legend.title = element_text(face="bold"), strip.text.x = element_text(face="bold"))+ facet_grid(~Production, scales="free_x", space="free")+ geom_hline(data=line, aes(yintercept = mean_val,linetype="solid"),size=1)+ geom_hline(data=line, aes(yintercept = median_val,linetype="dashed"),size=1)+ scale_linetype_discrete(name="Averages",labels=c("Mean","Median"))+ scale_fill_manual(values = c("#330066","#0066cc","#33cc66","#999999"))+ geom_text(size=5,colour="white", fontface="bold", position=position_stack(vjust=.5))

PD bar plot of butyrate production library(ggplot2) library(ggpubr) library(ggsignif)

119 library(dplyr) setwd("~/Microbiome/R stuff/MDS")

####PD butyrate barplot#### data<-read.csv("RUM_sub.csv",fileEncoding = "UTF-8-BOM") #change water to ctrl data$Trt <- as.character(data$Trt) data$Trt [data$Trt == "Water"] <- "Ctrl"

PD<-data

#format factors PD$Trt<-factor(PD$Trt, ordered = TRUE, levels = c("B.ado","R.bro","PS")) PD$Production<-factor(PD$Production, ordered = TRUE, levels = c("Low","Mid","High")) PD$Inoculum<-factor(PD$Inoculum, ordered=T, levels=c("T1","T2","T3","T4","T5","T11","T6","T8","T10","T7","T9")) production<-as.data.frame(c()) #remove NAs PD<-na.omit(PD) row.names(PD)<-PD$ID PD<-PD [,-c(1,2)] PD1<-PD [,c(1:3,9)]

#Function to get means/sd from dataframe data_summary <- function(data, varname, groupnames){ require(plyr) summary_func <- function(x, col){ c(mean = mean(x [ [col]], na.rm=TRUE), sd = sd(x [ [col]], na.rm=TRUE)) } data_sum<-ddply(data, groupnames, .fun=summary_func, varname) data_sum <- rename(data_sum, c("mean" = varname)) return(data_sum) }

PD.data<-data_summary(PD1, varname="Butyrate", groupnames=c("Inoculum","Trt")) PD.data$Production<-c(rep("Low",time=18), rep("Mid",time=9),rep("High", time=6)) PD.data$Production<-factor(PD.data$Production, levels=c("Low","Mid","High")) PD.data$Label<-c()

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windows() ggplot(PD.data, aes(x=Inoculum, y=Butyrate,fill=Trt)) + geom_bar(stat="identity", position="dodge") + geom_errorbar(aes(ymin=Butyrate-sd, ymax=Butyrate+sd), width=.2, position=position_dodge(.9))+ facet_grid(~Production, scales="free_x", space="free")+ labs(x="Inoculum",y="Butyrate(mM)")+ theme(axis.title.x=element_text(size=12, face="bold"), axis.title.y = element_text(face="bold"), legend.title = element_text(size=12, face="bold"), strip.text.x = element_text(size=12, face="bold"))+ scale_fill_manual(name="Treatment",labels=c("B. adolescentis","R. bromii","Control"), values=c('royalblue2','darkgreen','gray')) #See https://kohske.wordpress.com/2010/12/25/adjusting-the-relative-space-of-a- facet-grid/ #scale=free lets x axis be in order as defined in df #space=free lets relative size of facets be to scale

RS in whole food bar plot of total organic acid profile wf<-read.csv("WFstacked.csv",fileEncoding = "UTF-8-BOM") #replace negs with zeroes wf$Value [wf$Value<0]<-0 wf$Production<-factor(wf$Production, ordered = TRUE, levels = c("Low","Mid","High")) wf$Prod2<-factor(wf$Prod2, ordered = TRUE, levels = c("Low","Mid","High")) wf$OA<-factor(wf$OA, ordered=TRUE, levels=c("Butyrate","Acetate","Propionate","Lactate")) wf$Trt<-factor(wf$Trt, ordered=TRUE, levels=c("WF","ERS","PS"))

#Avg lines for each production level sums<-wf %>% group_by(ID) %>% dplyr::summarize(sum=sum(Value)) sums$Production<-c("High","Low","Mid","High","Low","Mid","High","Low","Mid") line1<-sums %>% group_by(Production) %>% dplyr::summarize(mean_val=mean(sum)) line2<-sums %>% group_by(Production) %>% dplyr::summarize(median_val=median(sum)) line3<-plyr::join(line1,line2,by="Production") line3$Production<-factor(line3$Production, ordered=TRUE, levels= c("Low","Mid","High"))

#plot #data values on stacked bars: https://stackoverflow.com/questions/6644997/showing- data-values-on-stacked-bar-chart-in-ggplot2

121 windows() ggplot(wf, aes(x=Trt, y=Value,fill=OA, label=Significance))+ geom_bar(stat="identity")+ geom_text(size=5, colour="white", fontface="bold", position=position_stack(vjust=.5))+ facet_grid(~Production, scales="free_x", space="free")+ labs(x="Treatment",y="",fill="Organic Acid")+ theme(axis.title.x=element_text(face="bold"), legend.title = element_text(face="bold"), strip.text.x = element_text(face="bold"))+ geom_hline(data=line3, aes(yintercept = mean_val,linetype="solid"),size=1)+ geom_hline(data=line3, aes(yintercept = median_val,linetype="dashed"),size=1)+ scale_linetype_discrete(name="Averages",labels=c("Mean","Median"))+ scale_fill_manual(values = c("#330066","#0066cc","#33cc66","#999999"))

Sequencing data preparation #Making clean OTU and Tax tables

####Meta#### Meta<-read.csv("metaMFIonly.csv") #Get rownames of Meta table to be sample names rownames(Meta)<-Meta$Group Meta$Production<-factor(Meta$Production, ordered = TRUE, levels = c("Low","Medium","High")) Meta$RS<-factor(Meta$RS, ordered = TRUE, levels = c("Inoculum","PS","TRS","HAM2","HAM4","Bn","TN","B.ado","R.bro", "Amy","CS","Water")) Meta$Inoculum<-factor(Meta$Inoculum, ordered = TRUE, levels = c("T1","T2","T3","T4","T5","T11","T6","T8","T10","T7","T9")) #change water to ctrl Meta$RS <- as.character(Meta$RS) Meta$RS [Meta$RS == "Water"] <- "Ctrl"

####OTU#### rawO<-read.table("otu_table.shared") #copy otu table so don't have to reload it OTU<-rawO

#samples as rownames, otu as colnames rownames [176]<-OTU$V2 colnames [176] <- as.character(unlist(OTU [1,])) #Turn factors into numerics OTU []<-lapply(OTU, function(x) { if(is.factor(x)) as.numeric(as.character(x)) else x

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}) #check ttl num OTUs the same in all samples #min(OTU$numOtus) #All good #remove extraneous labels OTU<-OTU [-1,-c(1:3)] #Find number of reads per sample reads<-rowSums [176] reads.avg<-mean(reads) reads.SD<-sd(reads) #remove E.marmotae Tax<-read.csv("TaxLineage_mfi.csv") marm<-subset(Tax,species == "Escherichia_marmotae") OTU<-OTU [,-which(names(OTU) %in% marm$OTU)]

#remove and collect PCR ctrls pcrs<-c("D118","D194","D209","D210") pcr<-subset(OTU, rownames(OTU) %in% pcrs) OTU<-subset(OTU, rownames(OTU)!="D118" &rownames [176]!="D194"&rownames [176]!= "D209"&rownames [176]!= "D210") Meta<-subset(Meta,Group!="D118" & Group!="D194"&Group!= "D209"& Group!= "D210") #rownumbers unique<-c(1:170) Meta$unique<-sprintf("%03d",unique) #subtract PCR ctrl counts from everything else. Do not change B.toyo. numbers pcr ["Avg",]<-colMeans(pcr) #Many samples have lower cts than the mean of the pcr ctrls, even within #samples that were processed at the same time

#Subset MFI exp only MFIotu<- subset(OTU, rownames(OTU) %in% Meta$Group) #The groups names are not in order

##B.toyonensis correction## #It's Otu00007 Btoyo_otu<-data.frame(t(otus.perc [7,])) #factor is the number all otus in that sample have to be multiplied by Btoyo_otu$factor<-.5/Btoyo_otu$Otu00007 #D0004 = 0 and several other samples have < .001% Btoyo ##correction## otu.abs<-data.frame(t(otus.perc))*Btoyo_otu$factor otu.abs<-data.frame(t(otu.abs))

####Normalization####

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#https://davetang.org/muse/2011/01/24/normalisation-methods-for-dge-data/ library(edgeR) mat<-as.matrix(t(MFIotu)) #work even better with dge obj instead of matracies dge<-DGEList(counts = mat) rle<-calcNormFactors(dge, method="RLE") cpm<-cpm(rle$counts) otus<-data.frame(t(cpm)) otus<-data.frame(otus/rowSums(otus)*100) otus<-data.frame(na.omit(otus)) otus<-data.frame(t(otus)) #taxa are rows #organize sample names otus<-otus [c("D1", "D2", "D3", "D4", "D5", "D6", "D7", "D8", "D9", "D10", "D11", "D12", "D13", "D16", "D17", "D18", "D19", "D20", "D21", "D22", "D23", "D24", "D25", "D26", "D27", "D28", "D29", "D32", "D33", "D34", "D51", "D52", "D55", "D56", "D57", "D58", "D59", "D60", "D61", "D62", "D63", "D64", "D65", "D66", "D42", "D43", "D46", "D47", "D48", "D49", "D50", "D35", "D36", "D37", "D38", "D39", "D40", "D41", "D70", "D71", "D74", "D75", "D76", "D77", "D78", "D79", "D80", "D81", "D82", "D67", "D68", "D69", "D83", "D84", "D90", "D91", "D92", "D93", "D94", "D95", "D96", "D97", "D98", "D99", "D100", "D101", "D102", "D103", "D106", "D107", "D108", "D109", "D110", "D111", "D112", "D113", "D114", "D115", "D116", "D117", "D119", "D120", "D123", "D124", "D125", "D126", "D127", "D128", "D129", "D130", "D131", "D132", "D133", "D134", "D135", "D136", "D139", "D140", "D141", "D142", "D143", "D144", "D145", "D146", "D147", "D148", "D149", "D150", "D151", "D152", "D155", "D156", "D157", "D158", "D159", "D160", "D161", "D162", "D163", "D164", "D165", "D166", "D180", "D181", "D182", "D183", "D184", "D185", "D186", "D187", "D188", "D189", "D190", "D191", "D192", "D193", "D195", "D196", "D197", "D198", "D199", "D200", "D201", "D202", "D203", "D204", "D205", "D206", "D207", "D208")]

###Tax table#### #remove otus with no counts MFItax <- subset(Tax, Tax$OTU %in% rownames(otus)) #get rownames of Tax table to be OTUs rownames(MFItax)<-MFItax$OTU #Tax list with g_s MFItax<-MFItax [,-c(1:3)] #remove E.marmotae MFItax<-subset(MFItax, species !="Escherichia_marmotae")

##Make phyloseq w/ OTU table w/ samples for columns=transposed## #phyloseq vignettes: https://bioconductor.org/packages/release/bioc/vignettes/phyloseq/inst/doc/phyloseq- analysis.html library(phyloseq)

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Phy.Otu<-otu_table(otus, taxa_are_rows = T) Phy.Met<-sample_data(Meta) Phy.Tax<-tax_table(as.matrix(MFItax)) physeq<-phyloseq(Phy.Tax,Phy.Otu, Phy.Met) physeq<- prune_taxa(taxa_sums(physeq) > 0, physeq) #16908 taxa

#Tax linage with only unique library(dplyr) uniquetax<-MFItax %>% distinct(species, .keep_all=TRUE) #800 unique tax/ 18930 = 4% uniquetaxtable<-data.frame(table(MFItax$species)) write.table(uniquetax, "uniqueTaxLineage.txt") Alpha-diversity – Shannon Index windows() new<-plot_richness(physeq, x = "Inoculum", measures = "Shannon", color = "RS") inoc<-new$data inoc<-subset(inoc, RS == "Inoculum") new+geom_boxplot(data = new$data, aes(x = Inoculum, y = value, color = NULL), alpha = .1)+ geom_point(size = 3)+ geom_point(data = inoc, aes(x = Inoculum, y = value, size = 10, shape= RS, color = RS))+ scale_shape_manual(values = 18)+ scale_color_manual(values = c("blue" ,"#FFFFB3" ,"#BEBADA" ,"#FB8072" ,"#80B1D3" ,"#FDB462", "#B3DE69", "#FCCDE5", "#D9D9D9" ,"#BC80BD", "#CCEBC5" ,"#FFED6F"))+ guides(size = FALSE)+ labs(shape = "Shape")+ theme(axis.title = element_text(face="bold"), legend.title = element_text(face = "bold"))

#average idat<-new$data idat<-idat [c(1:11),c(7,13)]

##Trt only Tphy<-subset_samples(physeq, RS!="Inoculum") t<-plot_richness(Tphy, x = "RS", measures = "Shannon", color = "Inoculum") t+geom_boxplot(data = t$data, aes(x = RS, y = value, color = NULL), alpha = .1)+ geom_point(size = 3)+ theme(axis.title = element_text(face="bold"), legend.title = element_text(face = "bold"))

#average tdat<-t$data tdat<-aggregate(tdat [,13],list(tdat$RS),mean)

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#PD only windows() PDphy<-subset_samples(physeq, RS == "B.ado" | RS == "R.bro" | RS == "PS") pdp<-plot_richness(PDphy, x = "Inoculum", measures = "Shannon", color = "RS") pdp+ geom_point(size = 3)+ theme(axis.title = element_text(face="bold"), legend.title = element_text(face = "bold"))+ geom_point(data = inoc, aes(x = Inoculum, y = value, size = 10, shape= RS))+ scale_shape_manual(values = 18)+ scale_color_manual(values = c("sienna1" ,"blue" , "gray48","springgreen3"), name = "Treatment", breaks=c("Inoculum","B.ado","R.bro", "PS"), labels=c("Inoculum", "B. adolescentis","R. bromii", "Ctrl"))+ guides(size = FALSE)

Beta-diversity – Bray-Curtis distance library(edgeR) library(raster) #bray curtis distance #remove rows with no cts otus<-subset(otus, rowSums(otus)!=0) brayRLE <- vegdist(t(otus)) mds<-ape::pcoa(brayRLE) mds$values$Relative_eig #20.6, 11.55% ggpt2<-data.frame(mds$vectors) #loadings load2<-t(otus) n <- nrow(load2) points.stand <- scale(mds$vectors [,c(1,2)]) S <- cov(load2, points.stand) U <- data.frame(S %*% diag((mds$values$Eigenvalues [c(1,2)]/(n-1))^(-0.5))) colnames(U) <- colnames(mds$vectors [,c(1,2)]) xmin = min(mds$vectors [,1]) xmax = max(mds$vectors [,1]) ymin = min(mds$vectors [,2]) ymax = max(mds$vectors [,2]) xl.min = min(0,min(U [,1]))

126 xl.max = max(0,max(U [,1])) yl.min = min(0,min(U [,2])) yl.max = max(0,max(U [,2])) xl.scale = max(abs(xmax),abs(xmin))/max(abs(xl.max),abs(xl.min))*0.75 yl.scale = max(abs(ymax),abs(ymin))/max(abs(yl.max),abs(yl.min))*0.75 vectors1<-data.frame(U [,1]*xl.scale, U [,2]*yl.scale) names(vectors1)<-c("a","b") rownames(vectors1)<-rownames(U) #find longest vectors len<-data.frame(cbind(rep(0,16908),rep(0,16908))) len<-data.frame(pointDistance(c(0,0),vectors1,lonlat=FALSE)) ##added when unique species were used, but doesn't produce a nice graph names(len)<-"len" len$otu<-rownames(len) len<-len [order(-len$len),] len<-len [c(1:5),] lentax<-MFItax [rownames(MFItax) %in% len$otu,] #can only see vectors longer than 0.05 vectors2<-vectors1 [rownames(vectors1) %in% len$otu,] vectors2$names<-lentax$species

#plot windows() ggplot(data=ggpt2, aes(x=Axis.1, y=Axis.2))+ geom_point(size=5, aes(color = Meta$Production))+ xlab(paste("PC1_20.6%"))+ ylab(paste("PC2_11.6%"))+ theme_bw()+ ggtitle("Species assosciated with butyrate production")+ geom_segment(data=vectors2, aes(x=0,y=0, xend=a, yend=b), size=1.2,arrow=arrow())+ geom_text(data=vectors2, aes(x=a,y=b),colour="red",size = 5, label=vectors2$names,fontface="bold",cex=2)+ theme(axis.title=element_text(face="bold"), title=element_text(face="bold"), text = element_text(size=20))+ labs(color = "Production")

Heatmap for T1, this template was used for all heatmaps ####T1#### T1_meta<-subset(Meta, Inoculum == "T1") T1_meta<-T1_meta [-c(4,5),] T1_otu<-data.frame(subset(t(otus), rownames(t(otus)) %in% T1_meta$Group)) #avg by trt

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T1_otu$trt<-as.character(T1_meta$RS) T1_otu [1,18692]<-c("Inoculum") T1_otu<-aggregate(T1_otu [,c(1:18691)],list(T1_otu$trt),mean) rownames(T1_otu)<-T1_otu$Group.1 T1_otu<-T1_otu [,-1] T1_otu<-data.frame(t(T1_otu)) #^run thru lefse, no sig spp. differences

## identify the maximum abundance of each OTU for a single sample maxab <- apply(T1_otu, 1, max) # for the purpose of display, remove OTUs with less than 1% max abundance =51otus n1 <- names(which(maxab< 1)) T1hm <- subset(T1_otu, !(rownames(T1_otu) %in% n1))

#replace rownames to genus_spp T1tax<-subset(MFItax,rownames(MFItax) %in% rownames(T1hm)) T1hm$tax<-T1tax$species T1hm<-aggregate(T1hm [,c(1:10)],list(T1hm$tax),sum) rownames(T1hm)<-T1hm$Group.1 T1hm<-T1hm [,-1] hm1_row<-data.frame(rownames(T1hm)) hm1_row$Function<-c(rep("None",47)) write.table(hm1_row,"hm1function.txt") hm1_row<-read.table("hm1function.txt",header = TRUE) hm1_row$Function<-factor(hm1_row$Function, order= TRUE, levels = c("Butyrate_producer","Primary_degrader","None"))

#subset buty-prod and PD only, change colnames hm1_row<-subset(hm1_row, Function != "None") T1hm<-subset(T1hm, rownames(T1hm) %in% rownames(hm1_row))

#colors for annotations Col<-list( Function = c(Butyrate_producer="springgreen2", Primary_degrader="orangered", None="midnightblue") ) library(pheatmap) windows() pheatmap(T1hm, color = colorRampPalette(c("blue4","blue","white","firebrick1","firebrick4"))(50), cluster_cols = T, cluster_rows = T, scale = "row",

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annotation_row = hm1_row, annotation_names_row = FALSE,annotation_colors = Col)

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