Gut microbiome and virome response to spinal cord injury
THESIS
Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University
By Jingjie Du
Graduate Program in Microbiology
The Ohio State University 2020
Thesis Committee:
Matthew Sullivan, Advisor
Phillip Popovich
Virginia Rich
Copyrighted by
Jingjie Du
2020
Abstract
The diversity and function of the human microbiome have been extensively studied over the last 10 years. Emerging evidence suggests that the gut microbiome plays an essential role in a wide range of human diseases, including acute, traumatic spinal cord injury (SCI). Previous work has characterized gut dysbiosis in SCI, correlating changes to clinical outcomes or other biomarkers. However, the study of gut virome in health and disease remain largely unknown, because viruses lack universal marker genes for taxonomic assignment. This work represents the first time the gut virome has been characterized in SCI and provides the first look at potential functional changes in the gut microbiome in SCI at the level of gene loss. As with our previous work, we present a unique multi-level SCI model, allowing us to compare partially preserved sympathetic enteric enervation (T10) with a total loss of sympathetic enteric enervation (T4). Our results reveal level-specific patterns of gut dysbiosis, which may inform microbe-based predictions of severity and locomotor outcome in SCI. Further, this research is the first time that the viral and functional components of the gut microbiome have been characterized in SCI areas, which represent possible therapeutic targets for improving outcomes in SCI.
i Acknowledgments
I would like to express my sincere gratitude to my advisor, Dr. Matthew Sullivan for his exceptional mentorship and professional support. I would also like to thank Dr. Phillip Popovich for his valuable support and guidance for this work. I would also like acknowledge my committee member Dr. Virginia Rich for her time invested in teaching me to be a good scientist. I also profoundly appreciate the personal and professional support from my collaborators, Kristina
Kigerl and Ahmed Zayed, and all the Sullivan lab and Rich lab members. I really appreciate the help and support from the Microbiology graduate students and the faculty and staff at the Ohio
State University’s Department of Microbiology. I greatly learned from being a teacher and scientist.
More importantly, I really appreciate the unconditional love and support from my parents. Thanks for your sacrifice and support for giving me the best education and sending me to elite schools, mom and dad! I really learned a lot during the three years’ hard transition to graduate school in a different country. This work was funded by a National Institutes of Neurological Disorders and
Stroke R35 award (1R35NS111582) to PGP, The Belford Center for Spinal Cord Injury (PGP),
The Ray W Poppleton Research Designated endowment (PGP), a National Institutes of Health
Medical Scientist Training Program T32 grant, and a Gordon and Betty Moore Foundation
Investigator Award (#3790) to MBS.
ii
Vita
2013…………………………………………Xiangyang NO.5 Middle School, China
2013………………………………………… National College Entrance Examination (NCEE)， natural-science-oriented area ranked top 1% of test takers, Hubei, China
2013 to 2017………………………………. SYSU Scholarships of the Academic Year / National Endeavor Fellowship/ SYSU Encouragement Scholarship
2017………………………………………... B.S. Biotechnology, Sun Yat-sen University, Guangzhou, China
2017 to present …………………….………Graduate Teaching/Research Associate, Department of Microbiology, The Ohio State University
Fields of Study
Major Field: Microbiology
iii Table of Contents
Abstract ...... i
Acknowledgments...... ii
Vita ...... iii
List of Figures ...... v
Chapter 1: Introduction ...... 1
Chapter 2: Spinal cord injury changes the structure and functional potential of gut bacterial and viral communities in a spinal-level dependent manner ...... 11
Abstract ...... 12
Background ...... 13
Results and Discussion ...... 16
Conclusion ...... 28
Methods ...... 29
List of abbreviations ...... 36
Declarations ...... 36
Major figures: ...... 38
Supplemental Figures ...... 48
Chapter 3: Conclusions ...... 61
References ...... 64
iv List of Figures
Figure 1. Intestinal microbial community composition was disturbed after spinal cord injury. . 38
Figure 2. Genus-level bacterial abundances are altered after SCI...... 40
Figure 3. Species-level bacterial abundances are altered after SCI...... 42
Figure 4. Predicted metabolic pathways are different between healthy and spinal cord injury animals...... 43
Figure 5. Phage communities are altered after SCI...... 45
Figure 6. Viral host-prediction reveals that phage abundances vary with their hosts...... 47
Figure 7. Flow diagrams showing the bioinformatic workflow...... 48
Figure 8. Differential abundance analysis of bacteria across three treatment groups...... 49
Figure 9. Species-level differential abundance analysis of bacteria across three treatment groups.
...... 52
Figure 10. Predicted metabolic pathways are different between Lam controls and SCI groups. 53
Figure 11. Caudovirales phage abundances increased after spinal cord injury...... 55
Figure 12. Prediction of temperate phages using different methods...... 57
Figure 13. Virus-host prediction...... 59
Figure 14. Heat map showing the differential abundance analysis of phages grouped by infected bacterial hosts...... 60
v Chapter 1: Introduction
Over the last 10 years, the diversity and function of the human microbiome have been extensively studied thanks to advanced sequencing technologies and methods (16s rRNA gene sequencing, metagenomics, metatranscriptomics, and metaproteomics) and large-scale initiatives such as the Human Microbiome Project (HMP)[1], the MetaHIT (Metagenomics of the Human
Intestinal Tract)[2] and the Integrative HMP(iHMP)[3]. In this chapter, we will focus on discussing the roles of gut microbes, especially bacteria and viruses, in human health and disease.
Development of gut microbiota
Humans are sterile at birth and infants obtain their first gut microbes from environmental microbiota during birth (as reviewed in[4]). These initial microbial sources include delivery modes
(virginal birth vs cesarean birth), the first forms of sustenance (breast milk vs formula), probiotics and antibiotics use. They shape the structure of the initial microbiota[5-7], and affect infant health as well as neurocognitive development in the later life[8, 9]. The development of the normal gut microbiota may be delayed or altered by cesarean section, formula feeding, and antibiotic exposures, which may be associated with a series of health risks (as reviewed in [9]). The use of antibiotics in early life has been reported to alter the gut microbiota at a critical developmental window and predispose infants to the development of asthma, inflammatory bowel disease, neurodevelopmental delays, and metabolic disorders in later life[4, 8, 10-15]. Similarly, cesarean section and formula feeding may diminish the infants exposure to maternal microbes, which has been linked to impaired immune development (as reviewed in [16] ), asthma, diabetes, and obesity[17, 18].
1 Microbial communities in individual humans experience dramatic shifts in the first year of life, with the dominance of Proteobacteria and Actinobacteria, and become more stable and diverse with the dominance of Bacteroidetes and Firmicutes during adulthood[2, 19-21]. The microbial composition remains relatively stable until old age when considerable differences are seen, with beneficial taxa, such as Bifidobacterium becoming less abundant and with a rise dominance of various pathobionts[22].
Roles of gut microbiota on human health and disease
The density of microbial cells associated with the human body is exceptionally high with an estimated 1013–1014 microbial cells per individual, mainly derived from the colon (about 3.8 ×
1013 microbial cells), and the ratio of microbial cells to human cells is roughly 1:1[23]. Emerging studies suggest that gut microbes play important roles in human health and disease (reviewed in[24]). The gut microbiota protects against pathogen colonization through several mechanisms, including producing antimicrobial effector molecules to directly inhibit pathogens, efficient competition with the pathogens for the available nutrients and niches, and stimulating the host's antimicrobial defense system[25, 26]. Commensal microbiota is essential to induce intestinal barrier structure, permeability, and functions[27] while limiting the transport of potentially harmful microbial antigens and intestinal inflammation[27, 28]. Moreover, gut microbes exclusively produce secondary bile acids (BAs), which can be beneficial or toxic to human health[29, 30]. At low concentrations, secondary BAs may promote colonic cell proliferation. However, at high concentrations, they may promote colonic inflammation and cancers[30]. Elevated secondary BA levels have been associated with colon cancer[31, 32], liver cancer, and some cholesterol gallstone disease[33-35].
2 In addition to influencing human health by protecting from pathogens, the microbial community is essential for dietary health[36]. Gut microbes can synthesize essential vitamins, such as vitamin B12 and vitamin K2, which can only be possessed by bacteria[37]. Vitamin B12 is required for central nervous system development and functions, and the production of red blood cells
(reviewed in[38, 39]). Deficiency of vitamin B12 has been associated with neurological disorders, impaired spinal cord injury recovery, anemia, malabsorption, diabetes, and susceptibility to infectious diseases[40-44]. Importantly, supplementation of vitamin B12 has been reported to enhance nerve repair, improve functional recovery after traumatic brain injury[45], and accelerate nerve regeneration after peripheral nerve injury [46]. Moreover, vitamin K2 plays an important role in bone health and keeping calcium from accumulating in the blood vessels walls [47-49]. An increased intake of vitamin K2 might be used to benefit cardiovascular health and lower peripheral arterial disease [48, 50]. Moreover, gut microbes can produce short-chain fatty acids (SCFA), which play a significant role in the maintenance of health and the deterrence of the disease by breaking down indigestible food sources using glycoside hydrolases (GHs)[51, 52]. Butyrate is an important energy source for colonocytes and can promote the growth of the colonic epithelium [53, 54]. Propionate is mainly taken up by the liver and has been associated with reduced stress behaviors [55], but has also been linked to autism-like symptoms, such as atypical social interactions and unusual motor movements, in rats [56]. A mouse experiment also indicates that long-term exposure of mice to a daily low dose of propionate results in a gradual weight gain and insulin resistance [57]. Acetate has been reported to cross the blood-brain barrier and can be utilized in the brain of rats, mice, and humans [58, 59], indicating an important role of SCFA in gut-brain-microbiome axis[60, 61].
Roles of gut microbiota in gut-brain-microbiome axis
3 The gut microbiome is now recognized as a key component of the brain-gut axis, the bidirectional system of communication between the central nervous system(CNS), and the digestive system[62]. Indeed, gut-derived microbes produce various neuroactive metabolites(e.g.,
SCFA) or precursor molecules (e.g., tryptophan), which are needed to synthesize serotonin, dopamine, GABA, acetylcholine, and melatonin[63]. These neuroactive metabolites signal the CNS via vagal afferents or they enter the circulation and pass directly into the neural parenchyma across the blood-brain barrier[64-66]. Gut microbes are also capable of indirectly signaling the CNS by influencing innate and adaptive immunity[67, 68]; and the immune system, like the gut, exert bidirectional communication with the CNS (reviewed in[69]).
Gut microbes interact with the gut-associated lymphoid tissue (GALT), composed of more than 70% of all immune cells, thus gut microbes are essential for normal CNS development and functions (as reviewed in [70]). As has been reported in studies using germ-free(GF) mice, gut microbiota plays an essential role in controlling the maturation and function of microglia, the tissue macrophages of the CNS [69, 71]. Gut microbes are also essential for regulating host neural activity and behavior in response to environmental cues[70, 72]. Increased motor activity and reduced anxiety behavior were displayed in GF mice as compared to mice with a normal gut microbiota, with increased levels of neurotransmitters in brain[72]; while a reduced social behavior was also observed in GF mice, indicating that the microbiota is crucial for social development [73].
Given the essential roles of normal gut microbiota on the development of CNS health, it’s not surprising that the disruption of gut microbiota has been reported to contribute to neurodegenerative disorders, mental health disorders, multiple sclerosis, and autism spectrum disorders[74]. The fecal microbiota transplanted into wild type or ger-free mice from mice with autism, depression, schizophrenia or irritable bowel syndrome has been reported to promote
4 autism-like, depressive-like or schizophrenia-relevant symptoms in recipient mice. In addition, the presence of certain bacteria in the gastrointestinal tract has been reported to influence behavior and brain function. Elevated anxiety-like behavior was observed by infection with pathogens, like
Campylobacter jejuni[75] and Citrobacter rodentium[76], while the anxiety- and depression-like behavior could be reduced through the treatment of probiotics like Lactobacillus rhamnosus in GF mice compared with conventional mice[77].
Roles of gut microbiota in spinal cord injury (SCI)
Spinal cord injury (SCI) is a serious disorder that can profoundly impact the physical and psychological health of a patient, with about 17,000 new SCI cases each year in the United
States[78, 79]. As a type of CNS injury, SCI can disrupt sympathetic tone to involved body sites and immunologically relevant organs, which include lymph nodes, bone marrow, thymus, lungs, liver, intestines, and spleen[80, 81]. In addition to their physical disabilities, SCI patients often suffer from severe bowel dysfunction, which adversely impacts the quality of life for these individuals [82].
Moreover, disruption of the gut microbial ecosystem has been linked to many disease states, including co-morbidities of SCI, such as metabolic disease, immune dysfunction, mental and cognitive health [80, 83, 84]. Likewise, disruption of the gut-brain axis due to SCI results in lasting changes in gut bacterial composition, which can last about 12 months [85]. Previous work has characterized gut dysbiosis at the phylum, order, genus, and species levels [82, 85-88]. More recently, three species, Lactobacillus intestinalis, Clostridium disporicum, and Bifidobacterium choerinum, were reported to be enriched, while Clostridium saccharogumia was depleted in a rat model of
SCI[82]. The proinflammatory cytokines, including interleukin (IL)-12, macrophage inflammatory protein-2 (MIP-2), and tumor necrosis factor alpha, also significantly elevated after SCI[82].
Moreover, SCI was reported to increase intestinal permeability and bacterial translocation from
5 the gut to distant organs[89]. The probiotics and/or neurotransmitters may improve the recovery of
SCI. It’s reported that the dysbiosis with antibiotics before SCI exacerbated neurological impairment after injury[89], and that administering a probiotic treatment including Lactobacillus and Bifidobacteria after injury improved locomotor recovery[89]. Similarly, another study recently reported that administration of melatonin after SCI correlates with increases in Lactobacillus, decreases in the pathogens Clostridiales, and improved gastrointestinal motility and intestinal barrier function[90]. However, because all published reports have used 16s rRNA amplicon sequencing to study SCI-induced gut dysbiosis, the composition and diversity of viruses remain elusive [89].
Roles of phages in human health and disease
The gut virome represents one of the most poorly understood parts of the human gut microbiome, due to the fact that viruses lack universal marker genes for taxonomic assignment, are challenging to cultivate, and viral genetic diversity remains largely unknown[91, 92]. It’s reported that 75% to 99% gut phage sequences cannot be aligned to known phage genomes [91, 93].
This is in marked contrast to the global human gut microbiome research, which has allowed for the cultivation and sequencing of more than 1,000 gut bacterial species [94-96], representing about
90% of total gut microbial species[96]. Bacteriophages(phages), or viruses that infect bacteria, account for the vast majority of viruses in the gut virome [91]. The existence of a healthy core gut virome has been called into question by recent longitudinal studies[97, 98] and the findings from the large scale gut virome database[99]. These studies suggest that phages are stable in healthy individuals over time, but most phages are unique to each individual at a nucleotide level [97, 98].
Bacteriophages may affect human health and disease by dramatically shaping the gut microbial communities and functions through active lysis(reviewed in[100]) and horizontal gene
6 transfer [101]. Lytic phages metabolically reprogram their bacterial hosts during infection in ways that alter the cell’s output, and ultimately kill their hosts, modulating host abundance and diversity[100, 102, 103]. A recent study suggested that two unrelated viruses could infect and metabolically reprogram the same marine Pseudoalteromonas bacterium in a different approach that transformed the bacterial hosts into two very different virocells(infected microbial cells)[103].
This indicates that phages can reprogram the bacteria's metabolisms for more efficient nutrient acquisition from the environment[103].
Phages can also impact the metabolism of bacteria through viral auxiliary metabolic genes
(AMGs). Viral AMGs are originally derived from the microbial host genomes but are maintained and adapted in viral genomes [100, 104]. The expression of AMGs is hypothesized to augment the host's metabolism, and/or facilitate the efficiency of nutrient acquisition or utilization during infection to enhance the production of new viruses[104-106]. AMGs have been best studied in aquatic environments where they are involved in photosynthesis, phosphate uptake, carbon turnover, nitrogen fixation, and stress response [100, 105, 107, 108]. Previous studies also suggest that rumen phages harbor potential AMGs, such as some glycoside hydrolases(GHs) that may potentially increase energy production by augmenting the breakdown of complex carbohydrates, and other
AMGs that may play a role in reprogramming carbon metabolism to the pentose phosphate pathway to boost viral replication[109]. However, little is known about how AMGs may modulate microbial-driven processes in the gut system and their roles in human health and disease.
The horizontal gene transfer events among microbiome, also critical to health, have been reported to frequently happen in mammalian intestine[110-113]. Recently, evidence of extensive transfer of genes, encoding proteins predicted to increase fitness, between Bacteroidales species within the human intestine has been reported[114]. Interestingly, bacteriophage-mediated
7 transduction is a significant mechanism of horizontal gene transfer and plays an important role in the antibiotic resistance determinants transfer among pathogens[110, 115], as has been reported in enterococci[116] and Salmonella species[117]. Given the fact that SCI patients frequently infected with antibiotic-resistant and multidrug-resistant bacteria[118], bacteriophage-mediated transduction maybe one of the major mechanisms of driving antibiotic resistance transformation in the intestines, which is worth further investigation.
In addition to the widely accepted roles of impacting gut microbiome through active lysis, temperate phages, which integrate themselves into host genomes, can regulate host gene function and/or provide their hosts with new functions, like antibiotic resistance, toxin production, and other functions that may promote the virulence of commensals or confer fitness and competitive advantages to the hosts(reviewed in[119-121]). For example, the induction of 933 W prophages in certain strains of Shiga toxigenic Escherichia coli can result in the expression of Shiga toxin, causing gastrointestinal illnesses in humans[122]. Moreover, previous studies using prophages deletion experiments in E. coli strains have indicated that temperate phages can improve the resistance to antibiotics and environmental stresses, as well as biofilm formation to help bacteria cope with adverse environments[123].
Most temperate phages are highly stable, but stressors that active the bacterial hosts DNA damages (“SOS” response), like antibiotics, radical oxygen species, changes in nutrients and PH, can induce temperate prophage and ultimately kill host cells[124-126]. The SOS response can mediate sustained colonization of the murine gut[127], thus the induction rates of temperate phages in the murine gut are reported to be global higher than in cultures[128, 129]. Quinolone antibiotics, which cause bacterial DNA damages, are the most reported stressors to induce temperate phages in the gut[130]. It’s worth noting that the lifestyles of gut phages might be influenced by diets. The dietary
8 fructose and SCFAs were shown to induce Lactobacillus reuteri temperate phages[129], and bile salts were shown to induce some Salmonella temperate phages[131]. Interestingly, the gut inflammation can trigger increased SOS response in resident gut bacteria[127] and has been reported to increase temperate phage induction in mice[132]. The resulting bacterial debris or cell wall component may trigger the host trigger inflammatory signaling cascades that can induce cytokines and worsen inflammation[133]. All these reports suggest that prophage induction in the gut might alter microbial composition and abundances, and impact human health[91].
In addition to the accepted role of bacteriophages in regulating gut microbiome communities, bacteriophages can affect human health and disease by interacting with the human immune system [134-137]. Gut phages contribute to gut immunity and protect mucosal barriers by binding to the mucus layer and lysing invading pathogens through the bacteriophage adherence to mucus(BAM) model[138]. It’s also known that gut phages can translocate into the systemic circulation to stimulate the host immune system (reviewed in[139]), with about 31 billion viral particles estimated to cross the human gut epithelial barrier each day[140]. Phages may also bypass the epithelial cell barrier via a “leaky gut”, known as intestinal barrier dysfunction or disruption[139]. Notably, phages can indirectly impact immune responses through the release of proteins, lipids, and nucleic acids, which can serve as pathogen-associated molecular patterns
(PAMPs)[141]; some prophage-encoded genes can aid pathogens in their abilities to translocate the gut epithelium and induce proinflammatory responses [139], and Pseudomonas aeruginosa phages can trigger antiviral immunity and prevent clearance of Pseudomonas aeruginosa infection[142].
Phages may affect the health of SCI patients through bypassing the leaky gut to trigger the immune systems, which needs the follow-up experiments.
9 Compared with healthy controls, alterations in the gut virome have been associated with many diseases, including inflammatory bowel disease (IBD)[143], ulcerative colitis[144], autism spectrum disorders[145], colorectal cancer[146], type 1 diabetes and type 2 diabetes[147], suggesting an important role for the gut virome in disease-mediated dysbiosis. Most recently, a whole-virome analysis elucidated viral dark matter in IBD and revealed IBD specific virome composition that may be used for the development of future biomarkers and therapeutics[148]. Similarly, fecal virome-based profiling was able to predict colorectal cancer status, and the Cytomegalovirus abundances may be biomarkers to help improve noninvasive colorectal cancer diagnoses accuracy[146]. Whether the gut virome is affected by SCI remains to be studied.
Here we used metagenomic analysis (chapter 2) to examine ecological and functional changes in gut bacterial and viral communities after SCI in a murine model. This study revealed
SCI level-specific gut bacterial and viral compositions, as well as related metabolic pathways, providing myriad new hypotheses to guide future SCI studies and for potentially improving outcomes in SCI patients in the future.
10 Chapter 2: Spinal cord injury changes the structure and functional potential of gut bacterial and viral communities in a spinal-level dependent manner
Material in this chapter is based upon a manuscript in preparation: “Jingjie Du1*, Kylie
Zane2*, Ahmed A. Zayed1*, Kristina A. Kigerl3-5*, Matthew B. Sullivan1,6,7^& Phillip G. Popovich3-
5^ (* equal contribution). Spinal cord injury changes the structure and functional potential of gut bacterial and viral communities in a spinal-level dependent manner.” I led the statistical analysis and co-led the bioinformatics analysis with Ahmed A. Zayed, co-led manuscript writing with Kylie
Zane. Kristina A. Kigerl performed the murine spinal cord injuries as well as daily care and sample collection. This chapter is written in a format consistent with submission to Microbiome.
11 Abstract
Emerging data indicate that gut dysbiosis causes or contributes to a wide range of human diseases including many comorbidities that develop after traumatic spinal cord injury (SCI). To date, all analyses of SCI-induced gut dysbiosis have used 16S rRNA amplicon sequencing to measure compositional changes in gut bacteria that are then correlated with cellular and molecular biomarkers or clinical outcomes. This technique has several limitations including being susceptible to taxonomic “blindspots”, primer bias, and an inability to profile microbiota functions or identify viruses at all. Conversely, metagenomics can reveal how SCI changes the taxonomic and functional profile of gut microbes and their viruses. Here we use genome- and gene-resolved metagenomics to analyze murine stool samples 21 days after experimental SCI performed at T4 or
T10 spinal levels to determine how complete or partial loss of sympathetic tone to the gut, respectively, affects the microbiome (and its functions that feedback to the CNS) relative to control mice receiving laminectomy (Lam) only. Among bacteria, we recovered 105 medium- to high- quality metagenome-assembled genomes (MAGs), with most (n=96) MAGs representing new bacterial species. Read-mapping revealed that after SCI the relative abundance of beneficial commensals (Lactobacillus johnsonii and CAG-1031 spp) decreased, while potentially pathogenic bacteria (Weissella cibaria, Lactococcus lactis_A and Bacteroides thetaiotaomicron) increased.
Functionally, microbial genes encoding proteins for tryptophan, vitamin B6, and folate biosynthesis, essential pathways for central nervous system function, were reduced after SCI.
Among viruses, we recovered 1,028 viral populations (approximately species-level taxonomy), which were virtually all novel and expands known murine gut viral species sequence space ~3- fold compared to public databases. Phages of beneficial commensal hosts (CAG-1031,
Lactobacillus and Turicibacter) decreased and phages of pathogenic hosts (Weissella, Lactococcus
12 and class Clostridia) increased after SCI. Though all SCI mice had significantly altered microbiomes and viromes, the largest changes occurred in T4 SCI mice, implicating loss of sympathetic tone as a mechanism underlying gut dysbiosis. These data and analyses reveal how metagenomic data may better predict microbiome impacts on SCI comorbidities by providing information on taxonomy, function and viruses.
Background
Gut microbiota protect mammals from pathogen colonization (reviewed in[25]), regulate gut permeability[27], stimulate the immune system (reviewed in[25]), synthesize essential vitamins
(reviewed in[149]), produce secondary bile acids[150, 151], produce short chain fatty acids and provide metabolic fuel for colonocytes by breaking down indigestible food sources[152, 153]. Gut microbes are also key components of the brain-gut axis, the bidirectional system of communication between the normal central nervous system (CNS0 and the digestive system. Gut microbes have been shown to be essential for CNS development, functioning and recovery after injury[154-157] and for regulating host neural activity and behavior in response to environmental cues[158]. Indeed, gut- derived microbes produce various neuroactive metabolites or precursor molecules (e.g., tryptophan), which are needed to synthesize serotonin, dopamine, GABA, acetylcholine and melatonin[63]. These neuroactive metabolites signal the CNS via vagal afferents or they enter the circulation and pass directly into the neural parenchyma across the blood-brain barrier[159, 160]. Gut microbes are also capable of indirectly signaling the CNS by influencing innate and adaptive immunity; and the immune system, like the gut, exerts bidirectional communication with the CNS
(reviewed in[161]).
13 Homeostasis of the gastrointestinal (GI) tract, including microbial homeostasis, is dependent on the enteric nervous system of the GI tract, which is innervated by the parasympathetic vagus nerve originating in the brainstem and sympathetic spinal nerves originating in the spinal cord[162, 163]. When the spinal cord is injured, axons normally descending from the brain/brainstem to control spinal sympathetic neurons are lost or damaged[162].
Consequently, after SCI, normal sympathetic control of the small bowel and colon is lost, leading to impaired gut motility, mucosal secretions, vascular tone and immune function[89]. Loss or disruption of one or more of these GI functions after SCI can disrupt the ecological balance of microorganisms in the gut causing gut dysbiosis[62, 164]. Indeed, lasting changes in gut bacterial composition has been recorded in multiple preclinical and clinical studies of SCI[82, 85-88].
Disruption of this gut microbial ecosystem has been linked to various comorbidities that develop after SCI, such as metabolic diseases, immune dysfunctions and mental and cognitive impairments[84, 165-167]. Unfortunately, because all published reports of SCI-induced gut dysbiosis have used 16s rRNA amplicon sequencing to characterize compositional changes in gut bacteria, reliable predictions about microbiota function remain elusive, as do compositional changes in novel microbial species, including viruses[164].
Because viruses lack universal marker genes for taxonomic assignment, the genetic diversity of the gut virome remains largely unknown[91, 126]. As the most abundant members of the enteric virome[99], bacteriophages may affect human health and disease by dramatically shaping gut bacterial communities and their functions. This can occur through predator-prey dynamics
(reviewed in[100]) and horizonal gene transfer[101], or by direct interactions between viruses and the immune system[134, 135, 139, 140], even including a phage-mediated, non-host-derived immunity to protect against invading pathogens[138]. There may or may not be a “healthy gut virome”[97-99, 168],
14 but it is clear that individuals have unique, persistent viromes[97, 98]. In the context of disease, disease-specific virome changes have been observed for inflammatory bowel disease (IBD)[143], ulcerative colitis[144], autism spectrum disorders (ASD)[145], colorectal cancer[146], type 1 diabetes[169, 170] and type 2 diabetes[171]. However, it is notable most of current gut virome studies predominantly limited their analyses to known taxonomy or missed the linkage of viruses to their hosts[172, 173] rather than considering all taxa as done in environmental studies[174-176]. Nothing is yet known about how the gut virome is affected by SCI.
Though studying viruses in complex communities and viral ecogenomic approaches are in their infancy, particularly for inferring phage lifestyle (e.g., lytic or temperate), understanding phage-host dynamics are likely essential for us to reveal the roles of the gut virome in disease[177].
Lytic phages metabolically reprogram their bacterial hosts during infection in ways that alter that cell’s output into the ecosystem[103], and ultimately kill their hosts to modulate host abundance and diversity through predator-prey dynamics (reviewed in[100]). Temperate phages integrate themselves into host genomes and can regulate host gene function (reviewed in[178]) and/or provide their hosts with new functions, like antibiotic resistance, toxin production, and other functions that may promote the virulence of commensals or confer fitness and competitive advantages to the hosts[120, 124]. However, stressors, like antibiotics, hydrogen peroxide, changes in nutrients and pH, which activate the bacterial hosts SOS response, can induce temperate prophages, causing them to become lytic and ultimately kill host cells[124, 127, 130]. Dietary fructose and short-chain fatty acids
(SCFAs) were shown to induce Lactobacillus reuteri temperate phages[129], and bile salts were shown to induce some Salmonella temperate phages[131]. Gut inflammation can also increase temperate phage induction in mice[179]. Thus, a changing SCI gut phage ecology will vary due to
15 actions by both temperate and lytic phages, particularly in response to dietary changes and repeated regimens of antibiotics or drug therapies.
Here we use metagenomics to examine ecological and functional changes in gut bacterial and viral communities after SCI in a murine model, controlling for diet and antibiotic exposure
(common confounders for microbiome studies)[85, 86, 180] . We show that the severity of gut dysbiosis is affected by spinal injury level; more robust changes were noted when SCI occurred at high spinal levels which causes greater imbalance in autonomic tone in the gut. Our novel data also reveal SCI-dependent changes in microbiome and virome, as well as related metabolic pathways, providing myriad new hypotheses to guide future SCI studies.
Results and Discussion
The composition and magnitude of change in gut microbiota caused by SCI vary as a function of spinal injury level
We hypothesized that sympathetic nervous system control over proximal and distal colon will vary as a function of spinal injury level, causing differences in colon function that will directly affect the composition of the gut microbiome. To test this hypothesis, three groups of mice were prepared (n=15 mice total; n=5/group). Sham-injured control mice underwent laminectomy surgery at vertebral level T4 without spinal cord injury (Lam). Mice in the remaining two groups received a severe crush injury of the spinal cord at either T10 or T4. These two distinct spinal injury levels either partially preserve (T10) or abolish (T4) sympathetic innervation of the gut.
Because gut microbes are transferrable among species that share the same habitat, all mice were singly housed throughout the study, starting 2 weeks before SCI. At 21 days post-injury (dpi), fecal samples were collected, then individually prepared for microbiome analysis (Fig. 1a).
16 To characterize microbial community composition in each group, microbial taxa were identified using read-based and assembly-based approaches (see Methods and Fig. 7). First, a set of 14 single copy marker genes were directly detected from the metagenomic reads using Hidden
Markov Model (HMM) profiles, allowing for higher taxonomic (strain-level) resolution than is achievable by 16S rRNA gene sequencing and also avoiding the copy-number variation problem that limits abundance estimation from 16S rRNA gene sequencing[181]. Abundance-based comparisons (principal co-ordinate analysis of Bray-Curtis dissimilarity using ribosomal protein
L2(rplB); see Methods) of all microbial taxa present within the microbiome of each mouse revealed that the SCI groups cluster separately from Lam controls (permanova p = 0.001), indicating that SCI, regardless of injury level, disrupts microbial community structure (Fig. 1b).
However, the Bray-Curtis dissimilarity of control and SCI groups indicated that gut dysbiosis is exacerbated in mice with greater imbalance in sympathetic control of the gut, i.e., those with the higher-level T4 SCI (Fig. 1c). Presence/absence analyses showed that only half of the microbial
OTUs (clustered at 97% nucleotide identity using the ribosomal protein L2) were shared between the different experimental groups, indicating a high level of dissimilarity between groups (Fig. 8a).
Most of the unshared OTUs are rare species (<0.01% relative abundance), indicating that most of the microbial OTUs are unique or fall below our identity cutoff threshold (Additional file 1).
At the phylum level, the relative abundance of Firmicutes decreased significantly after
T10 SCI (Fig. 1d, 1e), whereas Actinobacteria significantly increased after both T10 and T4 SCI
(Fig. 1d, 1f). Expansion in Actinobacteria may be associated with intestinal inflammation, as it has been observed in other inflammatory conditions like IBD[182], obesity[183] and rheumatoid arthritis (RA)[184].
17 At the genus level, 18 bacterial taxa were differentially abundant (Wilcoxon test, p < 0.05) between control and SCI groups (Fig. 8b). Of these, only one highly abundant taxon (>20% relative abundances in Lam) showed injury level-dependent differences; CAG-1031 consistently decreased after T4 SCI (Fig. 2a-c). Two other abundant taxa (>1% relative abundances in Lam) were affected in a spinal injury level-independent manner; Lactobacillus and Turicibacter decreased after T4 and T10 SCI. Turicibacter spp. has been shown to promote host serotonin biosynthesis[185], while members of the Lactobacillus and CAG-1031 genera are involved in key metabolic transformations in the gut (discussed below). A decrease in abundant commensals and probionts also could open niches for antagonistic commensals and pathobionts. Notably, seven taxa increased after T10 (Eubacterium_R and Lachnospira) or T4 (Bacteroides, Weissella,
Eubacterium_F, UBA9475, and Neglecta) SCIs, relative to controls (Fig. 2d-f, Fig. 8c). Most of these latter taxa fall within the class Clostridia (Fig. 2f). Previously, using 16S RNA sequencing, we found that Clostridiales, members of the class Clostridia, increase after SCI and their relative abundance inversely correlated with motor function, suggesting that these microbes may adversely affect spontaneous recovery of neurological function[89, 90]. Collectively, the above data suggest that gut dysbiosis is a consistent phenomenon after SCI but that greater disruption of sympathetic control over the colon, such as after high-level (T4) SCI, will exacerbate gut dysbiosis.
Genome-centric view of SCI-induced changes of gut microbiota
The above read-based analyses rely upon mapping to single-copy marker genes. To further assess changing taxonomic patterns and infer microbial functions, we assembled the shotgun sequence data from each sample then binned contigs to create de novo draft microbial genomes, i.e., metagenome-assembled genomes (MAGs). MAGs enable robust, genome-informed taxonomic classification within a sample and rather than predicting function from slow-evolving
18 taxonomic marker genes, MAGs provide maps of ecologically relevant functional potential for known taxa[186]. Using this approach, we recovered 112 MAGs(>60% complete, <10% contamination), including 105 MAGs of medium- (n=35; >70% complete, <10% contamination) to high- (n=70; >90% complete, <5% contamination) quality (see Methods) that recruited, on average, 50.4% of the quality-controlled sequencing reads (Additional file 2). Most (n=96) of these
105 MAGs represented previously unknown bacterial species as assessed against the GTDB-Tk v0.1.3[187], the largest curated genomic database available[186] (see Methods, Additional file 3).
Across the dataset, 29 MAGs were significantly differentially abundant between Lam and SCI groups (Wilcoxon test, p < 0.05, Fig. 9).
Only one previously known species, Lactobacillus johnsonii (L. johnsonii), was dramatically and significantly depleted in both T4 and T10 SCI models. In previous studies, this species has been widely considered beneficial due to its anti-inflammatory effects[188-191] and its ability to metabolize inulin into a prebiotic that is further metabolized to SCFAs like butyrate and propionate[192-195]. Analysis of our L. johnsonii MAG revealed that it also harbored the gene encoding the anti-inflammatory enzyme lactoceptin, though it lacks inulin metabolism genes, this
MAG is only ~85% complete, so we cannot rule out they are encoded in the native population (see
Additional file 4). Assuming this is functionally analogous, we hypothesize that after SCI, the dramatic decrease (>8x; Fig. 3a) of gut L. johnsonii markedly reduces beneficial SCFAs and impairs the immune regulatory properties of the gut microbiome. Consistent with this, we previously showed that post-injury oral supplementation with a probiotic mixture of Lactobacillus and Bifidobacterium boosted T-regulatory cells in the GALT of SCI mice and that these changes were associated with better recovery of locomotor function[88].
19 Additionally, of the novel species that decreased significantly across treatments, two belonged to the genus CAG-1031 (family Muribaculaceae) and were represented by two near- complete MAGs (Fig. 3b, 3c). CAG-1031 spp. decreased significantly with great magnitude (>5- fold) in T4 SCI mice, and though it decreased (>4 fold) in T10, this latter result was highly variable and not significant (Fig. 3b, 3c). Though there is no published CAG-1031 functions, as a newly classified taxon under the latest GTDB update[186], members of the Muribaculaceae are known to be able to synthesize folate and use diverse glycoside hydrolases to degrade complex polysaccharides[196]. Indeed, the CAG-1031 MAGs detected in our samples have the functional capacity to synthesize folate, break down complex polysaccharides, and synthesize vitamin B6
(Fig. 4c). The significant reduction in such an abundant species after SCI suggests a large degree of disruption in the gut microbiome’s capacity to metabolize complex carbohydrates and synthesize essential vitamins that cannot be made by mammalian host cells. Given their abundance, strong response and plausible beneficial roles, we hypothesize that CAG-1031 spp. are valuable probionts that, provided they can be grown in culture, could represent a novel probiotic therapy after SCI.
On the other hand, the relative abundance of three previously known species increased after
SCI (with two of them increasing significantly). First, Weissella cibaria (W. cibaria) and
Lactococcus lactis_A MAGs were significantly enriched in samples from T4 SCI mice (Figure 3d,
3e). Like L. johnsonii (above), W. cibaria and Lactococcus lactis_A are lactic acid producers[197,
198], and their expansion may indicate the opportunistic growth of a species that was previously outcompeted by L. johnsonii and/or CAG-1031 spp. In fact, both W. cibaria and Lactococcus lactis_A can act as opportunistic pathogens[197, 199], due to virulence factors such as hemolysins[197], which were also found in our W. cibaria MAG (Additional file 5). Second, B. thetaiotaomicron
20 (B. theta) MAG slightly increased in T4 SCI mice. B. theta is the second most common agent in anaerobic gram-negative infections in humans[200, 201], and is known to infect host immune cells in a sulfatase-dependent manner and express pro-inflammatory lipooligosaccharides (LOS), which are analogous to the lipopolysaccharides (LPS) found in other Gram-negative bacteria families[202,
203]. An analysis of our B. theta MAG predicts that these bacteria encode lipopolysaccharide (LPS), polysaccharide, O-antigen biosynthesis genes, as well as multiple antibiotic resistance and sulfatase genes (Additional file 6). Thus, an increase in B. theta after SCI is expected to increase inflammation locally in the gut, and systemically via translocation. Although non-significant, post-
SCI increases in B. theta were consistent and may be worthy of further investigation in larger, more strongly powered follow-up studies.
Collectively, our data suggest that SCI decreases commensal abundances, which may open niches for antagonistic commensals and opportunistic pathogens that can increase inflammation and impair recovery after SCI. However, since most (>90%) of these MAGs represent novel species, the metagenomic approach provides an opportunity to establish baseline hypotheses beyond taxonomy about metabolic versatility – such as the vitamin B6, virulence factors, and LPS genes identified and described here – that could contribute to pathological comorbidities common in SCI individuals.
SCI-induced gut dysbiosis is associated with loss of beneficial microbial functions
Beyond the MAG-constrained analyses, we sought next to more broadly evaluate functional changes in the microbiome since functions often vary more than taxonomy both in human[204] and environmental systems[205]. To this end, we translated predicted genes from assembled contigs (>500bp in size) into amino acid sequences, and ‘organized’ sequences into protein clusters. This eliminates bias against analyzing only proteins of known function (see
21 Methods). As observed before at the taxon-level (Fig. 1b), principal co-ordinate analysis (PcoA) revealed significant clustering of samples from the same group (permanova, p ≤ 0.006, Fig. 4a), with the largest separation occurring between Lam and SCI groups. This indicates that SCI- induced disruption of the composition of gut microbes is accompanied by a corresponding change in microbial function. To test the hypothesis that SCI impairs beneficial, microbially-encoded metabolic functions in the gut, we performed both gene-based abundance comparisons on the whole microbial community (from the assembled contigs) and genome-based metabolic reconstructions (from the recovered MAGs).
For gene-based analyses, all predicted functions in Lam controls were compared to those in SCI groups using the KEGG hierarchy to organize these functions into metabolic modules.
Genes that were differentially abundant between Lam and SCI groups (see Methods) fell into 5 major metabolic pathways - carbohydrate, lipid, amino acid, vitamin, and energy metabolism
(Wilcoxon test, p < 0.05) (Fig. 10). After SCI, especially T4 SCI, we found significant derangement of gut microbial function, including a reduction of many genes encoding components of the phosphotransferase system (PTS) and glycosyl hydrolases (GHs) (Fig. 4b, Fig. 10). The PTS system controls sugar uptake by microbes; a reduction in PTS genes can impair microbial sugar utilization and their physiological functions. Many PTS genes, including those that encode the transport proteins specific for maltose, galactose, mannose and lactose were significantly reduced after SCI (compared to Lam). Conversely, mannitol PTS genes were significantly increased after
T4 SCI (Fig. 4b, Fig. 10). Utilization of fructose and glucose can decrease the abundance of beneficial commensals[206], and promote inflammation throughout the body[207]. Conversely, utilization of lactose and mannose can decrease inflammation[208]. Sugar alcohols like mannitol and sorbitol are more abundant in the mouse gut after antibiotic treatment, which can promote the
22 growth of the pathogen, C. difficile[209]. Microbial glycosyl hydrolases (GHs) can break down indigestible food sources (e.g., fibers), producing neuroactive metabolites like short-chain fatty acids[210]. In our study, genes encoding GHs that degrade xylan, arabinan, mannan, maltose and cellobiose were significantly reduced after SCI compared to Lam (Fig. 4b, Fig. 10).
Other microbial genes involved in regulating the biosynthesis of folate, vitamin B6 and tryptophan also were reduced by SCI (Fig. 4b, Fig. 10). In mammals, folate is essential for de novo pyrimidine synthesis, a prerequisite for making DNA. Folate also is critical for the maintenance of gastrointestinal health and neurological function[211, 212]. In the context of SCI, recent data indicate that folate augments CNS repair and regeneration[213, 214]. Vitamin B6 also plays a significant role in CNS development and function, as it is required for the synthesis of key neurotransmitters including epinephrine, dopamine and serotonin[38, 215]. Similarly, microbial synthesis of tryptophan supports gut barrier integrity and stimulates epithelial renewal (as reviewed in[216]). Tryptophan synthesized by microbes is converted to 5-HTP by enterochromaffin cells in the gut and when further metabolized to serotonin, influences gut motility[216, 217]. 5-HTP also enters the circulation where it can cross the blood brain barrier and fuel serotonin synthesis in the brain and spinal cord[216-218]. Decreased serotonin production has been linked to many disorders like depression, anxiety and irritable bowel syndrome (IBS), indicating that loss or a reduction in microbe-dependent tryptophan metabolism after SCI could be an undiagnosed cause or contributor to the higher than normal incidence of depression, fatigue and anxiety in SCI individuals[219-222].
Finally, the microbial gene encoding the anti-inflammatory enzyme, lactoceptin was reduced after SCI. A reduction in the bacterial protease lactoceptin would favor gut inflammation.
Lactoceptin selectively degrades inflammatory chemokines thereby reducing inflammation.
Replenishing gut microbes that produce lactoceptin can effectively reduce gut pathology in a
23 murine colitis model[223]. After SCI, there is evidence that inflammatory cascades are initiated in the gut and that these can impair function of the enteric nervous system, which when combined with loss of normal autonomic tone to the gastrointestinal tract, could exacerbate the consequences of a neurogenic bowel[82, 89, 224] .
Next, using our de novo synthesized microbial reference genomes, we mapped functional changes (Fig. 4b) to specific microbial lineages that were enriched in Lam or SCI groups (Fig. 4c;
Fig. 9). Many MAGs encode genes controlling functions related to amino acids and secondary metabolites biosynthesis (chorismate synthase, indole-3-glycerol phosphate synthase and anthranilate synthase component II). The consistency with which these genes were detected in most MAGs is not surprising given the importance of these functions in building bacterial biomass, antibiotic production, and microbial community communication through quorum sensing[225-227].
However, some microbial functions were lost, mainly due to a loss or decrease in specific bacterial lineages. For example, the reduction of L. johnsonii after SCI leads to a decrease in lactoceptin,
PTS glucitol/sorbitol-specific components, galactitol-specific components and lactose-specific components (Fig.4c). Notably, the enzyme lactoceptin is considered to be specific to
Lactobacillus[223], and the abundance of lactoceptin in our L. johnsonii MAG constitutes about
61.1% of the total lactoceptin in the whole microbial community. In our analysis, lactoceptin was also present in the less abundant ASF356 MAG (Additional file7), another candidate probiotic that could be mixed with L. johnsonii and our two novel CAG-1031 spp. to collectively cover a large spectrum of microbial functions that are markedly reduced in the gut after SCI. Indeed, the consistent post-SCI reduction of each of these bacterial species could enhance gut inflammation and reduce the ability of the microbiota to contribute to the metabolism and biosynthesis of key molecules used throughout the body.
24 Taken together, these functional data support the hypothesis that SCI-induced gut dysbiosis promotes a pro-inflammatory environment in the gut which could adversely affect gut motility and epithelial barrier integrity. This in turn has the potential to enhance bacterial translocation and systemic inflammation, exacerbating neuroinflammation and impairing neurological recovery after SCI. Recovery may be impaired further due to a reduction in the production and release of precursors needed for neurotransmitter synthesis and also vitamins and cofactors needed for optimal CNS function and repair.
SCI alters the gut virome
Given the lack of marker genes for viruses[172], we applied de novo assembly and population-based approaches to characterize the gut virome, an approach now well-established in the oceans and soils[175, 176, 228]. Specifically, population or species level taxa were discerned by clustering genomes at >95% average nucleotide identity genome-wide, and genus-level taxa were assigned using gene sharing networks. In total, this revealed 1,028 viral populations
(approximately species-level taxonomy[176, 229, 230]; ≥10 kb; see Methods), of which 1,024 represent novel species (Additional file 8; see Methods) as compared to public databases including NCBI’s
RefSeq, v88 (4061 complete genomes; >10kb)[231] and the Human Gut Virome Database(6360 viral populations; >10kb)37. To further contextualize these findings, we built a mouse gut virome database by downloading murine gut viromes from the IMG/VR database[232] and one murine viral particle metagenome[233], and clustered genomes into populations (see Methods). This revealed
1,522 unique murine gut viral species, with 1,020 viral species exclusively in our study, which expands known murine gut viral species sequence space ~3-fold over the 502 viral species we recovered from public databases (Additional file 9; see Methods). In summary, most (1,016 out of
1,028) viral populations in our study represent novel species as compared to public databases
25 including NCBI’s RefSeq, v88 (4061 complete genomes; >10kb)[231], the Human Gut Virome
Database(6360 viral populations; >10kb)[99] and public murine gut virome databases(502 viral populations).
At the genus level, gene-sharing network analyses[234] of these data confidently placed nearly all (992 of 1,028) of the viral populations in the network as part of 163 viral clusters (VCs; equivalent to genus-level characterization). Of these, 89 VCs (containing 219 viral populations) were exclusive to our dataset, whereas 74 VCs (containing 182 viral populations) included in reference sequences (NCBI’s RefSeq, v88 and the Human Gut Virome Database) (Additional file
9, Fig. 11a). Only 58 murine gut viral populations could be assigned taxonomy (Fig. 11a), within the order Caudovirales, and the relative abundance of these phages increased significantly after
T4 SCI (Fig. 11b). Previous studies in murine colitis models and human IBD patients also revealed a disease-dependent increase in Caudovirales[143, 233]. These findings show that many of our murine gut viruses are unique at both the species and genus level, and that murine gut viruses clustered more closely with human gut viral genomes than non-gut derived viruses (Fig. 11a).
Together, this is promising for pre-clinical SCI murine applicability towards human disease.
With this reference dataset now in-hand, we next assessed viral population relative abundances via non-redundant read mapping. As with the bacterial component of the gut microbiome, principal co-ordinate analysis (PCoA) of the gut virome (viral contigs ≥5kb) revealed marked separation between Lam and SCI groups (permanova, p ≤ 0.002, Fig. 5a). Moreover, gut viral within-group community-dissimilarity (Fig. 5b) and diversity (Fig. 5c) increased significantly after SCI in a spinal level-dependent manner. Viral diversity also increases in obesity and inflammatory bowel disease[143, 235, 236], indicating that SCI-induced changes in the gut virome may be associated with an inflammatory disease state. Further analyses of changes in viral population
26 abundances among the groups revealed a relationship between the SCI level and the number of viral populations(>5kb) affected by SCI as significant changes were noted in 12 vs 45 viral populations for T10 vs T4 SCI, respectively (t-test adjusted, PDR P<0.05; Fig. 5d, Additional file
10).
Prophage-bacteria junctions(attachment sites)[237] and lysogeny markers(integrase/ site- specific recombinase, excisionase, repressor/anti-repressor and parA/parB)[124, 238-240] were then searched to identify the candidate temperate phages in the 2675 viral contigs(>5kb)(see methods).
Among these, we were able to identify 516 candidate temperate phages, which constitutes about
19.3% of our murine gut virome, including 49 confident temperate phages containing putative attachment sites and integrases (Fig. 12b). This is in concordance with previous studies in the establishment of temperate phages in human gut, which was established to constitute about 20%-
50 % of phages based on established methods[97, 241, 242]. It’s worth noting that among the 70 differential viral populations(>5kb) affected by SCI (Fig. 5d), 17 viral populations are candidate temperate phages with lysogenic genomic features, including 2 confident temperate phages with attachment sites and integrases (Additional file 11). This indicates that both temperate phages and lytic phages are involved in gut dysbiosis triggered by SCI.
In summary, as for the gut bacteria, these data suggest that SCI consistently alters the gut virome, but that these effects are magnified with progressive loss of sympathetic control over the colon, such as after high-level (T4) SCI. Both temperate phages and lytic phages are involved in gut dysbiosis triggered by SCI.
To develop hypotheses about how such virome changes might impact the microbiome, we sought to link viral contigs to microbial hosts (MAGs) via commonly used in silico approaches (k- mer signatures[243], prophages[228], tRNAs[233, 244] and CRISPR spacers[244, 245]; see Methods). These
27 analyses predicted hosts for 36% of the viral contigs (Fig. 13), which is 3X higher than a previous murine gut virome analysis where only reference microbial genome databases were available[233], but on par with findings in soils where co-sampled MAGs were also available[228]. These findings indicate the value of making such predictions using co-sampled microbial reference genomes.
With these predictions, we found that phages of the above discussed bacterial lineages that were significantly altered by SCI (Fig. 3), were similarly affected by SCI (Fig. 6). For example, the relative abundances of phages that infect Lactobacillus, CAG-1031 and Turicibacter decreased after SCI (Fig. 6a, Fig. 14), whereas those predicted to infect Weissella, Lactococcus and class
Clostridia increased after SCI (Fig. 6b, c, and Fig. 14). When compared to the microbial host data above (Fig. 3), these phage abundance patterns are concordant, indicating that phages may also serve as biomarkers of disease status
Conclusion
The gut microbiome has emerged as an essential component of human development, metabolism, and health, and growing evidence from gene marker data for microbes[246, 247], and metagenomic data for microbes and viruses (this study) suggest that this is also true for SCI. It is now clear that key probiotic bacterial populations and genes controlling their physiological functions are lost after SCI, suggesting that repopulating the gut with distinct bacterial taxa, such as Lactobacillus johnsonii and CAG-1031 spp, may help to improve outcomes after SCI and that there are phage-host interaction dynamics that respond to SCI and may alter clinical outcomes through their impacts on gut microbiota. Future work will benefit from experiments designed to explicitly test these metagenome-enabled hypotheses, particularly leveraging the expanding microbiome capabilities and resources[248-250] and the partnered viral ecogenomic toolkit for
28 capture[251, 252] and characterization[174-176]. With such augmented windows into the interactions modulating the gut-brain axis will be critical for understanding how SCI alters gut microbial ecology and microbiome-related clinical outcomes such that novel therapeutic interventions can be designed to improve SCI outcomes.
Methods
Animals and spinal cord injury
All surgical and post-operative care procedures were performed in accordance with the
Ohio State University Institutional Animal Care and Use Committee. N=15 female C57BL/6 mice from Jackson Laboratories (Bar Harbor, Maine) were used in this study. To prevent gut microbial cross-contamination due to co-habitation, all mice were single-housed upon arrival at our animal facility and for the duration of the study. Mice were anesthetized with an intraperitoneal cocktail of ketamine (80 mg/kg)/xylazine (10 mg/kg) after which a partial laminectomy was performed at the fourth thoracic spine (T4) or the tenth thoracic spine (T10). In mice modeling SCI, modified
#5 Dumont forceps were inserted laterally and held closed for 3 seconds to produce a complete crush spinal cord injury.
Post-operatively, animals were hydrated with 2 mL Ringer’s solution (subcutaneous) for 5 days. Bladders were voided manually at least twice daily for the duration of the study. No prophylactic antibiotics were used during or after surgery. Fecal samples were collected 21 days post-injury (dpi). Mice were removed from their home cage and placed into a ventilated, aseptic polystyrene compartment and fresh fecal samples were collected from each mouse into sterile tubes and immediately frozen in liquid nitrogen. Mice were returned to their home cage after
29 sample collection. In total, five mice received a complete crush at T4, 5 mice received a complete crush at T10, and 5 mice didn’t receive any crush after laminectomy (Lam).
Metagenomic sequencing, read quality control and contigs assembly
Bulk DNA was recovered from the 15 fecal samples separately using a ZymoBIOMICs extraction kit. Metagenomic library preparation and shotgun sequencing was conducted at
CosmosID® using an IonTorrent Ion S5 next-generation sequencing system. On average, 23.5 million single-end reads were generated per sample (range: 15.2 million to 36.2 million reads) with an average read-length of 180 bp. Reads were quality trimmed using bbduk
(https://jgi.doe.gov/data-and-tools/bbtools/) from both ends to remove bases with low quality scores (qtrim=rl, trimq=10) and positions with high compositional bias (ftl=10, ftr=range from 204 to 229 depending on the sample). Reads shorter than 30 bp (minlength=30), with Ns (maxns=0), or with an average quality below 10 (maq=10) were discarded. Mouse reads were removed from all the samples using bbmap (https://jgi.doe.gov/data-and-tools/bbtools/) by mapping against the genome of our model mouse strain C57BL/6NJ (downloaded from ncbi’s assembly database:
GCA_001632555.1) and removing reads with a minimum identity of 95% (minid=0.95). After quality control, all the clean single-end reads were cross-assembled using SPAdes(v3.11.1)[253] in the “read-error correction and assembling” mode and using the (--iontorrent) flag. The full k-mer size list (-k 21, 33, 55, 77, 99, 127) was used in the assembly. All bioinformatic analyses were performed within the Ohio Supercomputer Center[254]. For a visual overview of the bioinformatic analyses, see Figure S1a.
Read-based estimation of microbial diversity and community structure
Reads from each sample were piped through SingleM (singlem pipe; https://github.com/wwood/singlem) to estimate the abundance of discrete taxa down to the strain
30 level (Sensu[255]). Relative abundances of taxa, which were used for differential analysis, were calculated from the mean coverage of 14 single-copy marker genes to avoid copy-number variations associated with 16S-based estimates of abundance and to increase taxonomic resolution.
Abundances of taxa mapped to ribosomal protein L2(rplB) were used for principal co-ordinate analysis of Bray-Curtis dissimilarity. Within- and between-group Bray-Curtis dissimilarity analyses were performed using vegan in R[256]. Principal Coordinate analysis (function capscale with no constraints applied) was carried out on the Bray-Curtis dissimilarity matrix (function vegdist; method “bray”) after the log2 transformation. The three groups that emerged in the ordination plot were tested using a permanova test (function “anosim”) and were defined on the plot using function “ordihull”. For a visual overview of the bioinformatic analyses, see Figure 7b.
Construction of Metagenome assembled genomes (MAGs), estimating abundance and taxonomic classification
Microbial MAGs were recovered using the coassembly-optimized tool MaxBin 2.0
(v2.2.4)[257], which depends on the tetranuncleotide frequencies of the contigs, a phylogenetic marker gene set, and differential coverage binning. First, all reads from each sample were mapped to the coassembled contigs using Bowtie2(v2.3.4.1)[253]. The number of mapped bases (for average coverage calculation) and reads (for normalized RPKM calculation) were counted using
BEDTools(v2.23.0)[258] in ‘mapbamsamples.pl’ of SqueezeMeta (May2018 distribution)[259]. The
MAGs were then binned by MaxBin 2.0 using each sample’s contigs’ coverage (-abund_list) and the full marker gene set. CheckM (v1.0.12)[260] was then used to assess the quality (completeness and contamination) of the genome bins using the ‘lineage_wf’ pipeline, and genome bins were filtered at completeness ≥60% and contamination ≤10% (dRep_97 -comp 60 -con 10 -sa 0.97). dRep (v2.2.3)[261] was used then to dereplicate the MAGs at 97% average nucleotide identity. After
31 the dereplication, 112 MAGs were recorved (>60% complete, <5% contamination) including 70 high-quality MAGs (>90% complete, <5% contamination) and 35 medium-quality MAGs (>70% complete, <10% contamination) were recovered from our 15 samples. After the recovery of the
MAGs, GTDB-Tk (v0.1.3)[187, 262] was used to assign taxonomic classifications for the 112 MAGs in the ‘classify_wf’ mode. In total, 9 MAGs can be confidently resolved into bacterial species. In addition, among the rest of the MAGs, 73 MAGs can be assigned into bacterial genus. The coverm
(https://github.com/wwood/CoverM) was conducted for mapping the reads in different samples to the reference genome(MAGs) in ‘genome’ mode, and ‘single’ parameter. Additional file 3 describes the characteristics of the de-replicated MAGs and the predicted taxon of each MAG. For a visual overview of the bioinformatic analyses, see Figure 7 b.
Identification of viral contigs and establishing viral populations
To identify putative viral contigs following assembly, an ensemble approach was used where, first, all contigs were analyzed using VirSorter (v1.0.5)[263], DeepVirFinder (v1.0)[263],
MARVEL (v0.1)[264], and CAT (https://github.com/dutilh/CAT). This approach combines homology-based identification (CAT and VirSorter), sequence composition in deep learning
(DeepVirFinder), and genomic features in probabilistic models (VirSorter and MARVEL).
VirSorter was used in the ‘bulk metagenome’ mode and selecting the virome database, while
DeepVirFinder was allowed to predict contigs down to 300 bp in length. Next, linear contigs ≥
5kb and circular contigs ≥ 1.5kb that were sorted as VirSorter categories 1-6, DeepVirFinder score
≥ 0.7 (p-value < 0.05), and/or MARVEL Random Forest probability ≥ 70% were kept for further investigation. Of these contigs, those that were sorted as VirSorter (categories 1, 2, 4, 5),
DeepVirFinder (score ≥ 0.9), or MARVEL (≥ 90%) were considered viral. For the rest of the kept contigs, they were considered viral only if they were identified by at least two tools, VirSorter
32 (categories 3 or 6), DeepVirFinder (score ≥ 0.7 and <0.9), MARVEL (≥ 70% and <90%), and/or
CAT (annotated as viral or <40% of the genes were classified non-viral). In total, 29,143 viral contigs were identified, with 2,675 viral contigs ≥5 kb and 1,030 viral contigs ≥10 kb. Then, the identified putative viral contigs were clustered into viral populations using Clustergenomes
(v1.1.0; https://bitbucket.org/MAVERICLab/stampede-clustergenomes/src/master/) at ≥ 95% nucleotide identity across ≥ 80% of the shorter genome length[176]. This resulted in 2,658 viral populations ≥5 kb and 1028 viral populations ≥10 kb (see Additional file 12 for VirSorter, Deep
VirFinder, MARVEL and CAT results). Clustergenomes was also conducted of combining our
1028 viral populations with viral populations(>10kb) in public databases including NCBI’s
RefSeq, v88[231], the Human Gut Virome Database[99], and murine gut virome from IMG/VR database[232] and one murine viral particle metagenome[233] to determine the novelty of our viral species. For a visual overview of the bioinformatic analyses, see Figure 7c.
Viral taxonomy and virus-host predictions
Viral genus-level taxonomy was assigned using vConTACT 2.0 (v2-0.9.9)[234] by clustering (--rel-mode BLASTP --pcs-mode MCL --vcs-mode ClusterONE) our 1028 viral populations (>10kb) with both RefSeq viral genomes database (v88; 2,305 prokaryotic viral genomes)[231] and 13,203 viral genome sequences from a new human gut virome database
(GVD)[99]. The viral populations that can be clustered with a viral genome from RefSeq were likely to be assigned to a known viral taxonomic genus, family or order, classified by the International
Committee on Taxonomy of Viruses (ICTV) taxonomy. Additional file 13 summarizes the viral clusters using vConTACT 2.0. Four different computational methods were used to predict putative hosts for the viral populations, prophage-BLAST, CRISPR spacers matches, tRNA exact matches and k-mer-based sequence similarity. For prophage-BLAST, the viral genome nucleotide
33 sequences were compared to the MAGs using BLASTn based on a method described previously[265]. To improve prediction accuracy, only hits with 100% identity over 100% of the length of viral contigs were used for further prediction. Only prophage-BLAST prediction that can be identified using Phaster[266] were considered. For CRISPR linkages, MinCED (v0.2.0; github.com/ctSkennerton/minced) was used to search for prokaryotic CRISPR spacers (with a minimum of 2 repeats a CRISPR must contain) from our set of MAGs. Then, the prokaryotic
CRISPR spacers were matched to viral contigs using BLASTn. Only hits with at least 95% identity over the whole spacer length were considered. For tRNA linkages, tRNAscan-SE (v1.23)[267] was conducted to identify tRNAs from viral contigs (using the general model -G) and MAGs (using the bacterial model -B). Then, tRNAs secondary structure sequences from viral contigs were compared to those from MAGs using BLASTn. Only hits with 100% identity over 100% of the length, and at least one hit in each MAG that was consistent with prophage-BLAST prediction, were considered. For k-mer frequency linkages, host–virus connections were predicted using
WIsH (v1.0)[243]. To assign p-values to individual virus-host predictions, we built a null-model using all the sequences in the Refseq viral genomes database. Only WIsH prediction with p = 0 and the consistent predictions from both WIsH with p <0.05 and prophage-BLAST were considered. Figure 13 and Additional file 14 show the results from viral host prediction using different methods.
Annotating genes and making protein clusters
We annotated genes on all of the assembled contigs by first predicting the open reading frames (ORFs) using Prodigal (v2.6.3)[268] in the metagenomic mode (-p) and ignoring any masked non-protein-coding sequences (-m) produced by ‘barrnap.pl’ of SqueezeMeta. Next, the ORFs were annotated using a pipeline described previously[269]. Briefly, annotation were conducted by
34 running a combination of reciprocal best blast hit searches against the KEGG[270] and UniRef90[271] databases in tandem with HMM searches against Pfams[272]. About half of ORFs can be annotated using KEGG and KEGG annotations were then used for downstream analyses. MMseqs2 (version
4eb5e14267f64f2fb337995bd824ef279e04f266)[273] was used for clustering of all the protein sequence (cluster --min-seq-id 0.3 --cov-mode 1 -c 0.7 -e 0.00001) and the abundances of all the proteins within each cluster were summed to represent the total abundance of each unique protein clusters (PCs). In total, 335,087 protein clusters were identified for PcoA analyses.
Identification of candidate temperate phages
To infer phage lifestyle, prophage-bacteria junctions(attachment sites)[237] and lysogeny signatures were searched to identify candidate temperate phages. Candidate identifiable prophages were first identified with PHASTER[266] followed by manual inspection of the annotations for the presence of the attL and attR attachment sites (att). Then, lysogeny signatures[124, 238-240], including integrase(s)/site-specific recombinase, excisionase, phage repressor/antirepressor and ParA/B genes, were searched by annotating genes of the 2675 viral genomes (>5kb) against the KEGG[270] and UniRef90[271] databases in tandem with HMM searches against Pfams[272]. A prophage usually inserts into the host genome by integrases, which mediate site-specific recombination between the phage attachment site (attP) and the bacterial attachment site (attB)[274, 275]. This results in an inserted prophage flanked by two hybrid sites (attL and attR), also needed for the reverse reaction
(excision of the phage from the chromosome) along with excisionases[274, 275]. Thus, a viral contig can be considered as a typical prophage region if it has an integrase HMM match and attachments sites (attL and attR). Figure 12a is a clear example for a “confident” temperate phage, with putative
"att" sites next to an integrase gene in the non-coding region. A viral contig only contains lysogeny
35 markers is considered as “candidate” temperate phages. Figure 12b and Additional file 11 summarize the results from Phaster and lysogeny markers.
Statistical analyses
All the statistical analyses were conducted using RStudio. The Wilcoxon signed rank test was conducted using function ‘wilcox.test’ in R. The ‘ggplot2’ R package was used for boxplots and barplots. The R package ‘VennDiagram’ was used for Venn diagram plotting, and ‘pheatmap’ was used for heatmaps.
List of abbreviations
SCI: spinal cord injury
MAGs: metagenome-assembled genomes
CNS: central nervous system
PCoA: principal co-ordinate analysis
HMM: Hidden Markov Model
PTS: phosphotransferase system
GHs: glycosyl hydrolases
PCs: protein clusters
VCs: viral clusters
VHR: virus host ratios
Declarations
Ethics approval and consent to participate
The Institutional Animal Care and Use Committee of the Office of Responsible Research
Practices at The Ohio State University approved all animal protocols. All experiments were
36 performed in accordance with the guidelines and regulations of The Ohio State University and outlined in the Guide for the Care and Use of Laboratory Animals from the National Institutes of
Health.
Consent for publication
Not applicable.
Availability of data and material
Scripts used in this manuscript are available on the Sullivan laboratory bitbucket under
SCI. Raw reads and processed data are available through iVirus, including all assembled contigs, microbial MAGs, and viral populations.
Competing interests
The authors declare that they have no competing interests.
Funding
This work was funded by a National Institutes of Neurological Disorders and Stroke R35 award (1R35NS111582) to PGP, The Belford Center for Spinal Cord Injury (PGP), The Ray W
Poppleton Research Designated endowment (PGP), a National Institutes of Health Medical
Scientist Training Program T32 grant, and a Gordon and Betty Moore Foundation Investigator
Award (#3790) to MBS.
Acknowledgments
We appreciate the help and support from all members of the Popovich and Sullivan laboratories, especially, Zhen Guan, Jodie Hall, Dylan Cronin, Ben Bolduc, Dean Vik, Kia Adams,
Garrett Smith and Christine Sun who participated in animal surgery/care, sample collection, data analysis and story development. Computational support was provided by an award from the Ohio
Supercomputer Center[254] to MBS.
37 Major figures:
Figure 1. Intestinal microbial community composition was disturbed after spinal cord injury.
38 A Fifteen mice were equally divided among three treatment groups: a sham surgery control group
(Lam), an SCI group modeling injury at vertebral level T10, and an SCI group modeling injury at vertebral level T4. At 21 days post-injury, one fecal sample per mouse was collected for bulk microbiome isolation. B Principal coordinate analysis (PCoA) of Bray-Curtis distances shows that microbial communities are different between Lam, T4 and T10 (permanova, p=0.001). Each data point indicates an individual mouse sample. C Boxplot analysis showing the between group Bray-
Curtis dissimilarities between the controls (Lam) and T4 or T10 SCIs microbial communities. A higher score suggests higher dissimilarity of different individuals in the same group. D Intestinal microbial community composition at the phylum level. On the x-axis, the numbers 1–15 represent individual mice within each group. E, F Boxplots showing the relative abundance of the phyla
Firmicutes (left) and Actinobacteria (right). All boxplots shown display the median and quartiles, with each dot in the boxplot representing an individual mouse sample, and each group (Lam, T4,
T10) contains five samples. Read-based estimates of relative abundances of microbial taxa (see
Methods) were used for all the analyses displayed. ***p<0.001, **p<0.01, * p<0.05 by Wilcoxon rank sum test.
39
Figure 2. Genus-level bacterial abundances are altered after SCI.
Boxplot analysis of select bacterial genera (with at least 0.5% relative abundance in any experimental group) indicating that Lactobacillus, CAG-1031, and Turicibacter (A-C) decreased after SCI, while Bacteroides and Weissella (D, E) increased after SCI in one or both injury levels, compared to Lam controls. F Scatterplot of the relative abundances of genera in the class Clostridia that significantly changed after SCI. All boxplots shown display the median and quartiles, with each dot in the boxplot representing an individual mouse sample. Five individual mouse samples were used in each group. Read-based estimates of relative abundances of bacterial taxa (see
40 Methods) were used for all the analyses displayed. **p<0.01, * p<0.05 by Wilcoxon rank sum test.
Figures F was drawn using Prism 8.
41
Figure 3. Species-level bacterial abundances are altered after SCI.
Boxplot analysis of select bacterial species shows that Lactobacillus johnsonii and two CAG-1031
MAGs decreased after SCI (A-C), while Weissella cibaria, Lactococcus lactis_A, and Bacteroides thetaiotaomicron MAGs increased after SCI (D-F). All boxplots shown display the median and quartiles, with each dot in the boxplot representing an individual mouse sample. Five individual mouse samples are used in each group. All relative abundances shown here are represented by reads per kilobase per million mapped reads (RPKM, see Methods) of differentially abundant species-level MAGs. **p<0.01, * p<0.05 by Wilcoxon rank sum test.
42
Figure 4. Predicted metabolic pathways are different between healthy and spinal cord injury animals.
43 A Principal coordinate analysis (PCoA) using Bray-Curtis distances shows that predicted protein clusters are different between the control group Lam and disease groups T4 and T10 (permanova, p = 0.006). Each data point indicates an individual mouse sample. B Selected functions which are differentially abundant in different groups (p<0.05 by Wilcoxon rank sum test) and their predicted pathways. C MAG-resolved functional analysis for differentially abundant MAGs (p<0.05 by
Wilcoxon ran sum test). The x-axis represents the names of MAGs which are enriched in Lam or in T10/T4, and y-axis represents the microbial functions in (B) and their predicted pathways. Only the MAGs with at least 80% completeness and less than 10% contamination were displayed here.
Colored boxes indicate that a given differentially abundant gene is present in a given bacterial
MAG.
44
Figure 5. Phage communities are altered after SCI.
A Principal coordinate analysis (PCoA) of Bray-Curtis distances showing that viral community are different between Lam, T10 and T4 (ANOSIM, p=0.003). Each data point indicates an individual mouse sample (n = 15). B Boxplot analysis showing the within groups Bray-Curtis dissimilarities in Lam, T4, and T10 viral communities. A higher score suggests higher dissimilarity of different samples in the same group. ***p<0.001, **p<0.01 by Wilcoxon rank sum test. C
Boxplot analysis showing Shannon’s H of the viral communities between the Lam, T4, and T10.
Shannon’s H is an index of diversity and a higher Shannon’s H suggests higher diversity of viral populations in the communities. All boxplots shown display the median and quartiles, with each dot in the boxplot representing an individual mouse sample, and each group (Lam, T4, T10)
45 contains five samples. * p<0.05 by TuckyHSD test. D Volcano plots of t-tests corrected by the
Benjamini and Hochberg method for changes to viral populations abundances after spinal cord injury (SCI). A false discovery rate (FDR) cut off of 0.05 was used. Data points highlighted in red indicate viral populations that were significantly enriched in T10 or T4 mice, while data points highlighted in blue indicate viral populations that were significantly enriched in Lam.
46
Figure 6. Viral host-prediction reveals that phage abundances vary with their hosts.
A Boxplot analyses of select groups of phages are shown. Phages that were predicted to infect
CAG-1031, Lactobacillus, and Turicibacter genera decreased after spinal cord injury. B Phages that were predicted to infect Weissella and Lactococcus genera increased after spinal cord injury.
All boxplots shown display the median and quartiles, with each dot in the boxplot representing an individual mouse sample. Five individual mouse samples were used in each group. C Selected genus-specific phages constituting lower taxonomic ranks of the class Clostridia. **p<0.01, * p<0.05 by Wilcoxon rank sum test.
47 Supplemental Figures
Figure 7. Flow diagrams showing the bioinformatic workflow.
A The assembly of contigs, B the analysis of microbial community and construction of MAGs, and C the identification of viral populations
48 Figure 8. Differential abundance analysis of bacteria across three treatment groups.
49 A Venn diagram of the number of shared and unshared bacterial clustered OTUs in different groups. B Differential abundances of bacterial genera (p<0.05 by Wilcoxon rank sum test) in either two groups are indicated in red. Each row representing a unique bacterial genus was Z-score normalized. Bacterial genera on the y-axis are clustered using Euclidean distances. C Barplot depicting eight rare (<0.5%) bacterial genera that are altered after SCI and differentially abundant between Lam controls and either T4 or T10(p<0.05 by Wilcoxon rank sum test).
50
51 Figure 9. Species-level differential abundance analysis of bacteria across three treatment groups.
Differential abundances of bacterial species (p<0.05 by Wilcoxon rank sum test) in either two groups are indicated in red. Each row representing a unique bacterial genus was Z-score normalized.
52 Figure 10. Predicted metabolic pathways are different between Lam controls and SCI groups.
53 Predicted KEGG functions are listed in different pathways. Only differential abundances of predicted KEGG functions (p<0.05 by Wilcoxon rank sum test) in either two groups are indicated in this figure.
54 Figure 11. Caudovirales phage abundances increased after spinal cord injury.
A vConTACT 2.0 was used to construct the genome-sharing network analysis of shared protein content among three datasets: our SCI dataset (n = 992), RefSeq prokaryotic viral genomes, v88 and the Human Gut Viral Database (GVD) from Gregory, et al. 2019. Nodes (circles) represent
55 genomes and contigs, and edges (lines) indicate shared protein content. A pie chart shows the vConTACT 2.0 summary of our 992 viral populations (>10 kb). B Mapped read abundances represented by RPKM for the taxonomically classified viral populations (>10kb), which are assigned to the Caudovirales phage order. All boxplots shown display the median and quartiles, with each dot in the boxplot representing an individual mouse sample. * p<0.05 by Wilcoxon rank sum test. The genome-sharing network was constructed by Cytoscape_v3.7.1.
56 Fig. 12
A
B
20 19.29
) %
( 15
s g
i 11.74
t
n o
c 10
l
a r
i 6.62
v
f
o
t 5
n 3.07 e
c 1.83 1.94
r e P 0 r s e e e o B e s s s s r g ra a a s a a g r n e /p h e g io r A p t te is p r te in n c re a a + i x ti p r tt e n e a a p r/ m o e s t s re p re
Figure 12. Prediction of temperate phages using different methods.
A One clear example of confident prophage (phage NODE_5094) annotated and drawn by Phaster. att: attachment sites, int: integrase, PLP: phage like protein, Hyp: hypothetic protein, Ter:
57 terminase, Por: portal protein, Coa: coat protein. B Percent of viral populations(>5kb) using
Phaster and different lysogeny marker genes.
58
Figure 13. Virus-host prediction.
A 35.78% of the viral populations (≥5kb) can be linked to a host in this study. B Number of viral populations whose hosts can be predicted using each tool. The x-axis provides the name of the bioinformatic tool used.
59
Figure 14. Heat map showing the differential abundance analysis of phages grouped by infected bacterial hosts.
Differential abundances of phages (p<0.05 by Wilcoxon signed-rank test in either two groups are indicated in red. Each row representing a unique bacterial genus was Z-score normalized.
60 Chapter 3: Conclusions
The gut microbiome has emerged as an essential component of human development, metabolism, and health. Until recently, the role of the gut microbiome in recovery from spinal cord injury was largely unexplored. Motivated by our published data showing that SCI causes lasting gut dysbiosis and that post-injury treatment with probiotics containing Lactobacillus improves gut immune function and recovery of motor function in SCI mice[89], we have taken a closer genome- and gene-resolved look at SCI-induced gut dysbiosis. For the first time, we applied metagenomics to characterize the gut microbiome in a model of SCI. Using this approach, we show that key probiotic bacterial populations and genes controlling their physiological functions are lost after
SCI, suggesting that repopulation of the gut microbiota with distinct bacterial taxa may help to improve outcomes after SCI. However, a complete understanding of how SCI affects gut ecology is not complete without also studying gut virome. New data in this report show that both temperate phages and lytic phages are actively involved gut dysbiosis caused by SCI. Fully understanding the microbial ecology of gut dysbiosis in SCI, including interactions between bacteria and their viruses, temporal virus-host dynamics, and functional losses, is crucial to understanding how the gut microbiome influences recovery in SCI, and where we can intervene to improve outcomes.
Different patterns of gut dysbiosis across different injury levels have been noted in our research, indicating the importance of taking clinical factors into account while designing the experiments to study the gut microbiome and virome in SCI models. It is widely accepted that adult females have a stronger immune response than males[276], and different-sex chromosome genes and sex hormones can contribute to the immune differences between sexes[276-279]. Several studies also indicate that females and males have different neurologic and functional recovery after
61 SCI depending on levels of SCI[280-282], which may be associated with the neuroprotective effects of sex hormones[281]. The sex-specific differences in immunity may contribute to the different gut microbiota composition between males and females[283]. The impact of gut microbiota on the gut- brain axis by sex differences has been reported in previous studies, indicating a link between gut microbes, gender, and immunity[284]. In addition, gut microbial communities may change over time due to disease and are linked with the severity and progression of diseases, such as IBD[285, 286] and pediatric ulcerative colitis[287]. Unfortunately, SCI pathophysiology is complex and time- dependent, which hampers the development of novel and effective therapies. Characterization of the impact of sex on temporal dynamics of the gut microbiome and virome response to different levels of SCI may lead to improved predicted outcomes and treatment of SCI.
Infectious complications, such as urinary tract infections (UTIs), pressure sore, and bloodstream infections, have been reported as important causes of morbidity and mortality in SCI patients[118, 288]. As a result, SCI patients frequently receive antimicrobial therapy, which can disrupt normal gut microbiota and increase the risk of infection with antibiotic-resistant and multidrug-resistant bacteria, such as Staphylococcus aureus and Enterococcus, which are reported as the common causative bacteria for SCI patients[118]. The increasing of antibiotic-resistance bacteria indicates that new antimicrobial treatments for infections after SCI should be developed in the future. Current research indicates that phage therapy, which relies on using lytic phages or purified phage proteins for antimicrobial therapy, has the potential to be used as an alternative to antibiotic treatments(as reviewed in[289]). Phage therapy has been suggested to be efficient for the treatment of systemic infections, localized infections, gastrointestinal infections, and lung infections in animals or/and humans( as reviewed in[290]). For example, lytic phages have been suggested to protect mice from gut-derived sepsis, burn wounds infection, and chronic lung
62 infections caused by Pseudomonas aeruginosa[291-294]. Moreover, bacteriophages are efficient in reducing intestinal colonization of adherent-invasive E. coli and the progression of colitis symptoms in mice, indicating that phage therapy has the potential for the treatment of gastrointestinal tract diseases[295]. In humans, phage therapy has been tested in many in vitro studies, which have suggested that phages could be effective in prevention or/and treatment of cholera[296, 297], respiratory tract infections[298], cystic fibrosis[299, 300], wound infections[301] and diabetic foot ulcers[302, 303]. Given that phage therapy doesn’t trigger gut dysbiosis by eliminating beneficial bacteria and cause the emergence of antibiotics resistant bacteria as compared with antibiotics, phage therapy has the potential to be a new therapeutic avenue for management of UTI and other infectious complications in SCI patients in the future.
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