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Pathobiology and dysbiosis of the respiratory and intestinal microbiota in 14 months old Golden Syrian hamsters infected with SARS-CoV-2 [1]

['Brittany Seibert', 'Department Of Population Health', 'College Of Veterinary Medicine', 'University Of Georgia', 'Athens', 'Georgia', 'United States Of America', 'C. Joaquín Cáceres', 'Silvia Carnaccini', 'Stivalis Cardenas-Garcia']

Date: 2022-12 

The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS2) affected the geriatric population. Among research models, Golden Syrian hamsters (GSH) are one of the most representative to study SARS2 pathogenesis and host responses. However, animal studies that recapitulate the effects of SARS2 in the human geriatric population are lacking. To address this gap, we inoculated 14 months old GSH with a prototypic ancestral strain of SARS2 and studied the effects on virus pathogenesis, virus shedding, and respiratory and gastrointestinal microbiome changes. SARS2 infection led to high vRNA loads in the nasal turbinates (NT), lungs, and trachea as well as higher pulmonary lesions scores later in infection. Dysbiosis throughout SARS2 disease progression was observed in the pulmonary microbial dynamics with the enrichment of opportunistic pathogens (Haemophilus, Fusobacterium, Streptococcus, Campylobacter, and Johnsonella) and microbes associated with inflammation (Prevotella). Changes in the gut microbial community also reflected an increase in multiple genera previously associated with intestinal inflammation and disease (Helicobacter, Mucispirillum, Streptococcus, unclassified Erysipelotrichaceae, and Spirochaetaceae). Influenza A virus (FLUAV) pre-exposure resulted in slightly more pronounced pathology in the NT and lungs early on (3 dpc), and more notable changes in lungs compared to the gut microbiome dynamics. Similarities among aged GSH and the microbiome in critically ill COVID-19 patients, particularly in the lower respiratory tract, suggest that GSHs are a representative model to investigate microbial changes during SARS2 infection. The relationship between the residential microbiome and other confounding factors, such as SARS2 infection, in a widely used animal model, contributes to a better understanding of the complexities associated with the host responses during viral infections.

The SARS-CoV-2 pandemic led to millions of human losses, notably affecting the geriatric population, who are at greater risk of developing acute respiratory distress infection leading to prolonged hospitalization and death. However, the mechanism of age-related pathogenicity is not fully understood. Here, we utilized an aged Syrian hamster model resembling ~60-year-old humans to analyze the pathobiology, host response, and effects of SARS2 on the respiratory and intestinal microbiome. We identified specific microbial markers observed in severe COVID-19 patients within the lungs of aged hamsters infected with SARS-CoV-2. Prior influenza A virus (H1N1) exposure amplified these changes. Similarities among aged GSH and critically ill COVID-19 patients suggest that GSHs are a valuable model for investigating microbial changes during SARS2 infection. The relationship between the age, residential microbiome and viral pathogens contributes to a better understanding of the complexities associated with the host responses during viral infection while limiting potential environmental factors that may contribute to inter-individual variation.

Funding: This study was supported by a subcontract from the Center for Research on Influenza Pathogenesis (CRIP) to DRP under contract HHSN272201400008C from the National Institute of Allergy and Infectious Diseases (NIAID) Centers for Influenza Research and Surveillance (CEIRS). This research was also supported by the University of Georgia College of Veterinary Medicine Office of Research and Faculty and Graduate Affairs (ORFGA) Competitive Research Grant for Graduate Students. DRP receives funds from the Georgia Research Alliance and the Caswell S. Eidson endowment fund, University of Georgia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We recently analyzed the lung and cecum microbiota of K18-hACE2 mice after a low and high dose SARS2 infection [ 32 ]. Within this animal model, we reported diversity and compositional changes in the respiratory and intestinal microbiota in a virus dose-dependent manner [ 32 ]. However, the artificial expression of hACE2 does not mirror the natural susceptibility observed in humans. Therefore, we utilized an aged Syrian hamster model to analyze the pathobiology, host response and effects of SARS2 and pre-exposure of FLUAV H1N1 on the respiratory and intestinal microbiome in older patients. SARS2 challenged GSHs with and without prior FLUAV exposure had similar vRNA loads for respiratory and non-respiratory tissues except that FLUAV pre-exposure led to decreased SARS2 vRNA in the heart and nasal turbinates (NT). While SARS2 challenge led to overall higher pulmonary lesion scores later (6 days post challenge (dpc)) in infection than in the FLUAV pre-exposure group, FLUAV pre-exposed GSHs displayed slightly more pronounced pathology in the NT and lungs at 3 dpc. SARS2 challenge also impacted the microbial dynamics by enriching common opportunistic pathogens such as Haemophilus, Fusobacterium, Streptococcus, Campylobacter, and Johnsonella. Conjointly, SARS2 challenge led to dysbiosis in the small intestine (SI), cecum, and feces associated with more pronounced lesions in the cecum, notably in GSHs with FLUAV exposure. Several bacterial taxa were enriched in the SARS2 challenged GSH including Helicobacter, Mucispirillum, Ileibacterium, Streptococcus, unclassified Erysipelotrichaceae, Eubacteriaceae, and Spirochaetaceae while Allobaculum and unclassified Lachnospiraceae were depleted. Multiple taxa also correlated with vRNA load and pathological scores in respiratory and non-respiratory tissues. Altogether, SARS2 challenge with and without FLUAV exposure presented distinct disease pathology while both altered the respiratory and intestinal microbiota, suggesting that older GSH may be paramount in further investigating the effect of age on the host response to SARS2 infection.

Respiratory viral infections can also affect and induce changes in the gut microbiota through the gut-lung axis [ 22 , 23 ]. Further, gastrointestinal symptoms along with viral RNA (vRNA) in the feces have been observed occasionally during SARS2 infection [ 24 – 26 ]. While the direct mechanism is unclear, intestinal dysbiosis in COVID-19 patients has been previously described in various human cohort studies [ 21 , 27 , 28 ]. COVID-19 patients with severe symptoms presented dysbiosis of the gut microbiota characterized by commensal bacteria depletion and pathogenic bacteria enrichment [ 21 , 28 – 30 ]. Overall, fecal microbiome studies reported an association of opportunistic pathogens with COVID-19 infection, including Streptococcus, Rothia, Veillonella, Erysipelatoclostridium, Actinomyces, Collinsella, and Morganella [ 21 , 29 ]. While not fully understood, the intestinal microbiota affects various physiological functions, including metabolism, digestion, organ function, response to invading pathogens, and immune homeostasis [ 31 ]. While human clinical samples are informative, there are also limitations, such as the environmental factors influenced by daily life (age, geographic location, diet, sex) or clinical management (antiviral or antibiotic treatment) that contribute to inter-individual variation. Human microbiome studies are limited since samples are mostly restricted to those from the nasopharynx or feces; lungs and intestinal tissues are more difficult to collect. However, it is well accepted that microbial communities vary in composition and abundance depending on different parts of the respiratory and intestinal tracts. Therefore, it is valuable to characterize the effect of SARS2 infection on the respiratory and intestinal microbiota in established animal models.

While relatively established during adulthood, the residential host microbiota undergoes significant shifts in composition and diversity during the aging process, which can directly or indirectly affect the regulation of the immune system [ 15 , 16 ]. The deep respiratory microbiome is characterized by a diverse bacterial community in low abundances [ 17 ]. In healthy individuals, the lung microbiota is generally dominated by commensal bacteria within the phyla Firmicutes such as Veillonella spp and Bacteroidetes such as Prevotella spp [ 17 ]. Influenza A virus (FLUAV) and SARS2 infection affect the local bacterial community through abundance and composition changes [ 18 – 21 ]. Previous studies in humans reported that the lung microbiota of SARS2 infected patients consisted of opportunistic pathogens or enrichment of commensal bacteria such as Pseudomonas, Enterobacteriaceae, and Acinetobacter [ 18 , 20 ].

The severe acute respiratory syndrome coronavirus 2 (SARS2) is responsible for the ongoing coronavirus disease 2019 (COVID-19) pandemic. Clinical manifestations of infection are highly variable, ranging from asymptomatic or mild nonspecific flu-like symptoms to severe symptoms such as pneumonia, acute respiratory distress syndrome, multiple organ failure, and death [ 1 , 2 ]. Several risk factors have been identified for severe COVID-19 disease, including sex, age, obesity, cardiovascular conditions such as hypertension, smoking, among others [ 1 , 2 ]. Older individuals are at greater risk of developing acute respiratory distress amid SARS2 infection [ 3 ], leading to prolonged hospitalization and death [ 2 ]. Animal models resembling human disease have been essential in understanding SARS2 pathogenesis. Golden Syrian hamsters (GSH) are naturally susceptible to SARS2 replication in the respiratory tract, inducing severe lung pathology [ 4 , 5 ]. Generally, GSH at 5–8 weeks of age have been used in experimental studies [ 6 – 11 ]; however, multiple groups reported that 8-10-month-old GSHs develop more severe disease by exhibiting increased weight loss and slower lung recovery post-infection, emulating more severe disease presentation in older individuals [ 12 – 14 ]. The vulnerability of enhanced illness in the elderly population has been hypothesized to encompass a combination of immunopathology from exacerbated immune response and immunosenescence [ 2 ]. However, the mechanism and characteristics of age-related changes to the host such as the relationship between the immune response and host residential microbes during respiratory infections warrants further investigation.

A heatmap illustrating the Spearman correlation among different infection factors and the relative abundance of bacterial taxa within the small intestine (duodenum and ileum), cecum and feces. Correlations among pathology of the heart and small intestine were removed since the pathological score was the same for all GSH. Taxa are color-coded by phylum. Correlation labels and taxa displayed in bold have a spearman correlation value > 0.60 or < -0.60. FLUAV–inoculated with FLUAV and then challenged with SARS-CoV-2 (mock-challenged GSH were not included), SARSCoV2 –challenged with SARS-CoV-2, NT- nasal turbinates.

We further examined the association of genera and unclassified families to vRNA loads and pathology scores. Correlations among the heart and SI pathology were removed since the pathological score was the same for all GSH. We initially observed strong correlations among several bacterial taxa and the sample site ( S9 Fig ). However, when sample sites were analyzed separately, multiple genera, especially in the cecum and feces, had strong correlations among different infection factors, particularly with FLUAV pre-exposure and more severe pathology scores in the cecum ( Fig 6 ). Taxa such as Coxiella in the SI, UCG-005, and Fibrobacter in the cecum and feces positively correlated with FLUAV exposure before SARS2 challenge, while Peptococcus in the cecum and Lachnoclostridium in the feces negatively correlated with FLUAV pre-exposure. Interestingly, Bacteroidales RF16 group, Oscillibacter, and multiple other genera within the SI, Helicobacter and Christensenellaceae R-7 group within the cecum, and NK4A214 group within the feces positively correlated with more severe pathology in the cecum ( Fig 6 and S1 Table ). Meanwhile, Bifidobacterium in the SI, Fournierella and unclassified Bifidobacteriaceae and Christensenellaceae in the cecum, and unclassified Bifidobacteriaceae and Allobaculum in the feces negatively correlated with more severe pathology in the cecum. Other taxa were also strongly associated with other infection factors such as vRNA loads and histopathology in the respiratory tissues, particularly bacteria identified in the cecum. Overall, several bacteria were enriched among the different groups and correlated with vRNA loads and pathology, suggesting that intestinal microbes may play a role in disease pathology such as severity of lesions within the cecum during respiratory viral infection.

Multiple taxa were identified as differentially enriched or reduced within the different groups in at least 2 out of 3 differential analyses when sample types were combined ( S3 Table and S8 Fig ). Genera including Prevotella, Fibrobacter, and unclassified Spirochaetaceae within the SI and UCG-005 and unclassified Spirochaetaceae within the cecum were differentially enriched in the FLUAV-SARS2 group versus mock challenge GSH ( S4 Table ). No genera were considered differentially enriched in the SARS2 group when compared to the mock within the SI and cecum. On the other hand, Allobaculum, unclassified Prevotellaceae and Mycoplasma within the SI and unclassified Prevotellaceae and Allobaculum within the cecum were differentially enriched in the mock challenged GSH compared to both SARS2 challenged groups. While comparing SARS2 GSH and FLUAV-SARS2 GSH, Fibrobacter, unclassified [Eubacterium] coprostanoligenes group, and unclassified Spirochaetaceae within the SI and Fibrobacter and UCG-005 within the cecum were differentially enriched in FLUAV-SARS2 GSH. Within the feces, Elusimicrobium and unclassified Spriochaetaceae were differentially enriched in the SARS2 group while Parabacteroides, Elusimicrobium, Christensenellaceae R-7 group, NK4A214 group, UCG-005, and unclassified Spirochaetaceae were enriched in the FLUAV pre-exposed GSHs when compared to the mock. Unclassified Prevotellaceae, Ligilactobacillus, and unclassified Lachnospiraceae were differentially enriched in the mock. Compared to their respective pre-challenge feces, Bifidobacterium, Allobaculum, Ileibacterium, Faecalibaculum, and unclassified Eubacteriaceae were differentially enriched after SARS2 challenge, while an increase of Prevotella abundance was observed in the pre-challenge samples ( S5 Table ). No significant taxa were observed when comparing the FLUAV-SARS2 feces and their respective pre-challenge samples in at least 2/3 differential analysis ( S5 Table ). When comparing the two SARS2 challenge groups, Anaerostipes was enriched in the SARS2 GSHs while Fibrobacter and UCG-005 were enriched in FLUAV-SARS2 GSHs. When analyzing the effect of FLUAV exposure prior to SARS2 challenge, Anaerostipes was enriched within the mock pre-exposed GSH ( S5 Table ). Overall, SARS2 respiratory infection in aged GSH with and without prior FLUAV exposure led to enhanced gut dysbiosis.

At the phylum level, the intestinal and fecal microbiota were dominated by Bacteroidota ( : 44–74%) and Firmicutes ( : 20–40%) in all groups and sample sites ( S7C Fig ). However, relative abundance differences were observed at lower taxonomic levels among the different sample sites. The duodenum and ileum shared similar taxonomic profiles that were distinct from the cecum and fecal samples; however, unclassified Muribaculaceae ( : 56.69%) was the most prominent family among sample sites and groups when both dpc were combined (Figs 5D and S7D ). Markedly, decreased abundance of Allobaculum within both SARS2 groups was observed in the SI ( : SARS2: 6.72%, FLUAV-SARS2: 3.19%, mock: 12.88%), cecum ( : SARS2: 0.70%, FLUAV-SARS2: 0.61%, mock: 2.8%), and feces ( : SARS2: 4.50%, FLUAV-SARS2: 4.23%, mock: 7.36%). Within the SI, Streptococcus ( : SARS2: 2.42%, FLUAV-SARS2: 1.63%, mock: 0.0040%) and unclassified Erysipelotrichaceae, notably at 3 dpc, ( : SARS2 3 dpc: 9.704%, 6 dpc: 6.58%; FLUAV-SARS2 3 dpc: 8.04%, 6 dpc: 4.88%; mock 3 dpc: 3.20%, 6 dpc: 2.0%) were enriched while Desulfovibrio ( : SARS2: 1.39%, FLUAV-SARS2: 1.08%, mock: 3.30%) was reduced within both groups of SARS2 challenged GSH. Taxa enriched within the cecum in SARS2 challenged GSH included Prevotellaceae UCG-003 (x: SARS2: 3.03%, FLUAV-SARS2: 2.67%, mock: 0.11%), Helicobacter ( : SARS2: 3.47%, FLUAV-SARS2: 4.03%, mock: 1.81%), Mucispirillum ( : SARS2: 2.46%, FLUAV-SARS2: 2.18%, mock: 0.55%) and unclassified Spirochaetaceae ( : SARS2: 6.42%, FLUAV-SARS2: 4.56%, mock: 0.02%) while Treponema ( : SARS2: 0.068%, FLUAV-SARS2: 0.065%, mock: 3.76%) was decreased in both challenged groups compared to the mock. Within the feces, Ileibacterium ( : SARS2: 7.55%, FLUAV-SARS2: 6.47%, mock: 3.02%, Pre-challenge: 2.46%), unclassified Erysipelotrichaceae ( : SARS2: 3.14%, FLUAV-SARS2: 1.92%, mock: 0.86%, Pre-challenge: 0.88%), and unclassified Eubacteriaceae ( : SARS2: 7.13%, FLUAV-SARS2: 4.64%, mock: 0.94%, Pre-challenge: 1.66%) were enriched while unclassified Lachnospiraceae ( : SARS2: 1.04%, FLUAV-SARS2: 0.75%, Pock: 3.53%, Pre-challenge: 1.89%) was reduced in both challenged groups compared to the mock.

(A) Rarefaction curve of the intestine and fecal samples colored by sample type. The dashed line (x = 27,020) represents the number of reads each sample was rarified for downstream diversity analyses. (B) The observed number of ASVs between FLUAV-SARS2 (red), SARS2 (blue), mock (white), and pre-challenge (grey) containing all dpc of different sample types from the rarified ASV count table. Statistical analysis comparing the different groups within each sample type was performed using Kruskal-Wallis. (C) Comparison of unweighted Jaccard dissimilarity distance within each group and across multiple comparisons in feces. Intra-group variation is marked with grey shading, while the inter-group comparisons are colored gold. Pair-wise comparisons were performed using the Wilcox test with Bonferroni correction. Comparisons that are not indicated by an asterisk were considered non-significant (p > 0.05). (D) Heatmap of the relative abundances of taxa with a relative abundance > 1%. Relative abundances greater than 10% are shown in green. Taxa are color-coded by phylum. Taxa in bold are those discussed within the results. * <0.05, ** < 0.005, *** < 0.0005, **** < 0.00005.

The V4 region of 16S rRNA genes amplified from the SI (duodenum and ileum), large intestine (cecum), and feces collected at 3 and 6 dpc was sequenced and analyzed. Fecal samples from 1 day before challenge (-1 dpc) were analyzed as a separate pre-challenge group ( Fig 1A ). Rarefaction curves of alpha diversity (observed number of ASV) and Shannon diversity showed adequate coverage and no significant differences among the groups when both dpc were combined; however, a difference in observed species among the sample sites was apparent (Figs 5A, 5B and S5A ). The cecum had the highest number of ASVs, followed by feces, ileum, and duodenum ( Fig 5B ). When analyzing the effect of FLUAV exposure on pre-challenge fecal samples, no significant alpha diversity differences were observed ( S5B Fig ). For downstream analysis, the duodenum and ileum samples were combined to encompass the SI. Beta diversity using NMDS ordination plots showed limited clustering of groups in unweighted (Jaccard) and weighted (Bray-Curtis) dissimilarity metrics ( S6 Fig ); however, PERMANOVA analysis showed significant differences by group in the SI (R2 = 0.12, p<0.001) and feces (R2 = 0.14, p<0.001) but not within the cecum. There was also considerable overlap among the pre-exposed FLUAV GSH and mock GSH when analyzing the pre-challenge fecal samples (S6G-H). When analyzing the inter-variation among the groups, the dissimilarity distance was similar within the SI and the cecum ( S7A and S7B Fig ). The SARS2 GSHs had lower intra-group variation than the mock challenged GSHs in the feces but not considered statistically significant ( Fig 5C ). Also in the feces, the dissimilarity distance comparing both SARS2 challenged groups to the mock was larger than the distance between the comparison of SARS2 versus FLUAV-SARS2 (p < 0.001; Wilcox) ( Fig 5C ). The distance between SARS2 challenged GSH and pre-challenge was not significantly different from the distance between SARS2 and FLUAV-SARS2 GSH (p = 1; Wilcox) but was significantly different from the comparisons with the mock (SARS2: p = 0.0033 and FLUAV-SARS2: p = 0.0024; Wilcox). These findings suggest that the fecal microbiome composition of aged GSH challenged with SARS2 differs from the mock challenged GSH; however, the difference detected among the groups could be partially explained by intra-individual variations observed in the pre-challenge fecal samples.

Strong correlations were also observed between several bacterial taxa and histopathological scores ( Fig 4C and Tables 1 and S1 ). Prevotella 7 and Haemophilus positively correlated with more severe pathology scores in the NT and trachea. Actinomyces, Bifidobacterium, Streptococcus, and UCG-005 positively correlated with more severe pathology scores in the NT, and Actinomyces, Staphylococcus, Peptoniphilus, and Moraxella in the trachea. Within the lungs, Fusobacterium positively correlated with more severe pathology while Escherichia-Shigella negatively correlated with increased severity in the trachea and the lungs. In non-respiratory tissues, unclassified Actinomycetaceae positively correlated with more severe pathology in the brain, while no genera were found to correlate with severity of the cecum. Correlations among the heart and SI pathology were not determined since the pathological score was the same for all GSH groups ( S1 Table ).

We examined the association of multiple genera and unclassified families to SARS2 vRNA loads and pathology scores using Spearman correlation. Prevotella 7, Fusobacterium, and Haemophilus positively correlated with SARS2 challenge while Prevotellaceae UCG-003 and Anaerococcus positively correlated with FLUAV pre-exposure ( Fig 4C ). Escherichia-Shigella negatively correlated with SARS2 challenge. We also observed a strong correlation between several bacterial taxa and SARS2 vRNA load in tissues, particularly within the respiratory tract ( Fig 4C ). For instance, Rikenellaceae RC9 gut group, Johnsonella, Lachnoclostridium, and Dialister positively correlated with increased vRNA in the NT while Olsonella, Solobacterium, and Fenollaria positively correlated with increased vRNA in the lungs. Within the trachea and the lungs, Escherichia-Shigella negatively correlated with vRNA loads. In non-respiratory tissues, Moraxella positively correlated with increased vRNA in the heart, and Rikenellaceae RC9 gut group and Actinobacillus positively correlated with increased vRNA in the cecum. On the other hand, Anaerococcus negatively correlated with vRNA in the ileum.

(A-B) Cladogram of significantly (p<0.05) abundant taxa between (A) SARS2 (red) and mock (grey) or (B) FLUAV-SARS2 (blue) and mock (grey) using LEfSe analysis with default parameters. Taxa displayed in bold were significant in at least 2/3 differential analyses performed (Deseq2, ALDEx2, and LefSE). (C) A heatmap illustrating the spearman correlation among different infection factors and the relative abundance of bacterial taxa within the lungs. Correlations among pathology of the heart and SI were removed since the pathological score was the same for all GSH. Correlation labels and taxa displayed in bold have a spearman correlation value > 0.60 or < -0.60. SARS2 –challenged with SARS2, FLUAV–inoculated with FLUAV and then challenged with SARS2 (mock-challenged GSH were not included), NT- nasal turbinates, LI–large intestine.

We performed differential abundance testing at the genus level using three programs, DeSeq2, linear discriminant analysis effect size (LefSE), and ALDEx2. At the genus level, Campylobacter, Fusobacterium, Haemophilus, and Moraxella were identified as significantly enriched within the SARS2 group compared to the mock controls by 2 of 3 (Deseq2 and LefSE) differential analyses ( Fig 4A and S2 Table ). Prevotella 7, Johnsonella, Streptococcus, Fusobacterium, and Haemophilus were identified as significantly enriched within the FLUAV-SARS2 GSHs in comparison with mock-challenge animals ( Fig 4B and S2 Table ). Other taxa were considered significant in each group but were only identified in 1 of 3 differential analyses ( S4 Fig ). In contrast, no genera were considered significantly different in at least 2/3 differential analysis when comparing SARS2 and FLUAV-SARS2 GSHs ( S2 Table and S4E–S4G Fig ). Collectively, SARS2 challenge with and without prior FLUAV exposure impacts the microbiota by enriching opportunistic pathogens; however, previous FLUAV exposure partially modulates this phenomenon with enrichment of Haemophilus later (6 dpc) in SARS2 infection.

At the phylum level, the lung microbiota was dominated by Bacteroidota (Bacteroidetes) (mean ( ): 38.68%) and Firmicutes ( : 32.50%) in all three groups when both dpc were combined ( Fig 3D ). Prominently, the abundance of Fusobacteriota ( : SARS2: 6.57%, FLUAV-SARS2: 6.98%, mock: 0.60%) and Proteobacteria ( : SARS2: 25.79%, FLUAV-SARS2: 22.54%, mock: 2.6%) increased in the SARS2 challenged GSHs plausibly at the expense of Firmicutes ( : SARS2: 23.76%, FLUAV-SARS2: 28.87%, mock: 58.44%). A difference in relative abundance was also observed at lower taxonomic levels. Strikingly, a large increase in the relative abundance of Haemophilus was observed at 3 dpc ( : SARS2: 40.96%, FLUAV-SARS2: 24.74%, mock: 0%) and 6 dpc ( : SARS2: 15.49%, FLUAV-SARS2: 15.23%, mock: 0%) in both SARS2 challenged groups, consistent with overall increase of Proteobacteria ( Fig 3E ). While the cumulative Haemophilus relative abundance was similar at 6 dpc among the challenged groups, a higher prevalence of the genera was observed in FLUAV-SARS2 GSH (4/4) compared to SARS2 GSH (1/3). Increased relative abundances of Prevotella 7 ( : SARS2: 5.93%, FLUAV-SARS2: 4.14%, mock: 0%), Streptococcus ( : SARS2: 5.85%, FLUAV-SARS2: 2.24%, mock: 0%), Johnsonella ( : SARS2: 1.45%, FLUAV-SARS2: 2.01%, mock: 0%), and Fusobacterium ( : SARS2: 8.54%, FLUAV-SARS2: 9.31%, mock: 0%) were also observed in both SARS2 challenged groups compared to the mock ( Fig 3E ).

(A) Alpha diversity measure of the observed number of ASVs (left) and Shannon diversity (right) between SARS2 (red), FLUAV-SARS2 (blue), and mock (white) containing all dpc from the rarified ASV count table. Multiple group comparisons were performed using Kruskal-Wallis, while pair-wise comparisons were conducted using the Wilcox test with Bonferroni correction. (B) NMDS plot of unweighted Jaccard dissimilarity distance. The color designates groups (SARS2: red, FLUAV-SARS2: blue, and mock: black), and dpc is categorized by the shape (3 dpc: circle and 6 dpc: square). Ellipses were constructed using the standard deviation. (C) Comparison of unweighted Jaccard dissimilarity distance within each group and across multiple comparisons. Intra-group variation is marked with grey shading, while the inter-group comparisons are marked gold. Pair-wise comparisons were performed using the Wilcox test with Bonferroni correction. (D) Relative abundances agglomerated at the phylum level separated by group and dpc. Phyla in bold are those discussed within the results. (E) Relative abundances of each hamster at the lowest taxonomic rank identified separated by group and ordered by dpc. Taxa in bold are those discussed within the results. ns: non-significant (p > 0.05); * <0.05, ** < 0.005, *** < 0.0005, **** < 0.00005.

To characterize the lung microbiome, we sequenced the V4 region of 16S rRNA genes amplified from lung homogenates collected at 3 and 6 dpc. Alpha diversity (observed number of amplicon sequence variants (ASV’s)) and Shannon diversity showed a trend towards an increased number of ASVs and diversity following SARS2 challenge, particularly in the FLUAV-SARS2 group; however, no significant differences were observed among the SARS2 challenged groups and mock when dpc were combined ( Fig 3A ). To determine changes in the lung microbial composition after SARS2 challenge, we analyzed beta diversity using unweighted Jaccard and weighted Bray-Curtis’s dissimilarity metrics through non-metric multidimensional scaling (NMDS) ordination. Both ordinations showed clustering by group (R2 = 0.22, p = 0.0030; PERMANOVA) and dpc (R2 = 0.096, p = 0.025; PERMANOVA), though the interaction between the terms was not significant (Figs 3B and S3A ). The dissimilarity distance between the two SARS2 challenged groups and the mock was larger than the distance between the two challenged groups (FLUAV-SARS2: p < 0.0001 and SARS2: p = 0.00027; Wilcox) ( Fig 3C ). When analyzing the dispersion within the treatment groups using the unweighted and weighted distances, both groups challenged with SARS2 had lower intra-group variation than the mock (FLUAV-SARS2: p = 0.015 and SARS2: p = 0.037; Wilcox); however, the difference in sample size could explain the results (Figs 3C and S3B ). Overall, SARS2 challenge impacts the diversity within the lung microbial dynamics through changes in the relative abundance of different microbes, while prior FLUAV exposure amplified these changes.

Findings in the brain were inconsistent. Only 2 GSHs presented evidence of mild suppurative meningitis, one in SARS2 group and one in the FLUAV-SARS2 group at different time points ( S1 Table ). No significant findings were observed in the heart sections in any group and timepoints. These results suggest that in the absence of FLUAV-pre-exposure, increased pulmonary lesions were seen in the later part of infection after SARS2 challenge. In contrast, FLUAV pre-exposure led to exacerbated pathological scores in the NT and lungs early after SARS2 challenge. FLUAV pre-exposure also led to more pronounced lesions in the cecum throughout infection after SARS2 challenge.

Typhlocolitis was more pronounced within the large intestine in the FLUAV-SARS2 GSH than in SARS2 GSH at both timepoints. Enterocytes were degenerating, sloughing into the lumen or within the glands, and were colonized by moderate numbers of mixed coccobacilli and filamentous bacteria. Especially in the challenged groups, there was a visible increase in Entamoeba muris and bacteria numbers ( S2G , S2I and S2L Fig –inserts).

In the sections of small intestines, the inflammation was generally minimal and present in all groups, including the mock challenged, at both timepoints. Only in the SARS2 challenged groups, small numbers of eosinophils were migrating to the enterocyte lining ( S2A–S2E Fig ) and associated with mild non-specific lesions to the villi. Variable numbers of intraluminal protozoa and mixed bacteria appeared subjectively increased in the challenged GSHs. Protozoal morphologic features were mostly compatible with Giardia muris ( S2E Fig –insert).

At 3 dpc, pulmonary lesions were overall milder in SARS2 GSH than in FLUAV-SARS2 GSH ( Fig 2M and 2N and Table 1 ). In both SARS2 challenged groups, lesions were centered on the bronchi and bronchioles. They predominantly consisted of mild-moderate ( Fig 2M ) and moderate-severe ( Fig 2N ) multifocal, acute suppurative bronchitis/bronchiolitis with intraluminal exudate deposition. Syncytia were rarely observed in the bronchial respiratory epithelium at 3 dpc. At this time, peribronchial inflammation was mild, whereas perivascular cuffing and pulmonary vascular endothelialitis were accompanied by rare intravascular thrombi. Further, moderate amounts of virus antigen were present in bronchiolar epithelium and intraluminal necrotic cellular debris ( S1G and S1H Fig ). At 6 dpc more severe pulmonary lesion scores were observed in the SARS2 group than in FLUAV-SARS2 group. These consisted of moderate to severe, multifocal to diffuse, subacute interstitial pneumonia with marked alveolar proliferation of cuboidal epithelial cells (pneumocytes type II), replacing the necrotic pneumocytes type I ( Fig 2P and 2Q ). Pneumocytes type II often presented karyomegaly and multinucleation (syncytia). Alveoli were often collapsed or filled with hemorrhagic and suppurative exudate. Moderate multifocal, acute fibrinosuppurative pleuritis was observed mostly in the FLUAV-SARS2 group at 6 dpc. However, intracytoplasmic virus antigen staining was scarce at 6 dpc compared to 3 dpc ( S1J and S1K Fig ).

(A-F) Nasal turbinates. (G-L) Tracheas. (M-R) Lungs. (A-B) Moderate to severe necrotizing rhinitis in the infected hamsters at 3 dpc. The surface epithelium is necrotic, infiltrated by neutrophils and sloughing into the lumen (arrowhead). The lumen is filled with abundant intraluminal suppurative exudate (asterisk). (D-E) Rhinitis is still present in the infected hamsters at 6 dpc. The epithelium is irregular, with fewer necrotic cells sloughing into the lumen (arrowheads). The lumen is filled with abundant intraluminal suppurative exudate (asterisk). (C, F) No significant findings are observed in mock hamsters’ nasal turbinates. (G-H) Clusters of neutrophils and lymphocytes are expanding the mucosal lamina propria (arrowheads) of infected hamsters’ tracheas at 3 dpc. Occasionally, tracheal glands are dilated by intraluminal eosinophilic material (asterisk). (J-K) Similar small mix of neutrophils, macrophages, and lymphocytes (arrowheads) are observed in the tracheas of infected hamsters at 6 dpc. (I, L) No significant findings are observed in mock hamsters’ tracheas. (M-N) At 3 dpc, the bronchiolar mucosa in infected hamsters is necrotic and sloughing into the lumen (asterisk). Small to moderate clusters of neutrophils and lymphocytes are infiltrating the peribronchiolar interstitium and bronchiolar mucosa (arrowheads). (P-Q) At 6-dpc, pneumonia is characterized by florid pneumocyte type II hyperplasia following the outline of the alveoli (arrows within insert: bottom right corner; HE, 40X), and syncytia. Moderate to large numbers of neutrophils, macrophages and lymphocytes are expanding the septa and pouring into the alveoli (arrowheads). (O, R) No significant findings are observed in the mock hamsters’ lungs. All H&E images are pictured at 20X magnification.

Histopathological analysis of GSH groups revealed that the organs most affected upon infection were the NT, lungs, tracheas, and large intestine. Overall, lesions appeared slightly more pronounced in the FLUAV-SARS2 GSH, with a few exceptions ( Table 1 ). In both SARS2 challenged groups, lesions in the NT were more prominent at 3 dpc ( Fig 2A and 2B ) than at 6 dpc ( Fig 2D and 2E ). These ranged from mild-moderate to moderate-severe, multifocal, acute-subacute suppurative rhinitis with rare syncytia formation and abundant intraluminal catarrhal to fibrinosuppurative exudate deposition. Within both groups, variable amounts of intracytoplasmic virus antigen immunolabeling was present in epithelial cells ( S1A–S1F Fig ).

In addition to weight loss, high SARS2 vRNA loads were detected from the NT, trachea, and lungs in both the SARS2 and FLUAV-SARS2 groups with no statistically significant differences at 3 and 6 dpc ( Fig 1C and 1D ). The exception was on vRNA load data from NT at 6 dpc, where statistically significant higher viral loads were detected in the SARS2 group (p = 0.0038; Two-way ANOVA). Further, 2 out of 3 trachea samples and 1 out of 3 lung samples had vRNA loads below limit of detection in the FLUAV-SARS group at 6 dpc. Notably at 3 dpc, vRNA loads were detected in the heart from the SARS2 group, but not from the FLUAV-SARS2 group (p = 0.0096; Two-Way ANOVA). Low to undetectable vRNA loads were observed in the brain and intestine (duodenum, ileum, and cecum) at both 3 and 6 dpc in both the SARS2 and FLUAV-SARS2 challenged groups. Collectively, SARS2 challenge resulted in elevated vRNA loads in respiratory tissues and heart while FLUAV pre-exposure led to reduced SARS2 vRNA within the NT (at 6 dpc) and the heart (at 3 dpc). These observations suggest that prior FLUAV exposure led to a potential antiviral state that limited and/or delayed SARS2 replication in aged GSHs.

(A) 14-month female GSHs were randomly distributed into 3 groups: SARS2 GSHs were challenged with SARS2 (n = 6), FLUAV-SARS2 GSHs were pre-exposed to FLUAV twice and then challenged with SARS2 (n = 7), and mock GSH remained unchallenged (n = 3). A subset of GSH (n = 3 group/time point) were humanely euthanized at 3 and 6 dpc, and numerous tissues were collected. Feces was collected 1 day before challenge, and then 3 and 6 dpc for the remaining individuals. Illustration created with BioRender.com . (B) Weight change was calculated for all groups up to 6 dpc, including SARS2 (red triangles), FLUAV-SARS2 (blue triangles), and mock (white circles). Viral loads at (C) 3 dpc and (D) 6 dpc from the challenged groups were determined by RT-qPCR and conveyed as log10 TCID50 equivalent (TCID50eq) per gram of tissue. Error bars represent the mean ± SEM. Statistical analysis was performed by two-way ANOVA with a Dunnett’s multiple comparisons test, * <0.05. NT- nasal turbinates, LI–large intestine.

GSHs were divided into 3 groups: SARS2 GSHs were challenged intranasally with 1 x 10 5 TCID50/hamster of SARS2 while FLUAV-SARS2 GSHs were pre-exposed to FLUAV (primed twice, 14 days apart at 1 x 10 5 TCID50/hamster) 13 days before SARS2 challenge (as above). FLUAV exposure was used to determine if such treatment would affect SARS2 infection, disease progression, and microbiome composition outcomes. The mock GSH group was challenged with 1XPBS. Clinical signs, including weight loss, SARS2 shedding and histopathological changes of different tissues were analyzed at 3 and 6 dpc ( Fig 1A ). Assessment of humoral responses against FLUAV obtained on 1 day prior to SARS2 challenge (27 days post 1 st FLUAV dose) revealed antibody responses, hemagglutination inhibition (HAI) and enzyme-linked immunosorbent assay (ELISA) below the limit of detection suggesting that inoculation of FLUAV in GSH led to restricted and/or abortive FLUAV replication. Due to the limited number of ~14-month-old hamsters, active FLUAV replication was not evaluated within this study. The effects, if any, of prior-FLUAV exposure in GSH challenged with SARS2 would likely not be related to stimulation of antibody responses. After SARS2 challenge, the GSHs experienced clinical signs consistent with virus infection regardless of prior-FLUAV exposure starting at 2 dpc (SARS2: range 1.15–6.5% and FLUAV-SARS2: range 1.19–7.4%) and continued to deteriorate until 6 dpc when the study was terminated (SARS2: range 7.7–10.2% and FLUAV-SARS2: range 4.3–11.7%) ( Fig 1B ). Overall, a significant weight loss was observed upon SARS2 challenge regardless of previous FLUAV exposure.

Discussion

The present study uses an aged GSH model (14-months-old) to understand the pathobiology and respiratory/intestinal microbiota changes after SARS2 infection with and without previous FLUAV exposure. To our knowledge, there have been no previous studies that investigated the effect of SARS2 infection in 14-month aged GSH and how prior FLUAV exposure may affect the pathogenesis within this population subset. Investigating the interplay between FLUAV pre-exposure and SARS2 in the elderly population is imperative as FLUAV H1N1 was the most common detected co-infecting virus with SARS2, and patients with viral co-infection had more severe symptoms and required ICU admission in a recent study [33].

Aging has been shown to affect the immune response leading to increased susceptibility to infections [34]. Understanding the effect of FLUAV pre-exposure to COVID-19 in the aging population is beneficial as seasonal influenza viruses are one of the most prevalent causes of morbidity and mortality in the elderly [35]. In this old-age GSH model utilized within this study, SARS2 infection led to weight loss starting at 2 dpc and high vRNA loads in the NT, trachea, lungs, and heart at 3 and 6 dpc. This is consistent with previous studies using aged GSH at 7–9 months showing peak weight loss at 6 to 7 dpc [12–14]. SARS2 and FLUAV-SARS2 GSHs had similar vRNA loads for respiratory and non-respiratory tissues except for significantly increased vRNA loads in the heart at 3 dpc and the NTs at 6 dpc in GSHs exposed to SARS2 only. Comparably, similar SARS2 vRNA loads within respiratory tissues between 6–8-weeks old GSHs infected with SARS2 and co-infected with FLUAV (PR8) H1N1 were observed at 4 dpc [8]. While we observed a reduction of vRNA in the NT at 6 dpc, a recent report showed that prior FLUAV infection 14 days before SARS2 infection resulted in reduced SARS2 replication in the lung of male 6–8 weeks old GSH [36]. High SARS2 viral loads in respiratory samples, including NT, trachea, and lungs, at early time points, were also observed in 8–10-month-old GSH, consistent with our findings [12,13]. Here, pulmonary lesions were overall milder in SARS2 GSH than FLUAV-SARS2 GSH at 3 dpc, with moderate numbers of inflammatory cells, like what was previously observed in GSH co-infected with FLUAV [8]. However, more severe lesions were observed in SARS2 GSH compared to FLUAV-SARS2 GSH at 6 dpc. The dichotomy of these observations may be explained by faster disease progression and recovery in the FLUAV-SARS2 group compared to the SARS2 group or simply sample size effect.

Dysbiosis of the lung, intestine, and fecal microbiota composition was observed after SARS2 challenge with and without FLUAV pre-exposure. Numerous studies have reported that viral infections can affect the residential microbial composition and abundance within the respiratory tract (reviewed in [17]). In this study, beta diversity showed distinct clustering among both SARS2 challenged groups and the mock. Consistent with these observations, a previous study analyzing the oropharyngeal microbiota in humans showed beta diversity separations corresponding to COVID-19 severity [37]. In contrast, no statistically significant differences in alpha diversity in the lower respiratory tract were detected among SARS2 challenged GSHs and the mock controls, consistent with observations in the upper and lower respiratory tract of COVID19 patients [18,19,37].

The aged GSH lung microbiota was observed to be dominated by Firmicutes and Bacteroidetes at the phylum level and Prevotella spp., Veillona spp., and Streptococcus spp., at the genus level, which is consistent with previous studies of healthy human lungs [17]. GSHs challenged with SARS2 exhibited an increased relative abundance of Proteobacteria at 3 and 6 dpc. The expansion of Proteobacteria members has previously been associated with FLUAV H1N1 infection and has been positively correlated with alveolar and systemic inflammation in patients with acute respiratory distress syndrome [17,38]. At the genus level, we observed enrichment of predominantly gram-negative bacteria within the genus Prevotella 7, Campylobacter, Johnsonella, Fusobacterium, Haemophilus, Moraxella, and gram-positive Streptococcus following SARS2 challenge. We also observed a positive correlation between Actinomyces, Bifidobacterium, Prevotella 7, and Haemophilus with more severe NT histological scores, while Fusobacterium positively correlated with more severe lung scores. Streptococcus pneumoniae, Klebsiella pneumoniae, and Haemophilus influenzae are reported among the most common bacterial co-infections in COVID-19 patients [39]. Haemophilus was also previously described as a hallmark of the oropharyngeal microbiome in critically ill COVID-19 patients [37]. Further, Prevotella spp. have been associated with increased inflammation by stimulating epithelial cells to produce IL-6, IL-8, and CCL20, which can then prompt recruitment of neutrophils and mucosal Th17 immune responses [40]. A recent study showed increased TGFβ, IL-17, and neutrophil recruitment within the respiratory tract of SARS2 infected aged GSH (40+ weeks) compared to young GSH (6–8 weeks) [14]. Similarly, moderate to large numbers of neutrophils, notably at 6 dpc, were also observed within this study by histopathology in both SARS2 groups. Elevated levels of neutrophils within the lung are a hallmark of COVID-19 and has been hypothesized to contribute to the severe disease pathology observed in aged individuals [14,41,42]. Increased serum levels of IL-6 and IL-8 have also been associated with disease severity in COVID-19 adult patients and enrichment of CCL20 has been observed in mechanically ventilated patients with COVID-19 [43–45]. In contrast, other studies showed that bronchoalveolar lavage (BAL) samples of 50–77 years old COVID-19 positive patients were associated with Acinetobacter, Chryseobacterium, Burkholderia, and Enterobacteriaceae, while COVID-19 negative pneumonia patients were associated with Prevotella, Campylobacter, Streptococcus, and Haemophilus [18,46]. The SARS2 challenged GSHs within this study displayed more similar microbial signatures as observed in pneumonia patients as opposed to SARS2 patients, suggesting that bacteria within the genus Prevotella, Campylobacter, Streptococcus, and Haemophilus may be associated with a general respiratory disease pathology rather than a specific response to SARS2 infection.

Previous exposure to FLUAV in combination with SARS2 infection led to the enrichment of low abundance bacteria such as Prevotella 7, Streptococcus, Johnsonella, Fusobacterium, Haemophilus, and Moraxella within the lungs. Haemophilus was enriched in all GSHs at 3 and 6 dpc in the FLUAV-SARS2 group, while it was prevalent in only 1 out 3 GSHs at 6 dpc in the SARS2 group. These results suggest that previous innate immune response stimulation from FLUAV pre-exposure may influence the duration of opportunistic pathogen enrichment such as Haemophilus post-SARS2 infection. It has also been previously demonstrated that 6–8 week old GSH sustained heightened gene expression of interferon-stimulating genes 7 and 14 days post FLUAV infection [36]. The innate immune response, including macrophages and neutrophils, is important for viral clearance but also plays a role in regulating commensal microbes [47]. For instance, interferon-γ produced during T cell responses to FLUAV infection in mice correlated with suppression of phagocytosis by alveolar macrophages leading to the inhibition of initial bacterial clearance and the opportunity for secondary bacterial infections [48]. Respiratory viral infections can also promote bacterial colonization in the lower respiratory tract by excessive mucus production, decreased cilia movement, and impairment of the innate immune response leading to inefficient bacterial clearance resulting in the enrichment of opportunistic pathogens [47].

We also examined the response of the gut microbiota to infection. While previous studies in humans have detected vRNA in the intestine and feces correlating with occasional reports of gastrointestinal symptoms, we observed very low to no vRNA in the small or large intestine of GSH’s, consistent with previous reports [22,26,49]. Numerous studies have also shown that acute and chronic lung diseases induce changes in gut microbiota through the gut-lung axis [23,50]. Due to the difficulty accessing intestinal samples, most human samples collected for microbiota analysis are fecal samples. Within human fecal samples, SARS2 infection has been associated with an increased relative abundance of opportunistic bacteria such as Streptococcus, Rothia, Veillonella, Erysipelatoclostridium, and Actinomyces while patients infected with FLUAV H1N1 was enriched in Enterococcus, Prevotella, Finegoldia, and Peptoniphilus [21]. Meanwhile, fecal samples from young male GSHs infected with SARS2 displayed increased abundance of Oscillospiraceae UCG-007, Helicobacter, Escherichia-Shigella, and Parasuturella and decreased abundance of short-chain fatty acid (SCFA)-producing Firmicutes such as Ruminoccocus, Lachnospiraceae NK4A136, and Lachnospiraceae UCG-001 [50]. In accordance with previous studies, we observed enrichment of opportunistic bacteria including Streptococcus and Helicobacter in the SI and cecum while Lachnospiraceae was decreased in the feces of SARS2 challenged hamsters compared to the mock controls. Lachnospiraceae spp. have been previously described to produce SCFA, which plays a role in intestinal barrier integrity and modulation of pro-inflammatory factors, suggesting a beneficial role within a “healthy” gut microbiota [51]. In addition, increased relative abundance of Mucispirillum, Erysipelotrichaceae, and Spirochaetaceae (spirochetosis) was observed within cecum and feces of SARS2 challenged GSH with and without FLUAV exposure, in which spp. from these genera have been associated with inflammatory gastrointestinal disorders or diarrhea [52–54]. Ruminococcaceae NK4A214 group, Helicobacter, and Chirstensenellaceae R-7 group within the cecum and feces also positively correlated with more severe pathology in the cecum and have been previously associated with chronic diseases such as chronic obstructive pulmonary disease, colorectal cancer, and ulcers [55–57].

Interestingly, multiple genera including Bifidobacterium, Allobaculum, Ileibacterium, Faecalibaculum, and unclassified Eubacteriaceae were enriched after SARS2 challenge compared to their respective pre-challenge fecal samples; however, no taxa were considered significantly enriched in the FLUAV exposed GSHs when compared to their pre-challenge counterpart. The results suggest that initial stimulation of the innate immune response may result in greater changes of the intestinal microbiota compared to subsequent immune activations, or differences previously observed among the mock and FLUAV-SARS2 GSH can be explained by individual variability. The interplay between the gut microbiome and respiratory viral infections is bidirectional [47]. A respiratory virus infection can induce gut dysbiosis [47]; for instance, FLUAV infection favors type I interferon immune response which then promotes the outgrowth of Proteobacteria [58]. Contrastingly, the gut microbiome can also influence the immune response against the respiratory pathogen [47]; for example, macrophages from mice treated with antibiotics to induce depletion of the commensal bacteria exhibit decreased responses to type 1 and II interferons and reduced capacity to regulate viral replication during FLUAV infection [59].

Several limitations of this study must be noted: Despite the collection of multiple tissues for analysis, the study contained a relatively small sample size. And while the immune response plays a crucial role during early viral infection and the host microbiota, the immune response was not evaluated in this study. Future studies, including the analysis of the immune response, are needed to understand the effect of previous respiratory virus exposure to SARS2 infection and how the adaptive and innate immune response modulates the microbiota at the site of infection or through the gut-lung axis. We did not detect antibodies post-FLUAV H1N1 inoculation, which is similar to what has been previously observed in 2–3 month old GSHs [60]. While a recent study showed high viral titers of FLUAV H1N1 in GSHs, the study did not assess antibody titers for FLUAV [8]. Within this study, we did not evaluate FLUAV replication prior to SARS2 infection. However, FLUAV pre-exposure did have a distinct effect on the host microbial dynamics upon SARS2 infection compared to those that were only challenged with SARS2. Further research is needed to understand the impact of prior FLUAV infection and other respiratory pathogens and their role in stimulation of immune responses that might modulate outcomes in SARS2 infections due to effects on the residential respiratory and intestinal microbiota.

In summary, we characterized the pathogenicity and identified respiratory and gut microbiota changes during SARS2 infection with and without FLUAV pre-exposure in an animal model, emulating in this case, the elderly population. Information on the characterization of the lower respiratory tract in the elderly population is limited because of the difficulty of accessing samples from patients [23]. Because both SARS2 and FLUAV notably affect geriatric patients, understanding the disease pathology and the effect of the microbiota can potentially lead to the identification of differential host markers for disease severity within that demographic. While human BALF and fecal samples can be informative, human samples within elderly patients are more challenging to collect and tend to be highly variable and impacted by environmental conditions. As such, studying the effect of SARS2 infection on the microbiome in animal models in a controlled laboratory environment is a valuable alternative. In our work, specific microbial markers that have been previously reported in co-infections from opportunistic pathogens during COVID-19 were identified in the lungs of over aged GSHs challenged with SARS2 with and without FLUAV pre-exposure. Microbial changes within the different anatomical sections of the intestinal tract were also observed, warranting more in-depth studies investigating the complex relationship between the microbiota and pathogenicity in SARS2 in elderly patients.

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