key: cord-0283647-1z7ytywb
authors: Hernandez-Teran, A.; Vega-Sanchez, A. E.; Mejia-Nepomuceno, F.; Serna-Munoz, R.; Rodriguez-Llamazares, S.; Salido-Guadarrama, I.; Romero-Espinoza, J. A.; Guadarrama-Perez, C.; Sandoval, J. L.; Campos, F.; Mondrago-Rivero, E. N.; Ramirez-Venegas, A.; Castillejos, M.; Tellez-Navarrete, N. A.; Ormsby, C. E.; Perez-Padilla, R.; Vazquez-Perez, J. A.
title: Microbiota composition in the lower respiratory tract is associated with severity in patients with acute respiratory distress by influenza
date: 2021-12-08
journal: nan
DOI: 10.1101/2021.12.07.21267419
sha: c6de176d401ec0ac04be35a883ae99a749108072
doc_id: 283647
cord_uid: 1z7ytywb

Several factors are associated with the severity of the respiratory disease caused by the influenza virus. Although viral factors are one of the most studied, in recent years the role of the microbiota and co-infections in severe and fatal outcomes has been recognized. However, most of the work has focused on the microbiota of the upper respiratory tract (URT), hindering potential insights from the lower respiratory tract (LRT) that may help to understand the role of the microbiota in Influenza disease. In this work, we characterized the microbiota of the LRT of patients with Influenza A using 16S rRNA sequencing. We tested if patients with different outcomes (deceased/recovered), use of antibiotics, and different days of symptoms onset differ in their microbial community composition. We found striking differences in the diversity and composition of the microbiota depending on the days of symptoms onset and with mortality of the studied patients. We detected a high abundance of opportunistic pathogens such as Enterococcus, Granulicatella, and Staphylococcus in patients either deceased or with antibiotic treatment. Also, we found that antibiotic treatment deeply perturbs the microbial communities in the LRT and affect the probability of survival in Influenza A patients. Altogether, the loss of microbial diversity could, in turn, generate a disequilibrium in the community, potentially compromising the immune response increasing viral infectivity, promoting the growth of potentially pathogenic bacteria that, together with altered biochemical parameters, can be leading to severe forms of the disease. Overall, the present study gives one of the first characterizations of the diversity and composition of microbial communities in the LRT of Influenza patients and its relationship with clinical variables and disease severity.

Influenza patients and its relationship with clinical variables and disease severity. CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. polymerases have been related with severity [2] [3] [4] [5] . Other factors such as secondary 59 bacterial infections [6] have been related with severity, as well as several other studies that 60 have found bacterial co-infections associated with a more severe outcome or mortality 61 during influenza epidemics in the last century [6] [7] [8] . 62 Investigations into specific co-infecting pathogens that increase disease severity have 63 shown that the most frequent bacteria are Streptococcus pneumoniae, Haemophilus 64 influenzae, Neisseria meningitidis and Staphylococcus aureus [6] . In many cases, patients 65 with co-infections present longer hospital stays and more severe disease [9] . Although these 66 associations of co-infection and severity are well established, there is no conclusive 67 evidence that all cases with severe or fatal outcomes have occurred in patients with co- 68 infections, given that other clinical factors like obesity, hypertension, and other 69 comorbidities have an important role in severity [9] . 70 For the last ten years, modern technologies have allowed the study of whole microbial 71 communities associated with hosts (microbiota) and is the subject of increasing interest 72 [10, 11] . In human hosts, most of the efforts have been focused on characterizing the 73 microbiota in organs and tissues, such as the skin and the respiratory tract, but most have 74 focused on digestive tissues and processes [12] . In the respiratory tract, there has been 75 much progress describing and analyzing the diversity and composition of the microbiota in 76 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint 4 healthy and in pathological states. For instance, patients with chronic respiratory diseases 77 such as COPD showed striking differences in the community composition of the respiratory 78 tract compared with healthy controls [13, 14] . In acute respiratory infections, and in 79 particular influenza-like illness, there are several studies that characterize and analyze the 80 respiratory microbiota [15] [16] [17] . Nonetheless, most of them have focused on the microbiota 81 of the upper respiratory tract (URT) hindering potential insights from the lower respiratory 82 tract (LRT) that may help in the understanding of the role of the respiratory microbiota in 83 infectious diseases. 84 The composition of the microbiota has been recognized as an important factor in the 85 homeostatic state of healthy individuals, it has been shown that an imbalance in the 86 composition can cause or deepen the pathological conditions [18] . Factors such as age, 87 gender, geographic region, diet, diseases, and antibiotic treatment can modify the 88 composition and equilibrium of the microbiota, leading to a state known as dysbiosis [19-89 21] . Particularly, the respiratory microbiota plays a critical role in shaping the host´s 90 immune response, which is essential for effective elimination of invading viruses [20,22-91 24] . Some studies have shown that healthy commensal microbiota help maintain a robust 92 antiviral immunity, while dysbiosis increases viral infectivity due to the impaired capacity 93 of the immune system to limit viral infection [25] . Recently, several reports point out that In influenza disease, some reports have highlighted the importance of the microbiota and 98 that antibiotics treatments could affect the microbiota response against viral infectious 99 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint 5 diseases [20, 27] . For instance, it has been described that the microbiota present in mouse 100 lung stromal cells induce an antiviral state driven by an interferon response. However, the 101 antibiotic treatment has been found to affect this antiviral response in this model [20] and 102 can even modify the influenza vaccine efficacy in humans [27] .

In the present study, we aimed to characterize the microbiota of the LRT in patients with 104 acute respiratory syndrome associated with influenza virus infection. Specifically, we tested 105 if patients with different outcomes (deceased/recovered), use of antibiotics, and different 106 days of symptoms onset differ in their microbial community composition and correlate with 107 clinical data. We found striking differences in the diversity and composition of the 108 microbiota depending on the days of symptoms onset and with mortality of the studied 109 patients. We also found that the clinical use of antibiotics to reduce the severity of 110 respiratory diseases was associated with an altered microbial community composition in the 111 LRT which correlates with fatal outcomes. CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint 8 samples, and for the merged comparison groups in the phyloseq R package. Furthermore, 165 we selected the top abundant genera for each group and compared its relative abundance 166 using boxplots. Statistical differences in the abundance of such genera were calculated by a 167 Wilcoxon rank-sum test in the vegan R package.

To compare alpha diversity among the analyzed groups we calculated diversity as the 

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A total of 30 Influenza A (H1N1) patients with flu-like disease and severe acute respiratory 189 distress syndrome were analyzed. Demographic and health-related characteristics are 190 described in Table 1 . Briefly, the median age of our patient cohort was 45.5 (IQR: 36.5-51), CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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At comparing the microbial composition among deceased and recovered Influenza A 269 patients we found important differences in particular microbial groups. For instance, we 270 found that the whole respiratory microbial community of the deceased patients (Fig 2A) Tenericutes, Fusobacteria, and Actinobacteria. At the genus level (Fig 2B-C) we also 274 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. Prevotella, and Rothia) between deceased and recovered patients are described in Figure   281 2C. It is worth to mention that although Prevotella appears in both groups, is significantly 282 more abundant in recovered patients ( Fig 2C) . Moreover, we detected that recovered 283 patients showed statistically more richness (Chao1 index, Wilcoxon rank-sum test, p = 284 0.01), and more diversity (Shannon index, Wilcoxon rank-sum test, p = 0.004) than 285 deceased patients (Fig 2D) . Finally, regarding beta diversity (Fig 2E) , we also found CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint 13 significantly increased in patients with previous antibiotic treatment (AB, Fig 3C) .

Regarding alpha diversity (Fig 3D) , we found significantly less richness (Chao1 index,

Wilcoxon rank-sum test, p = 0.01) in the AB patients. In addition, we also found a When comparing patients with different days of symptoms onset we detected a distinct 306 community in the respiratory tract between patients with early (0-8 days) and late (>9 days) 307 symptoms onset (Fig 4) . For the early symptoms patients (0-8 days), Actinobacteria, 308 Bacteroidetes, and Firmicutes dominated the community at phylum level ( Fig 4A) . For the 309 late symptoms patients (>9 days) we found the same phyla except for Actinobacteria, 310 which appears absent. At the genus level (Fig 4B-C) , we found Veillonella, Streptococcus, 311 and Enterococcus significantly increased in the early symptoms patients (0-8 days), while 312 Granulicatella and Peptostreptococcus were significantly increased in the late symptoms 313 patients (>9 days). Regarding alpha diversity we do not find statistical differences among 314 the compared groups ( Fig 4D) . Lastly, we found a slightly differentiation in beta diversity 315 (Fig 4E) among the comparison groups (weighted UniFrac, PERMDISP, F= 6.5, p= 0.05). 317 We found some clinical variables related with survival probability and microbial diversity 318 and composition of the respiratory microbiota. For instance, after adjusting the CAP model,

. CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint we found six clinical variables that together explain the 22% of the total variation ( Fig 5A) . 320 We detected that all deceased patients were located in the upper half of the plot, positively 321 correlating with lymphocytes, creatinine, and urea vectors (PERMANOVA, F= 1.9, p = 322 0.004) . In contrast, although we found some recovered patients in the upper half plot, most 323 of them were found in the lower half of the plot, correlating with total protein, and 324 hematocrit vectors.

Furthermore, we found that patients with antibiotic treatment prior hospitalization exhibited 326 less probability of survival than patients which were not treated with antibiotics ( Fig 5B) 

(Kaplan-Meier curve, Cox test p = 0.04). Finally, we detected that, for all patients, both 328 diversity (Shannon index) and richness (Chao1 index) were negatively correlated with age 329 (R 2 = -0.9, p = 4.4e-13 for Shannon index; R 2 = -0.85, p = 3.9e-08 for Chao1 index) and 330 BMI (R 2 = -0.62, p = 0.02 for Shannon index; R 2 = -0.7, p = 0.04 for Chao1 index) (Fig   331   5C ).

The respiratory microbiota has proven to be related with disease courses in ARDS such as CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint could impact disease course (or the other way around) [8, 16, 41, 42, [44] [45] [46] , only studies 341 using mice models are available for the LRT [47] .

Some studies have correlated the microbial composition in the respiratory tract with the 343 outcome of patients with Influenza [7, 20, 45] and with other respiratory diseases [22, 48] .

On one hand, as it has been observed, patients with Influenza disease exhibit significantly 345 low diversity compared to healthy controls [17, 49] . In agreement with such previous 346 studies of microbial composition in the respiratory tract, some subjects within our cohort 347 exhibited a respiratory microbiota completely dominated by few genera, which is indicative 348 of severe dysbiosis (Fig 1B) . In many cases the dominating genera were microbial 349 pathogens that have been previously found as co-infections with Influenza virus (e.g. (Fig 2A) . Regarding the genus level, in the microbiota associated to deceased 357 patients we found Granulicatella and Staphylococcus to be enriched (Fig 2B-C) , which 358 have been associated to severe forms of Influenza in both children and adults [25, 41, 42] . 359 Moreover, some species of the genus Staphylococcus are frequently found in co-infections 360 with Influenza virus [6] . Thus, the commonly pathogenic bacteria frequently found 361 associated with Influenza patients are particularly enriched in more severe forms of the 362 disease and mortality within our cohort. Furthermore, we found that even between ill 363 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 patients, the deceased patients showed significantly less richness (Chao1 Index) and 364 diversity (Shannon-Wienner Index) than recovered patients (Fig 2D) , meaning that a 365 potential correlation between microbial diversity and outcome could exist. 366 It is known that antibiotic treatment affects microbial communities in hosts [19, 27] . In mice 367 pre-treated with antibiotics has been found an increased morbidity, mortality, and altered 368 respiratory microbiota during H1N1 infection [20, 23] . Particularly, it has been shown that In order to disentangle the relevance of the respiratory microbiota on disease progression, 378 we classified patients based on symptoms onset differences. We found important changes 379 in the composition and abundance of microbial groups (Fig 4) . Such alterations could be CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint bacteria such as Granulicatella, a known pathogen in oral cavities, can spread. Thus, viral 387 replication and immunological mechanisms could be acting together causing the changes 388 observed in patients with different days of symptoms onset, with potential consequences on 389 the outcome.

Finally, we also test for associations between clinical features and microbial composition in 391 the lower respiratory tract. For instance, the multivariate analysis (CAP) of clinical 392 variables ( Fig 5A) showed that some patients (in terms of Unifrac distance) were associated 393 with laboratory variables related to severity in infectious diseases. In particular, some CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 19 infection affect not only the URT but the microbial communities inhabiting the lungs. We 432 found that antibiotic treatment deeply perturb the diversity and composition of the 433 microbial communities in the LRT and the probability of survival in Influenza A patients.

The loss of microbial diversity could, in turn, generate a disequilibrium in the community, (PERMDISP). Asterisks denote statistical significant differences given by a Wilcoxon rank-sum test 469 (* p < 0.05, ** p < 0.001, *** p < 0.0001). 470 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint (PERMDISP). Asterisks denote statistical significant differences given by a Wilcoxon rank sum test 479 (* p < 0.05, ** p < 0.001, *** p < 0.0001). 480 481 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint Principal Coordinates Analysis (PCoA) with weighted Unifrac distance and dispersion test 489 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted December 8, 2021. ; https://doi.org/10.1101/2021.12.07.21267419 doi: medRxiv preprint (PERMDISP). Asterisks denote statistical significant differences given by a paired Wilcoxon rank 490 sum test (* p < 0.05, ** p < 0.001, *** p < 0.0001). 

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