Reduced olfactory performance is associated with changed microbial diversity, oralization and accumulation of dead biomaterial in the nasal olfactory area

doi:10.21203/rs.3.rs-2251236/v1

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Reduced olfactory performance is associated with changed microbial diversity, oralization and accumulation of dead biomaterial in the nasal olfactory area

https://doi.org/10.21203/rs.3.rs-2251236/v1

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BACKGROUND

The partial or complete loss of the sense of smell, which affects about 20% of the population, impairs the quality of life in many ways. Dysosmia and anosmia are mainly caused by aging, trauma, infections, or even neurodegenerative disease. Recently, the olfactory area – a site containing the olfactory receptor cells responsible for odor perception – was shown to harbor a complex microbiome that reflects the state of olfactory function. This initially observed correlation between microbiome composition and olfactory performance needed to be confirmed using a larger study cohort and additional analyses.

METHODS

A total of 120 participants (middle-aged, no neurodegenerative disease) were enrolled in the study to further analyze the microbial role in human olfactory function. Olfactory performance was assessed using the Sniffin’ Stick battery, and participants were grouped accordingly (normosmia: n = 93, dysosmia: n = 27). The olfactory microbiome was analyzed by 16S rRNA gene amplicon sequencing and supplemented by metatranscriptomics in a subset (Nose 2.0). Propidium monoazide (PMA) treatment was performed to distinguish between intact and non-intact microbiome components. The gastrointestinal microbiome of these participants was also characterized by amplicon sequencing and metabolomics, then correlated with food intake.

RESULTS

Our results confirm that normosmics and dysosmics indeed possess a distinguishable olfactory microbiome. Alpha diversity (i.e., richness) was significantly increased in dysosmics, reflected by an increase in the number of specific taxa (e.g., Rickettsia, Spiroplasma, and Brachybacterium). Lower olfactory performance was associated with microbial signatures from the oral cavity and periodontitis (Fusobacterium, Porphyromonas, and Selenomonas). However, PMA treatment revealed a higher accumulation of dead microbial material in dysosmic subjects. The gastrointestinal microbiome partially overlapped with the nasal microbiome but did not show substantial variation with respect to olfactory performance, although the diet of dysosmic individuals was shifted towards a higher meat intake.

CONCLUSION

Dysosmia is associated with a higher burden of dead microbial material in the olfactory area, indicating an impaired clearance mechanism. As the microbial community of dysosmics appears to be influenced by the oral microbiome, further studies should investigate the microbial oral-nasal interplay in individuals with partial or complete olfactory loss.

microbiome

olfaction

anosmia

nose

stool

dysosmia

The partial or complete loss of the sense of smell is a drastic event that substantially affects the quality of life by impacting psychological, behavioral and social performance. Various studies have already reported its negative effects on personal hygiene, which can range from weak to extensive, but effects on social interaction and internal safety warning systems for, for example, smoke, gas, or spoiled food are also possible (reviewed in ¹). In addition, the diet of those affected is often saltier and spicier, as the sense of smell is strongly linked to the sense of taste ². The term "dysosmia" describes the condition of impaired olfactory function and combines the two conditions of "hyposmia" (reduced olfactory function) and "anosmia" (complete loss of olfactory function).

Dysosmia is mainly caused by various diseases, mechanical impacts, the aging process, or infection ^3,4. This loss of smell usually recovers spontaneously after an upper respiratory tract infection (URT) ⁵ however, it is not yet clear why dysosmia can even persist for years in some cases.

In general, olfactory performance is mediated by olfactory receptor cells in the ceiling of the nasal cavity - the olfactory mucosa. These cells extend into the brain with their axons passing through the cribriform plate into the olfactory bulb (Fig. 1A) ^6–8 In addition to odor molecules, olfactory receptors are also able to recognize bacterial secondary metabolites (e.g., short-chain fatty acids, formyl-peptides). These molecules derived from bacteria can trigger the immune response mediated by the sense of smell. Thus, olfaction contributes to the individual’s defense against pathogens ^9–11. The receptor neurons are surrounded and supported by sustentacular cells, and the entire area is covered with mucus (Fig. 1B) ^8,12. The mucosa of the olfactory epithelium is also colonized by numerous microorganisms, referred to as the olfactory microbiome (Fig. 1)^13–15.

In a pilot study, our research group demonstrated that changes in the olfactory microbiome could be associated with altered olfactory performance. Significant differences in the microbiome diversity and composition were observed in hyposomics as compared to normosomics. Especially butyrate producing bacteria (usually found in the gut) were significantly more abundant in hyposmics ¹³. In a study of olfaction and the sinus microbiome, Biswas et al.,2020 observed a loss of microbial diversity in dysosmics, noting that Corynebacterium (ASV46) and Streptococcus (ASV65) were significantly more abundant in normosmics, while Streptococcus (ASV111) and Anaerococcus (ASV43) were significantly more abundant in dysosmics ¹⁶.

Both studies (i.e., Koskinen et al., 2017 and Biswas et al., 2020) focus on the total detectable microbial community, which also includes dead microbial material ^13,16. However, 7,000 L of air from the external environment passes through the upper respiratory tract of a healthy person daily. Therefore, it is likely that among the 10⁴–10⁶ bacterial cells per m³ of inhaled air per day ^17,18, inactive microbial material or external microbes may also be present that are not adapted to the habitat conditions. This material is usually transported by mucociliary clearance/ciliary beating towards the nasopharynx and down the throat ¹⁹.

This present study was conceptualized in order to deepen our current knowledge about the olfactory microbiome in normosmics and dysosmics. Guided by findings of our previous study ¹³, additional volunteers were recruited (n = 53) and subjected to olfactory performance testing, a more specific sampling of the olfactory mucosa only, and a thorough analysis of the bacterial and non-bacterial microbiome composition. We wanted to determine the potential influence of dead microbial material on olfactory function and discover whether this dead material originates from the gut. Furthermore, we were also interested in measuring the effect of a putative changed diet on the gastrointestinal microbiome, due to the reported changes in the diet of dysosmics.

Indeed, dysosmics tend to have a higher alpha diversity with more dead microbial material as compared to normosmics. However, the gut has not been identified as the source of these dead microbial signatures. Only Methanobrevibacter signatures belonging to a similar clade as fecal references have been found to be more prominent in dysosmics. In general, the diet of normosmics seems to be healthier than that of dysosmics.

3.0. Ethics

The study was approved by the Ethics Committee of the Medical University of Graz in accordance with the Declaration of Helsinki (EK-Nr. GZ. 39/44/63 ex 2017/18)). Before participation, all participants gave their informed consent.

3.1. Design and subjects of the study

To replicate our pilot study ¹³, we recruited an additional 53 healthy volunteers aged 18–45 years. Great efforts were made to allow for comparability between the pilot study and Nose 2.0. Participants of both studies had to meet the following criteria: Non-smoker, absence of psychiatric, neurological, or internal medicine conditions, use of probiotics and antibiotics within the month prior to sampling, no presence of nasal polyps, no use of nasal spray on the day of the study, and an absence of allergies (including acute hay fever or pollen) or acute illness (e.g., rhinitis). The only addition was the exclusion of participants with gastrointestinal disorders in Nose 2.0. Furthermore, no participant of the pilot study was included in the Nose 2.0 study. Because of the susceptibility of swab samples from the nasal olfactory region to contamination from other sites, we used sinus secretion collectors in Nose 2.0. While only 16S rRNA gene sequencing was performed in the pilot study for the microbiome analyses, the nose samples collected in Nose 2.0 underwent additional metagenomics and metatranscriptomics analyses. Furthermore, stool samples were collected for 16S rRNA gene-based amplicon sequencing and metabolomics. General metadata information (e.g., sex, age, BMI) was collected in both studies (Supplementary Table 1). In addition, the participants completed questionnaires in order to collect dietary as well as cognitive and emotional information (LEIDEN-R, SPM, FAIR; no significant differences observed; data not included herein) and underwent olfactory testing (Sniffin’ Sticks Battery). For study schemata, see also Supplementary Fig. 1.

3.2. Olfactory testing and olfactory group definition

The olfactory function of the study participants was investigated using the Sniffin' Sticks battery (Burghart Instruments, Wedel, Germany). Detailed information about this method can be found in Hummel et al.,1997 ²⁴. Briefly, a combined TDI score was defined by adding three different odor scores: the detection threshold (lowest odor concentration that can be detected), odor discrimination (ability to discriminate between odors), and odor identification (ability to name odors) ²⁴.

The main categorization of participants was based on the measured TDI scores as follows ²⁵: Normosmia (N) is defined by a TDI score of > 29 – dysosmia (D; TDI ≤ 29). For a more detailed analysis, both normosmics as well as dysosmics were further divided into subcategories: good normosmics (G; TDI ≥ 36.0), weak normosmics (W; 36.0 > TDI > 29.0), hyposmics (H; 29.0 ≥ TDI > 16.5), and anosmics (A; TDI ≤ 16.5).

3.3. Dietary questionnaires

All participants had to complete the “German Food Frequency Questionnaires'' (Robert Koch Institute, ²⁶) to evaluate nutritional intake information. The responses were analyzed using the Austrian specific nutrition software ²⁷. Values of nutritional intake were normalized based on the kcal intake (nutritional value per 100 kcal food intake) before being correlated with the olfactory performance measures (TDI scores and olfactory groups; Supplementary Table 14) and the microbiome (nose and stool).

4.0. Microbial sample collection and preparation

Nasal samples: Samples were collected by ENT physicians at the University Department of Otolaryngology, Medical University of Graz, Austria, using a sinus secretion collector (Medtronic Xomed, Inc.). Samples were immediately placed on ice until further processing. Nasal samples were dissolved in 500 µL of an aqueous 0.9% (w/v) NaCl solution and divided between two tubes. One aliquot (250 µL) was treated with propidium monoacid (PMA). The PMA treatment was performed in the dark. Samples were mixed with the PMA solution (final concentration: 50 µM), shaken briefly, then incubated on a shaker for 10 minutes in a PMA-Lite™ LED photolysis instrument (Biotum). Subsequently, both aliquots (PMA-treated and untreated) were stored at -20°C.

Stool samples: The stool samples were collected by the participants themselves on the same day that the nasal samples were collected. Samples were stored on ice until further processing. Within a maximum of 2h, the samples were aliquoted: 0.1 g of stool was dissolved in 1 mL of an aqueous 0.9% NaCl solution, and an aliquot of 500 µL was treated with PMA solution as previously described. Again, both aliquots (PMA treated and untreated) were stored at -20°C.

4.1. DNA and RNA extraction

DNA from the nasal (PMA-treated and -untreated) and stool (PMA-treated) samples was extracted using the DNeasy PowerSoil Kit (QIAGEN, USA). Untreated nasal samples which were frozen in liquid nitrogen immediately after sample collection were used for RNA extraction (RNeasy RNA Extraction Kit, QIAGEN, USA). DNA and RNA extractions were performed according to the manufacturer's protocol with minor modifications: Instead of vortexing the samples, a MagNaLyser was used at 6500 rpm for 2 cycles of 30 seconds. The nucleic acid concentration obtained was quantified using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, USA) before samples were stored at -20 (DNA) and − 80°C (RNA).

4.2. Amplicon sequencing

16S rRNA gene and ITS region-based amplicon sequencing.

Three different approaches were used for amplicon sequencing: The universal (primer pair: 515F-806R) and the archaeal approach (nested PCR: 344F-1041R, 519F-806R) were used to amplify the V4 region of the 16S rRNA gene, and the fungal approach (primer pair: ITS86F-TS4R) was used to amplify the ITS region. PCR was performed on DNA extracted from stool samples using all approaches, whereas only the universal approach was used to amplify DNA extracted from nasal samples (see ^28,29) for detailed information on protocol and primers). PCR products were sequenced using the Illumina MiSeq sequencing platform (Illumina, Eindhoven, The Netherlands) at the Core Facility for Molecular Biology of the Center for Medical Research in Graz, Austria ³⁰.

Sequence data procession and control

Raw sequences were quality filtered and processed using QIIME2 (Quantitative Insights Into Microbial Ecology) v2020.8 as previously described ³¹. The reference databases SILVA v138 (universal approach), SILVA v138 (archaeal approach), and Unite v8.3 (fungal approach) were used as a Naïve Bayes classifier ^32,33. After data processing, the R package decontam with a threshold of 0.5 and the prevalence method were used to treat extraction blanks and PCR negative controls ³⁴ (https://github.com/benjjneb/decontam). In addition to the controls, mitochondrial signatures, unassigned sequences (and also human mitochondrial assigned ones), and features with zero or only one read were removed.

SRS normalization (SRS = scaling with ranked subsampling) ³⁵ was then applied to the nasal samples with sample depths of 500 and 1006 (PMA-treated and untreated, respectively) and to the stool samples (bacterial approach only) with a cut-off at 2222 read counts. The archaeal and fungal approaches provided read counts that were too low for some samples; therefore, these were not normalized. The remaining RSVs (Supplementary Dataset 1–5) were visualized using various tools (including Microbiome Explorer v1.6.0 ³⁶, Rstudio v4.1.2 ³⁷, RawGraphs ³⁸). The colors and legends of charts and graphs were adjusted using Inkscape ²³.

4.3. Metatranscriptomics analysis

Metatranscriptome sequencing

RNA (without PMA treatment) extracted from 12 samples (six normosmics and six anosmics) was sent to a company (Macrogen, South Korea) for metatranscriptomics sequencing. The TruSeq Nano DNA Construction Kit (Illumina, Eindhoven, The Netherlands) was used to construct the library before sequencing with the Illumina NovaSeq 6000 technique (Illumina, Eindhoven, The Netherlands).

Metatranscriptomics data processing

The raw data were analyzed using the metagenomics pipeline as previously described ³¹. Briefly, the quality of the sequences was checked using fastqc (v0.11.8) ³⁹ before trimming (v0.38) ⁴⁰, and the information derived from the human host (chromosomes hg19 and GChr) was removed by binning using bowtie2 (v2.3.5) ⁴¹. The remaining reads were analyzed, first by using a gene-centric method (protein-based annotation using diamond (protein-based) and visualized with MEGAN using MEGAN ⁴²) and then second by using a genome-centric method (Megahit v1.1.3 ⁴³, MaxBin v2.2.4 ⁴⁴, and dRep v2.0.5 ⁴⁵. The latter did not provide MAGs (metagenome-assembled genomes); therefore, it not discussed further here.

Results from applying the gene-centered approach were filtered by removing all traits with fewer than three reads. To examine changes in all life domains, the following additional filters were set: Filtering all 1) non-bacterial, 2) non-archaeal, 3) non-fungal, or 4) non-viral signatures (Supplementary dataset 6–9).

4.4. Metabolomic analysis

Metabolomic measurements of PMA-untreated stool samples were performed at the Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University of Graz. A subset of 49 stool samples of the Nose 2.0 study were analyzed using untargeted NMR (nuclear magnetic resonance) spectroscopy for several metabolites in-house. Metabolites were extracted using a methanol/water solution and prepared for NMR measurements as described previously ⁴⁶. NMR was performed on an AVANCE™ Neo Bruker Ultrashield 600-MHz spectrometer equipped with a TXI probe head at 310 K. Briefly, the 1D CPMG (Carr-Purcell-Meiboom-Gill) pulse sequence (cpmgpr1d, 512 scans, 73,728 points in F1, 11904.76-Hz spectral width, 512 transients, recycle delays of 4 s) with water suppression by pre-saturation was used for ¹H 1D NMR experiments. Bruker Topspin version 4.0.2 was used for NMR data acquisition. Spectra for all samples were automatically processed (exponential line broadening of 0.3 Hz), phased and referenced using TSP at 0.0 ppm, using the Bruker Topspin 4.0.2 software (Bruker GmbH, Rheinstetten, Germany). Spectra were imported to Matlab2014b, and the regions around the water, TSP, and remaining methanol signals were excluded. To correct for the sample metabolite dilution, a probabilistic quotient normalization step was performed. Metabolite quantification was based on the signal integration of normalized spectra as described previously ⁴⁷.

4.5. BioEnv

Using R Studio v4.1.2 ³⁷ and its companion package (vegan ⁴⁸), BioEnv plots were generated with olfactory performance, sex, age, BMI, and food intake variables, showing the positive or negative correlations (Spearman’s rho) with microbial community dissimilarities.

4.6. Statistical analysis

IBM SPSS Amos v26 ⁴⁹ was used to perform statistical analyses (p-values). Parameters were tested for normal distribution before selecting the appropriate statistical test. In general, uncorrected significant values are reported as p-values and Bonferroni-corrected values as q-values in the manuscript.

4.7. Correlation heatmaps

Rstudio v4.1.2 ³⁷ with the following R packages was used to create the correlation heatmaps: ggpubr v0.4.0 ⁵⁰, tidyverse v1.3.2 ⁵¹, textshape v1.7.3 ⁵², dplyr v1.0.9 ⁵³, tibble v3.1.6 ⁵⁴, and ggplot2 v3.3.5 ⁵⁵. Since olfactory values are non-parametric (using SPSS ⁴⁹), the Spearman's rho correlation was used.

4.8. Source tracking

Source tracking was performed using sourceTracker2 (https://github.com/biota/sourcetracker2). The original version of SourceTracker was described in Knights et al. (2011) ⁵⁶. For source tracking, only the datasets from Nose 2.0 were used (reads from PMA-treated stool samples and non-PMA treated nasal samples from both olfactory groups). Reads from respective datasets were co-processed with Qiime2 as described above. Stool samples were assigned as the “source”, whereas nasal samples served as the “sink”. The selected rarefaction depth for source tracking was 480 (for both sink and source), to allow all samples to be kept in the analysis.

4.9. Methanobrevibacter tree

For the tree, all Methanobrevibacter sequences from the nasal samples (Nose 2.0) were used. Additional sequences (e.g., representatives of M. oralis and M. smithii), as well as environmental sources (rumen), were retrieved from SILVA ³². Environmental sequences were used to stabilize the tree. All archaeal sequences were subjected to the Silva Alignment, Classification, and Tree Service Tool (ACT; ³²). The tree was computed using the “Compute tree” function in SILVA ACT with standard settings (no positional variability filter). The tree was then annotated using iTOL ⁵⁷, and legends were added using Inkscape ²³.

Description of the study and the study cohorts

A new study (Nose 2.0) was conducted to add to the results of our published pilot study ¹³; Supplementary Fig. 1) which indicated the existence of a potential correlation between the nasal microbiome composition and olfactory function. In the following, the new study and the pilot study are referred to as “Nose 2.0” and “pilot”, respectively. In the new study, we recruited an additional 53 participants (22 women) and analyzed samples collected from these participants together with the already-collected 67 nasal samples (50 women) from the pilot study.

Based on the olfactory testing outcomes, the participants were categorized into two major groups, namely, normosmics (N: n = 33; TDI > 29) and dysosmics (D: n = 20; TDI ≤ 29), and four subgroups (good normosmics (G: n = 16; TDI ≥ 36.0), weak normosmics (W: n = 17; 36.0 > TDI > 29.0), hyposmics (H: n = 7; 29.0 ≥ TDI > 16.5), and anosmics (A: n = 13; TDI ≤ 16.5) (Supplementary Fig. 2; Supplementary Table 2). According to these cut-offs, participant groups from the pilot study were reclassified based on their olfactory performance. Only seven (D; N: n = 60) subjects met the criteria for olfactory dysfunction after adjustment (G: n = 24; W: n = 35; H: n = 8) (see also Supplementary Fig. 2).

The observed different olfactory performance in both cohorts could not be explained by any observed metadata, such as age, BMI, or sex) (Supplementary Table 2). The homogeneity of the cohorts was further supported by the results of analyses of the metadata information based on the olfactory groups (major and subgroups) (Supplementary Table 3).

Participants with dysosmia were selected based on the loss of smell they had experienced due to an infection that had occurred at least one year earlier. Based on inflammatory markers in the blood, no active inflammation was detected in any subject (Supplementary Table 1). Notably, samples were collected before the Covid-19 pandemic - April 2018 to November 2019.

Diversity of nasal microbial community differs based on olfactory performance.

To add to the collection of already-published data ¹³, the raw sequencing data from the pilot study had to be re-analyzed together with the Nose 2.0 data before combining/comparing the datasets (see Material and Methods). From both studies, an overall 2,480 unique microbial features (120,720 reads) were obtained by 16S rRNA gene sequencing the “olfactory microbiome. Notably, archaeal information made up approx. 2% of the dataset (see Supplementary dataset 1).

The predominant taxonomic signatures and microbial composition were similar in both studies, with Proteobacteria, Actinobacteriota, Firmicutes, Bacteroidota, and Euryarchaeota representing the predominant phyla, and Corynebacterium, Ralstonia, Staphylococcus, Lawsonella and Dolosigranulum representing the most abundant genera (Supplementary Table 4; Fig. 2A). However, only 243 features were shared between the studies, while 1.529 and 708 RSVs were found to be unique for the pilot and Nose 2.0 datasets, respectively (Fig. 2B; Supplementary Fig. 3; detailed list in Supplementary Table 5).

Consistent trends in the alpha diversity of both cohorts (Nose 2.0 and pilot) were observed. The Shannon indices and the evenness of the nasal samples were always higher in the dysosmics than in the normosmics; however, these differences were not significant. The increase in microbial richness based on the TDI score was significant. This result was supported by the trend observed in the Shannon index (Fig. 3A; p _{Nose 2.0_TDI_richness} = 0.022; p _{pilot_TDI_richness} = 0.017; Mann-Whitney U).

The differences observed in alpha diversity were even more pronounced if the main groups were further divided into olfactory subgroups (good and weak normosmia, hyposmia and anosmia), showing a gradual/stepwise increase as the olfactory perception decreased; again, this result may have been mainly driven by the data richness rather than evenness (Supplementary Fig. 4; not significant).

The microbial community information indicated a partial overlap of the clusters of both cohorts in a PCA plot (not significant). The Nose 2.0 cohort revealed a significant difference between normosmics and dysosmics; however, this was not reflected in the pilot cohort (Fig. 3B; p _NBA = 0.002; p _pilot = 0.505 TDI; Adonis test).

Both studies revealed a greater variability in the dysosmic microbiomes than in the normosmic microbiomes, revealed by the significant difference between these olfactory groups and subgroups seen in Nose 2.0 and pilot study, respectively (Fig. 3B; red dots; pNose2.0 + pilot = 0.002, pNose2.0 = 0.008, ppilot = 0.627 (Supplementary Fig. 4)).

Many taxa were identified that are unique to the microbiomes of either normosmics or dysosmics. Although both studies revealed slight differences with respect to the indicative microbial signatures, several taxa were identified as potential signatures for olfactory performance.

The phylum Acidobacteriota (p = 0.04, ↑normosmia) and the genus Ralstonia (p = 0.01, ↑N) were found to be significantly increased in normosmics; thus, these taxa could be associated with overall olfactory function. Brachybacterium (p = 0.01, ↑dysosmia), Rickettsia (p < 0.01, ↑D), and Spiroplasma (p < 0.05, ↑D) were correlated with the dysosmic situation. Notably, the abundance of Euryarchaeota (Methanobrevibacter) was strongly associated with dysosmia (Fig. 4).

The results of the analysis based on the underlying single olfactory scores (T, D, I) further confirm the results based on the major groups with additional taxa found to be associated with normosmics, such as Corynebacterium (p < 0.05, ↑N), Delftia (p = 0.01, ↑N), and Bradyrhizobium (p = 0.02, ↑N) (Supplementary Fig. 5). As already observed for the microbial diversity of the olfactory subgroups, the relative abundance of specific taxa revealed correlations with the TDI score. Alistipes and Muribaculaceae CAG873 were found to be associated with normosmic subjects (p = 0.03, Spearman’s rho). In addition to previously mentioned taxa such as Spiroplasma and Rickettsia, oral-associated microbes were also found, including Fusobacterium, Selenomonas, and Porphyromonas (Fig. 4). Moreover, the predicted community state types did not explain the observed differences in the microbial profiles of the olfactory groups (data not shown).

Similar trends in both cohorts that were found included an increased alpha diversity, indicating more microbial signatures in dysosmic than in normosmic participants with additional fluctuating beta diversity seen in dysosmics. These differences are possibly explained by the presence of biomarkers, and especially for dysosmics. Dysosmics were characterized by an increase in the relative abundance of gut- and oral-associated microbes (e.g., Methanobrevibacter and Fusobacterium, Porphyromonas and Selenomonas ^29,31) and mainly intracellular living microbes (e.g., Rickettsia, and Spiroplasma ^58,59).

These results raise the question of whether the observed microbial signatures belong to viable and actual inhabitants of the olfactory area or whether these signatures reach this area through respiration.

The olfactory area of dysosmics contains many signatures of dead cells

In order to approach the mechanistic question of the microbiome in the olfactory area in normosmics and dysosmics, we concentrated our analyses on the viable (propidium monoazide (PMA)-treated) and active (metatranscriptomics) fractions of the microbial community.

PMA is used to mask freely accessible DNA for subsequent PCR reactions and thus allows one to gain a reliable insight into the intact/viable fraction of microbiomes ⁶⁰. Many of the above-mentioned key findings were still detected after PMA treatment, such as the proportion of archaea (2%) and the most abundant phyla and genera (including the phyla Proteobacteria, Firmicutes, and Actinobacteria, and the genera Ralstonia, Corynebacterium, Staphylococcus, and Corynebacterium), where most showed similar trends for olfactory groups (Supplementary Table 7; Supplementary Dataset 2). However, the trends seen for Bacteroidota, Euryarchaeota, Ralstonia, and Dolosigranulum were exactly the opposite of those observed in the PMA-untreated samples (Supplementary Fig. 6A).

The analysis of the PMA dataset revealed opposite trends for microbial alpha diversity measures as compared to the untreated dataset. In untreated samples, as explained above, we observed an increased Shannon index, richness, and evenness that correlated with dysosmia; the PMA-treated samples showed decreases in terms of all measured alpha diversity analyses from good to bad olfactory performance. However, the results (Shannon index and richness) were not significant except for microbial evenness (p = 0.05, Supplementary Fig. 6A). In contrast to the previous results on the overall microbial community, the signatures of the viable community did not show significant differences in terms of their beta diversity (Supplementary Fig. 6A). An analysis of microbial biomarkers for olfactory performance did not reveal significant differences between the olfactory groups using Aldex2 (just DeSeq2).

Comparing the PMA and nPMA dataset at genus level, we revealed that 121 unique genera are shared between the datasets (PMA − 174 unique genera; nPMA − 316 unique genera). The read counts belonging to normosmia that are shared between both datasets made up 50% and 53%, respectively, whereas the proportion of normosmia and dysosmia in the nPMA dataset was lower in the nPMA (0.32 or 24% normosmia) than in the PMA (0.85 or 46% normosmia) dataset (Supplementary Fig. 7). Two of the five genera found in the dysosmics, namely, Rickettsia and Spiroplasma, were found only in nPMA samples (Supplementary Fig. 7; Supplementary Table 8).

All of these results lead us to conclude that dysosmics carry an increased proportion of dead microorganisms as compared to normosmics. To further evaluate the quantity and quality of the dead microbial material, 12 nasal samples (six normosmia and six dysosmia) were selected for further metatranscriptomics analysis.

The information derived from PMA-treated analyses was further confirmed by our metatranscriptomic analysis (Supplementary Table 9). Again, the alpha diversity showed the same opposite trend as seen in the PMA-treated as compared to untreated samples, and the core microbiome at phylum level was similar to that detected using the other methods (Supplementary Fig. 6B).

No feature that was identified as a biomarker for dysosmics in the amplicon dataset from the Nose 2.0 study could be found in the metatranscriptomics dataset (Fig. 4 and Supplementary Fig. 5; Amplicon, Supplementary dataset 1 and 6), which was probably due to the challenging nature of the metatranscriptomics analysis of nasal samples, which results in a small sample size and many un-annotated taxa. However, the phylum Actinobacteriota (p = 0.045) and the genus Corynebacterium (p > 0.05) were found to be indicative for normosmics in the metatranscriptomics output (DeSeq; Aldex2, data not shown). An analysis of information about the non-bacterial taxonomy (fungi, archaea, and viruses; Supplementary dataset 8 and 9) and functional capacity did not reveal statistically significant differences between the olfactory groups (Supplementary Fig. 8; Supplementary Table 10; Supplementary dataset 7).

Both the metatranscriptomics and PMA-based 16S rRNA gene analysis revealed a decrease in alpha-diversity measures that correlated with dysosmia. As this result contrasts with the observations made in the classical microbiome analysis (non-PMA-treated samples), we conclude that the olfactory area of dysosmics contains a higher proportion of signatures of dead/non-intact cells as compared to that of normosmics.

Signatures of dead microbes in the nasal cavity do not come from the gut

An increased relative abundance of butyrate producers in the nasal samples (usually found in the gastrointestinal tract) of dysosmics as compared to normosmics was already indicated by the results of our pilot study ¹³. These results suggest that dead microbial material is transferred from the gut to the nasal cavity. To determine whether microbes stray from the intestine into the nose, we also investigated the viable fecal microbiome (PMA) of all participants.

In this microbiome, 99.1% of the observed features belonged to Bacteria and 0.09%, to Archaea, when a universal amplicon sequencing approach was taken. Firmicutes (54.2%), Bacteroidota (36.2%), and Actinobacteriota (3.31%) accounted for around 90% of these features at the phylum level. The genera Bacteroides (24.6%), Alistipes (4.7%), Blautia (4.38%), Faecalibacterium (4.1%), Subdoligranulum (2.5%), and Ruminococcus (2.3%) were defined as the most abundant ones in all samples (Fig. 5, Supplementary Table 11, Supplementary dataset 3). In brief, as in the nasal samples (nPMA), the alpha diversity of the stool samples was significantly increased in dysosmics as compared to in normosmics (Shannon index: p = 0.043) with a stepwise decrease in the Shannon index and richness from bad to good olfactory performance (Shannon index/richness: AW p = 0.036; AG p = 0.018; Fig. 5). Although it is not obvious in the PCA plot, the Adonis test yielded a significant result (p = 0.04; Fig. 5). Several taxa, including Alistipes, Oscillospiraceae UCG002, and the Ruminococcus torques group, as well as Colidextribacter and Phascolarctobacterium, were found as putative biomarkers for dysosmia and normosmia, respectively (Aldex2; full list in Supplementary Table 12). An analysis of the community state types could not explain the observed differences based on olfactory performance (Supplementary Fig. 9).

Furthermore, no significant correlations between the key taxa of nasal and stool samples were found (Supplementary Fig. 10), and source tracking (at RSV and genus level) did not reveal a substantial overlap in the overall microbial signatures found in gut and nasal samples (0.49% for anosmics, 0.45% for normosmics; genus level).

For completeness, we also analyzed the information on the fungi and archaea from the stool dataset (Supplementary dataset 4 and 5). The fungal communities in both groups consisted mainly of food-borne fungi, including Saccharomyces (bread), Penicillium (cheese and meat), and Debaryomyces (production of vitamin B2 found on food) (Supplementary Fig. 11). Signatures of Debaryomyces (p = 0.05) and Scopulariopsis (p = 0.03; infections including sinusitis) were more common in dysosmics. Almost all archaeal signatures found (32 out of 45 features) belonged to the genus Methanobrevibacter (Supplementary Fig. 11). Due to the nested archaeal PCR approach taken, no differential abundance analysis of the olfactory groups could be performed.

One notable result is that Methanobrevibacter signatures were found to be the most abundant archaea in the nasal (PMA and nonPMA) and stool samples (universal and archaeal approach) in our study. Methanobrevibacter are widely distributed in the human aero-digestive tract. In particular, the archaeal signatures from anosmics clustered with signatures found in fecal samples, indicating a potential overlap (Supplementary Fig. 12).

Dysosmics tend to have a more heavily meat-based diet than normosmics.

Although one might assume that dietary habits would vary based on the different olfactory performance (i.e., the sense of smell is strongly linked to the sense of taste), we only found tendencies in this regard. The evident trends calculated based on TDI scores (p < 0.05; Spearman’s rho) included mainly the food categories meat (p = 0.002) and fish (p = 0.018), which were eaten more frequently in dysosmics, whereas the intake of fruits (p = 0.012) and legumes (p = 0.012) was higher in normosmics. When considering the microbiome aspect, only the processed meat intake (dysosmics; p = 0.025, Spearman’s rho) was significantly correlated with the fungal community of the dysosmics’ stool samples (Supplementary Fig. 13A,B), and the meat consumption, with the nasal microbiome of dysosmics (p = 0.018, nonPMA, Supplementary Fig. 13C,D). Several trends were also observed for nutrients (e.g., fatty acids, vitamins, carbohydrates), but only the essential omega-3 fatty acid ⍺-linolenic acid (q = 0.03; Spearman's rho), had a significantly higher correlation with olfactory function than dysfunction after p-value correction (Supplementary Tables 1 and 14). Interestingly, omega-3 fatty acids, in general, have been associated with an improvement in olfactory function in other studies ^61,62. All in all, normosmic participants seem to eat “healthier” (more fruits, less meat) than the dysosmics.

In addition to the amplicon information, the metabolites in the stool samples were determined. The presence of the metabolites D-fructose (p = 0.037), lactulose (p = 0.002), hypoxanthine (p = 0.037), and nicotinic acid (p = 0.007) were associated with better olfactory performance. Furthermore, an analysis of correlations between the metabolites and the key taxa found in stool and nasal samples revealed trends (p < 0.05) for both normosmics and dysosmics (Supplementary Tables 1 and 15; Supplementary Fig. 14).

Even though we detected trends, the changes in the diet could not explain the differences observed in the microbiome composition. However, in general, it seems as though normosmics are more likely to follow a vegetarian diet.

The full spectrum of the physiological effects of olfactory loss is still largely unknown, and, according to our findings, the olfactory microbiome is also affected or even involved in this devastating condition. By carrying out two independent studies, we could show that the microbiome composition varies with olfactory capacity. This was found to be mainly associated with an increased diversity in the dead/non-functional microbial cells at the olfactory epithelium. The source of these dead/non-functioning microbial cells has not been fully elucidated, but we found evidence that the oral cavity, rather than the gut, is a possible source.

The overall composition of the microbial community in the olfactory area varied as a function of olfactory performance. The same trends were observed in this study (Nose 2.0) and the pilot study, indicating reproducible results. Although the olfactory groups overlapped in terms of their microbial composition, the composition in dysosmics was more scattered with respect to beta diversity, suggesting that the microbial composition was not as stable as in the normosmics. Consistently, the analysis of the alpha diversity also revealed higher microbial richness (more different taxa) in samples with reduced olfactory performance.

In contrast to our results, Biswas et al. (2020) reported a loss of diversity in individuals with poor olfactory function; however, in this case, the authors investigated the microbiome of the sinuses, and thus the findings are not fully comparable ¹⁶. Other diseases of the upper respiratory tract have also been reported to be associated with lower levels of microbial diversity (e.g., chronic Rhinosinusitis ⁶³, cystic fibrosis ⁶⁴). Only allergic rhinitis has displayed the same trend as we found in our study, with higher levels of microbial diversity observed in diseased as compared to in healthy subjects ⁶⁵.

Interestingly, analyses of the data on viable/intact cells in the nasal samples revealed a nearly opposite trend, suggesting that dysosmics carry a higher load of dead/non-functional microorganisms. This result was also obtained when taking two independent molecular approaches, namely, PMA-treatment and metatranscriptomics. This finding is further supported by the fact that several taxa were found in both PMA-treated and untreated nasal samples, respectively; overlaps were observed among the groups at a genus level but also among an enormous number of the signatures that were specific for PMA-untreated samples (62% of total non-PMA signatures). Of the genera found in the non-PMA-specific dataset, only 24% belonged to the normosmic samples, while the normosmic proportion of the 121 genera found in both datasets (PMA and non-PMA) accounted for about 50%.

The excess number of RSVs might have been introduced from other body parts or the external environment. However, the impact of the gut microbiome was clarified by source tracking, and the results indicate that the dead material originated from other sources, such as other nasal areas, skin, the oral cavity, or the environment. We could even find evidence that in particular oral (or specifically subgingival) taxa such as Gemella, Capnocytophaga, Fusobacterium, and Porphyromonas were enriched in the olfactory area of dysosmics. Some of these have even been associated with periodontitis (e.g., Fusobacterium or Porphyromonas) in other studies ⁶⁶.

Chemosensory conditions have been associated with oral conditions in earlier studies ⁶⁷ and, in particular, with halitosis (e.g., Fusobacterium, Porphyromonas) that was associated with smell and taste disturbance ⁶⁸. Thus, our findings might indicate problematic dental conditions that would require additional and more thorough analyses. In any case, the higher load of dead/non-functional material might be due to a potentially reduced mucociliary clearance (i.e., where the dead material can no longer be transported towards the throat and/or microbes can migrate into the nasal cavity from lower body parts) in dysosmics.

Notably, dysosmics displayed a higher number of signatures for (dead/non-functional) intracellular microbes (e.g., Spiroplasma, Rickettsia; ^58,59); the reason for this observation is unclear, and the higher number of signatures is not correlated with increased inflammation. Although the cause of dysosmia in our participants was an infection, no significant difference in inflammatory markers (glucose, CRP, and IL6-L1) between the olfactory groups was observed. This result suggests that the initial inflammation/disease had been overcome, and the long-term smell loss was not driven by an active inflammation.

Interestingly, we found a significant difference in the stool microbiome based on olfactory performance. This stepwise decrease in alpha diversity from low to high olfactory performance and the distinctive microbial composition (normosmia: Bacteroides, Colidextribacter, Phascolarctobacterium; dysosmia: Alistipes, Oscillospiraceae UCG 002, Ruminococcus torques group) might be explained by changes in the diet, favoring different kinds of microbes. Overall, the dysosmics’ diet was characterized by a rather higher consumption of (processed) meat and fish as compared to the “healthier '' diet of normosmics (i.e., more fruits and legumes). Furthermore, we found a decreased intake of the omega-3 fatty acid, alpha-linolenic acid, in dysosmics. Consistent with our findings, other studies could show an improvement in olfactory function as a result of omega-3 fatty acid supplementation ^61,62. Since omega-3 fatty acids have neuroprotective and antioxidant effects, as well as boost the anti-inflammatory amino acid production, supplementation might serve as a putative therapy option for olfactory loss ^69,70.

Study limitations

To draw appropriate conclusions about the nasal microbiome and olfactory performance in humans, the olfactory mucosa was the sampling site of choice. This is a tiny area (8–10 mm) on the ceiling of the nasal cavity. Hence, the small volume of the specimens obtained from this area presented a major challenge, which was tackled for the first time in this study. Major issues that needed to be overcome were to find a less-contamination-prone, swab-less sampling procedure and a highly efficient DNA extraction method, both of which needed to be tested and established. However, the small amount of biomass retrieved still presented problems during the subsequent analyses. Thus, only 12 samples (six from 33 normosmics plus six from 20 dysosmics) were suitable for RNA sequencing and did not offer the possibility to re-run and improve the analyses. Therefore, we cannot make any statements about the link between olfaction and the functional profile of the microbiome; we were restricted to drawing conclusions from the taxonomic information.

We are aware that the overall number of participants (in total n = 53) is still low; the recruiting process was difficult due to a discrepancy in the personal olfactory perception of the study participants and the outcome of the sniffing-stick-based olfactory measurements. However, we were still able to include more participants with dysosmia than in our pilot study, which was included in order to run comparative analyses and to increase the total number of individuals analyzed. To minimize the study-bias, the pilot cohort was completely re-evaluated along with the new cohort.

Ongoing work

The results of our study show that the nasal microbiome differs according to olfactory performance and that the load of dead cells is significantly higher in dysosmics. Several independent studies have shown that the sense of smell can be improved by performing olfactory training ^71–73, but potential effects on the nasal microbiome have never been investigated. In the future, we plan to investigate whether the smell training can be used to revert the “dysosmic” microbiome to a more favorable “normosmic” one (e.g., less dead material) in a longitudinal study using three time points (before (TP0), during (TP1), and after (TP3) smell training). Furthermore, we want to use the gained knowledge to combine two distinct fields of the human life sciences, namely, microbiome research and neuroimaging, to investigate whether a reorganization of the brain structure takes place and to increase predictive and therapeutic opportunities.

People suffering from a loss of smell display a higher burden of dead microbial material near their olfactory receptor cells. The upper respiratory tract is equipped with highly efficient transport mechanisms, the so-called mucociliary clearance defense mechanism (reviewed in ¹⁵), which is responsible for the efficient removal of various kinds of material. This clearance process appears to be impaired in dysosmic subjects, which could lead to the increased migration and accumulation of microbes from other body sites or the external environment into the nasal passages.

We were able to exclude the gut as the source of these signatures, but linked specific periodontitis-associated microbial signatures to dysosmia. Investigating the relationship between olfactory function, the oral microbiome, and the functionality of mucociliary clearance may help researchers to understand the reason for the high burden of non-functioning microbes in the olfactory mucosa of dysosmics.

Nose 2.0 - new study cohort

pilot - old cohort (already published ¹³)

IL6- L1 - Interleukin 6

CRP - C-reactive protein

TDI - threshold/discrimination/Identification

ENT - ear nose throat

ITS - internal transcribed spacer

SRS - scaling with ranked subsampling

LEIDS-R = Leiden Index of Depression Sensitivity - revised; assesses the cognitive reactivity to sadness which is understood as cognitive vulnerability to depression

SPM = Standard Progressive Matrices; nonverbally assesses intelligence on a general level

FAIR = Frankfurter Aufmerksamkeits-Inventar, assesses the ability to concentrate and pay attention

PMA - propidium monoazide

N - normosmia

D - dysosmia

G - good normosmia

W - weak normosmia

H - hyposmia

A - anosmia

TP - timepoint

Data availability

Raw data obtained by amplicon sequencing (universal, archaeal, and fungal approaches) as well as metatranscriptomics can be found in the ENA Archive: Project Number PRJEB57226. Supplementary datasets (after preprocessing: decontam, removal of controls, mitochondrial and unassigned features, and features with less than 2 reads, and SRS normalization) are given in the Supplementary Data.

Acknowledgments

The authors acknowledge the staff at the MedBioNode at the Medical University Graz for providing access to computational resources and the support of the ZMF Galaxy Team: Core Facility Computational Bioanalytics, Medical University of Graz, funded by the Austrian Federal Ministry of Education, Science and Research, Hochschulraum-Strukturmittel 2016 grant, as part of BioTechMed Graz. We thank all study participants for providing sample material and questionnaires. We acknowledge the Team of the ENT Department who collected the nasal samples and the help of all students. We appreciate critical proof-reading and cross-checking of the figures and table by Charlotte Neumann.

Funding

This research was funded in whole, or in part, by the Austrian Science Fund (FWF) [KLI 639]. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. CME was additionally supported by FWF (P32697, P30796, DOC 129). TM was supported by FWF grants (P28854, I3792, DK-MCD W1226), the Austrian Research Promotion Agency (FFG) Grants 864690 and 870454; the Integrative Metabolism Research Center Graz; Austrian Infrastructure Program 2016/2017, the DOC-130, the City of Graz, the Styrian government doc. Fund program, the Styrian Government (Zukunftsfonds), and BioTechMed-Graz (Flagship project DYNIMO).

The funding bodies had no influence on the study, collection, analysis, or interpretation of data or on the writing of the manuscript.

Contributions

CK performed the lab work, including DNA and RNA extractions, PCR, and the data analysis (microbiome, metatranscriptome, correlation analyses), produced most of the displayed figures and tables, and wrote the manuscript. FF was responsible for the recruitment, sample and questionnaire/metadata collection, and the olfactory testing. SL and SH performed analysis of the dietary information, provided dietary indices, and contributed to manuscript writing. AW collected the nasal samples. FF and VS contributed to the manuscript writing. VS and CME initiated this project and were responsible for the study design. CME supervised all activities, performed analyses, and contributed to manuscript writing. TM and HJH performed NMR sample preparation and provided metabolomic data. The authors read and approved the final manuscript.

Ethics declarations

Ethics approval and consent to participate

The study was reviewed and approved by the local Ethics Committee of the University of Graz in accordance with the Declaration of Helsinki (EK-Nr. GZ. 39/44/63 ex 2017/18)). Before participation, all participants had to sign an informed consent form.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Conflict of interests

No conflict of interest and financial disclosures.

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No competing interests reported.

SuppFigure1.png
Supplementary Figure 1. Schematic overview of methodology used in the studies. Nasal (and stool) samples were collected; amplicon sequencing, metatranscriptomics and metabolomics were performed; and results were compared based on the metadata and study cohort. The boxes were color-coded as follows: dotted - nasal samples only, clear - stool samples only, grey - nasal and stool samples, orange – outputs.
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Supplementary Figure 2. Olfactory performance assessed via the standardized TDI score. A) The TDI scores of all participants based on the main olfactory groups (normosmia: TDI ≥ 31). B) For a more detailed analysis, normosmia as well as dysosmia were further divided into subcategories: good normosmia (TDI ≥ 41.25), weak normosmia (41.25 < TDI > 30.5), hyposmia (30.5 ≤ TDI > 16), and anosmia (TDI ≤ 16).
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Supplementary Figure 3. Venn diagram of the nasal microbiome based on both studies. The figure shows the unique as well as shared ASVs for the Nose 2.0 and pilot study results. For detailed information on these features, see Supplementary Table 5.
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Supplementary Figure 4. Fewer differences between olfactory groups and cohorts after division into olfactory subgroups. A) Bar chart of most abundant taxa at the phylum and genus levels. B) Alpha diversity indices tended to change stepwise from good to bad olfactory capability. C) Beta diversity did not show a shift of the clusters from good to bad olfactory performance as shown in the combined (grey box) as well as in each of the studies (green - Nose2.0; orange - pilot). D) Unweighted unifrac plot of the Nose 2.0 dataset at RSV level (based on TDI).
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Supplementary Figure 5. Putative microbial biomarkers were found for the olfactory main groups. CLR-transformed data were used to calculate significant differences between the olfactory main groups via aldex2 (p-values not corrected).
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Supplementary Figure 6. Intact cell signatures in the nasal samples show opposite trends compared to overall microbial signatures. This plot shows the A) amplicon sequencing results of the propidium monoazide (PMA)-treated nasal samples and B) the bacterial output of the metatranscriptomics analysis from the untreated nasal samples. Each of these shows the following: 1. Most abundant taxa at the phylum and genus levels. 2. Alpha diversity at the RSV level. 3. PCA plot at the RSV level. All analyses are based on TDI scores (p-values not corrected).
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Supplementary Figure 7. Approximately 50% of the shared signatures (between PMA-treated and -untreated) belonged to normosmics. Analysis based on Nose 2.0 only.
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Supplementary Figure 8. No significant differences were observed in fungal community or bacterial functions of nasal samples. A) Fungal community (colored by SEED level 1) B) Overview of the most abundant bacterial functions and insights into the diversity.
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Supplementary Figure 9. Community state types (CST) do not explain differences in stool microbiome based on olfaction. A) Associations among the top 11 and 25 abundant taxa at the phylum and genus levels. B) Three not-well-separated clusters of the predicted CSTs. C)LEfSe showing the biomarkers found for the three community state types. Analysis performed on Nose 2.0 only.
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Supplementary Figure 10. No significant correlations were observed between stool and nasal samples. A)Correlations between taxa found in nasal and stool samples of normosmics and dysosmics, respectively. B) Venn diagram showing the genera shared between nose and stool samples (see also Supplementary Table 13). Analysis based on Nose 2.0 only.
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Supplementary Figure 11. Non-bacterial signatures in nasal samples. A) Bar chart (and percentages) of the most abundant fungal taxa found in normosmics and dysosmics at different levels. B) Alpha diversity indices were similar when comparing the olfactory groups. C) Normosmic and dysosmic clusters overlapped in a PCA plot. D) All archaeal signatures found in the Nose 2.0 data. Analysis based on Nose 2.0 only.
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Supplementary Figure 12. Methanobrevibacter signatures from nasal samples in the context of oral, fecal, and environmental Methanobrevibacter signatures. Oral and fecal sequences were obtained from healthy individuals (unpublished data). Additional sequences (e.g., representatives of M. oralisand M. smithii), as well as environmental sources (rumen), were added to stabilize the tree. The origin of the sequences is shown in the outer ring, and the absolute abundance (sequence reads) of the nasal Methanobrevibacter sequences is displayed as a stacked bar. The most abundant nasal Methanobrevibacter sequences cluster separately (left), indicating a separate clade of respiratory-tract-associated Methanobrevibacter in both anosmics and normosmics. However, some Methanobrevibacter signatures obtained from anosmics clustered with archaeal sequences from fecal samples (right and elsewhere), indicating a potential overlap. Analysis based on Nose 2.0 only.
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Supplementary Figure 13. The dysosmics’ microbiome tended to be correlated with a meat-based diet. The BioEnvs analysis revealed a correlation between dietary information and taxa; on the one hand, this was found by analyzing stool samples using the A) universal approach or the B) fungal approach, and, on the other hand, this was found in C) non-PMA and D) PMA nasal samples. Analysis based on Nose 2.0 only.
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Supplementary Figure 14. Key taxa were associated with several stool metabolites. Heatmaps showing the correlations among metabolites found in stool samples and key taxa in A)stool samples and B) nasal samples, respectively (p-values not corrected; only key taxa of nasal and stool samples are shown). Analysis based on Nose 2.0 only.
SupplementaryData.xls
SupplementaryTables.xls

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Reduced olfactory performance is associated with changed microbial diversity, oralization and accumulation of dead biomaterial in the nasal olfactory area

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Abstract

BACKGROUND

METHODS

RESULTS

CONCLUSION

Figures

Background

Methods

3.0. Ethics

3.1. Design and subjects of the study

3.2. Olfactory testing and olfactory group definition

3.3. Dietary questionnaires

4.0. Microbial sample collection and preparation

4.1. DNA and RNA extraction

4.2. Amplicon sequencing

Sequence data procession and control

4.3. Metatranscriptomics analysis

Metatranscriptome sequencing

Metatranscriptomics data processing

4.4. Metabolomic analysis

4.5. BioEnv

4.6. Statistical analysis

4.7. Correlation heatmaps

4.8. Source tracking

4.9. Methanobrevibacter tree

Results

Description of the study and the study cohorts

Diversity of nasal microbial community differs based on olfactory performance.

The olfactory area of dysosmics contains many signatures of dead cells

Signatures of dead microbes in the nasal cavity do not come from the gut

Dysosmics tend to have a more heavily meat-based diet than normosmics.

Discussion

Study limitations

Ongoing work

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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