Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

The changes that occur in the microbiome of aging individuals are unclear, especially in light of the imperfect correlation of frailty with age. Studies in older human subjects have reported subtle effects, but these results may be confounded by other variables that often change with age such as diet and place of residence. To test these associations in a more controlled model system, we examined the relationship between age, frailty, and the gut microbiome of female C57BL/6 J mice. The frailty index, which is based on the evaluation of 31 clinical signs of deterioration in mice, showed a near-perfect correlation with age. We observed a statistically significant relationship between age and the taxonomic composition of the corresponding microbiome. Consistent with previous human studies, the Rikenellaceae family, which includes the Alistipes genus, was the most significantly overrepresented taxon within middle-aged and older mice. The functional profile of the mouse gut microbiome also varied with host age and frailty. Bacterial-encoded functions that were underrepresented in older mice included cobalamin (B12) and biotin (B7) biosynthesis, and bacterial SOS genes associated with DNA repair. Conversely, creatine degradation, associated with muscle wasting, was overrepresented within the gut microbiomes of the older mice, as were bacterial-encoded β-glucuronidases, which can influence drug-induced epithelial cell toxicity. Older mice also showed an overabundance of monosaccharide utilization genes relative to di-, oligo-, and polysaccharide utilization genes, which may have a substantial impact on gut homeostasis. We have identified taxonomic and functional patterns that correlate with age and frailty in the mouse microbiome. Differences in functions related to host nutrition and drug pharmacology vary in an age-dependent manner, suggesting that the availability and timing of essential functions may differ significantly with age and frailty. Future work with larger cohorts of mice will aim to separate the effects of age and frailty, and other factors.

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is an infectious virus that causes coronavirus disease 2019 (COVID-19) transmitted mainly through droplets and aerosol affecting the respiratory tract and lungs. Little is known regarding why some individuals are more susceptible than others and develop severe symptoms. In this study, we analyzed the nasopharyngeal microbiota profile of aged patients with COVID-19 (asymptomatic vs. symptomatic) vs. healthy individuals. We examined the nasopharynx swab of 84 aged-matched patients, out of which 27 were negative asymptomatic (NegA), 30 were positive asymptomatic (PA), and 27 patients were positive symptomatic (PSY). Our analysis revealed the presence of abundant Cyanobacterial taxa at phylum level in PA (p-value = 0.0016) and PSY (p-value = 0.00038) patients along with an upward trend in the population of Litoricola, Amylibacter, Balneola, and Aeromonas at the genus level. Furthermore, to know the relationship between the nasal microbiota composition and severity of COVID-19, we compared PA and PSY groups. Our data show that the nasal microbiota of PSY patients was significantly enriched with the signatures of two bacterial taxa: Cutibacterium (p-value = 0.045) and Lentimonas (p-value = 0.007). Furthermore, we also found a significantly lower abundance of five bacterial taxa, namely: Prevotellaceae (p-value = 7 × 10⁻⁶), Luminiphilus (p-value = 0.027), Flectobacillus (p-value = 0.027), Comamonas (p-value = 0.048), and Jannaschia (p-value = 0.012) in PSY patients. The dysbiosis of the nasal microbiota in COVID-19 positive patients might have a role in contributing to the severity of COVID-19. The findings of our study show that there is a strong correlation between the composition of the nasal microbiota and COVID-19 severity. Further studies are needed to validate our finding in large-scale samples and to correlate immune response (cytokine Strome) and nasal microbiota to identify underlying mechanisms and develop therapeutic strategies against COVID-19.