Research on the human microbiome has yielded numerous insights into health and disease, but also has resulted in a wealth of experimental artifacts. Here, we present suggestions for optimizing experimental design and avoiding known pitfalls, organized in the typical order in which studies are carried out. We first review best practices in experimental design and introduce common confounders such as age, diet, antibiotic use, pet ownership, longitudinal instability, and microbial sharing during cohousing in animal studies. Typically, samples will need to be stored, so we provide data on best practices for several sample types. We then discuss design and analysis of positive and negative controls, which should always be run with experimental samples. We introduce a convenient set of non-biological DNA sequences that can be useful as positive controls for high-volume analysis. Careful analysis of negative and positive controls is particularly important in studies of samples with low microbial biomass, where contamination can comprise most or all of a sample. Lastly, we summarize approaches to enhancing experimental robustness by careful control of multiple comparisons and to comparing discovery and validation cohorts. We hope the experimental tactics summarized here will help researchers in this exciting field advance their studies efficiently while avoiding errors.

Objectives Prior studies have suggested a potential link between nasal microbes and granulomatosis with polyangiitis (GPA; Wegener’s), but these studies relied on culture-dependent methods. This study comprehensively examined the entire community of nasal microbiota (bacteria and fungi) in participants with GPA compared with healthy controls using deep sequencing methods. Methods 16S rRNA and internal transcribed spacer gene sequencing were performed on nasal microbial DNA isolated from nasal swabs of 60 participants with GPA and 41 healthy controls. Alpha and beta diversity were assessed as well as the relative abundance of the most abundant bacterial and fungal taxa. The effects of covariates including disease activity and immunosuppressive therapies on microbial composition were evaluated. Results Compared with controls, participants with GPA had a significantly different microbial composition (weighted UniFrac p=0.04) and lower relative abundance of Propionibacterium acnes and Staphylococcus epidermidis (for both, false discovery rate-corrected p=0.02). Disease activity in GPA was associated with a lower abundance of fungal order Malasseziales compared with participants with GPA in remission (p=0.04) and controls (p=0.01). Use of non-glucocorticoid immunosuppressive therapy was associated with ‘healthy’ nasal microbiota while participants with GPA who were off immunosuppressive therapy had more dysbiosis (weighted UniFrac p=0.01). No difference in the relative abundance of Staphylococcus aureus was observed between GPA and controls. Conclusions GPA is associated with an altered nasal microbial composition, at both the bacterial and fungal levels. Use of immunosuppressive therapies and disease remission are associated with healthy microbial communities.

The Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants in microbial populations. Here, we present MIDAS2, which addresses the computational challenges presented by increasingly large reference genome databases, while adding functionality for building custom databases and leveraging paired-end reads to improve SNV accuracy. This fast and scalable reengineering of the MIDAS pipeline enables thousands of metagenomic samples to be efficiently genotyped. The source code is available at https://github.com/czbiohub/MIDAS2. Supplementary data are available at Bioinformatics online.

Metagenomics has greatly expanded our understanding of the human gut microbiome by revealing a vast diversity of bacterial species within and across individuals. Even within a single species, different strains can have highly divergent gene content, affecting traits such as antibiotic resistance, metabolism, and virulence. Methods that harness metagenomic data to resolve strain-level differences in functional potential are crucial for understanding the causes and consequences of this intraspecific diversity. The enormous size of pangenome references, strain mixing within samples, and inconsistent sequencing depth present challenges for existing tools that analyze samples one at a time. To address this gap, we updated the MIDAS pangenome profiler, now released as version 3, and developed StrainPGC, an approach to strain-specific gene content estimation that combines strain tracking and correlations across multiple samples. We validate our integrated analysis using a complex synthetic community of strains from the human gut and find that StrainPGC outperforms existing approaches. Analyzing a large, publicly available metagenome collection from inflammatory bowel disease patients and healthy controls, we catalog the functional repertoires of thousands of strains across hundreds of species, capturing extensive diversity missing from reference databases. Finally, we apply StrainPGC to metagenomes from a clinical trial of fecal microbiota transplantation for the treatment of ulcerative colitis. We identify twoEscherichia colistrains, from two different donors, that are both frequently transmitted to patients but have notable differences in functional potential. StrainPGC and MIDAS v3 together enable precise, intraspecific pangenomic investigations using large collections of metagenomic data without microbial isolation or de novo assembly.

Direct, amplification-free detection of RNA has the potential to transform molecular diagnostics by enabling simple on-site analysis of human or environmental samples. CRISPR-Cas nucleases offer programmable RNA-guided recognition of RNA that triggers cleavage and release of a fluorescent reporter molecule ^1,2 , but long reaction times hamper sensitivity and speed when applied to point-of-care testing. Here we show that unrelated CRISPR nucleases can be deployed in tandem to provide both direct RNA sensing and rapid signal generation, thus enabling robust detection of ∼30 RNA copies/microliter in 20 minutes. Combining RNA-guided Cas13 and Csm6 with a chemically stabilized activator creates a one-step assay that detected SARS-CoV-2 RNA from nasopharyngeal samples with PCR-derived Ct values up to 29 in microfluidic chips, using a compact imaging system. This Fast Integrated Nuclease Detection In Tandem (FIND-IT) approach enables direct RNA detection in a format amenable to point-of-care infection diagnosis, as well as to a wide range of other diagnostic or research applications.