Seasonal activities of the phyllosphere microbiome of perennial crops.

Understanding the interactions between plants and microorganisms can inform microbiome management to enhance crop productivity and resilience to stress. Here, we apply a genome-centric approach to identify ecologically important leaf microbiome members on replicated plots of field-grown switchgrass and miscanthus, and to quantify their activities over two growing seasons for switchgrass. We use metagenome and metatranscriptome sequencing and curate 40 medium- and high-quality metagenome-assembled-genomes (MAGs). We find that classes represented by these MAGs (Actinomycetia, Alpha- and Gamma- Proteobacteria, and Bacteroidota) are active in the late season, and upregulate transcripts for short-chain dehydrogenase, molybdopterin oxidoreductase, and polyketide cyclase. Stress-associated pathways are expressed for most MAGs, suggesting engagement with the host environment. We also detect seasonally activated biosynthetic pathways for terpenes and various non-ribosomal peptide pathways that are poorly annotated. Our findings support that leaf-associated bacterial populations are seasonally dynamic and responsive to host cues.

Identification and Evolution of Cas9 tracrRNAs.

Clustered regularly interspaced palindromic repeats (CRISPR)-associated (Cas)9 transactivating CRISPR RNAs (tracrRNAs) form distinct structures essential for target recognition and cleavage and dictate exchangeability between orthologous proteins. As noncoding RNAs that are often apart from the CRISPR array, their identification can be arduous. In this article, a new bioinformatic method for the detection of Cas9 tracrRNAs is presented. The approach utilizes a covariance model based on both sequence homology and predicted secondary structure to locate tracrRNAs. This method predicts a tracrRNA for 98% of CRISPR-Cas9 systems identified by us. To ensure accuracy, we also benchmark our approach against biochemically vetted tracrRNAs finding false-positive and false-negative rates of 5.5% and 7.1%, respectively. Finally, the association between Cas9 amino acid sequence-based phylogeny and tracrRNA secondary structure is evaluated, revealing strong evidence that secondary structure is evolutionarily conserved among Cas9 lineages. Altogether, our findings provide insight into Cas9 tracrRNA evolution and efforts to characterize the tracrRNA of Cas9 systems.

A catalogue of biochemically diverse CRISPR-Cas9 orthologs.

Bacterial Cas9 nucleases from type II CRISPR-Cas antiviral defence systems have been repurposed as genome editing tools. Although these proteins are found in many microbes, only a handful of variants are used for these applications. Here, we use bioinformatic and biochemical analyses to explore this largely uncharacterized diversity. We apply cell-free biochemical screens to assess the protospacer adjacent motif (PAM) and guide RNA (gRNA) requirements of 79 Cas9 proteins, thus identifying at least 7 distinct gRNA classes and 50 different PAM sequence requirements. PAM recognition spans the entire spectrum of T-, A-, C-, and G-rich nucleotides, from single nucleotide recognition to sequence strings longer than 4 nucleotides. Characterization of a subset of Cas9 orthologs using purified components reveals additional biochemical diversity, including both narrow and broad ranges of temperature dependence, staggered-end DNA target cleavage, and a requirement for long stretches of homology between gRNA and DNA target. Our results expand the available toolset of RNA-programmable CRISPR-associated nucleases.

Application of a new informatics tool for contamination screening in the HIV sequencing laboratory.

BACKGROUND: Current HIV-1 sequencing-based methods for detecting drug resistance-associated mutations are open and susceptible to contamination. Informatic identification of clinical sequences that are nearly identical to one another may indicate specimen-to-specimen contamination or another laboratory-associated issue. OBJECTIVES: To design an informatic tool to rapidly identify potential contamination in the clinical laboratory using sequence analysis and to establish reference ranges for sequence variation in the HIV-1 protease and reverse transcriptase regions among a U.S. patient population. STUDY DESIGN: We developed an open-source tool named HIV Contamination Detection (HIVCD). HIVCD was utilized to make pairwise comparisons of nearly 8000 partial HIV-1 pol gene sequences from patients across the United States and to calculate percent identities (PIDs) for each pair. ROC analysis and standard deviations of PID data were used to determine reference ranges for between-patient and within-patient comparisons and to guide selection of a threshold for identifying abnormally high PID between two unrelated sequences. RESULTS: The PID reference range for between-patient comparisons ranged from 83.8 to 95.7% while within-patient comparisons ranged from 96 to 100%. Interestingly, 48% of between-patient sequence pairs with a PID>96.5 were geographically related. The selected threshold for abnormally high PIDs was 96 (AUC=0.993, sensitivity=0.980, specificity=0.999). During routine use, HIVCD identified a specimen mix-up and the source of contamination of a negative control. CONCLUSIONS: In our experience, HIVCD is easily incorporated into laboratory workflow, useful for identifying potential laboratory errors, and contributes to quality testing. This type of analysis should be incorporated into routine laboratory practice.