We present genome engineering technologies that are capable of fundamentally reengineering genomes from the nucleotide to the megabase scale. We used multiplex automated genome engineering (MAGE) to site-specifically replace all 314 TAG stop codons with synonymous TAA codons in parallel across 32 Escherichia coli strains. This approach allowed us to measure individual recombination frequencies, confirm viability for each modification, and identify associated phenotypes. We developed hierarchical conjugative assembly genome engineering (CAGE) to merge these sets of codon modifications into genomes with 80 precise changes, which demonstrate that these synonymous codon substitutions can be combined into higher-order strains without synthetic lethal effects. Our methods treat the chromosome as both an editable and an evolvable template, permitting the exploration of vast genetic landscapes.

In most industrialized countries, screening programs for cervical cancer have shifted from cytology (Pap smear or ThinPrep) alone on clinician-obtained samples to the addition of screening for human papillomavirus (HPV), its main causative agent. For HPV testing, self-sampling instead of clinician-sampling has proven to be equally accurate, in particular for assays that use nucleic acid amplification techniques. In addition, HPV testing of self-collected samples in combination with a follow-up Pap smear in case of a positive result is more effective in detecting precancerous lesions than a Pap smear alone. Self-sampling for HPV testing has already been adopted by some countries, while others have started trials to evaluate its incorporation into national cervical cancer screening programs. Self-sampling may result in more individuals willing to participate in cervical cancer screening, because it removes many of the barriers that prevent women, especially those in low socioeconomic and minority populations, from participating in regular screening programs. Several studies have shown that the majority of women who have been underscreened but who tested HPV-positive in a self-obtained sample will visit a clinic for follow-up diagnosis and management. In addition, a self-collected sample can also be used for vaginal microbiome analysis, which can provide additional information about HPV infection persistence as well as vaginal health in general.

Experimental efforts to characterize the human microbiota often use bacterial strains that were chosen for historical rather than biological reasons. Here, we report an analysis of 380 whole-genome shotgun samples from 100 subjects from the NIH Human Microbiome Project. By mapping their reads to 1,751 reference genome sequences and analyzing the resulting relative strain abundance in each sample we present metrics and visualizations that can help identify strains of interest for experimentalists. We also show that approximately 14 strains of 10 species account for 80% of the mapped reads from a typical stool sample, indicating that the function of a community may not be irreducibly complex. Some of these strains account for >20% of the sequence reads in a subset of samples but are absent in others, a dichotomy that could underlie biological differences among subjects. These data should serve as an important strain selection resource for the community of researchers who take experimental approaches to studying the human microbiota.

Changes in the relative abundances of many intestinal microorganisms, both those that naturally occur in the human gut microbiome and those that are considered pathogens, have been associated with a range of diseases. To more accurately diagnose health conditions, medical practitioners could benefit from a molecular, culture-independent assay for the quantification of these microorganisms in the context of a healthy reference range. Here we present the targeted sequencing of the microbial 16S rRNA gene of clinically relevant gut microorganisms as a method to provide a gut screening test that could assist in the clinical diagnosis of certain health conditions. We evaluated the possibility of detecting 46 clinical prokaryotic targets in the human gut, 28 of which could be identified with high precision and sensitivity by a bioinformatics pipeline that includes sequence analysis and taxonomic annotation. These targets included 20 commensal, 3 beneficial (probiotic), and 5 pathogenic intestinal microbial taxa. Using stool microbiome samples from a cohort of 897 healthy individuals, we established a reference range defining clinically relevant relative levels for each of the 28 targets. Our assay quantifies 28 targets in the context of a healthy reference range and correctly reflected 38/38 verification samples of real and synthetic stool material containing known gut pathogens. Thus, we have established a method to determine microbiome composition with a focus on clinically relevant taxa, which has the potential to contribute to patient diagnosis, treatment, and monitoring. More broadly, our method can facilitate epidemiological studies of the microbiome as it relates to overall human health and disease.