Engineered phage with antibacterial CRISPR-Cas selectively reduce E. coli burden in mice.

Antibiotic treatments have detrimental effects on the microbiome and lead to antibiotic resistance. To develop a phage therapy against a diverse range of clinically relevant Escherichia coli, we screened a library of 162 wild-type (WT) phages, identifying eight phages with broad coverage of E. coli, complementary binding to bacterial surface receptors, and the capability to stably carry inserted cargo. Selected phages were engineered with tail fibers and CRISPR-Cas machinery to specifically target E. coli. We show that engineered phages target bacteria in biofilms, reduce the emergence of phage-tolerant E. coli and out-compete their ancestral WT phages in coculture experiments. A combination of the four most complementary bacteriophages, called SNIPR001, is well tolerated in both mouse models and minipigs and reduces E. coli load in the mouse gut better than its constituent components separately. SNIPR001 is in clinical development to selectively kill E. coli, which may cause fatal infections in hematological cancer patients.

Identification of representative species-specific genes for abundance measurements.

Motivation: Metagenomic binning facilitates the reconstruction of genomes and identification of Metagenomic Species Pan-genomes or Metagenomic Assembled Genomes. We propose a method for identifying a set of de novo representative genes, termed signature genes, which can be used to measure the relative abundance and used as markers of each metagenomic species with high accuracy. Results: An initial set of the 100 genes that correlate with the median gene abundance profile of the entity is selected. A variant of the coupon collector's problem was utilized to evaluate the probability of identifying a certain number of unique genes in a sample. This allows us to reject the abundance measurements of strains exhibiting a significantly skewed gene representation. A rank-based negative binomial model is employed to assess the performance of different gene sets across a large set of samples, facilitating identification of an optimal signature gene set for the entity. When benchmarked the method on a synthetic gene catalog, our optimized signature gene sets estimate relative abundance significantly closer to the true relative abundance compared to the starting gene sets extracted from the metagenomic species. The method was able to replicate results from a study with real data and identify around three times as many metagenomic entities. Availability and implementation: The code used for the analysis is available on GitHub: https://github.com/trinezac/SG_optimization. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

A computational framework to integrate high-throughput '-omics' datasets for the identification of potential mechanistic links.

We recently presented a three-pronged association study that integrated human intestinal microbiome data derived from shotgun-based sequencing with untargeted serum metabolome data and measures of host physiology. Metabolome and microbiome data are high dimensional, posing a major challenge for data integration. Here, we present a step-by-step computational protocol that details and discusses the dimensionality-reduction techniques used and methods for subsequent integration and interpretation of such heterogeneous types of data. Dimensionality reduction was achieved through a combination of data normalization approaches, binning of co-abundant genes and metabolites, and integration of prior biological knowledge. The use of prior knowledge to overcome functional redundancy across microbiome species is one central advance of our method over available alternative approaches. Applying this framework, other investigators can integrate various '-omics' readouts with variables of host physiology or any other phenotype of interest (e.g., connecting host and microbiome readouts to disease severity or treatment outcome in a clinical cohort) in a three-pronged association analysis to identify potential mechanistic links to be tested in experimental settings. Although we originally developed the framework for a human metabolome-microbiome study, it is generalizable to other organisms and environmental metagenomes, as well as to studies including other -omics domains such as transcriptomics and proteomics. The provided R code runs in ~1 h on a standard PC.