CRISPR/Cas9-Mediated Phage Resistance Is Not Impeded by the DNA Modifications of Phage T4

Bacteria rely on two known DNA-level defenses against their bacteriophage predators: restriction-modification and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR-associated (Cas) systems. Certain phages have evolved countermeasures that are known to block endonucleases. For example, phage T4 not only adds hydroxymethyl groups to all of its cytosines, but also glucosylates them, a strategy that defeats almost all restriction enzymes. We sought to determine whether these DNA modifications can similarly impede CRISPR-based defenses. In a bioinformatics search, we found naturally occurring CRISPR spacers that potentially target phages known to modify their DNA. Experimentally, we show that the Cas9 nuclease from the Type II CRISPR system of Streptococcus pyogenes can overcome a variety of DNA modifications in Escherichia coli. The levels of Cas9-mediated phage resistance to bacteriophage T4 and the mutant phage T4 gt, which contains hydroxymethylated but not glucosylated cytosines, were comparable to phages with unmodified cytosines, T7 and the T4-like phage RB49. Our results demonstrate that Cas9 is not impeded by N6-methyladenine, 5-methylcytosine, 5-hydroxymethylated cytosine, or glucosylated 5-hydroxymethylated cytosine

Complete Genome Sequences of T4-Like Bacteriophages RB3, RB5, RB6, RB7, RB9, RB10, RB27, RB33, RB55, RB59, and RB68

T4-like bacteriophages have been explored for phage therapy and are model organisms for phage genomics and evolution. Here, we describe the sequencing of 11 T4-like phages. We found a high nucleotide similarity among the T4, RB55, and RB59; RB32 and RB33; and RB3, RB5, RB6, RB7, RB9, and RB10 phages

Orthogonal Cas9 Proteins for RNA-Guided Gene Regulation and Editing

The Cas9 protein from the Streptococcus pyogenes CRISPR-Cas immune system has been adapted for both RNA-guided genome editing and gene regulation in a variety of organisms, but can mediate only a single activity at a time within any given cell. Here we characterize a set of fully orthogonal Cas9 proteins and demonstrate their ability to mediate simultaneous and independently targeted gene regulation and editing in bacteria and in human cells. We find that Cas9 orthologs display consistent patterns in their recognition of target sequences and identify a highly targetable protein from Neisseria meningitidis. Our results provide a basal set of orthogonal RNA-guided proteins for controlling biological systems and establish a general methodology for characterizing additional proteins and adapting them to eukaryotic cells

Towards in vivo editing of the human microbiome

Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 174-199).The human microbiota consists of 100 trillion microbial cells that naturally inhabit the body and harbors a rich reservoir of genetic elements collectively called the microbiome. Efforts based on metagenomic sequencing of microbiomes associated with healthy and diseased individuals have revealed vast effects of microbiota on human health. However, compared to the expanding amount of sequence data, little is known about the function of these microbes and their genes. Furthermore, current clinical approaches to modify the microbiota face several challenges, including colonization resistance in competitive environments such as the gut, and imprecise ecological perturbations using antibiotics and fecal transplants. The fundamental objective of this research is to develop safe methods to genetically edit the microbiome in vivo to promote human health. The abilities to introduce commensally fit strains and to control specificity of microbial modulations are critical steps towards ecological engineering of healthy microbiota. This thesis describes strategies to investigate, propagate, and ultimately engineer desired functions in microbiota. In particular, we developed a temporal functional metagenomics method to identify genes that improved microbial fitness in the mammalian gut in vivo. We also built foundational tools for delivering genetic elements and immunizing endogenous microbiota against acquiring antibiotic resistance and toxins. In addition to leveraging bacterial conjugation and the prokaryotic defense system CRISPR-Cas9, we employed bacteriophages for depleting native strains to empty the niche for an engineered version. Our work enables applications in engineering probiotic strains with augmented fitness and anti-pathogenesis properties, tempering host autoimmunity, and combating hospital-acquired infections and enteric diseases.by Stephanie J. Yaung.Ph. D

Plasma-Based Measurements of Tumor Heterogeneity Correlate with Clinical Outcomes in Metastatic Colorectal Cancer

Sequencing circulating tumor DNA (ctDNA) from liquid biopsies may better assess tumor heterogeneity than limited sampling of tumor tissue. Here, we explore ctDNA-based heterogeneity and its correlation with treatment outcome in STEAM, which assessed efficacy and safety of concurrent and sequential FOLFOXIRI-bevacizumab (BEV) vs. FOLFOX-BEV for first-line treatment of metastatic colorectal cancer. We sequenced 146 pre-induction and 89 post-induction patient plasmas with a 198-kilobase capture-based assay, and applied Mutant-Allele Tumor Heterogeneity (MATH), a traditionally tissue-based calculation of allele frequency distribution, on somatic mutations detected in plasma. Higher levels of MATH, particularly in the post-induction sample, were associated with shorter progression-free survival (PFS). Patients with high MATH vs. low MATH in post-induction plasma had shorter PFS (7.2 vs. 11.7 months; hazard ratio, 3.23; 95% confidence interval, 1.85–5.63; log-rank p < 0.0001). These results suggest ctDNA-based tumor heterogeneity may have potential prognostic value in metastatic cancers

From Information Overload to Actionable Insights: Digital Solutions for Interpreting Cancer Variants from Genomic Testing

Given the increase in genomic testing in routine clinical use, there is a growing need for digital technology solutions to assist pathologists, oncologists, and researchers in translating variant calls into actionable knowledge to personalize patient management plans. In this article, we discuss the challenges facing molecular geneticists and medical oncologists in working with test results from next-generation sequencing for somatic oncology, and propose key considerations for implementing a decision support software to aid the interpretation of clinically important variants. In addition, we review results from an example decision support software, NAVIFY Mutation Profiler. NAVIFY Mutation Profiler is a cloud-based software that provides curation, annotation, interpretation, and reporting of somatic variants identified by next-generation sequencing. The software reports a tiered classification based on consensus recommendations from AMP, ASCO, CAP, and ACMG. Studies with NAVIFY Mutation Profiler demonstrated that the software provided timely updates and accurate curation, as well as interpretation of variant combinations, demonstrating that decision support tools can help advance implementation of precision oncology

Cas9 reduces <i>E. coli</i> susceptibility to phages T7 and RB49.

<p>(A) Spacers against T7 were targeted against the primase/helicase gene (gene 4A and 4B). The PAM is underlined in the sequence and shown as a black box in the diagram showing the orientation and location of the protospacer (white box) on the gene. In a representative T7 plaque assay of protected and unprotected strains, there is substantial lysis on wild-type (wt) <i>E. coli</i> K-12, visible plaquing on cells with spacer 2 (sp 2), and no plaques on cells with spacer 1 (sp 1). (B) The efficiency of plating of T7 was calculated for each protected strain relative to the unprotected wild-type strain. Independent replicates of <i>E. coli</i> B (n = 4, 3, 3) and <i>E. coli</i> K-12 (n = 5, 5, 7) are plotted. Lines represent the median. (C) Spacers against RB49 were constructed against the major capsid protein (gp23). In a typical RB49 plaque assay, there is notable lysis on wild-type <i>E. coli</i> B, some plaques on cells with spacer 1, and a few plaques on cells protected with spacer 2. (D) The efficiency of plating of RB49 was quantified for each protected strain relative to the unprotected wild-type strain. Shown are independent replicates of <i>E. coli</i> B (n = 5, 3, 3) and <i>E. coli</i> K-12 (n = 3, 3, 3). Lines represent the median.</p

Cas9 cuts methylated cytosines and adenosines in <i>E. coli</i>.

<p>(A) Synthetic targets were designed to contain one to two dam (orange) or dcm (blue) sites. A control unmethylated sequence (+) was included. The PAM sequence NGG for SpCas9 recognition is underlined. (B) In serial transformations, we selected for the coexistence of DS-SPcas, the protospacer plasmid, and each spacer plasmid. The number of transformants was divided by the number of colonies resulting from a control transformation using a spacer plasmid (-) that did not target the protospacer plasmid. This relative number of transformants is plotted for <i>E. coli</i> K-12 and <i>E. coli</i> K-12 <i>dam⁻/dcm⁻</i> from three independent experiments. Lines represent the median.</p

Cas9 reduces <i>E. coli</i> susceptibility to phages T4 and T4 gt.

<p>(A) The structures of cytosine and modified cytosines are shown. T4 gt has 100% hydroxymethylated cytosines (hmCs). T4 has 100% glucosyl-hydroxymethylated cytosines (ghmCs), specifically 70% α- and 30% β-ghmCs. The ghmC structure shown is in the β-configuration. (B) Spacers against T4 were also designed against the major capsid protein (gp23), which is homologous to that of RB49. For comparison, the RB49 protospacers are aligned below in italics, where dots indicate identical nucleotides. In the T4 sequences, the PAM is underlined. The PAM (black box) and protospacer (white box) are represented on the gene. (C) In a typical plaque assay with T4 gt (left plate), there was complete lysis on wild-type (wt) restriction-less (r-l) <i>E. coli</i> K-12 and few plaques on cells with spacers 1, 2, or 3 (sp 1, sp 2, or sp 3). In an assay with T4 (right plate), there was complete lysis on wild-type <i>E. coli</i> K-12 MG1655, numerous plaques on cells with spacer 1 or 3, and about a dozen on spacer 2. (D) The efficiency of plating of T4 and T4 gt was quantified for each protected strain relative to the unprotected wild-type strain. Independent replicates of restriction-less <i>E. coli</i> K-12 (n = 5, 3, 3, 5), <i>E. coli</i> K-12 (n = 4, 4, 5, 6), and <i>E. coli</i> B (n = 5, 3, 3, 3) are plotted. Lines represent the median.</p

Native <i>E. coli</i> spacers target phage with modified DNA.

<p>In a BLASTn search, 1749 unique spacers from sequenced <i>E. coli</i> CRISPR arrays were queried against T4-like phage genomes. (A) Spacer S641 matches 25 of 32 nucleotides in phage T2. The putative protospacer has a permissible <i>E. coli</i> CRISPR PAM AAG and the matching nucleotides are concentrated at the 5′ end as a seed sequence. The spacer originated from the CRISPR1 locus of <i>E. coli</i> strain 579, a human-associated isolate from France. (B) Spacer S134 matches 29 of 32 nucleotides in phage CC31. While the protospacer in phage CC31 has five nucleotides inserted in the center of the sequence, there are 15 exactly matched nucleotides at the 5′ end in addition to 14 matched nucleotides after the insertion. The PAM GAG and strongly matched seed region suggest it is a plausible <i>E. coli</i> CRISPR target. This spacer was found in several strains, including <i>E. coli</i> C str. ATCC 8739, ECOR strains 17 through 21, one farm pig and two human fecal samples in France, duck and cattle fecal samples in Australia <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098811#pone.0098811-Sheludchenko1" target="_blank">[33]</a>, and enterotoxigenic <i>E. coli</i> (ETEC) strain UMNK88. The spacer and matching protospacer are in blue, the transcribed CRISPR RNA (crRNA) in bold black, and PAM sequence in red.</p