Addressing erroneous scale assumptions in microbe and gene set enrichment analysis.

By applying Differential Set Analysis (DSA) to sequence count data, researchers can determine whether groups of microbes or genes are differentially enriched. Yet sequence count data suffer from a scale limitation: these data lack information about the scale (i.e., size) of the biological system under study, leading some authors to call these data compositional (i.e., proportional). In this article, we show that commonly used DSA methods that rely on normalization make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these scale assumptions can lead to positive predictive values as low as 9%. To address this problem, we take three novel approaches. First, we introduce a sensitivity analysis framework to identify when modeling results are robust to such errors and when they are suspect. Unlike standard benchmarking studies, this framework does not require ground-truth knowledge and can therefore be applied to both simulated and real data. Second, we introduce a statistical test that provably controls Type-I error at a nominal rate despite errors in scale assumptions. Finally, we discuss how the impact of scale limitations depends on a researcher's scientific goals and provide tools that researchers can use to evaluate whether their goals are at risk from erroneous scale assumptions. Overall, the goal of this article is to catalyze future research into the impact of scale limitations in analyses of sequence count data; to illustrate that scale limitations can lead to inferential errors in practice; yet to also show that rigorous and reproducible scale reliant inference is possible if done carefully.

Non-viral Delivery of Zinc Finger Nuclease mRNA Enables Highly Efficient In Vivo Genome Editing of Multiple Therapeutic Gene Targets.

It has previously been shown that engineered zinc finger nucleases (ZFNs) can be packaged into adeno-associated viruses (AAVs) and delivered intravenously into mice, non-human primates, and most recently, humans to induce highly efficient therapeutic genome editing in the liver. Lipid nanoparticles (LNPs) are synthetic delivery vehicles that enable repeat administration and are not limited by the presence of preexisting neutralizing antibodies in patients. Here, we show that mRNA encoding ZFNs formulated into LNP can enable >90% knockout of gene expression in mice by targeting the TTR or PCSK9 gene, at mRNA doses 10-fold lower than has ever been reported. Additionally, co-delivering mRNA-LNP containing ZFNs targeted to intron 1 of the ALB locus with AAV packaged with a promoterless human IDS or FIX therapeutic transgene can result in high levels of targeted integration and subsequent therapeutically relevant levels of protein expression in mice. Finally, we show repeat administration of ZFN mRNA-LNP after a single AAV donor dose results in significantly increased levels of genome editing and transgene expression compared to a single dose. These results demonstrate LNP-mediated ZFN mRNA delivery can drive highly efficient levels of in vivo genome editing and can potentially offer a new treatment modality for a variety of diseases.

NmeCas9 is an intrinsically high-fidelity genome-editing platform.

BACKGROUND: The development of CRISPR genome editing has transformed biomedical research. Most applications reported thus far rely upon the Cas9 protein from Streptococcus pyogenes SF370 (SpyCas9). With many RNA guides, wildtype SpyCas9 can induce significant levels of unintended mutations at near-cognate sites, necessitating substantial efforts toward the development of strategies to minimize off-target activity. Although the genome-editing potential of thousands of other Cas9 orthologs remains largely untapped, it is not known how many will require similarly extensive engineering to achieve single-site accuracy within large genomes. In addition to its off-targeting propensity, SpyCas9 is encoded by a relatively large open reading frame, limiting its utility in applications that require size-restricted delivery strategies such as adeno-associated virus vectors. In contrast, some genome-editing-validated Cas9 orthologs are considerably smaller and therefore better suited for viral delivery. RESULTS: Here we show that wildtype NmeCas9, when programmed with guide sequences of the natural length of 24 nucleotides, exhibits a nearly complete absence of unintended editing in human cells, even when targeting sites that are prone to off-target activity with wildtype SpyCas9. We also validate at least six variant protospacer adjacent motifs (PAMs), in addition to the preferred consensus PAM (5'-N4GATT-3'), for NmeCas9 genome editing in human cells. CONCLUSIONS: Our results show that NmeCas9 is a naturally high-fidelity genome-editing enzyme and suggest that additional Cas9 orthologs may prove to exhibit similarly high accuracy, even without extensive engineering.