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Summary Despite the great diversity of vertebrate limb proportion and our deep understanding of the genetic mechanisms that drive skeletal elongation, little is known about how individual bones reach different lengths in any species. Here, we directly compare the transcriptomes of homologous growth cartilages of the mouse (Mus musculus) and bipedal jerboa (Jaculus jaculus), the latter of which has “mouse-like” arms but extremely long metatarsals of the feet. Intersecting gene-expression differences in metatarsals and forearms of the two species revealed that about 10% of orthologous genes are associated with the disproportionately rapid elongation of neonatal jerboa feet. These include genes and enriched pathways not previously associated with endochondral elongation as well as those that might diversify skeletal proportion in addition to their known requirements for bone growth throughout the skeleton. We also identified transcription regulators that might act as “nodes” for sweeping differences in genome expression between species. Among these, Shox2, which is necessary for proximal limb elongation, has gained expression in jerboa metatarsals where it has not been detected in other vertebrates. We show that Shox2 is sufficient to increase mouse distal limb length, and a nearby putative cis-regulatory region is preferentially accessible in jerboa metatarsals. In addition to mechanisms that might directly promote growth, we found evidence that jerboa foot elongation may occur in part by de-repressing latent growth potential. The genes and pathways that we identified here provide a framework to understand the modular genetic control of skeletal growth and the remarkable malleability of vertebrate limb proportion.

Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual-guide RNAs in three cell lines, altogether comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified and these patterns replicated with combinatorial drugs at 75% precision. Based on these results we anticipate cellular context will be critical to synthetic-lethal therapies.

Despite the great diversity of vertebrate limb proportion and our deep understanding of the genetic mechanisms that drive skeletal elongation, little is known about how individual bones reach different lengths in any species. Here, we directly compare the transcriptomes of homologous growth cartilages of the mouse (Mus musculus) and bipedal jerboa (Jaculus jaculus), which has extremely long metatarsals of the feet and ‘mouse-like’ arms. When we intersected gene expression differences in metatarsals of the two species with expression differences in forearms, we found that about 10% of all orthologous genes are associated with disproportionate elongation of jerboa feet. Among these,Shox2, has gained expression in jerboa metatarsals where it is not expressed in other vertebrates that have been assessed. This transcription factor is necessary for proximal limb elongation, and we show that it is sufficient to increase mouse distal limb length. Unexpectedly, we also found evidence that jerboa foot elongation occurs in part by releasing latent growth potential that is repressed in mouse feet. In jerboa metatarsals, we observed higher expression ofCrabp1, an antagonist of growth inhibitory retinoic acid, lower expression ofGdf10, an inhibitory TGFβ ligand, and lower expression ofMab21L2, a BMP signaling inhibitor that we show is sufficient to reduce limb bone elongation. By intersecting our data with prior expression analyses in other systems, we identify mechanisms that may both establish limb proportion during development and diversify proportion during evolution. The genes we identified here therefore provide a framework to understand the modular genetic control of skeletal growth and the remarkable malleability of vertebrate limb proportion.

RNA interference screening using pooled, short hairpin RNA (shRNA) is a powerful, high-throughput tool for determining the biological relevance of genes for a phenotype. Assessing an shRNA pooled screen’s performance is difficult in practice; one can estimate the performance only by using reproducibility as a proxy for power or by employing a large number of validated positive and negative controls. Here, we develop an open-source software tool, the Power Decoder simulator, for generating shRNA pooled screening experiments in silico that can be used to estimate a screen’s statistical power. Using the negative binomial distribution, it models both the relative abundance of multiple shRNAs within a single screening replicate and the biological noise between replicates for each individual shRNA. We demonstrate that this simulator can successfully model the data from an actual laboratory experiment. We then use it to evaluate the effects of biological replicates and sequencing counts on the performance of a pooled screen, without the necessity of gathering additional data. The Power Decoder simulator is written in R and Python and is available for download under the GNU General Public License v3.0.

The metabolic pathways fueling tumor growth have been well characterized, but the specific impact of transforming events on network topology and enzyme essentiality remains poorly understood. To this end, we performed combinatorial CRISPR-Cas9 screens on a set of 51 carbohydrate metabolism genes that represent glycolysis and the pentose phosphate pathway. This high-throughput methodology enabled systems-level interrogation of metabolic gene dispensability, interactions, and compensation across multiple cell types. The metabolic impact of specific combinatorial knockouts were validated using 13C and 2H isotope tracing, and, these assays together revealed key nodes controlling redox homeostasis along the KEAP1-NRF2 signaling axis. Specifically, targeting KEAP1 in combination with oxidative PPP enzymes mitigated the deleterious effects of these knockouts on growth rates. These results demonstrate how our integrated framework, combining genetic, transcriptomic, and flux measurements, can improve elucidation of metabolic network alterations, and guide precision targeting of metabolic vulnerabilities based on tumor genetics.

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

Off-target gene silencing can present a notable challenge in the interpretation of data from large-scale RNA interference (RNAi) screens. We performed a detailed analysis of off-targeted genes identified by expression profiling of human cells transfected with small interfering RNA (siRNA). Contrary to common assumption, analysis of the subsequent off-target gene database showed that overall identity makes little or no contribution to determining whether the expression of a particular gene will be affected by a given siRNA, except for near-perfect matches. Instead, off-targeting is associated with the presence of one or more perfect 3' untranslated region (UTR) matches with the hexamer or heptamer seed region (positions 2-7 or 2-8) of the antisense strand of the siRNA. These findings have strong implications for future siRNA design and the application of RNAi in high-throughput screening and therapeutic development.

Nat. Methods 3, 199–204 (2006). In Figure 3 of the original article, the genes in panels a–c were incorrectly identified. The correct gene names are: (a) Ppyr/LUC siRNA A, (b) ALPPL2 and (c) Ppyr/LUC siRNA B. The figure legend should read: Figure 3 | Systematic single base pair–mismatch analysis of siRNA functionality.

Addendum: 3′ UTR seed matches, but not overall identity, are associated with RNAi off-targets

The discovery that the bacterial clustered regularly interspaced short palindromic repeats (CRISPR)–CRISPR-associated protein 9 (Cas9) acquired immune system can be utilized to create double-strand breaks (DSBs) in eukaryotic genomes has resulted in the ability to create genomic changes more easily than with other genome engineering techniques. While there is significant potential for the CRISPR–Cas9 system to advance basic and applied research, several unknowns remain, including the specificity of the RNA-directed DNA cleavage by the small targeting RNA, the CRISPR RNA (crRNA). Here we describe a novel synthetic RNA approach that allows for high-throughput gene editing experiments. This was used with a functional assay for protein disruption to perform high-throughput analysis of crRNA activity and specificity. We performed a comprehensive test of target cleavage using crRNAs that contain one and two nucleotide mismatches to the DNA target in the 20mer targeting region of the crRNA, allowing for the evaluation of hundreds of potential mismatched target sites without the requirement for the off-target sequences and their adjacent PAMs to be present in the genome. Our results demonstrate that while many crRNAs are functional, less than 5% of crRNAs with two mismatches to their target are effective in gene editing; this suggests an overall high level of functionality but low level of off-targeting.

Human microbiome reference datasets provide epidemiological context for researchers, enabling them to uncover new insights into their own data through meta-analyses. In addition, large and comprehensive reference sets offer a means to develop or test hypotheses and can pave the way for addressing practical study design considerations such as sample size decisions. We discuss the importance of reference sets in human microbiome research, limitations of existing resources, technical challenges to employing reference sets, examples of their usage, and contributions of the American Gut Project to the development of a comprehensive reference set. Through engaging the general public, the American Gut Project aims to address many of the issues present in existing reference resources, characterizing health and disease, lifestyle, and dietary choices of the participants while extending its efforts globally through international collaborations.