Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 62,232 bioRxiv papers from 276,288 authors.
Most downloaded bioRxiv papers, since beginning of last month
49,529 results found. For more information, click each entry to expand.
615 downloads cell biology
At the end of mitosis, eukaryotic cells must segregate both copies of their replicated genome into two new nuclear compartments. They do this either by first dismantling and later reassembling the nuclear envelope in a so-called open mitosis, or by reshaping an intact nucleus and then dividing into two in a closed mitosis. However, while mitosis has been studied in a wide variety of eukaryotes for over a century, it is not known how the double membrane of the nuclear envelope is split into two at the end of a closed mitosis without compromising the impermeability of the nuclear compartment. In studying this problem in the fission yeast Schizosaccharomyces pombe , a classical model for closed mitosis, we use genetics, live cell imaging and electron tomography to show that nuclear fission is achieved via local disassembly of the nuclear envelope (NE) within the narrow bridge that links segregating daughter nuclei. In doing so, we identify a novel inner NE-localised protein Les1 that restricts the process of local NE breakdown (local NEB) to the bridge midzone and prevents the leakage of material from daughter nuclei. The mechanics of local NEB in a closed mitosis closely mirror those of NEB in open mitosis, revealing an unexpectedly deep conservation of nuclear remodelling mechanisms across diverse eukaryotes.
612 downloads neuroscience
Visual perception relies on cortical representations of visual objects that remain relatively stable with respect to the variation in object appearance typically encountered during natural vision (e.g., because of position changes). Such stability, known as transformation tolerance , is built incrementally along the ventral stream (the cortical hierarchy devoted to shape processing), but early evidence of position tolerance is already found in primary visual cortex (V1) for complex cells . To date, it remains unknown what mechanisms drive the development of this class of neurons, as well as the emergence of tolerance across the ventral stream. Leading theories suggest that tolerance is learned, in an unsupervised manner, either from the temporal continuity of natural visual experience– or from the spatial statistics of natural scenes,. However, neither learning principle has been empirically proven to be at work in the postnatal developing cortex. Here we show that passive exposure to temporally continuous visual inputs during early postnatal life is essential for normal development of complex cells in rat V1. This was causally demonstrated by rearing newborn rats with frame-scrambled versions of natural movies, resulting in temporally unstructured visual input, but with unaltered, natural spatial statistics. This led to a strong reduction of the fraction of complex cells, which also displayed an abnormally fast response dynamics and a reduced ability to support stable decoding of stimulus orientation over time. Conversely, our manipulation did not prevent the development of simple cells , which showed orientation tuning and multi-lobed, Gabor-like receptive fields as sharp as those found in rats reared with temporally continuous natural movies. Overall, these findings causally implicate unsupervised temporal learning in the postnatal development of transformation tolerance but not of shape tuning, in agreement with theories that place the latter under the control of unsupervised adaptation to spatial, rather than temporal, image statistics–. : #ref-1 : #ref-2 : #ref-10 : #ref-11 : #ref-12 : #ref-13 : #ref-16
606 downloads neuroscience
Hod Dana, Yi Sun, Boaz Mohar, Brad Hulse, Jeremy P Hasseman, Getahun Tsegaye, Arthur Tsang, Allan Wong, Ronak Patel, John J Macklin, Yang Chen, Arthur Konnerth, Vivek Jayaraman, Loren L Looger, Eric R Schreiter, Karel Svoboda, Douglas S Kim
Calcium imaging with genetically encoded calcium indicators (GECIs) is routinely used to measure neural activity in intact nervous systems. GECIs are frequently used in one of two different modes: to track activity in large populations of neuronal cell bodies, or to follow dynamics in subcellular compartments such as axons, dendrites and individual synaptic compartments. Despite major advances, calcium imaging is still limited by the biophysical properties of existing GECIs, including affinity, signal-to-noise ratio, rise and decay kinetics, and dynamic range. Using structure-guided mutagenesis and neuron-based screening, we optimized the green fluorescent protein-based GECI GCaMP6 for different modes of in vivo imaging. The jGCaMP7 sensors provide improved detection of individual spikes (jGCaMP7s,f), imaging in neurites and neuropil (jGCaMP7b), and tracking large populations of neurons using 2-photon (jGCaMP7s,f) or wide-field (jGCaMP7c) imaging.
606 downloads scientific communication and education
Gender inequality has been documented across a variety of high-prestige professions. Both structural bias (e.g., lack of proportionate representation) and interpersonal bias (e.g., sexism, discrimination) generate costs to underrepresented minorities. How can we estimate these costs and what interventions are most effective for reducing them? We used agent-based simulations, removing gender differences in interpersonal bias to isolate and quantify the impact and costs of structural bias (unequal gender ratios) on individuals and institutions. We compared the long-term impact of bias-confrontation strategies. Unequal gender ratios led to higher costs for female agents and institutions and increased sexism among male agents. Confronting interpersonal bias by targets and allies attenuated the impact of structural bias. However, bias persisted even after a structural intervention to suddenly make previously unequal institutions equal (50% women) unless the probability of interpersonal bias-confrontation was further increased among targets and allies. This computational approach allows for comparison of various policies to attenuate structural equality, and informs the design of new experiments to estimate parameters for more accurate predictions.
604 downloads neuroscience
The habenula complex is appreciated as a critical regulator of motivated and pathological behavioral states via its output to midbrain nuclei. Despite this, transcriptional definition of cell populations that comprise both the medial (MHb) and lateral habenular (LHb) subregions in mammals remain undefined. To resolve this, we performed single-cell transcriptional profiling and highly multiplexed in situ hybridization experiments of the mouse habenula complex in naive mice and those exposed to an acute aversive stimulus. Transcriptionally distinct neuronal cell types identified within the MHb and LHb, were spatially defined, and differentially engaged by aversive stimuli. Cell types identified in mice, also displayed a high degree of transcriptional similarity to those previously described in zebrafish, highlighting the well conserved nature of habenular cell types across the phylum. These data identify key molecular targets within habenula cell types, and provide a critical resource for future studies.
602 downloads neuroscience
The glycine receptor is a pentameric, neurotransmitter-activated ion channel that transitions between closed/resting, open and desensitized states. Glycine, a full agonist, produces an open channel probability (Po) of ~1.0 while partial agonists, such as taurine and γ-amino butyric acid (GABA) yield submaximal Po values. Despite extensive studies of pentameric Cys-loop receptors, there is little knowledge of the molecular mechanisms underpinning partial agonist action and how the receptor transitions from the closed to open and to desensitized conformations. Here we use electrophysiology and molecular dynamics (MD) simulations, together with a large ensemble of single-particle cryo-EM reconstructions, to show how agonists populate agonist-bound yet closed channel states, thus explaining their lesser efficacy, yet also populate agonist-bound open and desensitized states. Measurements within the neurotransmitter binding pocket, as a function of bound agonist, provide a metric to correlate the extent of agonist-induced conformational changes to open channel probability across the Cys-loop receptor family.
600 downloads microbiology
Ronan M. Doyle, Denise M. O’Sullivan, Sean D Aller, Sebastian Bruchmann, Taane Clark, Andreu Coello Pelegrin, Martin Cormican, Ernest Diez Benavente, Matthew J Ellington, Elaine McGrath, Yair Motro, Thi Phuong Thuy Nguyen, Jody Phelan, Liam P. Shaw, Richard A Stabler, Alex van Belkum, Lucy van Dorp, Neil Woodford, Jacob Moran-Gilad, Jim F. Huggett, Kathryn A Harris
Background Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a ‘one-stop’ test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data sequenced from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants and identify problem cases and factors that lead to discordant results. Methods We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams (‘participants’) were provided these sequence data without any other contextual information. Each participant used their own pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. Results Individual participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment a different antibiotic would have been recommended for each isolate by at least one participant. Conclusions We found that participants produced discordant predictions from identical WGS data. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases and standardisation in the comparisons between genotype and resistance phenotypes will be fundamental before AST prediction using WGS can be successfully implemented in standard clinical microbiology laboratories. * AMR : Antimicrobial resistance ARG-ANNOT : Antibiotic resistance gene-annotation ARIBA : Antimicrobial resistance identification by assembly AST : Antimicrobial susceptibility testing CARD : Comprehensive Antibiotic Resistance Database EUCAST : The European Committee on Antimicrobial Susceptibility Testing GOSH : Great Ormond Street Hospital NCBI : The National Center for Biotechnology Information SRST2 : Short read sequence typing 2 UHG : University Hospital Galway WGS : Whole-genome sequencing
599 downloads biophysics
Alexander Wolff, Iris Young, Raymond G. Sierra, Aaron S. Brewster, Michael W. Martynowycz, Eriko Nango, Michihiro Sugahara, Takanori Nakane, Kazutaka Ito, Andrew Aquila, Asmit Bhowmick, Justin T Biel, Sergio Carbajo, Aina E. Cohen, Saul Cortez, Ana Gonzalez, Tomoya Hino, Dohyun Im, Jake D Koralek, Minoru Kubo, Tomas S Lazarou, Takashi Nomura, Shigeki Owada, Avi Samelson, Rie Tanaka, Tomoyuki Tanaka, Erin M Thompson, Henry van den Bedem, Rahel A. Woldeyes, Fumiaki Yumoto, Wei Zhao, Kensuke Tono, Sebastien Boutet, So Iwata, Tamir Gonen, Nicholas K Sauter, James S. Fraser, Michael C Thompson
Innovative new crystallographic methods are facilitating structural studies from ever smaller crystals of biological macromolecules. In particular, serial X-ray crystallography and microcrystal electron diffraction (MicroED) have emerged as useful methods for obtaining structural information from crystals on the nanometer to micron scale. Despite the utility of these methods, their implementation can often be difficult, as they present many challenges not encountered in traditional macromolecular crystallography experiments. Here, we describe XFEL serial crystallography experiments and MicroED experiments using batch-grown microcrystals of the enzyme cyclophilin A (CypA). Our results provide a roadmap for researchers hoping to design macromolecular microcrystallography experiments, and they highlight the strengths and weaknesses of the two methods. Specifically, we focus on how the different physical conditions imposed by the sample preparation and delivery methods required for each type of experiment effect the crystal structure of the enzyme.
596 downloads genetics
Endre Neparaczki, Zoltán Maróti, Tibor Kalmár, Kitti Maár, István Nagy, Dóra Latinovics, Ágnes Kustár, György Pálfi, Erika Molnár, Antónia Marcsik, Csilla Balogh, Gábor Lőrinczy, Szilárd Sándor Gál, Péter Tomka, Bernadett Kovacsóczy, László Kovács, István Raskó, Tibor Török
Hun, Avar and conquering Hungarian nomadic groups arrived into the Carpathian Basin from the Eurasian Steppes and significantly influenced its political and ethnical landscape. In order to shed light on the genetic affinity of above groups we have determined Y chromosomal haplogroups and autosomal loci, from 49 individuals, supposed to represent military leaders. Haplogroups from the Hun-age are consistent with Xiongnu ancestry of European Huns. Most of the Avar-age individuals carry east Eurasian Y haplogroups typical for modern north-eastern Siberian and Buryat populations and their autosomal loci indicate mostly unmixed Asian characteristics. In contrast the conquering Hungarians seem to be a recently assembled population incorporating pure European, Asian and admixed components. Their heterogeneous paternal and maternal lineages indicate similar phylogeographic origin of males and females, derived from Central-Inner Asian and European Pontic Steppe sources. Composition of conquering Hungarian paternal lineages is very similar to that of Baskhirs, supporting historical sources that report identity of the two groups.
595 downloads genomics
Cristopher V Van Hout, Ioanna Tachmazidou, Joshua D Backman, Joshua X Hoffman, Bin Ye, Ashutosh K Pandey, Claudia Gonzaga-Jauregui, Shareef Khalid, Daren Liu, Nilanjana Banerjee, Alexander H Li, O’Dushlaine Colm, Anthony Marcketta, Jeffrey Staples, Claudia Schurmann, Alicia Hawes, Evan Maxwell, Leland Barnard, Alexander Lopez, John Penn, Lukas Habegger, Andrew L Blumenfeld, Ashish Yadav, Kavita Praveen, Marcus Jones, William J Salerno, Wendy K Chung, Ida Surakka, Cristen J. Willer, Kristian Hveem, Joseph B Leader, David J Carey, David H Ledbetter, Geisinger-Regeneron DiscovEHR Collaboration, Lon Cardon, George D Yancopoulos, Aris Economides, Giovanni Coppola, Alan R Shuldiner, Suganthi Balasubramanian, Michael Cantor, Matthew R. Nelson, John Whittaker, Jeffrey G Reid, Jonathan Marchini, John D Overton, Robert A Scott, Gonçalo Abecasis, Laura Yerges-Armstrong, Aris Baras, on behalf of the Regeneron Genetics Center
The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.
594 downloads genomics
Nanopore sequencing technology can rapidly and directly interrogate native DNA molecules. Often we are interested only in interrogating specific areas at high depth, but conventional enrichment methods have thus far proved unsuitable for long reads1. Existing strategies are currently limited by high input DNA requirements, low yield, short (<5kb) reads, time-intensive protocols, and/or amplification or cloning (losing base modification information). In this paper, we describe a technique utilizing the ability of Cas9 to introduce cuts at specific locations and ligating nanopore sequencing adaptors directly to those sites, a method we term 'nanopore Cas9 Targeted-Sequencing' (nCATS). We have demonstrated this using an Oxford Nanopore MinION flow cell (Capacity >10Gb+) to generate a median 165X coverage at 10 genomic loci with a median length of 18kb, representing a several hundred-fold improvement over the 2-3X coverage achieved without enrichment. We performed a pilot run on the smaller Flongle flow cell (Capacity ~1Gb), generating a median coverage of 30X at 11 genomic loci with a median length of 18kb. Using panels of guide RNAs, we show that the high coverage data from this method enables us to (1) profile DNA methylation patterns at cancer driver genes, (2) detect structural variations at known hot spots, and (3) survey for the presence of single nucleotide mutations. Together, this provides a low-cost method that can be applied even in low resource settings to directly examine cellular DNA. This technique has extensive clinical applications for assessing medically relevant genes and has the versatility to be a rapid and comprehensive diagnostic tool. We demonstrate applications of this technique by examining the well-characterized GM12878 cell line as well as three breast cell lines (MCF-10A, MCF-7, MDA-MB-231) with varying tumorigenic potential as a model for cancer.
587 downloads cancer biology
To understand the architecture of a tissue it is necessary to know both the cell populations and their physical relationships to one another. Single-cell RNA-Seq (scRNA-Seq) has made significant progress towards the unbiased and systematic characterization of the cell populations within a tissue, as well as their cellular states, by studying hundreds and thousands of cells in a single experiment. However, the characterization of the spatial organization of individual cells within a tissue has been more elusive. The recently introduced "spatial transcriptomics" method (ST) reveals the spatial pattern of gene expression within a tissue section at a resolution of one thousand 100 µm spots, each capturing the transcriptomes of ~10-20 cells. Here, we present an approach for the integration of scRNA-Seq and ST data generated from the same sample of pancreatic cancer tissue. Using markers for cell-types identified by scRNA-Seq, we robustly deconvolved the cell-type composition of each ST spot, to generate a spatial atlas of cell proportions across the tissue. Studying this atlas, we found that distinct spatial localizations accompany each of the three cancer cell populations that we identified. Strikingly, we find that subpopulations defined in the scRNA-Seq data also exhibit spatial segregation in the atlas, suggesting such an atlas may be used to study the functional attributes of subpopulations. Our results provide a framework for creating a tumor atlas by mapping single-cell populations to their spatial region, as well as the inference of cell architecture in any tissue.
585 downloads genomics
Oguz Kanca, Jonathan Zirin, Jorge Garcia-Marques, Shannon Marie Knight, Donghui Yang-Zhou, Gabriel Amador, Hyunglok Chung, Zhongyuan Zuo, Liwen Ma, Yuchun He, Wen-Wen Lin, Ying Fang, Ming Ge, Yamamoto Shinya, Karen L Schulze, Yanhui Hu, Allan C Spradling, Stephanie Mohr, Norbert Perrimon, Hugo Bellen
We previously reported a CRISPR-mediated knock-in strategy into introns of Drosophila genes, generating an attP - FRT-SA-T2A-GAL4-polyA-3XP3-EGFP-FRT-attP transgenic library for multiple uses (Lee et al., 2018b). The method relied on double stranded DNA (dsDNA) homology donors with ~1 kb homology arms. Here, we describe three new simpler ways to edit genes in flies. We create single stranded DNA (ssDNA) donors using PCR and add 100 nt of homology on each side of an integration cassette, followed by enzymatic removal of one strand. Using this method, we generated GFP-tagged proteins that mark organelles in S2 cells. We then describe two dsDNA methods using cheap synthesized donors flanked by 100 nt homology arms and gRNA target sites cloned into a plasmid. Upon injection, donor DNA (1 to 5 kb) is released from the plasmid by Cas9. The cassette integrates efficiently and precisely in vivo . The approach is fast, cheap, and scalable.
581 downloads neuroscience
Descending command neurons instruct spinal networks to execute basic locomotor functions, such as which gait and what speed. The command functions for gait and speed are symmetric, implying that a separate unknown system directs asymmetric movements—the ability to move left or right. Here we report the discovery that Chx10 -lineage reticulospinal neurons act to control the direction of locomotor movements in mammals. Chx10 neurons exhibit ipsilateral projection, and can decrease spinal limb-based locomotor activity ipsilaterally. This circuit mechanism acts as the basis for left or right locomotor movements in freely moving animals: selective unilateral activation of Chx10 neurons causes ipsilateral movements whereas inhibition causes contralateral movements. Spontaneous forward locomotion is thus transformed into an ipsilateral movement by braking locomotion on the ipsilateral side. We identify sensorimotor brain regions that project onto Chx10 reticulospinal neurons, and demonstrate that their unilateral activation can impart left/right directional commands. Together these data identify the descending motor system which commands left/right locomotor asymmetries in mammals.
580 downloads cell biology
Hematopoietic stem cells (HSC) can differentiate into all hematopoietic lineages to support hematopoiesis. Cells from the myeloid and lymphoid lineages fulfill distinct functions with specific shapes and intra-cellular architectures. The role of cytokines in the regulation of HSC differentiation has been intensively studied but our understanding of the potential contribution of inner cell architecture is relatively poor. Here we show that large invaginations are generated by microtubule constraints on the swelling nucleus of human HSCs during early commitment toward the myeloid lineage. These invaginations are associated with chromatin reorganization, local loss of H3K9 trimethylation and changes in expression of specific hematopoietic genes. This establishes the role of microtubules in defining the unique lobulated nuclear shape observed in myeloid progenitor cells and suggests that this shape is important to establish the gene expression profile specific to this hematopoietic lineage. It opens new perspectives on the implications of microtubule-generated forces, in the early specification of the myeloid lineage.
577 downloads bioinformatics
Accurate prediction of protein structure is one of the central challenges of biochemistry. Despite significant progress made by co-evolution methods to predict protein structure from signatures of residue-residue coupling found in the evolutionary record, a direct and explicit mapping between protein sequence and structure remains elusive, with no substantial recent progress. Meanwhile, rapid developments in deep learning, which have found remarkable success in computer vision, natural language processing, and quantum chemistry raise the question of whether a deep learning based approach to protein structure could yield similar advancements. A key ingredient of the success of deep learning is the reformulation of complex, human-designed, multi-stage pipelines with differentiable models that can be jointly optimized end-to-end. We report the development of such a model, which reformulates the entire structure prediction pipeline using differentiable primitives. Achieving this required combining four technical ideas: (1) the adoption of a recurrent neural architecture to encode the internal representation of protein sequence, (2) the parameterization of (local) protein structure by torsional angles, which provides a way to reason over protein conformations without violating the covalent chemistry of protein chains, (3) the coupling of local protein structure to its global representation via recurrent geometric units, and (4) the use of a differentiable loss function to capture deviations between predicted and experimental structures. To our knowledge this is the first end-to-end differentiable model for learning of protein structure. We test the effectiveness of this approach using two challenging tasks: the prediction of novel protein folds without the use of co-evolutionary information, and the prediction of known protein folds without the use of structural templates. On the first task the model achieves state-of-the-art performance, even when compared to methods that rely on co-evolutionary data. On the second task the model is competitive with methods that use experimental protein structures as templates, achieving 3-7Å accuracy despite being template-free. Beyond protein structure prediction, end-to-end differentiable models of proteins represent a new paradigm for learning and modeling protein structure, with potential applications in docking, molecular dynamics, and protein design.
574 downloads biophysics
The ultimate goal of biological superresolution fluorescence microscopy is to provide three-dimensional resolution at the size scale of a fluorescent marker. Here, we show that, by localizing individual switchable fluorophores with a probing doughnut-shaped excitation beam, MINFLUX nanoscopy provides 1 to 3 nanometer resolution in fixed and living cells. This progress has been facilitated by approaching each fluorophore iteratively with the probing doughnut minimum, making the resolution essentially uniform and isotropic over scalable fields of view. MINFLUX imaging of nuclear pore complexes of a mammalian cell shows that this true nanometer scale resolution is obtained in three dimensions and in two color channels. Relying on fewer detected photons than popular camera-based localization, MINFLUX nanoscopy is poised to open a new chapter in the imaging of protein complexes and distributions in fixed and living cells.
573 downloads genomics
The past five years have witnessed a tremendous growth of single-cell RNA-seq methodologies. Currently, there are three major commercial platforms for single-cell RNA-seq: Fluidigm C1, Clontech iCell8 (formerly Wafergen) and 10x Genomics Chromium. Here, we provide a systematic comparison of the throughput, sensitivity, cost and other performance statistics for these three platforms using single cells from primary human islets. The primary human islets represent a complex biological system where multiple cell types coexist, with varying cellular abundance, diverse transcriptomic profiles and differing total RNA contents. We apply standard pipelines optimized for each system to derive gene expression matrices. We further evaluate the performance of each system by benchmarking single-cell data with bulk RNA-seq data from sorted cell fractions. Our analyses can be generalized to a variety of complex biological systems and serve as a guide to newcomers to the field of single-cell RNA-seq when selecting platforms.
573 downloads genetics
Kendall R Sanson, Peter C DeWeirdt, Annabel K Sangree, Ruth E Hanna, Mudra Hegde, Teng Teng, Samantha M Borys, Christine Strand, J. Keith Joung, Benjamin P. Kleinstiver, Xuewen Pan, Alan Huang, John G. Doench
Cas12a enzymes have attractive properties for scalable delivery of multiplexed perturbations, yet widespread usage has lagged behind Cas9-based strategies. Here we describe the optimization of Cas12a from Acidaminococcus (AsCas12a) for use in pooled genetic screens in human cells. By assaying the activity of thousands of guides, we confirm on-target design rules and extend them to an enhanced activity variant, enAsCas12a. We also develop the first comprehensive set of off-target rules for Cas12a, and demonstrate that we can predict and exclude promiscuous guides. Finally, to enable efficient higher-order multiplexing via lentiviral delivery, we screen thousands of direct repeat variants and identify 38 that outperform the wildtype sequence. We validate this optimized AsCas12a toolkit by targeting 12 synthetic lethal gene pairs with up to 400 guide pairs each, and demonstrate effective triple knockout via flow cytometry. These results establish AsCas12a as a robust system for combinatorial applications of CRISPR technology.
567 downloads biophysics
The versatility of CRISPR-Cas endonucleases as a tool for biomedical research has lead to diverse applications in gene editing, programmable transcriptional control, and nucleic acid detection. Most CRISPR-Cas systems, however, suffer from off-target effects and unpredictable non-specific binding that negatively impact their reliability and broader applicability. To better evaluate the impact of mismatches on DNA target recognition and binding, we develop a massively parallel CRISPR interference (CRISPRi) assay to measure the binding energy between tens of thousands of CRISPR RNA (crRNA) and target DNA sequences. By developing a general thermodynamic model of CRISPR-Cas binding dynamics, our results unravel a comprehensive map of the energetic landscape of Francisella novicida Cas12a (FnCas12a) as it searches for its DNA target. Our results reveal concealed thermodynamic factors affecting FnCas12a DNA binding which should guide the design and optimization of crRNA that limit off-target effects, including the crucial role of an extended, 6-base long PAM sequence and the impact of the specific base composition of crRNA-DNA mismatches. Our generalizable approach should also provide a mechanistic understanding of target recognition and DNA binding when applied to other CRISPR-Cas systems.
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