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Results for “self-targeting”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Escherichia coli Cas1/2 Endonuclease Complex Modifies Self-Targeting CRISPR/Cascade Spacers Reducing Silencing Guide Stability

CRISPR-based interference has become common in various applications from genetic circuits to dynamic metabolic control. In E. coli, the native CRISPR Cascade system can be utilized for silencing by deletion of the cas3 nuclease along with expression of guide RNA arrays, where multiple genes can be silenced from a single transcript. We notice the loss of spacer sequences from guide arrays utilized for dynamic silencing. Here, we report that unstable guide arrays are due to expression of the Cas1/2 endonuclease complex. We propose a model wherein basal Cas1/2 endonuclease activity results in the loss of spacers from guide arrays. Subsequently, mutant guide arrays can be amplified through selection. Replacing a constitutive promoter driving Cascade complex expression with a tightly controlled inducible promoter improves guide array stability, while minimizing leaky gene silencing. Additionally, these results demonstrate the potential of Cas1/2 mediated guide deletion as a mechanism to avoid CRISPR based autoimmunity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Potent CRISPR-Cas9 inhibitors from Staphylococcus genomes

Anti-CRISPRs (Acrs) are small proteins that inhibit the RNA-guided DNA targeting activity of CRISPR-Cas enzymes. Encoded by bacteriophage and phage-derived bacterial genes, Acrs prevent CRISPR-mediated inhibition of phage infection and can also block CRISPR-Cas-mediated genome editing in eukaryotic cells. To identify Acrs capable of inhibiting Staphylococcus aureus Cas9 (SauCas9), an alternative to the most commonly used genome editing protein Streptococcus pyogenes Cas9 (SpyCas9), we used both self-targeting CRISPR screening and guilt-by-association genomic search strategies. Here we describe three potent inhibitors of SauCas9 that we name AcrIIA13, AcrIIA14, and AcrIIA15. These inhibitors share a conserved N-terminal sequence that is dispensable for DNA cleavage inhibition and have divergent C termini that are required in each case for inhibition of SauCas9-catalyzed DNA cleavage. In human cells, we observe robust inhibition of SauCas9-induced genome editing by AcrIIA13 and moderate inhibition by AcrIIA14 and AcrIIA15. We also find that the conserved N-terminal domain of AcrIIA13–AcrIIA15 binds to an inverted repeat sequence in the promoter of these Acr genes, consistent with its predicted helix-turn-helix DNA binding structure. These data demonstrate an effective strategy for Acr discovery and establish AcrIIA13–AcrIIA15 as unique bifunctional inhibitors of SauCas9.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Machine learning predicts new anti-CRISPR proteins

The increasing use of CRISPR–Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR–Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that inhibit the CRISPR–Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then applied AcRanker to predict candidate anti-CRISPRs from predicted prophage regions within self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA20 (ML1) and AcrIIA21 (ML8). We show that AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9) and SinCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1972

Shuttle ascent guidance and control.

The requirements of a unified optimal guidance scheme are discussed, giving attention to a general formulation, aspects of self-targeting, problems of optimum guidance within the atmosphere, and a unified concept for all flight phases. Since no previous guidance scheme meets these requirements, the shuttle demands a fundamentally new approach. A new unified optimal guidance scheme, called Mascot, was developed. The capabilities of Mascot include the real-time solution of general trajectory-optimization problems and the unification of guidance for all flight phases.

Lovingood, J. A.↗
NASA NTRS2004

Improved Timing Scheme for Spaceborne Precipitation Radar

An improved timing scheme has been conceived for operation of a scanning satellite-borne rain-measuring radar system. The scheme allows a real-time-generated solution, which is required for auto targeting. The current timing scheme used in radar satellites involves pre-computing a solution that allows the instrument to catch all transmitted pulses without transmitting and receiving at the same time. Satellite altitude requires many pulses in flight at any time, and the timing solution to prevent transmit and receive operations from colliding is usually found iteratively. The proposed satellite has a large number of scanning beams each with a different range to target and few pulses per beam. Furthermore, the satellite will be self-targeting, so the selection of which beams are used will change from sweep to sweep. The proposed timing solution guarantees no echo collisions, can be generated using simple FPGA-based hardware in real time, and can be mathematically shown to deliver the maximum number of pulses per second, given the timing constraints. The timing solution is computed every sweep, and consists of three phases: (1) a build-up phase, (2) a feedback phase, and (3) a build-down phase. Before the build-up phase can begin, the beams to be transmitted are sorted in numerical order. The numerical order of the beams is also the order from shortest range to longest range. Sorting the list guarantees no pulse collisions. The build-up phase begins by transmitting the first pulse from the first beam on the list. Transmission of this pulse starts a delay counter, which stores the beam number and the time delay to the beginning of the receive window for that beam. The timing generator waits just long enough to complete the transmit pulse plus one receive window, then sends out the second pulse. The second pulse starts a second delay counter, which stores its beam number and time delay. This process continues until an output from the first timer indicates there is less than one transmit pulse width until the start of the next receive event. This blocks future transmit pulses in the build-up phase. The feedback phase begins with the first timer paying off and starting the first receive window. When the first receive window is complete, the timing generator transmits the next beam from the list. When the second timer pays off, the second receive event is started. Following the second receive event, the timing generator will transmit the next beam on the list and start an additional timer. The timers work in a circular buffer fashion so there only need to be enough to cover the maximum number of echoes in flight.

Berkun, Andrew↗