A simple method for extracting phycocyanin from Arthrospira (Spirulina) platensis by autolysis.

Phycocyanin (PC) is a natural blue pigment that has great commercial value in food and pharmaceutical industry. Arthrospira (Spirulina) platensis is a photosynthetic spiral-shaped cyanobacterium containing a rich PC pigment. Autolysis is the enzymatic digestion of cells by the action of its own enzymes. To develop an effective and economical extraction process, an autolysis process was incorporated into the conventional freezing-thawing method. In the present study, 91% of maximal extraction yield of PC with 1.194 purity (A620/A280) was obtained via autolysis after 3 h of incubation at 37 °C without using an extraction salt solution or a successive freezing-thawing process. In addition to temperature, the initial concentration of bicarbonate in growth medium and the concentration of wet biomass are important parameters that influence the extraction yield of PC by autolysis.

In vivo gene correction with targeted sequence substitution through microhomology-mediated end joining.

Genome editing technology using programmable nucleases has rapidly evolved in recent years. The primary mechanism to achieve precise integration of a transgene is mainly based on homology-directed repair (HDR). However, an HDR-based genome-editing approach is less efficient than non-homologous end-joining (NHEJ). Recently, a microhomology-mediated end-joining (MMEJ)-based transgene integration approach was developed, showing feasibility both in vitro and in vivo. We expanded this method to achieve targeted sequence substitution (TSS) of mutated sequences with normal sequences using double-guide RNAs (gRNAs), and a donor template flanking the microhomologies and target sequence of the gRNAs in vitro and in vivo. Our method could realize more efficient sequence substitution than the HDR-based method in vitro using a reporter cell line, and led to the survival of a hereditary tyrosinemia mouse model in vivo. The proposed MMEJ-based TSS approach could provide a novel therapeutic strategy, in addition to HDR, to achieve gene correction from a mutated sequence to a normal sequence.

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

In vivo high-throughput profiling of CRISPR-Cpf1 activity.

CRISPR from Prevotella and Francisella 1 (Cpf1) is an effector endonuclease of the class 2 CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins) gene editing system. We developed a method for evaluating Cpf1 activity, based on target sequence composition in mammalian cells, in a high-throughput manner. A library of >11,000 target sequence and guide RNA pairs was delivered into human cells using lentiviral vectors. Subsequent delivery of Cpf1 into this cell library induced insertions and deletions (indels) at the integrated synthetic target sequences, which allowed en masse evaluation of Cpf1 activity by using deep sequencing. With this approach, we determined protospacer-adjacent motif sequences of two Cpf1 nucleases, one from Acidaminococcus sp. BV3L6 (hereafter referred to as AsCpf1) and the other from Lachnospiraceae bacterium ND2006 (hereafter referred to as LbCpf1). We also defined target-sequence-dependent activity profiles of AsCpf1, which enabled the development of a web tool that predicts the indel frequencies for given target sequences (http://big.hanyang.ac.kr/cindel). Both the Cpf1 characterization profile and the in vivo high-throughput evaluation method will greatly facilitate Cpf1-based genome editing.