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      Microfluidic screening and genomic mutation identification for enhancing cellulase production in Pichia pastoris

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          Abstract

          Background

          Pichia pastoris is a widely used host organism for heterologous production of industrial proteins, such as cellulases. Although great progress has been achieved in improving protein expression in P. pastoris, the potential of the P. pastoris expression system has not been fully explored due to unknown genomic impact factors. Recently, whole-cell directed evolution, employing iterative rounds of genome-wide diversity generation and high-throughput screening (HTS), has been considered to be a promising strategy in strain improvement at the genome level.

          Results

          In this study, whole-cell directed evolution of P. pastoris, employing atmospheric and room temperature plasma (ARTP) mutagenesis and droplet-based microfluidic HTS, was developed to improve heterogenous cellulase production. The droplet-based microfluidic platform based on a cellulase-catalyzed reaction of releasing fluorescence was established to be suitable for methanol-grown P. pastoris. The validation experiment showed a positive sorting efficiency of 94.4% at a sorting rate of 300 droplets per second. After five rounds of iterative ARTP mutagenesis and microfluidic screening, the best mutant strain was obtained and exhibited the cellulase activity of 11,110 ± 523 U/mL, an approximately twofold increase compared to the starting strain. Whole-genome resequencing analysis further uncovered three accumulated genomic alterations in coding region. The effects of point mutations and mutant genes on cellulase production were verified using reconstruction of point mutations and gene deletions. Intriguingly, the point mutation Rsc1 G22V was observed in all the top-performing producers selected from each round, and gene deletion analysis confirmed that Rsc1, a component of the RSC chromatin remodeling complex, might play an important role in cellulase production.

          Conclusions

          We established a droplet-based microfluidic HTS system, thereby facilitating whole-cell directed evolution of P. pastoris for enhancing cellulase production, and meanwhile identified genomic alterations by whole-genome resequencing and genetic validation. Our approaches and findings would provide guides to accelerate whole-cell directed evolution of host strains and enzymes of high industrial interest.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13068-022-02150-w.

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          Most cited references64

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

              High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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                Author and article information

                Contributors
                yuan_hl@tib.cas.cn
                tech@sunhy.cn
                lin_yp@tib.cas.cn
                tu_r@tib.cas.cn
                guo_yf@tib.cas.cn
                zhang_yy@tib.cas.cn
                wang_qh@tib.cas.cn
                Journal
                Biotechnol Biofuels Bioprod
                Biotechnol Biofuels Bioprod
                Biotechnology for Biofuels and Bioproducts
                BioMed Central (London )
                2731-3654
                14 May 2022
                14 May 2022
                2022
                : 15
                : 50
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, , Chinese Academy of Sciences, ; Tianjin, 300308 China
                [2 ]National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308 China
                [3 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [4 ]Wuhan Sunhy Biology Co., Ltd, Wuhan, 430206 China
                Article
                2150
                10.1186/s13068-022-02150-w
                9107654
                35568955
                68060394-4187-4a23-b075-7fc959ba82e1
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 11 November 2021
                : 5 May 2022
                Funding
                Funded by: National Key Research and Development Program of China
                Award ID: 2021YFC2103300
                Award Recipient :
                Funded by: Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project
                Award ID: TSBICIP-KJGG-004
                Award Recipient :
                Funded by: Instrument Developing Project of the Chinese Academy of Sciences
                Award ID: YJKYYQ20170023
                Award Recipient :
                Funded by: Industrial Synthetic Biology Innovation Team support funding
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                cellulase,pichia pastoris,high-throughput screening (hts),whole-cell directed evolution,droplet-based microfluidic,whole-genome resequencing

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