Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica

Functional genomic screening in Komagataella phaffii enabled by high-activity CRISPR-Cas9 library

CRISPR-based high-throughput genome-wide loss-of-function screens are a valuable approach to functional genetics and strain engineering. The yeast Komagataella phaffii is a host of particular interest in the biopharmaceutical industry and as a metabolic engineering host for proteins and metabolites. Here, we design and validate a highly active 6-fold coverage genome-wide sgRNA library for this biotechnologically important yeast containing 30,848 active sgRNAs targeting over 99% of its coding sequences. Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in K. phaffii, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guide's targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of K. phaffii essential genes, many of which relate to metabolic engineering targets, including protein production, secretion, and glycosylation. The high activity, genome-wide CRISPR library developed here enables functional genomic screening in K. phaffii, applied here to gene essentiality classification, and promises to enable other genetic screens.

Optimizing multicopy chromosomal integration for stable high-performing strains

The copy number of genes in chromosomes can be modified by chromosomal integration to construct efficient microbial cell factories but the resulting genetic systems are prone to failure or instability from triggering homologous recombination in repetitive DNA sequences. Finding the optimal copy number of each gene in a pathway is also time and labor intensive. To overcome these challenges, we applied a multiple nonrepetitive coding sequence calculator that generates sets of coding DNA sequence (CDS) variants. A machine learning method was developed to calculate the optimal copy number combination of genes in a pathway. We obtained an engineered Yarrowia lipolytica strain for eicosapentaenoic acid biosynthesis in 6 months, producing the highest titer of 27.5 g l-1 in a 50-liter bioreactor. Moreover, the lycopene production in Escherichia coli was also greatly improved. Importantly, all engineered strains of Y. lipolytica, E. coli and Saccharomyces cerevisiae constructed with nonrepetitive CDSs maintained genetic stability.

Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering

Genome editing is a crucial technology for obtaining desired phenotypes in a variety of species, ranging from microbes to plants, animals, and humans. With the advent of CRISPR-Cas technology, it has become possible to edit the intended sequence by modifying the target recognition sequence in guide RNA (gRNA). By expressing multiple gRNAs simultaneously, it is possible to edit multiple targets at the same time, allowing for the simultaneous introduction of various functions into the cell. This can significantly reduce the time and cost of obtaining engineered microbial strains for specific traits. In this review, we investigate the resolution of multiplex genome editing and its application in engineering microorganisms, including bacteria and yeast. Furthermore, we examine how recent advancements in artificial intelligence technology could assist in microbial genome editing and engineering. Based on these insights, we present our perspectives on the future evolution and potential impact of multiplex genome editing technologies in the agriculture and food industry.

Exploiting synthetic biology platforms for enhanced biosynthesis of natural products in Yarrowia lipolytica

With the rapid development of synthetic biology, researchers can design, modify, or even synthesize microorganisms de novo, and microorganisms endowed with unnatural functions can be considered "artificial life" and facilitate the development of functional products. Based on this concept, researchers can solve critical problems related to the insufficient supply of natural products, such as low yields, long production cycles, and cumbersome procedures. Due to its superior performance and unique physiological and biochemical characteristics, Yarrowia lipolytica is a favorable chassis cell used for green biomanufacturing by numerous researchers. This paper mainly reviews the development of synthetic biology techniques for Y. lipolytica and summarizes the recent research progress on the synthesis of natural products in Y. lipolytica. This review will promote the continued innovative development of Y. lipolytica by providing theoretical guidance for research on the biosynthesis of natural products.

CRISPR-COPIES: an in silico platform for discovery of neutral integration sites for CRISPR/Cas-facilitated gene integration

The CRISPR/Cas system has emerged as a powerful tool for genome editing in metabolic engineering and human gene therapy. However, locating the optimal site on the chromosome to integrate heterologous genes using the CRISPR/Cas system remains an open question. Selecting a suitable site for gene integration involves considering multiple complex criteria, including factors related to CRISPR/Cas-mediated integration, genetic stability, and gene expression. Consequently, identifying such sites on specific or different chromosomal locations typically requires extensive characterization efforts. To address these challenges, we have developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites. This tool leverages ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search, and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. As a proof of concept, we utilized CRISPR-COPIES to characterize neutral integration sites in three diverse species: Saccharomyces cerevisiae, Cupriavidus necator, and HEK293T cells. In addition, we developed a user-friendly web interface for CRISPR-COPIES (https://biofoundry.web.illinois.edu/copies/). We anticipate that CRISPR-COPIES will serve as a valuable tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, enable rapid strain construction to produce valuable biochemicals, and support human gene and cell therapy applications.

Detection of Parasites in the Field: The Ever-Innovating CRISPR/Cas12a

The rapid and accurate identification of parasites is crucial for prompt therapeutic intervention in parasitosis and effective epidemiological surveillance. For accurate and effective clinical diagnosis, it is imperative to develop a nucleic-acid-based diagnostic tool that combines the sensitivity and specificity of nucleic acid amplification tests (NAATs) with the speed, cost-effectiveness, and convenience of isothermal amplification methods. A new nucleic acid detection method, utilizing the clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) nuclease, holds promise in point-of-care testing (POCT). CRISPR/Cas12a is presently employed for the detection of Plasmodium falciparum, Toxoplasma gondii, Schistosoma haematobium, and other parasites in blood, urine, or feces. Compared to traditional assays, the CRISPR assay has demonstrated notable advantages, including comparable sensitivity and specificity, simple observation of reaction results, easy and stable transportation conditions, and low equipment dependence. However, a common issue arises as both amplification and cis-cleavage compete in one-pot assays, leading to an extended reaction time. The use of suboptimal crRNA, light-activated crRNA, and spatial separation can potentially weaken or entirely eliminate the competition between amplification and cis-cleavage. This could lead to enhanced sensitivity and reduced reaction times in one-pot assays. Nevertheless, higher costs and complex pre-test genome extraction have hindered the popularization of CRISPR/Cas12a in POCT.

Genome Editing, Transcriptional Regulation, and Forward Genetic Screening Using CRISPR-Cas12a Systems in Yarrowia lipolytica

Class II Type V endonucleases have increasingly been adapted to develop sophisticated and easily accessible synthetic biology tools for genome editing, transcriptional regulation, and functional genomic screening in a wide range of organisms. One such endonuclease, Cas12a, presents itself as an attractive alternative to Cas9-based systems. The ability to mature its own guide RNAs (gRNAs) from a single transcript has been leveraged for easy multiplexing, and its lack of requirement of a tracrRNA element, also allows for short gRNA expression cassettes. To extend these functionalities into the industrially relevant oleaginous yeast Yarrowia lipolytica, we developed a set of CRISPR-Cas12a vectors for easy multiplexed gene knockout, repression, and activation. We further extended the utility of this CRISPR-Cas12a system to functional genomic screening by constructing a genome-wide guide library targeting every gene with an eightfold coverage. Pooled CRISPR screens conducted with this library were used to profile Cas12a guide activities and develop a machine learning algorithm that could accurately predict highly efficient Cas12a gRNA. In this protocols chapter, we first present a method by which protein coding genes may be functionally disrupted via indel formation with CRISPR-Cas12a systems. Further, we describe how Cas12a fused to a transcriptional regulator can be used in conjunction with shortened gRNA to achieve transcriptional repression or activation. Finally, we describe the design, cloning, and validation of a genome-wide library as well as a protocol for the execution of a pooled CRISPR screen, to determine guide activity profiles in a genome-wide context in Y. lipolytica. The tools and strategies discussed here expand the list of available synthetic biology tools for facile genome engineering in this industrially important host.

High-Efficiency Multiplexed Cytosine Base Editors for Natural Product Synthesis in Yarrowia lipolytica

Yarrowia lipolytica is an industrial host with a high fatty acid flux. Even though CRISPR-based tools have accelerated its metabolic engineering, there remains a need to develop tools for rapid multiplexed strain engineering to accelerate the design-build-test-learn cycle. Base editors have the potential to perform high-efficiency multiplexed gene editing because they do not depend upon double-stranded DNA breaks. Here, we identified that base editors are less toxic than CRISPR-Cas9 for multiplexed gene editing. We increased the editing efficiency by removing the extra nucleotides between tRNA and gRNA and increasing the base editor and gRNA copy number in a Ku70 deficient strain. We achieved five multiplexed gene editing in the ΔKu70 strain at 42% efficiency. Initially, we were unsuccessful at performing multiplexed base editing in NHEJ competent strain; however, we increased the editing efficiency by using a co-selection approach to enrich base editing events. Base editor-mediated canavanine gene (CAN1) knockout provided resistance to the import of canavanine, which enriched the base editing in other unrelated genetic loci. We performed multiplexed editing of up to three genes at 40% efficiency in the Po1f strain through the CAN1 co-selection approach. Finally, we demonstrated the application of multiplexed cytosine base editor for rapid multigene knockout to increase naringenin production by 2-fold from glucose or glycerol as a carbon source.

Characterization of an efficient CRISPR-iCas9 system in Yarrowia lipolytica for the biosynthesis of carotenoids

The application of clustered regularly interspaced short palindromic repeats-Cas (CRISPR-Cas9) technology in the genetic modification of Yarrowia lipolytica is challenged by low efficiency and low throughput. Here, a highly efficient CRISPR-iCas9 (with D147Y and P411T mutants) genetic manipulation tool was established for Y. lipolytica, which was further utilized to integrate carotene synthetic key genes and significantly improve the target product yield. First, CRISPR-iCas9 could shorten the time of genetic modification and enable the rapid knockout of nonsense suppressors. iCas9 can lead to more than 98% knockout efficiency for NHEJ-mediated repair after optimal target disruption of a single gene, 100% knockout efficiency for a single gene-guided version, and more than 80% knockout efficiency for multiple genes simultaneously in Y. lipolytica. Subsequently, this technology allowed for rapid one-step integration of large fragments (up to 9902-bp) of genes into chromosomes. Finally, YL-ABTG and YL-ABTG2Z were further constructed through CRISPR-iCas9 integration of key genes in a one-step process, resulting in a maximum β-carotene and zeaxanthin content of 3.12 mg/g and 2.33 mg/g dry cell weight, respectively. Therefore, CRISPR-iCas9 technology provides a feasible approach to genetic modification for efficient biosynthesis of biological compounds in Y. lipolytica. KEY POINTS: • iCas9 with D147Y and P411T mutants improved the CRISPR efficiency in Y. lipolytica. • CRISPR-iCas9 achieved efficient gene knockout and integration in Y. lipolytica. • CRISPR-iCas9 rapidly modified Y. lipolytica for carotenoid bioproduction.

Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities

In silico design of single guide RNA (sgRNA) plays a critical role in clustered regularly interspaced, short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system. Continuous efforts are aimed at improving sgRNA design with efficient on-target activity and reduced off-target mutations. In the last 5 years, an increasing number of deep learning-based methods have achieved breakthrough performance in predicting sgRNA on- and off-target activities. Nevertheless, it is worthwhile to systematically evaluate these methods for their predictive abilities. In this review, we conducted a systematic survey on the progress in prediction of on- and off-target editing. We investigated the performances of 10 mainstream deep learning-based on-target predictors using nine public datasets with different sample sizes. We found that in most scenarios, these methods showed superior predictive power on large- and medium-scale datasets than on small-scale datasets. In addition, we performed unbiased experiments to provide in-depth comparison of eight representative approaches for off-target prediction on 12 publicly available datasets with various imbalanced ratios of positive/negative samples. Most methods showed excellent performance on balanced datasets but have much room for improvement on moderate- and severe-imbalanced datasets. This study provides comprehensive perspectives on CRISPR/Cas9 sgRNA on- and off-target activity prediction and improvement for method development.

A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets

The CRISPR/Cas9 nuclease from Streptococcus pyogenes (SpCas9) can be used with single guide RNAs (sgRNAs) as a sequence-specific antimicrobial agent and as a genome-engineering tool. However, current bacterial sgRNA activity models struggle with accurate predictions and do not generalize well, possibly because the underlying datasets used to train the models do not accurately measure SpCas9/sgRNA activity and cannot distinguish on-target cleavage from toxicity. Here, we solve this problem by using a two-plasmid positive selection system to generate high-quality data that more accurately reports on SpCas9/sgRNA cleavage and that separates activity from toxicity. We develop a machine learning architecture (crisprHAL) that can be trained on existing datasets, that shows marked improvements in sgRNA activity prediction accuracy when transfer learning is used with small amounts of high-quality data, and that can generalize predictions to different bacteria. The crisprHAL model recapitulates known SpCas9/sgRNA-target DNA interactions and provides a pathway to a generalizable sgRNA bacterial activity prediction tool that will enable accurate antimicrobial and genome engineering applications.

A DNA assembly toolkit to unlock the CRISPR/Cas9 potential for metabolic engineering

CRISPR/Cas9-based technologies are revolutionising the way we engineer microbial cells. One of the key advantages of CRISPR in strain design is that it enables chromosomal integration of marker-free DNA, eliminating laborious and often inefficient marker recovery procedures. Despite the benefits, assembling CRISPR/Cas9 editing systems is still not a straightforward process, which may prevent its use and applications. In this work, we have identified some of the main limitations of current Cas9 toolkits and designed improvements with the goal of making CRISPR technologies easier to access and implement. These include 1) A system to quickly switch between marker-free and marker-based integration constructs using both a Cre-expressing and standard Escherichia coli strains, 2) the ability to redirect multigene integration cassettes into alternative genomic loci via Golden Gate-based exchange of homology arms, 3) a rapid, simple in-vivo method to assembly guide RNA sequences via recombineering between Cas9-helper plasmids and single oligonucleotides. We combine these methodologies with well-established technologies into a comprehensive toolkit for efficient metabolic engineering using CRISPR/Cas9. As a proof of concept, we developed the YaliCraft toolkit for Yarrowia lipolytica, which is composed of a basic set of 147 plasmids and 7 modules with different purposes. We used the toolkit to generate and characterize a library of 137 promoters and to build a de novo strain synthetizing 373.8 mg/L homogentisic acid.

Deep learning in CRISPR-Cas systems: a review of recent studies

In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019-2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area.

acCRISPR: an activity-correction method for improving the accuracy of CRISPR screens

High throughput CRISPR screens are revolutionizing the way scientists unravel the genetic underpinnings of engineered and evolved phenotypes. One of the critical challenges in accurately assessing screening outcomes is accounting for the variability in sgRNA cutting efficiency. Poorly active guides targeting genes essential to screening conditions obscure the growth defects that are expected from disrupting them. Here, we develop acCRISPR, an end-to-end pipeline that identifies essential genes in pooled CRISPR screens using sgRNA read counts obtained from next-generation sequencing. acCRISPR uses experimentally determined cutting efficiencies for each guide in the library to provide an activity correction to the screening outcomes via calculation of an optimization metric, thus determining the fitness effect of disrupted genes. CRISPR-Cas9 and -Cas12a screens were carried out in the non-conventional oleaginous yeast Yarrowia lipolytica and acCRISPR was used to determine a high-confidence set of essential genes for growth under glucose, a common carbon source used for the industrial production of oleochemicals. acCRISPR was also used in screens quantifying relative cellular fitness under high salt conditions to identify genes that were related to salt tolerance. Collectively, this work presents an experimental-computational framework for CRISPR-based functional genomics studies that may be expanded to other non-conventional organisms of interest.

Efficient CRISPR/Cas9-mediated gene editing in mammalian cells by the novel selectable traffic light reporters

CRISPR/Cas9 is a powerful tool for gene editing in various cell types and organisms. However, it is still challenging to screen genetically modified cells from an excess of unmodified cells. Our previous studies demonstrated that surrogate reporters can be used for efficient screening of genetically modified cells. Here, we developed two novel traffic light screening reporters, puromycin-mCherry-EGFP (PMG) based on single-strand annealing (SSA) and homology-directed repair (HDR), respectively, to measure the nuclease cleavage activity within transfected cells and to select genetically modified cells. We found that the two reporters could be self-repaired coupling the genome editing events driven by different CRISPR/Cas nucleases, resulting in a functional puromycin-resistance and EGFP selection cassette that can be afforded to screen genetically modified cells by puromycin selection or FACS enrichment. We further compared the novel reporters with different traditional reporters at several endogenous loci in different cell lines, for the enrichment efficiencies of genetically modified cells. The results indicated that the SSA-PMG reporter exhibited improvements in enriching gene knockout cells, while the HDR-PMG system was very useful in enriching knock-in cells. These results provide robust and efficient surrogate reporters for the enrichment of CRISPR/Cas9-mediated editing in mammalian cells, thereby advancing basic and applied research.

A DNA assembly toolkit to unlock the CRISPR/Cas9 potential for metabolic engineering

CRISPR/Cas9-based technologies are revolutionising the way we engineer microbial cells. One of the key advantages of CRISPR in strain design is that it enables chromosomal integration of marker-free DNA, eliminating laborious and often inefficient marker recovery procedures. Despite the benefits, assembling CRISPR/Cas9 editing systems is still not a straightforward process, which may prevent its use and applications. In this work, we have identified some of the main limitations of current Cas9 toolkits and designed improvements with the goal of making CRISPR technologies easier to access and implement. These include 1) A system to quickly switch between marker-free and marker-based integration constructs using both a Cre-expressing and standard Escherichia coli strains, 2) the ability to redirect multigene integration cassettes into alternative genomic loci via Golden Gate-based exchange of homology arms, 3) a rapid, simple in-vivo method to assembly guide RNA sequences via recombineering between Cas9-helper plasmids and single oligonucleotides. We combine these methodologies with well-established technologies into a comprehensive toolkit for efficient metabolic engineering using CRISPR/Cas9. As a proof of concept, we generated and characterized a library of 137 promoters and built a de novo Yarrowia lipolytica strain synthetizing 373.8 mg/L homogentisic acid.

A review of synthetic biology tools in Yarrowia lipolytica

Yarrowia lipolytica is a non-conventional oleaginous yeast with great potential for industrial production. Y. lipolytica has a high propensity for flux through tricarboxylic acid cycle intermediates. Therefore, this host is currently being developed as a workhorse, and is rapidly emerging in biotechnology fields, especially for industrial chemical production, whole-cell bioconversion, and the treatment and recycling of industrial waste. In recent studies, Y. lipolytica has been rewritten and introduced with non-native metabolites of certain compounds of interest owing to the advancement in synthetic biology tools. In this review, we collate recent progress to present a detailed and insightful summary of the major developments in synthetic biology tools and techniques for Y. lipolytica, including promoters, terminators, selection markers, autonomously replicating sequences, DNA assembly techniques, genome editing techniques, and subcellular organelle engineering. This comprehensive overview would be a useful resource for future genetic engineering studies to improve the yield of desired metabolic products in Y. lipolytica.

Application of CRISPR/Cas9 Technology in Cancer Treatment: A Future Direction

Gene editing, especially with clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR-Cas9), has advanced gene function science. Gene editing's rapid advancement has increased its medical/clinical value. Due to its great specificity and efficiency, CRISPR/Cas9 can accurately and swiftly screen the whole genome. This simplifies disease-specific gene therapy. To study tumor origins, development, and metastasis, CRISPR/Cas9 can change genomes. In recent years, tumor treatment research has increasingly employed this method. CRISPR/Cas9 can treat cancer by removing genes or correcting mutations. Numerous preliminary tumor treatment studies have been conducted in relevant fields. CRISPR/Cas9 may treat gene-level tumors. CRISPR/Cas9-based personalized and targeted medicines may shape tumor treatment. This review examines CRISPR/Cas9 for tumor therapy research, which will be helpful in providing references for future studies on the pathogenesis of malignancy and its treatment.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Genome-wide CRISPR-Cas9 screen reveals a persistent null-hyphal phenotype that maintains high carotenoid production in Yarrowia lipolytica

Yarrowia lipolytica is a metabolic engineering host of growing industrial interest due to its ability to metabolize hydrocarbons, fatty acids, glycerol, and other renewable carbon sources. This dimorphic yeast undergoes a stress-induced transition to a multicellular hyphal state, which can negatively impact biosynthetic activity, reduce oxygen and nutrient mass transfer in cell cultures, and increase culture viscosity. Identifying mutations that prevent the formation of hyphae would help alleviate the bioprocess challenges that they create. To this end, we conducted a genome-wide CRISPR screen to identify genetic knockouts that prevent the transition to hyphal morphology. The screen identified five mutants with a null-hyphal phenotype-ΔRAS2, ΔRHO5, ΔSFL1, ΔSNF2, and ΔPAXIP1. Of these hits, only ΔRAS2 suppressed hyphal formation in an engineered lycopene production strain over a multiday culture. The RAS2 knockout was also the only genetic disruption characterized that did not affect lycopene production, producing more than 5 mg L^-1 OD^-1 from a heterologous pathway with enhanced carbon flux through the mevalonate pathway. These data suggest that a ΔRAS2 mutant of Y. lipolytica could prove useful in engineering a metabolic engineering host of the production of carotenoids and other biochemicals.

New roles for Yarrowia lipolytica in molecules synthesis and biocontrol

Reprogramming of host metabolism is a common strategy for improving desired compounds in host cells and is essential to generate overproducing strains in biotechnology. As a promising feedstock converter, Yarrowia lipolytica has been engineered to extend its bioproduction ability related to the synthesis of new value-added molecules relevant to human food and disease treatment. New synthetic tools have been reported and new enzymes with biotechnological importance are recovered. Additionally, metabolic events occurring during substrate utilization and recombinant protein production have been elucidated. Its contributions as feed and in controlling disease in the food industry have also been provided. Likewise, the recent abilities of Yarrowia lipolytica in the bioconversion of food waste into single-cell protein have been reported. These aforementioned events made the novelty of this review compared to the existing ones on this oleaginous yeast. KEY POINTS: • The production of biolipids by the heterotrophic yeast Yarrowia lipolytica is examined. • A Summary of information concerning new value-added molecules has been highlighted. • Special focus on the importance of Yarrowia lipolytica in regulating the immune system has been provided.

A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage

CRISPR/Cas9 technology has greatly accelerated genome engineering research. The CRISPR/Cas9 complex, a bacterial immune response system, is widely adopted for RNA-driven targeted genome editing. The systematic mapping study presented in this paper examines the literature on machine learning (ML) techniques employed in the prediction of CRISPR/Cas9 sgRNA on/off-target cleavage, focusing on improving support in sgRNA design activities and identifying areas currently being researched.

This area of research has greatly expanded recently, and we found it appropriate to work on a Systematic Mapping Study (SMS), an investigation that has proven to be an effective secondary study method. Unlike a classic review, in an SMS, no comparison of methods or results is made, while this task can instead be the subject of a systematic literature review that chooses one theme among those highlighted in this SMS. The study is illustrated in this paper. To the best of the authors' knowledge, no other SMS studies have been published on this topic.

Fifty-seven papers published in the period 2017–2022 (April, 30) were analyzed. This study reveals that the most widely used ML model is the convolutional neural network (CNN), followed by the feedforward neural network (FNN), while the use of other models is marginal. Other interesting information has emerged, such as the wide availability of both open code and platforms dedicated to supporting the activity of researchers or the fact that there is a clear prevalence of public funds that finance research on this topic.