Jun 2026· Journal of Genome Biotechnology and Genetics· 0 citations· 135 references
TL;DR
The convergence of standardised experimental data, specialised microbial datasets, and advanced AI architectures is paving the way to transform microbial biotechnology by accelerating metabolic engineering and synthetic biology applications.
Abstract
CRISPR-Cas9 has transformed microbial biotechnology by enabling precise genome modifications; however, achieving high editing efficiency remains a challenge due to multiple determinants, including on-target specificity, off-target events, PAM sequence, sgRNA scaffold composition, and RNA secondary structure. Our review foresees how artificial intelligence (AI) can address those challenges by enabling automated identification as well as highly active guide RNA (gRNA) optimisation. We highlight the influence of a data-driven training strategy that is focused on high-quality, diverse, and accurately labelled microbial datasets—mainly, given the limitations of models derived from mammalian systems that are not directly transferable to microbial organisms. Moreover, we discuss the key role of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and centralised, curated CRISPR-Cas databases as foundational elements for developing robust and predictive frameworks. Emerging directions are also explored, including generative AI approaches capable of supporting automated experimental planning. By considering the potential dual use of such technologies, the review further addresses bioethical considerations and regulatory frameworks necessary to ensure responsible genome engineering as a milestone, as well as the implementation of safeguards against misuse, particularly in pathogenic microorganisms. Furthermore, the convergence of standardised experimental data, specialised microbial datasets, and advanced AI architectures is paving the way to transform microbial biotechnology by accelerating metabolic engineering and synthetic biology applications.
Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute adaptive immune systems in prokaryotes and have transformed life sciences, precision medicine, and synthetic biology as programmable genome-editing tools. Despite their broad utility, naturally occurring DNA-targeting Cas effectors remain constrained by several intrinsic limitations, including large protein size that complicates delivery, stringent protospacer adjacent motif (PAM) requirements that restrict targetable genomic space, and mismatch tolerance that can lead to off-target activity and potential genotoxicity. These challenges have made Cas protein engineering and the discovery of novel CRISPR and CRISPR-like systems from metagenomic resources central to the development of next-generation genome-editing platforms. This Review places recent advances within an integrated synthetic biology engineering continuum that links natural effector discovery, structure-guided hypothesis generation, high-throughput functional screening, machine learning-enabled model construction, and iterative redesign. This Review summarizes progress in the screening, optimization, and functional engineering of DNA-targeting CRISPR and CRISPR-like effectors, with emphasis on structure-guided rational design, directed evolution coupled with high-throughput screening, bioinformatics- and evolution-guided mining of novel systems from large-scale sequence databases, and artificial intelligence-assisted development. By integrating these strategies, we highlight how CRISPR effector engineering is moving toward design-build-test-learn (DBTL)-inspired workflows that expand the functional landscape of genome-editing technologies and advance genome editing toward improved efficiency, safety, and programmability.
Lingwei She, Zeyu Liang, Qin Zou et al.· ACS Synthetic Biology· 0 citations
CRISPR-Cas systems, with their programmable nucleic acid-targeting capabilities, represent an ideal platform for constructing next-generation, highly sensitive biosensors. However, the clinical translation of these platforms is hindered by key limitations inherent to native single-guide RNAs (sgRNAs), including insufficient stability, potential immunogenicity, and off-target effects. To address these challenges, engineering sgRNAs has emerged as a central strategy to overcome such barriers and enhance overall biosensor performance. In this review, we provide a systematic overview of the field, beginning with the classification, molecular mechanisms, and structural features of representative CRISPR-Cas effector proteins to establish their foundational role as sensing elements. We then examine the specific limitations of native sgRNAs in biosensing applications. Building on this analysis, we highlight recent advances in sgRNA engineering strategies, which encompass three major approaches, including chemical modifications, structural remodeling, and modular functional integration. Furthermore, we review the integration of these engineered sgRNAs into advanced biosensor platforms, including microfluidic paper-based devices, centrifugal platforms, wearable patches, microneedles, and point-of-care testing (POCT) systems, and present a comparative table summarizing their performance in terms of detection signals, limits of detection, and other key metrics. Finally, we discuss persistent challenges such as the fine control of off-target effects, in vivo delivery bottlenecks, and system robustness in complex environments, and outline future directions toward amplification-free, multiplexed, and clinically translatable CRISPR-based biosensors. Overall, the engineering of sgRNAs offers a powerful means to systematically enhance the stability, specificity, and reliability of CRISPR-based biosensors, thereby accelerating their practical deployment in clinical diagnostics.
Xiaojing Liu, Tingyi Chen, Xiangjun Li et al.· ACS Sensors· 0 citations
This article synthesizes contemporary advancements in CRISPR-mediated mammalian genome modification, detailing core mechanisms – such as guide RNA and the Cas9 endonuclease – alongside next-generation modalities, including base and prime editing.
Olga Aldoshina, Dmitriy Lazarev, E. Smirnova· Veterinariya, Zootekhniya i...· 0 citations
Clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing and artificial intelligence (AI) are increasingly presented as a unified route to precision plant breeding. Their convergence is scientifically plausible but unevenly demonstrated. CRISPR systems can create targeted sequence changes, whereas AI can prioritise candidate genes, integrate genomic and phenomic data, optimise guide RNAs and editors, predict editing outcomes, and support iterative genotype-to-phenotype learning. This critical narrative review evaluates the evidence linking these capabilities across the crop-improvement pipeline. Literature published from 1 January 2012 to 5 June 2026 was selected through transparent searches of accessible scholarly indexes and bibliographic resources, followed by citation tracking, metadata verification and thematic appraisal. Evidence is strongest for CRISPR-mediated improvement of discrete, biologically well-characterised traits, including disease resistance, quality attributes, plant architecture and selected stress responses. AI has also achieved useful performance in phenotyping, genomic prediction and CRISPR design, but superiority over conventional statistical or rule-based approaches is not consistent across datasets, species or prediction tasks. Direct evidence for fully integrated, AI-guided CRISPR breeding programmes that deliver stable field performance remains limited. Major constraints include uncertain causal target identification, small and non-representative training datasets, poor transferability across genetic backgrounds, polyploidy, genotype-by-environment interaction, transformation and regeneration bottlenecks, incomplete detection of unintended outcomes, and heterogeneous regulation. The most defensible interpretation is therefore that AI and CRISPR are complementary components of an emerging design-build-test-learn framework rather than a mature autonomous breeding platform. Progress will depend on plant-specific benchmark datasets, prospective validation, multi-environment field trials, interoperable data standards, equitable access to transformation and computational infrastructure, and governance focused on the properties and evidence of resulting products. Their integration can accelerate precision breeding, but biological causality, experimental validation and breeding judgement remain indispensable.
Anilkumar Lalasing Chavan, Pavan Rathod G. P., Chandana Suresh K. S. et al.· Plant cell biotechnology and...· 0 citations
CRISPR‐Cas systems have far‐reaching implications for genome editing and molecular diagnostics, and are emerging as important tools in biomedical research and clinical practice. These systems enable precise genetic manipulation and highly sensitive nucleic acid detection, capabilities that are invaluable for therapeutic development and pathogen surveillance. Nevertheless, native CRISPR‐Cas systems possess inherent limitations that constrain their broader utility. These limitations in genome editing include insufficient editing efficiency, a restricted range of targets due to protospacer adjacent motif constraints and non‐negligible off‐target effects. In molecular diagnostics, the remaining problems include detecting low‐abundance targets, achieving high specificity, and ensuring effective compatibility with isothermal amplification. To address these limitations, researchers have proposed many enhancement strategies that mainly center around four aspects: engineering of Cas effector protein, optimizing guide RNA designs, developing advanced reporter probes, and utilizing chemical additives. This review summarizes the current developments in these areas and highlights their significant contribution to increasing the fidelity of editing and diagnostic sensitivity. In addition, we discuss future prospects for enhanced CRISPR‐Cas systems, focusing on their accelerating clinical translation and expanding biomedical applications.
Sheng Li, Han Li, Bojie Chen et al.· Interdisciplinary Medicine· 0 citations
CRISPR-Cas9 has revolutionized molecular biology with its precise genome-editing ability, whose precision largely relies on computationally designed guide RNAs (gRNAs) to minimize off-target effects. This paper reviews major bioinformatics tools for CRISPR, focusing on their algorithms and computational frameworks, classifying them into four layers. The first consists of rule-based systems such as CHOPCHOP and CRISPOR, using linear scoring functions with O(n) complexity. The second includes alignment-based tools like Cas-OFFinder, which applies FM-index for off-target detection with over 90% sensitivity. The third involves machine learning-based methods such as CRISPRscan with random forest, yet suffering 50–70% false positives due to expanded search space. The fourth layer comprises deep learning systems including CRISMER and DeepCRISPR, employing Transformers and CNNs with AUC 0.85–0.95, though they lack cross-dataset generalization. Moreover, current tools are fragmented, requiring manual integration of multiple platforms and poor reproducibility. This review identifies the layered separation as a key bottleneck and highlights the demand for a unified programming framework to streamline workflows and advance CRISPR algorithm innovation.