CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems

Interrogating two extensively self-targeting Type I CRISPR-Cas systems in Xanthomonas albilineans reveals distinct anti-CRISPR proteins that block DNA degradation

CRISPR-Cas systems store fragments of invader DNA as spacers to recognize and clear those same invaders in the future. Spacers can also be acquired from the host's genomic DNA, leading to lethal self-targeting. While self-targeting can be circumvented through different mechanisms, natural examples remain poorly explored. Here, we investigate extensive self-targeting by two CRISPR-Cas systems encoding 24 self-targeting spacers in the plant pathogen Xanthomonas albilineans. We show that the native I-C and I-F1 systems are actively expressed and that CRISPR RNAs are properly processed. When expressed in Escherichia coli, each Cascade complex binds its PAM-flanked DNA target to block transcription, while the addition of Cas3 paired with genome targeting induces cell killing. While exploring how X. albilineans survives self-targeting, we predicted putative anti-CRISPR proteins (Acrs) encoded within the bacterium's genome. Screening of identified candidates with cell-free transcription-translation systems and in E. coli revealed two Acrs, which we named AcrIC11 and AcrIF12Xal, that inhibit the activity of Cas3 but not Cascade of the respective system. While AcrF12Xal is homologous to AcrIF12, AcrIC11 shares sequence and structural homology with the anti-restriction protein KlcA. These findings help explain tolerance of self-targeting through two CRISPR-Cas systems and expand the known suite of DNA degradation-inhibiting Acrs.

Dynamics of CRISPR-mediated virus-host interactions in the human gut microbiome

Arms races between mobile genetic elements and prokaryotic hosts are major drivers of ecological and evolutionary change in microbial communities. Prokaryotic defense systems such as CRISPR-Cas have the potential to regulate microbiome composition by modifying the interactions among bacteria, plasmids, and phages. Here, we used longitudinal metagenomic data from 130 healthy and diseased individuals to study how the interplay of genetic parasites and CRISPR-Cas immunity reflects on the dynamics and composition of the human gut microbiome. Based on the coordinated study of 80 000 CRISPR-Cas loci and their targets, we show that CRISPR-Cas immunity effectively modulates bacteriophage abundances in the gut. Acquisition of CRISPR-Cas immunity typically leads to a decrease in the abundance of lytic phages but does not necessarily cause their complete disappearance. Much smaller effects are observed for lysogenic phages and plasmids. Conversely, phage-CRISPR interactions shape bacterial microdiversity by producing weak selective sweeps that benefit immune host lineages. We also show that distal (and chronologically older) regions of CRISPR arrays are enriched in spacers that are potentially functional and target crass-like phages and local prophages. This suggests that exposure to reactivated prophages and other endemic viruses is a major selective pressure in the gut microbiome that drives the maintenance of long-lasting immune memory.

Mobilizable plasmids drive the spread of antimicrobial resistance genes and virulence genes in Klebsiella pneumoniae

BACKGROUND

Klebsiella pneumoniae is a notorious clinical pathogen and frequently carries various plasmids, which are the main carriers of antimicrobial resistance and virulence genes. In comparison to self-transmissible conjugative plasmids, mobilizable plasmids have received much less attention due to their defects in conjugative elements. However, the contribution of mobilizable plasmids to the horizontal transfer of antimicrobial resistance genes and virulence genes of K. pneumoniae remains unclear. In this study, the transfer, stability, and cargo genes of the mobilizable plasmids of K. pneumoniae were examined via genetic experiments and genomic analysis.

METHODS

Carbapenem-resistant (CR) plasmid pHSKP2 and multidrug-resistant (MDR) plasmid pHSKP3 of K. pneumoniae HS11286, virulence plasmid pRJF293 of K. pneumoniae RJF293 were employed in conjugation assays to assess the transfer ability of mobilizable plasmids. Mimic mobilizable plasmids and genetically modified plasmids were constructed to confirm the cotransfer models. The plasmid morphology was evaluated through XbaI and S1 nuclease pulsed-field gel electrophoresis and/or complete genome sequencing. Mobilizable plasmid stability in transconjugants was analyzed via serial passage culture. In addition, in silico genome analysis of 3923 plasmids of 1194 completely sequenced K. pneumoniae was performed to investigate the distribution of the conjugative elements, the cargo genes, and the targets of the CRISPR-Cas system. The mobilizable MDR plasmid and virulence plasmid of K. pneumoniae were investigated, which carry oriT but lack other conjugative elements.

RESULTS

Our results showed that mobilizable MDR and virulence plasmids carrying oriT but lacking the relaxase gene were able to cotransfer with a helper conjugative CR plasmid across various Klebsiella and Escherichia coli strains. The transfer and stability of mobilizable plasmids rather than conjugative plasmids were not interfered with by the CRISPR-Cas system of recipient strains. According to the in silico analysis, the mobilizable plasmids carry about twenty percent of acquired antimicrobial resistance genes and more than seventy-five percent of virulence genes in K. pneumoniae.

CONCLUSIONS

Our work observed that a mobilizable MDR or virulence plasmid that carries oriT but lacks the relaxase genes transferred with the helper CR conjugative plasmid and mobilizable plasmids escaped from CRISPR-Cas defence and remained stable in recipients. These results highlight the threats of mobilizable plasmids as vital vehicles in the dissemination of antibiotic resistance and virulence genes in K. pneumoniae.

CRISPR genome editing using computational approaches: A survey

Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has been widely used in various cell types and organisms. To make genome editing with Clustered regularly interspaced short palindromic repeats far more precise and practical, we must concentrate on the design of optimal gRNA and the selection of appropriate Cas enzymes. Numerous computational tools have been created in recent years to help researchers design the best gRNA for Clustered regularly interspaced short palindromic repeats researches. There are two approaches for designing an appropriate gRNA sequence (which targets our desired sites with high precision): experimental and predicting-based approaches. It is essential to reduce off-target sites when designing an optimal gRNA. Here we review both traditional and machine learning-based approaches for designing an appropriate gRNA sequence and predicting off-target sites. In this review, we summarize the key characteristics of all available tools (as far as possible) and compare them together. Machine learning-based tools and web servers are believed to become the most effective and reliable methods for predicting on-target and off-target activities of Clustered regularly interspaced short palindromic repeats in the future. However, these predictions are not so precise now and the performance of these algorithms -especially deep learning one's-depends on the amount of data used during training phase. So, as more features are discovered and incorporated into these models, predictions become more in line with experimental observations. We must concentrate on the creation of ideal gRNA and the choice of suitable Cas enzymes in order to make genome editing with Clustered regularly interspaced short palindromic repeats far more accurate and feasible.

Analysis of genetic structure and function of clustered regularly interspaced short palindromic repeats loci in 110 Enterococcus strains

Clustered regularly interspaced short palindromic repeats (CRISPR) and their CRISPR-associated proteins (Cas) are an adaptive immune system involved in specific defenses against the invasion of foreign mobile genetic elements, such as plasmids and phages. This study aims to analyze the gene structure and to explore the function of the CRISPR system in the Enterococcus genome, especially with regard to drug resistance. The whole genome information of 110 enterococci was downloaded from the NCBI database to analyze the distribution and the structure of the CRISPR-Cas system including the Cas gene, repeat sequences, and spacer sequence of the CRISPR-Cas system by bioinformatics methods, and to find drug resistance-related genes and analyze the relationship between them and the CRISPR-Cas system. Multilocus sequence typing (MLST) of enterococci was performed against the reference MLST database. Information on the drug resistance of Enterococcus was retrieved from the CARD database, and its relationship to the presence or absence of CRISPR was statistically analyzed. Among the 110 Enterococcus strains, 39 strains (35.45%) contained a complete CRISPR-Cas system, 87 CRISPR arrays were identified, and 62 strains contained Cas gene clusters. The CRISPR system in the Enterococcus genome was mainly type II-A (59.68%), followed by type II-C (33.87%). The phylogenetic analysis of the cas1 gene sequence was basically consistent with the typing of the CRISPR-Cas system. Of the 74 strains included in the study for MLST typing, only 19 (25.68%) were related to CRISPR-Cas typing, while the majority of the strains (74.32%) of MLST typing were associated with the untyped CRISPR system. Additionally, the CRISPR-Cas system may only be related to the carrying rate of some drug-resistant genes and the drug-resistant phenotype. In conclusion, the distribution of the enterococcus CRISPR-Cas system varies greatly among different species and the presence of CRISPR loci reduces the horizontal transfer of some drug resistance genes.

CRISPR/Cas12a-Enabled Multiplex Biosensing Strategy Via an Affordable and Visual Nylon Membrane Readout

Most multiplex nucleic acids detection methods require numerous reagents and high-priced instruments. The emerging clustered regularly interspaced short palindromic repeats (CRISPR)/Cas has been regarded as a promising point-of-care (POC) strategy for nucleic acids detection. However, how to achieve CRISPR/Cas multiplex biosensing remains a challenge. Here, an affordable means termed CRISPR-RDB (CRISPR-based reverse dot blot) for multiplex target detection in parallel, which possesses the advantages of high sensitivity and specificity, cost-effectiveness, instrument-free, ease to use, and visualization is reported. CRISPR-RDB integrates the trans-cleavage activity of CRISPR-Cas12a with a commercial RDB technique. It utilizes different Cas12a-crRNA complexes to separately identify multiple targets in one sample and converts targeted information into colorimetric signals on a piece of accessible nylon membrane that attaches corresponding specific-oligonucleotide probes. It has demonstrated that the versatility of CRISPR-RDB by constructing a four-channel system to simultaneously detect influenza A, influenza B, respiratory syncytial virus, and SARS-CoV-2. With a simple modification of crRNAs, the CRISPR-RDB can be modified to detect human papillomavirus, saving two-thirds of the time compared to a commercial PCR-RDB kit. Further, a user-friendly microchip system for convenient use, as well as a smartphone app for signal interpretation, is engineered. CRISPR-RDB represents a desirable option for multiplexed biosensing and on-site diagnosis.

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.

CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection

MOTIVATION

The CRISPR-Cas9 system is a Type II CRISPR system that has rapidly become the most versatile and widespread tool for genome engineering. It consists of two components, the Cas9 effector protein, and a single guide RNA that combines the spacer (for identifying the target) with the tracrRNA, a trans-activating small RNA required for both crRNA maturation and interference. While there are well-established methods for screening Cas effector proteins and CRISPR arrays, the detection of tracrRNA remains the bottleneck in detecting Class 2 CRISPR systems.

RESULTS

We introduce a new pipeline CRISPRtracrRNA for screening and evaluation of tracrRNA candidates in genomes. This pipeline combines evidence from different components of the Cas9-sgRNA complex. The core is a newly developed structural model via covariance models from a sequence-structure alignment of experimentally validated tracrRNAs. As additional evidence, we determine the terminator signal (required for the tracrRNA transcription) and the RNA-RNA interaction between the CRISPR array repeat and the 5'-part of the tracrRNA. Repeats are detected via an ML-based approach (CRISPRidenify). Providing further evidence, we detect the cassette containing the Cas9 (Type II CRISPR systems) and Cas12 (Type V CRISPR systems) effector protein. Our tool is the first for detecting tracrRNA for Type V systems.

AVAILABILITY AND IMPLEMENTATION

The implementation of the CRISPRtracrRNA is available on GitHub upon requesting the access permission, (https://github.com/BackofenLab/CRISPRtracrRNA). Data generated in this study can be obtained upon request to the corresponding person: Rolf Backofen (backofen@informatik.uni-freiburg.de).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CRISPRCasStack: a stacking strategy-based ensemble learning framework for accurate identification of Cas proteins

CRISPR-Cas system is an adaptive immune system widely found in most bacteria and archaea to defend against exogenous gene invasion. One of the most critical steps in the study of exploring and classifying novel CRISPR-Cas systems and their functional diversity is the identification of Cas proteins in CRISPR-Cas systems. The discovery of novel Cas proteins has also laid the foundation for technologies such as CRISPR-Cas-based gene editing and gene therapy. Currently, accurate and efficient screening of Cas proteins from metagenomic sequences and proteomic sequences remains a challenge. For Cas proteins with low sequence conservation, existing tools for Cas protein identification based on homology cannot guarantee identification accuracy and efficiency. In this paper, we have developed a novel stacking-based ensemble learning framework for Cas protein identification, called CRISPRCasStack. In particular, we applied the SHAP (SHapley Additive exPlanations) method to analyze the features used in CRISPRCasStack. Sufficient experimental validation and independent testing have demonstrated that CRISPRCasStack can address the accuracy deficiencies and inefficiencies of the existing state-of-the-art tools. We also provide a toolkit to accurately identify and analyze potential Cas proteins, Cas operons, CRISPR arrays and CRISPR-Cas locus in prokaryotic sequences. The CRISPRCasStack toolkit is available at https://github.com/yrjia1015/CRISPRCasStack.

Anti-CRISPR prediction using deep learning reveals an inhibitor of Cas13b nucleases

As part of the ongoing bacterial-phage arms race, CRISPR-Cas systems in bacteria clear invading phages whereas anti-CRISPR proteins (Acrs) in phages inhibit CRISPR defenses. Known Acrs have proven extremely diverse, complicating their identification. Here, we report a deep learning algorithm for Acr identification that revealed an Acr against type VI-B CRISPR-Cas systems. The algorithm predicted numerous putative Acrs spanning almost all CRISPR-Cas types and subtypes, including over 7,000 putative type IV and VI Acrs not predicted by other algorithms. By performing a cell-free screen for Acr hits against type VI-B systems, we identified a potent inhibitor of Cas13b nucleases we named AcrVIB1. AcrVIB1 blocks Cas13b-mediated defense against a targeted plasmid and lytic phage, and its inhibitory function principally occurs upstream of ribonucleoprotein complex formation. Overall, our work helps expand the known Acr universe, aiding our understanding of the bacteria-phage arms race and the use of Acrs to control CRISPR technologies.

CRISPRs in the human genome are differentially expressed between malignant and normal adjacent to tumor tissue

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) have been identified in bacteria, archaea and mitochondria of plants, but not in eukaryotes. Here, we report the discovery of 12,572 putative CRISPRs randomly distributed across the human chromosomes, which we termed hCRISPRs. By using available transcriptome datasets, we demonstrate that hCRISPRs are distinctively expressed as small non-coding RNAs (sncRNAs) in cell lines and human tissues. Moreover, expression patterns thereof enabled us to distinguish normal from malignant tissues. In prostate cancer, we confirmed the differential hCRISPR expression between normal adjacent and malignant primary prostate tissue by RT-qPCR and demonstrate that the SHERLOCK and DETECTR dipstick tools are suitable to detect these sncRNAs. We anticipate that the discovery of CRISPRs in the human genome can be further exploited for diagnostic purposes in cancer and other medical conditions, which certainly will lead to the development of point-of-care tests based on the differential expression of the hCRISPRs.

Shotgun whole genome sequencing of drug-resistance Streptococcus anginosus strain 47S1 isolated from a patient with pharyngitis in Saudi Arabia

BACKGROUND

Streptococcus anginosus is an emergence opportunistic pathogen that colonize the human upper respiratory tract (URT), S. anginosus alongside with S. intermedius and S. constellatus, members of S. anginosus group, are implicated in several human infections. However, our understanding this bacterium to the genotype level with determining the genes associated with pathogenicity and antimicrobial resistance (AMR) is scarce. S. anginosus 47S1 strain was isolated from sore throat infection, the whole genome was characterized and the virulence & AMR genes contributing in pathogenicity were investigated.

METHODOLOGY

The whole genome of 47S1 was sequenced by Illumina sequencing technology. Strain 47S1 genome was de novo assembled with different strategies and annotated via PGAP, PROKKA and RAST pipelines. Identifying the CRISPR-Cass system and prophages sequences was performed using CRISPRloci and PhiSpy tools respectively. Prediction the virulence genes were performed with the VFDB database. AMR genes were detected in silico using NCBI AMRFinderPlus pipeline and CARD database and compared with in vitro AST findings.

RESULTS

β-hemolytic strain 47S1 was identified with conventional microbiology techniques and confirmed by the sequences of 16S rRNA gene. Genome of 47S1 comprised of 1981512 bp. Type I-C CRISPR-Cas system and 4 prophages were detected among the genome of 47S1. Several virulence genes were predicted, most of these genes are found in other pathogenic streptococci, mainly lmb, pavA, htrA/degP, eno, sagA, psaA and cpsI which play a significant role in colonizing, invading host tissues and evade form immune system. In silico AMR findings showed that 47S1 gnome harbors (tetA, tetB &tet32), (aac(6')-I, aadK &aph(3')-IVa), fusC, and PmrA genes that mediated-resistance to tetracyclines, aminoglycosides, fusidic acid, and fluoroquinolone respectively which corresponds with in vitro AST obtained results. In conclusion, WGS is a key approach to predict the virulence and AMR genes, results obtained in this study may contribute for a better understanding of the opportunistic S. anginosus pathogenicity.