Biology subjects

Alkhnbashi, O. S.

Publications and source records attributed to Alkhnbashi, O. S..

7 recordsLinked to original sources

bioRxiv2025

Analysis of tracrRNAs reveals subgroup V2 of type V-K CAST systems

CRISPR-associated transposons (CAST) consist of an integration between certain class 1 or class 2 CRISPR-Cas systems and Tn7-like transposons. Class 2 type V-K CAST systems are restricted to cyanobacteria. Here, we identified a unique subgroup of type V-K systems through phylogenetic analysis, classified as V-K_V2. Subgroup V-K_V2 CAST systems are characterized by an alternative tracrRNA, the exclusive use of Arc_2-type transcriptional regulators, and distinct differences in TnsB and TnsC proteins. Although the occurrence of V-K_V2 CAST systems is restricted to Nostocales cyanobacteria, it shows signs of horizontal gene transfer, indicating its capability for genetic mobility. The predicted V-K_V2 tracrRNA secondary structure has been integrated into an updated version of the CRISPRtracrRNA program available on GitHub under https://github.com/BackofenLab/CRISPRtracrRNA/releases/tag/2.0.

microbiology↗

bioRxiv2024

SpacerPlacer: Ancestral reconstruction of CRISPR arrays reveals the evolutionary dynamics of spacer deletions

Bacteria employ CRISPR-Cas systems for defense by integrating invader-derived sequences, termed spacers, into the CRISPR array, which constitutes an immunity memory. While spacer deletions occur randomly across the array, newly acquired spacers are predominantly integrated at the leader end. Consequently, spacer arrays can be used to derive the chronology of spacer insertions. Reconstruction of ancestral spacer acquisitions and deletions could help unravel the coevolution of phages and bacteria, the evolutionary dynamics in microbiomes, or track pathogens. However, standard reconstruction methods produce misleading results by overlooking insertion order and joint deletions of spacers. Here, we present SpacerPlacer, a maximum likelihood-based ancestral reconstruction approach for CRISPR array evolution. We used SpacerPlacer to reconstruct and investigate ancestral deletion events of 4565 CRISPR arrays, revealing that spacer deletions occur 374 times more frequently than mutations and are regularly deleted jointly, with an average of 2.7 spacers. Surprisingly, we observed a decrease in the spacer deletion frequency towards both ends of the reconstructed arrays. While the resulting trailer-end conservation is commonly observed, a reduced deletion frequency is now also detectable towards the variable leader end. Finally, our results point to the hypothesis that frequent loss of recently acquired spacers may provide a selective advantage.

bioinformatics↗

bioRxiv2021

RiboReport - Benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria

Small proteins, those encoded by open reading frames, with less than or equal to 50 codons, are emerging as an important class of cellular macromolecules in all kingdoms of life. However, they are recalcitrant to detection by proteomics or in silico methods. Ribosome profiling (Ribo-seq) has revealed widespread translation of sORFs in diverse species, and this has driven the development of ORF detection tools using Ribo-seq read signals. However, only a handful of tools have been designed for bacterial data, and have not yet been systematically compared. Here, we have performed a comprehensive benchmark of ORF prediction tools which handle bacterial Ribo-seq data. For this, we created a novel Ribo-seq dataset for E. coli, and based on this plus three publicly available datasets for different bacteria, we created a benchmark set by manual labeling of translated ORFs using their Ribo-seq expression profile. This was then used to investigate the predictive performance of four Ribo-seq-based ORF detection tools we found are compatible with bacterial data (Reparation_blast, DeepRibo, Ribo-TISH and SPECtre). The tool IRSOM was also included as a comparison for tools using coding potential and RNA-seq coverage only. DeepRibo and Reparation_blast robustly predicted translated ORFs, including sORFs, with no significant difference for those inside or outside of operons. However, none of the tools was able to predict a set of recently identified, novel, experimentally-verified sORFs with high sensitivity. Overall, we find there is potential for improving the performance, applicability, usability, and reproducibility of prokaryotic ORF prediction tools that use Ribo-Seq as input. Key pointsO_LICreated a benchmark set for Ribo-seq based ORF prediction in bacteria C_LIO_LIDeepRibo the first choice for bacterial ORF prediction tasks C_LIO_LITool performance is comparable between operon vs single gene regions C_LIO_LIIdentification of novel sORF with DeepRibo is, with restrictions, possible, by using the top 100 novel sORFs sorted by rank. C_LIO_LIExperimental results show that considering translation initiation site data could boost the detection of novel small ORFs C_LIO_LIDetermination of novel sORFs in E. coli using a new experimental protocol to enrich for translation initiation site. These data-set shows that still a significant part (here 8 out 24, so 1/3) are not detected dispute sufficient Ribo-seq signal. An additional 7 could be recovered using translation initiation site protocols. C_LIO_LITools should embrace the use of replicate data and improve packaging, usability and documentation. C_LI

bioinformatics↗

bioRxiv2021

CdrS is a global transcriptional regulator influencing cell division in Haloferax volcanii

Transcriptional regulators that integrate cellular and environmental signals to control cell division are well known in bacteria and eukaryotes, but their existence is poorly understood in archaea. We identified a conserved gene (cdrS) that encodes a small protein and is highly transcribed in the model archaeon Haloferax volcanii. The cdrS gene could not be deleted, but CRISPRi-mediated repression of the cdrS gene caused slow growth, cell division defects, and changed the expression of multiple genes and their products associated with cell division, protein degradation and metabolism. Consistent with this complex regulatory network, overexpression of cdrS inhibited cell division, whereas overexpression of the operon encoding both CdrS and a tubulin-like cell division protein (FtsZ2) stimulated division. ChIP-Seq identified 18 DNA-binding sites of the CdrS protein including one upstream of the promoter for diadenylate cyclase, which is an essential gene involved in c-di-AMP signalling implicated in the regulation of cell division. These findings suggest that CdrS is a transcription factor that plays a central role in a regulatory network coordinating metabolism and cell division. ImportanceCell division is a central mechanism of life, and is essential for growth and development. Bacteria and Eukarya have different mechanisms for cell division, which have been studied in detail. In contrast, cell division in Archaea is still understudied, and its regulation is poorly understood. Interestingly, different cell division machineries appear in the Archaea, with the Euryarchaeota using a cell division apparatus based on the tubulin-like cytoskeletal protein FtsZ, as in bacteria. Here we identify the small protein CdrS as essential for survival and a central regulator of cell division in the Euryarchaeon Haloferax volcanii. CdrS also appears to coordinate other cellular pathways including synthesis of signalling molecules and protein degradation. Our results show that CdrS plays a sophisticated role in cell division, including regulation of numerous associated genes. These findings are expected to initiate investigations into conditional regulation of division in archaea.

microbiology↗

bioRxiv2020

CRISPRIdentify: Identification of CRISPR arrays using machine learning approach

CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR- Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are produced by processing of pre-crRNA, the transcript of CRISPR arrays, and guide Cas protein(s) to the cognate invading nucleic acids, enabling their destruction. Several bioinformatics tools have been developed to detect CRISPR arrays based solely on DNA sequences, but all these tools employ the same strategy of looking for repetitive patterns, which might correspond to CRISPR array repeats. The identified patterns are evaluated using a fixed, built-in scoring function, and arrays exceeding a cut-off value are reported. Here, we instead introduce a data-driven approach that uses machine learning to detect and differentiate true CRISPR arrays from false ones based on several features. Our CRISPR detection tool, CRISPRidentify, performs three steps: detection, feature extraction and classification based on manually curated sets of positive and negative examples of CRISPR arrays. The identified CRISPR arrays are then reported to the user accompanied by detailed annotation. We demonstrate that our approach identifies not only previously detected CRISPR arrays, but also CRISPR array candidates not detected by other tools. Compared to other methods, our tool has a drastically reduced false positive rate. In contrast to the existing tools, our approach not only provides the user with the basic statistics on the identified CRISPR arrays but also produces a certainty score as a practical measure of the likelihood that a given genomic region is a CRISPR array.

bioinformatics↗

bioRxiv2019

CRISPRCasIdentifier: Machine learning for accurate identification and classification of CRISPR-Cas systems

CRISPR-Cas genes are extraordinarily diverse and evolve rapidly when compared to other prokaryotic genes. With the rapid increase in newly sequenced archaeal and bacterial genomes, manual identification of CRISPR-Cas systems is no longer viable. Thus, an automated approach is required for advancing our understanding of the evolution and diversity of these systems, and for finding new candidates for genome engineering in eukaryotic models. In this paper, we introduce a holistic strategy that combines regression and classification models for improving the quality of protein cascades, predicting their subtypes, detecting signature genes and extracting potential rules that reveal functional modules for CRISPR.

bioinformatics↗

bioRxiv2018

Conserved accessory proteins encoded with archaeal and bacterial Type III CRISPR-Cas gene cassettes that may specifically modulate, complement or extend interference activity

A study was undertaken to identify conserved proteins that are encoded either within, or directly adjacent to, cas gene cassettes of Type III CRISPR-Cas interference modules. These Type III modules are especially versatile functionally and have been shown to target and degrade dsDNA, ssDNA and ssRNA. In addition, the interference gene cassettes are frequently intertwined with other accessory genes, including genes encoding CARF domains, some of which are likely to be cofunctional. Using a comparative genomics approach, and defining a Type III association score accounting for coevolution and specificity of flanking genes, we identified and classified 39 new Type III associated gene families. Most archaeal and bacterial Type III modules were seen to be flanked by several accessory genes, around half of which did not encode CARF domains and remain of unknown function. Non-CARF accessory genes were found to be more diverse than their CARF counterparts, encoding nuclease, helicase, protease, ATPase, transporter and transmembrane domains and including a considerable fraction that encoded no known domains. The diversity of non-CARF Type III accessory genes found in this study suggests that additional families exist which remain undetected because of the limited number of annotated genomes currently available. The method employed is scalable for potential application on metagenomic data once automated pipelines for annotation of CRISPR-Cas systems have been developed. All accessory genes found in this study are presented online in a readily accessible and searchable format for researchers to audit their model organism of choice: http://accessory.crispr.dk.

bioinformatics↗