Rapid and accurate species identification for ecological studies and monitoring using CRISPR-based SHERLOCK.
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ABSTRACT:
SUBMITTER: Baerwald MR
PROVIDER: S-EPMC7497203 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Decades of ongoing research has established that oral microbial communities play a role in oral diseases such as periodontitis and caries. Yet the detection of oral bacteria and the profiling of oral polymicrobial communities currently rely on methods that are costly, slow, and technically complex, such as qPCR or next-generation sequencing. For the widescale screening of oral microorganisms suitable for point-of-care settings, there exists the need for a low-cost, rapid detection technique. Here, we tailored the novel CRISPR-Cas-based assay SHERLOCK for the species-specific detection of oral bacteria. We developed a computational pipeline capable of generating constructs suitable for SHERLOCK and experimentally validated the detection of seven oral bacteria. We achieved detection within the single-molecule range that remained specific in the presence of off-target DNA found within saliva. Further, we adapted the assay for detecting target sequences directly from unprocessed saliva samples. The results of our detection, when tested on 30 healthy human saliva samples, fully aligned with 16S rRNA sequencing. Looking forward, this method of detecting oral bacteria is highly scalable and can be easily optimized for implementation at point-of-care settings.
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Project description:INTRODUCTION:Invertebrate communities are central to many environmental monitoring programs. In freshwater ecosystems, aquatic macroinvertebrates are collected, identified and then used to infer ecosystem condition. Yet the key step of species identification is often not taken, as it requires a high level of taxonomic expertise, which is lacking in most organizations, or species cannot be identified as they are morphologically cryptic or represent little known groups. Identifying species using DNA sequences can overcome many of these issues; with the power of next generation sequencing (NGS), using DNA sequences for routine monitoring becomes feasible. RESULTS:In this study, we test if NGS can be used to identify species from field-collected samples in an important bioindicator group, the Chironomidae. We show that Cytochrome oxidase I (COI) and Cytochrome B (CytB) sequences provide accurate DNA barcodes for chironomid species. We then develop a NGS analysis pipeline to identifying species using megablast searches of high quality sequences generated using 454 pyrosequencing against comprehensive reference libraries of Sanger-sequenced voucher specimens. We find that 454 generated COI sequences successfully identified up to 96% of species in samples, but this increased up to 99% when combined with CytB sequences. Accurate identification depends on having at least five sequences for a species; below this level species not expected in samples were detected. Incorrect incorporation of some multiplex identifiers (MID's) used to tag samples was a likely cause, and most errors could be detected when using MID tags on forward and reverse primers. We also found a strong quantitative relationship between the number of 454 sequences and individuals showing that it may be possible to estimate the abundance of species from 454 pyrosequencing data. CONCLUSIONS:Next generation sequencing using two genes was successful for identifying chironomid species. However, when detecting species from 454 pyrosequencing data sets it was critical to include known individuals for quality control and to establish thresholds for detecting species. The NGS approach developed here can lead to routine species-level diagnostic monitoring of aquatic ecosystems.
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Project description:Differentiation between mitis group streptococci (MGS) bacteria in routine laboratory tests has become important for obtaining accurate epidemiological information on the characteristics of MGS and understanding their clinical significance. The most reliable method of MGS species identification is multilocus sequence analysis (MLSA) with seven house-keeping genes; however, because this method is time-consuming, it is deemed unsuitable for use in most clinical laboratories. In this study, we established a scheme for identifying 12 species of MGS (S. pneumoniae, S. pseudopneumoniae, S. mitis, S. oralis, S. peroris, S. infantis, S. australis, S. parasanguinis, S. sinensis, S. sanguinis, S. gordonii, and S. cristatus) using the MinION nanopore sequencer (Oxford Nanopore Technologies, Oxford, UK) with the taxonomic aligner "What's in My Pot?" (WIMP; Oxford Nanopore's cloud-based analysis platform) and Kraken2 pipeline with the custom database adjusted for MGS species identification. The identities of the species in reference genomes (n = 514), clinical isolates (n = 31), and reference strains (n = 4) were confirmed via MLSA. The nanopore simulation reads were generated from reference genomes, and the optimal cut-off values for MGS species identification were determined. For 31 clinical isolates (S. pneumoniae = 8, S. mitis = 17 and S. oralis = 6) and 4 reference strains (S. pneumoniae = 1, S. mitis = 1, S. oralis = 1, and S. pseudopneumoniae = 1), a sequence library was constructed via a Rapid Barcoding Sequencing Kit for multiplex and real-time MinION sequencing. The optimal cut-off values for the identification of MGS species for analysis by WIMP and Kraken2 pipeline were determined. The workflow using Kraken2 pipeline with a custom database identified all 12 species of MGS, and WIMP identified 8 MGS bacteria except S. infantis, S. australis, S. peroris, and S. sinensis. The results obtained by MinION with WIMP and Kraken2 pipeline were consistent with the MGS species identified by MLSA analysis. The practical advantage of whole genome analysis using the MinION nanopore sequencer is that it can aid in MGS surveillance. We concluded that MinION sequencing with the taxonomic aligner enables accurate MGS species identification and could contribute to further epidemiological surveys.
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Project description:Plasmalogens (Pls) levels are reported to be altered in several neurological and metabolic diseases. Identification of sn-1 fatty alcohols and sn-2 fatty acids of different Pls species is necessary to determine the roles and mechanisms of action of Pls in different diseases. Previously, full-scan tandem mass spectrometry (MS/MS) was used for this purpose but is not effective for low-abundance Pls species. Recently, multiplexed selected reaction monitoring MS (SRM/MS) was found to be more selective and sensitive than conventional full-scan MS/MS for the identification of low-abundance compounds. In the present study, we developed a liquid chromatography (LC)-targeted multiplexed SRM/MS system for the identification and quantification of different Pls choline (Pls-PC) and Pls ethanolamine (Pls-PE) species. We determined five precursor-product ion transitions to identify sn-1 and sn-2 fragments of each Pls species. Consequently, sn-1 and sn-2 fatty acyl chains of 22 Pls-PC and 55 Pls-PE species were identified in mouse brain samples. Among them, some species had C20:0 and C20:1 fatty alcohols at the sn-1 position. For quantification of Pls species in mouse brain samples, a single SRM transition was employed. Thus, our results suggest that the LC-targeted multiplexed SRM/MS system is very sensitive for the identification and quantification of low-abundance lipids such as Pls, and is thus expected to make a significant contribution to basic and clinical research in this field in the future.
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Project description:Simple PCR and sequencing assays that utilize a single pair of degenerate primers were used to characterize a 429-bp-long DNA fragment internal (sodA(int)) to the sodA gene encoding the manganese-dependent superoxide dismutase in 40 coagulase-negative staphylococcal (CNS) type strains. The topology of the phylogenetic tree obtained was in general agreement with that which was inferred from an analysis of their 16S rRNA or hsp60 gene sequences. Sequence analysis revealed that the staphylococcal sodA genes exhibit a higher divergence than does the corresponding 16S ribosomal DNA. These results confirm that the sodA gene constitutes a highly discriminative target sequence for differentiating closely related bacterial species. Clinical isolates that could not be identified at the species level by phenotypical tests were identified by use of this database. These results demonstrate the usefulness of this method for rapid and accurate species identification of CNS isolates, although it does not allow discrimination of subspecies. The sodA sequence polymorphisms observed with staphylococcal species offer good opportunities for the development of assays based on DNA chip technologies.
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Project description:Mycobacterium tuberculosis infection and nontuberculous mycobacteria (NTM) infections exhibit similar clinical symptoms; however, the therapies for these two types of infections are different. Therefore, the rapid and accurate identification of M. tuberculosis and NTM species is very important for the control of tuberculosis and NTM infections. In the present study, a Cas12a/guide RNA (gRNA)-based platform was developed to identify M. tuberculosis and most NTM species. By designing species-specific gRNA probes targeting the rpoB sequence, a Cas12a/gRNA-based platform successfully identified M. tuberculosis and six major NTM species (Mycobacterium abscessus, Mycobacterium intracellulare, Mycobacterium avium, Mycobacterium kansasii, Mycobacterium gordonae, and Mycobacterium fortuitum) without cross-reactivity. In a blind assessment, a total of 72 out of 73 clinical Mycobacterium isolates were correctly identified, which is consistent with previous rpoB sequencing results. These results suggest that the Cas12a/gRNA-based platform is a promising tool for the rapid, accurate, and cost-effective identification of both M. tuberculosis and NTM species.
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Project description:Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.
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Project description:BACKGROUND:Tsetse flies (Diptera: Glossinidae) are solely responsible for the transmission of African trypanosomes, causative agents of sleeping sickness in humans and nagana in livestock. Due to the lack of efficient vaccines and the emergence of drug resistance, vector control approaches such as the sterile insect technique (SIT), remain the most effective way to control disease. SIT is a species-specific approach and therefore requires accurate identification of natural pest populations at the species level. However, the presence of morphologically similar species (species complexes and sub-species) in tsetse flies challenges the successful implementation of SIT-based population control. RESULTS:In this study, we evaluate different molecular tools that can be applied for the delimitation of different Glossina species using tsetse samples derived from laboratory colonies, natural populations and museum specimens. The use of mitochondrial markers, nuclear markers (including internal transcribed spacer 1 (ITS1) and different microsatellites), and bacterial symbiotic markers (Wolbachia infection status) in combination with relatively inexpensive techniques such as PCR, agarose gel electrophoresis, and to some extent sequencing provided a rapid, cost effective, and accurate identification of several tsetse species. CONCLUSIONS:The effectiveness of SIT benefits from the fine resolution of species limits in nature. The present study supports the quick identification of large samples using simple and cost effective universalized protocols, which can be easily applied by countries/laboratories with limited resources and expertise.
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Project description:BackgroundSequencing of prokaryotic genomes has recently revealed the presence of CRISPR elements: short, highly conserved repeats separated by unique sequences of similar length. The distinctive sequence signature of CRISPR repeats can be found using general-purpose repeat- or pattern-finding software tools. However, the output of such tools is not always ideal for studying these repeats, and significant effort is sometimes needed to build additional tools and perform manual analysis of the output.ResultsWe present PILER-CR, a program specifically designed for the identification and analysis of CRISPR repeats. The program executes rapidly, completing a 5 Mb genome in around 5 seconds on a current desktop computer. We validate the algorithm by manual curation and by comparison with published surveys of these repeats, finding that PILER-CR has both high sensitivity and high specificity. We also present a catalogue of putative CRISPR repeats identified in a comprehensive analysis of 346 prokaryotic genomes.ConclusionPILER-CR is a useful tool for rapid identification and classification of CRISPR repeats. The software is donated to the public domain. Source code and a Linux binary are freely available at http://www.drive5.com/pilercr.
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Project description:The increasing spread of drug-resistant bacterial strains presents great challenges to clinical antibacterial treatment and public health, particularly with regard to β-lactamase-producing Enterobacteriaceae. A rapid and accurate detection method that can expedite precise clinical diagnostics and rational administration of antibiotics is urgently needed. Targeted proteomics, a technique involving selected reaction monitoring or multiple reaction monitoring, has been developed for detecting specific peptides. In the present study, a rapid single-colony-processing procedure combined with an improved parallel reaction monitoring (PRM) workflow based on HRAM Orbitrap MS was developed to detect carbapenemases (Klebsiella pneumoniae carbapenemase, KPC; imipenemase, IMP; Verona integron-encoded metallo-β-lactamase, VIM; New Delhi metallo-β-lactamase, NDM; and oxacillinase, OXA), extended spectrum β-lactamases (TEM and CTX-M), and AmpC (CMY-2) produced by Enterobacteriaceae. Specific peptides were selected and validated, and their coefficients of variation and stability were evaluated. In total, 188 Enterobacteriaceae strains were screened using the workflow. Fourteen out of total 19 peptides have 100% specificity; three peptides have specificity >95% and two peptides have specificity ranged from 74∼85%. On the sensitivity, only nine peptides have 95∼100% sensitivity. The other 10 peptides have sensitivity ranged from 27∼94%. Thus, a screening method based on peptide groups was developed for the first time. Taken together, this study described a rapid extraction and detection workflow for widespread β-lactamases, including KPC, IMP, VIM, NDM, OXA, CMY, CTX-M, and TEM, using single colonies of Enterobacteriaceae strains. PRM-targeted proteomics was proven to be a promising approach for the detection of drug-resistant enzymes.
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