Identifying microbial species by single-molecule DNA optical mapping and resampling statistics

Unlocking the microbial treasure trove: advances in Streptomyces derived secondary metabolites in the battle against cancer

Streptomyces is widely recognized as the "biological factory" of specialized metabolites comprising a huge variety of bioactive molecules with diverse chemical properties. The potential of this Gram-positive soil bacteria to produce such diversified secondary metabolites with significant biological properties positions them as an ideal candidate for anticancer drug discovery. Some of the Streptomyces-derived secondary metabolites include siderophores (enterobactin, desferrioxamine), antibiotics (xiakemycin, dinactin) pigments (prodigiosin, melanin), and enzymes (L-methioninase, L-asperginase, cholesterol oxidase) which exhibit a pronounced anticancer effect on both in vitro and in vivo system. These secondary metabolites are endowed with antiproliferative, pro-apoptotic, antimetastatic, and antiangiogenic properties, presenting several promising characteristics that make them suitable candidates in the battle against this deadly disease. In this comprehensive review, we have dived deep and explored their history of discovery, their role as anticancer agents, underlying mechanisms, the approaches for the discovery of anticancer molecules from the secondary metabolites of Streptomyces (isolation of Streptomyces, characterization of bacterial strain, screening for anticancer activity and determination of in vitro and in vivo toxicity, structure-activity relationship studies, clinical translation, and drug development studies). The hurdles and challenges associated with this process and their future prospect were also illustrated. This review highlights the efficacy of Streptomyces as a "microbial treasure island" for novel anticancer agents, which warrants sustained research and exploration in this field to disclose more molecules from Streptomyces that are unidentified and to translate the clinical application of these secondary metabolites for cancer patients.

DeepMAP: Deep CNN Classifiers Applied to Optical Mapping for Fast and Precise Species-Level Metagenomic Analysis

DNA optical mapping is a powerful technique commonly used for structural variant calling and genome assembly verification. Despite being inherently high-throughput, the method has not yet been applied to highly complex settings such as species identification in microbiome analysis due to the lack of alignment algorithms that can both assign large numbers of reads in minutes and handle large database size. In this work, we present a novel genomic classification pipeline based on deep convolutional neural networks for optical mapping data (DeepMAP), which can perform fast and accurate assignment of individual optical maps to their respective genomes. We furthermore achieve a superior performance of DeepMAP in the presence of evolutionary divergent sequences, making it robust to the presence of unknown strains within metagenomic samples. We evaluate DeepMAP on genomic DNA extracted from bacterial mixtures, reaching species-level resolution with true positive rates of around 75% and a false positive rate of less than 1%, with measured classification speeds significantly outpacing those of previously developed approaches for high-density optical mapping data alignment.

Sequence-Specific Minor Groove Binders in Labeling and Single-Molecule Analysis of DNA

The ability to address specific sequences within DNA is of tremendous interest in biotechnology and biomedicine. Various technologies have been established over the past few decades, such as nicking enzymes and methyltransferase-directed sequence-specific labeling, transcription activator-like effector nucleases (TALENs), the CRISPR-Cas9 system, and polyamides of heterocycles as sequence-specific DNA minor groove binders. Pyrrole-imidazole polyamides have been reported to recognize predetermined DNA sequences, and some successful attempts have demonstrated their potential in regulating gene expression. However, few studies on single-molecule labeling and analysis of DNA have been explored, particularly at single-targeting sites. In this study, we rationally designed and synthesized a set of functional minor groove binders, varying in structures, sequence information addressed, and methods of dye introduction. Their potential for sequence-specific labeling and single-molecule DNA analysis was evaluated through chromatographic and on-surface optical assays. First results indicated that, while they yielded excellent imaging output, the labeling specificity of the hairpin polyamides for single-molecule use was hindered by single-mismatch sites. To address this issue, and in an unprecedented approach, we devised a competitive binding strategy that utilizes ethidium bromide as a nonspecific binder to competitively block the mismatches, significantly enhancing the labeling specificity. These findings provide valuable insights into the use of hairpin polyamides as sequence-programmable and enzyme-free DNA labelers in the field of sequence-specific DNA recognition and modification.

Sequencing and Optical Genome Mapping for the Adventurous Chemist

This review provides a comprehensive overview of the chemistries and workflows of the sequencing methods that have been or are currently commercially available, providing a very brief historical introduction to each method. The main optical genome mapping approaches are introduced in the same manner, although only a subset of these are or have ever been commercially available. The review comes with a deck of slides containing all of the figures for ease of access and consultation.

The potential of including the microbiome as biomarker in population-based health studies: methods and benefits

The key role of our microbiome in influencing our health status, and its relationship with our environment and lifestyle or health behaviors, have been shown in the last decades. Therefore, the human microbiome has the potential to act as a biomarker or indicator of health or exposure to health risks in the general population, if information on the microbiome can be collected in population-based health surveys or cohorts. It could then be associated with epidemiological participant data such as demographic, clinical or exposure profiles. However, to our knowledge, microbiome sampling has not yet been included as biological evidence of health or exposure to health risks in large population-based studies representative of the general population. In this mini-review, we first highlight some practical considerations for microbiome sampling and analysis that need to be considered in the context of a population study. We then present some examples of topics where the microbiome could be included as biological evidence in population-based health studies for the benefit of public health, and how this could be developed in the future. In doing so, we aim to highlight the benefits of having microbiome data available at the level of the general population, combined with epidemiological data from health surveys, and hence how microbiological data could be used in the future to assess human health. We also stress the challenges that remain to be overcome to allow the use of this microbiome data in order to improve proactive public health policies.

OM2Seq: learning retrieval embeddings for optical genome mapping

Motivation

Genomics-based diagnostic methods that are quick, precise, and economical are essential for the advancement of precision medicine, with applications spanning the diagnosis of infectious diseases, cancer, and rare diseases. One technology that holds potential in this field is optical genome mapping (OGM), which is capable of detecting structural variations, epigenomic profiling, and microbial species identification. It is based on imaging of linearized DNA molecules that are stained with fluorescent labels, that are then aligned to a reference genome. However, the computational methods currently available for OGM fall short in terms of accuracy and computational speed.

Results

This work introduces OM2Seq, a new approach for the rapid and accurate mapping of DNA fragment images to a reference genome. Based on a Transformer-encoder architecture, OM2Seq is trained on acquired OGM data to efficiently encode DNA fragment images and reference genome segments to a common embedding space, which can be indexed and efficiently queried using a vector database. We show that OM2Seq significantly outperforms the baseline methods in both computational speed (by 2 orders of magnitude) and accuracy.

Availability and implementation

https://github.com/yevgenin/om2seq.

Optical Mapping: Detecting Genomic Resistance Cassettes in MRSA

Methicillin-resistant Staphylococcus aureus (MRSA) is a multidrug-resistant bacterium with a global presence in healthcare facilities as well as community settings. The resistance of MRSA to beta-lactam antibiotics can be attributed to a mobile genetic element called the staphylococcal cassette chromosome mec (SCCmec), ranging from 23 to 68 kilobase pairs in length. The mec gene complex contained in SCCmec allows MRSA to survive in the presence of penicillin and other beta-lactam antibiotics. We demonstrate that optical mapping (OM) is able to identify the bacterium as S. aureus, followed by an investigation of the presence of kilobase pair range SCCmec elements by examining the associated OM-generated barcode patterns. By employing OM as an alternative to traditional DNA sequencing, we showcase its potential for the detection of complex genetic elements such as SCCmec in MRSA. This approach holds promise for enhancing our understanding of antibiotic resistance mechanisms and facilitating the development of targeted interventions against MRSA infections.

Design of optimal labeling patterns for optical genome mapping via information theory

MOTIVATION

Optical genome mapping (OGM) is a technique that extracts partial genomic information from optically imaged and linearized DNA fragments containing fluorescently labeled short sequence patterns. This information can be used for various genomic analyses and applications, such as the detection of structural variations and copy-number variations, epigenomic profiling, and microbial species identification. Currently, the choice of labeled patterns is based on the available biochemical methods and is not necessarily optimized for the application.

RESULTS

In this work, we develop a model of OGM based on information theory, which enables the design of optimal labeling patterns for specific applications and target organism genomes. We validated the model through experimental OGM on human DNA and simulations on bacterial DNA. Our model predicts up to 10-fold improved accuracy by optimal choice of labeling patterns, which may guide future development of OGM biochemical labeling methods and significantly improve its accuracy and yield for applications such as epigenomic profiling and cultivation-free pathogen identification in clinical samples.

AVAILABILITY AND IMPLEMENTATION

https://github.com/yevgenin/PatternCode.

Fast and simple super-resolution with single images

We present a fast and simple algorithm for super-resolution with single images. It is based on penalized least squares regression and exploits the tensor structure of two-dimensional convolution. A ridge penalty and a difference penalty are combined; the former removes singularities, while the latter eliminates ringing. We exploit the conjugate gradient algorithm to avoid explicit matrix inversion. Large images are handled with ease: zooming a 100 by 100 pixel image to 800 by 800 pixels takes less than a second on an average PC. Several examples, from applications in wide-field fluorescence microscopy, illustrate performance.

Combining dense and sparse labeling in optical DNA mapping

Optical DNA mapping (ODM) is based on fluorescent labeling, stretching and imaging of single DNA molecules to obtain sequence-specific fluorescence profiles, DNA barcodes. These barcodes can be mapped to theoretical counterparts obtained from DNA reference sequences, which in turn allow for DNA identification in complex samples and for detecting structural changes in individual DNA molecules. There are several types of DNA labeling schemes for ODM and for each labeling type one or several types of match scoring methods are used. By combining the information from multiple labeling schemes one can potentially improve mapping confidence; however, combining match scores from different labeling assays has not been implemented yet. In this study, we introduce two theoretical methods for dealing with analysis of DNA molecules with multiple label types. In our first method, we convert the alignment scores, given as output from the different assays, into p-values using carefully crafted null models. We then combine the p-values for different label types using standard methods to obtain a combined match score and an associated combined p-value. In the second method, we use a block bootstrap approach to check for the uniqueness of a match to a database for all barcodes matching with a combined p-value below a predefined threshold. For obtaining experimental dual-labeled DNA barcodes, we introduce a novel assay where we cut plasmid DNA molecules from bacteria with restriction enzymes and the cut sites serve as sequence-specific markers, which together with barcodes obtained using the established competitive binding labeling method, form a dual-labeled barcode. All experimental data in this study originates from this assay, but we point out that our theoretical framework can be used to combine data from all kinds of available optical DNA mapping assays. We test our multiple labeling frameworks on barcodes from two different plasmids and synthetically generated barcodes (combined competitive-binding- and nick-labeling). It is demonstrated that by simultaneously using the information from all label types, we can substantially increase the significance when we match experimental barcodes to a database consisting of theoretical barcodes for all sequenced plasmids.

Assessing the Resolution of Methyltransferase-Mediated DNA Optical Mapping

Interest in the human microbiome is growing and has been, for the past decade, leading to new insights into disease etiology and general human biology. Stimulated by these advances and in a parallel trend, new DNA sequencing platforms have been developed, radically expanding the possibilities in microbiome research. While DNA sequencing plays a pivotal role in this field, there are some technological hurdles that are yet to be overcome. Targeting of the 16S rRNA gene with amplicon sequencing, for instance, is frequently used for sample composition profiling due to its short sample-to-result time and low cost, which counterbalance its low resolution (genus to species level). On the other hand, more comprehensive methods, namely, whole-genome sequencing (WGS) and shallow shotgun sequencing, are capable of yielding single-gene- and functional-level resolution at a higher cost and much higher sample processing time. It goes without saying that the existing gap between these two types of approaches still calls for the development of a fast, robust, and low-cost analytical platform. In search of the latter, we investigated the taxonomic resolution of methyltransferase-mediated DNA optical mapping and found that strain-level identification can be achieved with both global and whole-genome analyses as well as using a unique identifier (UI) database. In addition, we demonstrated that UI selection in DNA optical mapping, unlike variable region selection in 16S amplicon sequencing, is not limited to any genomic location, explaining the increase in resolution. This latter aspect was highlighted by SCCmec typing in methicillin-resistant Staphylococcus aureus (MRSA) using a simulated data set. In conclusion, we propose DNA optical mapping as a method that has the potential to be highly complementary to current sequencing platforms.

A resampling strategy for studying robustness in virus detection pipelines

Next-generation sequencing is regularly used to identify viral sequences in DNA or RNA samples of infected hosts. A major step of most pipelines for virus detection is to map sequence reads against known virus genomes. Due to small differences between the sequences of related viruses, and due to several biological or technical errors, mapping underlies uncertainties. As a consequence, the resulting list of detected viruses can lack robustness. A new approach for generating artificial sequencing reads together with a strategy of resampling from the original findings is proposed that can help to assess the robustness of the originally identified list of viruses. From the original mapping result in form of a SAM file, a set of statistical distributions are derived. These are used in the resampling pipeline to generate new artificial reads which are again mapped versus the reference genomes. By summarizing the resampling procedure, the analyst receives information about whether the presence of a particular virus in the sample gains or losses evidence, and thus about the robustness of the original mapping list but also that of individual viruses in this list. To judge robustness, several indicators are derived from the resampling procedure such as the correlation between original and resampling read counts, or the statistical detection of outliers in the differences of read counts. Additionally, graphical illustrations of read count shifts via Sankey diagrams are provided. To demonstrate the use of the new approach, the resampling approach is applied to three real-world data samples, one of them with laboratory-confirmed Influenza sequences, and to artificially generated data where virus sequences have been spiked into the sequencing data of a host. By applying the resampling pipeline, several viruses drop from the original list while new viruses emerge, showing robustness of those viruses that remain in the list. The evaluation of the new approach shows that the resampling approach is helpful to analyze the viral content of a biological sample, to rate the robustness of original findings and to better show the overall distribution of findings. The method is also applicable to other virus detection pipelines based on read mapping.

Self-contained and modular structured illumination microscope

We present a modular implementation of structured illumination microscopy (SIM) that is fast, largely self-contained and that can be added onto existing fluorescence microscopes. Our strategy, which we call HIT-SIM, can theoretically deliver well over 50 super-resolved images per second and is readily compatible with existing acquisition software packages. We provide a full technical package consisting of schematics, a list of components and an alignment scheme that provides detailed specifications and assembly instructions. We illustrate the performance of the instrument by imaging optically large samples containing sequence-specifically stained DNA fragments.

Electrical DNA Sequence Mapping Using Oligodeoxynucleotide Labels and Nanopores

Identifying DNA species is crucial for diagnostics. For DNA identification, single-molecule DNA sequence mapping is an alternative to DNA sequencing toward fast point-of-care testing, which traditionally relies on targeting and labeling DNA sequences with fluorescent labels and readout using optical imaging methods. A nanopore is a promising sensor as a complement to optical mapping with advantages of electric measurement suitable for portable devices and potential for high resolution. Here, we demonstrate a high-resolution nanopore-based DNA sequence mapping by labeling specific short sequence motifs with oligodeoxynucleotides (ODNs) using DNA methyltransferase (MTase) and detecting them using nanopores. We successfully detected ODNs down to the size of 11 nucleotides without introducing extra reporters and resolved neighboring sites with a distance of 141 bp (∼48 nm) on a single DNA molecule. To accurately locate the sequence motif positions on DNA, a nanopore data analysis method is proposed by considering DNA velocity change through nanopores and using ensemble statistics to translate the time-dependent signals to the location information. Our platform enables high-resolution detection of small labels on DNA and high-accuracy localization of them for DNA species identification in an all-electrical format. The method presents an alternative to optical techniques relying on fluorescent labels and is promising for miniature-scale integration for diagnostic applications.

Cultivation-Free Typing of Bacteria Using Optical DNA Mapping

A variety of pathogenic bacteria can infect humans, and rapid species identification is crucial for the correct treatment. However, the identification process can often be time-consuming and depend on the cultivation of the bacterial pathogen(s). Here, we present a stand-alone, enzyme-free, optical DNA mapping assay capable of species identification by matching the intensity profiles of large DNA molecules to a database of fully assembled bacterial genomes (>10 000). The assay includes a new data analysis strategy as well as a general DNA extraction protocol for both Gram-negative and Gram-positive bacteria. We demonstrate that the assay is capable of identifying bacteria directly from uncultured clinical urine samples, as well as in mixtures, with the potential to be discriminative even at the subspecies level. We foresee that the assay has applications both within research laboratories and in clinical settings, where the time-consuming step of cultivation can be minimized or even completely avoided.

Fluorescent SAM analogues for methyltransferase based DNA labeling

In this work, the preparation of new S-adenosyl-l-methionine (SAM) analogues for sequence specific DNA labeling is evaluated. Fluorescent cofactors were synthesized and their applicability in methyltransferase based optical mapping is demonstrated.