June 1, 2021  |  

Automated, non-hybrid de novo genome assemblies and epigenomes of bacterial pathogens.

Understanding the genetic basis of infectious diseases is critical to enacting effective treatments, and several large-scale sequencing initiatives are underway to collect this information. Sequencing bacterial samples is typically performed by mapping sequence reads against genomes of known reference strains. While such resequencing informs on the spectrum of single-nucleotide differences relative to the chosen reference, it can miss numerous other forms of variation known to influence pathogenicity: structural variations (duplications, inversions), acquisition of mobile elements (phages, plasmids), homonucleotide length variation causing phase variation, and epigenetic marks (methylation, phosphorothioation) that influence gene expression to switch bacteria from non- pathogenic to pathogenic states. Therefore, sequencing methods which provide complete, de novo genome assemblies and epigenomes are necessary to fully characterize infectious disease agents in an unbiased, hypothesis-free manner. Hybrid assembly methods have been described that combine long sequence reads from SMRT DNA Sequencing with short reads (SMRT CCS (circular consensus) or second-generation reads), wherein the short reads are used to error-correct the long reads which are then used for assembly. We have developed a new paradigm for microbial de novo assemblies in which SMRT sequencing reads from a single long insert library are used exclusively to close the genome through a hierarchical genome assembly process, thereby obviating the need for a second sample preparation, sequencing run, and data set. We have applied this method to achieve closed de novo genomes with accuracies exceeding QV50 (>99.999%) for numerous disease outbreak samples, including E. coli, Salmonella, Campylobacter, Listeria, Neisseria, and H. pylori. The kinetic information from the same SMRT Sequencing reads is utilized to determine epigenomes. Approximately 70% of all methyltransferase specificities we have determined to date represent previously unknown bacterial epigenetic signatures. With relatively short sequencing run times and automated analysis pipelines, it is possible to go from an unknown DNA sample to its complete de novo genome and epigenome in about a day.


June 1, 2021  |  

Automated, non-hybrid de novo genome assemblies and epigenomes of bacterial pathogens

Understanding the genetic basis of infectious diseases is critical to enacting effective treatments, and several large-scale sequencing initiatives are underway to collect this information. Sequencing bacterial samples is typically performed by mapping sequence reads against genomes of known reference strains. While such resequencing informs on the spectrum of single nucleotide differences relative to the chosen reference, it can miss numerous other forms of variation known to influence pathogenicity: structural variations (duplications, inversions), acquisition of mobile elements (phages, plasmids), homonucleotide length variation causing phase variation, and epigenetic marks (methylation, phosphorothioation) that influence gene expression to switch bacteria from non-pathogenic to pathogenic states. Therefore, sequencing methods which provide complete, de novo genome assemblies and epigenomes are necessary to fully characterize infectious disease agents in an unbiased, hypothesis-free manner. Hybrid assembly methods have been described that combine long sequence reads from SMRT DNA sequencing with short, high-accuracy reads (SMRT (circular consensus sequencing) CCS or second-generation reads) to generate long, highly accurate reads that are then used for assembly. We have developed a new paradigm for microbial de novo assemblies in which long SMRT sequencing reads (average readlengths >5,000 bases) are used exclusively to close the genome through a hierarchical genome assembly process, thereby obviating the need for a second sample preparation, sequencing run and data set. We have applied this method to achieve closed de novo genomes with accuracies exceeding QV50 (>99.999%) to numerous disease outbreak samples, including E. coli, Salmonella, Campylobacter, Listeria, Neisseria, and H. pylori. The kinetic information from the same SMRT sequencing reads is utilized to determine epigenomes. Approximately 70% of all methyltransferase specificities we have determined to date represent previously unknown bacterial epigenetic signatures. The process has been automated and requires less than 1 day from an unknown DNA sample to its complete de novo genome and epigenome.


June 1, 2021  |  

Complete microbial genomes, epigenomes, and transcriptomes using long-read PacBio Sequencing.

For comprehensive metabolic reconstructions and a resulting understanding of the pathways leading to natural products, it is desirable to obtain complete information about the genetic blueprint of the organisms used. Traditional Sanger and next-generation, short-read sequencing technologies have shortcomings with respect to read lengths and DNA-sequence context bias, leading to fragmented and incomplete genome information. The development of long-read, single molecule, real-time (SMRT) DNA sequencing from Pacific Biosciences, with >10,000 bp average read lengths and a lack of sequence context bias, now allows for the generation of complete genomes in a fully automated workflow. In addition to the genome sequence, DNA methylation is characterized in the process of sequencing. PacBio® sequencing has also been applied to microbial transcriptomes. Long reads enable sequencing of full-length cDNAs allowing for identification of complete gene and operon sequences without the need for transcript assembly. We will highlight several examples where these capabilities have been leveraged in the areas of industrial microbiology, including biocommodities, biofuels, bioremediation, new bacteria with potential commercial applications, antibiotic discovery, and livestock/plant microbiome interactions.


June 1, 2021  |  

Whole genome sequencing and epigenome characterization of cancer cells using the PacBio platform.

The comprehensive characterization of cancer genomes and epigenomes for understanding drug resistance remains an important challenge in the field of oncology. For example, PC-9, a non-small cell lung cancer (NSCL) cell line, contains a deletion mutation in exon 19 (DelE746A750) of EGRF that renders it sensitive to erlotinib, an EGFR inhibitor. However, sustained treatment of these cells with erlotinib leads to drug-tolerant cell populations that grow in the presence of erlotinib. However, the resistant cells can be resensitized to erlotinib upon treatment with methyltransferase inhibitors, suggesting a role of epigenetic modification in development of drug resistance. We have characterized for the first time cancer genomes of both drug-sensitive and drug-resistant PC- 9 cells using long-read PacBio sequencing. The PacBio data allowed us to generate a high-quality, de novo assembly of this cancer genome, enabling the detection of forms of genomic variations at all size scales, including SNPs, structural variations, copy number alterations, gene fusions, and translocations. The data simultaneously provide a global view of epigenetic DNA modifications such as methylation. We will present findings on large-scale changes in the methylation status across the cancer genome as a function of drug sensitivity.


April 21, 2020  |  

DNA methylation analysis.

DNA methylation is a process by which methyl groups are added to cytosine or adenine. DNA methylation can change the activity of the DNA molecule without changing the sequence. Methylation of 5-methylcytosine (5mC) is widespread in both eukaryotes and prokaryotes, and it is a very important epigenetic modification event, which can regulate gene activity and influence a number of key processes such as genomic imprinting, cell differentiation, transcriptional regulation, and chromatin remodeling. Profiling DNA methylation across the genome is critical to understanding the influence of methylation in normal biology and diseases including cancer. Recent discoveries of 5-methylcytosine (5mC) oxidation derivatives including 5-hydroxymethylcytosine (5hmC), 5-formylcytsine (5fC), and 5-carboxycytosine (5caC) in mammalian genome further expand our understanding of the methylation regulation. Genome-wide analyses such as microarrays and next-generation sequencing technologies have been used to assess large fractions of the methylome. A number of different quantitative approaches have also been established to map the DNA epigenomes with single-base resolution, as represented by the bisulfite-based methods, such as classical bisulfite sequencing, pyrosequencing etc. These methods have been used to generate base-resolution maps of 5mC and its oxidation derivatives in genomic samples. The focus of this chapter is to provide the methodologies that have been developed to detect the cytosine derivatives in the genomic DNA.


April 21, 2020  |  

Deciphering bacterial epigenomes using modern sequencing technologies.

Prokaryotic DNA contains three types of methylation: N6-methyladenine, N4-methylcytosine and 5-methylcytosine. The lack of tools to analyse the frequency and distribution of methylated residues in bacterial genomes has prevented a full understanding of their functions. Now, advances in DNA sequencing technology, including single-molecule, real-time sequencing and nanopore-based sequencing, have provided new opportunities for systematic detection of all three forms of methylated DNA at a genome-wide scale and offer unprecedented opportunities for achieving a more complete understanding of bacterial epigenomes. Indeed, as the number of mapped bacterial methylomes approaches 2,000, increasing evidence supports roles for methylation in regulation of gene expression, virulence and pathogen-host interactions.


April 21, 2020  |  

Genome-wide systematic identification of methyltransferase recognition and modification patterns.

Genome-wide analysis of DNA methylation patterns using single molecule real-time DNA sequencing has boosted the number of publicly available methylomes. However, there is a lack of tools coupling methylation patterns and the corresponding methyltransferase genes. Here we demonstrate a high-throughput method for coupling methyltransferases with their respective motifs, using automated cloning and analysing the methyltransferases in vectors carrying a strain-specific cassette containing all potential target sites. To validate the method, we analyse the genomes of the thermophile Moorella thermoacetica and the mesophile Acetobacterium woodii, two acetogenic bacteria having substantially modified genomes with 12 methylation motifs and a total of 23 methyltransferase genes. Using our method, we characterize the 23 methyltransferases, assign motifs to the respective enzymes and verify activity for 11 of the 12 motifs.


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