Nanopore sequencing technology, bioinformatics and applications

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Identification and characterisation of carbapenem-resistant Streptococcus nidrosiense sp. nov. isolated from blood culture

Background

This study aimed to investigate a highly resistant strain of Streptococcus sp. isolated from a patient with bloodstream infection and determine its taxonomic classification.

Methods

The strain was isolated from blood culture from a 65-year-old male patient admitted to St. Olavs University hospital, Trondheim, Norway, in 2023. Antimicrobial susceptibility testing as well as phenotypic and biochemical characterization were performed. Whole genome sequencing was conducted and genomic comparison to Streptococcus type strains was carried out.

Results

The strain was initially identified as Streptococcus mitis/oralis but showed significant genetic differences, suggesting that it belonged to an undescribed species within the Streptococcus genus. Phenotypic and biochemical characterization identified the strain as a non-motile, facultative anaerobic bacterium with α-hemolysis. Antimicrobial susceptibility testing showed resistance to all beta-lactams tested. Genomic analyses confirmed the classification of the strain as a novel species, which was designated Streptococcus nidrosiense.

Conclusion

This study combines conventional phenotypic tests with whole genome sequencing for accurate taxonomic classification of a bacterial strain isolated from blood culture. The identification of a novel species within the Streptococcus genus contributes to the understanding of microbial diversity and antibiotic resistance of the Streptococcus genus in clinical settings.

Giraffe: A tool for comprehensive processing and visualization of multiple long-read sequencing data

Third-generation sequencing techniques have become increasingly popular due to their capacity to produce long, high-quality reads. Effective comparative analysis across various samples and sequencing platforms is essential for understanding biological mechanisms and establishing benchmark baselines. However, existing tools for long-read sequencing predominantly focus on quality control (QC) and processing for individual samples, complicating the comparison of multiple datasets. The lack of comprehensive tools for data comparison and visualization presents challenges for researchers with limited bioinformatics experience. To address this gap, we present Giraffe (https://github.com/lrslab/Giraffe_View), a Python3-based command-line tool designed for comparative analysis and visualization across diverse samples and platforms. Giraffe facilitates the assessment of read quality, sequencing bias, and genomic regional methylation proportions for both DNA and direct RNA sequencing reads. Its effectiveness has been demonstrated in various scenarios, including comparisons of sequencing methods (whole genome amplification vs. shotgun), sequencing platforms (Oxford Nanopore Technology, ONT vs. Pacific Biosciences, PacBio), tissues (kidney marrow with and without blood), and biological replicates (kidney marrows).

Insights into the role of JAK2-I724T variant in myeloproliferative neoplasms from a unique cohort of New Zealand patients

OBJECTIVES

This study aimed to compile bioinformatic and experimental information for JAK2 missense variants previously reported in myeloproliferative neoplasms (MPN) and determine if germline JAK2-I724T, recently found to be common in New Zealand Polynesians, associates with MPN.

METHODS

For all JAK2 variants found in the literature, gnomAD_exome allele frequencies were extracted and REVEL scores were calculated using the dbNSFP database. We investigated the prevalence of JAK2-I724T in a cohort of 111 New Zealand MPN patients using a TaqMan assay, examined its allelic co-occurrence with JAK2-V617F using Oxford Nanopore sequencing, and modelled the impact of I724T on JAK2 using I-Mutant and ChimeraX software.

RESULTS

Several non-V617F JAK2 variants previously reported in MPN had REVEL scores greater than 0.5, suggesting pathogenicity. JAK2-I724T (REVEL score 0.753) was more common in New Zealand Polynesian MPN patients (n = 2/27; 7.4%) than in other New Zealand patients (n = 0/84; 0%) but less common than expected for healthy Polynesians (n = 56/377; 14.9%). Patients carrying I724T (n = 2), one with polycythaemia vera and one with essential thrombocythaemia, had high-risk MPN. Both patients with JAK2-I724T were also positive for JAK2-V617F, found on the same allele as I724T, as well as separately. In silico modelling did not identify noticeable structural changes that would give JAK2-I724T a gain-of-function.

CONCLUSION

Several non-canonical JAK2 variants with high REVEL scores have been reported in MPN, highlighting the need to further understand their relationship with disease. The JAK2-I724T variant does not drive MPN, but additional investigations are required to exclude any potential modulatory effect on the MPN phenotype.

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.

Multi-scale DNA language model improves 6 mA binding sites prediction

DNA methylation at the N6 position of adenine (N6-methyladenine, 6 mA), which refers to the attachment of a methyl group to the N6 site of the adenine (A) of DNA, is an important epigenetic modification in prokaryotic and eukaryotic genomes. Accurately predicting the 6 mA binding sites can provide crucial insights into gene regulation, DNA repair, disease development and so on. Wet experiments are commonly used for analyzing 6 mA binding sites. However, they suffer from high cost and expensive time. Therefore, various deep learning methods have been widely used to predict 6 mA binding sites recently. In this study, we develop a framework based on multi-scale DNA language model named "iDNA6mA-MDL". "iDNA6mA-MDL" integrates multiple kmers and the nucleotide property and frequency method for feature embedding, which can capture a full range of DNA sequence context information. At the prediction stage, it also leverages DNABERT to compensate for the incomplete capture of global DNA information. Experiments show that our framework obtains average AUC of 0.981 on a classic 6 mA rice gene dataset, going beyond all existing advanced models under fivefold cross-validations. Moreover, "iDNA6mA-MDL" outperforms most of the popular state-of-the-art methods on another 11 6 mA datasets, demonstrating its effectiveness in 6 mA binding sites prediction.

The Effect of Electrolyte Properties on Ionic Transport through Solid-State Nanopores: Experiment and Simulation

Nanopore membranes enable versatile technologies that are employed in many different applications, ranging from clean energy generation to filtration and sensing. Improving the performance can be achieved by conducting numerical simulations of the system, for example, by studying how the nanopore geometry or surface properties change the ionic transport behavior or fluid dynamics of the system. A widely employed tool for numerical simulations is finite element analysis (FEA) using software, such as COMSOL Multiphysics. We found that the prevalent method of implementing the electrolyte in the FEA can diverge significantly from physically accurate values. It is often assumed that salt molecules fully dissociate, and the effect of the temperature is neglected. Furthermore, values for the diffusion coefficients of the ions, as well as permittivity, density, and viscosity of the fluid, are assumed to be their bulk values at infinite dilution. By performing conductometry experiments with an amorphous SiO2 nanopore membrane with conical pores and simulating the pore system with FEA, it is shown that the common assumptions do not hold for different mono- and divalent chlorides (LiCl, NaCl, KCl, MgCl2, and CaCl2) at concentrations above 100 mM. Instead, a procedure is presented where all parameters are implemented based on the type of salt and concentration. This modification to the common approach improves the accuracy of the numerical simulations and thus provides a more comprehensive insight into ion transport in nanopores that is otherwise lacking.

Improved sub-genomic RNA prediction with the ARTIC protocol

Viral subgenomic RNA (sgRNA) plays a major role in SARS-COV2's replication, pathogenicity, and evolution. Recent sequencing protocols, such as the ARTIC protocol, have been established. However, due to the viral-specific biological processes, analyzing sgRNA through viral-specific read sequencing data is a computational challenge. Current methods rely on computational tools designed for eukaryote genomes, resulting in a gap in the tools designed specifically for sgRNA detection. To address this, we make two contributions. Firstly, we present sgENERATE, an evaluation pipeline to study the accuracy and efficacy of sgRNA detection tools using the popular ARTIC sequencing protocol. Using sgENERATE, we evaluate periscope, a recently introduced tool that detects sgRNA from ARTIC sequencing data. We find that periscope has biased predictions and high computational costs. Secondly, using the information produced from sgENERATE, we redesign the algorithm in periscope to use multiple references from canonical sgRNAs to mitigate alignment issues and improve sgRNA and non-canonical sgRNA detection. We evaluate periscope and our algorithm, periscope_multi, on simulated and biological sequencing datasets and demonstrate periscope_multi's enhanced sgRNA detection accuracy. Our contribution advances tools for studying viral sgRNA, paving the way for more accurate and efficient analyses in the context of viral RNA discovery.

Combining Short- and Long-Read Sequencing Technologies to Identify SARS-CoV-2 Variants in Wastewater

During the COVID-19 pandemic, the monitoring of SARS-CoV-2 RNA in wastewater was used to track the evolution and emergence of variant lineages and gauge infection levels in the community, informing appropriate public health responses without relying solely on clinical testing. As more sublineages were discovered, it increased the difficulty in identifying distinct variants in a mixed population sample, particularly those without a known lineage. Here, we compare the sequencing technology from Illumina and from Oxford Nanopore Technologies, in order to determine their efficacy at detecting variants of differing abundance, using 248 wastewater samples from various Quebec and Ontario cities. Our study used two analytical approaches to identify the main variants in the samples: the presence of signature and marker mutations and the co-occurrence of signature mutations within the same amplicon. We observed that each sequencing method detected certain variants at different frequencies as each method preferentially detects mutations of distinct variants. Illumina sequencing detected more mutations with a predominant lineage that is in low abundance across the population or unknown for that time period, while Nanopore sequencing had a higher detection rate of mutations that are predominantly found in the high abundance B.1.1.7 (Alpha) lineage as well as a higher sequencing rate of co-occurring mutations in the same amplicon. We present a workflow that integrates short-read and long-read sequencing to improve the detection of SARS-CoV-2 variant lineages in mixed population samples, such as wastewater.

Utilizing solid-state nanopore sensing for high-efficiency and precise targeted localization in antiviral drug development

The efficient identification and validation of drug targets are paramount in drug discovery and development. Excessive costs, intricate procedures, and laborious sample handling frequently encumber contemporary methodologies. In this study, we introduce an innovative approach for the expeditious screening of drug targets utilizing solid-state nanopores. These nanopores provide a label-free, ultra-sensitive, and high-resolution platform for the real-time detection of biomolecular interactions. By observing the changes in relative ion currents over time after mixing different peptides with small molecule drugs, and supplementing this with noise analysis, we can pinpoint specific regions of drug action, thereby enhancing both the speed and cost-efficiency of drug development. This research offers novel insights into drug discovery, expands current perspectives, and lays the groundwork for formulating effective therapeutic strategies across a spectrum of diseases.

Full-Length Immune Repertoire Reconstruction and Profiling at the Transcriptome Level Using Long-Read Sequencing

BACKGROUND

Due to the diversity of the immune repertoire (IR), reconstructing full-length IR using traditional short-read sequencing has proven challenging.

METHODS

A full-length IR sequencing (FLIRseq) work flow was developed with linear rolling circle amplification and nanopore sequencing. Its accuracy and quantification ability were verified by plasmid mixtures and commercial B-cell receptor/T-cell receptor sequencing (BCR/TCR-seq) based on short reads. IRs in tissues and the peripheral blood from 8 patients with acute lymphoblastic leukemia, 3 patients with allergic diseases, 4 patients with psoriasis, and 5 patients with prostate cancer were analyzed using FLIRseq.

RESULTS

FLIRseq reads had lower mismatch rates and gap rates, and higher identify rates than nanopore reads (all P < 2.2 × -16). The relative quantification of components by FLIRseq was consistent with the actual quantification (P > 0.05). FLIRseq had superiority over BCR/TCR-seq, providing the long complementarity-determining region 3, B-cell isotype, and the rarely used V gene sequence. FLIRseq observed an increase in clonotype diversity (P < 0.05) and a decrease in the percentage of abnormal BCRs/TCRs in patients with leukemia in remission. For patients with allergic diseases or psoriasis, FLIRseq provided direct insights into V(D)J recombination and specific immunoglobulin classes. Compared with that in prostate cancer tissues, the full-length V segment of the biased T-cell receptor β chain from lymphocytes in psoriatic tissues showed a more consistent AlphaFold2-predicted protein structure (P < 0.05).

CONCLUSIONS

FLIRseq enables unbiased and comprehensive analyses of direct V(D)J recombination and immunoglobulin classes, thereby contributing to characterizing pathogenic mechanisms, monitoring minimal residual disease, and customizing adoptive cell therapy.

A deep learning-based method enables the automatic and accurate assembly of chromosome-level genomes

The application of high-throughput chromosome conformation capture (Hi-C) technology enables the construction of chromosome-level assemblies. However, the correction of errors and the anchoring of sequences to chromosomes in the assembly remain significant challenges. In this study, we developed a deep learning-based method, AutoHiC, to address the challenges in chromosome-level genome assembly by enhancing contiguity and accuracy. Conventional Hi-C-aided scaffolding often requires manual refinement, but AutoHiC instead utilizes Hi-C data for automated workflows and iterative error correction. When trained on data from 300+ species, AutoHiC demonstrated a robust average error detection accuracy exceeding 90%. The benchmarking results confirmed its significant impact on genome contiguity and error correction. The innovative approach and comprehensive results of AutoHiC constitute a breakthrough in automated error detection, promising more accurate genome assemblies for advancing genomics research.

Nanopore sequencing: flourishing in its teenage years

Over the past decade, nanopore sequencing has experienced significant advancements and changes, transitioning from an initially emerging technology to a significant instrument in the field of genomic sequencing. However, as advancements in next-generation sequencing technology persist, nanopore sequencing also improves. This paper reviews the developments, applications, and outlook on nanopore sequencing technology. Currently, nanopore sequencing supports both DNA and RNA sequencing, making it widely applicable in areas such as telomere-to-telomere (T2T) genome assembly, direct RNA sequencing (DRS), and metagenomics. The openness and versatility of nanopore sequencing have established it as a preferred option for an increasing number of research teams, signaling a transformative influence on life science research. As nanopore sequencing technology advances, it provides a faster, more cost-effective approach with extended read lengths, demonstrating the significant potential for complex genome assembly, pathogen detection, environmental monitoring, and human disease research, offering a fresh perspective in sequencing technologies.

Mitogenomic profiling and gut microbial analysis of the newly identified polystyrene-consuming lesser mealworm in Kenya

Plastic waste has recently become a major global environmental concern and one of the biggest challenges has been seeking for alternative management options. Several studies have revealed the potential of several coleopteran species to degrade plastics, and this is the first research paper on plastic-degradation potential by lesser mealworms from Africa. This study evaluated the whole mitogenomic profile of the lesser mealworm to further identify the insect. The ability of the mealworm to consume Polystyrene (PS) was also evaluated alongside its associated gut microbiota diversity. Our results showed a complete circular mitochondrial genome which clustered closely to the Alphitobius genus but also suggested that our insect might be a new subspecies which require further identification. During the PS feeding trials, overall survival rates of the larvae decreased when fed a sole PS diet while PS intake was observed to increase over a 30-day period. The predominant bacteria observed in larvae fed PS diets were Kluyvera, Lactococcus, Klebsiella, Enterobacter, and Enterococcus, while Stenotrophomonas dominated the control diet. These findings demonstrated that the newly identified lesser mealworm can survive on a PS diet and has a consortium of important bacteria strongly associated with PS degradation. This work provides a better understanding of bioremediation applications, paving the way for further research into the metabolic pathways of plastic-degrading microbes and bringing hope to solving plastic waste pollution while providing high-value insect protein towards a circular economy.

A novel barcoded nanopore sequencing workflow of high-quality, full-length bacterial 16S amplicons for taxonomic annotation of bacterial isolates and complex microbial communities

Due to recent improvements, Nanopore sequencing has become a promising method for experiments relying on amplicon sequencing. We describe a flexible workflow to generate and annotate high-quality, full-length 16S rDNA amplicons. We evaluated it for two applications, namely, (i) identification of bacterial isolates and (ii) species-level profiling of microbial communities. We assessed the identification of single bacterial isolates by sequencing, using a set of barcoded full-length 16S rRNA gene primer pairs (pair A), on 47 isolates encompassing multiple genera and compared those results with matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS)-based identification. Species-level community profiling was tested with two sets of barcoded full-length 16S primer pairs (A and B) and compared to the results obtained with shotgun Illumina sequencing using 27 stool samples. We developed a Nextflow pipeline to retain high-quality reads and taxonomically annotate them. We found high agreement between our workflow and MALDI-TOF data for isolate identification (positive predictive value = 0.90, Cramér's V = 0.857, and Theil's U = 0.316). For species-level community profiling, we found strong correlations (rs > 0.6) of alpha diversity indices between the two primer sets and Illumina sequencing. At the community level, we found significant but small differences when comparing sequencing techniques. Finally, we found a moderate to strong correlation when comparing the relative abundances of individual species (average rs = 0.6 and 0.533 for primers A and B). Despite identified shortcomings, the proposed workflow enabled accurate identification of single bacterial isolates and prominent features in microbial communities, making it a worthwhile alternative to MALDI-TOF MS and Illumina sequencing.IMPORTANCEA quick, robust, simple, and cost-effective method to identify bacterial isolates and communities in each sample is indispensable in the fields of microbiology and infection biology. Recent technological advances in Oxford Nanopore Technologies sequencing make this technique an attractive option considering the adaptability, portability, and cost-effectiveness of the platform, even with small sequencing batches. Here, we validated a flexible workflow to identify bacterial isolates and characterize bacterial communities using the Oxford Nanopore Technologies sequencing platform combined with the most recent v14 chemistry kits. For bacterial isolates, we compared our nanopore-based approach to matrix-assisted laser desorption ionization-time of flight mass spectrometry-based identification. For species-level profiling of complex bacterial communities, we compared our nanopore-based approach to Illumina shotgun sequencing. For reproducibility purposes, we wrapped the code used to process the sequencing data into a ready-to-use and self-contained Nextflow pipeline.

Complete genome sequence of Klebsiella pneumoniae RX.G5M15, a methanol-metabolizing strain recovered from the sole of a shoe

The methanol-metabolizing strain Klebsiella pneumoniae RX.G5M15 was isolated from the sole of a shoe in Hong Kong. Its complete genome, a single chromosome and two plasmids totaling 5,381,940 bp (G+C 57.43%), was established through the hybrid assembly.

Single-Molecule Identification and Quantification of Steviol Glycosides with a Deep Learning-Powered Nanopore Sensor

Steviol glycosides (SGs) are a class of high-potency noncalorie natural sweeteners made up of a common diterpenoid core and varying glycans. Thus, the diversity of glycans in composition, linkage, and isomerism results in the tremendous structural complexity of the SG family, which poses challenges for the precise identification and leads to the fact that SGs are frequently used in mixtures and their variances in biological activity remain largely unexplored. Here we show that a wild-type aerolysin nanopore can detect and discriminate diverse SG species through the modulable electro-osmotic flow effect at varied applied voltages. At low voltages, the neutral SG molecule was drawn and stuck in the pore entrance due to an energy barrier around R220 sites. The ensuing binding events enable the identification of the majority of SG species. Increasing the voltage can break the barrier and cause translocation events, allowing for the unambiguous identification of several pairs of SGs differing by only one hydroxyl group through recognition accumulation from multiple sensing regions and sites. Based on nanopore data of 15 SGs, a deep learning-based artificial intelligence (AI) model was created to process the individual blockage events, achieving the rapid, automated, and precise single-molecule identification and quantification of SGs in real samples. This work highlights the value of nanopore sensing for precise structural analysis of complex glycans-containing glycosides, as well as the potential for sensitive and rapid quality assurance analysis of glycoside products with the use of AI.

Evaluation of Commercial RNA Extraction Protocols for Avian Influenza Virus Using Nanopore Metagenomic Sequencing

Avian influenza virus (AIV) is a significant threat to the poultry industry, necessitating rapid and accurate diagnosis. The current AIV diagnostic process relies on virus identification via real-time reverse transcription-polymerase chain reaction (rRT-PCR). Subsequently, the virus is further characterized using genome sequencing. This two-step diagnostic process takes days to weeks, but it can be expedited by using novel sequencing technologies. We aim to optimize and validate nucleic acid extraction as the first step to establishing Oxford Nanopore Technologies (ONT) as a rapid diagnostic tool for identifying and characterizing AIV from clinical samples. This study compared four commercially available RNA extraction protocols using AIV-known-positive clinical samples. The extracted RNA was evaluated using total RNA concentration, viral copies as measured by rRT-PCR, and purity as measured by a 260/280 absorbance ratio. After NGS testing, the number of total and influenza-specific reads and quality scores of the generated sequences were assessed. The results showed that no protocol outperformed the others on all parameters measured; however, the magnetic particle-based method was the most consistent regarding CT value, purity, total yield, and AIV reads, and it was less error-prone. This study highlights how different RNA extraction protocols influence ONT sequencing performance.

Characterization of sequence variations in the extended flanking regions using massively parallel sequencing in 21 A-STRs and 21 Y-STRs

In forensic genetics, utilizing massively parallel sequencing (MPS) to analyze short tandem repeats (STRs) has demonstrated several advantages compared to conventional capillary electrophoresis (CE). Due to the current technical limitations, although flanking region polymorphisms had been mentioned in several previous studies, most studies focused on the core repeat regions of STRs or the variations in the adjacent flanking regions. In this study, we developed an MPS system consisting of two sets of multiplex PCR systems to detect not only the STR core repeat regions but also to observe variants located at relatively distant positions in the flanking regions. The system contained 42 commonly used forensic STRs, including 21 autosomal STRs (A-STRs) and 21 Y-chromosomal STRs (Y-STRs), and a total of 350 male individuals from a Chinese Han population were genotyped. The length and sequence variants per locus were tallied and categorized based on length (length-based, LB), sequence without flanking region (core repeat regions sequence-based, RSB), and sequence with flanking region (core repeat and flanking regions sequence-based, FSB), respectively. Allele frequencies, Y-haplotype frequencies, and forensic parameters were calculated based on LB, RSB, and FSB, respectively, to evaluate the improvement in discrimination power, heterozygosity, and effectiveness of forensic systems. The results suggested the sequence variations have more influence on A-STRs and could improve the identification ability of MPS-STR genotyping. Concordance between MPS and CE methods was confirmed by using commercial CE-based STR kits. The impact of flanking region variations on STR genotype analysis and potential factors contributing to discordances were discussed. A total of 58 variations in the flanking regions (53 SNPs/SNVs and 5 InDels) were observed and most variations (48/58) were distributed in A-STRs. In summary, this study delved deeper into the genetic information of forensic commonly used STR and advanced the application of massively parallel sequencing in forensic genetics.

Transcriptional activation of PINK1 by MyoD1 mediates mitochondrial homeostasis to induce renal calcification in pediatric nephrolithiasis

This study aims to uncover the molecular mechanisms underlying pediatric kidney stone formation induced by renal calcium deposition by utilizing high-throughput sequencing data to reveal the regulation of PINK1 by MyoD1. We performed transcriptome sequencing on peripheral blood samples from healthy children and children with kidney stones to obtain differentially expressed genes (DEGs). Genes related to mitochondrial oxidative stress were obtained from the Genecards website and intersected with DEGs to obtain candidate target genes. Additionally, we conducted protein-protein interaction (PPI) analysis using the STRING database to identify core genes involved in pediatric kidney stone disease (KSD) and predicted their transcription factors using the hTFtarget database. We assessed the impact of MyoD1 on the activity of the PINK1 promoter using dual-luciferase reporter assays and investigated the enrichment of MyoD1 on the PINK1 promoter through chromatin immunoprecipitation (ChIP) experiments. To validate our hypothesis, we selected HK-2 cells and established an in vitro kidney stone model induced by calcium oxalate monohydrate (COM). We evaluated the expression levels of various genes, cell viability, volume of adherent crystals in each group, as well as mitochondrial oxidative stress in cells by measuring mitochondrial membrane potential (Δψm), superoxide dismutase (SOD) activity, reactive oxygen species (ROS), and malondialdehyde (MDA) content. Mitochondrial autophagy was assessed using mtDNA fluorescence staining and Western blot analysis of PINK1-related proteins. Apoptosis-related proteins were evaluated using Western blot analysis, and cell apoptosis was measured using flow cytometry. Furthermore, we developed a rat model of KSD and assessed the expression levels of various genes, as well as the pathologic changes in rat renal tissues using H&E and von Kossa staining, transmission electron microscopy (TEM), and the expression of creatinine, blood urea nitrogen, neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury molecule-1 (KIM-1) to evaluate the mitochondrial oxidative stress in vivo (through measurement of Δψm, SOD activity, ROS, and MDA content). Mitochondrial autophagy was evaluated by Western blot analysis of PINK1-associated proteins. Apoptosis-related proteins were detected using Western blot analysis, and cellular apoptosis was examined using cell flow cytometry and TUNEL staining. Bioinformatics analysis revealed that the PINK1 gene is upregulated and vital in pediatric kidney stone patients. Our in vitro and in vivo experiments demonstrated that silencing PINK1 could inhibit kidney stone formation by suppressing mitochondrial oxidative stress both in vitro and in vivo. We identified MyoD1 as an upstream transcription factor of PINK1 that contributes to the occurrence of pediatric kidney stones through the activation of PINK1. Our in vivo and in vitro experiments collectively confirmed that silencing MyoD1 could inhibit mitochondrial oxidative stress, mitochondrial autophagy, and cellular apoptosis in a rat model of kidney stones by downregulating PINK1 expression, consequently suppressing the formation of kidney stones. In this study, we discovered that MyoD1 may promote kidney stone formation and development in pediatric patients by transcriptionally activating PINK1 to induce mitochondrial oxidative stress.

A comprehensive review of influenza B virus, its biological and clinical aspects

Influenza B virus (IBV) stands as a paradox, often overshadowed by its more notorious counterpart, influenza A virus (IAV). Yet, it remains a captivating and elusive subject of scientific inquiry. Influenza B is important because it causes seasonal flu outbreaks that can lead to severe respiratory illnesses, including bronchitis, pneumonia, and exacerbations of chronic conditions like asthma. Limitations in the influenza B virus's epidemiological, immunological, and etiological evolution must be addressed promptly. This comprehensive review covers evolutionary epidemiology and pathogenesis, host-virus interactions, viral isolation and propagation, advanced molecular detection assays, vaccine composition and no animal reservoir for influenza B virus. Complex viral etiology begins with intranasal transmission of influenza B virus with the release of a segmented RNA genome that attacks host cell machinery for transcription and translation within the nucleus and the release of viral progeny. Influenza B virus prevalence in domesticated and wild canines, sea mammals, and birds is frequent, yet there is no zoonosis. The periodic circulation of influenza B virus indicates a 1-3-year cycle for monophyletic strain replacement within the Victoria strain due to frequent antigenic drift in the HA near the receptor-binding site (RBS), while the antigenic stability of Yamagata viruses portrays a more conservative evolutionary pattern. Additionally, this article outlines contemporary antiviral strategies, including pharmacological interventions and vaccination efforts. This article serves as a resource for researchers, healthcare professionals, and anyone interested in the mysterious nature of the influenza B virus. It provides valuable insights and knowledge essential for comprehending and effectively countering this viral foe, which continues to pose a significant public health threat.

Unlocking plant genetics with telomere-to-telomere genome assemblies

Contiguous genome sequence assemblies will help us to realize the full potential of crop translational genomics. Recent advances in sequencing technologies, especially long-read sequencing strategies, have made it possible to construct gapless telomere-to-telomere (T2T) assemblies, thus offering novel insights into genome organization and function. Plant genomes pose unique challenges, such as a continuum of ancient to recent polyploidy and abundant highly similar and long repetitive elements. Owing to progress in sequencing approaches, for most crop plants, chromosome-scale reference genome assemblies are available, but T2T assembly construction remains challenging. Here we describe methods for haplotype-resolved, gapless T2T assembly construction in plants, including various crop species. We outline the impact of T2T assemblies in elucidating the roles of repetitive elements in gene regulation, as well as in pangenomics, functional genomics, genome-assisted breeding and targeted genome manipulation. In conjunction with sequence-enriched germplasm repositories, T2T assemblies thus hold great promise for basic and applied plant sciences.

Reviewing the impact of hydroxyurea on DNA methylation and its potential clinical implications in sickle cell disease

Hydroxyurea (HU) is the most common drug therapy for sickle cell disease (SCD). The clinical benefits of HU derive from its upregulation of fetal hemoglobin (HbF), which reduces aggregation of the mutated sickle hemoglobin protein (HbS) and reduces SCD symptoms and complications. However, some individuals do not respond to HU, or stop responding over time. Unfortunately, current understanding of the mechanism of action of HU is limited, hindering the ability of clinicians to identify those patients who will respond to HU and to optimize treatment for those receiving HU. Given that epigenetic modifications are essential to erythropoiesis and HbF expression, we hypothesize that some effects of HU may be mediated by epigenetic modifications, specifically DNA methylation. However, few studies have investigated this possibility and the effects of HU on DNA methylation remain relatively understudied. In this review, we discuss the evidence linking HU treatment to DNA methylation changes and associated gene expression changes, with an emphasis on studies that were performed in individuals with SCD. Overall, although HU can affect DNA methylation, research on these changes and their clinical effects remains limited. Further study is likely to contribute to our understanding of hematopoiesis and benefit patients suffering from SCD.

Microcystis abundance is predictable through ambient bacterial communities: A data-oriented approach

The number of cyanobacterial harmful algal blooms (cyanoHABs) has increased, leading to the widespread development of prediction models for cyanoHABs. Although bacteria interact closely with cyanobacteria and directly affect cyanoHABs occurrence, related modeling studies have rarely utilized microbial community data compared to environmental data such as water quality. In this study, we built a machine learning model, the multilayer perceptron (MLP), for the prediction of Microcystis dynamics using both bacterial community and weekly water quality data from the Daechung Reservoir and Nakdong River, South Korea. The modeling performance, indicated by the R2 value, improved to 0.97 in the model combining bacterial community data with environmental factors, compared to 0.78 in the model using only environmental factors. This underscores the importance of microbial communities in cyanoHABs prediction. Through the post-hoc analysis of the MLP models, we revealed that nitrogen sources played a more critical role than phosphorus sources in Microcystis blooms, whereas the bacterial amplicon sequence variants did not have significant differences in their contribution to each other. Similar to the MLP model results, bacterial data also had higher predictability in multiple linear regression (MLR) than environmental data. In both the MLP and MLR models, Microscillaceae showed the strongest association with Microcystis. This modeling approach provides a better understanding of the interactions between bacteria and cyanoHABs, facilitating the development of more accurate and reliable models for cyanoHABs prediction using ambient bacterial data.

A generalized protein identification method for novel and diverse sequencing technologies

Protein sequencing is a rapidly evolving field with much progress towards the realization of a new generation of protein sequencers. The early devices, however, may not be able to reliably discriminate all 20 amino acids, resulting in a partial, noisy and possibly error-prone signature of a protein. Rather than achieving de novo sequencing, these devices may aim to identify target proteins by comparing such signatures to databases of known proteins. However, there are no broadly applicable methods for this identification problem. Here, we devise a hidden Markov model method to study the generalized problem of protein identification from noisy signature data. Based on a hypothetical sequencing device that can simulate several novel technologies, we show that on the human protein database (N = 20 181) our method has a good performance under many different operating conditions such as various levels of signal resolvability, different numbers of discriminated amino acids, sequence fragments, and insertion and deletion error rates. Our results demonstrate the possibility of protein identification with high accuracy on many early experimental devices. We anticipate our method to be applicable for a wide range of protein sequencing devices in the future.

Advances in Host Depletion and Pathogen Enrichment Methods for Rapid Sequencing-Based Diagnosis of Bloodstream Infection

Bloodstream infection is a major cause of morbidity and death worldwide. Timely and appropriate treatment can reduce mortality among critically ill patients. Current diagnostic methods are too slow to inform precise antibiotic choice, leading to the prescription of empirical antibiotics, which may fail to cover the resistance profile of the pathogen, risking poor patient outcomes. Additionally, overuse of broad-spectrum antibiotics may lead to more resistant organisms, putting further pressure on the dwindling pipeline of antibiotics, and risk transmission of these resistant organisms in the health care environment. Therefore, rapid diagnostics are urgently required to better inform antibiotic choice early in the course of treatment. Sequencing offers great promise in reducing time to microbiological diagnosis; however, the amount of host DNA compared with the pathogen in patient samples presents a significant obstacle. Various host-depletion and bacterial-enrichment strategies have been used in samples, such as saliva, urine, or tissue. However, these methods have yet to be collectively integrated and/or extensively explored for rapid bloodstream infection diagnosis. Although most of these workflows possess individual strengths, their lack of analytical/clinical sensitivity and/or comprehensiveness demands additional improvements or synergistic application. This review provides a distinctive classification system for various methods based on their working principles to guide future research, and discusses their strengths and limitations and explores potential avenues for improvement to assist the reader in workflow selection.

Implementing next-generation sequencing for diagnosis and management of hereditary hearing impairment: a comprehensive review

INTRODUCTION

Sensorineural hearing impairment (SNHI), a common childhood disorder with heterogeneous genetic causes, can lead to delayed language development and psychosocial problems. Next-generation sequencing (NGS) offers high-throughput screening and high-sensitivity detection of genetic etiologies of SNHI, enabling clinicians to make informed medical decisions, provide tailored treatments, and improve prognostic outcomes.

AREAS COVERED

This review covers the diverse etiologies of HHI and the utility of different NGS modalities (targeted sequencing and whole exome/genome sequencing), and includes HHI-related studies on newborn screening, genetic counseling, prognostic prediction, and personalized treatment. Challenges such as the trade-off between cost and diagnostic yield, detection of structural variants, and exploration of the non-coding genome are also highlighted.

EXPERT OPINION

In the current landscape of NGS-based diagnostics for HHI, there are both challenges (e.g. detection of structural variants and non-coding genome variants) and opportunities (e.g. the emergence of medical artificial intelligence tools). The authors advocate the use of technological advances such as long-read sequencing for structural variant detection, multi-omics analysis for non-coding variant exploration, and medical artificial intelligence for pathogenicity assessment and outcome prediction. By integrating these innovations into clinical practice, precision medicine in the diagnosis and management of HHI can be further improved.

Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts

Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a novel paradigm for building modification-tolerant nanopore sequencing basecallers.

Sensitivity and specificity of Nanopore sequencing for detecting carbapenem and 3rd-generation cephalosporin-resistant Enterobacteriaceae in urine samples: Real-time simulation with public antimicrobial resistance gene database

Objectives

To evaluate the accuracy of beta-lactamase gene detection directly from urine samples by Nanopore sequencing.

Methods

DNA was extracted from bacterial pellets in spun urine. The purified DNA was then sequenced in native form by a Nanopore sequencer (MinION) to identify the organisms and beta-lactamase genes. Results were compared to routine urine cultures and standard antimicrobial susceptibility tests (AST).

Results

We processed 60 urine samples of which routine cultures grew Enterobacteriaceae, including 28 carbapenem-resistant (CRE), 17 extended-spectrum beta-lactamase (ESBL) or AmpC producing, and 15 non-ESBL/AmpC phenotypes. We excluded 7 samples with extremely low DNA amounts (<1 ng/μl) for a final case of 53 in total. The sensitivity of antimicrobial resistance gene detection within 6 h, the optimal duration from real-time simulation, of Nanopore sequencing for the diagnosis of carbapenem-resistant and ceftriaxone-resistant phenotypes was 73.9 % (95%CI 56.0-91.9 %) and 81.1 % (95%CI 68.5-93.7 %), while the specificity was 96.7 % (95%CI 90.2-100.0 %) and 56.3 % (95%CI 31.9-80.6 %), respectively. The median times for MinION to generate DNA reads containing carbapenemase and ESBL/AmpC genes were 93 min (IQR 17-245.5) and 99 min (IQR 31.25-269.75) after sequencing commencement, respectively.

Conclusions

Nanopore sequencing can identify bacterial genotypic resistance in urine and may enable clinicians to adjust antimicrobial therapy earlier than routine AST.

CRISPR/Cas9 gene targeting plus nanopore DNA sequencing with the plasmid pBR322 in the classroom

Both nanopore-based DNA sequencing and CRISPR/Cas-based gene editing represent groundbreaking innovations in molecular biology and genomics, offering unprecedented insights into and tools for working with genetic information. For students, reading, editing, and even writing DNA will be part of their everyday life. We have developed a laboratory procedure that includes (i) the biosynthesis of a guide RNA for, (ii) targeting Cas9 to specifically linearize the pBR322 plasmid, and (iii) the identification of the cutting site through nanopore DNA sequencing. The protocol is intentionally kept simple and requires neither living organisms nor biosafety laboratories. We divided the experimental procedures into separate activities to facilitate customization. Assuming access to a well-equipped molecular biology laboratory, an initial investment of approximately $2,700 is necessary. The material costs for each experiment group amount to around $130. Furthermore, we have developed a freely accessible website (https://dnalesen.hs-mittweida.de) for sequence read analysis and visualization, lowering the required computational skills to a minimum. For those with strong computational skills, we provide instructions for terminal-based data processing. With the presented activities, we aim to provide a hands-on experiment that engages students in modern molecular genetics and motivates them to discuss potential implications. The complete experiment can be accomplished within half a day and has been successfully implemented by us at high schools, in teacher training, and at universities. Our tip is to combine CRISPR/Cas gene targeting with nanopore-based DNA sequencing. As a tool, we provide a website that facilitates sequence data analysis and visualization.

Evolution of Pyrrolysyl-tRNA Synthetase: From Methanogenesis to Genetic Code Expansion

Over 20 years ago, the pyrrolysine encoding translation system was discovered in specific archaea. Our Review provides an overview of how the once obscure pyrrolysyl-tRNA synthetase (PylRS) tRNA pair, originally responsible for accurately translating enzymes crucial in methanogenic metabolic pathways, laid the foundation for the burgeoning field of genetic code expansion. Our primary focus is the discussion of how to successfully engineer the PylRS to recognize new substrates and exhibit higher in vivo activity. We have compiled a comprehensive list of ncAAs incorporable with the PylRS system. Additionally, we also summarize recent successful applications of the PylRS system in creating innovative therapeutic solutions, such as new antibody-drug conjugates, advancements in vaccine modalities, and the potential production of new antimicrobials.

Robust genome and cell engineering via in vitro and in situ circularized RNAs

Circularization can improve RNA persistence, yet simple and scalable approaches to achieve this are lacking. Here we report two methods that facilitate the pursuit of circular RNAs (cRNAs): cRNAs developed via in vitro circularization using group II introns, and cRNAs developed via in-cell circularization by the ubiquitously expressed RtcB protein. We also report simple purification protocols that enable high cRNA yields (40-75%) while maintaining low immune responses. These methods and protocols facilitate a broad range of applications in stem cell engineering as well as robust genome and epigenome targeting via zinc finger proteins and CRISPR-Cas9. Notably, cRNAs bearing the encephalomyocarditis internal ribosome entry enabled robust expression and persistence compared with linear capped RNAs in cardiomyocytes and neurons, which highlights the utility of cRNAs in these non-dividing cells. We also describe genome targeting via deimmunized Cas9 delivered as cRNA and a long-range multiplexed protein engineering methodology for the combinatorial screening of deimmunized protein variants that enables compatibility between persistence of expression and immunogenicity in cRNA-delivered proteins. The cRNA toolset will aid research and the development of therapeutics.

Accurate long-read transcript discovery and quantification at single-cell, pseudo-bulk and bulk resolution with Isosceles

Accurate detection and quantification of mRNA isoforms from nanopore long-read sequencing remains challenged by technical noise, particularly in single cells. To address this, we introduce Isosceles, a computational toolkit that outperforms other methods in isoform detection sensitivity and quantification accuracy across single-cell, pseudo-bulk and bulk resolution levels, as demonstrated using synthetic and biologically-derived datasets. Here we show Isosceles improves the fidelity of single-cell transcriptome quantification at the isoform-level, and enables flexible downstream analysis. As a case study, we apply Isosceles, uncovering coordinated splicing within and between neuronal differentiation lineages. Isosceles is suitable to be applied in diverse biological systems, facilitating studies of cellular heterogeneity across biomedical research applications.

Nanopore Sequencing Technology: A Reliable Method for Pathogen Diagnosis in Elderly Patients with Community-Acquired Pneumonia

Purpose

Next-generation sequencing of the metagenome (mNGS) is gaining traction as a valuable tool for diagnosing infectious diseases. Compared to mNGS, pathogen detection based on Oxford Nanopore Technology further shortens the detection time. This study seeks to assess the efficacy of Nanopore sequencing in identifying pathogens associated with community-acquired pneumonia (CAP) among elderly individuals in China.

Patients and Methods

From January 2023 to June 2023, elderly patients with CAP were prospectively recruited from Hangzhou First People's Hospital. A comprehensive set of clinical data was gathered, and bronchoalveolar lavage (BAL) fluid samples were collected. Concurrently, pathogen identification was performed using conventional microbiological diagnostic methods, Illumina sequencing, and Nanopore sequencing, and the diagnostic efficacy of pathogen detection was compared.

Results

The study included a total of 29 patients. The diagnostic positivity rates of traditional microbiological detection, Illumina sequencing, and Nanopore sequencing were 24.1%, 51.7%, and 48.3%, respectively. Their diagnostic specificities were 91.7%, 50%, and 75%, respectively. Compared to traditional microbiological detection, both Nanopore and Illumina sequencing showed significantly increased sensitivity. However, Nanopore sequencing exhibited relatively better consistency with the final clinical comprehensive diagnosis, with a Kappa value of 0.574. This outperformed traditional microbiological detection and Illumina sequencing, which had a Kappa value of 0.296 and 0.402, respectively. In addition, Nanopore sequencing required the shortest turnaround time.

Conclusion

Nanopore sequencing technology demonstrates as a reliable and rapid method for detecting pathogens in elderly patients with CAP.

PathoTracker: an online analytical metagenomic platform for Klebsiella pneumoniae feature identification and outbreak alerting

Clinical metagenomics (CMg) Nanopore sequencing can facilitate infectious disease diagnosis. In China, sub-lineages ST11-KL64 and ST11-KL47 Carbapenem-resistant Klebsiella pneumoniae (CRKP) are widely prevalent. We propose PathoTracker, a specially compiled database and arranged method for strain feature identification in CMg samples and CRKP traceability. A database targeting high-prevalence horizontal gene transfer in CRKP strains and a ST11-only database for distinguishing two sub-lineages in China were created. To make the database user-friendly, facilitate immediate downstream strain feature identification from raw Nanopore metagenomic data, and avoid the need for phylogenetic analysis from scratch, we developed data analysis methods. The methods included pre-performed phylogenetic analysis, gene-isolate-cluster index and multilevel pan-genome database and reduced storage space by 10-fold and random-access memory by 52-fold compared with normal methods. PathoTracker can provide accurate and fast strain-level analysis for CMg data after 1 h Nanopore sequencing, allowing early warning of outbreaks. A user-friendly page ( http://PathoTracker.pku.edu.cn/ ) was developed to facilitate online analysis, including strain-level feature, species identifications and phylogenetic analyses. PathoTracker proposed in this study will aid in the downstream analysis of CMg.

Advancing long-read nanopore genome assembly and accurate variant calling for rare disease detection

More than 50% of families with suspected rare monogenic diseases remain unsolved after whole genome analysis by short read sequencing (SRS). Long-read sequencing (LRS) could help bridge this diagnostic gap by capturing variants inaccessible to SRS, facilitating long-range mapping and phasing, and providing haplotype-resolved methylation profiling. To evaluate LRS's additional diagnostic yield, we sequenced a rare disease cohort of 98 samples, including 41 probands and some family members, using nanopore sequencing, achieving per sample ∼36x average coverage and 32 kilobase (kb) read N50 from a single flow cell. Our Napu pipeline generated assemblies, phased variants, and methylation calls. LRS covered, on average, coding exons in ∼280 genes and ∼5 known Mendelian disease genes that were not covered by SRS. In comparison to SRS, LRS detected additional rare, functionally annotated variants, including SVs and tandem repeats, and completely phased 87% of protein-coding genes. LRS detected additional de novo variants, and could be used to distinguish postzygotic mosaic variants from prezygotic de novos . Eleven probands were solved, with diverse underlying genetic causes including de novo and compound heterozygous variants, large-scale SVs, and epigenetic modifications. Our study demonstrates LRS's potential to enhance diagnostic yield for rare monogenic diseases, implying utility in future clinical genomics workflows.

Formation of a planar biomimetic membrane with a novel zwitterionic polymer for nanopore sequencing

Biological membranes containing transmembrane channels play a crucial role in numerous cellular processes, and mimicking of cell membranes has garnered significant interest in various biomedical applications, particularly nanopore sequencing technology, where remarkable progress has been made with nanopore membranes. Considering the fragility of biomimetic membranes formed by artificial lipids and the limited mimicry of those formed by common block copolymers, this study developed a novel amphiphilic polymer by covalently linking hydrophilic heads of phospholipids to the ends of hydrophobic poly(dimethyl siloxane) (PDMS) chains. The absence of hydrophilic blocks allowed for good control over the polydispersity of this polymer within a narrow range. The high flexibility of PDMS chains, combined with relatively uniform molecular weights, would confer enhanced stability and robustness to polymeric membranes. Dynamic light scattering measurements and microdroplet formation tests demonstrated good amphipathic properties of these novel polymers when maintaining an appropriate hydrophilic-hydrophobic ratio. Moreover, the high similarity between the hydrophilic heads and natural phospholipids makes this polymer more compatible with biomolecules. A successful protein insertion experiment confirmed both the stability of this polymeric membrane and its compatibility with membrane proteins. As a result, this novel amphiphilic polymer exhibits great potential for biomembrane mimicking and paves a new path for material design in biomedical applications.

New insights into decoding the lifestyle of endophytic Fusarium lateritium Fl617 via comparing genomes

Fungal-plant interactions have persisted for 460 million years, and almost all terrestrial plants on Earth have endophytic fungi. However, the mechanism of symbiosis between endophytic fungi and host plants has been inconclusive. In this dissertation, we used a strain of endophytic Fusarium lateritium (Fl617), which was found in the previous stage to promote disease resistance in tomato, and selected the pathogenic Fusarium oxysporum Fo4287 and endophytic Fusarium oxysporum Fo47, which are in the same host and the closest relatives of Fl617, to carry out a comparative genomics analysis of the three systems and to provide a new perspective for the elucidation of the special lifestyle of the fungal endophytes. We found that endophytic F. lateritium has a smaller genome, fewer clusters and genes associated with pathogenicity, and fewer plant cell wall degrading enzymes (PCWDEs). There were also relatively fewer secondary metabolisms and typical Fusarium spp. toxins, and a lack of the key Fusarium spp. pathogenicity factor, secreted in xylem (SIX), but the endophytic fungi may be more sophisticated in their regulation of the colonization process. It is hypothesized that the endophytic fungi may have maintained their symbiosis with plants due to the relatively homogeneous microenvironment in plants for a long period of time, considering only plant interactions and discarding the relevant pathogenicity factors, and that their endophytic evolutionary tendency may tend to be genome streamlining and to enhance the fineness of the regulation of plant interactions, thus maintaining their symbiotic status with plants.

Structure and rational engineering of the PglX methyltransferase and specificity factor for BREX phage defence

Bacteria have evolved a broad range of systems that provide defence against their viral predators, bacteriophages. Bacteriophage Exclusion (BREX) systems recognise and methylate 6 bp non-palindromic motifs within the host genome, and prevent replication of non-methylated phage DNA that encodes these same motifs. How BREX recognises cognate motifs has not been fully understood. In this study we characterise BREX from pathogenic Salmonella and present X-ray crystallographic structures of the conserved BREX protein, PglX. The PglX N-terminal domain encodes the methyltransferase, whereas the C-terminal domain is for motif recognition. We also present the structure of PglX bound to the phage-derived DNA mimic, Ocr, an inhibitor of BREX activity. Our analyses propose modes for DNA-binding by PglX and indicate that both methyltransferase activity and defence require larger BREX complexes. Through rational engineering of PglX we broaden both the range of phages targeted, and the host motif sequences that are methylated by BREX. Our data demonstrate that PglX is used to recognise specific DNA sequences for BREX activity, contributing to motif recognition for both phage defence and host methylation.

Chromosome-contiguous genome for the Haecon-5 strain of Haemonchus contortus reveals marked genetic variability and enables the discovery of essential gene candidates

Millions of livestock animals worldwide are infected with the haematophagous barber's pole worm, Haemonchus contortus, the aetiological agent of haemonchosis. Despite the major significance of this parasite worldwide and its widespread resistance to current treatments, the lack of a high-quality genome for the well-defined strain of this parasite from Australia, called Haecon-5, has constrained research in a number of areas including host-parasite interactions, drug discovery and population genetics. To enable research in these areas, we report here a chromosome-contiguous genome (∼280 Mb) for Haecon-5 with high-quality models for 19,234 protein-coding genes. Comparative genomic analyses show significant genomic similarity (synteny) with a UK strain of H. contortus, called MHco3(ISE).N1 (abbreviated as "ISE"), but we also discover marked differences in genomic structure/gene arrangements, distribution of nucleotide variability (single nucleotide polymorphisms (SNPs) and indels) and orthology between Haecon-5 and ISE. We used the genome and extensive transcriptomic resources for Haecon-5 to predict a subset of essential single-copy genes employing a "cross-species" machine learning (ML) approach using a range of features from nucleotide/protein sequences, protein orthology, subcellular localisation, single-cell RNA-seq and/or histone methylation data available for the model organisms Caenorhabditis elegans and Drosophila melanogaster. From a set of 1,464 conserved single copy genes, transcribed in key life-cycle stages of H. contortus, we identified 232 genes whose homologs have critical functions in C. elegans and/or D. melanogaster, and prioritised 10 of them for further characterisation; nine of the 10 genes likely play roles in neurophysiological processes, germline, hypodermis and/or respiration, and one is an unknown (orphan) gene for which no detailed functional information exists. Future studies of these genes/gene products are warranted to elucidate their roles in parasite biology, host-parasite interplay and/or disease. Clearly, the present Haecon-5 reference genome and associated resources now underpin a broad range of fundamental investigations of H. contortus and could assist in accelerating the discovery of novel intervention targets and drug candidates to combat haemonchosis.

Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches

Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing. Recent improvements in the accuracy of long-read sequencing technologies have expanded the scope for novel splice isoform detection and have also enabled a far more accurate reconstruction of complex splicing patterns and transcriptomes. Additionally, the incorporation and advancements of machine learning and deep learning algorithms in bioinformatic software have significantly improved the reliability of long-read sequencing transcriptomic studies. However, there is a lack of consensus on what bioinformatic tools and pipelines produce the most precise and consistent results. Thus, this review aims to discuss and compare the performance of available methods for novel isoform discovery with long-read sequencing technologies, with 25 tools being presented. Furthermore, this review intends to demonstrate the need for developing standard analytical pipelines, tools, and transcript model conventions for novel isoform discovery and transcriptomic studies.

Direct RNA sequencing in plants: Practical applications and future perspectives

The transcriptome serves as a bridge that links genomic variation to phenotypic diversity. A vast number of studies using next-generation RNA sequencing (RNA-seq) over the last 2 decades have emphasized the essential roles of the plant transcriptome in response to developmental and environmental conditions, providing numerous insights into the dynamic changes, evolutionary traces, and elaborate regulation of the plant transcriptome. With substantial improvement in accuracy and throughput, direct RNA sequencing (DRS) has emerged as a new and powerful sequencing platform for precise detection of native and full-length transcripts, overcoming many limitations such as read length and PCR bias that are inherent to short-read RNA-seq. Here, we review recent advances in dissecting the complexity and diversity of plant transcriptomes using DRS as the main technological approach, covering many aspects of RNA metabolism, including novel isoforms, poly(A) tails, and RNA modification, and we propose a comprehensive workflow for processing of plant DRS data. Many challenges to the application of DRS in plants, such as the need for machine learning tools tailored to plant transcriptomes, remain to be overcome, and together we outline future biological questions that can be addressed by DRS, such as allele-specific RNA modification. This technology provides convenient support on which the connection of distinct RNA features is tightly built, sustainably refining our understanding of the biological functions of the plant transcriptome.

Average Nucleotide Identity and Digital DNA-DNA Hybridization Analysis Following PromethION Nanopore-Based Whole Genome Sequencing Allows for Accurate Prokaryotic Typing

Whole-genome sequencing (WGS) is revolutionizing clinical bacteriology. However, bacterial typing remains investigated by reference techniques with inherent limitations. This stresses the need for alternative methods providing robust and accurate sequence type (ST) classification. This study optimized and evaluated a GridION nanopore sequencing protocol, adapted for the PromethION platform. Forty-eight Escherichia coli clinical isolates with diverse STs were sequenced to assess two alternative typing methods and resistance profiling applications. Multi-locus sequence typing (MLST) was used as the reference typing method. Genomic relatedness was assessed using Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (DDH), and cut-offs for discriminative strain resolution were evaluated. WGS-based antibiotic resistance prediction was compared to reference Minimum Inhibitory Concentration (MIC) assays. We found ANI and DDH cut-offs of 99.3% and 94.1%, respectively, which correlated well with MLST classifications and demonstrated potentially higher discriminative resolution than MLST. WGS-based antibiotic resistance prediction showed categorical agreements of ≥ 93% with MIC assays for amoxicillin, ceftazidime, amikacin, tobramycin, and trimethoprim-sulfamethoxazole. Performance was suboptimal (68.8-81.3%) for amoxicillin-clavulanic acid, cefepime, aztreonam, and ciprofloxacin. A minimal sequencing coverage of 12× was required to maintain essential genomic features and typing accuracy. Our protocol allows the integration of PromethION technology in clinical laboratories, with ANI and DDH proving to be accurate and robust alternative typing methods, potentially offering superior resolution. WGS-based antibiotic resistance prediction holds promise for specific antibiotic classes.

The potential of RNA methylation in the treatment of cardiovascular diseases

RNA methylation has emerged as a dynamic regulatory mechanism that impacts gene expression and protein synthesis. Among the known RNA methylation modifications, N6-methyladenosine (m6A), 5-methylcytosine (m5C), 3-methylcytosine (m3C), and N7-methylguanosine (m7G) have been studied extensively. In particular, m6A is the most abundant RNA modification and has attracted significant attention due to its potential effect on multiple biological processes. Recent studies have demonstrated that RNA methylation plays an important role in the development and progression of cardiovascular disease (CVD). To identify key pathogenic genes of CVD and potential therapeutic targets, we reviewed several common RNA methylation and summarized the research progress of RNA methylation in diverse CVDs, intending to inspire effective treatment strategies.

Exploration of biomarkers for nursing physical examination early screening of multiple tumors

Nursing and physical examination early screening of multiple tumors is helpful to find tumors early, so as to improve the cure rate. Studying its molecular mechanisms is urgent. By logging into gene expression omnibus database, we found laryngeal cancer dataset GSE127165, bladder cancer dataset GSE65635, oral cancer dataset GSE146483, obtain differentially expressed genes, subsequently, weighted gene co-expression network analysis, protein-protein interaction networks, functional enrichment analysis, immune infiltration analysis, survival analysis, comparative toxicogenomics database analysis were conducted. Draw a heatmap of gene expression. Use targetScan to search for miRNA information about core DEG. Got 53 differentially expressed genes. In GOKEGG analysis, they were clustered in cell cycle processes, spindle poles, and protein serine/threonine/tyrosine kinase activity cell cycle, transcriptional dysregulation in cancer, RIG-I-like receptor signaling pathway, P53 signaling pathway. Protein-protein interaction analysis screened out 5 genes (NEK2, BUB1, HMMR, TTK, CCNB2). Cyclin B2 (CCNB2) and budding uninhibited by benzimidazole 1 (BUB1) were highly expressed in laryngeal cancer, bladder cancer, oral cancer. Comparative toxicogenomics database analysis found that core genes (CCNB2, BUB1) are associated with tumors, necrosis, and inflammation. Related miRNA of CCNB2 gene is hsa-miR-670-3p; related miRNAs of BUB1 gene are hsa-miR-5688, hsa-miR-495-3p. CCNB2 and BUB1 exhibit high expression in laryngeal cancer, bladder cancer, and oral cancer, suggesting their potential as molecular targets for precision therapy in these cancers.

A highly sensitive nanopore platform for measuring RNase A activity

Ribonuclease A (RNase A) plays significant roles in several physiological and pathological conditions and can be used as a valuable diagnostic biomarker for human diseases such as myocardial infarction and cancer. Hence, it is of great importance to develop a rapid and cost-effective method for the highly sensitive detection of RNase A. The significance of RNase A assay is further enhanced by the growing attention from the biotechnology and pharmaceutical industries to develop RNA-based vaccines and drugs in large part as a result of the successful development of mRNA vaccines in the COVID-19 pandemic. Herein, we report a label-free method for the detection of RNase A by monitoring its proteolytic cleavage of an RNA substrate in a nanopore. The method is ultra-sensitive with the limit of detection reaching as low as 30 fg per milliliter. Furthermore, sensor selectivity and the effects of temperature, incubation time, metal ion, salt concentration on sensor sensitivity were also investigated.

Scaling up for success: from bioactive natural products to new medicines

Covering 1986 to presentNatural product drug discovery at Novartis has a long and successful history of delivering life saving medicines to millions of patients. In this viewpoint, we are presenting the tools we use and challenges we face as we advance natural products from early research into development and beyond. We are leveraging our collection of 90 000 microbial strains and 20 000 isolated natural products to find new medications in an interdisciplinary approach that requires expertise in microbiology, computational biology, synthetic biology, chemistry, and process development. Technological advances, particularly in genome engineering and data science have transformed our field, accelerating discovery and facilitating sustainable compound supply. Emerging new modalities such as antibody drug conjugates, radioligand therapies and xRNA-based medications offer new opportunities for natural product-derived drugs. By taking advantage of these new modalities and the most recent research technologies, natural products will significantly contribute to the medicines of the future.

Quantitative analysis of dose dependent DNA fragmentation in dry pBR322 plasmid using long read sequencing and Monte Carlo simulations

Exposure to ionizing radiation can induce genetic aberrations via unrepaired DNA strand breaks. To investigate quantitatively the dose-effect relationship at the molecular level, we irradiated dry pBR322 plasmid DNA with 3 MeV protons and assessed fragmentation yields at different radiation doses using long-read sequencing from Oxford Nanopore Technologies. This technology applied to a reference DNA model revealed dose-dependent fragmentation, as evidenced by read length distributions, showing no discernible radiation sensitivity in specific genetic sequences. In addition, we propose a method for directly measuring the single-strand break (SSB) yield. Furthermore, through a comparative study with a collection of previous works on dry DNA irradiation, we show that the irradiation protocol leads to biases in the definition of ionizing sources. We support this scenario by discussing the size distributions of nanopore sequencing reads in the light of Geant4 and Geant4-DNA simulation toolkit predictions. We show that integrating long-read sequencing technologies with advanced Monte Carlo simulations paves a promising path toward advancing our comprehension and prediction of radiation-induced DNA fragmentation.