Next-generation sequencing technologies: An overview

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Development of a screening system of gene sets for estimating the time of early skeletal muscle injury based on second-generation sequencing technology

The present study is aimed to address the challenge of wound age estimation in forensic science by identifying reliable genetic markers using low-cost and high-precision second-generation sequencing technology. A total of 54 Sprague-Dawley rats were randomly assigned to a control group or injury groups, with injury groups being further divided into time points (4 h, 8 h, 12 h, 16 h, 20 h, 24 h, 28 h, and 32 h after injury, n = 6) to establish rat skeletal muscle contusion models. Gene expression data were obtained using second-generation sequencing technology, and differential gene expression analysis, weighted gene co-expression network analysis (WGCNA) and time-dependent expression trend analysis were performed. A total of six sets of biomarkers were obtained: differentially expressed genes at adjacent time points (127 genes), co-expressed genes most associated with wound age (213 genes), hub genes exhibiting time-dependent expression (264 genes), and sets of transcription factors (TF) corresponding to the above sets of genes (74, 87, and 99 genes, respectively). Then, random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were constructed for wound age estimation from the above gene sets. The results estimated by transcription factors were all superior to the corresponding hub genes, with the transcription factor group of WGCNA performed the best, with average accuracy rates of 96% for three models' internal testing, and 91.7% for the highest external validation. This study demonstrates the advantages of the indicator screening system based on second-generation sequencing technology and transcription factor level for wound age estimation.

GWAS advancements to investigate disease associations and biological mechanisms

Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. State of the literature demonstrate the capabilities of these techniques in enhancing the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined Single Nucleotide Polymorphism (SNP) sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications (SDs). Mendelian Randomization has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques like HuBMAP, enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aims to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.

Effects of different assembly strategies on gene annotation in activated sludge

Activated sludge comprises diverse bacteria, fungi, and other microorganisms, featuring a rich repertoire of genes involved in antibiotic resistance, pollutant degradation, and elemental cycling. In this regard, hybrid assembly technology can revolutionize metagenomics by detecting greater gene diversity in environmental samples. Nonetheless, the optimal utilization and comparability of genomic information between hybrid assembly and short- or long-read technology remain unclear. To address this gap, we compared the performance of the hybrid assembly, short- and long-read technologies, abundance and diversity of annotated genes, and taxonomic diversity by analysing 46, 161, and 45 activated sludge metagenomic datasets, respectively. The results revealed that hybrid assembly technology exhibited the best performance, generating the most contiguous and longest contigs but with a lower proportion of high-quality metagenome-assembled genomes than short-read technology. Compared with short- or long-read technologies, hybrid assembly technology can detect a greater diversity of microbiota and antibiotic resistance genes, as well as a wider range of potential hosts. However, this approach may yield lower gene abundance and pathogen detection. Our study revealed the specific advantages and disadvantages of hybrid assembly and short- and long-read applications in wastewater treatment plants, and our approach could serve as a blueprint to be extended to terrestrial environments.

Optical detection of infectious SARS-CoV-2 virions by counting spikes

Existing methods for the mass detection of viruses are limited to the registration of small amounts of a viral genome or specific protein markers. In spite of high sensitivity, the applied methods cannot distinguish between virulent viral particles and non-infectious viral particle debris. We report an approach to solve this long-standing challenge using the SARS-CoV-2 virus as an example. We show that wide-field optical microscopy with the state-of-the-art mesoscopic fluorescent labels, formed by a core-shell plasmonic nanoparticle with fluorescent dye molecules in the core-shell that are strongly coupled to the plasmonic nanoparticle, not only rapidly, i.e. in less than 20 minutes after sampling, detects SARS-CoV-2 virions directly in a patient sample without a pre-concentration step, but can also distinguish between infectious and non-infectious virus strains by counting the spikes on the lipid envelope of individual viral particles.

Next-generation data filtering in the genomics era

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne).

Detection of four isomers of the human cytomegalovirus genome using nanopore long-read sequencing

Human cytomegalovirus has a linear DNA genome with a total length of approximately 235 kb. This large genome is divided into two domains, "Long" and "Short". There are four isomers of the cytomegalovirus genome with different orientations of each domain. To confirm the presence of four types of isomers, it is necessary to identify the sequence of the junction between the domains. However, due to the presence of repeat sequences, it is difficult to determine the junction sequences by next-generation sequencing analysis. To solve this problem, long-read sequencing was performed using the Oxford Nanopore sequencer and the junctions were successfully identified in four isomers in strain Merin and ATCC-2011-3. Nanopore sequencing also revealed the presence of multiple copies of the "a" sequence (a-seq) in the junctions, indicating the diversity of the junction sequences. These results strongly suggest that long-read sequencing using the nanopore sequencer would be beneficial for identifying the complex structure of the cytomegalovirus genome.

Sequencing Strategy to Ensure Accurate Plasmid Assembly

Despite the wide use of plasmids in research and clinical production, verifying plasmid sequences is a bottleneck often underestimated in the manufacturing process. While sequencing platforms continue to improve, the chosen method and assembly pipeline still significantly influence the final plasmid assembly sequence. Furthermore, few dedicated tools exist for plasmid assembly, particularly for de novo assembly. Here, we evaluated short-read, long-read, and hybrid (both short and long reads) de novo assembly pipelines across three replicates of a 24-plasmid library. Consistent with previous characterizations of each sequencing technology, short-read assemblies faced challenges in resolving GC-rich regions, and long-read assemblies commonly exhibited small insertions and deletions, especially in repetitive regions. The hybrid approach facilitated the most accurate and consistent assembly generation, identifying mutations relative to the reference sequence. While Sanger sequencing can verify specific regions, some GC-rich and repetitive regions were challenging to resolve using any method, indicating that easily sequenced genetic parts should be prioritized in designing new genetic constructs.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

A pipeline for the development and analysis of extracellular vesicle-based transcriptomic biomarkers in molecular diagnostics

Extracellular vesicles are shed by every cell type and can be found in any biofluid. They contain different molecules that can be utilized as biomarkers, including several RNA species which they protect from degradation. Here, we present a pipeline for the development and analysis of extracellular vesicle-associated transcriptomic biomarkers that our group has successfully applied multiple times. We highlight the key steps of the pipeline and give particular emphasis to the necessary quality control checkpoints, which are linked to numerous available guidelines that should be considered along the workflow. Our pipeline starts with patient recruitment and continues with blood sampling and processing. The purification and characterization of extracellular vesicles is explained in detail, as well as the isolation and quality control of extracellular vesicle-associated RNA. We point out the possible pitfalls during library preparation and RNA sequencing and present multiple bioinformatic tools to pinpoint biomarker signature candidates from the sequencing data. Finally, considerations and pitfalls during the validation of the biomarker signature using RT-qPCR will be elaborated.

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

Application of Next-Generation Sequencing (NGS) Techniques for Selected Companion Animals

Next-Generation Sequencing (NGS) techniques have revolutionized veterinary medicine for cats and dogs, offering insights across various domains. In veterinary parasitology, NGS enables comprehensive profiling of parasite populations, aiding in understanding transmission dynamics and drug resistance mechanisms. In infectious diseases, NGS facilitates rapid pathogen identification, characterization of virulence factors, and tracking of outbreaks. Moreover, NGS sheds light on metabolic processes by elucidating gene expression patterns and metabolic pathways, essential for diagnosing metabolic disorders and designing tailored treatments. In autoimmune diseases, NGS helps identify genetic predispositions and molecular mechanisms underlying immune dysregulation. Veterinary oncology benefits from NGS through personalized tumor profiling, mutation analysis, and identification of therapeutic targets, fostering precision medicine approaches. Additionally, NGS plays a pivotal role in veterinary genetics, unraveling the genetic basis of inherited diseases and facilitating breeding programs for healthier animals. Physiological investigations leverage NGS to explore complex biological systems, unraveling gene-environment interactions and molecular pathways governing health and disease. Application of NGS in treatment planning enhances precision and efficacy by enabling personalized therapeutic strategies tailored to individual animals and their diseases, ultimately advancing veterinary care for companion animals.

High-throughput nanopore targeted sequencing for efficient drug resistance assay of Mycobacterium tuberculosis

Drug-resistant tuberculosis (TB), especially multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB), is one of the urgent clinical problems and public health challenges. Culture-based phenotypic drug susceptibility testing (pDST) is time-consuming, and PCR-based assays are limited to hotspot mutations. In this study, we developed and validated a convenient and efficient approach based on high-throughput nanopore sequencing technology combined with multiplex PCR, namely nanopore targeted sequencing (NTS), to simultaneously sequence 18 genes associated with antibiotic resistance in Mycobacterium tuberculosis (MTB). The analytical performance of NTS was evaluated, and 99 clinical samples were collected to assess its clinical performance. The NTS results showed that MTB and its drug resistance were successfully identified in approximately 7.5 h. Furthermore, compared to the pDST and Xpert MTB/RIF assays, NTS provided much more drug resistance information, covering 14 anti-TB drugs, and it identified 20 clinical cases of drug-resistant MTB. The mutations underlying these drug-resistant cases were all verified using Sanger sequencing. Our approach for this TB drug resistance assay offers several advantages, including being culture-free, efficient, high-throughput, and highly accurate, which would be very helpful for clinical patient management and TB infection control.

RNA isoform expression landscape of the human dorsal root ganglion generated from long-read sequencing

ABSTRACT

Splicing is a posttranscriptional RNA processing mechanism that enhances genomic complexity by creating multiple isoforms from the same gene. We aimed to characterize the isoforms expressed in the human peripheral nervous system, with the goal of creating a resource to identify novel isoforms of functionally relevant genes associated with somatosensation and nociception. We used long-read sequencing to document isoform expression in the human dorsal root ganglia from 3 organ donors and validated in silico by confirming expression in short-read sequencing from 3 independent organ donors. Nineteen thousand five hundred forty-seven isoforms of protein-coding genes were detected and validated. We identified 763 isoforms with at least one previously undescribed splice junction. Previously unannotated isoforms of multiple pain-associated genes, including ASIC3, MRGPRX1, and HNRNPK, were identified. In the novel isoforms of ASIC3, a region comprising approximately 35% of the 5'UTR was excised. By contrast, a novel splice junction was used in isoforms of MRGPRX1 to include an additional exon upstream of the start codon, consequently adding a region to the 5'UTR. Novel isoforms of HNRNPK were identified, which used previously unannotated splice sites to both excise exon 14 and include a sequence in the 3' end of exon 13. This novel insertion is predicted to introduce a tyrosine phosphorylation site potentially phosphorylated by SRC. We also independently confirm a recently reported DRG-specific splicing event in WNK1 that gives insight into how painless peripheral neuropathy occurs when this gene is mutated. Our findings give a clear overview of mRNA isoform diversity in the human dorsal root ganglia obtained using long-read sequencing.

miR-153 promotes neural differentiation by activating the cell adhesion/Ca2+ signaling pathway and targeting ion channel activity in HT-22 cells by bioinformatic analysis

MicroRNAs have been studied extensively in neurodegenerative diseases. In a previous study, miR-153 promoted neural differentiation and projection formation in mouse hippocampal HT-22 cells. However, the pathways and molecular mechanism underlying miR-153-induced neural differentiation remain unclear. To explore the molecular mechanism of miR-153 on neural differentiation, we performed RNA sequencing on miR-153-overexpressed HT-22 cells. Based on RNA sequencing, differentially expressed genes (DEGs) and pathways in miR-153-overexpressed cells were identified. The Database for Annotation, Visualization and Integrated Discovery and Gene Set Enrichment Analysis were used to perform functional annotation and enrichment analysis of DEGs. Targetscan predicted the targets of miR-153. The Search Tool for the Retrieval of Interacting Genes and Cytoscape, were used to construct protein-protein interaction networks and identify hub genes. Q-PCR was used to detect mRNA expression of the identified genes. The expression profiles of the identified genes were compared between embryonic days 9.5 (E9.5) and E11.5 in the embryotic mouse brain of the GDS3442 dataset. Cell Counting Kit-8 assay was used to determine cell proliferation and cellular susceptibility to amyloid β-protein (Aβ) toxicity in miR-153-overexpressed cells. The results indicated that miR-153 increased cell adhesion/Ca2+ (Cdh5, Nrcam, and P2rx4) and Bdnf/Ntrk2 neurotrophic signaling pathway, and decreased ion channel activity (Kcnc3, Kcna4, Clcn5, and Scn5a). The changes in the expression of the identified genes in miR-153-overexpressed cells were consistent with the expression profile of GDS3442 during neural differentiation. In addition, miR-153 overexpression decreased cellular susceptibility to Aβ toxicity in HT-22 cells. In conclusion, miR-153 overexpression may promote neural differentiation by inducing cell adhesion and the Bdnf/Ntrk2 pathway, and regulating electrophysiological maturity by targeting ion channels. MiR-153 may play an important role in neural differentiation; the findings provide a useful therapeutic direction for neurodegenerative diseases.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Improving Bacterial Metagenomic Research through Long-Read Sequencing

Metagenomic sequencing analysis is central to investigating microbial communities in clinical and environmental studies. Short-read sequencing remains the primary approach for metagenomic research; however, long-read sequencing may offer advantages of improved metagenomic assembly and resolved taxonomic identification. To compare the relative performance for metagenomic studies, we simulated short- and long-read datasets using increasingly complex metagenomes comprising 10, 20, and 50 microbial taxa. Additionally, we used an empirical dataset of paired short- and long-read data generated from mouse fecal pellets to assess real-world performance. We compared metagenomic assembly quality, taxonomic classification, and metagenome-assembled genome (MAG) recovery rates. We show that long-read sequencing data significantly improve taxonomic classification and assembly quality. Metagenomic assemblies using simulated long reads were more complete and more contiguous with higher rates of MAG recovery. This resulted in more precise taxonomic classifications. Principal component analysis of empirical data demonstrated that sequencing technology affects compositional results as samples clustered by sequence type, not sample type. Overall, we highlight strengths of long-read metagenomic sequencing for microbiome studies, including improving the accuracy of classification and relative abundance estimates. These results will aid researchers when considering which sequencing approaches to use for metagenomic projects.

Enhancing Trypanosomatid Identification and Genotyping with Oxford Nanopore Sequencing: Development and Validation of an 18S rRNA Amplicon-Based Method

Trypanosomatids, including Trypanosoma and Leishmania species, present significant medical and veterinary challenges, causing substantial economic losses, health complications, and even fatalities. Diagnosing and genotyping these species and their genotypes is often complex, involving multiple steps. This study aimed to develop an amplicon-based sequencing (ABS) method using Oxford Nanopore long-read sequencing to enhance Trypanosomatid detection and genotyping. The 18S rDNA gene was targeted for its inter-species conservation. The Trypanosomatid-ABS method effectively distinguished between 11 Trypanosoma species (including Trypanosoma evansi, Trypanosoma theileri, Trypanosoma vivax, and Trypanosoma rangeli) and 6 Trypanosoma cruzi discrete typing units (TcI to TcVI and TcBat), showing strong concordance with conventional methods (κ index of 0.729, P < 0.001). It detected co-infections between Trypanosomatid genera and T. cruzi, with a limit of detection of one parasite per mL. The method was successfully applied to human, animal, and triatomine samples. Notably, TcI predominated in chronic Chagas samples, whereas TcII and TcIV were found in the acute stage. Triatomine vectors exhibited diverse Trypanosomatid infections, with Triatoma dimidiata mainly infected with TcI and occasional TcBat co-infections, and Rhodnius prolixus showing TcI and TcII infections, along with T. rangeli co-infections and mixed TcII infections. Animals were infected with T. vivax, T. theileri, and T. evansi. The ABS method's high resolution, sensitivity, and accuracy make it a valuable tool for understanding Trypanosomatid dynamics, enhancing disease control strategies, and enabling targeted interventions.

Computationally guided AAV engineering for enhanced gene delivery

Gene delivery vehicles based on adeno-associated viruses (AAVs) are enabling increasing success in human clinical trials, and they offer the promise of treating a broad spectrum of both genetic and non-genetic disorders. However, delivery efficiency and targeting must be improved to enable safe and effective therapies. In recent years, considerable effort has been invested in creating AAV variants with improved delivery, and computational approaches have been increasingly harnessed for AAV engineering. In this review, we discuss how computationally designed AAV libraries are enabling directed evolution. Specifically, we highlight approaches that harness sequences outputted by next-generation sequencing (NGS) coupled with machine learning (ML) to generate new functional AAV capsids and related regulatory elements, pushing the frontier of what vector engineering and gene therapy may achieve.

Recent advances in high-performance liquid chromatography tandem mass spectrometry techniques for analysis of DNA damage and epigenetic modifications

Environmental exposure would cause DNA damage and epigenetic modification changes, potentially resulting in physiological dysfunction, thereby triggering diseases and even cancer. DNA damage and epigenetic modifications are thus promising biomarkers for environmental exposures and disease states. Benefiting from its high sensitivity and accuracy, high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) is considered the "gold standard technique" for investigating epigenetic DNA modifications. This review summarizes the recent advancements of UHPLC-MS/MS-based technologies for DNA damage and epigenetic modifications analysis, mainly focusing on the innovative methods developed for UHPLC-MS/MS-related pretreatment technologies containing efficient genomic DNA digestion and effective removal of the inorganic salt matrix, and the new strategies for improving detection sensitivity of liquid chromatography-mass spectrometry. Moreover, we also summarized the novel hyphenated techniques of the advanced UHPLC-MS/MS coupled with other separation and analysis methods for the measurement of DNA damage and epigenetic modification changes in special regions and fragments of chromosomes.

Low-frequency and rare genetic variants associated with rheumatoid arthritis risk

Rheumatoid arthritis (RA) has an estimated heritability of nearly 50%, which is particularly high in seropositive RA. HLA alleles account for a large proportion of this heritability, in addition to many common single-nucleotide polymorphisms with smaller individual effects. Low-frequency and rare variants, such as those captured by next-generation sequencing, can also have a large role in heritability in some individuals. Rare variant discovery has informed the development of drugs such as inhibitors of PCSK9 and Janus kinases. Some 34 low-frequency and rare variants are currently associated with RA risk. One variant (19:10352442G>C in TYK2) was identified in five separate studies, and might therefore represent a promising therapeutic target. Following a set of best practices in future studies, including studying diverse populations, using large sample sizes, validating RA and serostatus, replicating findings, adjusting for other variants and performing functional assessment, could help to ensure the relevance of identified variants. Exciting opportunities are now on the horizon for genetics in RA, including larger datasets and consortia, whole-genome sequencing and direct applications of findings in the management, and especially treatment, of RA.

Transforming Big Data into AI-ready data for nutrition and obesity research

OBJECTIVE

Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions.

METHODS

We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed.

RESULTS

Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented.

CONCLUSIONS

Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.

Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling

BACKGROUND

Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction.

OBJECTIVE

To reveal the correlation between cholelithiasis and gut bacteria.

METHODS

Stool samples were collected from 100 cholelithiasis and 250 healthy individuals from Huzhou Central Hospital; The 16S rRNA of gut bacteria in the stool samples was sequenced using the third-generation Pacbio sequencing platform; Mothur v.1.21.1 was used to analyze the diversity of gut bacteria; Wilcoxon rank-sum test and linear discriminant analysis of effect sizes (LEfSe) were used to analyze differences in gut bacteria between patients suffering from cholelithiasis and healthy individuals; Chord diagram and Plot-related heat maps were used to analyze the correlation between cholelithiasis and gut bacteria; six machine algorithms were used to construct models to predict cholelithiasis.

RESULTS

There were differences in the abundance of gut bacteria between cholelithiasis and healthy individuals, but there were no differences in their community diversity. Increased abundance of Costridia, Escherichia flexneri, and Klebsiella pneumonae were found in cholelithiasis, while Bacteroidia, Phocaeicola, and Phocaeicola vulgatus were more abundant in healthy individuals. The top four bacteria that were most closely associated with cholelithiasis were Escherichia flexneri, Escherichia dysenteriae, Streptococcus salivarius, and Phocaeicola vulgatus. The cholelithiasis model based on CatBoost algorithm had the best prediction effect (sensitivity: 90.48%, specificity: 88.32%, and AUC: 0.962).

CONCLUSION

The identification of characteristic gut bacteria may provide new predictive targets for gallstone screening. As being screened by the predictive model, people at high risk of cholelithiasis can determine the need for further testing, thus enabling early warning of cholelithiasis.

Estimating bloodstain age in the short term based on DNA fragment length using nanopore sequencer

We used a nanopore sequencer to quantify DNA fragments > 10,000 bp in size and then evaluated their relationship with short-term bloodstain age. Moreover, DNA degradation was investigated after bloodstains were wetted once with water. Bloodstain samples on cotton gauze were stored at room temperature and low humidity for up to 6 months. Bloodstains stored for 1 day were wetted with nuclease-free water, allowed to dry, and stored at room temperature and low humidity for up to 1 week. The proportion of fragments > 20,000 bp in dry bloodstains tended to decrease over time, particularly for fragments > 50,000 bp in size. This trend was modeled using a power approximation curve, with the highest R2 value (0.6475) noted for fragments > 50,000 bp in size; lower values were recorded for shorter fragments. The proportion of longer fragments was significantly reduced in bloodstains that were dried after being wetted once, and there was significant difference in fragments > 50,000 bp between dry conditions and once-wetted. This result suggests that even temporary exposure to water causes significant DNA fragmentation, but not extensive degradation. Thus, bloodstains that appear fresh but have a low proportion of long DNA fragments may have been wetted previously. Our results indicate that evaluating the proportion of long DNA fragments yields information on both bloodstain age and the environment in which they were stored.

Characterization of the olive endophytic community in genotypes displaying a contrasting response to Xylella fastidiosa

BACKGROUND

Endophytes mediate the interactions between plants and other microorganisms, and the functional aspects of interactions between endophytes and their host that support plant-growth promotion and tolerance to stresses signify the ecological relevance of the endosphere microbiome. In this work, we studied the bacterial and fungal endophytic communities of olive tree (Olea europaea L.) asymptomatic or low symptomatic genotypes sampled in groves heavily compromised by Xylella fastidiosa subsp. pauca, aiming to characterize microbiota in genotypes displaying differential response to the pathogen.

RESULTS

The relationships between bacterial and fungal genera were analyzed both separately and together, in order to investigate the intricate correlations between the identified Operational Taxonomic Units (OTUs). Results suggested a dominant role of the fungal endophytic community compared to the bacterial one, and highlighted specific microbial taxa only associated with asymptomatic or low symptomatic genotypes. In addition, they indicated the occurrence of well-adapted genetic resources surviving after years of pathogen pressure in association with microorganisms such as Burkholderia, Quambalaria, Phaffia and Rhodotorula.

CONCLUSIONS

This is the first study to overview endophytic communities associated with several putatively resistant olive genotypes in areas under high X. fastidiosa inoculum pressure. Identifying these negatively correlated genera can offer valuable insights into the potential antagonistic microbial resources and their possible development as biocontrol agents.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics.

New Insights into Glioblastoma

Glioblastoma (GBM) is the most aggressive malignant primary central nervous system (CNS) tumor and, despite decades of research, it remains a lethal disease with a median overall survival of less than two years [...].

Multi-omics technologies and molecular biomarkers in brain tumor-related epilepsy

BACKGROUND

Brain tumors are one of the leading causes of epilepsy, and brain tumor-related epilepsy (BTRE) is recognized as the major cause of intractable epilepsy, resulting in huge treatment cost and burden to patients, their families, and society. Although optimal treatment regimens are available, the majority of patients with BTRE show poor resolution of symptoms. BTRE has a very complex and multifactorial etiology, which includes several influencing factors such as genetic and molecular biomarkers. Advances in multi-omics technologies have enabled to elucidate the pathophysiological mechanisms and related biomarkers of BTRE. Here, we reviewed multi-omics technology-based research studies on BTRE published in the last few decades and discussed the present status, development, opportunities, challenges, and prospects in treating BTRE.

METHODS

First, we provided a general review of epilepsy, BTRE, and multi-omics techniques. Next, we described the specific multi-omics (including genomics, transcriptomics, epigenomics, proteomics, and metabolomics) techniques and related molecular biomarkers for BTRE. We then presented the associated pathogenetic mechanisms of BTRE. Finally, we discussed the development and application of novel omics techniques for diagnosing and treating BTRE.

RESULTS

Genomics studies have shown that the BRAF gene plays a role in BTRE development. Furthermore, the BRAF V600E variant was found to induce epileptogenesis in the neuronal cell lineage and tumorigenesis in the glial cell lineage. Several genomics studies have linked IDH variants with glioma-related epilepsy, and the overproduction of D2HG is considered to play a role in neuronal excitation that leads to seizure occurrence. The high expression level of Forkhead Box O4 (FOXO4) was associated with a reduced risk of epilepsy occurrence. In transcriptomics studies, VLGR1 was noted as a biomarker of epileptic onset in patients. Several miRNAs such as miR-128 and miRNA-196b participate in BTRE development. miR-128 might be negatively associated with the possibility of tumor-related epilepsy development. The lncRNA UBE2R2-AS1 inhibits the growth and invasion of glioma cells and promotes apoptosis. Quantitative proteomics has been used to determine dynamic changes of protein acetylation in epileptic and non-epileptic gliomas. In another proteomics study, a high expression of AQP-4 was detected in the brain of GBM patients with seizures. By using quantitative RT-PCR and immunohistochemistry assay, a study revealed that patients with astrocytomas and oligoastrocytomas showed high BCL2A1 expression and poor seizure control. By performing immunohistochemistry, several studies have reported the relationship between D2HG overproduction and seizure occurrence. Ki-67 overexpression in WHO grade II gliomas was found to be associated with poor postoperative seizure control. According to metabolomics research, the PI3K/AKT/mTOR pathway is associated with the development of glioma-related epileptogenesis. Another metabolomics study found that SV2A, P-gb, and CAD65/67 have the potential to function as biomarkers for BTRE.

CONCLUSIONS

Based on the synthesized information, this review provided new research perspectives and insights into the early diagnosis, etiological factors, and personalized treatment of BTRE.

Telomere-specific regulation of TERRA and its impact on telomere stability

TERRA is a class of telomeric repeat-containing RNAs that are expressed from telomeres in multiple organisms. TERRA transcripts play key roles in telomere maintenance and their physiological levels are essential to maintain the integrity of telomeric DNA. Indeed, deregulated TERRA expression or its altered localization can impact telomere stability by multiple mechanisms including fueling transcription-replication conflicts, promoting resection of chromosome ends, altering the telomeric chromatin, and supporting homologous recombination. Therefore, a fine-tuned control of TERRA is important to maintain the integrity of the genome. Several studies have reported that different cell lines express substantially different levels of TERRA. Most importantly, TERRA levels markedly vary among telomeres of a given cell type, indicating the existence of telomere-specific regulatory mechanisms which may help coordinate TERRA functions. TERRA molecules contain distinct subtelomeric sequences, depending on their telomere of origin, which may instruct specific post-transcriptional modifications or mediate distinct functions. In addition, all TERRA transcripts share a repetitive G-rich sequence at their 3' end which can form DNA:RNA hybrids and fold into G-quadruplex structures. Both structures are involved in TERRA functions and can critically affect telomere stability. In this review, we examine the mechanisms controlling TERRA levels and the impact of their telomere-specific regulation on telomere stability. We compare evidence obtained in different model organisms, discussing recent advances as well as controversies in the field. Furthermore, we discuss the importance of DNA:RNA hybrids and G-quadruplex structures in the context of TERRA biology and telomere maintenance.

Nuclear genome of dinoflagellates: Size variation and insights into evolutionary mechanisms

Recent progress in high-throughput sequencing technologies has dramatically increased availability of genome data for prokaryotes and eukaryotes. Dinoflagellates have distinct chromosomes and a huge genome size, which make their genomic analysis complicated. Here, we reviewed the nuclear genomes of core dinoflagellates, focusing on the genome and cell size. Till now, the genome sizes of several dinoflagellates (more than 25) have been measured by certain methods (e.g., flow cytometry), showing a range of 3-250 pg of genomic DNA per cell. In contrast to their relatively small cell size, their genomes are huge (about 1-80 times the human haploid genome). In the present study, we collected the genome and cell size data of dinoflagellates and compared their relationships. We found that dinoflagellate genome size exhibits a positive correlation with cell size. On the other hand, we recognized that the genome size is not correlated with phylogenetic relatedness. These may be caused by genome duplication, increased gene copy number, repetitive non-coding DNA, transposon expansion, horizontal gene transfer, organelle-to-nucleus gene transfer, and/or mRNA reintegration into the genome. Ultimate verification of these factors as potential causative mechanisms would require sequencing of more dinoflagellate genomes in the future.

Technology-enabled great leap in deciphering plant genomes

Plant genomes provide essential and vital basic resources for studying many aspects of plant biology and applications (for example, breeding). From 2000 to 2020, 1,144 genomes of 782 plant species were sequenced. In the past three years (2021-2023), 2,373 genomes of 1,031 plant species, including 793 newly sequenced species, have been assembled, representing a great leap. The 2,373 newly assembled genomes, of which 63 are telomere-to-telomere assemblies and 921 have been generated in pan-genome projects, cover the major phylogenetic clades. Substantial advances in read length, throughput, accuracy and cost-effectiveness have notably simplified the achievement of high-quality assemblies. Moreover, the development of multiple software tools using different algorithms offers the opportunity to generate more complete and complex assemblies. A database named N3: plants, genomes, technologies has been developed to accommodate the metadata associated with the 3,517 genomes that have been sequenced from 1,575 plant species since 2000. We also provide an outlook for emerging opportunities in plant genome sequencing.

Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data

Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computational efficiency and lower coverage requirements, are prominent. Alternative approaches, relying solely on available reads for de novo genome assembly and employing assembly-based tools for SV detection via comparison to a reference genome, demand significantly more computational resources. However, the lack of comprehensive benchmarking constrains our comprehension and hampers further algorithm development. Here we systematically compare 14 read alignment-based SV calling methods (including 4 deep learning-based methods and 1 hybrid method), and 4 assembly-based SV calling methods, alongside 4 upstream aligners and 7 assemblers. Assembly-based tools excel in detecting large SVs, especially insertions, and exhibit robustness to evaluation parameter changes and coverage fluctuations. Conversely, alignment-based tools demonstrate superior genotyping accuracy at low sequencing coverage (5-10×) and excel in detecting complex SVs, like translocations, inversions, and duplications. Our evaluation provides performance insights, highlighting the absence of a universally superior tool. We furnish guidelines across 31 criteria combinations, aiding users in selecting the most suitable tools for diverse scenarios and offering directions for further method development.

Pan-Genome Plasticity and Virulence Factors: A Natural Treasure Trove for Acinetobacter baumannii

Acinetobacter baumannii is a Gram-negative pathogen responsible for a variety of community- and hospital-acquired infections. It is recognized as a life-threatening pathogen among hospitalized individuals and, in particular, immunocompromised patients in many countries. A. baumannii, as a member of the ESKAPE group, encompasses high genomic plasticity and simultaneously is predisposed to receive and exchange the mobile genetic elements (MGEs) through horizontal genetic transfer (HGT). Indeed, A. baumannii is a treasure trove that contains a high number of virulence factors. In accordance with these unique pathogenic characteristics of A. baumannii, the authors aim to discuss the natural treasure trove of pan-genome and virulence factors pertaining to this bacterial monster and try to highlight the reasons why this bacterium is a great concern in the global public health system.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Species delimitation of tea plants (Camellia sect. Thea) based on super-barcodes

BACKGROUND

The era of high throughput sequencing offers new paths to identifying species boundaries that are complementary to traditional morphology-based delimitations. De novo species delimitation using traditional or DNA super-barcodes serve as efficient approaches to recognizing putative species (molecular operational taxonomic units, MOTUs). Tea plants (Camellia sect. Thea) form a group of morphologically similar species with significant economic value, providing the raw material for tea, which is the most popular nonalcoholic caffeine-containing beverage in the world. Taxonomic challenges have arisen from vague species boundaries in this group.

RESULTS

Based on the most comprehensive sampling of C. sect. Thea by far (165 individuals of 39 morphospecies), we applied three de novo species delimitation methods (ASAP, PTP, and mPTP) using plastome data to provide an independent evaluation of morphology-based species boundaries in tea plants. Comparing MOTU partitions with morphospecies, we particularly tested the congruence of MOTUs resulting from different methods. We recognized 28 consensus MOTUs within C. sect. Thea, while tentatively suggesting that 11 morphospecies be discarded. Ten of the 28 consensus MOTUs were uncovered as morphospecies complexes in need of further study integrating other evidence. Our results also showed a strong imbalance among the analyzed MOTUs in terms of the number of molecular diagnostic characters.

CONCLUSION

This study serves as a solid step forward for recognizing the underlying species boundaries of tea plants, providing a needed evidence-based framework for the utilization and conservation of this economically important plant group.

Importance about use of high-throughput sequencing in pediatric: case report of a patient with Fanconi-Bickel syndrome

BACKGROUND

Fanconi-Bickel syndrome is characterized by hepatorenal disease caused by anomalous glycogen storage. It occurs due to variants in the SLC2A2 gene. We present a male patient of 2 years 7 months old, with failure to thrive, hepatomegaly, metabolic acidosis, hypophosphatemia, hypokalemia, hyperlactatemia.

RESULTS

Exome sequencing identified the homozygous pathogenic variant NM_000340.2(SLC2A2):c.1093 C > T (p.Arg365Ter), related with Fanconi-Bickel syndrome. He received treatment with bicarbonate, amlodipine, sodium citrate and citric acid solution, enalapril, alendronate and zolendronate, and nutritional management with uncooked cornstarch, resulting in an improvement of one standard deviation in weight and height.

CONCLUSIONS

The importance of knowing the etiology in rare genetic disease is essential, not only to determine individual and familial recurrence risk, but also to establish the treatment and prognosis; in this sense, access to a new genomic technology in low- and middle-income countries is essential to shorten the diagnostic odyssey.

Development and proof-of-concept demonstration of a clinical metagenomics method for the rapid detection of bloodstream infection

BACKGROUND

The timely and accurate diagnosis of bloodstream infection (BSI) is critical for patient management. With longstanding challenges for routine blood culture, metagenomics is a promising approach to rapidly provide sequence-based detection and characterisation of bloodborne bacteria. Long-read sequencing technologies have successfully supported the use of clinical metagenomics for syndromes such as respiratory illness, and modified approaches may address two requisite factors for metagenomics to be used as a BSI diagnostic: depletion of the high level of host DNA to then detect the low abundance of microbes in blood.

METHODS

Blood samples from healthy donors were spiked with different concentrations of four prevalent causative species of BSI. All samples were then subjected to a modified saponin-based host DNA depletion protocol and optimised DNA extraction, whole genome amplification and debranching steps in preparation for sequencing, followed by bioinformatical analyses. Two related variants of the protocol are presented: 1mL of blood processed without bacterial enrichment, and 5mL of blood processed following a rapid bacterial enrichment protocol-SepsiPURE.

RESULTS

After first identifying that a large proportion of host mitochondrial DNA remained, the host depletion process was optimised by increasing saponin concentration to 3% and scaling the reaction to allow more sample volume. Compared to non-depleted controls, the 3% saponin-based depletion protocol reduced the presence of host chromosomal and mitochondrial DNA < 106 and < 103 fold respectively. When the modified depletion method was further combined with a rapid bacterial enrichment method (SepsiPURE; with 5mL blood samples) the depletion of mitochondrial DNA improved by a further > 10X while also increasing detectable bacteria by > 10X. Parameters during DNA extraction, whole genome amplification and long-read sequencing were also adjusted, and subsequently amplicons were detected for each input bacterial species at each of the spiked concentrations, ranging from 50-100 colony forming units (CFU)/mL to 1-5 CFU/mL.

CONCLUSION

In this proof-of-concept study, four prevalent BSI causative species were detected in under 12 h to species level (with antimicrobial resistance determinants) at concentrations relevant to clinical blood samples. The use of a rapid and precise metagenomic protocols has the potential to advance the diagnosis of BSI.

Horizontal transfer potential of antibiotic resistance genes in wastewater treatment plants unraveled by microfluidic-based mini-metagenomics

Wastewater treatment plants (WWTPs) are known to harbor antibiotic resistance genes (ARGs), which can potentially spread to the environment and human populations. However, the extent and mechanisms of ARG transfer in WWTPs are not well understood due to the high microbial diversity and limitations of molecular techniques. In this study, we used a microfluidic-based mini-metagenomics approach to investigate the transfer potential and mechanisms of ARGs in activated sludge from WWTPs. Our results show that while diverse ARGs are present in activated sludge, only a few highly similar ARGs are observed across different taxa, indicating limited transfer potential. We identified two ARGs, ermF and tla-1, which occur in a variety of bacterial taxa and may have high transfer potential facilitated by mobile genetic elements. Interestingly, genes that are highly similar to the sequences of these two ARGs, as identified in this study, display varying patterns of abundance across geographic regions. Genes similar to ermF found are widely found in Asia and the Americas, while genes resembling tla-1 are primarily detected in Asia. Genes similar to both genes are barely detected in European WWTPs. These findings shed light on the limited horizontal transfer potential of ARGs in WWTPs and highlight the importance of monitoring specific ARGs in different regions to mitigate the spread of antibiotic resistance.

Full-length transcriptome profiling of Acanthopanax gracilistylus provides new insight into the kaurenoic acid biosynthesis pathway

Acanthopanax gracilistylus is a deciduous plant in the family Araliaceae, which is commonly used in Chinese herbal medicine, as the root bark has functions of nourishing the liver and kidneys, removing dampness and expelling wind, and strengthening the bones and tendons. Kaurenoic acid (KA) is the main effective substance in the root bark of A. gracilistylus with strong anti-inflammatory effects. To elucidate the KA biosynthesis pathway, second-generation (DNA nanoball) and third-generation (Pacific Biosciences) sequencing were performed to analyze the transcriptomes of the A. gracilistylus leaves, roots, and stems. Among the total 505,880 isoforms, 408,954 were annotated by seven major databases. Sixty isoforms with complete open reading frames encoding 11 key enzymes involved in the KA biosynthesis pathway were identified. Correlation analysis between isoform expression and KA content identified a total of eight key genes. Six key enzyme genes involved in KA biosynthesis were validated by real-time quantitative polymerase chain reaction. Based on the sequence analysis, the spatial structure of ent-kaurene oxidase was modeled, which plays roles in the three continuous oxidations steps of KA biosynthesis. This study greatly enriches the transcriptome data of A. gracilistylus and facilitates further analysis of the function and regulation mechanism of key enzymes in the KA biosynthesis pathway.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-024-01436-7.

Comparison of nine extraction methods for bacterial identification using the ONT MinION sequencer

The Anthrax mailings bioterrorism attack in 2001 revealed the need for universal and rapid microbial forensic analyses on unknown biological evidence. However, the gold standard for bacterial identification includes culturing isolates, which is laborious. Molecular approaches for bacterial identification revolve around 16S ribosomal gene sequencing using Sanger or next generation sequencing (NGS) platforms, but these techniques are laboratory-based and can also be time-consuming. The Oxford Nanopore Technologies (ONT) MinION sequencer can generate long read lengths that span the entire bacterial 16S rRNA gene and accurately identify the species level. This platform can be used in the field, allowing on-site evidence analysis. However, it requires higher quantities of pure DNA compared to other sequencing platforms; thus, the extraction method for bacterial DNA is critical for downstream analysis, which to date are tailored toward a priori knowledge of the species' taxonomic grouping. During an attack, the investigative team may not know what species they are handling; therefore, identifying an extraction method that can handle all bacterial groups and generate clean DNA for the MinION is useful for microbial forensic analysis. The purpose of this study was to identify a "universal" extraction method that can be coupled with ONT MinION sequencing for use in forensic situations for rapid identification. It also evaluated the cloud-based data analysis software provided by ONT, EPI2ME. No "universal" extraction method was identified as optimal for downstream MinION sequencing. However, the DNeasy PowerSoil Kit and Noda et al. Chelex-100 method gave comparable sequencing results and could be used as rapid extraction techniques. This study showed that the ONT 16S Barcoding Kit 1-24 coupled with the 16S FASTQ workflow might not be the best for use in forensic situations where species-level identification needs to be obtained, as most alignments were approximately 89% accurate. In all seven test organisms and nine extraction methods, accurate species identification was only obtained in 63% of the cases.

Rapid diagnosis of Aspergillus fumigatus endocarditis using mNGS assay: A case report and review of the literature

Fungal endocarditis is caused mainly by Candida albicans and Aspergillus spp. and was first reported in the 1950s. Natural-valve endocarditis caused by Aspergillus is relatively uncommon. In this case, a 56-year-old male patient was admitted to the hospital on account of a cough accompanied by chills and fever and ineffective self-medication. Infective endocarditis was initially suspected based on echocardiography (indicating right atrial growth) and clinical manifestations. However, routine pathogen detections were always negative. The patient's condition was identified as Aspergillus fumigatus endocarditis (AFE) and was treated with targeted therapy, considering the detection of significant AFE sequences in the blood through metagenomic next-generation sequencing (mNGS). On this basis, the paper further summarizes the clinical manifestations, diagnosis, treatments, and outcomes of AFE endocarditis cases reported in recent years, aiming to provide a reference to better understand this rare infective disease and guide medical practitioners in choosing the right diagnostic and therapeutic strategy.

Dynamic changes in the gut microbiota after bismuth quadruple therapy and high-dose dual therapy for Helicobacter pylori eradication

BACKGROUND

A novel regimen with high-dose dual therapy (HDDT) has emerged, but its impact on the gut microbiota is not well understood. This study aimed to evaluate the impact of HDDT on the gut microbiota and compare it with that of bismuth quadruple therapy (BQT).

METHODS

We enrolled outpatients (18-70 years) diagnosed with Helicobacter pylori infection by either histology or a positive 13C-urea breath test (13C-UBT) and randomly assigned to either the BQT or HDDT group. Subjects consented to provide fecal samples which were collected at baseline, Week 2, and Week 14. Amplification of the V1 and V9 regions of the 16S rRNA was conducted followed by high-throughput sequencing.

RESULTS

Ultimately, 78 patients (41 patients in the HDDT group and 37 in the BQT group) were enrolled in this study. Eradication therapy significantly altered the diversity of the gut microbiota. However, the alpha diversity rebounded only in the HDDT group at 12 weeks post-eradication. Immediately following eradication, the predominance of Proteobacteria, replacing commensal Firmicutes and Bacteroidetes, did not recover after 12 weeks. Species-level analysis showed that the relative abundances of Klebsiella pneumoniae and Escherichia fergusonii significantly increased in both groups at Week 2. Enterococcus faecium and Enterococcus faecalis significantly increased in the BQT group, with no significant difference observed in the HDDT group. After 12 weeks of treatment, the relative abundance of more species in the HDDT group returned to baseline levels.

CONCLUSION

Eradication of H. pylori can lead to an imbalance in gut microbiota. Compared to BQT, the HDDT is a regimen with milder impact on gut microbiota.

The diagnosis of central nervous system infections in resource-limited settings and the use of novel and molecular diagnostic platforms to improve diagnosis

INTRODUCTION

Central nervous system infections (CNSI) disproportionately affect individuals in low-resource settings where diagnosis is challenging; large proportions of patients never receive a confirmed microbiological diagnosis resulting in inadequate management and high mortality. The epidemiology of CNSI varies globally and conventional diagnostics deployed in resource-limited settings have significant limitations, with an urgent need for improved diagnostic strategies.

AREAS COVERED

This review describes molecular platforms and other novel diagnostics used in the diagnosis of CNSI that are applicable to resource-limited settings. An extensive literature search of Medline and PubMed was performed. The emphasis is on investigations targeting infections of relevance to resource-limited settings either due to variation in regional CNSI epidemiology or due to increased prevalence in patients with immunosuppression. This includes commercially available multiplex PCR platforms, mycobacterial PCR platforms, and rapid diagnostics tests. To offer a framework for the optimal implementation in clinical settings, existing evidence highlighting the advantages and limitations of available platforms is reviewed.

EXPERT OPINION

The implementation of molecular platforms and other novel diagnostics has the potential to transform CNSI diagnosis in resource-limited settings, with several examples of successful rollout of novel diagnostics such as Xpert MTB/RIF Ultra and cryptococcal antigen testing.

Cryptographic approaches to authenticating synthetic DNA sequences

In a bioeconomy that relies on synthetic DNA sequences, the ability to ensure their authenticity is critical. DNA watermarks can encode identifying data in short sequences and can be combined with error correction and encryption protocols to ensure that sequences are robust to errors and securely communicated. New digital signature techniques allow for public verification that a sequence has not been modified and can contain sufficient information for synthetic DNA to be self-documenting. In translating these techniques from bacteria to more complex genetically modified organisms (GMOs), special considerations must be made to allow for public verification of these products. We argue that these approaches should be widely implemented to assert authorship, increase the traceability, and detect the unauthorized use of synthetic DNA.

One-pot DTECT enables rapid and efficient capture of genetic signatures for precision genome editing and clinical diagnostics

The detection of genomic sequences and their alterations is crucial for basic research and clinical diagnostics. However, current methodologies are costly and time-consuming and require outsourcing sample preparation, processing, and analysis to genomic companies. Here, we establish One-pot DTECT, a platform that expedites the detection of genetic signatures, only requiring a short incubation of a PCR product in an optimized one-pot mixture. One-pot DTECT enables qualitative, quantitative, and visual detection of biologically relevant variants, such as cancer mutations, and nucleotide changes introduced by prime editing and base editing into cancer cells and human primary T cells. Notably, One-pot DTECT achieves quantification accuracy for targeted genetic signatures comparable with Sanger and next-generation sequencing. Furthermore, its effectiveness as a diagnostic platform is demonstrated by successfully detecting sickle cell variants in blood and saliva samples. Altogether, One-pot DTECT offers an efficient, versatile, adaptable, and cost-effective alternative to traditional methods for detecting genomic signatures.

Sustainability implications and relevance of using omics sciences to investigate cheeses with protected designation of origin

Cheese, a fundamental component of the human diet and a cornerstone of the global food economy, has a significance beyond its role as a commodity, playing a crucial part in the cultural identity of various communities. The intricate natural aging process known as maturation involves a series of reactions that induce changes in the cheese's physical, biochemical, microbiological, and particularly sensory characteristics, making it a complex aspect of cheese production. Recently, the adoption of omics sciences (e.g., metagenomics, metabolomics, proteomics) has emerged as a new trend in studies related to protected designation of origin (PDO) cheese. This mini-summary aims to outline the relationship between omics studies in these food matrices and all the sustainability facets of the production chain in general, and to discuss and recognize that the importance of these studies goes beyond comprehending the cheese biome and extends to fostering and ensuring the sustainability of the production chain. In this context, numerous studies in recent years have linked the identification of intrinsic characteristics of PDO cheeses through omics sciences to crucial sustainability themes such as territoriality, biodiversity, and the preservation of product authenticity. The trajectory suggests that, increasingly, multidisciplinary studies spanning various omics sciences will not only contribute to the characterization of these products but will also address sustainability aspects directly related to the production chain (e.g., authenticity, microbial biodiversity, functionality). This expansion underscores the multidisciplinary nature of these studies, broadening their social impact beyond the academic realm. Consequently, these pivotal studies play a crucial role in advancing discussions on PDO products and sustainability. © 2024 Society of Chemical Industry.

A comprehensive review on Gossypium hirsutum resistance against cotton leaf curl virus

Cotton (Gossypium hirsutum L.) is a significant fiber crop. Being a major contributor to the textile industry requires continuous care and attention. Cotton is subjected to various biotic and abiotic constraints. Among these, biotic factors including cotton leaf curl virus (CLCuV) are dominant. CLCuV is a notorious disease of cotton and is acquired, carried, and transmitted by the whitefly (Bemisia tabaci). A cotton plant affected with CLCuV may show a wide range of symptoms such as yellowing of leaves, thickening of veins, upward or downward curling, formation of enations, and stunted growth. Though there are many efforts to protect the crop from CLCuV, long-term results are not yet obtained as CLCuV strains are capable of mutating and overcoming plant resistance. However, systemic-induced resistance using a gene-based approach remained effective until new virulent strains of CLCuV (like Cotton Leaf Curl Burewala Virus and others) came into existence. Disease control by biological means and the development of CLCuV-resistant cotton varieties are in progress. In this review, we first discussed in detail the evolution of cotton and CLCuV strains, the transmission mechanism of CLCuV, the genetic architecture of CLCuV vectors, and the use of pathogen and nonpathogen-based approaches to control CLCuD. Next, we delineate the uses of cutting-edge technologies like genome editing (with a special focus on CRISPR-Cas), next-generation technologies, and their application in cotton genomics and speed breeding to develop CLCuD resistant cotton germplasm in a short time. Finally, we delve into the current obstacles related to cotton genome editing and explore forthcoming pathways for enhancing precision in genome editing through the utilization of advanced genome editing technologies. These endeavors aim to enhance cotton's resilience against CLCuD.

Genomics landscape of mitochondrial DNA variations in patients from South Italy affected by mitochondriopathies

Mitochondrial DNA (mtDNA) is a 16,569 base pairs, double-stranded, circular molecule that contains 37 genes coding for 13 subunits of the respiratory chain plus 2 rRNAs and 22 tRNAs. Mutations in these genes have been identified in patients with a variety of disorders affecting every system in the body. The advent of next generation sequencing technologies has provided the possibility to perform the whole mitochondrial DNA sequencing, allowing the identification of disease-causing pathogenic variants in a single platform. In this study, the whole mtDNA of 100 patients from South Italy affected by mitochondrial diseases was analyzed by using an amplicon-based approach and then the enriched libraries were deeply sequenced on the ION Torrent platform (Thermofisher Scientific Waltham, MA, USA). After bioinformatics analysis and filtering, we were able to find 26 nonsynonymous variants with a MAF <1% that were associated with different pathological phenotypes, expanding the mutational spectrum of these diseases. Moreover, among the new mutations found, we have also analyzed the 3D structure of the MT-ATP6 A200T gene variation in order to confirm suspected functional alterations. This work brings light on new variants possibly associated with several mitochondriopathies in patients from South Italy and confirms that deep sequencing approach, compared to the standard methods, is a reliable and time-cost reducing strategy to detect all the variants present in the mitogenome, making the possibility to create a genomics landscape of mitochondrial DNA variations in human diseases.