Next-generation Sequencing Technologies Research Articles

Genome sequencing of SARS-Co-V-2 reveals mutations including F559I and V781D in S protein and LI123-124L in the nsp6 in 21K and 21L clades.

Contagious and virulent virus variants like B.1.1.529 have complicated the 2019 global COVID-19 pandemic from Wuhan, China. Omicron, with extensive mutations and high transmissibility, is replacing Delta in some regions. Remarkably, Omicron exhibits reduced disease severity and resistance to certain vaccines and treatments. Our research sought to identify Egypt-specific variants of concern (VOCs) and their mutation patterns, aiming to provide critical insights for tailored public health strategies. We also looked at vaccine compatibility with these VOCs, as well as the efficacy of current treatments against new SARS-CoV-2 variants. We collected 103 PCR-confirmed COVID-19 cases from an Egyptian army hospital and used next-generation sequencing technology to sequence the entire viral genome. The viral genome was then assembled and reconstructed. Nextclade tools aided in clade assignment and Phylogenetic analysis, allowing classification, and understanding of these genomes' Phylogenetic relationships. Our findings reveal that the dominant VOCs in Egypt are the 21K clade, mainly Pango lineages BA.1 (34%), BA.1.1 (30.1%), and BA.1.17 (6.8%), and the 21L clade represented by Pango lineages BA.2. We also identified novel mutations, including F559I in the S protein (consistent in the 21K clade), V781D in the S protein (present in > 50% of both 21K and 21L clades), and LI123-124L in the nsp6 gene (found in both 21K and 21L clades). Finally, our research provides important insights into Egypt's evolving COVID-19 landscape, allowing for tailored responses and risk mitigation strategies for emerging variants in the region.

Evolution of dirofilariasis diagnostic techniques from traditional morphological analysis to molecular-based techniques: a comprehensive review

Dirofilariasis, caused by the nematode Dirofilaria spp., poses significant challenges in diagnosis due to its diverse clinical manifestations and complex life cycle. This comprehensive literature review focuses on the evolution of diagnostic methodologies, spanning from traditional morphological analyses to modern emerging techniques in the context of dirofilariasis diagnosis. The review traces the historical progression of diagnostic modalities, encompassing traditional approaches such as microscopic examination, serological tests (including ELISA and IFA), radiographic imaging, ultrasonography, and necropsy, which laid the foundation for subsequent advancements. The integration of molecular diagnostics marks a significant turning point in dirofilariasis diagnosis with the adoption of polymerase chain reaction (PCR) assays and real-time PCR (qPCR) facilitating enhanced sensitivity and specificity. Furthermore, recent strides in next-generation sequencing (NGS) technologies, including whole–genome sequencing (WGS), targeted sequencing (TS), metagenomic sequencing (MS), and RNA sequencing (transcriptome sequencing), have revolutionized the landscape of dirofilariasis diagnostics. Emerging techniques such as loop-mediated isothermal amplification (LAMP), digital PCR (dPCR), and digital microfluidics are also explored for their potential to augment diagnostic accuracy. The review addresses challenges associated with standardizing molecular protocols, tackling false positives/negatives, and discusses the advantages and limitations of each technique. By providing a comprehensive overview of dirofilariasis diagnostic strategies, from traditional to cutting-edge methods, this review aims to enhance understanding of the disease’s diagnostic landscape. The insights gained have implications for improved disease management and guide future research endeavors toward refining diagnostic protocols and advancing therapeutic interventions.

Transcriptome Analysis of the Effect of Nickel on Lipid Metabolism in Mouse Kidney.

Although the human body needs nickel as a trace element, too much nickel exposure can be hazardous. The effects of nickel on cells include inducing oxidative stress, interfering with DNA damage repair, and altering epigenetic modifications. Glucose metabolism and lipid metabolism are closely related to oxidative stress; however, their role in nickel-induced damage needs further study. In Institute of Cancer Research (ICR) mice, our findings indicated that nickel stress increased the levels of blood lipid indicators (triglycerides, high-density lipoprotein, and cholesterol) by about 50%, blood glucose by more than two-fold, and glycated serum protein by nearly 20%. At the same time, nickel stress increased oxidative stress (malondialdehyde) and inflammation (Interleukin 6) by about 30% in the kidney. Based on next-generation sequencing technology, we detected and analyzed differentially expressed genes in the kidney caused by nickel stress. Bioinformatics analysis and experimental verification showed that nickel inhibited the expression of genes related to lipid metabolism and the AMPK and PPAR signaling pathways. The finding that nickel induces kidney injury and inhibits key genes involved in lipid metabolism and the AMPK and PPAR signaling pathways provides a theoretical basis for a deeper understanding of the mechanism of nickel-induced kidney injury.

Application of the Agilent 2100 Bioanalyzer instrument as quality control for next-generation sequencing.

Next-generation sequencing (NGS) technologies have expanded the spectrum of forensic DNA analysis by facilitating efficient and precise genotyping of a large number of genetic markers. Yet, challenges persist regarding complex sample processing and assurance of equal molar concentrations across pooled samples. Since optimal cluster density is crucial for sequencing performance, the determination of both quantity and quality is indispensable for library preparation. In this study, we investigated the application of the Agilent 2100 Bioanalyzer for library quality control, as studies for forensic approaches, particularly for highly degraded postmortem samples, are rare. Our analysis encompassed assessing total DNA concentrations, fluorescence unit (FU) values, and adapter dimer concentrations in purified DNA libraries derived from buccal swabs and tissue samples of decomposed corpses. The sensitivity study tested a serial dilution derived from buccal swabs and revealed a decrease in FU values and an increase in adapter dimers with declining DNA input concentrations. Deviations in total DNA concentrations and average peak heights between the Agilent 2100 Bioanalyzer runs indicated a lack of repeatability in data and presented challenges in accurate quantification, which was also observed in previous studies. Yet, the analysis of degraded samples from decomposed human remains has shown the ability to detect adapter dimer concentrations, which can be crucial for the quality of subsequent NGS library preparation and sequencing success. Therefore, the Agilent 2100 Bioanalyzer proves to be a valuable tool for NGS quality control.

Role of HSP90 in Type 2 Diabetes Mellitus and Its Association with Liver Diseases.

Non-alcoholic fatty acid liver disease (NAFLD), non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC) are the fatal liver diseases which encompass a spectrum of disease severity associated with increased risk of type 2 diabetes mellitus (T2DM), a metabolic disorder. Heat shock proteins serve as markers in early prognosis and diagnosis of early stages of liver diseases associated with metabolic disorder. This review aims to comprehensively investigate the significance of HSP90 isoforms in T2DM and liver diseases. Additionally, we explore the collective knowledge on plant-based drug compounds that regulate HSP90 isoform targets, highlighting their potential in treating T2DM-associated liver diseases. Furthermore, this review focuses on the computational systems' biology and next-generation sequencing technology approaches that are used to unravel the potential medicine for the treatment of pleiotropy of these 2 diseases and to further elucidate the mechanism.

Assessment the carrier frequency of monogenic diseases in populations requiring assisted reproductive technology

PurposeThe objective of this study is to assess the carrier frequency and pathogenic variation of monogenetic diseases in a population of 114 subjects in Han Chinese from Hebei province who are undergoing assisted reproductive technology through the utilization of Expanded Carrier Screening (ECS).MethodsThe study utilized a panel consisting of 155 severe monogenic recessive genetic diseases for ECS. Next-generation sequencing technology was employed to identify specific variants associated with ECS in a cohort of 114 subjects from 97 couples, comprising 97 females and 17 male spouses.ResultsA total of 114 individuals received ECS. The carrier rate of pathogenic genes in the enrolled population was 44.74% (51/114). Among the 97 females, the carrier rate of pathogenic genes was higher in those without assisted reproduction indicators than in those with assisted reproduction indicators (59.09% vs. 41.33%). However, the carrier rate of pathogenic genes in males without assisted reproductive technology was slightly lower than that with assisted reproductive technology (40% vs. 41.67%). Among both female and male participants, the carrier rate of pathogenic genes between individuals without indicators of assisted reproduction and those with such indicators was 55.55% vs. 41.38%. In 51 carriers, 72.55% (37/51) carried one genetic variant, 25.49% (13/51) carried two genetic variants, and 1.96% (1/51) carried three genetic variants. A total of 38 pathogenic genes were detected in this study, and GJB2 and MMACHC were most common. The carrier rates of the two genes were both 5.26% (6/114). A total of 55 variations were detected, and c.235delC was most frequently found. The carrier rate was 3.51% (4/114). The incidence of couples carrying the same pathogenic genes was 1.03% (1/97).ConclusionsThe findings elucidate the carrier rate of pathogenic genes among 155 severe monogenic recessive genetic diseases and underscore the significance of ECS as a preventive measure against congenital anomalies. When both partners carry the same genetic mutation for a monogenic disease, preventive strategies can be taken in offspring through preimplantation genetic testing (PGT), prenatal genetic testing, or the utilization of donor gametes. ECS is instrumental in assessing reproductive risk, guiding fertility-related decisions, and reducing the prevalence of monogenic recessive genetic disorders in subsequent generations.

Combined Diagnostic Value of Hsa-miR-592 and Hsa-miR-9-3p in Plasma for Methamphetamine Addicts.

A number of studies have reported that drug addiction is associated with microRNAs (miRNAs). However, the roles of plasma miRNAs in methamphetamine (METH) addicts have not been clearly explained. This study aimed to profile a panel of miRNAs as non-invasive predictive biomarkers and therapeutic targets for METH addiction. Differentially expressed miRNAs were derived from next-generation sequencing technology (NGS) and were validated by quantitative real-time PCR (RT-qPCR). The diagnostic value of specific altered miRNAs was evaluated by receiver operating characteristic (ROC) analysis and area under the curve (AUC). NGS results revealed that 63 miRNAs were significantly altered in the METH-exposed paradigm. The levels of hsa-miR-592, hsa-miR-9-3p, hsa-miR-206 and hsa-let-7b-3p were significantly elevated in the plasma of METH addicts. Hsa-miR-9-3p was a useful biomarker discriminating METH addicts from normal (AUC was 0.756). Importantly, combining detection of hsa-miR-592 and hsa-miR-9-3p achieved the highest AUC of 0.87, with a sensitivity and specificity of 82.7% and 78.9%, respectively. Target gene BDNF decreased significantly in METH addicts. Although METH addicts showed significant depressive symptoms, there was no correlation between the expression level of miR-592 and miR-9-3p and the degree of depression. Our findings suggested that hsa-miR-592, hsa-miR-9-3p, hsa-miR-206, and hsa-let-7b-3p may play a potential role in the pathology of METH addiction, and a combination of hsa-miR-592 and hsa-miR-9-3p could serve as potential peripheral biomarker and therapeutic target for METH addiction.

Different Prostatic Tissue Microbiomes between High- and Low-Grade Prostate Cancer Pathogenesis.

Numerous human pathologies, such as neoplasia, are related to particular bacteria and changes in microbiome constituents. To investigate the association between an imbalance of bacteria and prostate carcinoma, the microbiome and gene functionality from tissues of patients with high-grade prostate tumor (HGT) and low-grade prostate tumor (LGT) were compared utilizing next-generation sequencing (NGS) technology. The results showed abnormalities in the bacterial profiles between the HGT and LGT specimens, indicating alterations in the make-up of bacterial populations and gene functionalities. The HGT specimens showed higher frequencies of Cutibacterium, Pelomonas, and Corynebacterium genera than the LGT specimens. Cell proliferation and cytokine assays also showed a significant proliferation of prostate cancer cells and elevated cytokine levels in the cells treated with Cutibacterium, respectively, supporting earlier findings. In summary, the HGT and LGT specimens showed differences in bacterial populations, suggesting that different bacterial populations might characterize high-grade and low-grade prostate malignancies.

Enhancing Precision in HIV Treatment: Validation of a Robust Next-Generation Sequencing System for Drug Resistance Mutation Analysis.

Multidrug-resistant HIV strains challenge treatment efficacy and increase mortality rates. Next-generation sequencing (NGS) technology swiftly detects variants, facilitating personalized antiretroviral therapy. This study aimed to validate the Vela Diagnostics NGS platform for HIV drug resistance mutation analysis, rigorously assessed with clinical samples and CAP proficiency testing controls previously analyzed by Sanger sequencing. The experimental approach involved the following: RNA extraction from clinical specimens, reverse transcription polymerase chain reaction (RT-PCR) utilizing the Sentosa SX 101 platform, library preparation with the Sentosa SQ HIV Genotyping Assay, template preparation, sequencing using the Sentosa SQ301 instrument, and subsequent data analysis employing the Sentosa SQ Suite and SQ Reporter software. Drug resistance profiles were interpreted using the Stanford HIV Drug Resistance Database (HIVdb) with the HXB2 reference sequence. The Vela NGS system successfully identified a comprehensive array of drug resistance mutations across the tested samples: 28 nucleoside reverse transcriptase inhibitors (NRTI), 25 non-nucleoside reverse transcriptase inhibitors (NNRTI), 25 protease inhibitors (PI), and 10 integrase gene-specific variants. Dilution experiments further validated the system's sensitivity, detecting drug resistance mutations even at viral loads lower than the recommended threshold (1000 copies/mL) set by Vela Diagnostics. This study underscores the validation and clinical applicability of the Vela NGS system, and its implementation may offer clinicians enhanced precision in therapeutic decision-making for individuals living with HIV.

Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges.

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.

Novel PTPRQ variants associated with hearing loss in a Chinese family PTPRQ variants in Chinese hearing loss.

Hearing loss is one of the most prevalent congenital sensory disorders. Over 50% of congenital hearing loss cases are attributed to genetic factors. The PTPRQ gene encodes the protein tyrosine phosphatase receptor Q, which plays an important role in maintaining the structure and function of the stereocilia of hair cells. Variants in the PTPRQ gene have been implicated in hereditary sensorineural hearing loss. Utilizing next-generation sequencing technology, we identified novel compound heterozygous variants (c.977G>A:p.W326X and c.6742C>T:p.Q2248X) in the PTPRQ gene within a Chinese national lineage, marking the first association of these variants with hereditary sensorineural hearing loss. Our findings further emphasize the critical role of PTPRQ in auditory function and contribute to a more comprehensive understanding of PTPRQ-associated hearing loss mechanisms, aiding in clinical management and genetic counseling.

Enhancing metagenomic classification with compression-based features

Metagenomics is a rapidly expanding field that uses next-generation sequencing technology to analyze the genetic makeup of environmental samples. However, accurately identifying the organisms in a metagenomic sample can be complex, and traditional reference-based methods may need to be more effective in some instances.In this study, we present a novel approach for metagenomic identification, using data compressors as a feature for taxonomic classification. By evaluating a comprehensive set of compressors, including both general-purpose and genomic-specific, we demonstrate the effectiveness of this method in accurately identifying organisms in metagenomic samples. The results indicate that using features from multiple compressors can help identify taxonomy. An overall accuracy of 95% was achieved using this method using an imbalanced dataset with classes with limited samples. The study also showed that the correlation between compression and classification is insignificant, highlighting the need for a multi-faceted approach to metagenomic identification.This approach offers a significant advancement in the field of metagenomics, providing a reference-less method for taxonomic identification that is both effective and efficient while revealing insights into the statistical and algorithmic nature of genomic data. The code to validate this study is publicly available at https://github.com/ieeta-pt/xgTaxonomy.

A Standardized Pipeline for Assembly and Annotation of African Swine Fever Virus Genome.

Obtaining a complete good-quality sequence and annotation for the long double-stranded DNA genome of the African swine fever virus (ASFV) from next-generation sequencing (NGS) technology has proven difficult, despite the increasing availability of reference genome sequences and the increasing affordability of NGS. A gap analysis conducted by the global African swine fever research alliance (GARA) partners identified that a standardized, automatic pipeline for NGS analysis was urgently needed, particularly for new outbreak strains. Whilst there are several diagnostic and research labs worldwide that collect isolates of the ASFV from outbreaks, many do not have the capability to analyze, annotate, and format NGS data from outbreaks for submission to NCBI, and some publicly available ASFV genomes have missing or incorrect annotations. We developed an automated, standardized pipeline for the analysis of NGS reads that directly provides users with assemblies and annotations formatted for their submission to NCBI. This pipeline is freely available on GitHub and has been tested through the GARA partners by examining two previously sequenced ASFV genomes; this study also aimed to assess the accuracy and limitations of two strategies present within the pipeline: reference-based (Illumina reads) and de novo assembly (Illumina and Nanopore reads) strategies.

Cardiomyocyte proliferation and regeneration in congenital heart disease.

Despite advances in prenatal screening and a notable decrease in mortality rates, congenital heart disease (CHD) remains the most prevalent congenital disorder in newborns globally. Current therapeutic surgical approaches face challenges due to the significant rise in complications and disabilities. Emerging cardiac regenerative therapies offer promising adjuncts for CHD treatment. One novel avenue involves investigating methods to stimulate cardiomyocyte proliferation. However, the mechanism of altered cardiomyocyte proliferation in CHD is not fully understood, and there are few feasible approaches to stimulate cardiomyocyte cell cycling for optimal healing in CHD patients. In this review, we explore recent progress in understanding genetic and epigenetic mechanisms underlying defective cardiomyocyte proliferation in CHD from development through birth. Targeting cell cycle pathways shows promise for enhancing cardiomyocyte cytokinesis, division, and regeneration to repair heart defects. Advancements in human disease modeling techniques, CRISPR-based genome and epigenome editing, and next-generation sequencing technologies will expedite the exploration of abnormal machinery governing cardiomyocyte differentiation, proliferation, and maturation across diverse genetic backgrounds of CHD. Ongoing studies on screening drugs that regulate cell cycling are poised to translate this nascent technology of enhancing cardiomyocyte proliferation into a new therapeutic paradigm for CHD surgical interventions.

OncoGPT: An AI assistant for genomic-driven precision oncology.

160 Background: Cancer patient’s unique genomic profile can help oncologists select a course of precise personalized treatment for the subject. High throughput next-generation sequencing (NGS) technology allows us to identify genomic alterations simultaneously within a patient’s tumor tissue and blood-circulating tumor DNA (ctDNA) in a single test, however, robust tool for accessing mutation-treatment matching is limited. Methods: OncoGPT was built on a cloud-based elastic computing platform. The NGS data analytical module consists of a cascade of computational algorithms for NGS data processing and gene variant calling. The interactive and univariate Cox proportional hazards models were used for mutation-treatment matching and prognostic effect analysis, respectively. Machine learning algorithms including decision tree, random forest, and neural network were trained and tested with novel features for tissue-of-origin (TO) classification across 8 caner types. Results: We built an AI-driven NGS data analytical platform by integrating computational models, matching algorithms and variant annotation databases to highly accurate achieve cancer-related gene mutations, copy number variation, and structure variants using NGS data from 35,122 tumors across 8 cancer types from three institutions. Next, an AI-driven TO classifier was developed and achieved a weighted F1 score of 0.926 for high confidence predictions (≥ 0.9) on tumor samples. Furthermore, augmented AI matching algorithms were applied to match the optimal personalized treatment and provide prognostic prediction for cancer patients with significantly bett survival outcomes (hazard ratio (HR) = 0.326; 95% confidence interval (CI) = 0.213–0.565; P = 2.52×10−5). Conclusions: We have successfully developed an innovative intelligent system (OncoGPT) with AI capabilities to help accurately find actionable targets from patient tumor or blood ctDNA NGS sequencing data and precisely match individualized therapeutic and clinical trial options for patients. In addition, OncoGPT classified primary tumor sites across 8 different cancer types with high confidence predictions. We believe that OncoGPT would help clinicians make optimal treatment decisions for cancer patients through genomic-driven precision oncology.

Development of multiplex digital PCR based droplex NSCLC panel test for detecting major mutations in patients with non-small cell lung cancer (NSCLC).

46 Background: Recently, in the treatment of non-small cell lung cancer(NSCLC) patient, immunotherapies and targeted therapies aimed at various genes have been developed. Next-Generation Sequencing(NGS) technology is currently known as the only method for detecting genetic alterations in many different genes simultaneously. However, this technology requires a substantial amount of DNA or RNA input from patients to obtain high-quality results in a clinical setting. We have developed a Droplex NSCLC Panel Test Kit based on multiplex digital PCR, which is known as ideal molecular diagnostic technique due to its simple workflow, minimal sample input, high sensitivity and specificity. In addition, it has a one-day TAT (Turn around time) and low costs compared NGS, resulting in a lower financial burden for patients. Recently, the development of non-invasive liquid biopsy diagnostics as an alternative to tissue biopsy is emerging important issue. The kit, applying a digital PCR platform, enables multiple mutation detection using plasma samples of blood. The digital PCR-based Droplex NSCLC Panel Test Kit is designed to detect over 170 key mutations in 11 genes, namely EGFR, ALK, ROS1, BRAF, MET, RET, KRAS, HER2, NTRK1, NTRK2, and NTRK3, using DNA and RNA extracted from NSCLC FFPET and plasma samples. Methods: The Droplex NSCLC Panel Test Kit consists of four Oligo Mixtures (OMs) for detecting mutations of 4 genes on DNA and two OMs for detecting 7 gene rearrangements on RNA. After extracting DNA and RNA from a single sample, cDNA synthesis from RNA is prioritized, followed by simultaneous execution of droplet generation, PCR processes, and data analysis. The kit test was based on QX600 droplet digital PCR system of Bio-rad with 6 fluorescent channels. To validate the analytical performance of the Droplex NSCLC Panel Test Kit for each type of sample, DNA and RNA samples were extracted from FFPET sections and plasma isolated from NSCLC patients. Each type of samples, contrived samples were manufactured and tested for analytical performance. Results: The results showed that DNA/RNA multiple mutations can be detected simultaneously on a 6-fluorescent channel system. The kit was highly sensitive for low-input sample in dilutions studies ranging from 5 to 20ng DNA or RNA for FFPET samples and plasma sample. The Digital PCR NSCLC panel test detected an analytical specificity of 100% and detection limit of 0.25% or higher for all genes included in the kit. Conclusions: We demonstrated that the performance of the Droplex NSCLC Panel Test is reliable and effective for the detection of above 170 clinically relevant mutations of 11 genes in FFPET and plasma samples of patients with NSCLC. Additionally, it will be necessary to evaluate the utility of the kit through further clinical studies with NSCLC patients, as well as expanding its compatibility to various digital PCR platforms.

Genomic and transcriptomic analyses of the elite rice variety Huizhan provide insight into disease resistance and heat tolerance

The indica rice variety Huizhan shows elite traits of disease resistance and heat tolerance. However, the underlying genetic basis of these traits is not fully understood due to limited genomic resources. Here, we used Nanopore long-read and next-generation sequencing technologies to generate a chromosome-scale genome assembly of Huizhan. Comparative genomics analysis uncovered a large chromosomal inversion and expanded gene families that are associated with plant growth, development and stress responses. Functional rice blast resistance genes, including Pi2, Pib and Ptr, and bacterial blight resistance gene Xa27, contribute to disease resistance of Huizhan. Furthermore, integrated genomics and transcriptomics analyses showed that OsHIRP1, OsbZIP60, the SOD gene family, and various transcription factors are involved in heat tolerance of Huizhan. The high-quality genome assembly and comparative genomics results presented in this study facilitate the use of Huizhan as an elite parental line in developing rice varieties adapted to disease pressure and climate challenges.

Comparative analysis of complete chloroplast genomes of Synotis species (Asteraceae, Senecioneae) for identification and phylogenetic analysis

BackgroundThe Synotis (C. B. Clarke) C. Jeffrey & Y. L. Chen is an ecologically important genus of the tribe Senecioneae, family Asteraceae. Because most species of the genus bear similar morphology, traditional morphological identification methods are very difficult to discriminate them. Therefore, it is essential to develop a reliable and effective identification method for Synotis species. In this study, the complete chloroplast (cp.) genomes of four Synotis species, S. cavaleriei (H.Lév.) C. Jeffrey & Y.L. Chen, S. duclouxii (Dunn) C. Jeffrey & Y.L. Chen, S. nagensium (C.B. Clarke) C. Jeffrey & Y.L. Chen and S. erythropappa (Bureau & Franch.) C. Jeffrey & Y. L. Chen had been sequenced using next-generation sequencing technology and reported here.ResultsThese four cp. genomes exhibited a typical quadripartite structure and contained the large single-copy regions (LSC, 83,288 to 83,399 bp), the small single-copy regions (SSC, 18,262 to 18,287 bp), and the inverted repeat regions (IR, 24,837 to 24,842 bp). Each of the four cp. genomes encoded 134 genes, including 87 protein-coding genes, 37 tRNA genes, 8 rRNA genes, and 2 pseudogenes (ycf1 and rps19). The highly variable regions (trnC-GCA-petN, ccsA-psaC, trnE-UUC-rpoB, ycf1, ccsA and petN) may be used as potential molecular barcodes. The complete cp. genomes sequence of Synotis could be used as the potentially effective super-barcode to accurately identify Synotis species. Phylogenetic analysis demonstrated that the four Synotis species were clustered into a monophyletic group, and they were closed to the Senecio, Crassocephalum and Dendrosenecio in tribe Senecioneae.ConclusionsThis study will be useful for further species identification, evolution, genetic diversity and phylogenetic studies within this genus Synotis and the tribe Senecioneae.

A case of non-specific infection in the pharynx and larynx assisted the confirmation by targeted next-generation sequencing technology

A case of non-specific infection in the pharynx and larynx assisted the confirmation by targeted next-generation sequencing technology

Integration of Artificial Intelligence and Quantum Transport toward Stereoselective Identification of Carbohydrate Isomers.

Detection of stereoisomers of carbohydrates with molecular resolution, a challenging goal analysts desire to achieve, is key to the full development of glycosciences. Despite the promise that analytical techniques made, including widely used nuclear magnetic resonance and mass spectrometry, high throughput de novo carbohydrate sequencing remains an unsolved issue. Notably, while next-generation sequencing technologies are readily available for DNA and proteins, they are conspicuously absent for carbohydrates due to the immense stereochemical and structural complexity inherent in these molecules. In this work, we report a novel computational technique that employs quantum tunneling coupled with artificial intelligence to detect complex carbohydrate anomers and stereoisomers with excellent sensitivity. The quantum tunneling footprints of carbohydrate isomers show high distinguishability with an in-depth analysis of underlying chemistry. Our findings open up a new route for carbohydrate sensing, which can be seamlessly integrated with next-generation sequencing technology for real-time analysis.