Overview of Next-generation Sequencing Platforms Used in Published Draft Plant Genomes in Light of Genotypization of Immortelle Plant (Helichrysium Arenarium)

The Next Chapter in Cancer Diagnostics: Advances in HPV-Positive Head and Neck Cancer

Human papillomavirus (HPV)-associated head and neck squamous cell carcinoma (HNSCC), particularly oropharyngeal squamous cell carcinoma (OPSCC), is an increasingly prevalent pathology worldwide, especially in developed countries. For diagnosing HPV in HNSCC, the combination of p16 immunohistochemistry (IHC) and polymerase chain reaction (PCR) offers high sensitivity and specificity, with p16 IHC being a reliable initial screen and PCR confirming HPV presence. Advanced techniques like next-generation sequencing (NGS) and RNA-based assays provide detailed insights but are primarily used in research settings. Regardless of HPV status, standard oncological treatments currently include surgery, radiation, and/or chemotherapy. This conventional approach does not account for the typically better prognosis of HPV-positive HNSCC patients, leading to increased chemo/radiation-induced secondary morbidities and reduced quality of life. Therefore, it is crucial to identify and detect HPV positivity and other molecular characteristics of HNSCC to personalize treatment strategies. This comprehensive review aims to summarize current knowledge on various HPV detection techniques and evaluate their advantages and disadvantages, with a focus on developing methodologies to identify new biomarkers in HPV-positive HNSCC. The review discusses direct and indirect HPV examination in tumor tissue, DNA- and RNA-based detection techniques, protein-based markers, liquid biopsy potentials, immune-related markers, epigenetic markers, novel biomarkers, and emerging technologies, providing an overall insight into the current state of knowledge.

Next-Generation Sequencing to Study the DNA Interaction

Next-generation sequencing (NGS) has transformed genomics by allowing researchers to sequence DNA and RNA at highest speed, accuracy, and cost-effectiveness. Researchers investigate DNA interactions with the help next-generation sequencing with a great deal of information. Over the last decade, NGS technologies have advanced significantly, with the development of several platforms, including Illumina, PacBio, and Oxford Nanopore, each offering distinct advantages and uses. The use of next-generation sequencing (NGS) has aided in the discovery of genetic variations, gene expression patterns, and epigenetic modifications connected with a variety of diseases, including cancer, neurological disorders, and infectious diseases. By identifying these regions, we can control the expression of genes, cellular signaling pathways, and other key biological processes. NGS is an effective method for researching DNA interactions that has completely transformed the area of genomics. NGS has also played an important part in personalized medicine, enabling the discovery of disease-causing mutations and the creation of targeted medicines. Finally, NGS has transformed the field of genomics, resulting in new discoveries and applications in medicine, environmental sciences, and other fields.

Analysis of the bacterial and fungal populations in South African sorghum beer (umqombothi) using full-length 16S rRNA amplicon sequencing

There is a need to profile microorganisms which exist pre-and-post-production of umqombothi, to understand its microbial diversity and the interactions which subsequently influence the final product. Thus, this study sought to determine the relative microbial abundance in umqombothi and predict the functional pathways of bacterial and fungal microbiota present. Full-length bacterial 16S rRNA and internal transcribed spacer (ITS) gene sequencing using PacBio single-molecule, real-time (SMRT) technology was used to assess the microbial compositions. PICRUSt2 was adopted to infer microbial functional differences. A mixture of harmful and beneficial microorganisms was observed in all samples. The microbial diversity differed significantly between the mixed raw ingredients (MRI), customary beer brew (CB), and optimised beer brew (OPB). The highest bacterial species diversity was observed in the MRI, while the highest fungal species diversity was observed in the OPB. The dominant bacterial species in the MRI, CB, and OPB were Kosakonia cowanii, Apilactobacillus pseudoficulneus, and Vibrio alginolyticus, respectively, while the dominant fungal species was Apiotrichum laibachii. The predicted functional annotations revealed significant (p < 0.05) differences in the microbial pathways of the fermented and unfermented samples. The most abundant pathways in the MRI were the branched-chain amino acid biosynthesis super pathway and the pentose phosphate pathway. The CB sample was characterised by folate (vitamin B9) transformations III, and mixed acid fermentation. Biotin (vitamin B7) biosynthesis I and L-valine biosynthesis characterised the OPB sample. These findings can assist in identifying potential starter cultures for the commercial production of umqombothi. Specifically, A. pseudoficulneus can be used for controlled fermentation during the production of umqombothi. Likewise, the use of A. laibachii can allow for better control over the fermentation kinetics such as carbohydrate conversion and end-product characteristics, especially esters and aroma compounds.

Isolating Linum usitatissimum L. Nuclear DNA Enabled Assembling High-Quality Genome

High-quality genome sequences help to elucidate the genetic basis of numerous biological processes and track species evolution. For flax (Linum usitatissimum L.)-a multifunctional crop, high-quality assemblies from Oxford Nanopore Technologies (ONT) data were unavailable, largely due to the difficulty of isolating pure high-molecular-weight DNA. This article proposes a scheme for gaining a contiguous L. usitatissimum assembly using Nanopore data. We developed a protocol for flax nuclei isolation with subsequent DNA extraction, which allows obtaining about 5 μg of pure high-molecular-weight DNA from 0.5 g of leaves. Such an amount of material can be collected even from a single plant and yields more than 30 Gb of ONT data in two MinION runs. We performed a comparative analysis of different genome assemblers and polishers on the gained data and obtained the final 447.1-Mb assembly of L. usitatissimum line 3896 genome using the Canu-Racon (two iterations)-Medaka combination. The genome comprised 1695 contigs and had an N50 of 6.2 Mb and a completeness of 93.8% of BUSCOs from eudicots_odb10. Our study highlights the impact of the chosen genome construction strategy on the resulting assembly parameters and its eligibility for future genomic studies.

Transcriptome repository of North-Western Himalayan endangered medicinal herbs: a paramount approach illuminating molecular perspective of phytoactive molecules and secondary metabolism

Medicinal plants of the North-Western Himalayan region are known for their unprecedented biodiversity and valuable secondary metabolites that are unique to this dynamic geo-climatic region. From ancient times these medicinal herbs have been used traditionally for their therapeutic potentials. But from the last 2 decades increasing pharmaceutical demand, illegal and unorganized trade of these medicinal plants have accelerated the rate of over-exploitation in a non-scientific manner. In addition, climate change and anthropogenic activities also affected their natural habitat and driving most of these endemic plant species to critically endangered that foresee peril of mass extinction from this eco-region. Hence there is an urgent need for developing alternative sustainable approaches and policies to utilize this natural bioresource ensuring simultaneous conservation. Hither, arise the advent of sequencing-based transcriptomic studies significantly contributes to better understand the background of important metabolic pathways and related genes/enzymes of high-value medicinal herbs, in the absence of genomic information. The use of comparative transcriptomics in conjunction with biochemical techniques in North-Western Himalayan medicinal plants has resulted in significant advances in the identification of the molecular players involved in the production of secondary metabolic pathways over the last decade. This information could be used to further engineer metabolic pathways and breeding programs, ultimately leading to the development of in vitro systems dedicated to the production of pharmaceutically important secondary metabolites at the industrial level. Collectively, successful adoption of these approaches can certainly ensure the sustainable utilization of Himalayan bioresource by reducing the pressure on the wild population of these critically endangered medicinal herbs. This review provides novel insight as a transcriptome-based bioresource repository for the understanding of important secondary metabolic pathways genes/enzymes and metabolism of endangered high-value North-Western Himalayan medicinal herbs, so that researchers across the globe can effectively utilize this information for devising effective strategies for the production of pharmaceutically important compounds and their scale-up for sustainable usage and take a step forward in omics-based conservation genetics.

Next Generation High Throughput Sequencing to Assess Microbial Communities: An Application Based on Water Quality

Traditional techniques to identify different contaminants (biological or chemical) in the waters are slow, laborious, and can require specialized expertise. Hence, the rapid determination of water quality using more sensitive and reliable metagenomic based approaches attains special importance. Metagenomics deals with the study of genetic material that is recovered from microbial communities present in environmental samples. In traditional techniques cultivation-based methodologies were used to describe the diversity of microorganisms in environmental samples. It has failed to function as a robust marker because of limited taxonomic and phylogenetic implications. In this backdrop, high-throughput DNA sequencing approaches have proven very powerful in microbial source tracking because of investigating the full variety of genome-based analysis such as microbial genetic diversity and population structure played by them. Next generation sequencing technologies can reveal a greater proportion of microbial communities that have not been reported earlier by traditional techniques. The present review highlights the shift from traditional techniques for the basic study of community composition to next-generation sequencing (NGS) platforms and their potential applications to the biomonitoring of water quality in relation to human health.

Novel perspectives for SARS-CoV-2 genome browsing

SARS-CoV-2 has spread worldwide and caused social, economic, and health turmoil. The first genome assembly of SARS-CoV-2 was produced in Wuhan, and it is widely used as a reference. Subsequently, more than a hundred additional SARS-CoV-2 genomes have been sequenced. While the genomes appear to be mostly identical, there are variations. Therefore, an alignment of all available genomes and the derived consensus sequence could be used as a reference, better serving the science community. Variations are significant, but representing them in a genome browser can become, especially if their sequences are largely identical. Here we summarize the variation in one track. Other information not currently found in genome browsers for SARS-CoV-2, such as predicted miRNAs and predicted TRS as well as secondary structure information, were also added as tracks to the consensus genome. We believe that a genome browser based on the consensus sequence is better suited when considering worldwide effects and can become a valuable resource in the combating of COVID-19. The genome browser is available at http://cov.iaba.online.

The Application of Next-Generation Sequencing to Define Factors Related to Oral Cancer and Discover Novel Biomarkers

Despite the introduction of next-generation sequencing in the realm of DNA sequencing technology, it is not often used in the investigation of oral squamous cell carcinoma (OSCC). Oral cancer is one of the most frequently occurring malignancies in some parts of the world and has a high mortality rate. Patients with this malignancy are likely to have a poor prognosis and may suffer from severe facial deformity or mastication problems even after successful treatment. Therefore, a thorough understanding of this malignancy is essential to prevent and treat it. This review sought to highlight the contributions of next-generation sequencing (NGS) in unveiling the genetic alterations and differential expressions of miRNAs involved in OSCC progression. By applying an appropriate eligibility criterion, we selected relevant studies for review. Frequently identified mutations in genes such as TP53, NOTCH1, and PIK3CA are discussed. The findings of existing miRNAs (e.g., miR-21) as well as novel discoveries pertaining to OSCC are also covered. Lastly, we briefly mention the latest findings in targeted gene therapy and the potential use of miRNAs as biomarkers. Our goal is to encourage researchers to further adopt NGS in their studies and give an overview of the latest findings of OSCC treatment.

Artificial intelligence (AI) and big data in cancer and precision oncology

Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging, accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery. NGS generates large datasets that demand specialised bioinformatics resources to analyse the data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images. Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical application of NGS remains to be validated. By continuing to enhance the progression of innovation and technology, the future of AI and precision oncology show great promise.

Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma

Even with a rare occurrence of only 1.35% of cancer cases in the United States of America, brain tumors are considered as one of the most lethal malignancies. The most aggressive and invasive type of brain tumor, glioblastoma, accounts for 60–70% of all gliomas and presents with life expectancy of only 12–18 months. Despite trimodal treatment and advances in diagnostic and therapeutic methods, there are no significant changes in patient outcome. Our understanding of glioblastoma was significantly improved with the introduction of next generation sequencing technologies. This led to the identification of different genetic and molecular subtypes, which greatly improve glioblastoma diagnosis. Still, because of the poor life expectancy, novel diagnostic, and treatment methods are broadly explored. Epigenetic modifications like methylation and changes in histone acetylation are such examples. Recently, in addition to genetic and molecular characteristics, epigenetic profiling of glioblastomas is also used for sample classification. Further advancement of next generation sequencing technologies is expected to identify in detail the epigenetic signature of glioblastoma that can open up new therapeutic opportunities for glioblastoma patients. This should be complemented with the use of computational power i.e., machine and deep learning algorithms for objective diagnostics and design of individualized therapies. Using a combination of phenotypic, genotypic, and epigenetic parameters in glioblastoma diagnostics will bring us closer to precision medicine where therapies will be tailored to suit the genetic profile and epigenetic signature of the tumor, which will grant longer life expectancy and better quality of life. Still, a number of obstacles including potential bias, availability of data for minorities in heterogeneous populations, data protection, and validation and independent testing of the learning algorithms have to be overcome on the way.

Resequencing core accessions of a pedigree identifies derivation of genomic segments and key agronomic trait loci during cotton improvement

Upland cotton (Gossypium hirsutum) is the world's largest source of natural fibre and dominates the global textile industry. Hybrid cotton varieties exhibit strong heterosis that confers high fibre yields, yet the genome-wide effects of artificial selection that have influenced Upland cotton during its breeding history are poorly understood. Here, we resequenced Upland cotton genomes and constructed a variation map of an intact breeding pedigree comprising seven elite and 19 backbone parents. Compared to wild accessions, the 26 pedigree accessions underwent strong artificial selection during domestication that has resulted in reduced genetic diversity but stronger linkage disequilibrium and higher extents of selective sweeps. In contrast to the backbone parents, the elite parents have acquired significantly improved agronomic traits, with an especially pronounced increase in the lint percentage. Notably, identify by descent (IBD) tracking revealed that the elite parents inherited abundant beneficial trait segments and loci from the backbone parents and our combined analyses led to the identification of a core genomic segment which was inherited in the elite lines from the parents Zhong 7263 and Ejing 1 and that was strongly associated with lint percentage. Additionally, SNP correlation analysis of this core segment showed that a non-synonymous SNP (A-to-G) site in a gene encoding the cell wall-associated receptor-like kinase 3 (GhWAKL3) protein was highly correlated with increased lint percentage. Our results substantially increase the valuable genomics resources available for future genetic and functional genomics studies of cotton and reveal insights that will facilitate yield increases in the molecular breeding of cotton.

Association study of frameshift and splice variant polymorphisms with risk of idiopathic recurrent pregnancy loss

Recurrent pregnancy loss (RPL) is defined as ≥2 consecutive pregnancy losses, and can be caused by various factors, including genetics, chromosomal abnormalities, thrombophilia, immune disorders, nutritional factors, environmental factors, psychological stress or maternal infections; however, as many as 50% of RPL cases are idiopathic. In the present study, the role of genetic polymorphisms in RPL was investigated. Four gene polymorphisms were selected by whole exome sequencing, including membrane spanning 4‑domains A14 (MS4A14)D>I (rs3217518), solute carrier family 2 member 7 (SLC2A7)D>I (rs60746313), pregnancy specific β‑1‑glycoprotein 9 (PSG9)C>T (rs3746297) and ATP binding cassette subfamily B member 5 (ABCB5)C>G (rs17143187), and the aim was to investigate their association with RPL in Korean women. Genotyping was performed using polymerase chain reaction‑restriction fragment length polymorphism assay. Allele combination analysis revealed that the four‑allele combination I‑D‑T‑G, (MS4A14/SLC2A7/PSG9/ABCB5) was associated with a decreased risk for RPL. Interaction analysis demonstrated that the following genotypes: MS4A14 DI+II, SLC2A DI+II and ABCB 5 CG+GG, were associated with a prothrombin time ≥12 sec and with RPL risk. It may be concluded that the four gene polymorphisms do not affect RPL individually, but are associated with RPL when in combination with other genes or blood coagulation factors. Notably, the MS4A14 I allele, with a prothrombin time ≥12 sec, may be a potential biomarker for diagnosis, prevention and prognosis of RPL.

Proteomic Contributions to Medicinal Plant Research: From Plant Metabolism to Pharmacological Action

Herbal medicine is a clinical practice of utilizing medicinal plant derivatives for therapeutic purposes. It has an enduring history worldwide and plays a significant role in the fight against various diseases. Herbal drug combinations often exhibit synergistic therapeutic action compared with single-constituent dosage, and can also enhance the cytotoxicity induced by chemotherapeutic drugs. To explore the mechanism underlying the pharmacological action of herbs, proteomic approaches have been applied to the physiology of medicinal plants and its effects on animals. This review article focuses on the existing proteomics-based medicinal plant research and discusses the following topics: (i) plant metabolic pathways that synthesize an array of bioactive compounds; (ii) pharmacological action of plants tested using in vivo and in vitro studies; and (iii) the application of proteomic approaches to indigenous plants with scarce sequence information. The accumulation of proteomic information in a biological or medicinal context may help in formulating the effective use of medicinal plants.