Next-Generation Sequencing Strategies

Genome Tunisia Project: paving the way for precision medicine in North Africa

BACKGROUND

Key discoveries and innovations in the field of human genetics have led to the foundation of molecular and personalized medicine. Here, we present the Genome Tunisia Project, a two-phased initiative (2022-2035) which aims to deliver the reference sequence of the Tunisian Genome and to support the implementation of personalized medicine in Tunisia, a North African country that represents a central hub of population admixture and human migration between African, European, and Asian populations. The main goal of this initiative is to develop a healthcare system capable of incorporating omics data for use in routine medical practice, enabling medical doctors to better prevent, diagnose, and treat patients.

METHODS

A multidisciplinary partnership involving Tunisian experts from different institutions has come to discern all requirements that would be of high priority to fulfill the project's goals. One of the most urgent priorities is to determine the reference sequence of the Tunisian Genome. In addition, extensive situation analysis and revision of the education programs, community awareness, appropriate infrastructure including sequencing platforms and biobanking, as well as ethical and regulatory frameworks, have been undertaken towards building sufficient capacity to integrate personalized medicine into the Tunisian healthcare system.

RESULTS

In the framework of this project, an ecosystem with all engaged stakeholders has been implemented including healthcare providers, clinicians, researchers, pharmacists, bioinformaticians, industry, policymakers, and advocacy groups. This initiative will also help to reinforce research and innovation capacities in the field of genomics and to strengthen discoverability in the health sector.

CONCLUSIONS

Genome Tunisia is the first initiative in North Africa that seeks to demonstrate the major impact that can be achieved by Human Genome Projects in low- and middle-income countries to strengthen research and to improve disease management and treatment outcomes, thereby reducing the social and economic burden on healthcare systems. Sharing this experience within the African scientific community is a chance to turn a major challenge into an opportunity for dissemination and outreach. Additional efforts are now being made to advance personalized medicine in patient care by educating consumers and providers, accelerating research and innovation, and supporting necessary changes in policy and regulation.

Integrative analysis of autophagy-related genes reveals that CAPNS1 is a novel prognostic biomarker and promotes the malignancy of melanoma via Notch signaling pathway

Skin cutaneous melanoma (SKCM) is a highly fatal form of skin cancer that develops from the malignant transformation of epidermal melanocytes. There is substantial evidence linking autophagy to cancer etiology and immunotherapy efficacy. This study aimed to conduct a comprehensive analysis of autophagy-related genes (ARGs) using TCGA datasets and further explore the potential function of critical ARGs in SKCM progression. We performed comprehensive bioinformatics analysis uses the TCGA dataset. RT-PCR was applied to examine the expression of CAPNS1 in SKCM cells. Lost-of-function experiments were performed to detect the expression of the related proteins. In this search, we screed 70 differentially expressed autophagy-related genes (DE-ARGs), including 33 up-DE-ARGs and 37 down-DE-ARGs. Enrichment assays revealed that these 70 DE-ARGs may exert influence on critical cellular processes such as autophagy, protein kinase activity, and signaling pathways, impacting cell growth, differentiation, survival, and tumor development. Then, we further explore the prognostic value of 70 DE-ARGs and confirmed 18 survival-related DE-ARGs in SKCM patients. Nearly all the 18 DE-ARGs' methylation was negatively correlated with their corresponding expression in SKCM. The 12 survival-related DE-ARGs were used to develop a unique predictive model that effectively classified SKCM patients into high- and low-risk groups with regard to overall survival. Furthermore, tumor environment analysis indicated that the risk score was associated with several immune cells. Among the 12 survival-related DE-ARGs, our attention focused on CAPNS1 which was highly expressed in SKCM patients and predicted a poor prognosis. In addition, we confirmed that knockdown of CAPNS1 distinctly suppressed the proliferation, metastasis and EMT of SKCM cells, and promoted autophagy via regulating Notch signaling pathway. Overall, this study enhances our understanding of the intricate molecular landscape of SKCM progression and presents promising avenues for future research and clinical applications.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

Evolution and advancements in genomics and epigenomics in OA research: How far we have come

OBJECTIVE

Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput "omic" technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics - including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease.

DESIGN

In this narrative review, we selected articles discussing advancements and applications of these technologies to OA biology and pathology. We discuss how genomics, deoxyribonucleic acid (DNA) methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients.

RESULTS

Genomics investigations into the genetic links of OA, including using genome-wide association studies, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease.

CONCLUSIONS

Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.

Single-Cell Sequencing Technology and Its Application in the Study of Central Nervous System Diseases

The mammalian central nervous system consists of a large number of cells, which contain not only different types of neurons, but also a large number of glial cells, such as astrocytes, oligodendrocytes, and microglia. These cells are capable of performing highly refined electrophysiological activities and providing the brain with functions such as nutritional support, information transmission and pathogen defense. The diversity of cell types and individual differences between cells have brought inspiration to the study of the mechanism of central nervous system diseases. In order to explore the role of different cells, a new technology, single-cell sequencing technology has emerged to perform specific analysis of high-throughput cell populations, and has been continuously developed. Single-cell sequencing technology can accurately analyze single-cell expression in mixed-cell populations and collect cells from different spatial locations, time stages and types. By using single-cell sequencing technology to compare gene expression profiles of normal and diseased cells, it is possible to discover cell subsets associated with specific diseases and their associated genes. Therefore, scientists can understand the development process, related functions and disease state of the nervous system from an unprecedented depth. In conclusion, single-cell sequencing technology provides a powerful technology for the discovery of novel therapeutic targets for central nervous system diseases.

Genomic and Transcriptomic Analyses Reveal Multiple Strategies for Vibrio parahaemolyticus to Tolerate Sub-Lethal Concentrations of Three Antibiotics

Vibrio parahaemolyticus can cause acute gastroenteritis, wound infections, and septicemia in humans. The overuse of antibiotics in aquaculture may lead to a high incidence of the multidrug-resistant (MDR) pathogen. Nevertheless, the genome evolution of V. parahaemolyticus in aquatic animals and the mechanism of its antibiotic tolerance remain to be further deciphered. Here, we investigated the molecular basis of the antibiotic tolerance of V. parahaemolyticus isolates (n = 3) originated from shellfish and crustaceans using comparative genomic and transcriptomic analyses. The genome sequences of the V. parahaemolyticus isolates were determined (5.0-5.3 Mb), and they contained 4709-5610 predicted protein-encoding genes, of which 823-1099 genes were of unknown functions. Comparative genomic analyses revealed a number of mobile genetic elements (MGEs, n = 69), antibiotic resistance-related genes (n = 7-9), and heavy metal tolerance-related genes (n = 2-4). The V. parahaemolyticus isolates were resistant to sub-lethal concentrations (sub-LCs) of ampicillin (AMP, 512 μg/mL), kanamycin (KAN, 64 μg/mL), and streptomycin (STR, 16 μg/mL) (p < 0.05). Comparative transcriptomic analyses revealed that there were significantly altered metabolic pathways elicited by the sub-LCs of the antibiotics (p < 0.05), suggesting the existence of multiple strategies for antibiotic tolerance in V. parahaemolyticus. The results of this study enriched the V. parahaemolyticus genome database and should be useful for controlling the MDR pathogen worldwide.

Development and emerging trends of drug resistance mutations in HIV: a bibliometric analysis based on CiteSpace

Background

Antiretroviral therapy has led to AIDS being a chronic disease. Nevertheless, the presence of constantly emerging drug resistance mutations poses a challenge to clinical treatment. A systematic analysis to summarize the advancements and uncharted territory of drug resistance mutations is urgently needed and may provide new clues for solving this problem.

Methods

We gathered 3,694 publications on drug resistance mutations from the Web of Science Core Collection with CiteSpace software and performed an analysis to visualize the results and predict future new directions and emerging trends. Betweenness centrality, count, and burst value were taken as standards.

Results

The number of papers on HIV medication resistance mutations during the last 10 years shows a wave-like trend. In terms of nation, organization, and author, the United States (1449), University of London (193), and Mark A. Wainberg (66) are the most significant contributors. The most frequently cited article is "Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update." Hot topics in this field include "next-generation sequencing," "tenofovir alafenamide," "children," "regimens," "accumulation," "dolutegravir," "rilpivirine," "sex," "pretreatment drug resistance," and "open label." Research on drug resistance in teenagers, novel mutation detection techniques, and drug development is ongoing, and numerous publications have indicated the presence of mutations related to current medications. Therefore, testing must be performed regularly for patients who have used medications for a long period. Additionally, by choosing medications with a longer half-life, patients can take fewer doses of their prescription, increasing patient compliance.

Conclusion

This study involved a bibliometric visualization analysis of the literature on drug resistance mutations, providing insight into the field's evolution and emerging patterns and offering academics a resource to better understand HIV drug resistance mutations and contribute to the field's advancement.

Technical Advances in Circulating Cell-Free DNA Detection and Analysis for Personalized Medicine in Patients' Care

Circulating cell-free DNA (cfDNA) refers to small fragments of DNA molecules released after programmed cell death and necrosis in several body fluids such as blood, saliva, urine, and cerebrospinal fluid. The discovery of cfDNA has revolutionized the field of non-invasive diagnostics in the oncologic field, in prenatal testing, and in organ transplantation. Despite the potential of cfDNA and the solid results published in the recent literature, several challenges remain, represented by a low abundance, a need for highly sensitive assays, and analytical issues. In this review, the main technical advances in cfDNA analysis are presented and discussed, with a comprehensive examination of the current available methodologies applied in each field. Considering the potential advantages of cfDNA, this biomarker is increasing its consensus among clinicians, as it allows us to monitor patients' conditions in an easy and non-invasive way, offering a more personalized care. Nevertheless, cfDNA analysis is still considered a diagnostic marker to be further validated, and very few centers are implementing its analysis in routine diagnostics. As technical improvements are enhancing the performances of cfDNA analysis, its application will transversally improve patients' quality of life.

SARS-CoV-2 resistance to monoclonal antibodies and small-molecule drugs

Over four years have passed since the beginning of the COVID-19 pandemic. The scientific response has been rapid and effective, with many therapeutic monoclonal antibodies and small molecules developed for clinical use. However, given the ability for viruses to become resistant to antivirals, it is perhaps no surprise that the field has identified resistance to nearly all of these compounds. Here, we provide a comprehensive review of the resistance profile for each of these therapeutics. We hope that this resource provides an atlas for mutations to be aware of for each agent, particularly as a springboard for considerations for the next generation of antivirals. Finally, we discuss the outlook and thoughts for moving forward in how we continue to manage this, and the next, pandemic.

Advances in Brain-Gut-Microbiome Interactions: A Comprehensive Update on Signaling Mechanisms, Disorders, and Therapeutic Implications

The complex, bidirectional interactions between the brain, the gut, and the gut microbes are best referred to as the brain gut microbiome system. Animal and clinical studies have identified specific signaling mechanisms within this system, with gut microbes communicating to the brain through neuronal, endocrine, and immune pathways. The brain, in turn, modulates the composition and function of the gut microbiota through the autonomic nervous system, regulating gut motility, secretion, permeability, and the release of hormones impacting microbial gene expression. Perturbations at any level of these interactions can disrupt the intricate balance, potentially contributing to the pathogenesis of intestinal, metabolic, neurologic, and psychiatric disorders. Understanding these interactions and their underlying mechanisms holds promise for identifying biomarkers, as well as novel therapeutic targets, and for developing more effective treatment strategies for these complex disorders. Continued research will advance our knowledge of this system, with the potential for improved understanding and management of a wide range of disorders. This review provides an update on the current state of knowledge regarding this system, with a focus on recent advancements and emerging research areas.

Single-cell RNA sequencing to understand host-virus interactions

Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.

Machine learning identifies SLC6A14 as a novel biomarker promoting the proliferation and metastasis of pancreatic cancer via Wnt/β-catenin signaling

Pancreatic cancer (PC) has the poorest prognosis compared to other common cancers because of its aggressive nature, late detection, and resistance to systemic treatment. In this study, we aimed to identify novel biomarkers for PC patients and further explored their function in PC progression. We analyzed GSE62452 and GSE28735 datasets, identifying 35 differentially expressed genes (DEGs) between PC specimens and non-tumors. Based on 35 DEGs, we performed machine learning and identified eight diagnostic genes involved in PC progression. Then, we further screened three critical genes (CTSE, LAMC2 and SLC6A14) using three GEO datasets. A new diagnostic model was developed based on them and showed a strong predictive ability in screen PC specimens from non-tumor specimens in GEO, TCGA datasets and our cohorts. Then, clinical assays based on TCGA datasets indicated that the expression of LAMC2 and SLC6A14 was associated with advanced clinical stage and poor prognosis. The expressions of LAMC2 and SLC6A14, as well as the abundances of a variety of immune cells, exhibited a significant positive association with one another. Functionally, we confirmed that SLC6A14 was highly expressed in PC and its knockdown suppressed the proliferation, migration, invasion and EMT signal via regulating Wnt/β-catenin signaling pathway. Overall, our findings developed a novel diagnostic model for PC patients. SLC6A14 may promote PC progression via modulating Wnt/β-catenin signaling. This work offered a novel and encouraging new perspective that holds potential for further illuminating the clinicopathological relevance of PC as well as its molecular etiology.

SINGLE-CELL TRANSCRIPTOME ANALYSIS IN HEALTH AND DISEASE

ABSTRACT

The analysis of the single-cell transcriptome has emerged as a powerful tool to gain insights on the basic mechanisms of health and disease. It is widely used to reveal the cellular diversity and complexity of tissues at cellular resolution by RNA sequencing of the whole transcriptome from a single cell. Equally, it is applied to discover an unknown, rare population of cells in the tissue. The prime advantage of single-cell transcriptome analysis is the detection of stochastic nature of gene expression of the cell in tissue. Moreover, the availability of multiple platforms for the single-cell transcriptome has broadened its approaches to using cells of different sizes and shapes, including the capture of short or full-length transcripts, which is helpful in the analysis of challenging biological samples. And with the development of numerous packages in R and Python, new directions in the computational analysis of single-cell transcriptomes can be taken to characterize healthy versus diseased tissues to obtain novel pathological insights. Downstream analysis such as differential gene expression analysis, gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, cell-cell interaction analysis, and trajectory analysis has become standard practice in the workflow of single-cell transcriptome analysis to further examine the biology of different cell types. Here, we provide a broad overview of single-cell transcriptome analysis in health and disease conditions currently applied in various studies.

Leveraging transformers-based language models in proteome bioinformatics

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.

Developments in Genetics: Better Management of Ovarian Cancer Patients

The purpose of this article is to highlight the new advancements in molecular and diagnostic genetic testing and to properly classify all ovarian cancers. In this article, we address statistics, histopathological classification, molecular pathways implicated in ovarian cancer, genetic screening panels, details about the genes, and also candidate genes. We hope to bring new information to the medical field so as to better prevent and diagnose ovarian cancer.

Recent advances in triterpenoid pathway elucidation and engineering

Triterpenoids are among the most assorted class of specialized metabolites found in all the taxa of living organisms. Triterpenoids are the leading active ingredients sourced from plant species and are utilized in pharmaceutical and cosmetic industries. The triterpenoid precursor 2,3-oxidosqualene, which is biosynthesized via the mevalonate (MVA) pathway is structurally diversified by the oxidosqualene cyclases (OSCs) and other scaffold-decorating enzymes such as cytochrome P450 monooxygenases (P450s), UDP-glycosyltransferases (UGTs) and acyltransferases (ATs). A majority of the bioactive triterpenoids are harvested from the native hosts using the traditional methods of extraction and occasionally semi-synthesized. These methods of supply are time-consuming and do not often align with sustainability goals. Recent advancements in metabolic engineering and synthetic biology have shown prospects for the green routes of triterpenoid pathway reconstruction in heterologous hosts such as Escherichia coli, Saccharomyces cerevisiae and Nicotiana benthamiana, which appear to be quite promising and might lead to the development of alternative source of triterpenoids. The present review describes the biotechnological strategies used to elucidate complex biosynthetic pathways and to understand their regulation and also discusses how the advances in triterpenoid pathway engineering might aid in the scale-up of triterpenoid production in engineered hosts.

Comparing DNA isolation methods for forest trees: quality, plastic footprint, and time-efficiency

BACKGROUND

Genetic and genomic studies are seeing an increase in sample sizes together with a wider range of species investigated in response to environmental change concerns. In turn, these changes may come with challenges including the time and difficulty to isolate nucleic acids (DNA or RNA), the sequencing cost and environmental impacts of the growing amount of plastic waste generated in the process. Pseudotsuga menziesii var. menziesii (Mirbel) Franco (PM), Tsuga heterophylla (Raf.) Sarg. (TH) and Thuja plicata Donn ex D.Don (TP) are conifer species found in diverse woodlands both as natives and naturalized exotics. Our study was carried out whilst investigating their genetics to understand their population structure and potential for adaptation.

RESULTS

In the present study, we compared two different DNA isolation methods, i.e., spin-column DNeasy plant mini kit (QIAGEN), and temperature-driven enzymatic cocktail Plant DNA Extraction (MicroGEM). The quantity of recovered DNA and the quality of DNA were assessed along with the plastic footprint and time needed for three tree species. Both methods were optimised and proven to provide enough DNA for each studied species. The yield of DNA for each method depended on the species: QIAGEN showed higher yield in P. menziesii and T. heterophylla, while T. plicata recovered similar amount of DNA for both methods. The DNA quality was investigated using DNA barcoding techniques by confirming species identity and species discrimination. No difference was detected in the PCR amplification of the two barcoding loci, (rbcL and trnH-psbA), and the recovered sequences between DNA isolation methods. Measurement of the plastic use and the processing time per sample indicated that MicroGEM had a 52.64% lower plastic footprint and was 51.8% faster than QIAGEN.

CONCLUSIONS

QIAGEN gave higher yields in two of the species although both methods showed similar quality results across all species. However, MicroGEM was clearly advantageous to decrease the plastic footprint and improve the time efficiency. Overall, MicroGEM recovers sufficient and reliable DNA to perform common downstream analyses such as PCR and sequencing. Our findings illustrate the benefits of research and efforts towards developing more sustainable methods and techniques to reduce the environmental footprint of molecular analyses.

Analysis of two-gene signatures and related drugs in small-cell lung cancer by bioinformatics

Small-cell lung cancer (SCLC) has a poor prognosis and can be diagnosed with systemic metastases. Nevertheless, the molecular mechanisms underlying the development of SCLC are unclear, requiring further investigation. The current research aims to identify relevant biomarkers and available drugs to treat SCLC. The bioinformatics analysis comprised three Gene Expression Omnibus datasets (including GSE2149507, GSE6044, and GSE30219). Using the limma R package, we discovered differentially expressed genes (DEGs) in the current work. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were made by adopting the DAVID website. The DEG protein-protein interaction network was built based on the Search Tool for the Retrieval of Interacting Genes/Proteins website and visualized using the CytoHubba plugin in Cytoscape, aiming to screen the top ten hub genes. Quantitative real-time polymerase chain reaction was adopted for verifying the level of the top ten hub genes. Finally, the potential drugs were screened and identified using the QuartataWeb database. Totally 195 upregulated and 167 downregulated DEGs were determined. The ten hub genes were NCAPG, BUB1B, TOP2A, CCNA2, NUSAP1, UBE2C, AURKB, RRM2, CDK1, and KIF11. Ten FDA-approved drugs were screened. Finally, two genes and related drugs screened could be the prospective drug targets for SCLC treatment.

GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning

Introduction: Identifying significant sets of genes that are up/downregulated under specific conditions is vital to understand disease development mechanisms at the molecular level. Along this line, in order to analyze transcriptomic data, several computational feature selection (i.e., gene selection) methods have been proposed. On the other hand, uncovering the core functions of the selected genes provides a deep understanding of diseases. In order to address this problem, biological domain knowledge-based feature selection methods have been proposed. Unlike computational gene selection approaches, these domain knowledge-based methods take the underlying biology into account and integrate knowledge from external biological resources. Gene Ontology (GO) is one such biological resource that provides ontology terms for defining the molecular function, cellular component, and biological process of the gene product. Methods: In this study, we developed a tool named GeNetOntology which performs GO-based feature selection for gene expression data analysis. In the proposed approach, the process of Grouping, Scoring, and Modeling (G-S-M) is used to identify significant GO terms. GO information has been used as the grouping information, which has been embedded into a machine learning (ML) algorithm to select informative ontology terms. The genes annotated with the selected ontology terms have been used in the training part to carry out the classification task of the ML model. The output is an important set of ontologies for the two-class classification task applied to gene expression data for a given phenotype. Results: Our approach has been tested on 11 different gene expression datasets, and the results showed that GeNetOntology successfully identified important disease-related ontology terms to be used in the classification model. Discussion: GeNetOntology will assist geneticists and scientists to identify a range of disease-related genes and ontologies in transcriptomic data analysis, and it will also help doctors design diagnosis platforms and improve patient treatment plans.

Bioinformatics analysis and single-cell RNA sequencing: elucidating the ubiquitination pathways and key enzymes in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer associated with high mortality rates. We aimed to utilize single-cell multiomics analysis to identify the key molecules involved in ubiquitination modification, which plays a role in LUAD development and progression.

METHODS

We use a systematic approach to analyze LUAD-related single-cell and bulk transcriptome datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Single-cell RNA sequencing (scRNA-seq) data were normalized, clustered, and annotated with the Seurat package in R. InferCNV was used to distinguish malignant from epithelial cells, and AUCell evaluated the area under the curve (AUC) score of ubiquitination-related enzymes. Survival and differential analyses identified significant molecular markers associated with ubiquitination. PSMD14 expression was confirmed using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot assays, and its knockdown cell lines were assessed for effects on cellular processes and tumor formation in mice. PSMD14's interacting proteins were predicted, and its impact on AGR2 protein half-life and ubiquitination was evaluated. Rescue experiments involving PSMD14 overexpression and AGR2 silencing assessed their impact on malignant behaviors.

RESULTS

By means of single-cell sequencing analysis, we probed the ubiquitination modification landscape in the LUAD microenvironment. Malignant cells had elevated scores for enzymes and ubiquitin-binding domains compared to normal epithelial cells, with 53 ubiquitination-related molecules showing prognostic disparities. FGR, PSMD14, and ZBTB16 were identified as genes with prognostic significance, with PSMD14 showing higher expression in epithelial and malignant cells. Two missense mutation sites were identified in PSMD14, which had a high copy number amplification ratio and positive correlation with messenger RNA (mRNA) expression. PSMD14 expression and tumor stage were found to be independent prognostic factors, and interfering with PSMD14 expression reduced the malignant behavior of LUAD cells. PSMD14 was found to bind to AGR2 protein and reduce its ubiquitination, leading to increased AGR2 stability. Knockdown of AGR2 inhibited the enhancement of cell viability, invasion, and migration resulting from PSMD14 overexpression.

CONCLUSIONS

This study examined ubiquitination modifications in LUAD using sequencing data, identifying PSMD14's critical role in malignancy regulation and its potential as a prognostic and therapeutic biomarker. These insights enhance understanding of LUAD mechanisms and treatment.

Genomic determinants in advanced endometrial cancer patients with sustained response to hormonal therapy- case series and review of literature

The incidence of endometrial cancer is increasing, however treatment options for advanced disease are limited. Hormonal therapy has demonstrated positive outcomes for Stage IV EC. Next generation sequencing (NGS) has increased our understanding of molecular mechanisms driving EC. In this case series, we selected six patients at our institution with Stage IV, hormone receptor positive, endometrial cancer currently being treated with hormonal therapy. All patients achieved SD for at least ≥ 1.5 years. We studied NGS data on all six patients to assess for any common genomic marker which could predict the SD of at least 1.5 years achieved in this group. Institutional Review Board (IRB) approval was obtained from Staten Island University Hospital and Northwell Health, New York. PTEN, PIK3CA, PIK3R1, and ARID1A mutations were found in 83%, 67% 50%, and 67% of patients respectively. TP53 and FGFR2 were both found in 50% of patients. All patients were positive for estrogen and/or progesterone receptor (ER+ and/or PR+). We did not find any one common mutation that could have predicted the observed response (or SD of ≥1.5 years) to hormone therapy. However, our data reflects the prevalence of various mutations reported in literature: (1) Hormone Receptor status is a positive prognostic indicator (2) PTEN/PIK3CA mutations can occur concurrently in EC (3) ARID1A coexists with PTEN (4) FGFR and PTEN pathways may be interlinked. We suggest NGS be employed frequently in patients with endometrial cancer to identify targetable mutations. Additional larger studies are needed to characterize the interplay between mutations.

Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges

The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.

Novel CaLB-like Lipase Found Using ProspectBIO, a Software for Genome-Based Bioprospection

Enzymes have been highly demanded in diverse applications such as in the food, pharmaceutical, and industrial fuel sectors. Thus, in silico bioprospecting emerges as an efficient strategy for discovering new enzyme candidates. A new program called ProspectBIO was developed for this purpose as it can find non-annotated sequences by searching for homologs of a model enzyme directly in genomes. Here we describe the ProspectBIO software methodology and the experimental validation by prospecting for novel lipases by sequence homology to Candida antarctica lipase B (CaLB) and conserved motifs. As expected, we observed that the new bioprospecting software could find more sequences (1672) than a conventional similarity-based search in a protein database (733). Additionally, the absence of patent protection was introduced as a criterion resulting in the final selection of a putative lipase-encoding gene from Ustilago hordei (UhL). Expression of UhL in Pichia pastoris resulted in the production of an enzyme with activity towards a tributyrin substrate. The recombinant enzyme activity levels were 4-fold improved when lowering the temperature and increasing methanol concentrations during the induction phase in shake-flask cultures. Protein sequence alignment and structural modeling showed that the recombinant enzyme has high similarity and capability of adjustment to the structure of CaLB. However, amino acid substitutions identified in the active pocket entrance may be responsible for the differences in the substrate specificities of the two enzymes. Thus, the ProspectBIO software allowed the finding of a new promising lipase for biotechnological application without the need for laborious and expensive conventional bioprospecting experimental steps.

Metagenomics by next-generation sequencing (mNGS) in the etiological characterization of neonatal and pediatric sepsis: A systematic review

Introduction

Pediatric and neonatal sepsis is one of the main causes of mortality and morbidity in these age groups. Accurate and early etiological identification is essential for guiding antibiotic treatment, improving survival, and reducing complications and sequelae. Currently, the identification is based on culture-dependent methods, which has many limitations for its use in clinical practice, and obtaining its results is delayed. Next-generation sequencing enables rapid, accurate, and unbiased identification of multiple microorganisms in biological samples at the same time. The objective of this study was to characterize the etiology of neonatal and pediatric sepsis by metagenomic techniques.

Methods

A systematic review of the literature was carried out using the PRISMA-2020 guide. Observational, descriptive, and case report studies on pediatric patients were included, with a diagnostic evaluation by clinical criteria of sepsis based on the systemic inflammatory response, in sterile and non-sterile biofluid samples. The risk of bias assessment of the observational studies was carried out with the STROBE-metagenomics instrument and the CARE checklist for case reports.

Results and Discussion

Five studies with a total of 462 patients were included. Due to the data obtained from the studies, it was not possible to perform a quantitative synthesis (meta-analysis). Based on the data from the included studies, the result identified that mNGS improves the etiological identification in neonatal and pediatric sepsis, especially in the context of negative cultures and in the identification of unusual microorganisms (bacteria that are difficult to grow in culture, viruses, fungi, and parasites). The number of investigations is currently limited, and the studies are at high risk of bias. Further research using this technology would have the potential to improve the rational use of antibiotics.

Molecular mapping of drought-responsive QTLs during the reproductive stage of rice using a GBS (genotyping-by-sequencing) based SNP linkage map

BACKGROUND

In rice, drought stress at reproductive stage drastically reduces yield, which in turn hampers farmer's efforts towards crop production. The majority of the rice varieties have resistance genes against several abiotic and biotic stresses. Therefore, the traditional landraces were studied to identify QTLs/candidate genes associated with drought tolerance.

METHODS AND RESULTS

A high-density SNP-based genetic map was constructed using a Genotyping-by-sequencing (GBS) approach. The recombinant inbred lines (RILs) derived from crossing 'Banglami × Ranjit' were used for QTL analysis. A total map length of 1306.424 cM was constructed, which had an average inter-marker distance of 0.281 cM. The phenotypic evaluation of F₆ and F₇ RILs were performed under drought stress and control conditions. A total of 42 QTLs were identified under drought stress and control conditions for yield component traits explaining 1.95-13.36% of the total phenotypic variance (PVE). Among these, 19 QTLs were identified under drought stress conditions, whereas 23 QTLs were located under control conditions. A total of 4 QTLs explained a PVE ≥ 10% which are considered as the major QTLs. Moreover, bioinformatics analysis revealed the presence of 6 candidate genes, which showed differential expression under drought and control conditions.

CONCLUSION

These QTLs/genes may be deployed for marker-assisted pyramiding to improve drought tolerance in the existing rice varieties.

Methods of isolation and identification of nontuberculous mycobacteria from environmental samples: A scoping review

Nontuberculous mycobacteria (NTM) are ubiquitous in the environment. Some species of NTM are pathogenic and cause lung disease in susceptible persons. Epidemiologic studies of environmental NTM infection risk rely on both culture-dependent and culture-independent techniques for NTM isolation and identification. In this review, we summarized current methods used to isolate and identify NTM from the environment. We searched PubMed, Embase, Scopus, Web of Science: Core Collection, and Global Health (CAB Direct) for peer-reviewed studies from the last 12 years. We identified 1685 unique citations and 110 studies met our inclusion and exclusion criteria. Approximately half (55%) of the studies identified in this review used a combination of culture-independent and culture-dependent methods. The most common environmental substrate analyzed was water (n = 90). Identification of current, common methods for the isolation and identification of NTM from environmental samples may contribute to the development of standard methodological practices in the future. The choice of isolation method is based on the research question, environment, and species. A summary of common methods may contribute to the development of standard practices for isolation and identification of NTM from environmental samples, which may lead to more robust and comparable results.

Genomic and Transcriptomic Analysis Reveal Multiple Strategies for the Cadmium Tolerance in Vibrio parahaemolyticus N10-18 Isolated from Aquatic Animal Ostrea gigas Thunberg

The waterborne Vibrio parahaemolyticus can cause acute gastroenteritis, wound infection, and septicemia in humans. Pollution of heavy metals in aquatic environments is proposed to link high incidence of the multidrug-resistant (MDR) pathogen. Nevertheless, the genome evolution and heavy metal tolerance mechanism of V. parahaemolyticus in aquatic animals remain to be largely unveiled. Here, we overcome the limitation by characterizing an MDR V. parahaemolyticus N10-18 isolate with high cadmium (Cd) tolerance using genomic and transcriptomic techniques. The draft genome sequence (4,910,080 bp) of V. parahaemolyticus N10-18 recovered from Ostrea gigas Thunberg was determined, and 722 of 4653 predicted genes had unknown function. Comparative genomic analysis revealed mobile genetic elements (n = 11) and heavy metal and antibiotic-resistance genes (n = 38 and 7). The bacterium significantly changed cell membrane structure to resist the Cd2+ (50 μg/mL) stress (p < 0.05). Comparative transcriptomic analysis revealed seven significantly altered metabolic pathways elicited by the stress. The zinc/Cd/mercury/lead transportation and efflux and the zinc ATP-binding cassette (ABC) transportation were greatly enhanced; metal and iron ABC transportation and thiamine metabolism were also up-regulated; conversely, propanoate metabolism and ribose and maltose ABC transportation were inhibited (p < 0.05). The results of this study demonstrate multiple strategies for the Cd tolerance in V. parahaemolyticus.

Quantum computing algorithms: getting closer to critical problems in computational biology

The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics

Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.

NanoRTax, a real-time pipeline for taxonomic and diversity analysis of nanopore 16S rRNA amplicon sequencing data

Background

The study of microbial communities and their applications have been leveraged by advances in sequencing techniques and bioinformatics tools. The Oxford Nanopore Technologies long-read sequencing by nanopores provides a portable and cost-efficient platform for sequencing assays. While this opens the possibility of sequencing applications outside specialized environments and real-time analysis of data, complementing the existing efficient library preparation protocols with streamlined bioinformatic workflows is required.

Results

Here we present NanoRTax, a Nextflow pipeline for nanopore 16S rRNA gene amplicon data that features state-of-the-art taxonomic classification tools and real-time capability. The pipeline is paired with a web-based visual interface to enable user-friendly inspections of the experiment in progress. NanoRTax workflow and a simulated real-time analysis were used to validate the prediction of adult Intensive Care Unit patient mortality based on full-length 16S rRNA sequencing data from respiratory microbiome samples.

Conclusions

This constitutes a proof-of-concept simulation study of how real-time bioinformatic workflows could be used to shorten the turnaround times in critical care settings and provides an instrument for future research on early-response strategies for sepsis.

Integrated analysis of bulk and single-cell RNA sequencing reveals the interaction of PKP1 and tumor-infiltrating B cells and their therapeutic potential for nasopharyngeal carcinoma

Immunotherapy is an individualized therapeutic strategy for nasopharyngeal carcinoma (NPC). However, few molecular targets are clinically satisfactory. This work aimed to integrate bulk and single-cell RNA sequencing data to identify novel biomarkers involved in NPC. We performed differentially expressed gene (DEG) analysis, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and immune cell infiltration analysis prior to correlation analysis of the identified genes and immune cells and further assessed the prognostic effects of the biomarkers and immune cells in NPC. As a result, PKP1, a potential molecular biomarker associated with immune infiltration, and tumor-infiltrating lymphocyte-B cells (TIL-Bs) were identified as promising therapeutic targets for NPC. Importantly, immunohistochemistry (IHC) validated that PKP1 protein expression was mainly found in NPC cells rather than noncancerous cells. In addition, the tumor microenvironment (TME) of NPC was characterized by the infiltration of more dendritic cells (DCs) and γδT cells but fewer B cells. Our results suggest that the interaction of PKP1 and TIL-B cells is involved in NPC development. It is possible that TIL-B cells produce immunoglobulin G (IgG) to tumor antigens, such as PKP1, or viral antigens, including EBV and HPV, to execute antitumor ability through DC and T cells. In response, NPC cells express proteins such as PKP1 (absent in normal nasopharynx) to induce myeloid-derived suppressor cell (MDSC) expansion, which subsequently impairs the proliferation of B cells and results in B-cell death by generating iNOS and NOX2. In summary, our findings provide a potential therapeutic strategy for NPC by disrupting the interaction of PKP1 and TIL-Bs in the TME.

The power, potential, benefits, and challenges of implementing high-throughput sequencing in food safety systems

The development and application of modern sequencing technologies have led to many new improvements in food safety and public health. With unprecedented resolution and big data, high-throughput sequencing (HTS) has enabled food safety specialists to sequence marker genes, whole genomes, and transcriptomes of microorganisms almost in real-time. These data reveal not only the identity of a pathogen or an organism of interest in the food supply but its virulence potential and functional characteristics. HTS of amplicons, allow better characterization of the microbial communities associated with food and the environment. New and powerful bioinformatics tools, algorithms, and machine learning allow for development of new models to predict and tackle important events such as foodborne disease outbreaks. Despite its potential, the integration of HTS into current food safety systems is far from complete. Government agencies have embraced this new technology, and use it for disease diagnostics, food safety inspections, and outbreak investigations. However, adoption and application of HTS by the food industry have been comparatively slow, sporadic, and fragmented. Incorporation of HTS by food manufacturers in their food safety programs could reinforce the design and verification of effectiveness of control measures by providing greater insight into the characteristics, origin, relatedness, and evolution of microorganisms in our foods and environment. Here, we discuss this new technology, its power, and potential. A brief history of implementation by public health agencies is presented, as are the benefits and challenges for the food industry, and its future in the context of food safety.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.

The Application of Whole-Exome Sequencing in Patients With FUO

Background

Fever of unknown origin (FUO) is still a challenge for clinicians. Next-generation sequencing technologies, such as whole exome sequencing (WES), can be used to identify genetic defects in patients and assist in diagnosis. In this study, we investigated the application of WES in individuals with FUO.

Methods

We performed whole-exome sequencing on 15 FUO patients. Clinical information was extracted from the hospital information system.

Results

In 7/15 samples, we found positive results, including potentially causative mutations across eight different genes: CFTR, CD209, IRF2BP2, ADGRV 1, TYK2, MEFV, THBD and GATA2.

Conclusions

Our results show that whole-exome sequencing can promote the genetic diagnosis and treatment of patients with FUO.

Identification of the Potential Key Genes and Pathways Involved in Lens Changes of High Myopia

Aim

Methods

Results

Conclusion

Building Personalized Cancer Therapeutics through Multi-Omics Assays and Bacteriophage-Eukaryotic Cell Interactions

Bacteriophage-eukaryotic cell interaction provides the biological foundation of Phage Display technology, which has been widely adopted in studies involving protein-protein and protein-peptide interactions, and it provides a direct link between the proteins and the DNA encoding them. Phage display has also facilitated the development of new therapeutic agents targeting personalized cancer mutations. Proteins encoded by mutant genes in cancers can be processed and presented on the tumor cell surface by human leukocyte antigen (HLA) molecules, and such mutant peptides are called Neoantigens. Neoantigens are naturally existing tumor markers presented on the cell surface. In clinical settings, the T-cell recognition of neoantigens is the foundation of cancer immunotherapeutics. This year, we utilized phage display to successfully develop the 1st antibody-based neoantigen targeting approach for next-generation personalized cancer therapeutics. In this article, we discussed the strategies for identifying neoantigens, followed by using phage display to create personalized cancer therapeutics-a complete pipeline for personalized cancer treatment.

Fisher vs. the Worms: Extraordinary Sex Ratios in Nematodes and the Mechanisms that Produce Them

Parker, Baker, and Smith provided the first robust theory explaining why anisogamy evolves in parallel in multicellular organisms. Anisogamy sets the stage for the emergence of separate sexes, and for another phenomenon with which Parker is associated: sperm competition. In outcrossing taxa with separate sexes, Fisher proposed that the sex ratio will tend towards unity in large, randomly mating populations due to a fitness advantage that accrues in individuals of the rarer sex. This creates a vast excess of sperm over that required to fertilize all available eggs, and intense competition as a result. However, small, inbred populations can experience selection for skewed sex ratios. This is widely appreciated in haplodiploid organisms, in which females can control the sex ratio behaviorally. In this review, we discuss recent research in nematodes that has characterized the mechanisms underlying highly skewed sex ratios in fully diploid systems. These include self-fertile hermaphroditism and the adaptive elimination of sperm competition factors, facultative parthenogenesis, non-Mendelian meiotic oddities involving the sex chromosomes, and environmental sex determination. By connecting sex ratio evolution and sperm biology in surprising ways, these phenomena link two "seminal" contributions of G. A. Parker.

Targeted gene panel sequencing for molecular diagnosis of congenital adrenal hyperplasia

CONTEXT

Congenital adrenal hyperplasia (CAH) is a group of autosomal recessive genetic diseases caused by genetic deficiency in nine genes encoding steroidogenesis enzymes and cofactors.

OBJECTIVE

To establish a targeted next-generation sequencing (NGS) assay for all nine CAH candidate genes.

METHODS

We developed a customized targeted NGS assay of CAH candidate genes (CYP21A2, CYP17A1, CYP11B1, StAR, CYP11A1, POR, HSD3B2, H6PD, CYP11B2) and apply this assay plus MLPA of CYP21A2 in a total of 469 patients with CAH like signs and symptoms.

RESULTS

We totally identified 125 variants with seven variant types in eight genes. Variant types included missense variant (46.8 %), splicing variant (21.5 %), small indel (12.5 %), large structure variation (11.8 %), nonsense variant (4.1 %), UTR variant (2.9 %), synonymous variant (0.3 %). Successful genotyping, defined as biallelic pathogenic or likely pathogenic variants, was achieved in 98.5 % (336/341) of cases, including biallelic variants in CYP21A2 (n = 254), CYP17A1 (n = 45), CYP11B1 (n = 23), StAR (n = 7), HSD3B2 (n = 4), POR (n = 1), CYP11A1 (n = 1) and CYP11B2 (n = 1) gene. Importantly, the assay found one patient with CYP11B1 deficiency, one patient with non-classic POR deficiency and two patients with non-classic CYP17A1 deficiency while clinically diagnosed differently.

CONCLUSIONS

Our NGS-based assay plus MLPA of CYP21A2 is a useful tool to genotype all subtypes of CAH. The test successfully achieved genotype in 98.5 % of patients with clinically determined CAH. It also efficiently facilitated the diagnosis of CAH in patients with rare subtypes as well as non-classic phenotypes.

Targeted Genome Sequencing (TG-Seq) Approaches to Detect Plant Viruses

Globally, high-throughput sequencing (HTS) has been used for virus detection in germplasm certification programs. However, sequencing costs have impeded its implementation as a routine diagnostic certification tool. In this study, the targeted genome sequencing (TG-Seq) approach was developed to simultaneously detect multiple (four) viral species of; Pea early browning virus (PEBV), Cucumber mosaic virus (CMV), Bean yellow mosaic virus (BYMV) and Pea seedborne mosaic virus (PSbMV). TG-Seq detected all the expected viral amplicons within multiplex PCR (mPCR) reactions. In contrast, the expected PCR amplicons were not detected by gel electrophoresis (GE). For example, for CMV, GE only detected RNA1 and RNA2 while TG-Seq detected all the three RNA components of CMV. In an mPCR to amplify all four viruses, TG-Seq readily detected each virus with more than 732,277 sequence reads mapping to each amplicon. In addition, TG-Seq also detected all four amplicons within a 10-8 serial dilution that were not detectable by GE. Our current findings reveal that the TG-Seq approach offers significant potential and is a highly sensitive targeted approach for detecting multiple plant viruses within a given biological sample. This is the first study describing direct HTS of plant virus mPCR products. These findings have major implications for grain germplasm healthy certification programs and biosecurity management in relation to pathogen entry into Australia and elsewhere.

Pharmacogenomics of Pain Management: The Impact of Specific Biological Polymorphisms on Drugs and Metabolism

PURPOSE OF REVIEW

Pain is multifactorial and complex, often with a genetic component. Pharmacogenomics is a relative new field, which allows for the development of a truly unique and personalized therapeutic approach in the treatment of pain.

RECENT FINDINGS

Until recently, drug mechanisms in humans were determined by testing that drug in a population and calculating response averages. However, some patients will inevitably fall outside of those averages, and it is nearly impossible to predict who those outliers might be. Pharmacogenetics considers a patient's unique genetic information and allows for anticipation of that individual's response to medication. Pharmacogenomic testing is steadily making progress in the management of pain by being able to identify individual differences in the perception of pain and susceptibility and sensitivity to drugs based on genetic markers. This has a huge potential to increase efficacy and reduce the incidence of iatrogenic drug dependence and addiction. The streamlining of relevant polymorphisms of genes encoding receptors, transporters, and drug-metabolizing enzymes influencing the pain phenotype can be an important guide to develop safe new strategies and approaches to personalized pain management. Additionally, some challenges still prevail and preclude adoption of pharmacogenomic testing universally. These include lack of knowledge about pharmacogenomic testing, inadequate standardization of the process of data handling, questionable benefits about the clinical and financial aspects of pharmacogenomic testing-guided therapy, discrepancies in clinical evidence supporting these tests, and doubtful reimbursement of the tests by health insurance agencies.