Coming of age: ten years of next-generation sequencing technologies

Toward understanding the role of genomic repeat elements in neurodegenerative diseases

Neurodegenerative diseases cause great medical and economic burdens for both patients and society; however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

A consensus molecular subtypes classification strategy for clinical colorectal cancer tissues

Consensus Molecular Subtype (CMS) classification of colorectal cancer (CRC) tissues is complicated by RNA degradation upon formalin-fixed paraffin-embedded (FFPE) preservation. Here, we present an FFPE-curated CMS classifier. The CMSFFPE classifier was developed using genes with a high transcript integrity in FFPE-derived RNA. We evaluated the classification accuracy in two FFPE-RNA datasets with matched fresh-frozen (FF) RNA data, and an FF-derived RNA set. An FFPE-RNA application cohort of metastatic CRC patients was established, partly treated with anti-EGFR therapy. Key characteristics per CMS were assessed. Cross-referenced with matched benchmark FF CMS calls, the CMSFFPE classifier strongly improved classification accuracy in two FFPE datasets compared with the original CMSClassifier (63.6% versus 40.9% and 83.3% versus 66.7%, respectively). We recovered CMS-specific recurrence-free survival patterns (CMS4 versus CMS2: hazard ratio 1.75, 95% CI 1.24-2.46). Key molecular and clinical associations of the CMSs were confirmed. In particular, we demonstrated the predictive value of CMS2 and CMS3 for anti-EGFR therapy response (CMS2&3: odds ratio 5.48, 95% CI 1.10-27.27). The CMSFFPE classifier is an optimized FFPE-curated research tool for CMS classification of clinical CRC samples.

A data science roadmap for open science organizations engaged in early-stage drug discovery

The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design.

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

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

Surveillance and phylogenetic analysis of a pathogenic bacterium candidate in nasal discharge from children

"The infectious organism lurking in human airways (IOLA)" is a candidate pathogenic bacterium detected in bronchoalveolar lavage fluid specimens derived from adult patients with chronic lower respiratory tract infections. Genomic analyses of IOLA have revealed that it possesses the smallest and most AT-rich genome among human-derived bacteria. However, its biological properties remain unclear because no culture method has been established for IOLA. Here, we conducted a large-scale IOLA surveillance study of nasal discharge specimens from children in Japan and investigated the correlation between IOLA detection frequency and patient characteristics. We detected IOLA in 5.4% (103 of 1,920) of pediatric nasal discharge samples. No significant differences were observed in the frequency of detection based on the patient's background. However, with respect to age, the frequency of detection tended to be significantly high at 2-3 and 6 years old. Phylogenetic analysis revealed five phylotypes in the IOLA 16S rRNA gene sequences, and the sequences detected in adult patients with respiratory infections in a previous study belonged to one of the five phylotypes. The involvement of IOLA in the symptoms is not clear, but IOLA is detected at a relatively high frequency in pediatric nasal discharge. Many subjects with detected IOLA were not always IOLA positive, and IOLA was detected transiently. Our findings suggest that IOLA is horizontally transmitted through groups in nursery and elementary schools, and there are differences in biological characteristics among the IOLA phylotypes.IMPORTANCE"The infectious organism lurking in human airways (IOLA)" is a candidate pathogenic bacterium strongly suspected to be infectious to the respiratory tracts of humans and animals. However, a culture method for IOLA has not been established yet, and its properties remain unclear. In this study, IOLA was detected at a relatively high frequency in the nasal discharge of children, and five phylotypes of IOLA were identified. One of these phylotypes was found in the bronchoalveolar lavage fluid from adult patients, suggesting lineage-specific differences in the pathogenicity of IOLA. Moreover, it was suggested that IOLA is horizontally transmitted when children gather in groups such as nursery and elementary schools. These findings strongly indicate that IOLAs have been clinically undetected so far but are spreading among children, with one lineage being involved in respiratory diseases in adults. Examining the presence of IOLA in clinical specimens may help to understand the etiology of respiratory diseases with unknown causes.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Cancer cytogenetics in a genomics world: Wedding the old with the new

Since the discovery of the Philadelphia chromosome in 1960, cytogenetic studies have been instrumental in detecting chromosomal abnormalities that can inform cancer diagnosis, treatment, and risk assessment efforts. The initial expansion of cancer cytogenetics was with fluorescence in situ hybridization (FISH) to assess submicroscopic alterations in dividing or non-dividing cells and has grown into the incorporation of chromosomal microarrays (CMA), and next generation sequencing (NGS). These molecular technologies add additional dimensions to the genomic assessment of cancers by uncovering cytogenetically invisible molecular markers. Rapid technological and bioinformatic advances in NGS are so promising that the idea of performing whole genome sequencing as part of routine patient care may soon become economically and logistically feasible. However, for now cytogenetic studies continue to play a major role in the diagnostic testing and subsequent assessments in leukemia with other genomic studies serving as complementary testing options for detection of actionable genomic abnormalities. In this review, we discuss the role of conventional cytogenetics (karyotyping, chromosome analysis) and FISH studies in hematological malignancies, highlighting the continued clinical utility of these techniques, the subtleties and complexities that are relevant to treating physicians and the unique strengths of cytogenetics that cannot yet be paralleled by the current high-throughput molecular technologies. Additionally, we describe how CMA, optical genome mapping (OGM), and NGS detect abnormalities that were beyond the capacity of cytogenetic studies and how an integrated approach (broad molecular testing) can contribute to the detection of actionable targets and variants in malignancies. Finally, we discuss advances in the field of genomic testing that are bridging the advantages of individual (single) cell based cytogenetic testing and broad genomic testing.

GWAS advancements to investigate disease associations and biological mechanisms

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

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Viral genome sequencing methods: benefits and pitfalls of current approaches

Whole genome sequencing of viruses provides high-resolution molecular insights, enhancing our understanding of viral genome function and phylogeny. Beyond fundamental research, viral sequencing is increasingly vital for pathogen surveillance, epidemiology, and clinical applications. As sequencing methods rapidly evolve, the diversity of viral genomics applications and catalogued genomes continues to expand. Advances in long-read, single molecule, real-time sequencing methodologies present opportunities to sequence contiguous, haplotype resolved viral genomes in a range of research and applied settings. Here we present an overview of nucleic acid sequencing methods and their applications in studying viral genomes. We emphasise the advantages of different viral sequencing approaches, with a particular focus on the benefits of third-generation sequencing technologies in elucidating viral evolution, transmission networks, and pathogenesis.

Knowledge Graph Convolutional Network with Heuristic Search for Drug Repositioning

Drug repositioning is a strategy of repurposing approved drugs for treating new indications, which can accelerate the drug discovery process, reduce development costs, and lower the safety risk. The advancement of biotechnology has significantly accelerated the speed and scale of biological data generation, offering significant potential for drug repositioning through biomedical knowledge graphs that integrate diverse entities and relations from various biomedical sources. To fully learn the semantic information and topological structure information from the biological knowledge graph, we propose a knowledge graph convolutional network with a heuristic search, named KGCNH, which can effectively utilize the diversity of entities and relationships in biological knowledge graphs, as well as topological structure information, to predict the associations between drugs and diseases. Specifically, we design a relation-aware attention mechanism to compute the attention scores for each neighboring entity of a given entity under different relations. To address the challenge of randomness of the initial attention scores potentially impacting model performance and to expand the search scope of the model, we designed a heuristic search module based on Gumbel-Softmax, which uses attention scores as heuristic information and introduces randomness to assist the model in exploring more optimal embeddings of drugs and diseases. Following this module, we derive the relation weights, obtain the embeddings of drugs and diseases through neighborhood aggregation, and then predict drug-disease associations. Additionally, we employ feature-based augmented views to enhance model robustness and mitigate overfitting issues. We have implemented our method and conducted experiments on two data sets. The results demonstrate that KGCNH outperforms competing methods. In particular, case studies on lithium and quetiapine confirm that KGCNH can retrieve more actual drug-disease associations in the top prediction results.

MethPhaser: methylation-based long-read haplotype phasing of human genomes

The assignment of variants across haplotypes, phasing, is crucial for predicting the consequences, interaction, and inheritance of mutations and is a key step in improving our understanding of phenotype and disease. However, phasing is limited by read length and stretches of homozygosity along the genome. To overcome this limitation, we designed MethPhaser, a method that utilizes methylation signals from Oxford Nanopore Technologies to extend Single Nucleotide Variation (SNV)-based phasing. We demonstrate that haplotype-specific methylations extensively exist in Human genomes and the advent of long-read technologies enabled direct report of methylation signals. For ONT R9 and R10 cell line data, we increase the phase length N50 by 78%-151% at a phasing accuracy of 83.4-98.7% To assess the impact of tissue purity and random methylation signals due to inactivation, we also applied MethPhaser on blood samples from 4 patients, still showing improvements over SNV-only phasing. MethPhaser further improves phasing across HLA and multiple other medically relevant genes, improving our understanding of how mutations interact across multiple phenotypes. The concept of MethPhaser can also be extended to non-human diploid genomes. MethPhaser is available at https://github.com/treangenlab/methphaser .

Inconsistencies between prenatal diagnostic and genetic testing laboratories on variant validation of rare monogenic diseases

BACKGROUND

The advent of next-generation sequencing (NGS) has enhanced the diagnostic efficacy for monogenic diseases, while presenting challenges in achieving consistent diagnoses.

METHOD

We retrospectively analyzed the concordance rate and reasons for the inconsistency between the original diagnostic result from the genetic testing laboratory and the variant validation result from the prenatal diagnostic center. The validation procedure comprised three stages: validation of variant detection, reevaluation of variant classification, and assessment of recurrence risk, which involved verifying the mode of inheritance and parental carriage.

RESULT

In total, 17 (6%) of the 286 families affected by rare monogenic diseases showed different results during the variant validation procedure. These cases comprised four (23.5%) with variant detection errors, 12 (70.5%) with inconsistent interpretation, and one (6%) with non-Mendelian inheritance patterns. False-positive NGS results confirmed by Sanger sequencing were related to pseudogenes and GC-rich regions. The classification of the 17 variants was altered in the 12 cases owing to various factors. The case with an atypical inheritance pattern was originally considered autosomal recessive inheritance, but was diagnosed as maternal uniparental disomy after additional genetic analysis.

CONCLUSION

We underscored the significance of variant validation by prenatal diagnostic centers. Families affected by monogenic diseases with reproductive plans should be referred to prenatal genetic centers as early as possible to avoid different results that may postpone subsequent prenatal diagnosis.

Deciphering the pathogenic role of rare RAF1 heterozygous missense mutation in the late-presenting DDH

Background

Developmental Dysplasia of the Hip (DDH) is a skeletal disorder where late-presenting forms often escape early diagnosis, leading to limb and pain in adults. The genetic basis of DDH is not fully understood despite known genetic predispositions.

Methods

We employed Whole Genome Sequencing (WGS) to explore the genetic factors in late-presenting DDH in two unrelated families, supported by phenotypic analyses and in vitro validation.

Results

In both cases, a novel de novo heterozygous missense mutation in RAF1 (c.193A>G [p.Lys65Glu]) was identified. This mutation impacted RAF1 protein structure and function, altering downstream signaling in the Ras/ERK pathway, as demonstrated by bioinformatics, molecular dynamics simulations, and in vitro validations.

Conclusion

This study contributes to our understanding of the genetic factors involved in DDH by identifying a novel mutation in RAF1. The identification of the RAF1 mutation suggests a possible involvement of the Ras/ERK pathway in the pathogenesis of late-presenting DDH, indicating its potential role in skeletal development.

Biobanks in chronic disease management: A comprehensive review of strategies, challenges, and future directions

Biobanks, through the collection and storage of patient blood, tissue, genomic, and other biological samples, provide unique and rich resources for the research and management of chronic diseases such as cardiovascular diseases, diabetes, and cancer. These samples contain valuable cellular and molecular level information that can be utilized to decipher the pathogenesis of diseases, guide the development of novel diagnostic technologies, treatment methods, and personalized medical strategies. This article first outlines the historical evolution of biobanks, their classification, and the impact of technological advancements. Subsequently, it elaborates on the significant role of biobanks in revealing molecular biomarkers of chronic diseases, promoting the translation of basic research to clinical applications, and achieving individualized treatment and management. Additionally, challenges such as standardization of sample processing, information privacy, and security are discussed. Finally, from the perspectives of policy support, regulatory improvement, and public participation, this article provides a forecast on the future development directions of biobanks and strategies to address challenges, aiming to safeguard and enhance their unique advantages in supporting chronic disease prevention and treatment.

Culture-acquired genetic variation in human pluripotent stem cells: Twenty years on

Genetic changes arising in human pluripotent stem cells (hPSC) upon culture may bestow unwanted or detrimental phenotypes to cells, thus potentially impacting on the applications of hPSCs for clinical use and basic research. In the 20 years since the first report of culture-acquired genetic aberrations in hPSCs, a characteristic spectrum of recurrent aberrations has emerged. The preponderance of such aberrations implies that they provide a selective growth advantage to hPSCs upon expansion. However, understanding the consequences of culture-acquired variants for specific applications in cell therapy or research has been more elusive. The rapid progress of hPSC-based therapies to clinics is galvanizing the field to address this uncertainty and provide definitive ways both for risk assessment of variants and reducing their prevalence in culture. Here, we aim to provide a timely update on almost 20 years of research on this fascinating, but a still unresolved and concerning, phenomenon.

Fecal microbiota transplantation: current challenges and future landscapes

SUMMARYGiven the importance of gut microbial homeostasis in maintaining health, there has been considerable interest in developing innovative therapeutic strategies for restoring gut microbiota. One such approach, fecal microbiota transplantation (FMT), is the main "whole gut microbiome replacement" strategy and has been integrated into clinical practice guidelines for treating recurrent Clostridioides difficile infection (rCDI). Furthermore, the potential application of FMT in other indications such as inflammatory bowel disease (IBD), metabolic syndrome, and solid tumor malignancies is an area of intense interest and active research. However, the complex and variable nature of FMT makes it challenging to address its precise functionality and to assess clinical efficacy and safety in different disease contexts. In this review, we outline clinical applications, efficacy, durability, and safety of FMT and provide a comprehensive assessment of its procedural and administration aspects. The clinical applications of FMT in children and cancer immunotherapy are also described. We focus on data from human studies in IBD in contrast with rCDI to delineate the putative mechanisms of this treatment in IBD as a model, including colonization resistance and functional restoration through bacterial engraftment, modulating effects of virome/phageome, gut metabolome and host interactions, and immunoregulatory actions of FMT. Furthermore, we comprehensively review omics technologies, metagenomic approaches, and bioinformatics pipelines to characterize complex microbial communities and discuss their limitations. FMT regulatory challenges, ethical considerations, and pharmacomicrobiomics are also highlighted to shed light on future development of tailored microbiome-based therapeutics.

Unveiling inflammatory and prehypertrophic cell populations as key contributors to knee cartilage degeneration in osteoarthritis using multi-omics data integration

OBJECTIVES

Single-cell and spatial transcriptomics analysis of human knee articular cartilage tissue to present a comprehensive transcriptome landscape and osteoarthritis (OA)-critical cell populations.

METHODS

Single-cell RNA sequencing and spatially resolved transcriptomic technology have been applied to characterise the cellular heterogeneity of human knee articular cartilage which were collected from 8 OA donors, and 3 non-OA control donors, and a total of 19 samples. The novel chondrocyte population and marker genes of interest were validated by immunohistochemistry staining, quantitative real-time PCR, etc. The OA-critical cell populations were validated through integrative analyses of publicly available bulk RNA sequencing data and large-scale genome-wide association studies.

RESULTS

We identified 33 cell population-specific marker genes that define 11 chondrocyte populations, including 9 known populations and 2 new populations, that is, pre-inflammatory chondrocyte population (preInfC) and inflammatory chondrocyte population (InfC). The novel findings that make this an important addition to the literature include: (1) the novel InfC activates the mediator MIF-CD74; (2) the prehypertrophic chondrocyte (preHTC) and hypertrophic chondrocyte (HTC) are potentially OA-critical cell populations; (3) most OA-associated differentially expressed genes reside in the articular surface and superficial zone; (4) the prefibrocartilage chondrocyte (preFC) population is a major contributor to the stratification of patients with OA, resulting in both an inflammatory-related subtype and a non-inflammatory-related subtype.

CONCLUSIONS

Our results highlight InfC, preHTC, preFC and HTC as potential cell populations to target for therapy. Also, we conclude that profiling of those cell populations in patients might be used to stratify patient populations for defining cohorts for clinical trials and precision medicine.

WGS of intrauterine E. coli from cows with early postpartum uterine infection reveals a non-uterine specific genotype and virulence factors

Escherichia coli has been attributed to playing a major role in a cascade of events that affect the prevalence and severity of uterine disease in cattle. The objectives of this project were to (i) define the association between the prevalence of specific antimicrobial resistance and virulence factor genes in E. coli with the clinical status related to uterine infection, (ii) identify the genetic relationship between E. coli isolates from cows with diarrhea, with mastitis, and with and without metritis, and (iii) determine the association between the phenotypic and genotypic antimicrobial resistance identified on the E. coli isolated from postpartum cattle. Bacterial isolates (n = 148) were obtained from a larger cross-sectional study. Cows were categorized into one of three clinical groups before enrollment: metritis, cows with purulent discharge, and control cows. For genomic comparison, public genomes (n = 130) from cows with diarrhea, mastitis, and metritis were included in a genome-wide association study, to evaluate differences between the drug classes or the virulence factor category among clinical groups. A distinct E. coli genotype associated with metritis could not be identified. Instead, a high genetic diversity among the isolates from uterine sources was present. A virulence factor previously associated with metritis (fimH) using PCR was not associated with metritis. There was moderate accuracy for whole-genome sequencing to predict phenotypic resistance, which varied depending on the antimicrobial tested. Findings from this study contradict the traditional pathotype classification and the unique intrauterine E. coli genotype associated with metritis in dairy cows.IMPORTANCEMetritis is a common infectious disease in dairy cattle and the second most common reason for treating a cow with antimicrobials. The pathophysiology of the disease is complex and is not completely understood. Specific endometrial pathogenic Escherichia coli have been reported to be adapted to the endometrium and sometimes lead to uterine disease. Unfortunately, the specific genomic details of the endometrial-adapted isolates have not been investigated using enough genomes to represent the genomic diversity of this organism to identify specific virulence genes that are consistently associated with disease development and severity. Results from this study provide key microbial ecological advances by elucidating and challenging accepted concepts for the role of Intrauterine E. coli in metritis in dairy cattle, especially contradicting the existence of a unique intrauterine E. coli genotype associated with metritis in dairy cows, which was not found in our study.

Single nucleotide polymorphism-based biomarker in primary hypertension

Primary hypertension is a multiplex and multifactorial disease influenced by various strong components including genetics. Extensive research such as Genome-wide association studies and candidate gene studies have revealed various single nucleotide polymorphisms (SNPs) related to hypertension, providing insights into the genetic basis of the condition. This review summarizes the current status of SNP research in primary hypertension, including examples of hypertension-related SNPs, their location, function, and frequency in different populations. The potential clinical implications of SNP research for primary hypertension management are also discussed, including disease risk prediction, personalized medicine, mechanistic understanding, and lifestyle modifications. Furthermore, this review highlights emerging technologies and methodologies that have the potential to revolutionize the vast understanding of the basis of genetics in primary hypertension. Gene editing holds the potential to target and correct any kind of genetic mutations that contribute to the development of hypertension or modify genes involved in blood pressure regulation to prevent or treat the condition. Advances in computational biology and machine learning enable researchers to analyze large datasets and identify complex genetic interactions contributing to hypertension risk. In conclusion, SNP research in primary hypertension is rapidly evolving with emerging technologies and methodologies that have the potential to transform the knowledge about genetic basis related to the condition. These advances hold promise for personalized prevention and treatment strategies tailored to an individual's genetic profile ultimately improving patient outcomes and reducing healthcare costs.

Ochrobactrum anthropi infection following corneal transplantation -a case report and review of literature

BACKGROUND

Ochrobactrum anthropi is widely distributed and primarily infects patients with compromised immune functions . Historically, O. anthropi has been considered to possess low toxicity and pathogenicity; however, recent studies suggest that it may in fact cause severe purulent infections. In this case study, we examine a case of O. anthropi infection following corneal transplantation, exploring the occurrence and outcomes of such post-operative infections.

CASE PRESENTATION

A retrospective analysis of cases involved examinations, genetic testing for diagnosis, and subsequent treatment. In patients undergoing partial penetrating keratoplasty with a fungal corneal ulcer perforation, anterior chamber exudation and purulence were observed post-surgery. Despite antifungal treatment, genetic testing of the anterior chamber fluid and purulent material confirmed O. anthropi infection. The use of antimicrobial treatment specifically targeting O. anthropi was found to be effective in treating the infection.

CONCLUSION

Inflammatory reactions following corneal transplantation should be should be monitored for the presence of other infections. Genetic testing has significant implications for clinical diagnosis and treatment.

Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network

In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.

Next generation sequencing technologies to address aberrant mRNA translation in cancer

In this review, we explore the transformative impact of next generation sequencing technologies in the realm of translatomics (the study of how translational machinery acts on a genome-wide scale). Despite the expectation of a direct correlation between mRNA and protein content, the complex regulatory mechanisms that affect this relationship remark the limitations of standard RNA-seq approaches. Then, the review characterizes crucial techniques such as polysome profiling, ribo-seq, trap-seq, proximity-specific ribosome profiling, rnc-seq, tcp-seq, qti-seq and scRibo-seq. All these methods are summarized within the context of cancer research, shedding light on their applications in deciphering aberrant translation in cancer cells. In addition, we encompass databases and bioinformatic tools essential for researchers that want to address translatome analysis in the context of cancer biology.

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

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

Predicting class switch recombination in B-cells from antibody repertoire data

Statistical and machine learning methods have proved useful in many areas of immunology. In this paper, we address for the first time the problem of predicting the occurrence of class switch recombination (CSR) in B-cells, a problem of interest in understanding antibody response under immunological challenges. We propose a framework to analyze antibody repertoire data, based on clonal (CG) group representation in a way that allows us to predict CSR events using CG level features as input. We assess and compare the performance of several predicting models (logistic regression, LASSO logistic regression, random forest, and support vector machine) in carrying out this task. The proposed approach can obtain an unweighted average recall of71 % $71\%$ with models based on variable region descriptors and measures of CG diversity during an immune challenge and, most notably, before an immune challenge.

Development of new techniques and clinical applications of liquid biopsy in lung cancer management

Lung cancer is an exceedingly malignant tumor reported as having the highest morbidity and mortality of any cancer worldwide, thus posing a great threat to global health. Despite the growing demand for precision medicine, current methods for early clinical detection, treatment and prognosis monitoring in lung cancer are hampered by certain bottlenecks. Studies have found that during the formation and development of a tumor, molecular substances carrying tumor-related genetic information can be released into body fluids. Liquid biopsy (LB), a method for detecting these tumor-related markers in body fluids, maybe a way to make progress in these bottlenecks. In recent years, LB technology has undergone rapid advancements. Therefore, this review will provide information on technical updates to LB and its potential clinical applications, evaluate its effectiveness for specific applications, discuss the existing limitations of LB, and present a look forward to possible future clinical applications. Specifically, this paper will introduce technical updates from the prospectives of engineering breakthroughs in the detection of membrane-based LB biomarkers and other improvements in sequencing technology. Additionally, it will summarize the latest applications of liquid biopsy for the early detection, diagnosis, treatment, and prognosis of lung cancer. We will present the interconnectedness of clinical and laboratory issues and the interplay of technology and application in LB today.

Microbial diagnostics in periodontal diseases

Microbial analytical methods have been instrumental in elucidating the complex microbial etiology of periodontal diseases, by shaping our understanding of subgingival community dynamics. Certain pathobionts can orchestrate the establishment of dysbiotic communities that can subvert the host immune system, triggering inflammation and tissue destruction. Yet, diagnosis and management of periodontal conditions still rely on clinical and radiographic examinations, overlooking the well-established microbial etiology. This review summarizes the chronological emergence of periodontal etiological models and the co-evolution with technological advances in microbial detection. We additionally review the microbial analytical approaches currently accessible to clinicians, highlighting their value in broadening the periodontal assessment. The epidemiological importance of obtaining culture-based antimicrobial susceptibility profiles of periodontal taxa for antibiotic resistance surveillance is also underscored, together with clinically relevant analytical approaches to guide antibiotherapy choices, when necessary. Furthermore, the importance of 16S-based community and shotgun metagenomic profiling is discussed in outlining dysbiotic microbial signatures. Because dysbiosis precedes periodontal damage, biomarker identification offers early diagnostic possibilities to forestall disease relapses during maintenance. Altogether, this review highlights the underutilized potential of clinical microbiology in periodontology, spotlighting the clinical areas most conductive to its diagnostic implementation for enhancing prevention, treatment predictability, and addressing global antibiotic resistance.

Exploring the comparative genome of rice pathogen Burkholderia plantarii: unveiling virulence, fitness traits, and a potential type III secretion system effector

This study presents a comprehensive genomic analysis of Burkholderia plantarii, a rice pathogen that causes blight and grain rot in seedlings. The entire genome of B. plantarii KACC 18964 was sequenced, followed by a comparative genomic analysis with other available genomes to gain insights into its virulence, fitness, and interactions with rice. Multiple secondary metabolite gene clusters were identified. Among these, 12 demonstrated varying similarity levels to known clusters linked to bioactive compounds, whereas eight exhibited no similarity, indicating B. plantarii as a source of potentially novel secondary metabolites. Notably, the genes responsible for tropolone and quorum sensing were conserved across the examined genomes. Additionally, B. plantarii was observed to possess three complete CRISPR systems and a range of secretion systems, exhibiting minor variations among the analyzed genomes. Genomic islands were analyzed across the four genomes, and a detailed study of the B. plantarii KACC 18964 genome revealed 59 unique islands. These islands were thoroughly investigated for their gene contents and potential roles in virulence. Particular attention has been devoted to the Type III secretion system (T3SS), a crucial virulence factor. An in silico analysis of potential T3SS effectors identified a conserved gene, aroA. Further mutational studies, in planta and in vitro analyses validated the association between aroA and virulence in rice. Overall, this study enriches our understanding of the genomic basis of B. plantarii pathogenicity and emphasizes the potential role of aroA in virulence. This understanding may guide the development of effective disease management strategies.

Characterization of telomere variant repeats using long reads enables allele-specific telomere length estimation

Telomeres are regions of repetitive DNA at the ends of linear chromosomes which protect chromosome ends from degradation. Telomere lengths have been extensively studied in the context of aging and disease, though most studies use average telomere lengths which are of limited utility. We present a method for identifying all 92 telomere alleles from long read sequencing data. Individual telomeres are identified using variant repeats proximal to telomere regions, which are unique across alleles. This high-throughput and high-resolution characterization of telomeres could be foundational to future studies investigating the roles of specific telomeres in aging and disease.

Enzyme-assisted high throughput sequencing of an expanded genetic alphabet at single base resolution

With just four building blocks, low sequence information density, few functional groups, poor control over folding, and difficulties in forming compact folds, natural DNA and RNA have been disappointing platforms from which to evolve receptors, ligands, and catalysts. Accordingly, synthetic biology has created "artificially expanded genetic information systems" (AEGIS) to add nucleotides, functionality, and information density. With the expected improvements seen in AegisBodies and AegisZymes, the task for synthetic biologists shifts to developing for expanded DNA the same analytical tools available to natural DNA. Here we report one of these, an enzyme-assisted sequencing of expanded genetic alphabet (ESEGA) method to sequence six-letter AEGIS DNA. We show how ESEGA analyses this DNA at single base resolution, and applies it to optimized conditions for six-nucleotide PCR, assessing the fidelity of various DNA polymerases, and extending this to AEGIS components with functional groups. This supports the renewed exploitation of expanded DNA alphabets in biotechnology.

The Third-Generation Sequencing Challenge: Novel Insights for the Omic Sciences

The understanding of the human genome has been greatly improved by the advent of next-generation sequencing technologies (NGS). Despite the undeniable advantages responsible for their widespread diffusion, these methods have some constraints, mainly related to short read length and the need for PCR amplification. As a consequence, long-read sequencers, called third-generation sequencing (TGS), have been developed, promising to overcome NGS. Starting from the first prototype, TGS has progressively ameliorated its chemistries by improving both read length and base-calling accuracy, as well as simultaneously reducing the costs/base. Based on these premises, TGS is showing its potential in many fields, including the analysis of difficult-to-sequence genomic regions, structural variations detection, RNA expression profiling, DNA methylation study, and metagenomic analyses. Protocol standardization and the development of easy-to-use pipelines for data analysis will enhance TGS use, also opening the way for their routine applications in diagnostic contexts.

A Two-color Single-molecule Sequencing Platform and Its Clinical Applications

DNA sequencers have become increasingly important research and diagnostic tools over the past 20 years. In this study, we developed a single-molecule desktop sequencer, GenoCare 1600 (GenoCare), which utilizes amplification-free library preparation and two-color sequencing-by-synthesis chemistry, making it more user-friendly compared with previous single-molecule sequencing platforms for clinical use. Using the GenoCare platform, we sequenced an Escherichia coli standard sample and achieved a consensus accuracy exceeding 99.99%. We also evaluated the sequencing performance of this platform in microbial mixtures and coronavirus disease 2019 (COVID-19) samples from throat swabs. Our findings indicate that the GenoCare platform allows for microbial quantitation, sensitive identification of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and accurate detection of virus mutations, as confirmed by Sanger sequencing, demonstrating its remarkable potential in clinical application.

Whole-genome sequencing of Ganoderma boninense, the causal agent of basal stem rot disease in oil palm, via combined short- and long-read sequencing

The hemibiotrophic Basidiomycete pathogen Ganoderma boninense (Gb) is the dominant causal agent of oil palm basal stem rot disease. Here, we report a complete chromosomal genome map of Gb using a combination of short-read Illumina and long-read Pacific Biosciences (PacBio) sequencing platforms combined with chromatin conformation capture data from the Chicago and Hi-C platforms. The genome was 55.87 Mb in length and assembled to a high contiguity (N50: 304.34 kb) of 12 chromosomes built from 112 scaffolds, with a total of only 4.34 Mb (~ 7.77%) remaining unplaced. The final assemblies were evaluated for completeness of the genome by using Benchmarking Universal Single Copy Orthologs (BUSCO) v4.1.4, and based on 4464 total BUSCO polyporales group searches, the assemblies yielded 4264 (95.52%) of the conserved orthologs as complete and only a few fragmented BUSCO of 42 (0.94%) as well as a missing BUSCO of 158 (3.53%). Genome annotation predicted a total of 21,074 coding genes, with a GC content ratio of 59.2%. The genome features were analyzed with different databases, which revealed 2471 Gene Ontology/GO (11.72%), 5418 KEGG (Kyoto Encyclopedia of Genes and Genomes) Orthologous/KO (25.71%), 13,913 Cluster of Orthologous Groups of proteins/COG (66.02%), 60 ABC transporter (0.28%), 1049 Carbohydrate-Active Enzymes/CAZy (4.98%), 4005 pathogen-host interactions/PHI (19%), and 515 fungal transcription factor/FTFD (2.44%) genes. The results obtained in this study provide deep insight for further studies in the future.

High-throughput DNA synthesis for data storage

With the explosion of digital world, the dramatically increasing data volume is expected to reach 175 ZB (1 ZB = 1012 GB) in 2025. Storing such huge global data would consume tons of resources. Fortunately, it has been found that the deoxyribonucleic acid (DNA) molecule is the most compact and durable information storage medium in the world so far. Its high coding density and long-term preservation properties make itself one of the best data storage carriers for the future. High-throughput DNA synthesis is a key technology for "DNA data storage", which encodes binary data stream (0/1) into quaternary long DNA sequences consisting of four bases (A/G/C/T). In this review, the workflow of DNA data storage and the basic methods of artificial DNA synthesis technology are outlined first. Then, the technical characteristics of different synthesis methods and the state-of-the-art of representative commercial companies, with a primary focus on silicon chip microarray-based synthesis and novel enzymatic DNA synthesis are presented. Finally, the recent status of DNA storage and new opportunities for future development in the field of high-throughput, large-scale DNA synthesis technology are summarized.

Long-Read Nanopore-Based Sequencing of Anelloviruses

Routinely used metagenomic next-generation sequencing (mNGS) techniques often fail to detect low-level viremia (<104 copies/mL) and appear biased towards viruses with linear genomes. These limitations hinder the capacity to comprehensively characterize viral infections, such as those attributed to the Anelloviridae family. These near ubiquitous non-pathogenic components of the human virome have circular single-stranded DNA genomes that vary in size from 2.0 to 3.9 kb and exhibit high genetic diversity. Hence, species identification using short reads can be challenging. Here, we introduce a rolling circle amplification (RCA)-based metagenomic sequencing protocol tailored for circular single-stranded DNA genomes, utilizing the long-read Oxford Nanopore platform. The approach was assessed by sequencing anelloviruses in plasma drawn from people who inject drugs (PWID) in two geographically distinct cohorts. We detail the methodological adjustments implemented to overcome difficulties inherent in sequencing circular genomes and describe a computational pipeline focused on anellovirus detection. We assessed our protocol across various sample dilutions and successfully differentiated anellovirus sequences in conditions simulating mixed infections. This method provides a robust framework for the comprehensive characterization of circular viruses within the human virome using the Oxford Nanopore.

MASTR-seq: Multiplexed Analysis of Short Tandem Repeats with sequencing

More than 60 human disorders have been linked to unstable expansion of short tandem repeat (STR) tracts. STR length and the extent of DNA methylation is linked to disease pathology and can be mosaic in a cell type-specific manner in several repeat expansion disorders. Mosaic phenomenon have been difficult to study to date due to technical bias intrinsic to repeat sequences and the need for multi-modal measurements at single-allele resolution. Nanopore long-read sequencing accurately measures STR length and DNA methylation in the same single molecule but is cost prohibitive for studies assessing a target locus across multiple experimental conditions or patient samples. Here, we describe MASTR-seq, M ultiplexed A nalysis of S hort T andem R epeats, for cost-effective, high-throughput, accurate, multi-modal measurements of DNA methylation and STR genotype at single-allele resolution. MASTR-seq couples long-read sequencing, Cas9-mediated target enrichment, and PCR-free multiplexed barcoding to achieve a >ten-fold increase in on-target read mapping for 8-12 pooled samples in a single MinION flow cell. We provide a detailed experimental protocol and computational tools and present evidence that MASTR-seq quantifies tract length and DNA methylation status for CGG and CAG STR loci in normal-length and mutation-length human cell lines. The MASTR-seq protocol takes approximately eight days for experiments and one additional day for data processing and analyses.

Key points

We provide a protocol for MASTR-seq: M ultiplexed A nalysis of S hort T andem R epeats using Cas9-mediated target enrichment and PCR-free, multiplexed nanopore sequencing. MASTR-seq achieves a >10-fold increase in on-target read proportion for highly repetitive, technically inaccessible regions of the genome relevant for human health and disease.MASTR-seq allows for high-throughput, efficient, accurate, and cost-effective measurement of STR length and DNA methylation in the same single allele for up to 8-12 samples in parallel in one Nanopore MinION flow cell.

Are cereal grasses a single genetic system?

In 1993, a passionate and provocative call to arms urged cereal researchers to consider the taxon they study as a single genetic system and collaborate with each other. Since then, that group of scientists has seen their discipline blossom. In an attempt to understand what unity of genetic systems means and how the notion was borne out by later research, we survey the progress and prospects of cereal genomics: sequence assemblies, population-scale sequencing, resistance gene cloning and domestication genetics. Gene order may not be as extraordinarily well conserved in the grasses as once thought. Still, several recurring themes have emerged. The same ancestral molecular pathways defining plant architecture have been co-opted in the evolution of different cereal crops. Such genetic convergence as much as cross-fertilization of ideas between cereal geneticists has led to a rich harvest of genes that, it is hoped, will lead to improved varieties.

Nanopore-based glycan sequencing: state of the art and future prospects

Sequencing of biomacromolecules is a crucial cornerstone in life sciences. Glycans, one of the fundamental biomolecules, derive their physiological and pathological functions from their structures. Glycan sequencing faces challenges due to its structural complexity and current detection technology limitations. As a highly sensitive sensor, nanopores can directly convert nucleic acid sequence information into electrical signals, spearheading the revolution of third-generation nucleic acid sequencing technologies. However, their potential for deciphering complex glycans remains untapped. Initial attempts demonstrated the significant sensitivity of nanopores in glycan sensing, which provided the theoretical basis and insights for the realization of nanopore-based glycan sequencing. Here, we present three potential technical routes to employ nanopore technology in glycan sequencing for the first time. The three novel technical routes include: strand sequencing, capturing glycan chains as they translocate through nanopores; sequential hydrolysis sequencing, capturing released monosaccharides one by one; splicing sequencing, mapping signals from hydrolyzed glycan fragments to an oligosaccharide database/library. Designing suitable nanopores, enzymes, and motors, and extracting characteristic signals pose major challenges, potentially aided by artificial intelligence. It would be highly desirable to design an all-in-one high-throughput glycan sequencer instrument by integrating a sample processing unit, nanopore array, and signal acquisition system into a microfluidic device. The nanopore sequencer invention calls for intensive multidisciplinary cooperation including electrochemistry, glycochemistry, engineering, materials, enzymology, etc. Advancing glycan sequencing will promote the development of basic research and facilitate the discovery of glycan-based drugs and disease markers, fostering progress in glycoscience and even life sciences.

A review of the role of transcription factors in regulating adipogenesis and lipogenesis in beef cattle

In the past few decades, genomic selection and other refined strategies have been used to increase the growth rate and lean meat production of beef cattle. Nevertheless, the fast growth rates of cattle breeds are often accompanied by a reduction in intramuscular fat (IMF) deposition, impairing meat quality. Transcription factors play vital roles in regulating adipogenesis and lipogenesis in beef cattle. Meanwhile, understanding the role of transcription factors in regulating adipogenesis and lipogenesis in beef cattle has gained significant attention to increase IMF deposition and meat quality. Therefore, the aim of this paper was to provide a comprehensive summary and valuable insight into the complex role of transcription factors in adipogenesis and lipogenesis in beef cattle. This review summarizes the contemporary studies in transcription factors in adipogenesis and lipogenesis, genome-wide analysis of transcription factors, epigenetic regulation of transcription factors, nutritional regulation of transcription factors, metabolic signalling pathways, functional genomics methods, transcriptomic profiling of adipose tissues, transcription factors and meat quality and comparative genomics with other livestock species. In conclusion, transcription factors play a crucial role in promoting adipocyte development and fatty acid biosynthesis in beef cattle. They control adipose tissue formation and metabolism, thereby improving meat quality and maintaining metabolic balance. Understanding the processes by which these transcription factors regulate adipose tissue deposition and lipid metabolism will simplify the development of marbling or IMF composition in beef cattle.

A genome assembly of ginger (Zingiber officinale Roscoe) provides insights into genome evolution and 6-gingerol biosynthesis

Ginger is cultivated in tropical and subtropical regions and is one of the most crucial spices worldwide owing to its special taste and scent. Here, we present a high-quality genome assembly for 'Small Laiwu Ginger', a famous cultivated ginger in northern China. The ginger genome was phased into two haplotypes, haplotype A (1.55Gb), and haplotype B (1.44Gb). Analysis of Ty1/Copia and Ty3/Gypsy LTR retrotransposon families revealed that both have undergone multiple retrotransposon bursts about 0-1 million years ago. In addition to a recent whole-genome duplication event, there has been a lineage-specific expansion of genes involved in stilbenoid, diarylheptanoid, and gingerol biosynthesis, thereby enhancing 6-gingerol biosynthesis. Furthermore, we focused on the biosynthesis of 6-gingerol, the most important gingerol, and screened key transcription factors ZoMYB106 and ZobHLH148 that regulate 6-gingerol synthesis by transcriptomic and metabolomic analysis in the ginger rhizome at four growth stages. The results of yeast one-hybrid, electrophoretic mobility shift, and dual-luciferase reporter gene assays showed that both ZoMYB106 and ZobHLH148 bind to the promoters of the key rate-limiting enzyme genes ZoCCOMT1 and ZoCCOMT2 in the 6-gingerol synthesis pathway and promote their transcriptional activities. The reference genome, transcriptome, and metabolome data pave the way for further research on the molecular mechanism underlying the biosynthesis of 6-gingerol. Furthermore, it provides precious new resources for the study on the biology and molecular breeding of ginger.

Next-Generation Sequencing and Emerging Technologies

Genetic sequencing technologies are evolving at a rapid pace with major implications for research and clinical practice. In this review, the authors provide an updated overview of next-generation sequencing (NGS) and emerging methodologies. NGS has tremendously improved sequencing output while being more time and cost-efficient in comparison to Sanger sequencing. The authors describe short-read sequencing approaches, such as sequencing by synthesis, ion semiconductor sequencing, and nanoball sequencing. Third-generation long-read sequencing now promises to overcome many of the limitations of short-read sequencing, such as the ability to reliably resolve repeat sequences and large genomic rearrangements. By combining complementary methods with massively parallel DNA sequencing, a greater insight into the biological context of disease mechanisms is now possible. Emerging methodologies, such as advances in nanopore technology, in situ nucleic acid sequencing, and microscopy-based sequencing, will continue the rapid evolution of this area. These new technologies hold many potential applications for hematological disorders, with the promise of precision and personalized medical care in the future.

Paving the path for implementation of clinical genomic sequencing globally: Are we ready?

Despite the emerging evidence in recent years, successful implementation of clinical genomic sequencing (CGS) remains limited and is challenged by a range of barriers. These include a lack of standardized practices, limited economic assessments for specific indications, limited meaningful patient engagement in health policy decision-making, and the associated costs and resource demand for implementation. Although CGS is gradually becoming more available and accessible worldwide, large variations and disparities remain, and reflections on the lessons learned for successful implementation are sparse. In this commentary, members of the Global Economics and Evaluation of Clinical Genomics Sequencing Working Group (GEECS) describe the global landscape of CGS in the context of health economics and policy and propose evidence-based solutions to address existing and future barriers to CGS implementation. The topics discussed are reflected as two overarching themes: (1) system readiness for CGS and (2) evidence, assessments, and approval processes. These themes highlight the need for health economics, public health, and infrastructure and operational considerations; a robust patient- and family-centered evidence base on CGS outcomes; and a comprehensive, collaborative, interdisciplinary approach.

Nanomedomics

Nanomaterials have attractive physicochemical properties. A variety of nanomaterials such as inorganic, lipid, polymers, and protein nanoparticles have been widely developed for nanomedicine via chemical conjugation or physical encapsulation of bioactive molecules. Superior to traditional drugs, nanomedicines offer high biocompatibility, good water solubility, long blood circulation times, and tumor-targeting properties. Capitalizing on this, several nanoformulations have already been clinically approved and many others are currently being studied in clinical trials. Despite their undoubtful success, the molecular mechanism of action of the vast majority of nanomedicines remains poorly understood. To tackle this limitation, herein, this review critically discusses the strategy of applying multiomics analysis to study the mechanism of action of nanomedicines, named nanomedomics, including advantages, applications, and future directions. A comprehensive understanding of the molecular mechanism could provide valuable insight and therefore foster the development and clinical translation of nanomedicines.

Clinical sequencing defines the somatic and germline mutation landscapes of Chinese HER2-Low Breast Cancer

More than half of the breast cancer initially labeled as human epidermal growth factor receptor 2 (HER2)-negative actually exhibited low HER2 levels (IHC 1+ or IHC 2+/FISH-) and were classified as HER2-low breast cancer. Previous research emphasized the significant biological heterogeneity in HER2-low breast cancer, highlighting the importance of accurately characterizing HER2-low tumors to promote the precise management of antibody‒drug conjugates. In this study, we established a large-scale targeted sequencing cohort (N = 1907) representing Chinese HER2-low breast cancer patients with detailed clinical annotation. Our research findings revealed that HER2-low breast cancer demonstrated distinct clinical pathological characteristics and mutation landscapes compared to HER2-zero group. When compared to HER2-zero tumors, HER2-low tumors exhibited a higher proportion of Luminal B subtypes and better disease-free survival. In hormone receptor (HR)-positive breast cancer, HER2-low group showed a higher frequency of GATA3 somatic mutations, BRCA2 germline mutations, and mutations in the DNA damage repair pathway. In contrast, in HR-negative breast cancer, the HER2-low group displayed a higher frequency of PIK3CA mutations and PI3K pathway alterations. These findings offered valuable insights for the precise targeted treatment of HER2-low breast cancer in different HR statuses.

Development, High-Throughput Profiling, and Biopanning of a Large Phage Display Single-Domain Antibody Library

Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or "VHHs"), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library's diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library's vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.

S100a9 might act as a modulator of the Toll-like receptor 4 transduction pathway in chronic rhinosinusitis with nasal polyps

Chronic rhinosinusitis with nasal polyp (CRSwNP) is a highly prevalent disorder characterized by persistent nasal and sinus mucosa inflammation. Despite significant morbidity and decreased quality of life, there are limited effective treatment options for such a disease. Therefore, identifying causal genes and dysregulated pathways paves the way for novel therapeutic interventions. In the current study, a three-way interaction approach was used to detect dynamic co-expression interactions involved in CRSwNP. In this approach, the internal evolution of the co-expression relation between a pair of genes (X, Y) was captured under a change in the expression profile of a third gene (Z), named the switch gene. Subsequently, the biological relevancy of the statistically significant triplets was confirmed using both gene set enrichment analysis and gene regulatory network reconstruction. Finally, the importance of identified switch genes was confirmed using a random forest model. The results suggested four dysregulated pathways in CRSwNP, including "positive regulation of intracellular signal transduction", "arachidonic acid metabolic process", "spermatogenesis" and "negative regulation of cellular protein metabolic process". Additionally, the S100a9 as a switch gene together with the gene pair {Cd14, Tpd52l1} form a biologically relevant triplet. More specifically, we suggested that S100a9 might act as a potential upstream modulator in toll-like receptor 4 transduction pathway in the major CRSwNP pathologies.

Whole-Genome Resequencing Revealed Selective Signatures for Growth Traits in Hu and Gangba Sheep

A genomic study was conducted to uncover the selection signatures in sheep that show extremely significant differences in growth traits under the same breed, age in months, nutrition level, and management practices. Hu sheep from Gansu Province and Gangba sheep from the Tibet Autonomous Region in China were selected. We collected whole-genome data from 40 sheep individuals (24 Hu sheep and 16 Gangba sheep), through whole-genome sequencing. Selection signals were analyzed using parameters such as FST, π ratio, and Tajima's D. We have identified several candidate genes that have undergone strong selection, particularly those associated with growth traits. Specifically, five growth-related genes were identified in both the Hu sheep group (HDAC1, MYH7B, LCK, ACVR1, GNAI2) and the Gangba sheep group (RBBP8, ACSL3, FBXW11, PLAT, CRB1). Additionally, in a genomic region strongly selected in both the Hu and Gangba sheep groups (Chr 22: 51,425,001-51,500,000), the growth-associated gene CYP2E1 was identified, further highlighting the genetic factors influencing growth characteristics in these breeds. This study analyzes the genetic basis for significant differences in sheep phenotypes, identifies candidate genes related to sheep growth traits, lays the foundation for molecular genetic breeding in sheep, and accelerates the genetic improvement in livestock.

Optimizing Nucleic Acid Delivery Systems through Barcode Technology

Conventional biological experiments often focus on in vitro assays because of the inherent limitations when handling multiple variables in vivo, including labor-intensive and time-consuming procedures. Often only a subset of samples demonstrating significant efficacy in the in vitro assays can be evaluated in vivo. Nonetheless, because of the low correlation between the in vitro and in vivo tests, evaluation of the variables under examination in vivo and not solely in vitro is critical. An emerging approach to achieve high-throughput in vivo tests involves using a barcode system consisting of various nucleotide combinations. Unique barcodes for each variant enable the simultaneous testing of multiple entities, eliminating the need for separate individual tests. Subsequently, to identify crucial parameters, samples were collected and analyzed using barcode sequencing. This review explores the development of barcode design and its applications, including the evaluation of nucleic acid delivery systems and the optimization of gene expression in vivo.