Sequencing technologies — the next generation

A predictive model for MGMT promoter methylation status in glioblastoma based on terahertz spectral data

O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a crucial biomarker in glioblastoma (GBM) that influences response to temozolomide. Traditional detection methods, such as gene sequencing, are time-consuming and limited to postoperative analysis. This study explores the use of terahertz time-domain spectroscopy (THz-TDS) combined with machine learning to predict MGMT methylation status intraoperatively. By analyzing 180 GBM tissue samples, a Random Forest model was developed, achieving an AUC of 0.862. The findings suggest that THz spectroscopy offers a rapid, intraoperative alternative to traditional MGMT methylation detection methods, potentially enhancing surgical decision-making and personalized treatment strategies in GBM.

Diagnostic tools in respiratory medicine (Review)

Recent advancements in diagnostic technologies have significantly transformed the landscape of respiratory medicine, aiming for early detection, improved specificity and personalized therapeutic strategies. Innovations in imaging such as multi-slice computed tomography (CT) scanners, high-resolution CT and magnetic resonance imaging (MRI) have revolutionized our ability to visualize and assess the structural and functional aspects of the respiratory system. These techniques are complemented by breakthroughs in molecular biology that have identified specific biomarkers and genetic determinants of respiratory diseases, enabling targeted diagnostic approaches. Additionally, functional tests including spirometry and exercise testing continue to provide valuable insights into pulmonary function and capacity. The integration of artificial intelligence is poised to further refine these diagnostic tools, enhancing their accuracy and efficiency. The present narrative review explores these developments and their impact on the management and outcomes of respiratory conditions, underscoring the ongoing shift towards more precise and less invasive diagnostic modalities in respiratory medicine.

Sequencing and characterizing human mitochondrial genomes in the biobank-based genomic research paradigm

Human mitochondrial DNA (mtDNA) harbors essential mutations linked to aging, neurodegenerative diseases, and complex muscle disorders. Due to its uniparental and haploid inheritance, mtDNA captures matrilineal evolutionary trajectories, playing a crucial role in population and medical genetics. However, critical questions about the genomic diversity patterns, inheritance models, and evolutionary and medical functions of mtDNA remain unresolved or underexplored, particularly in the transition from traditional genotyping to large-scale genomic analyses. This review summarizes recent advancements in data-driven genomic research and technological innovations that address these questions and clarify the biological impact of nuclear-mitochondrial segments (NUMTs) and mtDNA variants on human health, disease, and evolution. We propose a streamlined pipeline to comprehensively identify mtDNA and NUMT genomic diversity using advanced sequencing and computational technologies. Haplotype-resolved mtDNA sequencing and assembly can distinguish authentic mtDNA variants from NUMTs, reduce diagnostic inaccuracies, and provide clearer insights into heteroplasmy patterns and the authenticity of paternal inheritance. This review emphasizes the need for integrative multi-omics approaches and emerging long-read sequencing technologies to gain new insights into mutation mechanisms, the influence of heteroplasmy and paternal inheritance on mtDNA diversity and disease susceptibility, and the detailed functions of NUMTs.

Mapping-based genome size estimation

While the size of chromosomes can be measured under a microscope, obtaining the exact size of a genome remains a challenge. Biochemical methods and k-mer distribution-based approaches allow only estimations. An alternative approach to estimate the genome size based on high contiguity assemblies and read mappings is presented here. Analyses of Arabidopsis thaliana and Beta vulgaris data sets are presented to show the impact of different parameters. Oryza sativa, Brachypodium distachyon, Solanum lycopersicum, Vitis vinifera, and Zea mays were also analyzed to demonstrate the broad applicability of this approach. Further, MGSE was also used to analyze Escherichia coli, Saccharomyces cerevisiae, and Caenorhabditis elegans datasets to show its utility beyond plants. Mapping-based Genome Size Estimation (MGSE) and additional scripts are available on GitHub: https://github.com/bpucker/MGSE . MGSE predicts genome sizes based on short reads or long reads requiring a minimal coverage of 5-fold.

A comprehensive analysis of induced pluripotent stem cell (iPSC) production and applications

The ability to reprogram mature, differentiated cells into induced pluripotent stem cells (iPSCs) using exogenous pluripotency factors opened up unprecedented opportunities for their application in biomedicine. iPSCs are already successfully used in cell and regenerative therapy, as various drug discovery platforms and for in vitro disease modeling. However, even though already 20 years have passed since their discovery, the production of iPSC-based therapies is still associated with a number of hurdles due to low reprogramming efficiency, the complexity of accurate characterization of the resulting colonies, and the concerns associated with the safety of this approach. However, significant progress in many areas of molecular biology facilitated the production, characterization, and thorough assessment of the safety profile of iPSCs. The number of iPSC-based studies has been steadily increasing in recent years, leading to the accumulation of significant knowledge in this area. In this review, we aimed to provide a comprehensive analysis of methods used for reprogramming and subsequent characterization of iPSCs, discussed barriers towards achieving these goals, and various approaches to improve the efficiency of reprogramming of different cell populations. In addition, we focused on the analysis of iPSC application in preclinical and clinical studies. The accumulated breadth of data helps to draw conclusions about the future of this technology in biomedicine.

Transduction of Lentiviral Vectors and ADORA3 in HEK293T Cells Modulated in Gene Expression and Alternative Splicing

For steady transgenic expression, lentiviral vector-mediated gene delivery is a commonly used technique. One question that needs to be explored is how external lentiviral vectors and overexpressed genes perturb cellular homeostasis, potentially altering transcriptional networks. In this study, two Human Embryonic Kidney 293T (HEK293T)-derived cell lines were established via lentiviral transduction, one overexpressing green fluorescent protein (GFP) and the other co-overexpressing GFP and ADORA3 following puromycin selection to ensure stable genomic integration. Genes with differentially transcript utilization (gDTUs) and differentially expressed genes (DEGs) across cell lines were identified after short-read and long-read RNA-seq. Only 31 genes were discovered to have changed in expression when GFP was expressed, although hundreds of genes showed variations in transcript use. In contrast, even when co-overexpression of GFP and ADORA3 alters the expression of more than 1000 genes, there are still less than 1000 gDTUs. Moreover, DEGs linked to ADORA3 overexpression play a major role in RNA splicing, whereas gDTUs are highly linked to a number of malignancies and the molecular mechanisms that underlie them. For the analysis of gene expression data from stable cell lines derived from HEK293T, our findings provide important insights into changes in gene expression and alternative splicing.

Stained DNA Dot Detection (SD3): An automated tool for quantifying fluorescent features along single stretched DNA molecules

The main information in DNA is its four-letter sequence that builds up the genetic information and that is traditionally read using sequencing methodologies. DNA can, however, also carry other important information, such as epigenetic marks and DNA damage. This information has recently been visualized along single DNA molecules using fluorescent labels. Quantifying fluorescent labels along DNA is done by counting the number of "dots" per length of each DNA molecule on DNA stretched on a glass surface. So far, a major challenge has been the lack of standardized data analysis tools. Focusing on DNA damage, we here present a Matlab-based automated software, Stained DNA Dot Detection (SD3), which uses a robust method for finding DNA molecules and estimating the number of dots along each molecule. We have validated SD3 by comparing the outcome to manual analysis using DNA extracted from cells exposed to H2O2 as a model system. Our results show that SD3 achieves high accuracy and reduced analysis time relative to manual counting. SD3 allows the user to define specific parameters regarding the DNA molecule and the location of dots to include during analysis via a user-friendly interface. We foresee that our open-source software can have broad use in the analysis of single DNA molecules and their modifications in research and in diagnostics.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Multidrug-resistant Proteus mirabilis in a critically endangered Malayan pangolin: clinical and genomic insights

Proteus mirabilis, an important zoonotic opportunistic pathogen, is widely found in nature and the intestinal tracts of animals, which can cause diarrhea, pneumonia, urinary tract infections, and other symptoms in domestic animals including sheep, pigs, cattle and chickens. In this study, necropsy of a deceased critically endangered Malayan pangolin revealed lobar pneumonia in the lungs and hepatocyte necrosis with hepatic cord disintegration in the liver. A strain of Proteus mirabilis (PM2022) was isolated from the affected lungs and liver. This bacterium exhibited multidrug resistance, being susceptible only to cefoxitin and amikacin. Whole-genome sequencing identified 26 antibiotic resistance genes, including CTX-M-65, FosA3, which mediate resistance to five classes of antibiotics, such as penicillins and quinolones. Additionally, 20 virulence factors (including the T6SS secretion system, hemolysins HpmA/B, among others) were detected. Mouse experiments confirmed its high pathogenicity (LD50 = 1.45 × 109 CFU/mL). Based on experimental and genomic testing results, the initial symptoms of Proteus mirabilis infection in pangolins manifest in the lungs, liver, and intestines, and the use of penicillins and quinolones should be avoided during treatment. This study offers clinical guidance for diagnosing and treating P. mirabilis infections in pangolins, informing evidence-based antimicrobial strategies.

Next-generation sequencing in cancer diagnosis and treatment: clinical applications and future directions

Next-generation sequencing (NGS) has emerged as a pivotal technology in the field of oncology, transforming the approach to cancer diagnosis and treatment. This paper provides a comprehensive overview of the integration of NGS into clinical settings, emphasizing its significant contributions to precision medicine. NGS enables detailed genomic profiling of tumors, identifying genetic alterations that drive cancer progression and facilitating personalized treatment plans targeting specific mutations, thereby improving patient outcomes. This capability facilitates the development of personalized treatment plans targeting specific mutations, leading to improved patient outcomes and the potential for better prognosis. The application of NGS extends beyond identifying actionable mutations; it is instrumental in detecting hereditary cancer syndromes, thus aiding in early diagnosis and preventive strategies. Furthermore, NGS plays a crucial role in monitoring minimal residual disease, offering a sensitive method to detect cancer recurrence at an early stage. Its use in guiding immunotherapy by identifying biomarkers that predict response to treatment is also highlighted. Ethical issues related to genetic testing, such as concerns around patient consent and data privacy, are also important considerations that need to be addressed for the broader implementation of NGS. These include the complexities of data interpretation, the need for robust bioinformatics support, cost considerations, and ethical issues related to genetic testing. Addressing these challenges is essential for the widespread adoption of NGS. Looking forward, advancements such as single-cell sequencing and liquid biopsies promise to further enhance the precision of cancer diagnostics and treatment. This review emphasizes the transformative impact of NGS in oncology and advocates for its incorporation into routine clinical practice to promote molecularly driven cancer care.

Comparative Analysis of Metopograpsus quadridentatus (Crustacea: Decapoda: Grapsidae) Mitochondrial Genome Reveals Gene Rearrangement and Phylogeny

The taxonomy of the genus Metopograpsus is still a matter of debate because its species have limited morphological differences. Mitochondrial genomes, which are highly informative and easily accessible genetic markers, have been widely used to study molecular evolution and clarify relationships among species. This study aims to investigate the mitochondrial genome of Metopograpsus quadridentatus, a species with unique ecological and evolutionary significance. By analyzing the mitochondrial genome, we seek to address taxonomic uncertainties and provide insights into the evolutionary history of this species. In this study, we sequenced and analyzed the mitochondrial genome of M. quadridentatus to investigate its gene rearrangement patterns and its place within Brachyura. We compared the gene order of M. quadridentatus with that of 40 other Brachyuran species and created phylogenetic trees based on the nucleotide and amino acid sequences of 13 protein-coding genes (PCGs). We found that the mitochondrial gene arrangement of M. quadridentatus is mostly unchanged, similar to the original crustacean pattern, except for the movement of the trnH gene. Notably, the gene orders of several families, such as Eriphiidae, Grapsidae, Camptandriidae, Dotillidae, Plagusiidae, Ocypodidae, and Gecarcinidae, are the same. Phylogenetic analyses consistently placed M. quadridentatus within the genus Metopograpsus and the family Grapsidae, confirming its current taxonomic classification. These results offer important insights into evolutionary relationships and gene order conservation within Brachyura. Our study highlights the significance of mitochondrial genomes in resolving taxonomic uncertainties within the genus Metopograpsus.

A novel genome-wide association study method for detecting quantitative trait loci interacting with complex population structures in plant genetics

In plant genetics, most modern association analyses are performed on panels that bring together individuals from several populations, including admixed individuals whose genomes comprise chromosomal regions from different populations. These panels can identify quantitative trait loci (QTLs) with population-specific effects and epistatic interactions between QTLs and polygenic backgrounds. However, analyzing a diverse panel constitutes a challenge for statistical analysis. The statistical model must account for possible interactions between a QTL and the panel structure while strictly controlling the detection error rate. Although models to detect population-specific QTLs have already been developed, they rely on prior information about the population structure. In practice, this prior information may be missing as many genome-wide association study (GWAS) panels exhibit complex population structures. The present study introduces 2 new models for detecting QTLs interacting with complex population structures. Both incorporate an interaction term between single nucleotide polymorphism/haplotype block and genetic background into conventional GWAS models. The proposed models were compared with state-of-the-art models through simulation studies that considered QTLs with different levels of interaction with their genetic backgrounds. Results showed that models matching simulation settings were most effective for detecting corresponding QTLs while the proposed models outperformed classical models in detecting QTLs interacting with polygenes. Additionally, when applied to a soybean dataset, one of our models identified putative associated QTLs that conventional models failed to detect. The new models, implemented in the RAINBOWR package available on CRAN, are expected to help uncover complex trait genetic architectures.

Establishing genome sequencing and assembly for non-model and emerging model organisms: a brief guide

Reference genome assemblies are the basis for comprehensive genomic analyses and comparisons. Due to declining sequencing costs and growing computational power, genome projects are now feasible in smaller labs. De novo genome sequencing for non-model or emerging model organisms requires knowledge about genome size and techniques for extracting high molecular weight DNA. Next to quality, the amount of DNA obtained from single individuals is crucial, especially, when dealing with small organisms. While long-read sequencing technologies are the methods of choice for creating high quality genome assemblies, pure short-read assemblies might bear most of the coding parts of a genome but are usually much more fragmented and do not well resolve repeat elements or structural variants. Several genome initiatives produce more and more non-model organism genomes and provide rules for standards in genome sequencing and assembly. However, sometimes the organism of choice is not part of such an initiative or does not meet its standards. Therefore, if the scientific question can be answered with a genome of low contiguity in intergenic parts, missing the high standards of chromosome scale assembly should not prevent publication. This review describes how to set up an animal genome sequencing project in the lab, how to estimate costs and resources, and how to deal with suboptimal conditions. Thus, we aim to suggest optimal strategies for genome sequencing that fulfil the needs according to specific research questions, e.g. "How are species related to each other based on whole genomes?" (phylogenomics), "How do genomes of populations within a species differ?" (population genomics), "Are differences between populations relevant for conservation?" (conservation genomics), "Which selection pressure is acting on certain genes?" (identification of genes under selection), "Did repeats expand or contract recently?" (repeat dynamics).

Emerging therapeutic strategies in glioblastsoma: drug repurposing, mechanisms of resistance, precision medicine, and technological innovations

Glioblastoma (GBM) is an aggressive Grade IV brain tumor with a poor prognosis. It results from genetic mutations, epigenetic changes, and factors within the tumor microenvironment (TME). Traditional treatments like surgery, radiotherapy, and chemotherapy provide limited survival benefits due to the tumor's heterogeneity and resistance mechanisms. This review examines novel approaches for treating GBM, focusing on repurposing existing medications such as antipsychotics, antidepressants, and statins for their potential anti-GBM effects. Advances in molecular profiling, including next-generation sequencing, artificial intelligence (AI), and nanotechnology-based drug delivery, are transforming GBM diagnosis and treatment. The TME, particularly GBM stem cells and immune evasion, plays a key role in therapeutic resistance. Integrating multi-omics data and applying precision medicine show promise, especially in combination therapies and immunotherapies, to enhance clinical outcomes. Addressing challenges such as drug resistance, targeting GBM stem cells, and crossing the blood-brain barrier is essential for improving treatment efficacy. While current treatments offer limited benefits, emerging strategies such as immunotherapies, precision medicine, and drug repurposing show significant potential. Technologies like liquid biopsies, AI-powered diagnostics, and nanotechnology could help overcome obstacles like the blood-brain barrier and GBM stem cells. Ongoing research into combination therapies, targeted drug delivery, and personalized treatments is crucial. Collaborative efforts and robust clinical trials are necessary to translate these innovations into effective therapies, offering hope for improved survival and quality of life for GBM patients.

Validating a Target-Enrichment Design for Capturing Uniparental Haplotypes in Ancient Domesticated Animals

In the last three decades, DNA sequencing of ancient animal osteological assemblages has become an important tool complementing standard archaeozoological approaches to reconstruct the history of animal domestication. However, osteological assemblages of key archaeological contexts are not always available or do not necessarily preserve enough ancient DNA for a cost-effective genetic analysis. Here, we develop an in-solution target-enrichment approach, based on 80-mer species-specific RNA probes (ranging from 306 to 1686 per species) to characterise (in single experiments) the mitochondrial genetic variation from eight domesticated animal species of major economic interest: cattle, chickens, dogs, donkeys, goats, horses, pigs and sheep. We also illustrate how our design can be adapted to enrich DNA library content and map the Y-chromosomal diversity within Equus caballus. By applying our target-enrichment assay to an extensive panel of ancient osteological remains, farm soil, and cave sediments spanning the last 43 kyrs, we demonstrate that minimal sequencing efforts are necessary to exhaust the DNA library complexity and to characterise mitogenomes to an average depth-of-coverage of 19.4 to 2003.7-fold. Our assay further retrieved horse mitogenome and Y-chromosome data from Late Pleistocene coprolites, as well as bona fide mitochondrial sequences from species that were not part of the probe design, such as bison and cave hyena. Our methodology will prove especially useful to minimise costs related to the genetic analyses of maternal and paternal lineages of a wide range of domesticated and wild animal species, and for mapping their diversity changes over space and time, including from environmental samples.

Transcriptome analysis of nitrogen assimilation preferences in Burkholderia sp. M6-3 and Arthrobacter sp. M7-15

Introduction

Ammonium (NH4 +) and nitrate (NO3 -) are the two main forms of inorganic nitrogen (N) that exist in soil and both can be absorbed and utilized by plants. As a vast and crucial biome, soil microorganisms are responsible for mediating the inorganic N assimilation process and enhancing nitrogen use efficiency. Understanding how these microorganisms assimilate different forms of inorganic nitrogen is crucial. There are a handful of microorganisms that play a dominant role in the process of soil inorganic nitrogen assimilation and have a significant advantage in abundance. However, microbial preferences for ammonium or nitrate, as well as differences in their metabolic pathways under co-existing ammonium and nitrate conditions, remain unclear.

Methods

In this study, two microbial strains with nitrogen assimilation advantages, Burkholderia sp. M6-3 and Arthrobacter sp. M7-15 were isolated from an acidic Chinese soil and then incubated by different sources of inorganic N to investigate their N preferences. Furthermore, RNA sequencing-based transcriptome analysis was used to map the metabolic pathways of the two strains and explore their explanatory potential for N preferences.

Results

The results showed that strain M6-3 preferred to utilize NH4 + while strain M7-15 preferred to utilize NO3 -. Although both strains shared similar nitrogen metabolic pathways, the differential expression of the glutamine synthetase-coding gene glnA played a crucial role in regulating their inorganic N preferences. This inconsistency in glnA expression may be attributed to GlnR, a global regulator of nitrogen utilization.

Discussion

This research strengthens the theoretical basis for exploring the underlying causes of differential preferences for inorganic N forms and provided key clues for screening functional microorganisms to ultimately enhance inorganic nitrogen use efficiency.

Dimension Reduction Using Local Principal Components for Regression-Based Multi-SNP Analysis in 1000 Genomes and the Canadian Longitudinal Study on Aging (CLSA)

For genetic association analysis based on multiple SNP regression of genotypes obtained by dense DNA sequencing or array data imputation, multi-collinearity can be a severe issue causing failure to fit the regression model. In this study, we propose a method of Dimension Reduction using Local Principal Components (DRLPC) which aims to resolve multi-collinearity by removing SNPs under the assumption that the remaining SNPs can capture the effect of a removed SNP due to high linear dependency. This approach to dimension reduction is expected to improve the power of regression-based statistical tests. We apply DRLPC to chromosome 22 SNPs of two data sets, the 1000 Genomes Project (phase 3) and the Canadian Longitudinal Study on Aging (CLSA), and calculate variance inflation factors (VIF) in various SNP-sets before and after implementing DRLPC as a metric of collinearity. Notably, DRLPC addresses multi-collinearity by excluding variables with a VIF exceeding a predetermined threshold (VIF = 20), thereby improving applicability for subsequent regression analyses. The number of variables in a final set for regression analysis is reduced to around 20% on average for larger-sized genes, whereas for smaller ones, the proportion is around 48%; suggesting that DRLPC is particularly effective for larger genes. We also compare the power of several multi-SNP statistics constructed for gene-specific analysis to evaluate power gains achieved by DRLPC. In simulation studies based on 100 genes with ≤ 500 SNPs per gene, DRLPC increases the power of the multiple regression Wald test from 60% to around 80%.

Engineering Gizmos for Short Cancer Genetic Fragments Discrimination

Currently, mankind is fiercely struggling with cancer. Recently, we have been winning the battle against cancer through precision medicine and accompanying diagnostic methods, and we are raising many hopes with blockbuster drugs. It would be even better if we could read the cancer nucleotide sequence, identify them in advance, and suggest treatments simultaneously. However, this may be an impossible dream because it takes a lot of time and effort to diagnose and ensure all the long gene sequences of cancer at once. Thus, victory will be even closer if a rapid and accurate diagnosis of the cancer-specific gene biomarkers that will soon be imprinted can be made. With the advent of nanotechnology, a new short cancer diagnostic toolkit has been proposed to achieve the goal. This review presents a small diagnostic device that detects certain cancers' genetic fragments (simply 'Gizmo'). The development of numerous diagnostic methods has focused on (1) directly detecting pre-selectively targeted genes using novel diagnostic systems, and (2) indirectly detecting substantial improvements in diagnostic sensitivity only through detection signal amplification without existing gene amplification steps. Our fight against cancer is not a dream, but the result of success, and it is assumed that victory will accelerate as soon as possible.

Knowledge of the genetics of human pain gained over the last decade from next-generation sequencing

Next-generation sequencing (NGS) technologies have revolutionized pain research by providing comprehensive insights into genetic variation across the genome. Recent studies have expanded the known spectrum of mutations in genes such as SCN9A and NTRK1, which are commonly mutated in hereditary sensory neuropathies. NGS has uncovered critical alternative splicing events and facilitated single-cell transcriptomics, revealing cellular heterogeneity within tissues. An NGS-based classifier predicted extremely high opioid requirements with 80 % accuracy, highlighting the importance of tailoring opioid therapy based on genetic profiles. Key genes such as GDF5, COL11A1, and TRPV1 have been linked to osteoarthritis risk and pain sensitivity, while HLA-DRB1, TNF, and P2X7 play critical roles in inflammation and pain modulation in rheumatoid arthritis. Innovative tools, such as an atlas of the somatosensory system in neuropathic pain, have been developed based on NGS data, focusing on the dorsal root and trigeminal ganglia. This approach allows the analysis of cellular changes during the development of chronic pain. In the study of rare variants, NGS outperforms single nucleotide variant candidate studies and classical genome-wide association approaches. The complex data generated by NGS enables integrated multi-omics approaches, allowing deeper exploration of the molecular and cellular basis of pain perception. In addition, the characterization of non-coding RNAs has opened new therapeutic avenues. NGS-based pain research faces challenges related to complex data analysis and interpretation of rare genetic variants with unknown biological functions. Nevertheless, NGS offers significant potential for improving personalized pain management and highlights the need for interdisciplinary collaboration to translate findings into clinical practice.

Molluscan systematics: historical perspectives and the way ahead

Mollusca, the second-most diverse animal phylum, is estimated to have over 100,000 living species with great genetic and phenotypic diversity, a rich fossil record, and a considerable evolutionary significance. Early work on molluscan systematics was grounded in morphological and anatomical studies. With the transition from oligo gene Sanger sequencing to cutting-edge genomic sequencing technologies, molecular data has been increasingly utilised, providing abundant information for reconstructing the molluscan phylogenetic tree. However, relationships among and within most major lineages of Mollusca have long been contentious, often due to limited genetic markers, insufficient taxon sampling and phylogenetic conflict. Fortunately, remarkable progress in molluscan systematics has been made in recent years, which has shed light on how major molluscan groups have evolved. In this review of molluscan systematics, we first synthesise the current understanding of the molluscan Tree of Life at higher taxonomic levels. We then discuss how micromolluscs, which have adult individuals with a body size smaller than 5 mm, offer unique insights into Mollusca's vast diversity and deep phylogeny. Despite recent advancements, our knowledge of molluscan systematics and phylogeny still needs refinement. Further advancements in molluscan systematics will arise from integrating comprehensive data sets, including genome-scale data, exceptional fossils, and digital morphological data (including internal structures). Enhanced access to these data sets, combined with increased collaboration among morphologists, palaeontologists, evolutionary developmental biologists, and molecular phylogeneticists, will significantly advance this field.

Diversity, Complexity, and Challenges of Viral Infectious Disease Data in the Big Data Era: A Comprehensive Review

Viral infectious diseases, characterized by their intricate nature and wide-ranging diversity, pose substantial challenges in the domain of data management. The vast volume of data generated by these diseases, spanning from the molecular mechanisms within cells to large-scale epidemiological patterns, has surpassed the capabilities of traditional analytical methods. In the era of artificial intelligence (AI) and big data, there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information. Despite the rapid accumulation of data associated with viral infections, the lack of a comprehensive framework for integrating, selecting, and analyzing these datasets has left numerous researchers uncertain about which data to select, how to access it, and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels, from the molecular details of pathogens to broad epidemiological trends. The scope extends from the micro-scale to the macro-scale, encompassing pathogens, hosts, and vectors. In addition to data summarization, this review thoroughly investigates various dataset sources. It also traces the historical evolution of data collection in the field of viral infectious diseases, highlighting the progress achieved over time. Simultaneously, it evaluates the current limitations that impede data utilization.Furthermore, we propose strategies to surmount these challenges, focusing on the development and application of advanced computational techniques, AI-driven models, and enhanced data integration practices. By providing a comprehensive synthesis of existing knowledge, this review is designed to guide future research and contribute to more informed approaches in the surveillance, prevention, and control of viral infectious diseases, particularly within the context of the expanding big-data landscape.

Biological characteristics and transcriptomic profile of adipose-derived mesenchymal stem cells isolated from prion-infected murine model

BACKGROUND

Prion diseases are characterized by accumulation of misfolded host prion proteins (PrPSc) that produce aggregates in brain tissue. Mesenchymal stem cells (MSCs) have been identified as potential therapeutic candidates for prion diseases. However, it has been demonstrated that MSCs maintained and expressed PrPSc levels following inoculation, raising concerns regarding their safe and effective use in medical applications. Prion infectivity has been reported in fat tissues, thus the response of adipose-derived MSCs (AdMSCs) to prion infection needs to be fully studied.

METHODS

For this study, we analyzed the properties of AdMSCs isolated from mice infected with the ME7 scrapie strain and compared them with negative controls. We investigated morphology, viability, immunophenotyping, markers of inflammation, migration activity, and neurotrophic factors. RNA sequencing (RNA-Seq) was performed to identify transcriptome profile changes.

RESULTS

AdMSCs derived from ME7-infected mice displayed immunophenotypes similar to cells from negative controls, but they were larger with lower viability (p < 0.05). ME7 infection caused higher expression of inflammatory mediators CCL5, TNF-α, C3, and IL6 (p < 0.05 and p < 0.01) and low expression of the stem cell marker, CXCR4 (p < 0.05) which was confirmed by immunofluorescence staining. The results showed decreased migration activity and wound closure ability of AdMSCs isolated from ME7-infected mice as confirmed by Transwell migration and scratch wound assays (p < 0.05 and p < 0.001), respectively. The RNA-Seq results detected 367 differentially expressed genes between AdMSCs from ME7-infected mice and those from the negative controls, and negative regulation of locomotion, extracellular matrix (ECM) organization, collagen-containing ECM, and extracellular structure organization genes were common in AdMSCs from ME7-infected mice. Transcriptomic analysis revealed that pathways enriched in AdMSCs from ME7-infected mice included those involved in the PI3K-Akt signaling pathway, cell adhesion, protein digestion and absorption, and cytokine-cytokine receptor interactions. Interestingly, genes related to the regulation of iron storage, such as Hp and hepcidin, were upregulated in AdMSCs isolated from ME7-infected mice.

CONCLUSIONS

Based on these data, therapeutic strategies for AdMSCs in prion disease should be further investigated.

Differential performance of polygenic prediction across traits and populations depending on genotype discovery approach

Polygenic scores (PGS) are widely used for estimating genetic predisposition to complex traits by aggregating the effects of common variants into a single measure. They hold promise in identifying individuals at increased risk for diseases, allowing earlier screening and interventions. Genotyping arrays, commonly used for PGS computation, are affordable and computationally efficient, while whole-genome sequencing (WGS) offers a comprehensive view of genetic variation. Using the same set of individuals, we compared PGS derived from arrays and WGS across multiple traits to evaluate differences in predictive performance, portability across populations, and computational efficiency. We computed PGS for 10 traits across the spectrum of heritability and polygenicity in the three largest genetic ancestry groups in All of Us (European, African American, Admixed American), trained on the multi-ancestry meta-analyses from the Pan-UK Biobank. Using the clumping and thresholding (C+T) method, we found that WGS-based PGS outperformed array-based PRS for highly polygenic traits but showed differentially reduced accuracy for sparse traits in certain populations. This may be attributable to the lower allele frequency observed in clumped variants from WGS compared to arrays. Using the LD-informed PRS-CS method, we observed overall improved prediction performance compared to C+T, with WGS outperforming arrays across most non-cancer traits. In conclusion, while PGS computed using WGS generally provide superior predictive power with PRS-CS, the advantage over arrays is context-dependent, varying by trait, population, and the PGS method. This study provides insights into the complexities and potential advantages of using different genotype discovery approach for polygenic predictions in diverse populations.

Graphical abstract

Solid-State Nanopore Sizing for cfDNA Sample Quality Control in Point-of-Need Sequencing

DNA sequencing is a powerful tool for diagnosing conditions like infectious diseases and cancers. Even though current workflows demand rigorous quality control (QC) of DNA samples, this QC is typically limited to lab settings, despite recent advances in portable nanopore sequencers. For personalized healthcare to truly benefit from the portable sequencer, QC must be performed right where the samples are processed. Here, we present a solid-state nanopore device that provides label-free, controlled quantification and qualification of cell-free DNA (cfDNA). We demonstrated the use of a 1 kbp double-stranded DNA internal marker at a known concentration to measure the concentration of a representative cfDNA target in the presence of genomic DNA. We also found that nanopores with diameters ranging from 6 to 19 nm yield consistent measurements, with a maximum coefficient of variation (CV) of less than 15%. Moreover, analyzing data from multiple nanopores over longer acquisition times can reduce the uncertainty to below 10% CV. Finally, we applied our nanopore QC assay to a plasma cfDNA sample and compared the results with those from a capillary electrophoresis (CE) assay. Both methods produced highly correlated measurements, demonstrating the potential of our nanopore QC assay for effective cfDNA assessment at the point of need.

Advances and Challenges in Solid-State Nanopores for DNA Sequencing

Solid-state nanopore sensing, a state-of-the-art technology for single-molecule detection, has rapidly advanced in recent years and demonstrates significant potential in DNA sequencing. This technology determines the nucleotide sequences by analyzing the electrical or optical signal variations that occur when DNA molecules pass through the nanopore. It offers notable advantages, including high-throughput, single-molecule detection, real-time monitoring, and the elimination of the need for polymerase chain reaction (PCR) amplification, thereby presenting broad application prospects in areas such as the diagnosis and treatment of genetic diseases. This paper reviews the solid-state nanopore DNA sequencing technology by discussing advancements in nanopore types, preparation techniques, and sequencing detection methods. It examines various nanopore materials, including silicon-based materials and two-dimensional (2D) materials, as well as preparation techniques such as transmission electron microscopy (TEM), focused ion beam (FIB) etching, and controlled breakdown (CBD). Additionally, it elucidates sequencing detection mechanisms, including ion-current blockade, transverse-current detection, and optical detection. However, this technology faces numerous challenges in its implementation and future commercialization. For instance, limited spatial resolution hampers single-base identification; the rapid translocation speed of DNA impacts time resolution; and various types of noise significantly disrupt detection signals. In response, researchers have proposed several solutions, including local thinning of the film, adjustment of surface charges, and optimization of detection materials and structures. With interdisciplinary integration and technological innovation, solid-state nanopore DNA sequencing technology is expected to make breakthroughs, bringing transformations to life sciences research and medical diagnosis.

Design and synthesis of fluorescent-dye-labeled nucleotide with a new cleavable azo linker for DNA sequencing by synthesis

A new cleavable azo linker was synthesized and reacted with 5-iodo-2'-deoxyuridine, followed by triphosphorylation, and finally labeled with Cy3 to give the desired product dUTP-azo linker-Cy3 as a potential reversible terminator for DNA sequencing. The synthesized 3'-OH-unblocked nucleotide triphosphate can be faithfully incorporated into the DNA strands as catalyzed by DNA polymerase (Bst 3.0) with 100% yield. Meanwhile, further incorporation is terminated temporarily by the overall steric hindrance of the nucleotide triphosphate, ensuring that only one molecule can be incorporated into the DNA strand within one sequencing cycle even for a template containing multiple identical bases in a row. The next incorporation can proceed smoothly upon complete removal of the labeled dye with only aniline left on the elongated DNA strand. The synthesized nucleotide triphosphate is the first reversible terminator that can be considered crucial for DNA sequening. These preliminary evaluations indicate that the synthesized nucleotide triphosphate holds substantial potential value in DNA sequencing.

Advanced RPL19-TRAPKI-seq method reveals mechanism of action of bioactive compounds

Natural products play a crucial role in new drug development, but their druggability is often limited by uncertain molecular targets and insufficient research on mechanisms of action. In this study, we developed a new RPL19-TRAPKI-seq method, combining CRISPR/Cas9 and TRAP technologies, to investigate these mechanisms. We identified and validated seven ribosomal large subunit surface proteins suitable for TRAP, selecting RPL19 for its high enrichment. We successfully established a stable cell line expressing EGFP-RPL19 using CRISPR knock-in and verified its efficiency and specificity in enriching ribosomes and translating mRNA. Integrated with next-generation sequencing, this method allows precise detection of translating mRNA. We validated RPL19-TRAPKI-seq by investigating rapamycin, an mTOR inhibitor, yielding results consistent with previous reports. This optimized TRAP technology provides an accurate representation of translating mRNA, closely reflecting protein expression levels. Furthermore, we investigated SBF-1, a 23-oxa-analog of natural saponin OSW-1 with significant anti-tumor activity but an unclear mechanism. Using RPL19-TRAPKI-seq, we found that SBF-1 exerts its cytotoxic effects on tumor cells by disturbing cellular oxidative phosphorylation. In conclusion, our method has been proven to be a promising tool that can reveal the mechanisms of small molecules with greater accuracy, setting the stage for future exploration of small molecules and advancing the fields of pharmacology and therapeutic development.

A Robust and Efficient Representation-based DNA Storage Architecture by Deep Learning

As one main form of multimedia data, images play a critical role in various applications. In this paper, a representation-based architecture is proposed which takes advantage of the outstanding representation and image-generation abilities of deep learning (DL). This architecture includes two DL models: an autoencoder and a U-Net network which achieve the representation, construction, and refinement of images from the noisy reads in DNA storage. Simulation experiments demonstrate that it can reconstruct images of moderate quality in scenarios where insertion-deletion-substitution (IDS) errors are less than 6%. Combined with the feature quantization, it also offers a flexible way to achieve a balanced trade-off between compression ratio and image quality by selecting an approximate representation channel number. Additionally, the quality of images can be boosted by using multiple reads which are a common situation in DNA storage. A wet lab practice that successfully reconstructs an image stored in 14 plasmids further proves the feasibility of the proposed architecture. Instead of storing the original image information, the representation-based architecture provides a competitive solution which achieves robust and efficient DNA storage for large-scale image applications.

Enhancing clinical research with pharmacogenomics: a practical perspective

Pharmacogenomics (PGx) is transforming therapeutic development by providing insights into how genetic variations influence drug response, safety, and efficacy. This review provides a structured analysis of PGx in clinical research, beginning with an overview of key genes involved in drug metabolism, transport, and targets. Following this, it examines strategies for identifying PGx-relevant genes, including phenotype-driven, hypothesis-driven, population-focused, and clinical-driven approaches. Technical platforms such as PCR, MassARRAY, and next-generation sequencing are analyzed for their suitability in PGx studies. The discussion then shifts to assay validation processes, covering both analytical and clinical validation, to ensure data reliability in clinical trials. Finally, regulatory expectations for PGx in clinical trials are discussed, focusing on key requirements across all phases of drug development. This review aims to provide a clear and practical framework for integrating PGx into clinical research to enhance drug safety, efficacy, and personalized medicine.

Computational advances in biosynthetic gene cluster discovery and prediction

Biosynthetic gene clusters (BGCs) are groups of clustered genes found in bacteria, fungi, and some plants and animals that are crucial for synthesizing secondary metabolites. In recent years, genome mining of BGCs has emerged as a prominent research focus, particularly in natural product discovery and drug development. Compared to traditional experimental methods, applying computational techniques has significantly enhanced the efficiency of BGC identification and annotation, thereby facilitating the discovery of novel metabolites. The advent of artificial intelligence, particularly machine learning models and more advanced deep learning algorithms, has significantly enhanced both the speed and precision of BGC mining. This review offers a comprehensive introduction to currently developed BGC databases and prediction tools, highlighting the potential of machine learning technologies in BGC mining. Additionally, it summarizes the challenges computational methods face in this area and discusses future research directions.

Unravelling phylogenetic relationships within the genus Lispe (Diptera: Muscidae) through genome-assisted and de novo analyses of RAD-seq data

Lispe represents a species-rich genus within the family Muscidae. The current subdivision of Lispe species into species groups is based mainly on adult morphology and ecology, with the only available phylogenetic study based on three molecular markers. Nonetheless, certain species groups remain unclear and the relationships and composition of these groups are still unresolved. This study employs restriction-site associated DNA sequencing (RAD-seq) with both reference-based and de novo reads assembly approaches to investigate relationships within Lispe. To apply a reference-based approach we utilised Oxford Nanopore Technologies (ONT) long read sequencing to assemble a draft genome of L. tentaculata. We evaluated various assemblers for ONT reads of L. tentaculata in order to demonstrate the highest effectiveness in terms of completeness and assembly quality. The resulting phylogenetic trees topologies are well supported and present a consistent division into three main clades: 1) the palposa-, rigida- and caesia-groups, 2) the nicobarensis-, nivalis-, scalaris- and tentaculata-groups and 3) the longicollis-, desjardinsii-, uliginosa- and kowarzi-groups. The primary discrepancy between topologies obtained under our various analytical approaches is the relationship between the leucospila-group and all other ingroup taxa, being a sister taxon either to all remaining Lispe or to a clade consisting of the longicollis-, desjardinsii-, uliginosa- and kowarzi-groups. Lispe polonaise, included for the first time in a molecular phylogenetic analysis, is nested within the caesia-group. Similarly, L. capensis and the hitherto unassigned L. mirabilis belong to the tentaculata-group. Our study confirms the validity of the 14 species groups currently recognised in the genus Lispe.

The novel Bacillus thuringiensis HSY204 as a potential bioinsecticide with efficacy against Aedes aegypti larvae

Bacillus thuringiensis (Bt), renowned for its potential, rapid action and environmental sustainability, remains an understudied group, particularly in newly identified strains. In this study, we screened a novel Bt strain, HSY204, extracted from soil samples. Our results revealed that HSY204 belonged to a new species of Bt with remarkable efficacy against Aedes aegypti larvae. The complete HSY204 genome analysis revealed nine toxin genes. In particular, a protein similar to Xpp37Aa showed very promising effectiveness in mosquito suppression, though surpassed by Bacillus thuringiensis subsp. israelensis (Bti). This research provides a valuable background for future biotechnological applications of HSY204 strain as a basis for the production of commercial bioinsecticides, thereby contributing to the development of innovative and environmentally friendly bioinsecticide strategies.

Mitochondrial Genome of Grapsus albolineatus and Insights into the Phylogeny of Brachyura

Brachyura is among the most diverse groups of crustaceans, with over 7000 described species. Crab mitogenomes are important for understanding molecular evolution and phylogenetic relationships. Grapsus albolineatus exhibits specific rearrangements compared with the Pancrustacean ground pattern and other Brachyura species. The gene arrangement of G. albolineatus is similar to that of ancestral crustaceans, barring that of the translocated trnH gene. In phylogenetic analyses, the Bayesian inference estimation was observed to be superior to the maximum likelihood estimation when the nodal support values were compared. Considering the results of the gene rearrangement pattern and phylogenetic analysis, we speculate that G. albolineatus belongs to Grapsidae. Our comparative study indicated that mitogenomes are a useful phylogenetic tool at the subfamily level within Brachyura. The findings indicate that mitogenomes could be a useful tool for systematics in other Brachyuran species.

CamITree: a streamlined software for phylogenetic analysis of viral and mitochondrial genomes

BACKGROUND

Over the past decade, the continuous and rapid advances in bioinformatics have led to an increasingly common use of molecular sequence comparison for phylogenetic analysis. However, the use of multi-software and cross-platform strategies has increased the complexity of phylogenetic tree estimation. Therefore, the development and application of streamlined phylogenetic analysis tools are growing in significance in the field of biology. Particularly for genomes with relatively short sequences, there is a lack of simple and integrative tools for phylogenetic analysis.

RESULTS

In this study, we present CamlTree (Concatenated alignments maximum-likelihood tree), a user-friendly desktop software designed to simplify phylogenetic analysis for viral and mitochondrial genomes, ultimately facilitating related research. CamlTree provides a workflow including gene concatenation (or coalescence), sequence alignment, alignment optimization, and the estimation of phylogenetic trees using both maximum-likelihood (ML) and Bayesian inference (BI) methods. CamlTree was written in TypeScript and developed using the Electron framework. It offers a primarily user-friendly interface based on the React framework.

CONCLUSIONS

CamlTree software has been released for the Windows OS. It integrates several popular analysis tools to optimize and simplify the process of estimating polygenic phylogenetic trees. The establishment of software can assist researchers in reducing their workload and enhancing data processing efficiency, enabling them to expedite their research progress. The software, along with a detailed user manual, is available at https://github.com/BioCrossCoder/camltree .

Data Readout Techniques for DNA-Based Information Storage

DNA is a natural chemical substrate that carries genetic information, which also serves as a powerful toolkit for storing digital data. Compared to traditional storage media, DNA molecules offer higher storage density, longer lifespan, and lower maintenance energy consumption. In DNA storage process, data readout is a critical step that bridges the gap between DNA molecular/structures with stored digital information. With the continued development of strategies in DNA data storage technology, the readout techniques have evolved. However, there is a lack of systematic introduction and discussion on the readout techniques for reported DNA data storage systems, especially the correlation between the design of the data storage system and the corresponding selection of readout techniques. This review first introduces two main categories of DNA data storage units (i.e., sequence and structure) and their corresponding readout techniques (i.e., sequencing and nonsequencing methods), and then reviewed representative examples of notable advancements in DNA data storage technology, focusing on data storage unit design, and readout technique selection. It also introduces emerging approaches to assist data readout techniques, such as implementation of microfluidic and fluorescent probes. Finally, the paper discusses the limitations, challenges, and potential of DNA data readout approaches.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Clinical Validation of Local Versus Commercial Genomic Testing in Cancer: A Comparison of Tissue and Plasma Concordance

Genomic sequencing of tumours improves patient outcomes through implementation of precision oncology. At present, genomic testing is mainly confined to research settings, with samples sent to biopharmaceutical companies for analysis. The ever-expanding catalogue approved of targeted therapies has created an urgent unmet need for local genomic testing facilities, to enable upscaling of testing. Here, we compare the outcomes of local (IonTorrent™) and commercial (Foundation Medicine) genomic testing collected from 30 cancer patients in from plasma and tissue samples. Overall concordance was high in both tissue (98%) and plasma (94.2%). Variants identified by both platforms had a strong correlation in variant allele frequencies (VAF%): plasma: r = 0.99 p < 0.0001, tissue: r = 0.91 p < 0.0001. However, numerous low VAF% variants resulted in low positive percentage agreement (tissue 78.8% plasma 16.1%) and positive predictive values (tissue 56.3% plasma 71.4%). Local sequencing demonstrated higher fidelity in detecting fusions but low fidelity in detecting indels. Overall, this study supports the use of local genomic testing for routine molecular diagnostics but highlights outstanding issues before widespread implementation. Processing of variants detected at low VAF% and the limit of detection of assays needs to be addressed. Construction of gene panels requires careful consideration, including incorporation of markers of genomic instability.

Genetics of sorghum: grain quality, molecular aspects, and drought responses

MAIN CONCLUSION

Sorghum kernel composition is a crucial characteristic that determines its functional qualities. The total protein content of sorghum grain increases under drought stress, but starch, protein digestibility, and micronutrient contents decrease. Sorghum (Sorghum bicolor L.) is a staple source of starch, protein, and micronutrients in Ethiopia, where it is a key ingredient in local foods like injera and traditional beverages such as tela and areke. It has adapted remarkably to the diverse climatic conditions and soils of both highland and lowland regions. However, grain quality is influenced by climate change, drought stress, and genotype-environment interactions. Under drought conditions, sorghum shows reduced starch content, protein digestibility, and micronutrient levels, as well as increased kernel hardness and total protein content. The genetic and geographic diversity of sorghum makes it an adaptable crop, essential for breeding and diversity studies. Genome-wide association studies (GWAS) have emerged as essential tools for identifying candidate genes linked to key traits, thereby advancing genetic improvement initiatives, particularly for Ethiopian sorghum landraces. Advances in genotyping techniques, particularly genotyping-by-sequencing (GBS) and association mapping, have facilitated the identification of quantitative trait loci (QTL) associated with grain quality, enhancing breeding efficiency and the development of resilient, high-quality sorghum varieties. This review explored the genetic and phenotypic diversity of sorghum, focusing on grain quality traits, molecular mechanisms, and responses to drought stress.

Innovations in Transgene Integration Analysis: A Comprehensive Review of Enrichment and Sequencing Strategies in Biotechnology

Understanding the integration of transgene DNA (T-DNA) in transgenic crops, animals, and clinical applications is paramount for ensuring the stability and expression of inserted genes, which directly influence desired traits and therapeutic outcomes. Analyzing T-DNA integration patterns is essential for identifying potential unintended effects and evaluating the safety and environmental implications of genetically modified organisms (GMOs). This knowledge is crucial for regulatory compliance and fostering public trust in biotechnology by demonstrating transparency in genetic modifications. This review highlights recent advancements in T-DNA integration analysis, specifically focusing on targeted DNA enrichment and sequencing strategies. We examine key technologies, such as polymerase chain reaction (PCR)-based methods, hybridization capture, RNA/DNA-guided endonuclease-mediated enrichment, and high-throughput resequencing, emphasizing their contributions to enhancing precision and efficiency in transgene integration analysis. We discuss the principles, applications, and recent developments in these techniques, underscoring their critical role in advancing biotechnological products. Additionally, we address the existing challenges and future directions in the field, offering a comprehensive overview of how innovative DNA-targeted enrichment and sequencing strategies are reshaping biotechnology and genomics.

Genetic Mechanism Analysis Related to Cold Tolerance of Red Swamp Crayfish, Procambarus clarkii

In China, the red swamp crayfish (Procambarus clarkii), a notorious invasive species, has become an important economic freshwater species. In order to compare the genetic diversity and population structure of crayfish from northern and southern China, we collected 60 crayfish individuals from 4 crayfish populations in northern China and 2 populations in southern China for sequencing using the 2b-RAD technique. Additionally, the whole genome sequence information obtained by 2b-RAD of 90 individuals from 2 populations in northern China and 7 populations in southern China were downloaded from NCBI. After quality control, a total of 25,371 SNPs were detected from approximately 54.22 billion raw reads. Based on these SNPs, high genetic diversity was observed in the 15 crayfish populations in China. The pairwise FST values indicated that there was a large genetic differentiation of crayfish populations in northern and southern China. Despite common genetic backgrounds, due to geographical barriers, genetic divergence has been observed in northern and southern China crayfishes. The principal component analysis in combination with Admixture and Neighbor-Joining tree analysis showed that the crayfish fell into two clusters corresponding to geographical regions. The integrated analysis of whole genome and transcriptome data showed that two genes (CETN4 and CPEB2) might play important roles during crayfish resistance to a cold environment. This study reveals the genetic differentiation of crayfish populations in northern and southern China and provides clues to the genetic mechanism related to cold adaptation.

Genetic determinants of silver nanoparticle resistance and the impact of gamma irradiation on nanoparticle stability

BACKGROUND

One of the main issues facing public health with microbial infections is antibiotic resistance. Nanoparticles (NPs) are among the best alternatives to overcome this issue. Silver nanoparticle (AgNPs) preparations are widely applied to treat multidrug-resistant pathogens. Therefore, there is an urgent need for greater knowledge regarding the effects of improper and excessive use of these medications. The current study describes the consequences of long-term exposure to sub-lethal concentrations of AgNPs on the bacterial sensitivity to NPs and the reflection of this change on the bacterial genome.

RESULTS

Chemical methods have been used to prepare AgNPs and gamma irradiation has been utilized to produce more stable AgNPs. Different techniques were used to characterize and identify the prepared AgNPs including UV-visible spectrophotometer, Fourier Transform Infrared (FT-IR), Dynamic light scattering (DLS), and zeta potential. Transmission electron microscope (TEM) and Scanning electron microscope (SEM) showed 50-100 nm spherical-shaped AgNPs. Eleven gram-negative and gram-positive bacterial isolates were collected from different wound infections. The minimum inhibitory concentrations (MICs) of AgNPs against the tested isolates were evaluated using the agar dilution method. This was followed by the induction of bacterial resistance to AgNPs using increasing concentrations of AgNPs. All isolates changed their susceptibility level to become resistant to high concentrations of AgNPs upon recultivation at increasing concentrations of AgNPs. Whole genome sequencing (WGS) was performed on selected susceptible isolates of gram-positive Staphylococcus lentus (St.L.1), gram-negative Klebsiella pneumonia (KP.1), and their resistant isolates St.L_R.Ag and KP_R.Ag to detect the genomic changes and mutations.

CONCLUSIONS

For the detection of single-nucleotide polymorphisms (SNPs) and the identification of all variants (SNPs, insertions, and deletions) in our isolates, the Variation Analysis Service tool available in the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) was used. Compared to the susceptible isolates, the AgNPs-resistant isolates St.L_R.Ag and KP_R.Ag had unique mutations in specific efflux pump systems, stress response, outer membrane proteins, and permeases. These findings might help to explain how single-nucleotide variants contribute to AgNPs resistance. Consequently, strict regulations and rules regarding the use and disposal of nano waste worldwide, strict knowledge of microbe-nanoparticle interaction, and the regulated disposal of NPs are required to prevent pathogens from developing nanoparticle resistance.

Exploring RNA-Seq Data Analysis Through Visualization Techniques and Tools: A Systematic Review of Opportunities and Limitations for Clinical Applications

RNA sequencing (RNA-seq) has emerged as a prominent resource for transcriptomic analysis due to its ability to measure gene expression in a highly sensitive and accurate manner. With the increasing availability of RNA-seq data analysis from clinical studies and patient samples, the development of effective visualization tools for RNA-seq analysis has become increasingly important to help clinicians and biomedical researchers better understand the complex patterns of gene expression associated with health and disease. This review aims to outline the current state-of-the-art data visualization techniques and tools commonly used to frame clinical inferences from RNA-seq data and point out their benefits, applications, and limitations. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included "RNA-seq", "visualization", "plots", and "clinical". Only full-text studies reported between 2017 and 2024 were included for analysis. Following PRISMA guidelines, a total of 126 studies were identified, of which 33 studies met the inclusion criteria. We found that 18% of studies have visualization techniques and tools for circular RNA-seq data, 56% for single-cell RNA-seq data, 23% for bulk RNA-seq data, and 3% for long non-coding RNA-seq data. Overall, this review provides a comprehensive overview of the common visualization tools and their potential applications, which is a useful resource for researchers and clinicians interested in using RNA-seq data for various clinical purposes (e.g., diagnosis or prognosis).

Health Professionals' Preferences for Next-Generation Sequencing in the Diagnosis of Suspected Genetic Disorders in the Paediatric Population

Background/Objectives: Next-generation sequencing (NGS) can explain how genetics influence morbidity and mortality in children. However, it is unclear whether health providers will perceive and use such treatments. We conducted a discrete choice experiment (DCE) to understand Italian health professionals' preferences for NGS to improve the diagnosis of paediatric genetic diseases. Methods: The DCE was administered online to 125 health professionals in Italy. We documented attributes influencing professionals' decisions of NGS, including higher diagnostic yield, shorter counselling periods, cost, turnaround time, and the identification of fewer variants of unknown significance. Results: Results show that factors such as higher diagnostic yield, shorter counselling periods, lower costs, and faster turnaround times positively influenced the adoption of NGS tests. Willingness to pay (WTP) estimates varied from EUR 387 (95% CI, 271.8-502.9) for 7% increase in the diagnostic yield to EUR 469 (95% CI, 287.2-744.9) for a decrease of one week in the turnaround time. Responders would reduce diagnostic yield by 7% to decrease the turnaround time by one week in both the preference and the willingness to trade (WTT) spaces. Respondents prioritised diagnostic yield (RI = 50.36%; 95% CI 40.2-67.2%) compared to other attributes. Conclusions: therefore, health professionals value NGS for allowing earlier, more accurate genetic diagnoses.

Structures of a DNA Polymerase Caught while Incorporating Responsive Dual-Functional Nucleotide Probes

Functionalizing nucleic acids using DNA polymerases is essential in biophysical and biotechnology applications. This study focuses on understanding how DNA polymerases recognize and incorporate nucleotides with diverse chemical modifications, aiming to develop advanced nucleotide probes. We present the crystal structures of ternary complexes of Thermus aquaticus DNA polymerase (KlenTaq) with C5-heterocycle-modified environment-sensitive 2'-deoxyuridine-5'-triphosphate (dUTP) probes. These nucleotides include SedUTP, BFdUTP and FBFdUTP, which bear selenophene, benzofuran and fluorobenzofuran, respectively, at the C5 position of uracil, and exhibit high conformational sensitivity. SedUTP and FBFdUTP serve as dual-app probes, combining a fluorophore with X-ray anomalous scattering Se or 19F NMR labels. Our study reveals that the size of the heterocycle influences how DNA polymerase families A and B incorporate these modified nucleotides during single nucleotide incorporation and primer extension reactions. Remarkably, the responsiveness of FBFdUTP enabled real-time monitoring of the binary complex formation and polymerase activity through fluorescence and 19F NMR spectroscopy. Comparative analysis of incorporation profiles, fluorescence, 19F NMR data, and crystal structures of ternary complexes highlights the plasticity of the enzyme. Key insight is provided into the role of gatekeeper amino acids (Arg660 and Arg587) in accommodating and processing these modified substrates, offering a structural basis for next-generation nucleotide probe development.

Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data

Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications. This leads to the requirement for prognostic and diagnostic biomarker development. Our study integrates machine learning models and protein network analysis to identify protein biomarkers for colorectal cancer. Our methodology leverages an extensive collection of proteome profiles from both healthy and colorectal cancer individuals. To identify a potential biomarker with high predictive ability, we used three machine learning models. To enhance the interpretability of our models, we quantify each protein's contribution to the model's predictions using SHapley Additive exPlanations values. Three classifiers-LASSO, XGBoost, and LightGBM were evaluated for predictive performance along with hyperparameter tuning of each model using grid search, with LASSO achieving the highest AUC of 75% in the UK Biobank dataset and the AUCs for LightGBM and XGBoost are 69.61% and 71.42%, respectively. Using SHapley Additive exPlanations values, TFF3, LCN2, and CEACAM5 were found to be key biomarkers associated with cell adhesion and inflammation. Protein quantitative trait loci analyze studies provided further evidence for the involvement of TFF1, CEACAM5, and SELE in colorectal cancer, with possible connections to the PI3K/Akt and MAPK signaling pathways. By offering insights into colorectal cancer diagnostics and targeted therapeutics, our findings set the stage for further biomarker validation.

Moving Toward Metaproteogenomics: A Computational Perspective on Analyzing Microbial Samples via Proteogenomics

Microbial sample analysis has received growing attention within the last decade, driven by important findings in microbiome research and promising applications in the biotechnological field. Modern mass spectrometry-based methodology has been established in this context, providing sufficient sensitivity, resolution, dynamic range, and throughput to analyze the so-called metaproteome of complex microbial mixtures from clinical or environmental samples. While proteomic analyses were previously restricted to common model organisms, next-generation sequencing technologies nowadays allow for the rapid and cost-efficient characterization of whole metagenomes of microbial consortia and specific genomes from non-model organisms to which microbes contribute by significant amounts. This proteogenomic approach, meaning the combined application of genomic and proteomic methods, enables researchers to create a protein database that presents a tailored blueprint of the microbial sample under investigation. This contribution provides an overview of the computational challenges and opportunities in proteogenomics and metaproteomics as of January 2018. For practical application, we first showcase an integrative proteogenomic method that circumvents existing reference databases by creating sample-specific transcripts. The underlying algorithm uses a graph network approach that combines RNA-Seq and peptide information. As a second example, we provide a tutorial for a simulation tool that estimates the computational limits of detecting microbial non-model organisms. This method evaluates the potential influence of error-tolerant searches and proteogenomic approaches on databases of interest. Finally, we discuss recommendations for developing future strategies that may help overcome present limitations by combining the strengths of genome- and proteome-based methods and moving toward an integrated metaproteogenomics approach.

Sequencing and Optical Genome Mapping for the Adventurous Chemist

This review provides a comprehensive overview of the chemistries and workflows of the sequencing methods that have been or are currently commercially available, providing a very brief historical introduction to each method. The main optical genome mapping approaches are introduced in the same manner, although only a subset of these are or have ever been commercially available. The review comes with a deck of slides containing all of the figures for ease of access and consultation.

From Tradition to Innovation: Diverse Molecular Techniques in the Fight Against Infectious Diseases

Infectious diseases impose a significant burden on global health systems due to high morbidity and mortality rates. According to the World Health Organization, millions die from infectious diseases annually, often due to delays in accurate diagnosis. Traditional diagnostic methods in clinical microbiology, primarily culture-based techniques, are time-consuming and may fail with hard-to-culture pathogens. Molecular biology advancements, notably the polymerase chain reaction (PCR), have revolutionized infectious disease diagnostics by allowing rapid and sensitive detection of pathogens' genetic material. PCR has become the gold standard for many infections, particularly highlighted during the COVID-19 pandemic. Following PCR, next-generation sequencing (NGS) has emerged, enabling comprehensive genomic analysis of pathogens, thus facilitating the detection of new strains and antibiotic resistance tracking. Innovative approaches like CRISPR technology are also enhancing diagnostic precision by identifying specific DNA/RNA sequences. However, the implementation of these methods faces challenges, particularly in low- and middle-income countries due to infrastructural and financial constraints. This review will explore the role of molecular diagnostic methods in infectious disease diagnosis, comparing their advantages and limitations, with a focus on PCR and NGS technologies and their future potential.

Bioinformatic approach to identifying causative missense polymorphisms in animal genomes

BACKGROUND

Trends in the development of genetic markers for the purposes of genomic and marker-assisted selection primarily focus on identifying causative polymorphisms. Using these polymorphisms as markers enables a more accurate association between genotype and phenotype. Bioinformatic analysis allows calculating the impact of missense polymorphisms on the structural and functional characteristics of proteins, which makes it promising for identifying causative polymorphisms. In this study, a bioinformatic approach is applied to evaluate and differentiate polymorphisms based on their causality in genes that affect the production traits of pigs and cows, which are two important livestock species.

RESULTS

The influence of both known causative and candidate missense polymorphisms in the MC4R, NR6A1, PRKAG3, RYR1, and SYNGR2 genes of pigs, as well as the ABCG2, DGAT1, GHR, and MSTN genes of cows, was assessed. The study included an evaluation of the effect of polymorphisms on protein functions, considering the evolutionary and physicochemical characteristics of amino acids at polymorphic sites. Additionally, it examined the impact of polymorphisms on the stability of tertiary protein structures, including changes in folding, binding of protein monomers, and interaction with ligands.

CONCLUSIONS

The comprehensive bioinformatic analysis used in this study enables the differentiation of polymorphisms into neutral, where both amino acids in the polymorphic site do not significantly affect the structure and function of the protein, and causative, where one of the amino acids significantly impacts the protein's properties. This approach can be employed in future research to screen extensive sets of polymorphisms in animal genomes, identifying the most promising polymorphisms for further investigation in association studies.