Next-Generation DNA Sequencing Methods

Empowering protein single-molecule sequencing: nanopore technology toward sensing gene sequences

The investigation of proteins at the single-molecule level is urgent to reveal the relationship between their structure and function. Unlike traditional techniques for attaining the overall average effect of group systems, nanopore sensing mode can provide information on the characteristics of proteins at the single-molecule level. Assisting with the intensity, frequency, and period of current changes, nanopore sequencing technology is rapidly advancing due to its merits, including fast readout, high accuracy, low cost, and portability. In particular, the single-molecule nanopore sequencing mode enables in-depth studies of DNA-protein interactions, protein conformation, DNA sequencing, and microbial assay, including genome sequencing of new species. This review summarizes the sensing mechanisms of nanopore sequencing technology in DNA damage, DNA methylation, RNA sequencing, and protein post-translational modifications and unfolding, covering both biological and solid-state nanopores. Due to these significant advantages, nanopore sequencing provides new insights into complex biological processes and enables more precise real-time monitoring of molecular changes. Its applications extend to clinical diagnostics, environmental monitoring, food safety, and forensic analysis. Moreover, the review outlines the present challenges faced by nanopore sequencing patterns, such as the choice of raw reagents and the design of special construction, offering a deep understanding of nanoporous single-molecule sensing toward protein sequence information and structure prediction.

StructMAn 2.0 Web: a web server for structural annotation of protein sequences and mutations

StructMAn is a method for protein structural annotation. It describes each position of a protein sequence or specific variants in it in terms of their importance for the three-dimensional (3D) structure of the protein and its interactions with other molecules. StructMAn maps, aligns, and aggregates data from experimentally resolved and predicted 3D structures of proteins and their homologs for any given protein sequence and/or a combination of mutations in it. The results provide structural annotation for every amino acid position allowing a detailed structural analysis. Furthermore, StructMAn enables generation of a wide variety of position-specific high-quality structural features that can be leveraged in machine learning applications. With the new web server StructMAn 2.0 Web, we provide a user-friendly way to use StructMAn offering an easy-to-use input interface and a comprehensive visualization for the various results of StructMAn. StructMAn 2.0 Web is available at https://tools.helmholtz-hips.de/structman.

Nano-enabled pharmacogenomics: revolutionizing personalized drug therapy

The combination of pharmacogenomics and nanotechnology science of pharmacogenomics into a highly advanced single entity has given birth to personalized medicine known as nano-enabled pharmacogenomics. This review article covers all aspects starting from pharmacogenomics to gene editing tools, how these have evolved or are likely to be evolved for pharmacogenomic application, and how these can be delivered using nanoparticle delivery systems. In this prior work, we explore the evolution of pharmacogenomics over the years, as well as new achievements in the field of genomic sciences, the challenges in drug creation, and application of the strategy of personalized medicine. Particular attention is paid to how nanotechnology helps avoid the problems that accompanied the development of pharmacogenomics earlier, for example, the question of drug resistance and targeted delivery. We also review the latest developments in nano-enabled pharmacogenomics, such as the coupling with other nanobio-technologies, artificial intelligence, and machine learning in pharmacogenomics, and the ethical and regulatory aspects of these developing technologies. The possible uses of nanotechnology in improving the chances of pated and treating drug-resistant cancers are exemplified by case studies together with the current clinical uses of nanotechnology. In the last section, we discuss the future trends and research prospects in this dynamically growing area, stressing the importance of further advancements and collaborations which will advance the nano-enabled pharmacogenomics to their maximum potential.

Clinical Bioinformatician Body of Knowledge-Molecular Diagnostics Core: A Report of the Association for Molecular Pathology

Clinical bioinformaticians play a critical role in clinical molecular diagnostics laboratories as developers of data analysis pipelines, tools, and databases. They also contribute to a variety of other tasks, such as genomic data interpretation, database administration, hardware engineering, informatics, information technology, infrastructure support, and software engineering. To effectively perform these functions, the clinical bioinformatician must possess a strong foundational knowledge of molecular biology, genetics, genomics, computational biology, and the relevant federal, state, and/or regional regulations, laboratory accreditation requirements, and other standards and best practices. This first article in the Association for Molecular Pathology's Clinical Bioinformatician Body of Knowledge series provides a comprehensive core knowledge base on molecular biology, genetics, genomics, clinical laboratory practices, sequencing technologies, databases, and clinical applications. This resource serves not only to equip clinical bioinformaticians for their professional roles but also as a valuable reference for laboratorians.

Nanopore sequencing for precise detection of Mycobacterium tuberculosis and drug resistance: a retrospective multicenter study in China

Tuberculosis (TB) management in endemic regions often grapples with resource constraints, including the scarcity of Mycobacterium tuberculosis (M.tb) culture laboratories. The emergence of M.tb strains with complex drug resistance profiles necessitates rapid and comprehensive drug susceptibility testing (DST) to guide patient treatment. However, traditional phenotypic DST (pDST) for M.tb is costly and time-consuming. In this study, we retrospectively enrolled 829 participants from six specialized TB treatment hospitals in China from September 2022 to July 2023. The diagnostic performance of TBseq test, a targeted-nanopore sequencing assay, was compared head-to-head with M.tb culture, acid-fast bacillus smear, quantitative polymerase chain reaction, and Xpert MTB/RIF Ultra by using clinical diagnosis as the reference standard. Subsequently, pDST for seven anti-TB drugs (rifampicin, isoniazid, ethambutol, streptomycin, levofloxacin, amikacin, and capreomycin) was performed. The resistance predictions provided by the TBseq test were compared with pDST results, which were used as a reference standard. The performance estimates of TBseq test were quantified through sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (AUC), providing a comprehensive assessment of its diagnostic accuracy. We found that TBseq test demonstrated significantly superior diagnostic performance for TB compared to other methods, achieving a sensitivity of 90.9% (95% CI: 88.9%-93.0%), specificity of 93.0% (95% CI: 97.2%-99.5%), and an AUC of 0.92 (95% CI: 0.876-0.963). TBseq test also exhibited robust predictive capabilities for drug resistance to the seven anti-TB drugs, with sensitivity and specificity consistently above 90% for all drugs. The AUC values ranged from 0.919 to 0.998, indicative of high diagnostic accuracy in forecasting drug resistance.IMPORTANCEOur results show that TBseq test offers excellent identification performance for tuberculosis (TB), significantly outperforming Mycobacterium tuberculosis (M.tb) culture, acid-fast bacillus (AFB) smear, qPCR, and Xpert MTB/RIF. Its diagnostic accuracy for anti-TB drug resistance is also superior, with sensitivity and specificity above 90% for all drugs tested. This method can be integrated into routine clinical diagnostic workflows, enabling early diagnosis and reporting of drug resistance simultaneously.

Latroeggtoxin-VI improves depression by regulating the composition and function of gut microbiota in a mouse model of depression

Introduction. Depression has become one of the mental diseases that seriously affect human health. Its mechanism is very complex, and many factors influence the condition. An imbalance of the gut microbiota is being considered as a factor that impacts the occurrence and progression of depression. Future therapies may therefore tap into this connection, treating depression through manipulation of the gut microbiome.Hypothesis/Gap Statement. Latroeggtoxin-VI (LETX-VI), a proteinaceous neurotoxin from Latrodectus tredecimguttatus eggs, was previously demonstrated to inhibit excessive inflammation and improve depression behaviours, suggesting that it might be able to regulate the balance of gut microbiota. The aim of this study was to explore the effects of LPS and LETX-VI on depressive behaviours and gut microbiota and to analyse correlations between changes in the gut microbiota and depressive behaviours.Methodology. A murine model of depression was established, and the effects of LPS and LETX-VI treatment on depressive behaviours and gut microbiota were investigated.Results. In the murine model, depressive behaviour was induced by LPS; the ratio of Firmicutes to Bacteroidetes (F/B) and the number of pro-inflammatory bacteria in the gut microbiota increased (P<0.01), while butyric acid-producing bacteria with anti-inflammatory effect decreased (P<0.05). Furthermore, the metabolic function of the gut microbiota was disrupted, and the level of virulence factors among gut microbiota was up-regulated (P<0.05). Association analysis showed that the changes in the composition and function of gut microbiota were closely related to the depression phenotype of mice, suggesting that the abnormal function of gut microbiota is linked to depression. However, when LETX-VI was applied before LPS injection, the LPS-induced changes in the gut microbiota were alleviated, and the depressive behaviour greatly improved.Conclusion. LETX-VI can prevent depressive behaviour by regulating the composition and/or function of the gut microbiota.

Quantifying the Dietary Overlap of Two Co-Occurring Mammal Species Using DNA Metabarcoding to Assess Potential Competition

Interspecific competition is often assumed in ecosystems where co-occurring species have similar resource requirements. The potential for competition can be investigated by measuring the dietary overlap of putative competitor species. The degree of potential competition between generalist species has often received less research attention than competition between specialist species. We examined dietary overlap between two naturally co-occurring dietary generalist species: the common brushtail possum Trichosurus vulpecula and the bush rat Rattus fuscipes. To gauge the potential for competition, we conducted a diet analysis using DNA extracted from faecal samples to identify the range of food items consumed by both species within a shared ecosystem and quantify their dietary overlap. We used DNA metabarcoding on faecal samples to extract plant, fungal, and invertebrate DNA, identifying diet items and quantifying dietary range and overlap. The species' diets were similar, with a Pianka's overlap index score of 0.84 indicating high dietary similarity. Bush rats had a large dietary range, consisting of many plant and fungal species and some invertebrates, with almost no within-species variation. Possums had a more restricted dietary range, consisting primarily of plants. We suggest that the larger dietary range of the bush rat helps buffer it from the impacts of competition from possums by providing access to more food types. We conclude that, despite the high ostensible overlap in the foods consumed by dietary generalist species, fine-scale partitioning of food resources may be a key mechanism to alleviate competition and permit co-existence.

Non-coding RNAs, a double-edged sword in breast cancer prognosis

Cancer is a rising issue worldwide, and numerous studies have focused on understanding the underlying reasons for its occurrence and finding proper ways to defeat it. By applying technological advances, researchers are continuously uncovering and updating treatments in cancer therapy. Their vast functions in the regulation of cell growth and proliferation and their significant role in the progression of diseases, including cancer. This review provides a comprehensive analysis of ncRNAs in breast cancer, focusing on long non-coding RNAs such as HOTAIR, MALAT1, and NEAT1, as well as microRNAs such as miR-21, miR-221/222, and miR-155. These ncRNAs are pivotal in regulating cell proliferation, metastasis, drug resistance, and apoptosis. Additionally, we discuss experimental approaches that are useful for studying them and highlight the advantages and challenges of each method. We then explain the results of these clinical trials and offer insights for future studies by discussing major existing gaps. On the basis of an extensive number of studies, this review provides valuable insights into the potential of ncRNAs in cancer therapy. Key findings show that even though the functions of ncRNAs are vast and undeniable in cancer, there are still complications associated with their therapeutic use. Moreover, there is an absence of sufficient experiments regarding their application in mouse models, which is an area to work on. By emphasizing the crucial role of ncRNAs, this review underscores the need for innovative approaches and further studies to explore their potential in cancer therapy.

On the Trail of the Longest Plant RNA Virus: Citrus Tristeza Virus

The devastating tristeza epidemic swept through South American citrus groves in the 1930s and subsequently spread to most citrus-growing regions worldwide, causing varying degrees of damage and prompting significant changes in citrus cultivation practices. The causal agent of the disease, citrus tristeza virus (CTV), belongs to the genus Closterovirus in the family Closteroviridae. CTV virions are approximately two microns long and possess the largest known positive-strand RNA genome in plants, spanning 19.3 kb. The history of tristeza disease and CTV's molecular biology and taxonomic relationships have been extensively reviewed in the scientific literature. This paper primarily focuses on the author's personal experiences with tristeza disease and its causal agent over the past six decades. The journey began during a period when biological indexing was the primary diagnostic tool. It later progressed through the isolation of purified CTV particles, which served as a practical diagnostic tool for CTV suppression efforts in Israel during the 1970s. However, biological indexing was first replaced by electron microscopy, followed by ELISA procedures; both were eventually abandoned after it was discovered that many ELISA-positive infections were caused by symptomless CTV isolates, even on trees grafted onto sour orange rootstocks. In retrospect, my work on CTV can be categorized into three main phases. It began with the biological phase, inherited from earlier generations of citrus virologists, followed by the isolation and partial characterization of CTV virions, and culminated in the genomic era. While we live in an age of remarkable biotechnological achievements, my recommendation for future CTV research is to integrate both biological and genomic approaches rather than viewing them as mutually exclusive. This is particularly important for economically significant pathogens such as CTV, which should be studied continuously as both biological agents and molecular pathogens.

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.

Profiling of rhizosphere-associated microbial communities in North Alabama soils infested with varied levels of reniform nematodes

Introduction

Plant roots, nematodes, and soil microorganisms have a complex interaction in the rhizosphere by exchanging or communicating through biomolecules or chemicals or signals. Some rhizospheric (including endophytic) microbes process such compounds via biogeochemical cycles to improve soil fertility, promote plant growth and development, and impart stress tolerance in plants. Some rhizospheric microbes can affect negatively on plant parasitic nematodes (PPNs) thus hindering the ability of nematodes in parasitizing the plant roots. Next-generation sequencing is one of the most widely used and cost-effective ways of determining the composition and diversity of microbiomes in such complex environmental samples.

Methods

This study employed amplicon sequencing (Illumina/NextSeq) of 16S ribosomal RNA (16S rRNA) for bacteria and Internal Transcribed Spacer (ITS2) region for fungi to profile the soil microbiome in the rhizosphere of cotton grown in North Alabama. We isolated DNA (ZymoBIOMICS) from soil samples in triplicates from four representative locations of North Alabama. Based on the level of Reniform Nematode (RN) Infestation, these locations were classified as Group A-RN Not-Detected (ND), Group B-RN Low Infestation (LI), Group C-RN Medium Infestation (MI), and Group D-RN High Infestation (HI) and determined using sieving method and microscopic examination.

Results and discussion

Our analyses identified 47,893 bacterial and 3,409 fungal Amplicon Sequence Variants (ASVs) across all groups. Among the bacterial ASVs, 12,758, 10,709, 12,153, and 11,360 unique ASVs were determined in Groups A, B, C, and D, respectively. While 663, 887, 480, and 326 unique fungal ASVs were identified in Groups A, B, C, and D, respectively. Also, the five most abundant rhizospheric bacterial genera identified were Gaiella, Conexibacter, Bacillus, Blastococcus, Streptomyces. Moreover, five abundant fungal genera belonging to Fusarium, Aspergillus, Gibberella, Cladosporium, Lactera were identified. The tight clustering of bacterial nodes in Actinobacteria, Acidobacteria, and Proteobacteria shows they are highly similar and often found together. On the other hand, the close association of Ascomycota and Basidiomycota suggesting that they have different ecological roles but occupy similar niches and contribute similar functions within the microbial community. The abundant microbial communities identified in this study had a role in nutrient recycling, soil health, plant resistance to some environmental stress and pests including nematodes, and biogeochemical cycles. Our findings will aid in broadening our understanding of how microbial communities interact with crops and nematodes in the rhizosphere, influencing plant growth and pest management.

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.

MOSMAP: Mosquito metagenome analysis pipeline

MosMAP is a bioinformatics pipeline designed for mosquito metagenome analysis. MosMAP automates essential processes like quality control, taxonomic classification, species abundance estimation and visualization by integrating tools such as Trimgalore, Kraken 2, Bracken and Krona into a user-friendly workflow. Each of these tools is integrated to ensure a smooth and efficient workflow from raw data to interpretable results. The pipeline simplifies complex bioinformatics tasks, making them accessible to researchers with limited computational expertise. MosMAP demonstrated high concordance with standard bioinformatics workflows such as Kraken and Bracken in terms of read retention, taxonomic accuracy and abundance estimation when applied to metagenomes of mosquito collected in Bhopal, India. This accessible pipeline promotes the simplification of meta-genomics, supporting research in microbiology, ecology and vector-borne diseases.

Sudden Cardiac Death: The Role of Molecular Autopsy with Next-Generation Sequencing

Molecular autopsy is a term employed to describe the investigation of the cause of death through the analysis of genetic information using biological samples collected post-mortem. Its utility becomes evident in situations where conventional medico-legal autopsy methods are not able to identify the cause of death, i.e., in sudden cardiac death (SCD) cases in young individuals, where deaths are commonly due to genetic cardiac conditions, such as cardiomyopathies and channelopathies. The recent advancement in high-throughput sequencing techniques, such as next-generation sequencing (NGS), has allowed the investigation of a high number of genomic regions in a more cost-effective and faster approach. Unlike traditional sequencing methods, which can only sequence one DNA fragment at a time, NGS can sequence millions of short polynucleotide fragments simultaneously. This parallel approach reduces both the time and cost required to generate large-scale genomic data, making it a useful tool for applications ranging from basic research to molecular autopsy. In the forensic context, by enabling the examination of multiple genes or entire exomes and genomes, NGS enhances the accuracy and depth of genetic investigations, contributing to a better understanding of complex inherited diseases. However, challenges remain, such as the interpretation of variants of unknown significance (VUS), the need for standardized protocols, and the high demand for specialized bioinformatics expertise. Despite these challenges, NGS continues to offer significant promise for enhancing the precision of molecular autopsies. The goal of this review is to assess the effectiveness of contemporary advancements in molecular autopsy methodologies when applied to cases of SCD in young individuals and to present an overview of the steps involved in the analysis of NGS data and the interpretation of genetic variants.

Comparative analysis of chloroplast genomes reveals molecular evolution and phylogenetic relationships within the Papilionoideae of Fabaceae

BACKGROUND

The structure of chloroplast genomes (cpDNAs) in Fabaceae (Fab.) has undergone significant evolutionary modifications. Within the Papilionoideae (Pap.), the emergence of the Inverted Repeat-Lacking Clade (IRLC) represents a major genomic alteration. However, the molecular evolution and phylogenetic relationships within Pap. remain poorly resolved due to limited molecular data and incomplete research, highlighting the need for systematic investigation.

PURPOSE

This study presents an in-depth analysis of the cpDNAs within the Pap., with the aim of unraveling the molecular evolution and phylogenetic interconnections among its species.

METHODS

Complete cpDNAs of 18 Pap. species were sequenced using the Illumina Novaseq 6000 platform, followed by assembly and annotation. Comparative genomic analyses were conducted to elucidate structural variations and phylogenetic relationships.

RESULTS

The research has uncovered significant differences in the structure and characteristics of the cpDNAs within the Pap.. The lengths of the cpDNAs of 18 species range from 121,190 bp to 158,539 bp, and they contain between 107 and 112 unique genes. Five species, namely Desmodium elegans and Indigofera bracteata, exhibit a typical quadripartite structure, while thirteen species from genera such as Astragalus (Ast.), Hedysarum (Hed.), and Caragana (Car.) are grouped within the Inverted Repeat-Lacking Clade (IRLC). Genetic characteristic analysis revealed a plentiful presence of SSR loci, with single-nucleotide repeats and dinucleotide (A/T) repeats being the most predominant. Notably, the cpDNAs of five species including D. elegans have experienced significant rearrangements. For example, an inversion of approximately 23 kilobase (kb) pairs was observed in Pueraria peduncularis and Sophora moorcroftiana. These species exhibit pronounced differences in their non-coding regions. Comparative genomic variations at cpDNA sites were identified. Moreover, by using D. elegans as a reference, six genes (ycf4, clpP, ycf1, trnI-GAU, accD, rpl32) displayed high nucleotide polymorphism (Pi > 0.1), and the Ka/Ks ratio for all protein-coding genes was determined to be less than 1. The topological structure of the constructed phylogenetic tree of 85 species was basically consistent with that of Pap.. Seven main clades were formed and relatively high bootstrap values were exhibited, further clarifying the evolutionary relationships among them.

CONCLUSION

This study provides novel insights into the molecular evolution and phylogeny of Pap., offering a foundational resource for future taxonomic and evolutionary research.

Gene Editing for Enhanced Swine Production: Current Advances and Prospects

Traditional pig breeding has improved production traits but faces limitations in genetic diversity, disease resistance, and environmental adaptation. Gene editing technologies, such as CRISPR/Cas9, base editing, and prime editing, enable precise genetic modifications, overcoming these limitations and expanding applications to biomedical research. Here, we reviewed the advancements in gene editing technologies in pigs and explored pathways toward optimized swine genetics for a resilient and adaptive livestock industry. This review synthesizes recent research on gene editing tools applied to pigs, focusing on CRISPR/Cas9 and its derivatives. It examines their impact on critical swine production traits and their role as human disease models. Significant advancements have been made in targeting genes for disease resistance, such as those conferring immunity to porcine reproductive and respiratory syndrome viruses. Additionally, gene-edited pigs are increasingly used as models for human diseases, demonstrating the technology's broader applications. However, challenges such as off-target effects, ethical concerns, and varying regulatory frameworks remain. Gene editing holds substantial potential for sustainable and productive livestock production by enhancing key traits and supporting biomedical applications. Addressing technical and ethical challenges through integrated approaches will be essential to realize its full potential, ensuring a resilient, ethical, and productive livestock sector for future generations.

Statistical Distributions of Genome Assemblies Reveal Random Effects in Ancient Viral DNA Reconstructions

Ancient human viruses have been detected in ancient DNA (aDNA) samples of both Anatomically Modern Humans and Neanderthals. Reconstructing genomes from aDNA using reference mapping presents numerous problems due to the unique nature of ancient samples, their degraded state, smaller read sizes and the limitations of current methodologies. The spurious alignments of reads to reference sequences (mapping) are a main source of false positives in aDNA assemblies and the assessment of signal-to-noise ratios is essential to differentiate bona fide reconstructions from random, noisy assemblies. Here, we analyzed the statistical distributions of viral genome assemblies, ancient and modern, and their respective random "mock" controls used to evaluate the signal-to-noise ratio. We tested if differences between real and random assemblies could be detected from their statistical distributions. Our analysis shows that the coverage distributions of (1) real viral aDNA assemblies of adenovirus (ADV), herpesvirus (HSV) and papillomavirus (HPV) do not follow power laws nor log-normal laws, (2) (ADV) and control aDNA assemblies are well approximated by log-normal laws, (3) negative control parvovirus B19 (real and random) follow a power law with infinite variance and (4) the mapDamage negative control with non-ancient DNA (modern ADV) and the mapDamage positive control (human mtDNA) are well approximated by the negative binomial distribution, consistent with the Lander-Waterman model. Our results show that the tails of the distributions of aDNA and their controls reveal the weight of random effects and can differentiate spurious assemblies, or false positives, from bona fide assemblies.

Characterizing flavor development in low-salt Chinese horse bean-chili paste through integrated metabolomics and metagenomics

This study utilized metabolomics and metagenomics to investigate the microbial composition and functions in low- and high-salt Chinese horse bean-chili pastes (CHCPs). The results showed that 25 key metabolites were identified to distinguish the flavor attributes between the two samples. Leuconostoc was identified as the dominant microbiota in low-salt CHCP, while Pantoea prevailed in the high-salt CHCP. Compared to traditional high-salt fermentation, low-salt and inoculated fermentation promoted the increase in the relative abundances of Companionlactobacillus, Levilactobacillus, Tetragenococcus, Zygosaccharomyces and Wickerhamiella as well as the enrichment of carbohydrate and amino acid metabolic pathways, which contributed to the enhancement of characteristic flavor compounds. Further metabolic pathway reconstruction elucidated 21 potential microbial genera associated with the formation of key metabolites, such as Leuconostoc, Levilactobacillus, Pantoea, and Pectobacterium. This study may provide insights for optimizing the fermentation process and improving the flavor quality of low-salt CHCP and similar fermentation products. KEYWORDS: Low-salt fermentation Hight-salt fermentation Chinese horse-bean chili paste Flavor formation Metabolomics Metagenomics.

Transcriptomic Evidence Reveals the Dysfunctional Mechanism of Synaptic Plasticity Control in ASD

BACKGROUND/OBJECTIVES

A prominent endophenotype in Autism Spectrum Disorder (ASD) is the synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigate the postsynaptic signal transduction network in glutamatergic neurons and integrate single-cell nucleus transcriptomics data from the Prefrontal Cortex (PFC) to unveil the malfunction of translation control.

METHODS

We devise an innovative and highly dependable pipeline to transform our acquired signal transduction network into an mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level. We employ Cell-Specific Network Inference via Integer Value Programming and Causal Reasoning (CS-NIVaCaR) to identify core modules and Cell-Specific Probabilistic Contextualization for mRNA Regulatory Networks (CS-ProComReN) to quantitatively reveal activated sub-pathways involving MAPK1, MKNK1, RPS6KA5, and MTOR across different cell types in ASD.

RESULTS

The results indicate that specific pivotal molecules, such as EIF4EBP1 and EIF4E, lacking Differential Expression (DE) characteristics and responsible for protein translation with long-term potentiation (LTP) or long-term depression (LTD), are dysregulated. We further uncover distinct activation patterns causally linked to the EIF4EBP1-EIF4E module in excitatory and inhibitory neurons.

CONCLUSIONS

Importantly, our work introduces a methodology for leveraging extensive transcriptomics data to parse the signal transduction network, transforming it into mSiReN, and mapping it back to the protein level. These algorithms can serve as potent tools in systems biology to analyze other omics and regulatory networks. Furthermore, the biomarkers within the activated sub-pathways, revealed by identifying convergent dysregulation, illuminate potential diagnostic and prognostic factors in ASD.

The application status of sequencing technology in global respiratory infectious disease diagnosis

Next-generation sequencing (NGS) has revolutionized clinical microbiology, particularly in diagnosing respiratory infectious diseases and conducting epidemiological investigations. This narrative review summarizes conventional methods for routine respiratory infection diagnosis, including culture, smear microscopy, immunological assays, image techniques as well as polymerase chain reaction(PCR). In contrast to conventional methods, there is a new detection technology, sequencing technology, and here we mainly focus on the next-generation sequencing NGS, especially metagenomic NGS(mNGS). NGS offers significant advantages over traditional methods. Firstly, mNGS eliminates assumptions about pathogens, leading to faster and more accurate results, thus reducing diagnostic time. Secondly, it allows unbiased identification of known and novel pathogens, offering broad-spectrum coverage. Thirdly, mNGS not only identifies pathogens but also characterizes microbiomes, analyzes human host responses, and detects resistance genes and virulence factors. It can complement targeted sequencing for bacterial and fungal classification. Unlike traditional methods affected by antibiotics, mNGS is less influenced due to the extended survival of pathogen DNA in plasma, broadening its applicability. However, barriers to full integration into clinical practice persist, primarily due to cost constraints and limitations in sensitivity and turnaround time. Despite these challenges, ongoing advancements aim to improve cost-effectiveness and efficiency, making NGS a cornerstone technology for global respiratory infection diagnosis.

Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives

The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which mainly encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in technology and methodologies that will address these challenges. Emerging innovations in data analytics, machine learning, and high-throughput sequencing are expected to enhance the integration of multi-omics data, making personalized medicine more accessible and effective. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial to overcoming these hurdles and fully harnessing the potential of multi-omics for individualized healthcare.

Metagenomic approaches and opportunities in arid soil research

Arid soils present unique challenges and opportunities for studying microbial diversity and bioactive potential due to the extreme environmental conditions they bear. This review article investigates soil metagenomics as an emerging tool to explore complex microbial dynamics and unexplored bioactive potential in harsh environments. Utilizing advanced metagenomic techniques, diverse microbial populations that grow under extreme conditions such as high temperatures, salinity, high pH levels, and exposure to metals and radiation can be studied. The use of extremophiles to discover novel natural products and biocatalysts emphasizes the role of functional metagenomics in identifying enzymes and secondary metabolites for industrial and pharmaceutical purposes. Metagenomic sequencing uncovers a complex network of microbial diversity, offering significant potential for discovering new bioactive compounds. Functional metagenomics, connecting taxonomic diversity to genetic capabilities, provides a pathway to identify microbes' mechanisms to synthesize valuable secondary metabolites and other bioactive substances. Contrary to the common perception of desert soil as barren land, the metagenomic analysis reveals a rich diversity of life forms adept at extreme survival. It provides valuable findings into their resilience and potential applications in biotechnology. Moreover, the challenges associated with metagenomics in arid soils, such as low microbial biomass, high DNA degradation rates, and DNA extraction inhibitors and strategies to overcome these issues, outline the latest advancements in extraction methods, high-throughput sequencing, and bioinformatics. The importance of metagenomics for investigating diverse environments opens the way for future research to develop sustainable solutions in agriculture, industry, and medicine. Extensive studies are necessary to utilize the full potential of these powerful microbial communities. This research will significantly improve our understanding of microbial ecology and biotechnology in arid environments.

An easy-to-use pipeline to analyze amplicon-based Next Generation Sequencing results of human mitochondrial DNA from degraded samples

Genome and transcriptome examinations have become more common due to Next-Generation Sequencing (NGS), which significantly increases throughput and depth coverage while reducing costs and time. Mitochondrial DNA (mtDNA) is often the marker of choice in degraded samples from archaeological and forensic contexts, as its higher number of copies can improve the success of the experiment. Among other sequencing strategies, amplicon-based NGS techniques are currently being used to obtain enough data to be analyzed. There are some pipelines designed for the analysis of ancient mtDNA samples and others for the analysis of amplicon data. However, these pipelines pose a challenge for non-expert users and cannot often address both ancient and forensic DNA particularities and amplicon-based sequencing simultaneously. To overcome these challenges, a user-friendly bioinformatic tool was developed to analyze the non-coding region of human mtDNA from degraded samples recovered in archaeological and forensic contexts. The tool can be easily modified to fit the specifications of other amplicon-based NGS experiments. A comparative analysis between two tools, MarkDuplicates from Picard and dedup parameter from fastp, both designed for duplicate removal was conducted. Additionally, various thresholds of PMDtools, a specialized tool designed for extracting reads affected by post-mortem damage, were used. Finally, the depth coverage of each amplicon was correlated with its level of damage. The results obtained indicated that, for removing duplicates, dedup is a better tool since retains more non-repeated reads, that are removed by MarkDuplicates. On the other hand, a PMDS = 1 in PMDtools was the threshold that allowed better differentiation between present-day and ancient samples, in terms of damage, without losing too many reads in the process. These two bioinformatic tools were added to a pipeline designed to obtain both haplotype and haplogroup of mtDNA. Furthermore, the pipeline presented in the present study generates information about the quality and possible contamination of the sample. This pipeline is designed to automatize mtDNA analysis, however, particularly for ancient samples, some manual analyses may be required to fully validate results since the amplicons that used to be more easily recovered were the ones that had fewer reads with damage, indicating that special care must be taken for poor recovered samples.

Scalable analysis of large multi-ancestry biobanks by leveraging sparse ancestry-adjusted sample-relatedness

Linear mixed-effects models (LMMs) and ridge regression are commonly applied in genetic association studies to control for population structure and sample-relatedness. To control for sample-relatedness, the existing methods use empirical genetic relatedness matrices (GRM) either explicitly or conceptually. This works well with mostly homogeneous populations, however, in multi-ancestry heterogeneous populations, GRMs are confounded with population structure which leads to inflated type I error rates, massively increased computation, and reduced power. Here, we propose FastSparseGRM, a scalable pipeline for multi-ancestry Genome-Wide Association studies (GWAS) and Whole Genome Sequencing (WGS) studies. It utilizes a block-diagonal sparse ancestry-adjusted (BDSA) GRM to model sample-relatedness, and ancestry PCs as fixed effects to control for population structure. It is ~ 2540/4100/54 times faster than BOLT-LMM/fast-GWA/REGENIE for fitting the null LMM on 50,000 heterogeneous subjects. Through numerical simulations and both single-variant GWAS and rare variant WGS analyses of five biomarkers (Triglycerides, HDL, LDL, BMI, Total Bilirubin) on the entire UK Biobank data, we demonstrate that our approach scales to nearly half-a-million subjects and provides accurate p-value calibration and improved power compared to the existing methods.

Prediction of YY1 loop anchor based on multi-omics features

The three-dimensional structure of chromatin is crucial for the regulation of gene expression. YY1 promotes enhancer-promoter interactions in a manner analogous to CTCF-mediated chromatin interactions. However, little is known about which YY1 binding sites can form loop anchors. In this study, the LightGBM model was used to predict YY1-loop anchors by integrating multi-omics data. Due to the large imbalance in the number of positive and negative samples, we use AUPRC to reflect the quality of the classifier. The results show that the LightGBM model exhibits strong predictive performance (AUPRC≥0.93). To verify the robustness of the model, the dataset was divided into training and test sets at a 4:1 ratio. The results show that the model performs well for YY1-loop anchor prediction on both the training and independent test sets. Additionally, we ranked the importance of the features and found that the formation of YY1-loop anchors is primarily influenced by the co-binding of transcription factors CTCF, SMC3, and RAD21, as well as histone modifications and sequence context.

Optimization and Clinical Application Potential of Single Nucleotide Polymorphism Detection Method Based on CRISPR/Cas12a and Recombinase Polymerase Amplification

Conventional methods for detecting single nucleotide polymorphisms (SNPs) in clinical practice often require substantial time, labor, and specialized equipment, limiting their widespread application. To address this limitation, we refined our previous SNP detection method, IMAS-RPA [introducing an extra mismatched base adjacent to the single-base mutant site by recombinase polymerase amplification (RPA)], resulting in an updated version termed IMAS-RPAv2. We began by introducing a suboptimal protospacer adjacent motif (PAM) sequence, GTTG, into the double-stranded DNA (dsDNA) products using either RPA or reverse transcription RPA. This modification decreased the efficiency with which CRISPR RNA (crRNA) recognizes the PAM and locally unwinds the dsDNA to form an R loop. After a delay, the R loop forms. However, due to the intentional incorporation of a mismatched base on the crRNA relative to the wild-type double-stranded DNA (WT-dsDNA), a continuous two-base mismatch is established between the crRNA and WT-dsDNA. Consequently, WT-dsDNA does not activate CRISPR/Cas12a's cleavage activity within a short time, while variant-type dsDNA continues to activate CRISPR/Cas12a and produce a robust fluorescence signal. This improvement significantly enhances the SNP discrimination sensitivity, allowing for detection at the single-copy level. Results were observed using both a conventional microplate reader and a specially designed portable device created through 3D printing. This device allows a direct fluorescence observation without the need for additional equipment. Consequently, the entire detection process becomes independent of large-scale equipment. This greatly expands its range of applications and offers promising prospects for clinical use.

Understanding the Biology and Testing Techniques for Pharmacogenomics in Oncology: A Practical Guide for the Clinician

Pharmacogenomic (PGx) testing is a growing area of personalized medicine with demonstrated clinical utility in improving patient outcomes in oncology. PGx testing of pharmacogenes affecting drug pharmacokinetics, pharmacodynamics, and response can help inform drug selection and dosing of several anticancer therapies and supportive care medications. Several PGx testing techniques exist including polymerase chain reaction (PCR), MassARRAY, microarray, and sequencing. This review article provides a clinician-friendly guide of these techniques. Understanding the advantages, limitations, ideal use, and potential clinical applications of each platform can help clinicians choose the appropriate PGx testing platform for specific use cases.

Next Generation Sequencing: Latent applications in clinical diagnostics with the advent of bioinformatic frameworks

For the past 3-4 decades, the discovery of Sanger's method of pyrosequencing was the only method unparalleled till 2005 being employed as a method of whole genome sequencing (WGS). Following this, a revolutionary extensive parallel sequencing method, Next Generation Sequencing (NGS), was engineered. NGS supported a substantial number of bases under a high throughput metagenomic interrogation. Bioinformatics contributed notably to this advancement. It provided alignment tools, assembly algorithms, and protocols such as Illumina and hybridization capture which have metamorphosed clinical and translational diagnostics. With the extension in precision medicine and targeted therapy under NGS sectors such as epigenetics, transcriptomics, mutation detection, prognosis, therapeutics, and patient management have been gaining progress. Using NGS in real-time clinical settings has been proven to produce positive outcomes. The most recent instrumental benefaction of NGS has been decoding the SARS-CoV-2 virus epidemiology with the assistance of multiplex PCR. So far, it had been employed to inspect different levels of viral loads from low to mid. This has been executed by amplification and phylogenetic examination of the load to raise a connective link with the evolutionary history leading up to the period of origin. The depletion in the consumed time and extensive genome size under analysis was further coupled by a cutback in the cost of sequencing while executing NGS. With the aid of this review paper, we aspire to manifest how the above-mentioned elements have boosted, tissue, microbial, and molecular data interrogation. Along with this, promoting, and stimulating an extensive evaluation and expansion in the paradigm of morphological and phenotypic study, via bioinformatics can facilitate further advancement in personalized and concise clinical research.

Early changes in the gut microbiome among HIV-infected Individuals in Uganda initiating daily TMP/SMX

Daily cotrimoxazole (TMP/SXT) prophylaxis is part of the HIV treatment package for all new HIV-infected individuals in Uganda. Although this treatment has shown reduced morbidity and mortality in HIV, it remains controversial due to its contribution to developing antibiotic-resistant bacteria. Moreover, the effects of daily use of a broad-spectrum antibiotic on the gut microbiome remain unknown. To study the early effects, we analysed shotgun metagenome sequence data from stool samples of five newly HIV-infected individuals initiating TMP/SXT prophylaxis longitudinally for the first 30 days of treatment. Using shotgun metagenomics sequencing, we generated both taxonomic and functional profiles from each patient and compared gut microbial changes Pre- TMP/SXT and post-TMP/SXT on Day 5, Day 14, and Day 30. Daily TMP/SXT prophylaxis resulted in a shift characterised by an enrichment of Prevetollea and Ruminococcus genera members and the depletion of Lactococcus and Bacteroides genera members. Furthermore, these microbial shifts were associated with changes in the functional profile revealed by a differential abundance of pathways of amino acid metabolism, carbohydrate metabolism, and nucleotide biosynthesis linked to members of the Bacteroidaceae and Enterobacteriaceae families. TMP/SXT daily prophylaxis in HIV-infected individuals is associated with dramatic changes in microbial composition and functional profiles; however, other factors such as Age, Gender, HIV clinical stage, and ART regiment are at play. Further investigation is needed to examine the implication of these shifts on clinical management and outcomes among HIV patients.

Single-Cell Analysis of Bone-Marrow-Disseminated Tumour Cells

Metastasis frequently targets bones, where cancer cells from the primary tumour migrate to the bone marrow, initiating new tumour growth. Not only is bone the most common site for metastasis, but it also often marks the first site of metastatic recurrence. Despite causing over 90% of cancer-related deaths, effective treatments for bone metastasis are lacking, with current approaches mainly focusing on palliative care. Circulating tumour cells (CTCs) are pivotal in metastasis, originating from primary tumours and circulating in the bloodstream. They facilitate metastasis through molecular interactions with the bone marrow environment, involving direct cell-to-cell contacts and signalling molecules. CTCs infiltrate the bone marrow, transforming into disseminated tumour cells (DTCs). While some DTCs remain dormant, others become activated, leading to metastatic growth. The presence of DTCs in the bone marrow strongly correlates with future bone and visceral metastases. Research on CTCs in peripheral blood has shed light on their release mechanisms, yet investigations into bone marrow DTCs have been limited. Challenges include the invasiveness of bone marrow aspiration and the rarity of DTCs, complicating their isolation. However, advancements in single-cell analysis have facilitated insights into these elusive cells. This review will summarize recent advancements in understanding bone marrow DTCs using single-cell analysis techniques.

FaTEDT1L of Octoploid Cultivated Strawberry Functions as a Transcriptional Activator and Enhances Abiotic Stress Tolerance in Transgenic Arabidopsis

Plants may encounter abiotic stresses, such as drought, flooding, salinity, and extreme temperatures, thereby negatively affecting their growth, development, and reproduction. In order to enhance their tolerance to such stresses, plants have developed intricate signaling networks that regulate stress-responsive gene expression. For example, Arabidopsis Enhanced Drought Tolerance1/HOMEODOMAIN GLABROUS 11 (AtEDT1/HDG11), one of the transcription factor genes from the group IV of homeodomain-leucine zipper (HD-ZIP) gene family, has been shown to increase drought tolerance in various transgenic plants. However, the underlying molecular mechanisms of enhanced stress tolerance remain unclear. In this study, we identified a homologous gene related to AtEDT1/HDG11, named FaTEDT1L, from the transcriptome sequencing database of cultivated strawberry. Phylogenetic analysis revealed the close relationship of FaTEDT1L with AtEDT1/HDG11, which is one of the group IV members of the HD-ZIP gene family. Yeast one-hybrid analysis showed that FaTEDT1L functions as a transcriptional activator. Transgenic Arabidopsis plants overexpressing FaTEDT1L under the control of the cauliflower mosaic virus (CaMV) 35S promoter exhibited significantly enhanced tolerance to osmotic stress (both drought and salinity) when compared to the wild-type (WT) plants. Under osmotic stress, the average root length was 3.63 ± 0.83 cm, 4.20 ± 1.03 cm, and 4.60 ± 1.14 cm for WT, 35S::FaTEDT1L T2 #3, and 35S:: FaTEDT1L T2 #5, respectively. Substantially increased root length in 35S::FaTEDT1L T2 #3 and 35S::FaTEDT1L T2 #5 was noted when compared to the WT. In addition, the average water loss rates were 64%, 57.1%, and 55.6% for WT, 35S::FaTEDT1L T2 #3, and 35S::FaTEDT1L T2 #5, respectively, after drought treatment, indicating a significant decrease in water loss rate of 35S:: FaTEDT1L T2 #3 and 35S::FaTEDT1L T2 #5 is a critical factor in enhancing plant drought resistance. These findings thus highlight the crucial role of FaTEDT1L in mitigating drought and salt stresses and regulating plant osmotic stress tolerance. Altogether, FaTEDT1L shows its potential usage as a candidate gene for strawberry breeding in improving crop resilience and increasing agricultural productivity under adverse environmental conditions.

Future directions for early detection of fracture related infections

Introduction

Fracture related infection (FRI) refers to pathogens infecting a fracture site and hence impeding fracture healing. It is a significant complication that carries substantial disease burden and socio-economic costs, but has had limited scientific development. Hence, this paper will review the existing strategies for early detection of FRI, in the form of serum markers, molecular diagnostics and imaging modalities, and further discuss potential future directions for improved detection of FRI.

Existing Strategies for Diagnosis of FRI

The Anti-infection Global Expert Committee (AIGEC) developed a consensus definition for FRI in 2017, which includes confirmatory and suggestive criteria for diagnosis of FRI. Existing strategies for diagnosis include clinical, laboratory, histopathological, microbiological and radiological investigations.

Future Directions for Early Detection of FRI

With increasing recognition of FRI, early detection is crucial for early treatment to be enforced. We have identified potential areas for future development in diagnostics for early detection of FRI, which are discussed in this manuscript. They include inflammatory cytokines, serum calcium levels, platelet count, improved management of histopathological and microbiological specimens, metagenomics, wound biomarkers, gut microbiota analysis, and novel imaging technologies.

scSwinFormer: A Transformer-Based Cell-Type Annotation Method for scRNA-Seq Data Using Smooth Gene Embedding and Global Features

Single-cell omics techniques have made it possible to analyze individual cells in biological samples, providing us with a more detailed understanding of cellular heterogeneity and biological systems. Accurate identification of cell types is critical for single-cell RNA sequencing (scRNA-seq) analysis. However, scRNA-seq data are usually high dimensional and sparse, posing a great challenge to analyze scRNA-seq data. Existing cell-type annotation methods are either constrained in modeling scRNA-seq data or lack consideration of long-term dependencies of characterized genes. In this work, we developed a Transformer-based deep learning method, scSwinFormer, for the cell-type annotation of large-scale scRNA-seq data. Sequence modeling of scRNA-seq data is performed using the smooth gene embedding module, and then, the potential dependencies of genes are captured by the self-attention module. Subsequently, the global information inherent in scRNA-seq data is synthesized using the Cell Token, thereby facilitating accurate cell-type annotation. We evaluated the performance of our model against current state-of-the-art scRNA-seq cell-type annotation methods on multiple real data sets. ScSwinFormer outperforms the current state-of-the-art scRNA-seq cell-type annotation methods in both external and benchmark data set experiments.

Spatiotemporal omics for biology and medicine

The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.

Cataloging the Genetic Response: Unveiling Drought-Responsive Gene Expression in Oil Tea Camellia (Camellia oleifera Abel.) through Transcriptomics

Drought stress is a critical environmental factor that significantly impacts plant growth and productivity. However, the transcriptome analysis of differentially expressed genes in response to drought stress in Camellia oleifera Abel. is still unclear. This study analyzed the transcriptome sequencing data of C. oleifera under drought treatments. A total of 20,674 differentially expressed genes (DEGs) were identified under drought stress, with the number of DEGs increasing with the duration of drought. Specifically, 11,793 and 18,046 DEGs were detected after 8 and 15 days of drought treatment, respectively, including numerous upregulated and downregulated genes. Gene Ontology (GO) enrichment analysis showed that the DEGs were primarily involved in various biological processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that carbon metabolism, glyoxylate and dicarboxylate metabolism, proteasome, glycine, serine, and threonine metabolism were the main affected pathways. Among the DEGs, 376 protein kinases, 42 proteases, 168 transcription factor (TF) genes, and 152 other potential functional genes were identified, which may play significant roles in the drought response of C. oleifera. The expression of relevant functional genes was further validated using quantitative real-time PCR (qRT-PCR). These findings contribute to the comprehension of drought tolerance mechanisms in C. oleifera and bolster the identification of drought-resistant genes for molecular breeding purposes.

A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology

The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.

Should AI-Powered Whole-Genome Sequencing Be Used Routinely for Personalized Decision Support in Surgical Oncology—A Scoping Review

In this scoping review, we delve into the transformative potential of artificial intelligence (AI) in addressing challenges inherent in whole-genome sequencing (WGS) analysis, with a specific focus on its implications in oncology. Unveiling the limitations of existing sequencing technologies, the review illuminates how AI-powered methods emerge as innovative solutions to surmount these obstacles. The evolution of DNA sequencing technologies, progressing from Sanger sequencing to next-generation sequencing, sets the backdrop for AI’s emergence as a potent ally in processing and analyzing the voluminous genomic data generated. Particularly, deep learning methods play a pivotal role in extracting knowledge and discerning patterns from the vast landscape of genomic information. In the context of oncology, AI-powered methods exhibit considerable potential across diverse facets of WGS analysis, including variant calling, structural variation identification, and pharmacogenomic analysis. This review underscores the significance of multimodal approaches in diagnoses and therapies, highlighting the importance of ongoing research and development in AI-powered WGS techniques. Integrating AI into the analytical framework empowers scientists and clinicians to unravel the intricate interplay of genomics within the realm of multi-omics research, paving the way for more successful personalized and targeted treatments.

TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer.

Oral microbial profiles of extrinsic black tooth stain in primary dentition: A literature review

The extrinsic black tooth stain (EBS) is commonly found in primary dentition. Patients cannot clean the EBS; this can only be done by professional scaling and debridement. It also has a tendency to reform, which significantly compromises children's aesthetics and even affects their quality of life. However, there is no conclusive evidence on the etiology of the EBS. The associations between the EBS and related oral microbial features is one of the research hot topics. No literature review summarized these research progresses in this area. Therefore, we reviewed the literature on the microbiology of the EBS since 1931 and reported as the following 5 aspects: molecular biotechnology, morphological structure and physiochemical characteristics, microbial etiology hypothesis and core microbial characteristics. The EBS is a special dental plaque mainly composed of Gram-positive bacilli and cocci with scattered calcium deposits that acquired salivary pellicle activates. Early studies showed that the Actinomyces was the main pathogenic bacteria. With advances in biological research techniques, the 'core microbiome' was proposed. The potential pathogenic genera were Actinomyces, Prevotella nigrescens, Pseudotropinibacterium, Leptotrichia, Neisseria and Rothia. However, the pathogenic species of the above genera were still unclear. Currently, it is believed that the EBS consists of iron compounds or black substances that oral bacterial metabolism produces or that the bacterial metabolites formed after chemical reactions in the micro-ecological environment.

PxBLAT: an efficient python binding library for BLAT

BACKGROUND

With the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem.

RESULTS

PxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT.

CONCLUSIONS

PxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment.

Targeting FGFR for cancer therapy

The FGFR signaling pathway is integral to cellular activities, including proliferation, differentiation, and survival. Dysregulation of this pathway is implicated in numerous human cancers, positioning FGFR as a prominent therapeutic target. Here, we conduct a comprehensive review of the function, signaling pathways and abnormal alterations of FGFR, as well as its role in tumorigenesis and development. Additionally, we provide an in-depth analysis of pivotal phase 2 and 3 clinical trials evaluating the performance and safety of FGFR inhibitors in oncology, thereby shedding light on the current state of clinical research in this field. Then, we highlight four drugs that have been approved for marketing by the FDA, offering insights into their molecular mechanisms and clinical achievements. Our discussion encompasses the intricate landscape of FGFR-driven tumorigenesis, current techniques for pinpointing FGFR anomalies, and clinical experiences with FGFR inhibitor regimens. Furthermore, we discuss the inherent challenges of targeting the FGFR pathway, encompassing resistance mechanisms such as activation by gatekeeper mutations, alternative pathways, and potential adverse reactions. By synthesizing the current evidence, we underscore the potential of FGFR-centric therapies to enhance patient prognosis, while emphasizing the imperative need for continued research to surmount resistance and optimize treatment modalities.

Differential response of bacteria and fungi to drought on the decomposition of Sarcocornia fruticosa woody stems in a saline stream

Inland saline ecosystems suffer multiple stresses (e.g., high radiation, salinity, water scarcity) that may compromise essential ecosystem functions such as organic matter decomposition. Here, we investigated the effects of drought on microbial colonization and decomposition of Sarcocornia fruticosa woody stems across different habitats in a saline watershed: on the dry floodplain, submerged in the stream channel and at the shoreline (first submerged, then emerged). Unexpectedly, weight loss was not enhanced in the submerged stems, while decomposition process differed between habitats. On the floodplain, it was dominated by fungi and high cellulolytic activity; in submerged conditions, a diverse community of bacteria and high ligninolytic activity dominated; and, on the shoreline, enzyme activities were like submerged conditions, but with a fungal community similar to the dry conditions. Results indicate distinct degradation paths being driven by different stress factors: strong water scarcity and photodegradation in dry conditions, and high salinity and reduced oxygen in wet conditions. This suggests that fungi are more resistant to drought, and bacteria to salinity. Overall, in saline watersheds, variations in multiple stress factors exert distinct environmental filters on bacteria and fungi and their role in the decomposition of plant material, affecting carbon cycling and microbial interactions.

Comparing full variation profile analysis with the conventional consensus method in SARS-CoV-2 phylogeny

This study proposes a novel approach to studying severe acute respiratory syndrome coronavirus 2 virus mutations through sequencing data comparison. Traditional consensus-based methods, which focus on the most common nucleotide at each position, might overlook or obscure the presence of low-frequency variants. Our method, in contrast, retains all sequenced nucleotides at each position, forming a genomic matrix. Utilizing simulated short reads from genomes with specified mutations, we contrasted our genomic matrix approach with the consensus sequence method. Our matrix methodology, across multiple simulated datasets, accurately reflected the known mutations with an average accuracy improvement of 20% over the consensus method. In real-world tests using data from GISAID and NCBI-SRA, our approach demonstrated an increase in reliability by reducing the error margin by approximately 15%. The genomic matrix approach offers a more accurate representation of the viral genomic diversity, thereby providing superior insights into virus evolution and epidemiology.

The Dark Side of the pollen: BSA-seq identified genomic regions linked to male sterility in globe artichoke

Globe artichoke (Cynara cardunculus var. scolymus; 2n = 2x = 34) is a food crop consumed for its immature flower heads. Traditionally, globe artichoke varietal types are vegetatively propagated. However, seed propagation makes it possible to treat the crop as annual, increasing field uniformity and reducing farmers costs, as well as pathogens diffusion. Despite globe artichoke's significant agricultural value and the critical role of heterosis in the development of superior varieties, the production of hybrids remains challenging without a reliable system for large-scale industrial seed production. Male sterility (MS) presents a promising avenue for overcoming these challenges by simplifying the hybridization process and enabling cost-effective seed production. However, within the Cynara genus, genic male sterility has been linked to three recessive loci in globe artichoke, with no definitive genetic mechanism elucidated to date. A 250 offsprings F2 population, derived from a cross between a MS globe artichoke and a male fertile (MF) cultivated cardoon (C. cardunculus var. altilis) and fitting a monogenic segregation model (3:1), was analyzed through BSA-seq, aiming at the identification of genomic regions/genes affecting male sterility. Four QTL regions were identified on chromosomes 4, 12, and 14. By analyzing the sequence around the highest pick on chromosome 14, a cytochrome P450 (CYP703A2) was identified, carrying a deleterious substitution (R/Q) fixed in the male sterile parent. A single dCAPS marker was developed around this SNP, allowing the discrimination between MS and MF genotypes within the population, suitable for applications in plant breeding programs. A 3D model of the protein was generated by homology modeling, revealing that the mutated amino acid is part of a highly conserved motif crucial for protein folding.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications

Background

Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field.

Methods

We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field.

Results

3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field.

Conclusion

Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

Elucidating the role of exogenous melatonin in mitigating alkaline stress in soybeans across different growth stages: a transcriptomic and metabolomic approach

BACKGROUND

Soybean (Glycine max), a vital grain and oilseed crop, serves as a primary source of plant protein and oil. Soil salinization poses a significant threat to soybean planting, highlighting the urgency to improve soybean resilience and adaptability to saline stress. Melatonin, recently identified as a key plant growth regulator, plays crucial roles in plant growth, development, and responses to environmental stress. However, the potential of melatonin to mitigate alkali stress in soybeans and the underlying mechanisms remain unclear.

RESULTS

This study investigated the effects of exogenous melatonin on the soybean cultivar Zhonghuang 13 under alkaline stress. We employed physiological, biochemical, transcriptomic, and metabolomic analyses throughout both vegetative and pod-filling growth stages. Our findings demonstrate that melatonin significantly counteracts the detrimental effects of alkaline stress on soybean plants, promoting plant growth, photosynthesis, and antioxidant capacity. Transcriptomic analysis during both growth stages under alkaline stress, with and without melatonin treatment, identified 2,834 and 549 differentially expressed genes, respectively. These genes may play a vital role in regulating plant adaptation to abiotic stress. Notably, analysis of phytohormone biosynthesis pathways revealed altered expression of key genes, particularly in the ARF (auxin response factor), AUX/IAA (auxin/indole-3-acetic acid), and GH3 (Gretchen Hagen 3) families, during the early stress response. Metabolomic analysis during the pod-filling stage identified highly expressed metabolites responding to melatonin application, such as uteolin-7-O-(2''-O-rhamnosyl)rutinoside and Hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)glucoside, which helped alleviate the damage caused by alkali stress. Furthermore, we identified 183 differentially expressed transcription factors, potentially playing a critical role in regulating plant adaptation to abiotic stress. Among these, the gene SoyZH13_04G073701 is particularly noteworthy as it regulates the key differentially expressed metabolite, the terpene metabolite Hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)glucoside. WGCNA analysis identified this gene (SoyZH13_04G073701) as a hub gene, positively regulating the crucial differentially expressed metabolite of terpenoids, Hederagenin-3-O-glucuronide-28-O-glucosyl(1,2)glucoside. Our findings provide novel insights into how exogenous melatonin alleviates alkali stress in soybeans at different reproductive stages.

CONCLUSIONS

Integrating transcriptomic and metabolomic approaches, our study elucidates the mechanisms by which exogenous melatonin ameliorates the inhibitory effects of alkaline stress on soybean growth and development. This occurs through modulation of biosynthesis pathways for key compounds, including terpenes, flavonoids, and phenolics. Our findings provide initial mechanistic insights into how melatonin mitigates alkaline stress in soybeans, offering a foundation for molecular breeding strategies to enhance salt-alkali tolerance in this crop.

Technology-enabled great leap in deciphering plant genomes

Plant genomes provide essential and vital basic resources for studying many aspects of plant biology and applications (for example, breeding). From 2000 to 2020, 1,144 genomes of 782 plant species were sequenced. In the past three years (2021-2023), 2,373 genomes of 1,031 plant species, including 793 newly sequenced species, have been assembled, representing a great leap. The 2,373 newly assembled genomes, of which 63 are telomere-to-telomere assemblies and 921 have been generated in pan-genome projects, cover the major phylogenetic clades. Substantial advances in read length, throughput, accuracy and cost-effectiveness have notably simplified the achievement of high-quality assemblies. Moreover, the development of multiple software tools using different algorithms offers the opportunity to generate more complete and complex assemblies. A database named N3: plants, genomes, technologies has been developed to accommodate the metadata associated with the 3,517 genomes that have been sequenced from 1,575 plant species since 2000. We also provide an outlook for emerging opportunities in plant genome sequencing.

Revealing the seed microbiome: Navigating sequencing tools, microbial assembly, and functions to amplify plant fitness

Microbial communities within seeds play a vital role in transmitting themselves to the next generation of plants. These microorganisms significantly impact seed vigor and early seedling growth, for successful crop establishment. Previous studies reported on seed-associated microbial communities and their influence on processes like dormancy release, germination, and disease protection. Modern sequencing and conventional methods reveal microbial community structures and environmental impacts, these information helps in microbial selection and manipulation. These studies form the foundation for using seed microbiomes to enhance crop resilience and productivity. While existing research has primarily focused on characterizing microbiota in dried mature seeds, a significant gap exists in understanding how these microbial communities assemble during seed development. The review also discusses applying seed-associated microorganisms to improve crops in the context of climate change. However, limited knowledge is available about the microbial assembly pattern on seeds, and their impact on plant growth. The review provides insight into microbial composition, functions, and significance for plant health, particularly regarding growth promotion and pest control.

Arthropod diversity in the alpine tundra using metabarcoding: Spatial and temporal differences in alpha- and beta-diversity

All ecosystems face ecological challenges in this century. Therefore, it is becoming increasingly important to understand the ecology and degree of local adaptation of functionally important Arctic-alpine biomes by looking at the most diverse taxon of metazoans: the Arthropoda. This is the first study to utilize metabarcoding in the Alpine tundra, providing insights into the effects of micro-environmental parameters on alpha- and beta-diversity of arthropods in such unique environments. To characterize arthropod diversity, pitfall traps were set at three middle-alpine sampling sites in the Scandinavian mountain range in Norway during the snow-free season in 2015. A metabarcoding approach was then used to determine the small-scale biodiversity patterns of arthropods in the Alpine tundra. All DNA was extracted directly from the preservative EtOH from 27 pitfall traps. In order to identify the controlling environmental conditions, all sampling locations were equipped with automatic data loggers for permanent measurement of the microenvironmental conditions. The variables measured were: air temperature [°C] at 15 cm height, soil temperature [°C] at 15 cm depth, and soil moisture [vol.%] at 15 cm depth. A total of 233 Arthropoda OTUs were identified. The number of unique OTUs found per sampling location (ridge, south-facing slope, and depression) was generally higher than the OTUs shared between the sampling locations, demonstrating that niche features greatly impact arthropod community structure. Our findings emphasize the fine-scale heterogeneity of arctic-alpine ecosystems and provide evidence for trait-based and niche-driven adaptation. The spatial and temporal differences in arthropod diversity were best explained by soil moisture and soil temperature at the respective locations. Furthermore, our results show that arthropod diversity is underestimated in alpine-tundra ecosystems using classical approaches and highlight the importance of integrating long-term functional environmental data and modern taxonomic techniques into biodiversity research to expand our ecological understanding of fine- and meso-scale biogeographical patterns.

Selective Sensing of DNA Nucleobases with Angular Discrimination

The fast and precise selective sensing of DNA nucleobases is a long-pursued method that can lead to huge advances in the field of genomics and have an impact on aspects such as the prevention of diseases, health enhancement, and, in general, all types of medical treatments. We present here a new type of nanoscale sensor based on carbon nanotubes with a specific geometry that can discriminate the type of nucleobase and also its angle of orientation. The proper differentiation of nucleobases is essential to clearly sequence DNA chains, while angular discrimination is key to improving the sensing selectivity. We perform first-principle and quantum transport simulations to calculate the transmission, conductance, and current of the nanotube-based nanoscale sensor in the presence of the four nucleotides (A, C, G, and T), each of them rotated 0, 90, 180, or 270°. Our results show that this system is able to effectively discriminate between the four nucleotides and their angle of orientation. We explain these findings in terms of the interaction between the phosphate group of the nucleotide and the nanotube wall. The phosphate specifically distorts the electronic structure of the nanotube depending on the distance and the orientation and leads to nontrivial changes in the transmission. This work provides a method for finer and more precise sequential DNA chains.