RNA sequencing: advances, challenges and opportunities

Alternative transcription increases isoform complexity in Long Non-Coding RNAs and alters their functions in cancer

Transcriptional start and end variance, a less-explored aspect of lncRNA biology, is a critical determinant of isoform diversity in human RNA. While alternative splicing (AS) has been extensively studied as a mechanism of isoform generation, differences in transcriptional start and termination site usage-whether from distinct promoters or varying initiation events at the same core promoter-contribute more to isoform diversity than alternative splicing. In the context of long non-coding RNAs (lncRNAs), even subtle alterations to transcriptional start and end sites can induce significant changes in the structural and functional capacities of individual lncRNA isoforms. This review highlights the underappreciated realm of transcriptional start and end variance in lncRNAs, exploring its pivotal role in shaping the diversity of lncRNA transcripts. In cancer, where lncRNAs are increasingly recognised as key players in tumorigenesis, understanding the ramifications of transcriptional start and end variance is crucial. With single nucleotide alterations capable of influencing the folding energy, shape, stability, and function of a lncRNA molecules, significant changes to transcriptional regulation may lead to aberrant isoforms with implications for cancer initiation, progression, and potentially, its treatment. As lncRNAs emerge as therapeutic targets, particularly with the advancement of antisense oligonucleotide (ASO) technologies, it becomes crucial to understand the regulatory landscape of transcriptional variation among lncRNA isoforms, to ensure selective targeting of oncogenic transcripts while sparing those with normal physiological functions. By highlighting the significance of transcriptional start and end site variation as major contributors to lncRNA diversity, the potential exploitation for precision therapeutic interventions in the field of non-coding RNA cancer research can be expanded.

Three-dimensional assessments are necessary to determine the true, spatially resolved composition of tissues

Methods for spatially resolved cellular profiling of tissue sections enable in-depth study of inter- and intra-sample heterogeneity but often profile small regions, requiring evaluation of many samples to compensate for limited assessment. Recent advances in three-dimensional (3D) tissue mapping offer deeper insights; however, attempts to quantify the information gained in transitioning to 3D remains limited. Here, to compare inter- and intra-sample tissue heterogeneity, we analyze >100 pancreas samples as cores, whole-slide images (WSIs), and cm3-sized 3D samples. We show that tens of WSIs and hundreds of tissue microarrays are needed to approximate the compositional tissue heterogeneity of tumors. Additionally, spatial correlations of pancreatic structures decay significantly within microns, demonstrating that isolated two-dimensional (2D) sections poorly represent their surroundings. Through 3D simulations, we determined the number of slides necessary to accurately measure tumor burden. These results quantify the power of 3D mapping and establish sampling methods for biological studies prioritizing composition or incidence.

KLC3 activates PI3K/AKT signaling and promotes ovarian cancer cell proliferation and migration through COL3A1

Ovarian cancer (OC) is a prevalent malignancy; however, the role of kinesin light chain 3 (KLC3) in OC remains unclear. The present study conducted a comprehensive investigation of KLC3 using bioinformatics analysis, as well as in vitro and in vivo experiments. The findings revealed that KLC3 expression was significantly upregulated in the OC group compared with that in the normal group, and high KLC3 expression in patients with OC was associated with a poorer overall survival. Functional studies demonstrated that targeting KLC3 effectively suppressed the proliferation, migration, epithelial‑mesenchymal transition and DNA damage resistance of OC cells in vitro, while also inhibiting tumor growth in vivo, underscoring the pivotal role of KLC3 in tumor progression and metastasis. Additionally, RNA‑sequencing analysis identified collagen type III α1 (COL3A1) as a potential downstream gene cooperating with KLC3 to promote the occurrence and development of OC through the PI3K/AKT signaling pathway. Rescue experiments revealed that the KLC3 knockdown‑induced suppression of the malignant phenotype could be partially reversed by overexpression of COL3A1. In summary, the present findings demonstrated that KLC3 acts as an oncogene by influencing COL3A1 expression to promote the proliferation and migration of OC cells in vivo and in vitro.

Transcriptome Derived Artificial neural networks predict PRRC2A as a potent biomarker for epilepsy

Epilepsy refers to the occurrence of two or more than two reiterative seizures. The occurrence of seizure is governed by the excessive electrical discharges in the cortex of the brain. Bioinformatics is crucial in diagnosing, prognosticating, and treating neurological disorders. It uses methodologies, computational tools, software, and databases to probe disease molecular underpinnings and identify biomarkers. It aids clinicians in addressing patient parameters and translational research. Artificial neural networks (ANNs) are computer models that attempt to mimic the neurons present in the human brain. This computerized neuronal model is used for analyzing and comprehending large and complex data sets. In the present study, three GEO datasets (GSE190451, GSE140393, and GSE134697) were retrieved from NCBI for the identification of differentially expressed genes using the DESeq2 package. The study identified 7 up-regulated genes (PRRC2A, FCGR3B, HLA-DRB, ENSG00000280614, ENSG00000281181, SLN, C4A) in patients with epilepsy. Furthermore, WEKA software was used for feature selection and classification of DEGs using feature selection algorithms namely Correlation Feature Selection, ReliefF, and Information Gain and classification methods such as Logistic regression, Classification via regression, Random forest, Random subspace, and Logistic model trees. After the analysis, out of the 7 genes, the C4A gene was removed as it yielded the lowest feature selection statistics. Lastly, R Studio was used for constructing the Artificial Neural Network of the 6 identified DEGs. The model's performance was evaluated using the "pROC" R package, and an AUC of 0.720 was obtained, indicating that the model had excellent classification accuracy. The NeuralNet package of R revealed that PRRC2A had the highest generalized weight value indicating the increased expression of these genes when all other parameters are constant. Therefore, PRRC2A can be used as a potential biomarker for the diagnosis of epilepsy.

Molecular endotypes in sepsis: integration of multicohort transcriptomics based on RNA sequencing

BACKGROUND

The heterogeneity of host responses in sepsis has hindered efforts to develop targeted therapies for this large patient population. Although growing evidence has identified sepsis endotypes based on the microarray data, studies using RNA-seq data-which offers higher sensitivity and a broader dynamic range-remain limited. We hypothesized that integrating RNA-seq data from patients with sepsis would reveal molecular endotypes with distinct biological and clinical signatures.

METHODS

In this meta-analysis, we systematically searched for publicly available RNA-seq datasets of sepsis. Using identified datasets, we applied a consensus clustering algorithm to identify distinct endotypes. To characterize the biological differences between these endotypes, we performed gene-set enrichment analysis and immune cell deconvolution. Next, we investigated the association between these endotypes and mortality risks. We finally developed gene classifiers for endotype stratification and validated our endotype classification by applying these classifiers to an external cohort.

RESULTS

A total of 280 adults with sepsis from four datasets were included in this analysis. Using an unsupervised approach, we identified three distinct endotypes: coagulopathic (n = 83, 30%), inflammatory (n = 118, 42%), and adaptive endotype (n = 79, 28%). The coagulopathic endotype exhibited upregulated coagulation signaling, along with an increased monocyte and neutrophil composition, although the adaptive endotype demonstrated enhanced adaptive immune cell responses, marked by elevated T and B cell compositions. The inflammatory endotype was characterized by upregulated TNF-α/NF-κB signaling and IL-6/JAK/STAT3 pathways with an increased neutrophil composition. Patients with the coagulopathic endotype had a significantly higher risk of mortality than those with the adaptive endotype (30% vs. 16%, odds ratio 2.19, 95% confidence interval 1.04-4.78, p = 0.04). To enable the practical application of these findings, we developed endotype classification models and identified 14 gene classifiers. In a validation cohort of 123 patients, we consistently identified these three endotypes. Furthermore, the mortality risk pattern was reproduced, with the coagulopathic endotype showing greater mortality risk than the adaptive endotype (34% vs 18%, p = 0.10).

CONCLUSIONS

This multicohort RNA-seq meta-analysis identified three biologically and clinically distinct sepsis endotypes characterized by coagulopathic, adaptive, and inflammatory responses. This endotype-based approach to patient stratification may facilitate the development of more precise therapeutic strategies for sepsis.

The impact of PCR duplication on RNAseq data generated using NovaSeq 6000, NovaSeq X, AVITI, and G4 sequencers

BACKGROUND

Transcriptome sequencing (RNA-seq) is a powerful technology for gene expression profiling. Selection of optimal parameters for cDNA library generation is crucial for acquisition of high-quality data. In this study, we investigate the impact of the amount of RNA and the number of PCR cycles used for sample amplification on the rate of PCR duplication and, in consequence, on the RNA-seq data quality.

RESULTS

For broader applicability, we sequenced the data on four short-read sequencing platforms: Illumina NovaSeq 6000, Illumina NovaSeq X, Element Biosciences AVITI, and Singular Genomics G4. The native Illumina libraries were converted for sequencing on AVITI and G4 to assess the effect of library conversion, containing additional PCR cycles. We find that the rate of PCR duplicates depends on the combined effect of RNA input material and the number of PCR cycles used for amplification. For input amounts lower than 125 ng, 34-96% of reads were discarded via deduplication with the percentage increasing with lower input amount and decreasing with increasing PCR cycles. The reduced read diversity for low input amounts leads to fewer genes detected and increased noise in expression counts.

CONCLUSIONS

Data generated with each of the four sequencing platforms presents similar associations between starting material amount and the number of PCR cycles on PCR duplicates, a similar number of detected genes, and comparable gene expression profiles.

Integrated miRNA-mRNA Profiling of C2C12 Myoblasts Indicates Regulatory Interactions Involved in Proliferation and Differentiation

Myogenesis is a complex biological process regulated by multiple factors. This study systematically revealed the dynamic changes of gene expression and its regulatory network in C2C12 myoblasts during proliferation and differentiation stages by integrating transcriptome and miRNA-omics data. The analysis results showed that in the early stage of proliferation, gene expression showed significant fluctuations, and key cell cycle and DNA replication-related genes were closely associated with specific miRNAs (miR-486a-5p, miR-486b-5p, and miR-351-5p), suggesting that these miRNAs play an important role in regulating cell proliferation. In the differentiation stage, the activation of key myogenic transcription factors and signaling pathways, such as MAPK and PI3K-Akt, synergizes with miRNA regulation to promote the myogenic program. In addition, we found that genes such as IGF1 and Dio2 were continuously upregulated during differentiation, and IGF1 might be regulated by multiple miRNAs during this process. This study provides key molecular insights for a deeper understanding of muscle development and regeneration.

Antibiotic-induced stress responses in Gram-negative bacteria and their role in antibiotic resistance

Bacteria adapt to changes in their natural environment through a network of stress responses that enable them to alter their gene expression to survive in the presence of stressors, including antibiotics. These stress responses can be specific to the type of stress and the general stress response can be induced in parallel as a backup mechanism. In Gram-negative bacteria, various envelope stress responses are induced upon exposure to antibiotics that cause damage to the cell envelope or result in accumulation of toxic metabolic by-products, while the heat shock response is induced by antibiotics that cause misfolding or accumulation of protein aggregates. Antibiotics that result in the production of reactive oxygen species (ROS) induce the oxidative stress response and those that cause DNA damage, directly and through ROS production, induce the SOS response. These responses regulate the expression of various proteins that work to repair the damage that has been caused by antibiotic exposure. They can contribute to antibiotic resistance by refolding, degrading or removing misfolded proteins and other toxic metabolic by-products, including removal of the antibiotics themselves, or by mutagenic DNA repair. This review summarizes the stress responses induced by exposure to various antibiotics, highlighting their interconnected nature, as well the roles they play in antibiotic resistance, most commonly through the upregulation of efflux pumps. This can be useful for future investigations targeting these responses to combat antibiotic-resistant Gram-negative bacterial infections.

TIRE-seq simplifies transcriptomics via integrated RNA capture and library preparation

RNA sequencing (RNA-seq) is widely used in biomedical research, advancing our understanding of gene expression across biological systems. Traditional methods require upstream RNA extraction from biological inputs, adding time and expense to workflows. We developed TIRE-seq (Turbocapture Integrated RNA Expression Sequencing) to address these challenges. TIRE-seq integrates mRNA purification directly into library preparation, eliminating the need for a separate extraction step. This streamlined approach reduces turnaround time, minimizes sample loss, and improves data quality. A comparative study with the widely used Prime-seq protocol demonstrates TIRE-seq's superior sequencing efficiency with crude cell lysates as inputs. TIRE-seq's utility was demonstrated across three biological applications. It captured transcriptional changes in stimulated human T cells, revealing activation-associated gene expression profiles. It also identified key genes driving murine dendritic cell differentiation, providing insights into lineage commitment. Lastly, TIRE-seq analyzed the dose-response and time-course effects of temozolomide on patient-derived neurospheres, identifying differentially expressed genes and enriched pathways linked to the drug's mechanism of action. With its simplified workflow and high sequencing efficiency, TIRE-seq offers a cost-effective solution for large-scale gene expression studies across diverse biological systems.

Citrus genomes: past, present and future

Over the past decade, genome sequencing and assembly approaches have been greatly improved, resulting in the assembly of many genomes for citrus, including wild, domesticated, and citrus-related genomes. Improvements in technologies have led to assembled genomes with higher completeness, contiguity, quality, and accuracy that have greatly facilitated annotation and analysis. This review summarizes the evolution of the sequencing, assembly, and annotation technologies leading to citrus genomes over the past 11 years, a comprehensive evaluation of their quality, contiguity, and completeness, and the major findings and applications. Of the 50 genomes now available, 35 have been assembled to chromosome level and 15 to draft level, and 14 were haplotype-resolved assemblies. To date there have been four pangenome-wide studies for citrus. The very recent genomes assembled with long-read sequencing have achieved >99% and >98% assembly and annotation completeness (BUSCO), respectively. However, some early genomes are not of the same high quality as more recently sequenced genomes and would benefit from re-sequencing. A more comprehensive pangenome based upon a larger set of species and genotypes assembled at the haplotype level would allow genomics to deliver the maximum benefits for citrus improvement and research.

Candidate Biomarkers for Hard-to-Heal Wounds Revealed by Single-Cell RNA Sequencing of Wound Fluid in Murine Wound Models

Wound healing is often hindered by hyperglycemia, chronic inflammation and ageing. Despite extensive research on the pathophysiology of hard-to-heal wounds, wound healing remains complex and poses challenges in treatment and management. Current wound treatments and care mostly target a single pathology, such as infection, while most hard-to-heal wounds are multifactorial. Therefore, exploring the factors that do not rely on a single pathology is crucial to fill the gap in current wound management. Despite containing more comprehensive information than commonly used wound tissue samples, cells in the wound fluid have not drawn much attention because of collection difficulties. This study aimed to use single-cell RNA sequencing (scRNA-seq) of cells from wound fluid to identify specific biomarkers for hard-to-heal wounds, with the hypothesis that common biomarkers among various wound models can be potentially applied to complex hard-to-heal wounds in clinical settings. Three representative delayed wound models, aged, diabetic and lipopolysaccharide-induced inflammatory wound models, were compared with normal young mice to explore commonly shared genes that exist in different pathological delayed wound healing models. The shared upregulation of cell cycle and cellular senescence-related genes such as Rpl11, Rpl26, Rps3, Rps15, Rps 20, Rps26, Ccl2, Cdk2ap2 and Ccnd3 and the downregulation of immune response regulation genes such as Tnfaip3, Junb, Il1r2, Plaur, Il1rn, Il1a, Cxcl2, Cd14, S100a8 and S100a9 in all delayed healing wound models were found in most immune cell subgroups, especially the macrophage subgroup. The results of this study suggested cellular senescence of cells in wound fluid could be related to hard-to-heal wounds.

Comprehensive analysis of the NAC transcription factor gene family in Sophora tonkinensis Gagnep

BACKGROUND

Sophora tonkinensis Gagnep. has long been utilized in the treatment of anti-inflammatory and pain-relieving, with its principal active compounds being alkaloids and flavonoids. NAC transcription factors, a large family of plant-specific regulators, play pivotal roles in growth, development, stress responses, and secondary metabolism. However, comprehensive genome-wide characterization of S. tonkinensis NAC gene family (StNAC) remains unexplored.

RESULTS

This study identified 85 NAC proteins from the S. tonkinensis genome database. Phylogenetic analysis revealed that StNAC proteins were categorized into 15 subgroups based on their homology with Arabidopsis thaliana NAC proteins. Gene structure analysis demonstrated a variation in intron numbers ranging from 1 to 7, with a majority of StNAC genes containing 2-3 introns. Chromosomal distribution analysis indicated an uneven spread of StNAC genes across 9 chromosomes, with the highest number of StNAC genes on Chr3. Detection of 4 tandem duplicates and 32 segmental duplicates revealed that segmental duplication primarily drive StNAC genes amplification. Prediction of cis-regulatory elements suggested the involvement of StNAC genes in growth, stress responses, and hormone regulation. Gene expression analysis showed substantial variability expression of StNAC genes across different tissues. Notably, eight StNAC genes were identified as significantly associated alkaloid and flavonoid levels. qRT-PCR validation indicated that five genes were highly expressed in tissues, corroborating transcriptome data.

CONCLUSION

These findings offer valuable insights for further functional characterization of NAC genes and their potential roles in alkaloid and flavonoid biosynthesis in S. tonkinensis.

Exogenous trehalose increased polysaccharide content and altered their properties and metabolism in Lentinula edodes mycelium

Trehalose promotes polysaccharide synthesis in edible mushrooms, yet its effect on the antioxidant activity and structure of Lentinula edodes mycelium polysaccharides is still unknown. We investigated the effect of trehalose on the antioxidant activity of L. edodes polysaccharides and expression of genes related to L. edodes polysaccharide metabolism. Five g/L trehalose increased the biomass and polysaccharide content of L. edodes mycelium by over 50 % compared to the control. Trehalose increased the superoxide anion radical scavenging rate of L. edodes polysaccharides up to 80.9 %. Compared to the control, the molecular weight of polysaccharides was lower, and their glucuronic acid content was higher in the trehalose treatment. RNA-seq analysis revealed 1045 differentially expressed genes in the trehalose treatment compared to the control. Genes related to glycolysis/gluconeogenesis pathway, starch and sucrose metabolic pathway, and the pentose and glucuronate interconversions pathway were differentially expressed, possibly accounting for the increased polysaccharide synthesis. In summary, exogenous trehalose has potential to increase the biosynthesis and biological activity of L. edodes polysaccharides.

Extended spectrum beta-lactamase producing Escherichia coli and antimicrobial resistance gene sharing at the interface of human, poultry and environment: results of ESBL tricycle surveillance in Kathmandu, Nepal

BACKGROUND

The spread of antimicrobial resistant pathogens, including extended-spectrum β-lactamase (ESBL) producing Enterobacteriaceae is a global health threat and can be addressed only through a One Health approach. We aimed to characterize ESBL producing Escherichia coli isolates from World Health Organization Tricycle surveillance using data from whole genome sequencing (WGS) to decipher the potential dynamics of their circulation at the human, poultry and environment interface.

METHODS

WGS was performed on 100 non-duplicate representative ESBL E. coli isolates including 28 isolates from humans, 36 from poultry caeca, and 36 from water samples. Minimum Inhibitory Concentration (MIC) was determined using Vitek 2 Compact. WGS was performed on Illumina NextSeq 2000 platform and open-source bioinformatics pipelines were used to analyze WGS data for genomic characterization including phylogenetic analysis and in silico multi-locus sequence typing and, serotyping and, ESBL gene detection.

RESULTS

Most isolates were susceptible to imipenem (98%), meropenem (94%) and tigecycline (94%). Six ESBL E. coli isolates from poultry were resistant to colistin (MIC ≥ 4 μg/ml). WGS revealed high genetic diversity representing 56 sequence types (ST) including three novel STs. ST131 (7 isolates) was the most prevalent comprising human and environment isolates, followed by ST2179 (6 isolates, all poultry) and ST155 (5 isolates across the three sectors). All eight recognized E. coli phylogroups were observed, with majority (86%) of the isolates belonging to A, B1, B2 and D phylogroups. Of the100 isolates, 98 carried blaCTX-M gene, with blaCTX-M-15 the most prevalent allele (76%). AmpC type ESBL genes were found in four and OXA type β lactamases in six isolates. In our study, blaNDM-5 was detected in two imipenem resistant isolates from human. Coexistence of more than one β-lactamase genes was seen in 26% isolates.

CONCLUSION

Our findings indicate high genetic diversity among ESBL E. coli strains from all three sectors and sharing of identical strains and resistance genes within and between sectors. ST131, the globally dominant ESBL E. coli clade is gaining prevalence in Nepal with blaCTX-M being the most common ESBL gene across the phylogroups and all source groups. Antimicrobial stewardship should be promoted in one health approach to combat antimicrobial resistance.

Deciphering IGH rearrangement complexity and detection strategies in acute lymphoblastic leukaemia

Acute lymphoblastic leukaemia is a highly heterogeneous malignancy characterised by various genomic alterations that influence disease progression and therapeutic outcomes. Gene fusions involving the immunoglobulin heavy chain gene represent a complex and diverse category. These fusions often result in enhancer hijacking, upregulation of partner proto-oncogenes and contribute to leukemogenesis. This review highlights the mechanisms underlying IGH gene fusions, the critical role they play in ALL pathogenesis, and current detection technologies.

Intracranial Aneurysm Biomarkers: A Convergence of Genetics, Inflammation, Oxidative Stress, and the Extracellular Matrix

Intracranial aneurysm (IA) is a common cerebrovascular disease in which sacral aneurysms occurring in the Wills ring region can lead to devastating subarachnoid hemorrhage. Despite advances in research, the underlying mechanisms of IA formation and rupture remain incompletely understood, hindering early diagnosis and effective treatment. This review comprehensively summarizes the current landscape of IA biomarkers, encompassing genetic markers, DNA, RNA, inflammatory molecules, oxidative stress proteins, and extracellular matrix (ECM) components. Accumulating evidence suggests that various biomarkers are associated with different stages of IA pathogenesis, including initiation, progression, and rupture. Aberrant ECM composition and remodeling have been observed in IA patients, and extracellular matrix-degrading enzymes are implicated in IA growth and rupture. Biomarker research in IA holds great potential for improving clinical outcomes. Future studies should focus on validating the existing biomarkers, identifying novel ones, and investigating their underlying mechanisms to facilitate the development of personalized preventive and therapeutic strategies for IA.

Integrated metabolomics and transcriptomics analysis reveals the potential mechanism by which Methyl jasmonate enhances the pungent flavor of soilless-cultivated Chinese chives (Allium tuberosum)

BACKGROUND

Methyl jasmonate (MeJA) is an effective plant elicitor that enhances secondary metabolism. Chinese chives are prized for their pungent flavor, yet the biosynthetic pathways and regulatory mechanisms of flavor compounds induced by MeJA remain unclear.

METHODOLOGY

This study integrated metabolomic and transcriptomic analyses to elucidate how MeJA modulates the biosynthesis of flavor substance precursors in soilless-cultivated Chinese chives.

RESULTS

MeJA treatment improved the dry matter content and nutritional quality of Chinese chives. We identified 36 volatile and 183 nonvolatile differentially abundant metabolites between the MeJA-treated and control groups. Gene expression analysis revealed 193 candidate genes associated with flavor formation. Among all the genes, a total of 2,667 DEGs were enriched primarily in metabolic pathways, including secondary metabolite biosynthesis, linoleic acid metabolism, and phenylpropanoid biosynthesis. Furthermore, exogenous MeJA inhibited the synthesis of endogenous jasmonic acid as well as enzyme activity and gene expression related to metabolic pathways. It also promoted the conversion of S-alkyl-L-cysteine to S-alk(en)ylcysteine sulfoxides (CSOs), increasing the accumulation of the flavor precursor CSOs and increasing the levels of S-methyl-L-cysteine. This led to increased concentrations of the key garlic flavor compounds methiin and alliin, intensifying the pungent flavor of Chinese chives. Notably, MeJA-induced AtuFMO1 was associated with enhanced pungent flavor and may be regulated by AtuPHL7 and AP2/ERF-ERF transcription factors.

CONCLUSION

In conclusion, exogenous MeJA activates key enzyme-encoding genes involved in the biosynthesis of garlic flavor precursor CSOs, leading to increased accumulation of the spicy compounds Methiin and Alliin. These findings establish AtuFMO1 as a central hub linking hormonal signaling to flavor biosynthesis and provide molecular targets for improving Allium crop flavor and quality through precision horticulture.

Omics advancements towards exploring arsenic toxicity and tolerance in plants: a review

MAIN CONCLUSION

Omics approaches provide comprehensive insights into plant arsenic stress responses, setting the stage for engineering arsenic-tolerant crops. Understanding arsenic (As) toxicity in plants is crucial for environmental and agricultural sustainability, considering the implications of As in impacting soil productivity and environmental health. Although some articles already examined the detailed molecular mechanisms behind As toxicity and tolerance, a comprehensive review of recent omics advancements in studying plant responses to As exposure is needed. The present review highlights the valuable contribution of omics approaches (genomics, transcriptomics, proteomics, and metabolomics) to characterize the intricate response to As overall, which could empower As-tolerant plant development. Genomic techniques, such as QTL mapping, GWAS, RAPD, and SSH, hold the potential to provide valuable insights into the genetic diversity and expression patterns associated with the plant response to As stress, highlighting also the power of new advanced technology such as CRISPR-Cas9. Transcriptomics approaches (e.g., microarrays and RNA sequencing) revealed gene expression patterns in plants under As stress, emphasizing the role of sulfur metabolism in As tolerance. Proteomics, using 2-DE combined with MALDI-ToF MS or ESI-MS/MS, offers insights into the stress-inducible proteins and their involvement in As toxicity mitigation, while iTRAQ-based proteomics enabled an understanding of cultivar-specific responses under high As concentration. Metabolomics, with LC-MS, GC-MS, (U)HPLC, and NMR, elucidated small molecule alterations and complex metabolic activities occurring under As plant exposure. Compendium of data and evidence-related tools offers a foundation for advancing As-tolerant plant development and promoting environmental and agricultural resilience.

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Differential gene expression analysis and machine learning identified structural, TFs, cytokine and glycoproteins, including SOX2, TOP2A, SPP1, COL1A1, and TIMP1 as potential drivers of lung cancer

BACKGROUND

Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understanding its molecular complexities is crucial for identifying potential targets for treatment. The goal is to slow disease progression and intervene early to prevent the development of advanced lung cancer cases. Hence, there's an urgent need for new biomarkers that can detect lung cancer in its early stages.

METHODS

The study conducted RNA-Seq analysis of lung cancer samples from the publicly available SRA database (NCBI SRP009408), including both control and tumour samples. The genes with differential expression between tumour and healthy tissues were identified using R and Bioconductor. Machine learning (ML) techniques, Random Forest, Lasso, XGBoost, Gradient Boosting and Elastic Net were employed to pinpoint significant genes followed by classifiers, Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbours (k-NN). Gene ontology and pathway analyses were performed on the significant differentially expressed genes (DEGs). The top genes from DEG and machine learning analyses were combined for protein-protein interaction (PPI) analysis, identifying 10 hub genes essential for lung cancer progression.

RESULTS

The integrated analysis of ML and DEGs revealed the significance of specific genes in lung cancer samples, identified the top 5 upregulated genes (COL11A1, TOP2A, SULF1, DIO2, MIR196A2) and the top 5 downregulated genes (PDK4, FOSB, FLYWCH1, CYB5D2, MIR328), along with their associated genes implicated in pathways or co-expression networks were identified. Among the various algorithms employed, Random Forest and XGBoost proved effective in identifying common genes, underscoring their potential significance in lung cancer pathogenesis. The MLP exhibited the highest accuracy in classifying samples using all genes. Additionally, the protein-protein interaction (PPI) analysis identified 10 hub genes that are pivotal in lung cancer pathogenesis: COL1A1, SOX2, SPP1, THBS2, POSTN, COL5A1, COL11A1, TIMP1, TOP2A and PKP1.

CONCLUSION

The study contributes to the early prediction of lung cancer by identifying potential biomarkers that could enhance early diagnosis and pave the way for practical clinical applications in the future. Integrating DEGs and machine learning-derived significant genes for PPI analysis offers a robust approach to uncovering critical molecular targets for lung cancer treatment.

Advancements in omics technologies: Molecular mechanisms of acute lung injury and acute respiratory distress syndrome (Review)

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is an inflammatory response arising from lung and systemic injury with diverse causes and associated with high rates of morbidity and mortality. To date, no fully effective pharmacological therapies have been established and the relevant underlying mechanisms warrant elucidation, which may be facilitated by multi‑omics technology. The present review summarizes the application of multi‑omics technology in identifying novel diagnostic markers and therapeutic strategies of ALI/ARDS as well as its pathogenesis.

Full-Length Transcriptome Sequencing and Comparative Transcriptomics Reveal the Molecular Mechanisms Underlying Gonadal Development in Sleepy Cod (Oxyeleotris lineolata)

Sleepy cod (Oxyeleotris lineolata) is native to Australia and is now an economically valuable fish cultured in China and Southern Asian countries. Its growth rate exhibits as sexually dimorphic, with males generally growing more rapidly and attaining a larger body size compared to females. Thus, the effective development of sex control breeding can significantly contribute to increased yields and output value. Nevertheless, due to the lack of genomic and transcriptomic data, the molecular mechanisms underlying sex determination and gonadal differentiation in sleepy cod remain poorly understood. In this study, long-read PacBio isoform sequencing (Iso-Seq) was performed to obtain a full-length transcriptome from a pooled sample of eight tissues (kidney, brain, liver, muscle, heart, spleen, ovary and testis). A total of 30.41 G subread bases were generated and 49,113 non-redundant full-length transcripts with an average length of 2948 bp were produced. Using the full-length transcriptome as a reference, short-read Illumina sequencing was performed to investigate the differences in gene expression at the transcriptome level between ovaries and testes from 12-month-old individuals. A total of 19,102 differentially expressed transcripts (DETs) were identified, of which 8510 (44.55%) were up-regulated in the ovary and 10,592 (55.45%) were up-regulated in the testis. The DETs were mainly clustered into 241 KEGG pathways, in which oocyte meiosis and arachidonic acid metabolism were the most relevant pathways involved in gonadal differentiation. To verify the validity of the transcriptomic data, 20 DETs were selected to investigate the gonad expression profiles based on qPCR. The expression levels of all 20 screened genes were consistent with the transcriptome sequencing results. The present study provides new genetic resources-including full-length transcriptome sequences and annotation information-as a coding genomic-level reference for sleepy cod-yielding valuable insights into the genetic mechanisms of sex determination and gonadal differentiation in this economically important species.

Mapping the landscape of gastric signet ring cell carcinoma: Overcoming hurdles and charting new paths for advancement

BACKGROUND

In recent years, the global prevalence of gastric cancer (GC) has witnessed a progressive decrease, accompanied by a step-growth in the incidence of gastric signet ring cell carcinoma (GSRCC). As precision medicine concepts progress, GSRCC, a distinct sub-type of GC, has drawn considerable attention from researchers. However, there still persist some controversies regarding the associated research findings.

AIM

To summarize the current obstacles and potential future directions for research on GSRCC.

METHODS

To begin with, all literature related to GSRCC published from January 1, 2004 to December 31, 2023 was subjected to bibliometric analysis in this article. Additionally, this paper analyzed the research data using CiteSpace, GraphPad Prism v8.0.2, and VOSviewer, which was obtained from the Web of Science Core Collection database. The analysis results were visually represented.

RESULTS

This study provided a comprehensive overview of the statistical characteristics of the 995 English articles related to GSRCC, including cited references, authors, journals, countries, institutions, and keywords. The popular keywords and clusters contain "prognosis", "survival", "expression", "histology", and "chemotherapy".

CONCLUSION

The prognosis, precise definition and classification, as well as chemoresistance of GSRCC, continue to be crucial areas of ongoing research, whose directions are closely tied to advancements in molecular biology research on GSRCC.

Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithms

Alzheimer's disease (AD) is the most common cause of dementia, emphasizing the critical need for the development of biomarkers that facilitate accurate and objective assessment of disease progression for early detection and intervention to delay its onset. In our study, three AD datasets from the Gene Expression Omnibus (GEO) database were integrated for differential expression analysis, followed by a weighted gene co-expression network analysis (WGCNA), and potential AD biomarkers were screened. Our study identified UBE2N as a promising biomarker for AD. Functional enrichment analysis revealed that UBE2N is associated with synaptic vesicle cycling and T cell/B cell receptor signaling pathways. Notably, UBE2N expression levels were found to be significantly reduced in the cortex and hippocampus of the TauP301S mice. Furthermore, analysis of single-cell data from AD patients demonstrated the association of UBE2N and T cell function. These findings underscore the potential of UBE2N as a valuable biomarker for AD, offering important insights for diagnosis and targeted therapeutic strategies.

Unlocking melanoma Suppression: Insights from Plasma-Induced potent miRNAs through PI3K-AKT-ZEB1 axis

INTRODUCTION

Melanoma is a rare but highly malignant form of skin cancer. Although recent targeted and immune-based therapies have improved survival rates by 10-15%, effective melanoma treatment remains challenging. Therefore, novel, combinatorial therapy options such as non-thermal atmospheric pressure plasma (NTP) are being investigated to inhibit and prevent chemoresistance. Although several studies have reported the apoptotic and inhibitory effects of reactive oxygen species produced by NTP in the context of melanoma, the intricate molecular network that determines the role of microRNAs (miRNAs) in regulating NTP-mediated cell death remains unexplored.

OBJECTIVES

This study aimed to explore the molecular mechanisms and miRNA networks regulated by NTP-induced oxidative stress in melanoma cells.

METHODS

Melanoma cells were exposed to NTP and then subjected to high-throughput miRNA sequencing to identify NTP-regulated miRNAs. Various biological processes and underlying molecular mechanisms were assessed using Alamar Blue, propidium iodide (PI) uptake, cell migration, and clonogenic assays followed by qRT-PCR and flow cytometry.

RESULTS

NTP exposure for 3 min was sufficient to modulate the expression of several miRNAs, inhibiting cell growth. Persistent NTP exposure for 5 min increased differential miRNA regulation, PI uptake, and the expression of genes involved in cell cycle arrest and death. qPCR confirmed that miR-200b-3p and miR-215-5p upregulation contributed to decreased cell viability and migration. Mechanistically, inhibiting miR-200b-3p and miR-215-5p in SK-2 cells enhancedZEB1, PI3K, and AKT expression, increasing cell proliferation and viability.

CONCLUSION

This study demonstrated that NTP exposure for 5 min results in the differential regulation of miRNAs related to the PI3K-AKT-ZEB1 axis and cell cycle dysregulation to facilitate melanoma suppression.

Discovery of paradoxical genes: reevaluating the prognostic impact of overexpressed genes in cancer

Oncogenes are typically overexpressed in tumor tissues and often linked to poor prognosis. However, recent advancements in bioinformatics have revealed that many highly expressed genes in tumors are associated with better patient outcomes. These genes, which act as tumor suppressors, are referred to as "paradoxical genes." Analyzing The Cancer Genome Atlas (TCGA) confirmed the widespread presence of paradoxical genes, and KEGG analysis revealed their role in regulating tumor metabolism. Mechanistically, discrepancies between gene and protein expression-affected by pre- and post-transcriptional modifications-may drive this phenomenon. Mechanisms like upstream open reading frames and alternative splicing contribute to these inconsistencies. Many paradoxical genes modulate the tumor immune microenvironment, exerting tumor-suppressive effects. Further analysis shows that the stage- and tumor-specific expression of these genes, along with their environmental sensitivity, influence their dual roles in various signaling pathways. These findings highlight the importance of paradoxical genes in resisting tumor progression and maintaining cellular homeostasis, offering new avenues for targeted cancer therapy.

A tale of two strands: Decoding chromatin replication through strand-specific sequencing

DNA replication, a fundamental process in all living organisms, proceeds with continuous synthesis of the leading strand by DNA polymerase ε (Pol ε) and discontinuous synthesis of the lagging strand by polymerase δ (Pol δ). This inherent asymmetry at each replication fork necessitates the development of methods to distinguish between these two nascent strands in vivo. Over the past decade, strand-specific sequencing strategies, such as enrichment and sequencing of protein-associated nascent DNA (eSPAN) and Okazaki fragment sequencing (OK-seq), have become essential tools for studying chromatin replication in eukaryotic cells. In this review, we outline the foundational principles underlying these methodologies and summarize key mechanistic insights into DNA replication, parental histone transfer, epigenetic inheritance, and beyond, gained through their applications. Finally, we discuss the limitations and challenges of current techniques, highlighting the need for further technological innovations to better understand the dynamics and regulation of chromatin replication in eukaryotic cells.

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).

Progress in Tandem Mass Spectrometry Data Analysis for Nucleic Acids

Mass spectrometry (MS) has become a critical tool in the characterization of covalently modified nucleic acids. Well-developed bottom-up approaches, where nucleic acids are digested with an endonuclease and the resulting oligonucleotides are separated before MS and MS/MS analysis, provide substantial insight into modified nucleotides in biological and synthetic nucleic. Top-down MS presents an alternative approach where the entire nucleic acid molecule is introduced to the mass spectrometer intact and then fragmented by MS/MS. Current top-down MS workflows have incorporated automated, on-line HPLC workflows to enable rapid desalting of nucleic acid samples for facile mass analysis without complication from adduction. Furthermore, optimization of MS/MS parameters utilizing collision, electron, or photon-based activation methods have enabled effective bond cleavage throughout the phosphodiester backbone while limiting secondary fragmentation, allowing characterization of progressively larger (~100 nt) nucleic acids and localization of covalent modifications. Development of software applications to perform automated identification of fragment ions has accelerated the broader adoption of mass spectrometry for analysis of nucleic acids. This review focuses on progress in tandem mass spectrometry for characterization of nucleic acids with particular emphasis on the software tools that have proven critical for advancing the field.

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patients (n = 23) and healthy controls (n = 10). 5148 differentially expressed genes were identified using the DESeq2 technique and 5636 differentially expressed genes were identified by the limma method(|Log2 Fold Change|≥2, FDR < 0.05). A total of 1793 immune-related genes were identified from the ImmPort database, with 358 genes identified in both groups. Next, a Biological association network was constructed, and five key hub genes (CD4, HLA-DOB, HLA-DRB1, HLA-DRA, AHNAK) were identified using a combination of three topological analysis algorithms (MCC, Closeness, and MNC) and four machine learning algorithms (Random Forest, LASSO regression, SVM, and XGBoost). immune cell distribution showed that the key genes correlated with multiple immune cell infiltrations. Gene Set Enrichment Analysis (GSEA) revealed that the key genes involved multiple immune response and inflammation-related signaling pathways. Subsequently, diagnostic models were constructed using four machine learning algorithms (Logistic regression, AdaBoost, KNN, and XGBoost) based on the identified key genes. Models with the highest performance were then selected. Ultimately, single-cell sequencing data revealed that the identified key genes were expressed in various immune cells, while Quantitative PCR (qPCR) tests confirmed their reduced expression in the sepsis group.

Applying AI/ML for Analyzing Gene Expression Patterns

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of genomics is not matching the levels others have achieved. Challenges include but are not limited to the handling and analysis of high volumes of complex genomic data, and the expertise needed to implement and execute AI/ML approaches. In this chapter, we highlight the importance of transcriptomics, and RNA-seq driven gene expression data exploration to discover novel biomarkers and predict rare, common, and complex diseases. We discuss relevant high volume sequence data generated in the recent past and its availability through various channels, development of orthodox bioinformatics tools and technologies to investigate significantly expressed and abundantly enriched genes, and the implementation of cutting-edge AI/ML approaches to observe disease specific patterns. Current challenges include but are not limited to the acceptance of AI/ML in the scientific research and clinical environments, especially in providing personalized diagnoses and treatments. Reasons include unavailability of user-friendly AI/ML applications and reproducible results. Addressing these issues, we discuss our recently developed Findable, Accessible, Intelligent, and Reproducible (FAIR) solutions, designed for the users with and without computational background to discover biomarkers and predict diseases with high accuracy. We strongly believe that the rightful application of AI/ML techniques has the potential to open avenues for broader research, ultimately leading to personalized interventions and novel treatment targets. Its widespread application will contribute to the public health at large in the United States and around the globe.

Identification of Ferroptosis-Associated Genes in Primary Open-Angle Glaucoma through Bioinformatics Analysis

This study aims to examine ferroptosis-associated genes in primary open-angle glaucoma (POAG) and offer new insights into the underlying disease mechanisms and potential therapeutic approaches. Differentially expressed genes (DEGs) between the POAG and control groups were identified using bioinformatics analysis and subsequently intersected with a ferroptosis gene set to isolate ferroptosis-related DEGs (Ferr DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to examine their biological functions. Core genes were identified through protein-protein interaction (PPI) network and Friends analysis. The diagnostic potential of core Ferr DEGs was assessed using receiver operating characteristic (ROC) curve analysis, while immune cell infiltration was examined using the CIBERSORT algorithm. Additionally, Spearman correlation analysis was used to examine the relationships between the identified genes and immune cell populations. A total of 25 Ferr DEGs were identified, with DDIT4, GDF15, NAMPT, HBA1, and IGFBP7 recognized as key core genes. ROC analysis demonstrated that these genes exhibited high diagnostic accuracy, with an AUC > 0.7. Additionally, the infiltration levels of memory B cells and macrophage_M2 were significantly elevated in POAG tissues compared to the control group. Notably, the core genes revealed significant correlations with various immune cell types. Our findings underscore the involvement of ferroptosis-related genes in POAG pathogenesis and highlight their potential as diagnostic biomarkers and therapeutic targets. Future research should focus on validating these findings in clinical settings and exploring the therapeutic modulation of ferroptosis in POAG management.

Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data

We develop a Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. The approach features a novel joint model of the sample variability and the deferential state of isoforms. Specifically, the within-sample variability and the between-sample variability of each isoform are modeled by a Poisson-Lognormal model and a Gamma-Gamma model, respectively. Using a Bayesian framework, the differential state of each isoform and the model parameters are jointly estimated by a Markov Chain Monte Carlo (MCMC) method. Extensive studies using simulation and real data demonstrate that BayesIso can effectively detect isoforms of less differentially expressed and differential transcripts for genes with multiple isoforms. We applied the approach to breast cancer RNA-seq data and uncovered a unique set of isoforms that form key pathways associated with breast cancer recurrence. First, PI3K/AKT/mTOR signaling and PTEN signaling pathways are identified as being involved in breast cancer development. Further integrated with protein-protein interaction data, pathways of Jak-STAT, mTOR, MAPK and Wnt signaling are revealed in association with breast cancer recurrence. Finally, several pathways are activated in the early recurrence of breast cancer. In tumors that occur early, members of pathways of cellular metabolism and cell cycle (such as CD36 and TOP2A) are upregulated, while immune response genes such as NFATC1 are downregulated.

Optimal endogenous controls for microRNA analysis of visceral adipose tissue in the NAFLD mouse model

The selection of proper reference genes and materials is critical in the design of PCR experiments, especially for differential expression studies. In this study, we propose a method to identify robust endogenous control miRNAs in the visceral adipose tissue of C57BL/6J mice with non-alcoholic fatty liver disease induced by alternating Western and control diets. This study outlines a comprehensive methodology for the analysis of microRNA endogenous controls using microfluidic cards in conjunction with miRNA profiling through small RNA sequencing and subsequent validation by quantitative PCR and the RefFinder algorithm. Criteria included were fold change, p-value, reads per million, and gene stability assessment. A set of six putative endogenous microRNAs was identified (miR-331-3p, let-7a-5p, miR-1839-5p, miR-151a-5p, let-7d-5p, and let-7c-5p). Subsequent validation and analysis using the RefFinder algorithm assessed the stability of the selected genes, and a combination of the three most stable endogenous miRNA controls (miR-331-3p, let-7a- 5p, and miR-1839-5p) exhibiting consistent expression patterns with minimal variability was set. Given the absence of universal endogenous controls, individual evaluation of normalizers for each experiment is imperative for accurate miRNA expression measurements. This approach, which combines multiple techniques and assessments, provides a reliable strategy for identifying and validating endogenous controls in miRNA studies.

Comparative Transcriptome Analysis Reveals Mechanisms of Differential Salinity Tolerance Between Suaeda glauca and Suaeda salsa

BACKGROUND

Suaeda glauca and Suaeda salsa have obvious morphological features and strongly tolerate saline-alkali environments. However, the mechanisms that lead to the differences in saline-alkali tolerance between them remain unclear.

METHODS

In this study, we employed comparative transcriptome analysis to investigate S. glauca and S. salsa under saline-alkali stress.

RESULTS

Our sequencing efforts resulted in the identification of 99,868 unigenes. We obtained 12,021 and 6227 differentially expressed genes (DEGs) from the S. glauca and S. salsa under salt stress compared with plants in the control. Notably, 1189 and 1864 were specifically upregulated DEGs in the roots and leaves of S. salsa under saline-alkali conditions, respectively. These genes were enriched in pathways such as "Plant hormone signal transduction", "Carbon metabolism" and "Starch and sucrose metabolism". Further analysis of stress-related pathways and gene expression levels revealed that key genes involved in abscisic acid (ABA) and jasmonic acid (JA) biosynthesis, ABA signal transduction, and their downstream transcription factors were upregulated in the roots of S. salsa under saline-alkali conditions. Additionally, 24 DEGs associated with stress response were identified in the roots and leaves of both species. The expression levels of these pathways and related genes were higher in S. salsa than in S. glauca, suggesting that S. salsa enhances its saline-alkali tolerance by elevating the expression of these genes.

CONCLUSIONS

This study provides a new research perspective for revealing the differences in saline-alkali tolerance mechanisms between S. glauca and S. salsa, bringing forth important candidate genes for studying their saline-alkali tolerance.

Identification of crucial drought-tolerant genes of barley through comparative transcriptomic analysis and yeast-based stress assay

Drought is a persistent and serious threat to crop yield and quality. The identification and functional characterization of drought tolerance-related genes is thus vital for efforts to support the genetic improvement of drought-tolerant crops. Barley is highly adaptable and renowned for its robust stress resistance, making it an ideal subject for efforts to explore genes related to drought tolerance. In this study, two barley materials with different drought tolerance were subjected to soil drought treatment, including a variety with strong drought tolerance (Hindmarsh) and a genotype with weaker drought tolerance (XZ5). Transcriptomic sequencing data from the aboveground parts of these plants led to the identification of 1,206 differentially expressed genes associated with drought tolerance. These genes were upregulated in Hindmarsh following drought stress exposure but downregulated or unchanged in XZ5 under these same conditions, or were unchanged in Hindmarsh but downregulated in XZ5. Pathway enrichment analyses suggested that these genes are most closely associated with defense responses, signal recognition, photosynthesis, and the biosynthesis of various secondary metabolites. Using protein-protein interaction networks, the ankyrin repeat domain-containing protein 17-like isoform X2 was predicted to impact other drought tolerance-related protein targets in Hindmarsh. In MapMan metabolic pathway analyses, genes found to be associated with the maintenance of drought tolerance in Hindmarsh under adverse conditions were predicted to include genes involved in the abscisic acid, cytokinin, and gibberellin phytohormone signaling pathways, genes associated with redox homeostasis related to ascorbate and glutathione S-transferase, transporters including ABC and AAAP, transcription factors such as AP2/ERF and bHLH, the heat shock proteins HSP60 and HSP70, and the sucrose non-fermenting-1-related protein kinase. Heterologous HvSnRK2 (one of the identified genes, which encodes the sucrose non-fermenting-1-related protein kinase) gene expression in yeast conferred significant drought tolerance, highlighting the functional importance of this gene as one linked with drought tolerance. This study revealed the drought tolerance mechanism of Hindmarsh by comparing transcriptomes while also providing a set of candidate genes for genetic efforts to improve drought tolerance in this and other crop species.

Key active mercury methylating microorganisms and their synergistic effects on methylmercury production in paddy soils

Rice contamination with neurotoxic methylmercury (MeHg) from paddy soils is an escalating global concern. Identifying the microorganisms responsible for mercury (Hg) methylation in these soils is essential for controlling Hg contamination in the food chain and mitigating health impacts. Current research often focuses on total Hg-methylating microorganisms, overlooking the contributions of active ones, which can lead to either overestimating or neglecting the specific roles of microorganisms in Hg methylation within paddy soils. In this study, active Hg-methylating microorganisms in paddy soils were identified using a combination of DNA-SIP, Hg isotope labelling, and Hg methylation gene sequencing techniques. Our findings revealed that Geobacter and Anaerolinea are pivotal active Hg-methylating microorganisms across a contamination gradient in paddy soils. Transcriptomic analysis of soils from major rice-producing provinces in China confirmed the widespread and synergistic involvement of these microorganisms. Microbial incubation further validated their interaction significantly enhances Hg methylation, with Me198Hg concentrations increasing 2.8-fold compared to Geobacter alone and 5.2-fold compared to Anaerolinea alone. These findings enhance our understanding of microbial Hg methylation in paddy soils, providing critical insights for accurately predicting soil MeHg load, rice grain MeHg contamination, and human MeHg exposure risks.

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Applications of Next-Generation Sequencing Technologies and Statistical Tools in Identifying Pathways and Biomarkers for Heat Tolerance in Livestock

The climate change-associated abnormal weather patterns negatively influences the productivity and performance of farm animals. Heat stress is the major detrimental factor hampering production, causing substantial economic loss to the livestock industry. Therefore, it is important to identify heat-tolerant breeds that can survive and produce optimally in any given environment. To achieve this goal, a clearer understanding of the genetic differences and the underlying molecular mechanisms associated with climate change impacts and heat tolerance are a prerequisite. Adopting next-generation biotechnological and statistical tools like whole transcriptome analysis, whole metagenome sequencing, bisulphite sequencing, genome-wide association studies (GWAS), and selection signatures provides an opportunity to achieve this goal. Through these techniques, it is possible to identify permanent genetic markers for heat tolerance, and by incorporating those markers in marker-assisted breeding selection, it is possible to achieve the target of breeding for heat tolerance in livestock. This review gives an overview of the recent advancements in assessing heat tolerance in livestock using such 'omics' approaches and statistical models. The salient findings from this research highlighted several candidate biomarkers that have the potential to be incorporated into future heat-tolerance studies. Such approaches could revolutionise livestock production in the changing climate scenario and support the food demands of the growing human population.

Quantitative Characterization of Gene Regulatory Circuits Associated With Fungal Secondary Metabolism to Discover Novel Natural Products

Microbial genetic circuits are vital for regulating gene expression and synthesizing bioactive compounds. However, assessing their strength and timing, especially in multicellular fungi, remains challenging. Here, an advanced microfluidic platform is combined with a mathematical model enabling precise characterization of fungal gene regulatory circuits (GRCs) at the single-cell level. Utilizing this platform, the expression intensity and timing of 30 transcription factor-promoter combinations derived from two representative fungal GRCs, using the model fungus Aspergillus nidulans are determined. As a proof of concept, the selected GRC combination is utilized to successfully refactor the biosynthetic pathways of bioactive molecules, precisely control their production, and activate the expression of the silenced biosynthetic gene clusters (BGCs). This study provides insights into microbial gene regulation and highlights the potential of platform in fungal synthetic biology applications and the discovery of novel natural products.

Machine learning and network analysis with focus on the biofilm in Staphylococcus aureus

Research on biofilm formation in Staphylococcus aureus has greatly benefited from the generation of high-throughput sequencing data to drive molecular analysis. The accumulation of high-throughput sequencing data, particularly transcriptomic data, offers a unique opportunity to unearth the network and constituent genes involved in biofilm formation using machine learning strategies and co-expression analysis. Herein, the available RNA sequencing data related to Staphylococcus aureus biofilm studies and identified influenced functional pathways and corresponding genes in the process of the transition of bacteria from planktonic to biofilm state by employing machine learning and differential expression analysis. Using weighted gene co-expression analysis and previously developed online prediction platform, important functional modules, potential biofilm-associated proteins, and subnetworks of the biofilm-formation pathway were uncovered. Additionally, several novel protein interactions within these functional modules were identified by constructing a protein-protein interaction (PPI) network. To make this data more straightforward for experimental biologists, an online database named SAdb was developed (http://sadb.biownmcli.info/), which integrates gene annotations, transcriptomics, and proteomics data. Thus, the current study will be of interest to researchers in the field of bacteriology, particularly those studying biofilms, which play a crucial role in bacterial growth, pathogenicity, and drug resistance.

Thousands of oscillating LncRNAs in the mouse testis

The long noncoding RNAs (lncRNAs) are involved in numerous fundamental biological processes, including circadian regulation. Although recent studies have revealed insights into the functions of lncRNAs, how the lncRNAs regulate circadian rhythms still requires a deeper investigation. In this study, we generate two datasets of RNA-seq profiles of the mouse (Mus musculus) testis under light-dark (LD) cycle. The first dataset included 18,613 unannotated transcripts measured at 12 time points, each with duplicate samples, under LD conditions; while the second dataset included 21,414 unannotated transcripts measured at six time points, each with three replicates, under desynchronized and control conditions. We identified 5964 testicular lncRNAs in each dataset by BLASTing these transcripts against the known mouse lncRNAs from the NONCODE database. MetaCycle analyses were performed to identify 519, 475, and 494 rhythmically expressed mouse testicular lncRNAs in the 12-time-point dataset, the six-time-point control dataset, and the six-time-point desynchronized dataset, respectively. A comparison of the expression profiles of the lncRNAs under desynchronized and control conditions revealed that 427 rhythmically expressed lncRNAs from the control condition became arrhythmic under the desynchronized condition, suggesting a possible loss of rhythmicity. In contrast, 446 arrhythmic lncRNAs from the control condition became rhythmic under the desynchronized condition, suggesting a possible gain of rhythmicity. Interestingly, 48 lncRNAs were rhythmically expressed under both desynchronized and control conditions. These oscillating lncRNAs were divided into morning lncRNAs, evening lncRNAs, and night lncRNAs based on their time-course expression patterns. We interrogated the promoter regions of these rhythmically expressed mouse testicular lncRNAs to predict their possible regulation by the E-box, D-box, or RORE promoter motifs. GO and KEGG analyses were performed to identify the possible biological functions of these rhythmically expressed mouse testicular lncRNAs. Further, we conducted conservation analyses of the rhythmically expressed mouse testicular lncRNAs with lncRNAs from humans, rats, and zebrafish, and uncovered three mouse testicular lncRNAs conserved across these four species. Finally, we computationally predicted the conserved lncRNA-encoded peptides and their 3D structures from each of the four species. Taken together, our study revealed thousands of rhythmically expressed lncRNAs in the mouse testis, setting the stage for further computational and experimental validations.

Integrated single-cell and bulk RNA sequencing identifies POSTN as a potential biomarker and therapeutic target for rheumatoid arthritis

BACKGROUND

This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data to identify potential biomarkers and therapeutic targets for rheumatoid arthritis (RA).

METHOD

Firstly, we obtained the synovial scRNA-seq data from the Immport database and bulk RNA-seq data from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene correlation network analysis (WGCNA) to screen for module genes most relevant to RA and intersected them with the differentially expressed genes (DEGs) obtained from scRNA-seq and bulk RNA-seq to obtain intersecting genes. Next, we constructed a protein-protein interaction (PPI) network of hub genes using the STRING database and Cytoscape software and validated its expression using external validation cohorts. Finally, we performed immune cell infiltration analysis using CIBERSORT and explored the expression and drug binding activity of key gene using clinical samples and molecular docking, respectively.

RESULT

We identified six cellular subgroups through dimensionality reduction and clustering, and fibroblasts may be the most important cell cluster in RA based on pseudotime and cell-cell communication analyses. Subsequently, we intersected module genes with DEGs obtained from scRNA-seq and bulk RNA-seq and constructed a PPI network of hub genes (BGN, COL11A1, COL1A1, GUCY1A1, POSTN). In external validation cohorts, POSTN was highly expressed and demonstrated the highest diagnostic performance (AUC = 0.716). In subsequent analyses, we defined POSTN as a key gene and found that its expression level was positively correlated with M2 macrophages in immune cell infiltration analysis. Additionally, POSTN was upregulated in clinical samples and exhibited favorable binding activity with nine anti-rheumatoid arthritis drugs (affinity ≤ -6.0 kcal/mol).

CONCLUSION

Through bioinformatics analysis, clinical sample validation, and molecular docking, we found that POSTN was highly expressed in RA and stably bound to common anti-rheumatoid arthritis drugs, which will provide new insights into potential biomarkers and therapeutic targets for RA.

RNA-seq and Ribosome Profiling Reveal the Translational Landscape of Rice in Response to Rice Stripe Virus Infection

Rice is a crucial staple food for over half the global population, and viral infections pose significant threats to rice yields. This study focuses on the Rice Stripe Virus (RSV), which is known to drastically reduce rice productivity. We employed RNA-seq and ribosome profiling to analyze the transcriptional and translational responses of RSV-infected rice seedlings. Our results reveal that translational reprogramming is a critical aspect of the plant's defense mechanism, operating independently of transcriptional changes. Notably, less than half of the differentially expressed genes showed concordance between transcription and translation. Furthermore, RSV infection led to significant alterations in translational efficiency for numerous genes, suggesting that the virus selectively manipulates translation to enhance its pathogenicity. Our findings underscore the necessity of examining both transcriptional and translational landscapes to fully understand plant responses to viral infections.

SGTCDA: Prediction of circRNA-drug sensitivity associations with interpretable graph transformers and effective assessment

CircRNAs are a type of circular non-coding RNA whose associations with drug sensitivities have been demonstrated in recent studies. Due to the high cost of biomedical experiments for detecting the associations between circRNAs and drug sensitivities, several computational methods have been developed. However, these methods were evaluated mainly based on 5- or tenfold cross-validation, which are often over-optimistic. Furthermore, there are technique issues with these models, such as over-smoothing and over-squashing. To address these issues, we propose a strategy to evaluate models based on independent test sets for association prediction-related studies. In the light of this effective assessment, we constructed a model, SGTCDA, by integrating structural deep network embedding (SDNE) and a graph transformer to predict the potential associations of circRNA-drug sensitivity, which can efficiently capture long-range dependencies and local structural information of nodes. Our results on the training sets and the independent test sets indicate that SGTCDA outperforms the other state-of-the-art models, demonstrating its capacity for accurate prediction of circRNA-drug sensitivity. Moreover, we leveraged EdgeSHAPer to explain the performance of the proposed SGTCDA model, which illustrates that the edges between drugs are more important than other edges for the performance of the model. The source code and dataset of SGTCDA are available at: https://github.com/hwxia/SGTCDA .

Screening of reference genes for gene expression study in different tissues from the transcriptome data of the vector leafhopper Psammotettix striatus

Selecting appropriate reference genes is crucial for ensuring the accuracy and reliability of gene expression study using reverse transcription-quantitative PCR (RT-qPCR). To screen the optimal reference genes for analyzing gene expression in different tissues of the vector leafhopper Psammotettix striatus which causes extensive damage to a wide range of crops by vectoring multiple plant pathogenic microorganisms, the transcriptome data from Malpighian tubules (MTs) of P. striatus were mined. Twenty alternative candidate reference genes were initially selected for screening, among which seven genes with diverse Gene Ontology (GO) annotations were choosed as candidate reference genes, i.e., ribosomal protein L7A (RPL7A), ribosomal protein S28 (RPS28), ribosomal protein L22 (RPL22), ribosomal protein LP2 (RPLP2), H3 histone family 3A (H3F3A), elongation factor 1γ (EF-1γ), and elongation factor 1α (EF-1α). Gene expression levels in different tissues of P. striatus adults were examined using RT-qPCR, and their expression stability was analyzed using multiple reference gene screening software. This study revealed EF-1α as the most abundantly expressed gene, while RPL22 exhibited the lowest expression levels. EF-1α showed the most stable expression, whereas RPS28 showed the least stability. Various software tools confirmed EF-1α as the most stable single reference gene, and EF-1α and RPLP2 an optimal combination. This study provides a foundation for future investigation of the transmission of pathogenic microorganisms mediated by the vector leafhoppers, the function of the MTs, the biosynthesis of brochosomes, the coevolutionary processes and nutritional interactions of symbionts and host insects, and the gene expression study of other sap-sucking insects.

Transcriptomic era of cancers in females: new epigenetic perspectives and therapeutic prospects

In the era of transcriptomics, the role of epigenetics in the study of cancers in females has gained increasing recognition. This article explores the impact of epigenetic modifications, such as DNA methylation, histone modification, and non-coding RNA, on cancers in females, including breast, cervical, and ovarian cancers (1). Our findings suggest that these epigenetic markers not only influence tumor onset, progression, and metastasis but also present novel targets for therapeutic intervention. Detailed analyses of DNA methylation patterns have revealed aberrant events in cancer cells, particularly promoter region hypermethylation, which may lead to silencing of tumor suppressor genes. Furthermore, we examined the complex roles of histone modifications and long non-coding RNAs in regulating the expression of cancer-related genes, thereby providing a scientific basis for developing targeted epigenetic therapies. Our research emphasizes the importance of understanding the functions and mechanisms of epigenetics in cancers in females to develop effective treatment strategies. Future therapeutic approaches may include drugs targeting specific epigenetic markers, which could not only improve therapeutic outcomes but also enhance patient survival and quality of life. Through these efforts, we aim to offer new perspectives and hope for the prevention, diagnosis, and treatment of cancers in females.

Update on antimicrobial resistance in Somalia: Current status, challenges, opportunities, and future perspectives

Antimicrobial resistance (AMR) is a critical global health challenge, and Somalia is no exception. This update examines the current status of AMR in Somalia, highlighting the prevalent patterns of resistance, contributing factors, and significant health impacts. Despite limited surveillance data, evidence suggests rising resistance to key antibiotics, exacerbated by inadequate healthcare infrastructure, overuse of antimicrobials, and lack of regulatory oversight. The review identifies key challenges, including insufficient diagnostic capabilities, poor infection control practices, and a need for robust stewardship programs. Opportunities for addressing AMR in Somalia are discussed, including strengthening surveillance systems, improving healthcare access, and fostering international collaboration. Future perspectives emphasize the importance of integrating AMR strategies into broader health policies, enhancing public awareness, and investing in research to develop new treatments and prevention methods. Addressing these issues is crucial for mitigating the impact of AMR and improving health outcomes in Somalia.

A single-stranded based library preparation method for virome characterization

BACKGROUND

The gut virome is an integral component of the gut microbiome, playing a crucial role in maintaining gut health. However, accurately depicting the entire gut virome is challenging due to the inherent diversity of genome types (dsDNA, ssDNA, dsRNA, and ssRNA) and topologies (linear, circular, or fragments), with subsequently biases associated with current sequencing library preparation methods. To overcome these problems and improve reproducibility and comparability across studies, universal or standardized virome sequencing library construction methods are highly needed in the gut virome study.

RESULTS

We repurposed the ligation-based single-stranded library (SSLR) preparation method for virome studies. We demonstrate that the SSLR method exhibits exceptional efficiency in quantifying viral DNA genomes (both dsDNA and ssDNA) and outperforms existing double-stranded (Nextera) and single-stranded (xGen, MDA + Nextera) library preparation approaches in terms of minimal amplification bias, evenness of coverage, and integrity of assembling viral genomes. The SSLR method can be utilized for the simultaneous library preparation of both DNA and RNA viral genomes. Furthermore, the SSLR method showed its ability to capture highly modified phage genomes, which were often lost using other library preparation approaches.

CONCLUSION

We introduce and improve a fast, simple, and efficient ligation-based single-stranded DNA library preparation for gut virome study. This method is compatible with Illumina sequencing platforms and only requires ligation reagents within 3-h library preparation, which is similar or even better than the advanced library preparation method (xGen). We hope this method can be further optimized, validated, and widely used to make gut virome study more comparable and reproducible. Video Abstract.

Development of a Flow Through-Based Limited Digestion Approach for High-Throughput and High-Sequence Coverage Mapping of Therapeutic mRNAs

Messenger RNAs (mRNAs) have rapidly emerged as a pivotal class of biotherapeutics with great promise in the prevention and treatment of various diseases. As with other biotherapeutics, the sequence accuracy and integrity of mRNAs are critical quality attributes (CQAs), influencing the translation efficiency and protein expression fidelity of mRNAs. Due to the generation of short and repetitive oligonucleotides, traditional sequence mapping methods, which utilize in-solution RNase T1 digestion followed by LC-MS/MS analysis, face challenges in achieving high sequence coverage. In this study, we developed a novel flow through (FT)-based strategy to achieve the limited RNase T1 digestion of therapeutic mRNAs, leading to improved mRNA sequence mapping by LC-MS/MS analysis. Compared with the traditional in-solution digestion methods, the FT-based digestion method could consistently generate an increased number of unique oligonucleotides with miscleavages, which significantly boosted the overall sequence coverage (over 93%) of therapeutic mRNAs of varying sizes. Moreover, the automated digestion workflow using the AssayMAP platform offers significant advantages in method reproducibility and throughput. The high throughput and high sequence coverage features of this method could facilitate its wide application in the development of mRNA-based therapeutics.