TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository

Augusta: From RNA-Seq to gene regulatory networks and Boolean models

Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

Bioprobe-RNA-seq-microRaman system for deep tracking of the live single-cell metabolic pathway chemometrics

The integration between RNA-sequencing and micro-spectroscopic techniques has recently profiled the advanced transcriptomic discoveries on the cellular level. In the current study, by combining the sensation approach (including bio-molecules structural evaluation, high throughput next-generation sequencing (HT-NGS), and confocal Raman microscopy) the functionality on the single live cancer cells' ferroptosis and apoptosis signaling pathways is visualized. Our study reveals a hydrophobic tunnel by phycocyanin-isoprene molecule (PC-SIM) electrostatic charge within hepatoma cells (HepG2) that activates the ferritin light chain (FTL) and caspase-8 associate protein (CASP8AP2) ferroptosis responsible genes. Moreover, this research proves that PC-vanillin (VAN) stimulation induces the actin-binding factor profilin-1 (PFN1), subsequently in situ tracking its expression at 1139.75 cm-1 microRaman wavenumber. While PC-thymol (THY) induces the lysophospholipase-2 (LYPLA2) (p-value = 0.009) and acetylneuraminate-9-O-acetyltransferase (CASD1) (p-value = 0.022) at 1143.19 cm-1. Our findings establish a new concept to promote the cross-disciplinary use of instant cellular-based detection technology for intermediary evaluating the signaling cellular transcriptome.

Occlusion enhanced pan-cancer classification via deep learning

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces "GENESO," a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called "Symmetrical Occlusion (SO)". It collaborates with the baseline LSTM network, mimicking the "gain of function" and "loss of function" of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test.

Different impact of short-term and long-term hindlimb disuse on bone homeostasis

Disuse osteoporosis is one of the major problems of bone health which commonly occurs in astronauts during long-term spaceflight and bedridden patients. However, the mechanisms underlying such mechanical unloading induced bone loss have not been fully understood. In this study, we employed hindlimb-unloading mice models with different length of tail suspension to investigate if the bone loss was regulated by distinct factors under different duration of disuse. Our micro-CT results showed more significant decrease of bone mass in 6W (6-week) tail-suspension mice compared to the 1W (1-week) tail-suspension ones, as indicated by greater reduction of BV/TV, Tb.N, B.Ar/T.Ar and Ct.Th. RNA-sequencing results showed significant effects of hindlimb disuse on cell locomotion and immune system process which could cause bone loss.Real-time quantitative PCR results indicated a greater number of bone formation related genes that were downregulated in short-term tail-suspension mice compared to the long-term ones. It is, thus, suggested while sustained hindlimb unloading continuously contributes to bone loss, molecular regulation of bone homeostasis tends to reach a balance during this process.

Insights into gemcitabine resistance in pancreatic cancer: association with metabolic reprogramming and TP53 pathogenicity in patient derived xenografts

BACKGROUND

With poor prognosis and high mortality, pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. Standard of care therapies for PDAC have included gemcitabine for the past three decades, although resistance often develops within weeks of chemotherapy initiation through an array of possible mechanisms.

METHODS

We reanalyzed publicly available RNA-seq gene expression profiles of 28 PDAC patient-derived xenograft (PDX) models before and after a 21-day gemcitabine treatment using our validated analysis pipeline to identify molecular markers of intrinsic and acquired resistance.

RESULTS

Using normalized RNA-seq quantification measurements, we first identified oxidative phosphorylation and interferon alpha pathways as the two most enriched cancer hallmark gene sets in the baseline gene expression profile associated with intrinsic gemcitabine resistance and sensitivity, respectively. Furthermore, we discovered strong correlations between drug-induced expression changes in glycolysis and oxidative phosphorylation genes and response to gemcitabine, which suggests that these pathways may be associated with acquired gemcitabine resistance mechanisms. Thus, we developed prediction models using baseline gene expression profiles in those pathways and validated them in another dataset of 12 PDAC models from Novartis. We also developed prediction models based on drug-induced expression changes in genes from the Molecular Signatures Database (MSigDB)'s curated 50 cancer hallmark gene sets. Finally, pathogenic TP53 mutations correlated with treatment resistance.

CONCLUSION

Our results demonstrate that concurrent upregulation of both glycolysis and oxidative phosphorylation pathways occurs in vivo in PDAC PDXs following gemcitabine treatment and that pathogenic TP53 status had association with gemcitabine resistance in these models. Our findings may elucidate the molecular basis for gemcitabine resistance and provide insights for effective drug combination in PDAC chemotherapy.

Identification of Pathogen-Specific Novel Sources of Genetic Resistance Against Ascochyta Blight and Identification of Their Underlying Genetic Control

Global chickpea production is restricted by Ascochyta blight caused by the necrotrophic fungi Ascochyta rabiei. Developing locally adapted disease-resistant cultivars is an economically and environmentally sustainable approach to combat this disease. However, the lack of genetic variability in cultivated chickpeas and breeder-friendly markers poses a significant challenge to Ascochyta blight-resistant breeding efforts in chickpeas. In this study, we screened the mini-core germplasm of Cicer reticulatum against a local pathotype of A. rabiei. A modified mini-dome screening approach resulted in the identification of five accessions showing a high level of resistance. The mean disease score of resistant accessions ranged between 1.75 ± 0.3 and 2.88 ± 0.4 compared to susceptible accessions, where the mean disease score ranged between 3.59 ± 0.62 and 8.86 ± 0.14. Genome-wide association study revealed a strong association on chromosome 5, explaining ∼58% of the phenotypic variance. The underlying region contained two candidate genes (Cr_14190.1_v2 and Cr_14189.1_v2), the characterization of which showed the presence of a DNA-binding domain (cl28899 and cd18793) in Cr_14190.1_v2 and its orthologs in C. arietinum, whereas Cr_14190.1_v2 carried an additional N-terminal domain (cl31759). qPCR expression analysis in resistant and susceptible accessions revealed ∼3- and ∼110-fold higher transcript abundance for Cr_14189.1 and Cr_14190.1, respectively.

A novel small non-coding RNA 562 mediates the virulence of Pseudomonas plecoglossicida by regulating the expression of fliP, a key component of flagella T3SS

Pseudomonas plecoglossicida is a vital pathogen that poses a substantial risk to aquaculture. Small RNAs (sRNAs) are non-coding regulatory molecules capable of sensing environmental changes and modulating virulence-associated signaling pathways, such as the assembly of flagella. However, the relevant researches on P. plecoglossicida are an urgent need. Here, we report a novel sRNA, sRNA562, which has potential to regulate the post-transcriptional of fliP, a key component of the lateral flagellar type III secretion system. In this study, the effects of sRNA562 on the virulence of P. plecoglossicida and its role in regulating the pathogenic process were investigated through the use of a constructed sRNA562 deletion strain. The deletion of sRNA562 resulted in an up-regulation of fliP in P. plecoglossicida, and leading to increased swarming motility and enhanced the ability of biofilm formation, adhesion and chemotaxis. Subsequent artificial infection experiment demonstrated that the deletion of sRNA562 increased the virulence of P. plecoglossicida towards hybrid grouper, as evidenced by a reduction in survival rate, elevation of tissue bacterial load, and the exacerbation of histopathological damage. Further studies have found that the deletion of sRNA562 lead to an up-regulation of fliP expression during hybrid grouper infection, thereby enhancing bacterial swarming ability and ultimately heightening pathogenicity, leading to a dysregulated host response to infection, tissue damage and eventually death. Our work revealed a sRNA that exerts negative regulation on the expression of lateral flagella in P. plecoglossicida, thereby impacting its virulence. These findings provide a new perspective on the virulence regulation mechanism of P. plecoglossicida, contributing to a more comprehensive understanding in the field of pathogenicity research.

Comprehensive Metatranscriptomic Analysis of Plant Viruses in Imported Frozen Cherries and Blueberries

The possibility of new viruses emerging in various regions worldwide has increased due to a combination of factors, including climate change and the expansion of international trading. Plant viruses spread through various transmission routes, encompassing well-known avenues such as pollen, seeds, and insects. However, research on potential transmission routes beyond these known mechanisms has remained limited. To address this gap, this study employed metatranscriptomic analysis to ascertain the presence of plant viruses in imported frozen fruits, specifically cherries and blueberries. This analysis aimed to identify pathways through which plant viruses may be introduced into countries. Virome analysis revealed the presence of six species of plant viruses in frozen cherries and blueberries: cherry virus A (CVA), prunus necrotic ringspot virus (PNRSV), prune dwarf virus (PDV), prunus virus F (PrVF), blueberry shock virus (BlShV), and blueberry latent virus (BlLV). Identifying these potential transmission routes is crucial for effectively managing and preventing the spread of plant viruses and crop protection. This study highlights the importance of robust quality control measures and monitoring systems for frozen fruits, emphasizing the need for proactive measures to mitigate the risk associated with the potential spread of plant viruses.

Genome-Wide Analyses of MADS-Box Genes Reveal Their Involvement in Seed Development and Oil Accumulation of Tea-Oil Tree (Camellia oleifera)

The seeds of Camellia oleifera produce high amount of oil, which can be broadly used in the fields of food, industry, and medicine. However, the molecular regulation mechanisms of seed development and oil accumulation in C. oleifera are unclear. In this study, evolutionary and expression analyses of the MADS-box gene family were performed across the C. oleifera genome for the first time. A total of 86 MADS-box genes (ColMADS) were identified, including 60 M-type and 26 MIKC members. More gene duplication events occurred in M-type subfamily (6) than that in MIKC subfamily (2), and SEP-like genes were lost from the MIKCC clade. Furthermore, 8, 15, and 17 differentially expressed ColMADS genes (DEGs) were detected between three developmental stages of seed (S1/S2, S2/S3, and S1/S3), respectively. Among these DEGs, the STK-like ColMADS12 and TT16-like ColMADS17 were highly expressed during the seed formation (S1 and S2), agreeing with their predicted functions to positively regulate the seed organogenesis and oil accumulation. While ColMADS57 and ColMADS07 showed increasing expression level with the seed maturation (S2 and S3), conforming to their potential roles in promoting the seed ripening. In all, these results revealed a critical role of MADS-box genes in the C. oleifera seed development and oil accumulation, which will contribute to the future molecular breeding of C. oleifera.

Insights into chlorantraniliprole exposure via activating cytochrome P450-mediated xenobiotic metabolism pathway in the Procambarus clarkii: Identification of P450 genes involved in detoxification

To investigate the impact of chlorantraniliprole on Procambarus clarkii, acute toxicity tests were performed. Results indicated that 96 h post-exposure to chlorantraniliprole (60 mg/L) led to the separation of the hepatopancreas basement membrane, causing cell swelling, rupture, and vacuolation. Moreover, acid phosphatase (ACP) and alkaline phosphatase (AKP) activities exhibited divergent trends across four concentrations of chlorantraniliprole (0, 30, 60, and 90 mg/L). Hydrogen peroxide (H2O2) and catalase (CAT) levels significantly increased, while total superoxide dismutase (T-SOD) and malonaldehyde (MDA) activities decreased, indicating oxidative stress in the hepatopancreas. A total of 276 differentially expressed genes (DEGs) were identified, with 204 up-regulated and 72 down-regulated. Out of these, 114 DEGs were successfully annotated and classified into 99 pathways, with a primary focus on the cytochrome P450-mediated xenobiotic metabolism pathway. The DEGs enriched in this pathway, along with transcriptome data, were validated using quantitative-polymerase chain reaction. This study enhances the transcriptome database of P. clarkii and provides fundamental insights into its immune defense and antioxidant mechanisms. Additionally, it lays a theoretical foundation for future research on disease prevention in P. clarkii within rice-shrimp culture systems.

Transcriptome analysis of CpGV in midguts of type II resistant codling moth larvae and identification of contaminant infections by SNP mapping of RNA-Seq data

Various isolates of the Cydia pomonella granulovirus (CpGV) are used as insect pest control agents against codling moth (CM, Cydia pomonella L.), a predominant pest in apple orchards. Three different types (I-III) of dominantly inherited field resistance of CM larvae to CpGV have been recently identified. In this study, transcription of virus genes in midgut cells of type II-resistant CM larvae infected with different CpGV isolates, i.e., CpGV-M and CpGV-S (both prone to type II resistance) as well as CpGV-E2 (breaking type II resistance) was determined by strand-specific RNA sequencing (RNA-Seq) at an early infection stage (72 h post infection). Based on principal component analysis of read counts and the quantitative distribution of single nucleotide polymorphisms (SNPs) in the RNA-Seq data, a bioinformatics analysis pipeline was developed for an a posteriori identification of the infective agents. We report that (i) identification of infective agent is crucial, especially in in vivo infection experiments, when activation of covert virus infections is a possibility, (ii) no substantial difference between CpGV-M and CpGV-S transcription was found in type II-resistant CM larvae despite a different resistance mechanism, (iii) the transcription level of CpGV-M and CpGV-S was much lower than that of CpGV-E2, and (iv) orf59 (sod), orf89 (pif-6), orf92 (p18), and orf137 (lef-10) were identified as significantly downregulated genes in resistance-prone isolates CpGV-M and CpGV-S. For type II resistance of CM larvae, we conclude that CpGV-M and CpGV-S are both able to enter midgut cells, but viral transcription is significantly impaired at an early stage of infection compared to the resistance-breaking isolate CpGV-E2.

IMPORTANCE

CpGV is a highly virulent pathogen of codling moth, and it has been developed into one of the most successful commercial baculovirus biocontrol agents for pome fruit production worldwide. The emergence of field resistance in codling moth to commercial CpGV products is a threat toward the sustainable use of CpGV. In recent years, different types of resistance (type I-III) were identified. For type II resistance, very little is known regarding the infection process. By studying the virus gene expression patterns of different CpGV isolates in midguts of type II-resistant codling moth larvae, we found that the type II resistance mechanism is most likely based on intracellular factors rather than a receptor component. By applying SNP mapping of the RNA-Seq data, we further emphasize the importance of identifying the infective agents in in vivo experiments when activation of a covert infection cannot be excluded.

RNA-seq validation: software for selection of reference and variable candidate genes for RT-qPCR

BACKGROUND

Real-time quantitative PCR (RT-qPCR) is one of the most widely used gene expression analyses for validating RNA-seq data. This technique requires reference genes that are stable and highly expressed, at least across the different biological conditions present in the transcriptome. Reference and variable candidate gene selection is often neglected, leading to misinterpretation of the results.

RESULTS

We developed a software named "Gene Selector for Validation" (GSV), which identifies the best reference and variable candidate genes for validation within a quantitative transcriptome. This tool also filters the candidate genes concerning the RT-qPCR assay detection limit. GSV was compared with other software using synthetic datasets and performed better, removing stable low-expression genes from the reference candidate list and creating the variable-expression validation list. GSV software was used on a real case, an Aedes aegypti transcriptome. The top GSV reference candidate genes were selected for RT-qPCR analysis, confirming that eiF1A and eiF3j were the most stable genes tested. The tool also confirmed that traditional mosquito reference genes were less stable in the analyzed samples, highlighting the possibility of inappropriate choices. A meta-transcriptome dataset with more than ninety thousand genes was also processed successfully.

CONCLUSION

The GSV tool is a time and cost-effective tool that can be used to select reference and validation candidate genes from the biological conditions present in transcriptomic data.

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons

The outstanding human cognitive capacities are computed in the cerebral cortex, a mammalian-specific brain region and the place of massive biological innovation. Long noncoding RNAs have emerged as gene regulatory elements with higher evolutionary turnover than mRNAs. The many long noncoding RNAs identified in neural tissues make them candidates for molecular sources of cerebral cortex evolution and disease. Here, we characterized the genomic and cellular shifts that occurred during the evolution of the long noncoding RNA repertoire expressed in the developing cerebral cortex and explored putative roles for these long noncoding RNAs in the evolution of the human brain. Using transcriptomics and comparative genomics, we comprehensively annotated the cortical transcriptomes of humans, rhesus macaques, mice, and chickens and classified human cortical long noncoding RNAs into evolutionary groups as a function of their predicted minimal ages. Long noncoding RNA evolutionary groups showed differences in expression levels, splicing efficiencies, transposable element contents, genomic distributions, and transcription factor binding to their promoters. Furthermore, older long noncoding RNAs showed preferential expression in germinative zones, outer radial glial cells, and cortical inhibitory (GABAergic) neurons. In comparison, younger long noncoding RNAs showed preferential expression in cortical excitatory (glutamatergic) neurons, were enriched in primate and human-specific gene co-expression modules, and were dysregulated in neurodevelopmental disorders. These results suggest different evolutionary routes for older and younger cortical long noncoding RNAs, highlighting old long noncoding RNAs as a possible source of molecular evolution of conserved developmental programs; conversely, we propose that the de novo expression of primate- and human-specific young long noncoding RNAs is a putative source of molecular evolution and dysfunction of cortical excitatory neurons, warranting further investigation.

Key co-expressed genes correlated with blood serum parameters of pigs fed with different fatty acid profile diets

This study investigated how gene expression is affected by dietary fatty acids (FA) by using pigs as a reliable model for studying human diseases that involve lipid metabolism. This includes changes in FA composition in the liver, blood serum parameters and overall metabolic pathways. RNA-Seq data from 32 pigs were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA). Our aim was to identify changes in blood serum parameters and gene expression between diets containing 3% soybean oil (SOY3.0) and a standard pig production diet containing 1.5% soybean oil (SOY1.5). Significantly, both the SOY1.5 and SOY3.0 groups showed significant modules, with a higher number of co-expressed modules identified in the SOY3.0 group. Correlated modules and specific features were identified, including enriched terms and pathways such as the histone acetyltransferase complex, type I diabetes mellitus pathway, cholesterol metabolism, and metabolic pathways in SOY1.5, and pathways related to neurodegeneration and Alzheimer's disease in SOY3.0. The variation in co-expression observed for HDL in the groups analyzed suggests different regulatory patterns in response to the higher concentration of soybean oil. Key genes co-expressed with metabolic processes indicative of diseases such as Alzheimer's was also identified, as well as genes related to lipid transport and energy metabolism, including CCL5, PNISR, DEGS1. These findings are important for understanding the genetic and metabolic responses to dietary variation and contribute to the development of more precise nutritional strategies.

Growth increase and gonadal dysfunction of the lined seahorse triggered by zinc exposure

Seahorses are characterized by unique characteristics such as a male pregnancy reproductive strategy and grasping preferences, which make these species vulnerable to various environmental risks. Zinc (Zn) is one of the most frequently occurring toxic elements in coastal waters; however, little is known about the effect of Zn exposure on seahorses. In the present study, line seahorses (Hippocampus erectus) were exposed to waterborne Zn (0.2 and 1.0 mg/L) and the impact on growth and gonadal development was investigated. Zn exposure induced growth improvement, but also led to gonadal dysfunction in the lined seahorse. Female seahorses exhibited high testosterone levels, immature follicles, and weight increase after Zn exposure, which is the typical characteristics of a polycystic ovary syndrome (PCOS)-like phenotype. Transcriptomic data suggested that the Zn-induced growth promotion resulted from the dysregulated expression of fat accumulation genes. Further investigation of gene expression profiles in the brain, ovaries, and testes indicated that Zn affected the expression of genes involved in growth, immunity, tissue remodeling, and gonadal development by regulating serum steroid hormone levels and androgen receptor expression. This study not only clarifies the complex impact of Zn on seahorses using physiological, histological, and molecular evidence but can also provide new insights into the mechanism underlying PCOS in reproductive-aged women. Moreover, this work demonstrates the risk of the common practice of Zn supplementation in the aquaculture industry as the consequent growth yield may not represent a healthy condition.

Combining a transcriptomic approach and a targeted metabolomics approach for deciphering the molecular bases of compatibility phenotype in the snail Biomphalaria glabrata toward Schistosoma mansoni

Biomphalaria glabrata is a freshwater snail and the obligatory intermediate host of Schistosoma mansoni parasite, the etiologic agent of intestinal Schistosomiasis, in South America and Caribbean. Interestingly in such host-parasite interactions, compatibility varies between populations, strains or individuals. This observed compatibility polymorphism is based on a complex molecular-matching-phenotype, the molecular bases of which have been investigated in numerous studies, notably by comparing between different strains or geographical isolates or clonal selected snail lines. Herein we propose to decipher the constitutive molecular support of this interaction in selected non-clonal resistant and susceptible snail strain originating from the same natural population from Brazil and thus having the same genetic background. Thanks to a global RNAseq transcriptomic approach on whole snail, we identified a total of 328 differentially expressed genes between resistant and susceptible phenotypes among which 129 were up-regulated and 199 down-regulated. Metabolomic studies were used to corroborate the RNAseq results. The activation of immune genes and specific metabolic pathways in resistant snails might provide them with the capacity to better respond to parasite infection.

MOI is a comprehensive database collecting processed multi-omics data associated with viral infection

Viral infections pose significant public health challenges, exemplified by the global impact of COVID-19 caused by SARS-CoV-2. Understanding the intricate molecular mechanisms governing virus-host interactions is pivotal for effective intervention strategies. Despite the burgeoning multi-omics data on viral infections, a centralized database elucidating host responses to viruses remains lacking. In response, we have developed a comprehensive database named 'MOI' (available at http://www.fynn-guo.cn/ ), specifically designed to aggregate processed Multi-Omics data related to viral Infections. This meticulously curated database serves as a valuable resource for conducting detailed investigations into virus-host interactions. Leveraging high-throughput sequencing data and metadata from PubMed and Gene Expression Omnibus (GEO), MOI comprises over 3200 viral-infected samples, encompassing human and murine infections. Standardized processing pipelines ensure data integrity, including bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), Chromatin Immunoprecipitation sequencing (ChIP-seq), and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). MOI offers user-friendly interfaces presenting comprehensive cell marker tables, gene expression data, and epigenetic landscape charts. Analytical tools for DNA sequence conversion, FPKM calculation, differential gene expression, and Gene Ontology (GO)/ Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment enhance data interpretation. Additionally, MOI provides 16 visualization plots for intuitive data exploration. In summary, MOI serves as a valuable repository for researchers investigating virus-host interactions. By centralizing and facilitating access to multi-omics data, MOI aims to advance our understanding of viral pathogenesis and expedite the development of therapeutic interventions.

A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data

The premise for this study emanated from the need to understand SARS-CoV-2 infections at the molecular level and to develop predictive tools for managing COVID-19 severity. With the varied clinical outcomes observed among infected individuals, creating a reliable machine learning (ML) model for predicting the severity of COVID-19 became paramount. Despite the availability of large-scale genomic and clinical data, previous studies have not effectively utilized multi-modality data for disease severity prediction using data-driven approaches. Our primary goal is to predict COVID-19 severity using a machine-learning model trained on a combination of patients' gene expression, clinical features, and co-morbidity data. Employing various ML algorithms, including Logistic Regression (LR), XGBoost (XG), Naïve Bayes (NB), and Support Vector Machine (SVM), alongside feature selection methods, we sought to identify the best-performing model for disease severity prediction. The results highlighted XG as the superior classifier, with 95% accuracy and a 0.99 AUC (Area Under the Curve), for distinguishing severity groups. Additionally, the SHAP analysis revealed vital features contributing to prediction, including several genes such as COX14, LAMB2, DOLK, SDCBP2, RHBDL1, and IER3-AS1. Notably, two clinical features, the absolute neutrophil count and Viremia Categories, emerged as top contributors. Integrating multiple data modalities has significantly improved the accuracy of disease severity prediction compared to using any single modality. The identified features could serve as biomarkers for COVID-19 prognosis and patient care, allowing clinicians to optimize treatment strategies and refine clinical decision-making processes for enhanced patient outcomes.

Cross-talk of MLST and transcriptome unveiling antibiotic resistance mechanism of carbapenem resistance Acinetobacter baumannii clinical strains isolated in Guiyang, China

Introduction

Acinetobacter baumannii (A. baumannii) is an important opportunistic pathogen causing nosocomial infection in the clinic. The occurrence rate of antibiotic resistance is increasing year by year, resulting in a highly serious situation of bacterial resistance.

Methods

To better understand the local epidemiology of multidrug-resistant A. baumannii, an investigation was conducted on the antibiotic resistance of different types of A. baumannii and its relationship with the genes of A. baumannii. Furthermore, the molecular mechanism underlying antibiotic resistance in A. baumannii was investigated through transcriptome analysis.

Results

These results showed that a total of 9 STs were detected. It was found that 99% of the strains isolated in the hospital belonged to the same STs, and the clone complex CC208 was widely distributed in various departments and all kinds of samples. Furthermore, these A. baumannii strains showed high resistance to ertapenem, biapenem, meropenem, and imipenem, among which the resistance to ertapenem was the strongest. The detection rate of bla OXA-51 gene in these carbapenem resistance A. baumannii (CRAB) reached 100%; Additionally, the transcriptome results showed that the resistance genes were up-regulated in resistance strains, and these genes involved in biofilm formation, efflux pumps, peptidoglycan biosynthesis, and chaperonin synthesis.

Discussion

These results suggest that the CC208 STs were the main clonal complex, and showed high carbapenem antibiotic resistance. All these resistant strains were distributed in various departments, but most of them were distributed in intensive care units (ICU). The bla OXA-23 was the main antibiotic resistance genotype; In summary, the epidemic trend of clinical A. baumannii in Guiyang, China was analyzed from the molecular level, and the resistance mechanism of A. baumannii to carbapenem antibiotics was analyzed with transcriptome, which provided a theoretical basis for better control of A. baumannii.

Observation of Agonistic Behavior in Pacific White Shrimp (Litopenaeus vannamei) and Transcriptome Analysis

Agonistic behavior has been identified as a limiting factor in the development of intensive L. vannamei aquaculture. However, the characteristics and molecular mechanisms underlying agonistic behavior in L. vannamei remain unclear. In this study, we quantified agonistic behavior through a behavioral observation system and generated a comprehensive database of eyestalk and brain ganglion tissues obtained from both aggressive and nonaggressive L. vannamei employing transcriptome analysis. The results showed that there were nine behavior patterns in L. vannamei which were correlated, and the fighting followed a specific process. Transcriptome analysis revealed 5083 differentially expressed genes (DEGs) in eyestalk and 1239 DEGs in brain ganglion between aggressive and nonaggressive L. vannamei. Moreover, these DEGs were primarily enriched in the pathways related to the energy metabolism process and signal transduction. Specifically, the phototransduction (dme04745) signaling pathway emerges as a potential key pathway for the adjustment of the L. vannamei agonistic behavior. The G protein-coupled receptor kinase 1-like (LOC113809193) was screened out as a significant candidate gene within the phototransduction pathway. Therefore, these findings contribute to an enhanced comprehension of crustacean agonistic behavior and provide a theoretical basis for the selection and breeding of L. vannamei varieties suitable for high-density aquaculture environments.

System analysis based on Anoikis-related genes identifies MAPK1 as a novel therapy target for osteosarcoma with neoadjuvant chemotherapy

BACKGROUND

Osteosarcoma (OS) is the most common bone malignant tumor in children, and its prognosis is often poor. Anoikis is a unique mode of cell death.However, the effects of Anoikis in OS remain unexplored.

METHOD

Differential analysis of Anoikis-related genes was performed based on the metastatic and non-metastatic groups. Then LASSO logistic regression and SVM-RFE algorithms were applied to screen out the characteristic genes. Later, Univariate and multivariate Cox regression was conducted to identify prognostic genes and further develop the Anoikis-based risk score. In addition, correlation analysis was performed to analyze the relationship between tumor microenvironment, drug sensitivity, and prognostic models.

RESULTS

We established novel Anoikis-related subgroups and developed a prognostic model based on three Anoikis-related genes (MAPK1, MYC, and EDIL3). The survival and ROC analysis results showed that the prognostic model was reliable. Besides, the results of single-cell sequencing analysis suggested that the three prognostic genes were closely related to immune cell infiltration. Subsequently, aberrant expression of two prognostic genes was identified in osteosarcoma cells. Nilotinib can promote the apoptosis of osteosarcoma cells and down-regulate the expression of MAPK1.

CONCLUSIONS

We developed a novel Anoikis-related risk score model, which can assist clinicians in evaluating the prognosis of osteosarcoma patients in clinical practice. Analysis of the tumor immune microenvironment and chemotherapeutic drug sensitivity can provide necessary insights into subsequent mechanisms. MAPK1 may be a valuable therapeutic target for neoadjuvant chemotherapy in osteosarcoma.

Modeling cardiomyocyte signaling and metabolism predicts genotype-to-phenotype mechanisms in hypertrophic cardiomyopathy

Familial hypertrophic cardiomyopathy (HCM) is a significant precursor of heart failure and sudden cardiac death, primarily caused by mutations in sarcomeric and structural proteins. Despite the extensive research on the HCM genotype, the complex and context-specific nature of many signaling and metabolic pathways linking the HCM genotype to phenotype has hindered therapeutic advancements for patients. Here, we have developed a computational model of HCM encompassing cardiomyocyte signaling and metabolic networks and their associated interactions. Utilizing a stochastic logic-based ODE approach, we linked cardiomyocyte signaling to the metabolic network through a gene regulatory network and post-translational modifications. We validated the model against published data on activities of signaling species in the HCM context and transcriptomes of two HCM mouse models (i.e., R403Q-αMyHC and R92W-TnT). Our model predicts that HCM mutation induces changes in metabolic functions such as ATP synthase deficiency and a transition from fatty acids to carbohydrate metabolism. The model indicated major shifts in glutamine-related metabolism and increased apoptosis after HCM-induced ATP synthase deficiency. We predicted that the transcription factors STAT, SRF, GATA4, TP53, and FoxO are the key regulators of cardiomyocyte hypertrophy and apoptosis in HCM in alignment with experiments. Moreover, we identified shared (e.g., activation of PGC1α by AMPK, and FHL1 by titin) and context-specific mechanisms (e.g., regulation of Ca2+ sensitivity by titin in HCM patients) that may control genotype-to-phenotype transition in HCM across different species or mutations. We also predicted potential combination drug targets for HCM (e.g., mavacamten plus ROS inhibitors) preventing or reversing HCM phenotype (i.e., hypertrophic growth, apoptosis, and metabolic remodeling) in cardiomyocytes. This study provides new insights into mechanisms linking genotype to phenotype in familial hypertrophic cardiomyopathy and offers a framework for assessing new treatments and exploring variations in HCM experimental models.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .

Acyl carrier protein OsMTACP2 confers rice cold tolerance at the booting stage

Low temperatures occurring at the booting stage in rice (Oryza sativa L.) often result in yield loss by impeding male reproductive development. However, the underlying mechanisms by which rice responds to cold at this stage remain largely unknown. Here, we identified MITOCHONDRIAL ACYL CARRIER PROTEIN 2 (OsMTACP2), the encoded protein of which mediates lipid metabolism involved in the cold response at the booting stage. Loss of OsMTACP2 function compromised cold tolerance, hindering anther cuticle and pollen wall development, resulting in abnormal anther morphology, lower pollen fertility, and seed setting. OsMTACP2 was highly expressed in tapetal cells and microspores during anther development, with the encoded protein localizing to both mitochondria and the cytoplasm. Comparative transcriptomic analysis revealed differential expression of genes related to lipid metabolism between the wild type and the Osmtacp2-1 mutant in response to cold. Through a lipidomic analysis, we demonstrated that wax esters, which are the primary lipid components of the anther cuticle and pollen walls, function as cold-responsive lipids. Their levels increased dramatically in the wild type but not in Osmtacp2-1 when exposed to cold. Additionally, mutants of two cold-induced genes of wax ester biosynthesis, ECERIFERUM1 and WAX CRYSTAL-SPARSE LEAF2, showed decreased cold tolerance. These results suggest that OsMTACP2-mediated wax ester biosynthesis is essential for cold tolerance in rice at the booting stage.

The KAT module of the SAGA complex maintains the oncogenic gene expression program in MYCN-amplified neuroblastoma

Pediatric cancers are frequently driven by genomic alterations that result in aberrant transcription factor activity. Here, we used functional genomic screens to identify multiple genes within the transcriptional coactivator Spt-Ada-Gcn5-acetyltransferase (SAGA) complex as selective dependencies for MYCN-amplified neuroblastoma, a disease of dysregulated development driven by an aberrant oncogenic transcriptional program. We characterized the DNA recruitment sites of the SAGA complex in neuroblastoma and the consequences of loss of SAGA complex lysine acetyltransferase (KAT) activity on histone acetylation and gene expression. We demonstrate that loss of SAGA complex KAT activity is associated with reduced MYCN binding on chromatin, suppression of MYC/MYCN gene expression programs, and impaired cell cycle progression. Further, we showed that the SAGA complex is pharmacologically targetable in vitro and in vivo with a KAT2A/KAT2B proteolysis targeting chimeric. Our findings expand our understanding of the histone-modifying complexes that maintain the oncogenic transcriptional state in this disease and suggest therapeutic potential for inhibitors of SAGA KAT activity in MYCN-amplified neuroblastoma.

Integrating GC-MS and comparative transcriptome analysis reveals that TsERF66 promotes the biosynthesis of caryophyllene in Toona sinensis tender leaves

Introduction

The strong aromatic characteristics of the tender leaves of Toona sinensis determine their quality and economic value.

Methods and results

Here, GC-MS analysis revealed that caryophyllene is a key volatile compound in the tender leaves of two different T. sinensis varieties, however, the transcriptional mechanisms controlling its gene expression are unknown. Comparative transcriptome analysis revealed significant enrichment of terpenoid synthesis pathway genes, suggesting that the regulation of terpenoid synthesis-related gene expression is an important factor leading to differences in aroma between the two varieties. Further analysis of expression levels and genetic evolution revealed that TsTPS18 is a caryophyllene synthase, which was confirmed by transient overexpression in T. sinensis and Nicotiana benthamiana leaves. Furthermore, we screened an AP2/ERF transcriptional factor ERF-IX member, TsERF66, for the potential regulation of caryophyllene synthesis. The TsERF66 had a similar expression trend to that of TsTPS18 and was highly expressed in high-aroma varieties and tender leaves. Exogenous spraying of MeJA also induced the expression of TsERF66 and TsTPS18 and promoted the biosynthesis of caryophyllene. Transient overexpression of TsERF66 in T. sinensis significantly promoted TsTPS18 expression and caryophyllene biosynthesis.

Discussion

Our results showed that TsERF66 promoted the expression of TsTPS18 and the biosynthesis of caryophyllene in T. sinensis leaves, providing a strategy for improving the aroma of tender leaves.

DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods.

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing to Identify an Immunogenic Cell Death-Related 5-Gene Prognostic Signature in Hepatocellular Carcinoma

Introduction

Immunogenic cell death (ICD) can enhance the potency of immunotherapy in cancer treatment. Nevertheless, it is ambiguous how ICD-related genes (ICDRGs) contribute to hepatocellular carcinoma (HCC).

Methods

Single-cell RNA sequencing (scRNA-seq) data were used to distinguish malignant cells from normal cells in the HCC tumor microenvironment(TME). Bulk RNA sequencing data was employed to acquire the landscape of the 33 ICDRGs. Unsupervised clustering identified two ICD molecular subtypes. The cellular infiltration characteristics and biological behavior in different subtypes were analyzed by ssGSEA. Subsequently, differentially expressed genes (DEGs) between the two subtypes were determined, based on which patients were classified into three gene clusters. Then, the prognostic model was constructed by Lasso-Cox analysis. Finally, we investigated the expression of risk genes in cancer cell line encyclopedia (CCLE) and validated the function of NKX3-2 in vitro experiments.

Results

ICD scores and ICDRGs expression in malignant cells were significantly lower than in normal cells by scRNA-seq analysis. ICD-high subtype was characterized by ICD-related gene overexpression and high levels of immune infiltration abundance and immune checkpoints; Three DEGs-related gene clusters were likewise strongly linked to stromal and immunological activation. In the ICD-related prognostic model consisting of NKX3-2, CHODL, MMP1, NR0B1, and CTSV, the low-risk group patients had a better endpoint and displayed increased susceptibility to immunotherapy and chemotherapeutic drugs like 5-Fluorouracil, afatinib, bortezomib, cediratinib, lapatinib, dasatinib, gefitinib and crizotinib. Moreover, NKX3-2 amplification in HCC samples has been verified by experiments, and its disruption suppressed the proliferation and invasion of tumor cells.

Conclusion

Our study highlighted the potential of the ICDRGs risk score as a prognostic indicator to aid in the accurate diagnosis and immunotherapy sensitivity of HCC.

DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data

Understanding the cellular composition of a disease-related tissue is important in disease diagnosis, prognosis, and downstream treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technique have allowed the measurement of gene expression profiles for individual cells. However, scRNA-seq is still too expensive to be used for large-scale population studies, and bulk RNA-seq is still widely used in such situations. An essential challenge is to deconvolve cellular composition for bulk RNA-seq data based on scRNA-seq data. Here, we present DeepDecon, a deep neural network model that leverages single-cell gene expression information to accurately predict the fraction of cancer cells in bulk tissues. It provides a refining strategy in which the cancer cell fraction is iteratively estimated by a set of trained models. When applied to simulated and real cancer data, DeepDecon exhibits superior performance compared to existing decomposition methods in terms of accuracy.

Multi-omics analysis revealed that the protein kinase MoKin1 affected the cellular response to endoplasmic reticulum stress in the rice blast fungus, Magnaporthe oryzae

BACKGROUND

Previous studies have shown that protein kinase MoKin1 played an important role in the growth, conidiation, germination and pathogenicity in rice blast fungus, Magnaporthe oryzae. ΔMokin1 mutant showed significant phenotypic defects and significantly reduced pathogenicity. However, the internal mechanism of how MoKin1 affected the development of physiology and biochemistry remained unclear in M. oryzae.

RESULT

This study adopted a multi-omics approach to comprehensively analyze MoKin1 function, and the results showed that MoKin1 affected the cellular response to endoplasmic reticulum stress (ER stress). Proteomic analysis revealed that the downregulated proteins in ΔMokin1 mutant were enriched mainly in the response to ER stress triggered by the unfolded protein. Loss of MoKin1 prevented the ER stress signal from reaching the nucleus. Therefore, the phosphorylation of various proteins regulating the transcription of ER stress-related genes and mRNA translation was significantly downregulated. The insensitivity to ER stress led to metabolic disorders, resulting in a significant shortage of carbohydrates and a low energy supply, which also resulted in severe phenotypic defects in ΔMokin1 mutant. Analysis of MoKin1-interacting proteins indicated that MoKin1 really took participate in the response to ER stress.

CONCLUSION

Our results showed the important role of protein kinase MoKin1 in regulating cellular response to ER stress, providing a new research direction to reveal the mechanism of MoKin1 affecting pathogenic formation, and to provide theoretical support for the new biological target sites searching and bio-pesticides developing.

DNMT3A promotes glioma growth and malignancy via TNF-α/NF-κB signaling pathway

Background

DNMT3A is the main molecule responsible for DNA methylation in cells. DNMT3A affects the progression of inflammation, degenerative diseases, and malignant tumors, and exhibits significant aberrantly expression in tumor tissues.

Methods

Transcriptome data and relevant clinical information were downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) datasets. Differential expression analysis and prognostic analysis were conducted based on above statistics. We constructed a clinical prognostic model and identified DNMT3A as an independent prognostic factor to accurately predict patient prognosis. Differential gene enrichment analysis revealed that DNMT3A affects the progression of glioma through multiple pathways, among which the tumor necrosis factor-α (TNF-α)/nuclear factor-kappa B (NF-κB) pathway shows a strong correlation. Immunological analysis also revealed a certain correlation between DNMT3A and tumor immunity. We demonstrated through gene editing that DNMT3A can affect the release of TNF-α in cells, thereby affecting the progression of glioma. Functional experiments have also demonstrated that DNMT3A plays a crucial role in tumors.

Results

RNA-sequencing and survival analyses of lower-grade glioma (LGG) patients in TCGA, CGGA, and GEO cohorts showed that high DNMT3A expression correlated with poor prognosis of LGG patients. Univariate and multivariate Cox regression analyses showed that DNMT3A expression was an independent prognostic indicator in LGG. The prognosis prediction nomogram with age, World Health Organization (WHO) grading, and DNMT3A expression showed reliable performance in predicting the 1-, 3-, and 5-year overall survival (OS) of LGG patients. Functional enrichment analysis, gene set enrichment analysis (GSEA), and ESTIMATE algorithm analyses showed that DNMT3A expression was associated with the tumor infiltration of immune cells and predicted response to immunotherapy in two immunotherapy cohorts of pan-cancer patients. Furthermore, short hairpin RNA (shRNA)-mediated knockdown of DNMT3A in the LGG cell lines suppressed proliferation, migration, and invasion of LGG cells by downregulating the TNF-α/NF-κB signaling pathway.

Conclusions

Our data showed that DNMT3A was a potential prognostic biomarker in glioma. DNMT3A promoted proliferation and malignancy of LGG cells through the TNF-α/NF-κB signaling pathway. DNMT3A is a promising therapeutic target for treating patients with LGG.

RNA viromes of Dermacentor nuttalli ticks reveal a novel uukuvirus in Qīnghǎi Province, China

Ticks are a major parasite on the Qīnghǎi-Tibet Plateau, western China, and represent an economic burden to agriculture and animal husbandry. Despite research on tick-borne pathogens that threaten humans and animals, the viromes of dominant tick species remain unknown. In this study, we collected Dermacentor nuttalli ticks near Qīnghǎi Lake and identified 13 viruses belonging to at least six families through metagenomic sequencing. Four viruses were of high abundance in pools, including Xīnjiāng tick-associated virus 1 (XJTAV1), and three novel viruses: Qīnghǎi Lake virus 1, Qīnghǎi Lake virus 2 (QHLV1, and QHLV2, unclassified), and Qīnghǎi Lake virus 3 (QHLV3, genus Uukuvirus of family Phenuiviridae in order Bunyavirales), which lacks the M segment. The minimum infection rates of the four viruses among the tick groups were 8.2%, 49.5%, 6.2%, and 24.7%, respectively, suggesting the prevalence of these viruses in D. nuttalli ticks. A putative M segment of QHLV3 was identified from next-generation sequencing data and further characterized for its signal peptide cleavage site, N-glycosylation, and transmembrane region. Furthermore, we probed the L, M, and S segments of other viruses using the putative M segment sequence with sequencing data of other tick pools. By revealing the viromes of D. nuttalli ticks, this study enhances our understanding of tick-borne viral communities in highland regions. The putative M segment identified in a novel uukuvirus suggests that previously identified uukuviruses without M segments should have had the same genome organization as typical bunyaviruses. These results will facilitate virus discovery and our understanding of the phylogeny of tick-borne uukuviruses.

Anaerostipes hadrus, a butyrate-producing bacterium capable of metabolizing 5-fluorouracil

Anaerostipes hadrus (A. hadrus) is a dominant species in the human gut microbiota and considered a beneficial bacterium for producing probiotic butyrate. However, recent studies have suggested that A. hadrus may negatively affect the host through synthesizing fatty acid and metabolizing the anticancer drug 5-fluorouracil, indicating that the impact of A. hadrus is complex and unclear. Therefore, comprehensive genomic studies on A. hadrus need to be performed. We integrated 527 high-quality public A. hadrus genomes and five distinct metagenomic cohorts. We analyzed these data using the approaches of comparative genomics, metagenomics, and protein structure prediction. We also performed validations with culture-based in vitro assays. We constructed the first large-scale pan-genome of A. hadrus (n = 527) and identified 5-fluorouracil metabolism genes as ubiquitous in A. hadrus genomes as butyrate-producing genes. Metagenomic analysis revealed the wide and stable distribution of A. hadrus in healthy individuals, patients with inflammatory bowel disease, and patients with colorectal cancer, with healthy individuals carrying more A. hadrus. The predicted high-quality protein structure indicated that A. hadrus might metabolize 5-fluorouracil by producing bacterial dihydropyrimidine dehydrogenase (encoded by the preTA operon). Through in vitro assays, we validated the short-chain fatty acid production and 5-fluorouracil metabolism abilities of A. hadrus. We observed for the first time that A. hadrus can convert 5-fluorouracil to α-fluoro-β-ureidopropionic acid, which may result from the combined action of the preTA operon and adjacent hydA (encoding bacterial dihydropyrimidinase). Our results offer novel understandings of A. hadrus, exceptionally functional features, and potential applications.

IMPORTANCE

This work provides new insights into the evolutionary relationships, functional characteristics, prevalence, and potential applications of Anaerostipes hadrus.

Genome-wide view and characterization of natural antisense transcripts in Cannabis Sativa L

Natural Antisense Transcripts (NATs) are a kind of complex regulatory RNAs that play crucial roles in gene expression and regulation. However, the NATs in Cannabis Sativa L., a widely economic and medicinal plant rich in cannabinoids remain unknown. In this study, we comprehensively predicted C. sativa NATs genome-wide using strand-specific RNA sequencing (ssRNA-Seq) data, and validated the expression profiles by strand-specific quantitative reverse transcription PCR (ssRT-qPCR). Consequently, a total of 307 NATs were predicted in C. sativa, including 104 cis- and 203 trans- NATs. Functional enrichment analysis demonstrated the potential involvement of the C. sativa NATs in DNA polymerase activity, RNA-DNA hybrid ribonuclease activity, and nucleic acid binding. Finally, 18 cis- and 376 trans- NAT-ST pairs were predicted to produce 621 cis- and 5,679 trans- small interfering RNA (nat-siRNAs), respectively. These nat-siRNAs were potentially involved in the biosynthesis of cannabinoids and cellulose. All these results will shed light on the regulation of NATs and nat-siRNAs in C. sativa.

Pure lignin induces overexpression of cytochrome P450 (CYP) encoding genes and brings insights into the lignocellulose depolymerization by Trametes villosa

Trametes villosa is a remarkable white-rot fungus (WRF) with the potential to be applied in lignocellulose conversion to obtain chemical compounds and biofuels. Lignocellulose breakdown by WRF is carried out through the secretion of oxidative and hydrolytic enzymes. Despite the existing knowledge about this process, the complete molecular mechanisms involved in the regulation of this metabolic system have not yet been elucidated. Therefore, in order to understand the genes and metabolic pathways regulated during lignocellulose degradation, the strain T. villosa CCMB561 was cultured in media with different carbon sources (lignin, sugarcane bagasse, and malt extract). Subsequently, biochemical assays and differential gene expression analysis by qPCR and high-throughput RNA sequencing were carried out. Our results revealed the ability of T. villosa CCMB561 to grow on lignin (AL medium) as the unique carbon source. An overexpression of Cytochrome P450 was detected in this medium, which may be associated with the lignin O-demethylation pathway. Clusters of up-regulated CAZymes-encoding genes were identified in lignin and sugarcane bagasse, revealing that T. villosa CCMB561 acts simultaneously in the depolymerization of lignin, cellulose, hemicellulose, and pectin. Furthermore, genes encoding nitroreductases and homogentisate-1,2-dioxygenase that act in the degradation of organic pollutants were up-regulated in the lignin medium. Altogether, these findings provide new insights into the mechanisms of lignocellulose degradation by T. villosa and confirm the ability of this fungal species to be applied in biorefineries and in the bioremediation of organic pollutants.

PM2.5, component cause of severe metabolically abnormal obesity: An in silico, observational and analytical study

Obesity is currently one of the most alarming pathological conditions due to the progressive increase in its prevalence. In the last decade, it has been associated with fine particulate matter suspended in the air (PM2.5). The purpose of this study was to explore the mechanistic interaction of PM2.5 with a high-fat diet (HFD) through the differential regulation of transcriptional signatures, aiming to identify the association of these particles with metabolically abnormal obesity. The research design was observational, using bioinformatic methods and an explanatory approach based on Rothman's causal model. We propose three new transcriptional signatures in murine adipose tissue. The sum of transcriptional differences between the group exposed to an HFD and PM2.5, compared to the control group, were 0.851, 0.265, and -0.047 (p > 0.05). The HFD group increased body mass by 20% with two positive biomarkers of metabolic impact. The group exposed to PM2.5 maintained a similar weight to the control group but exhibited three positive biomarkers. Enriched biological pathways (p < 0.05) included PPAR signaling, small molecule transport, adipogenesis genes, cytokine-cytokine receptor interaction, and HIF-1 signaling. Transcriptional regulation predictions revealed CpG islands and common transcription factors. We propose three new transcriptional signatures: FAT-PM2.5-CEJUS, FAT-PM2.5-UP, and FAT-PM2.5-DN, whose transcriptional regulation profile in adipocytes was statistically similar by dietary intake and HFD and exposure to PM2.5 in mice; suggesting a mechanistic interaction between both factors. However, HFD-exposed murines developed moderate metabolically abnormal obesity, and PM2.5-exposed murines developed severe abnormal metabolism without obesity. Therefore, in Rothman's terms, it is concluded that HFD is a sufficient cause of the development of obesity, and PM2.5 is a component cause of severe abnormal metabolism of obesity. These signatures would be integrated into a systemic biological process that would induce transcriptional regulation in trans, activating obesogenic biological pathways, restricting lipid mobilization pathways, decreasing adaptive thermogenesis and angiogenesis, and altering vascular tone thus inducing a severe metabolically abnormal obesity.

Revealing myopathy spectrum: integrating transcriptional and clinical features of human skeletal muscles with varying health conditions

Myopathy refers to a large group of heterogeneous, rare muscle diseases. Bulk RNA-sequencing has been utilized for the diagnosis and research of these diseases for many years. However, the existing valuable sequencing data often lack integration and clinical interpretation. In this study, we integrated bulk RNA-sequencing data from 1221 human skeletal muscles (292 with myopathies, 929 controls) from both databases and our local samples. By applying a method similar to single-cell analysis, we revealed a general spectrum of muscle diseases, ranging from healthy to mild disease, moderate muscle wasting, and severe muscle disease. This spectrum was further partly validated in three specific myopathies (97 muscles) through clinical features including trinucleotide repeat expansion, magnetic resonance imaging fat fraction, pathology, and clinical severity scores. This spectrum helped us identify 234 genuinely healthy muscles as unprecedented controls, providing a new perspective for deciphering the hallmark genes and pathways among different myopathies. The newly identified featured genes of general myopathy, inclusion body myositis, and titinopathy were highly expressed in our local muscles, as validated by quantitative polymerase chain reaction.

Viral Metatranscriptomic Analysis to Reveal the Diversity of Viruses Infecting Satsuma Mandarin (Citrus unshiu) in Korea

Citrus cultivation plays a pivotal role, making a significant contribution to global fruit production and dietary consumption. Accurate identification of viral pathogens is imperative for the effective management of plant viral disease in citrus crops. High-throughput sequencing serves as an alternative approach, enabling comprehensive pathogen identification on a large scale without requiring pre-existing information. In this study, we employed HTS to investigate viral pathogens infecting citrus in three different regions of South Korea: Jejudo (Jeju), Wando-gun (Wando), and Dangjin-si (Dangjin). The results unveiled diverse viruses and viroids that exhibited regional variations. Notably, alongside the identification of well-known citrus viruses such as satsuma dwarf virus, citrus tatter leaf virus, and citrus leaf blotch virus (CLBV), this study also uncovered several viruses and viroids previously unreported in Korean citrus. Phylogenetic analysis revealed that majority of identified viruses exhibited the closest affilations with isolates from China or Japan. However, CLBV and citrus viroid-I-LSS displayed diverse phylogenetic positions, reflecting their regional origins. This study advances our understanding of citrus virome diversity and regional dynamics through HTS, emphasizing its potential in unraveling intricate viral pathogens in agriculture. Consequently, it significantly contributes to disease management strategies, ensuring the resilience of the citrus industry.

Plasma-Free Blood as a Potential Alternative to Whole Blood for Transcriptomic Analysis

RNA sequencing (RNAseq) technology has become increasingly important in precision medicine and clinical diagnostics, and emerged as a powerful tool for identifying protein-coding genes, performing differential gene analysis, and inferring immune cell composition. Human peripheral blood samples are widely used for RNAseq, providing valuable insights into individual biomolecular information. Blood samples can be classified as whole blood (WB), plasma, serum, and remaining sediment samples, including plasma-free blood (PFB) and serum-free blood (SFB) samples that are generally considered less useful byproducts during the processes of plasma and serum separation, respectively. However, the feasibility of using PFB and SFB samples for transcriptome analysis remains unclear. In this study, we aimed to assess the suitability of employing PFB or SFB samples as an alternative RNA source in transcriptomic analysis. We performed a comparative analysis of WB, PFB, and SFB samples for different applications. Our results revealed that PFB samples exhibit greater similarity to WB samples than SFB samples in terms of protein-coding gene expression patterns, detection of differentially expressed genes, and immunological characterizations, suggesting that PFB can serve as a viable alternative to WB for transcriptomic analysis. Our study contributes to the optimization of blood sample utilization and the advancement of precision medicine research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00121-1.

Transcriptome analysis reveals the genes involved in spermatogenesis in white feather broilers

Semen volume is an important economic trait of broilers and one of the important indices for continuous breeding. The objective of this study was to identify genes related to semen volume through transcriptome analysis of the testis tissue of white feather broilers. The testis samples with the highest semen volume (H group, n = 5) and lowest semen volume (L group, n = 5) were selected from 400-day-old roosters for transcriptome analysis by RNA sequencing. During the screening of differentially expressed genes (DEGs) between the H and L groups, a total of 386 DEGs were identified, among which 348 were upregulated and 38 were downregulated. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the immune response, leukocyte differentiation, cell adhesion molecules and collagen binding played vital roles in spermatogenesis. The results showed that 4 genes related to spermatogenesis, namely, COL1A1, CD74, ARPC1B and APOA1, were significantly expressed in Group H, which was consistent with the phenotype results. Our findings may provide a basis for further research on the genetic mechanism of semen volume in white feather broilers.

Predicting drug activity against cancer through genomic profiles and SMILES

Due to the constant increase in cancer rates, the disease has become a leading cause of death worldwide, enhancing the need for its detection and treatment. In the era of personalized medicine, the main goal is to incorporate individual variability in order to choose more precisely which therapy and prevention strategies suit each person. However, predicting the sensitivity of tumors to anticancer treatments remains a challenge. In this work, we propose two deep neural network models to predict the impact of anticancer drugs in tumors through the half-maximal inhibitory concentration (IC50). These models join biological and chemical data to apprehend relevant features of the genetic profile and the drug compounds, respectively. In order to predict the drug response in cancer cell lines, this study employed different DL methods, resorting to Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). In the first stage, two autoencoders were pre-trained with high-dimensional gene expression and mutation data of tumors. Afterward, this genetic background is transferred to the prediction models that return the IC50 value that portrays the potency of a substance in inhibiting a cancer cell line. When comparing RSEM Expected counts and TPM as methods for displaying gene expression data, RSEM has been shown to perform better in deep models and CNNs model can obtain better insight in these types of data. Moreover, the obtained results reflect the effectiveness of the extracted deep representations in the prediction of the IC50 value that portrays the potency of a substance in inhibiting a tumor, achieving a performance of a mean squared error of 1.06 and surpassing previous state-of-the-art models.

Viromes of Haemaphysalis longicornis reveal different viral abundance and diversity in free and engorged ticks

Haemaphysalis longicornis ticks, commonly found in East Asia, can transmit various pathogenic viruses, including the severe fever with thrombocytopenia syndrome virus (SFTSV) that has caused febrile diseases among humans in Hubei Province. However, understanding of the viromes of H. longicornis was limited, and the prevalence of viruses among H. longicornis ticks in Hubei was not well clarified. This study investigates the viromes of both engorged (fed) and free (unfed) H. longicornis ticks across three mountainous regions in Hubei Province from 2019 to 2020. RNA-sequencing analysis identified viral sequences that were related to 39 reference viruses belonging to unclassified viruses and seven RNA viral families, namely Chuviridae, Nairoviridae, Orthomyxoviridae, Parvoviridae, Phenuiviridae, Rhabdoviridae, and Totiviridae. Viral abundance and diversity in these ticks were analysed, and phylogenetic characteristics of the Henan tick virus (HNTV), Dabieshan tick virus (DBSTV), Okutama tick virus (OKTV), and Jingmen tick virus (JMTV) were elucidated based on their full genomic sequences. Prevalence analysis demonstrated that DBSTV was the most common virus found in individual H. longicornis ticks (12.59%), followed by HNTV (0.35%), whereas JMTV and OKTV were not detected. These results improve our understanding of H. longicornis tick viromes in central China and highlight the role of tick feeding status and geography in shaping the viral community. The findings of new viral strains and their potential impact on public health raise the need to strengthen surveillance efforts for comprehensively assessing their spillover potentials.

A novel metagenomic approach uncovers phage genes as markers for increased disinfectant tolerance in mixed Listeria monocytogenes communities

Listeria monocytogenes is an important human pathogen with a high mortality rate. Consumption of contaminated ready-to-eat food is the main mode of transmission to humans. Disinfectant-tolerant L. monocytogenes have emerged, which are believed to have increased persistence potential. Elucidating the mechanisms of L. monocytogenes disinfectant tolerance has been the focus of previous studies using pure cultures. A limitation of such approach is the difficulty to identify strains with reduced susceptibility due to inter-strain variation and the need to screen large numbers of strains and genes. In this study, we applied a novel metagenomic approach to detect genes associated with disinfectant tolerance in mixed L. monocytogenes planktonic communities. Two communities, consisting of 71 and 80 isolates each, were treated with the food industry disinfectants benzalkonium chloride (BC, 1.75 mg/L) or peracetic acid (PAA, 38 mg/L). The communities were subjected to metagenomic sequencing and differences in individual gene abundances between biocide-free control communities and biocide-treated communities were determined. A significant increase in the abundance of Listeria phage-associated genes was observed in both communities after treatment, suggesting that prophage carriage could lead to an increased disinfectant tolerance in mixed L. monocytogenes planktonic communities. In contrast, a significant decrease in the abundance of a high-copy emrC-harbouring plasmid pLmN12-0935 was observed in both communities after treatment. In PAA-treated community, a putative ABC transporter previously found to be necessary for L. monocytogenes resistance to antimicrobial agents and virulence, was among the genes with the highest weight for differentiating treated from control samples. The undertaken metagenomic approach in this study can be applied to identify genes associated with increased tolerance to other antimicrobials in mixed bacterial communities.

Comprehensive Genome-Wide Natural Variation and Expression Analysis of Tubby-like Proteins Gene Family in Brachypodium distachyon

The Tubby-like proteins (TLPs) gene family is a group of transcription factors found in both animals and plants. In this study, we identified twelve B. distachyon TLPs, divided into six groups based on conserved domains and evolutionary relationships. We predicted cis-regulatory elements involved in light, hormone, and biotic and abiotic stresses. The expression patterns in response to light and hormones revealed that BdTLP3, 4, 7, and 14 are involved in light responses, and BdTLP1 is involved in ABA responses. Furthermore, BdTLP2, 7, 9, and 13 are expressed throughout vegetative and reproductive stages, whereas BdTLP1, 3, 5, and 14 are expressed at germinating grains and early vegetative development, and BdTLP4, 6, 8, and 10 are expressed at the early reproduction stage. The natural variation in the eleven most diverged B. distachyon lines revealed high conservation levels of BdTLP1-6 to high variation in BdTLP7-14 proteins. Based on diversifying selection, we identified amino acids in BdTLP1, 3, 8, and 13, potentially substantially affecting protein functions. This analysis provided valuable information for further functional studies to understand the regulation, pathways involved, and mechanism of BdTLPs.

Molecular adaptations underlying high-frequency hearing in the brain of CF bats species

BACKGROUND

The majority of bat species have developed remarkable echolocation ability, especially for the laryngeally echolocating bats along with high-frequency hearing. Adaptive evolution has been widely detected for the cochleae in the laryngeally echolocating bats, however, limited understanding for the brain which is the central to echolocation signal processing in the auditory perception system, the laryngeally echolocating bats brain may also undergo adaptive changes.

RESULT

In order to uncover the molecular adaptations related with high-frequency hearing in the brain of laryngeally echolocating bats, the genes expressed in the brain of Rhinolophus ferrumequinum (CF bat) and Myotis pilosus (FM bat) were both detected and also compared. A total of 346,891 genes were detected and the signal transduction mechanisms were annotated by the most abundant genes, followed by the transcription. In hence, there were 3,088 DEGs were found between the two bat brains, with 1,426 highly expressed in the brain of R. ferrumequinum, which were significantly enriched in the neuron and neurodevelopmental processes. Moreover, we found a key candidate hearing gene, ADCY1, playing an important role in the R. ferrumequinum brain and undergoing adaptive evolution in CF bats.

CONCLUSIONS

Our study provides a new insight to the molecular bases of high-frequency hearing in two laryngeally echolocating bats brain and revealed different nervous system activities during auditory perception in the brain of CF bats.

Unveiling the evolutionary history of lingonberry (Vaccinium vitis-idaea L.) through genome sequencing and assembly of European and North American subspecies

Lingonberry (Vaccinium vitis-idaea L.) produces tiny red berries that are tart and nutty in flavor. It grows widely in the circumpolar region, including Scandinavia, northern parts of Eurasia, Alaska, and Canada. Although cultivation is currently limited, the plant has a long history of cultural use among indigenous communities. Given its potential as a food source, genomic resources for lingonberry are significantly lacking. To advance genomic knowledge, the genomes for 2 subspecies of lingonberry (V. vitis-idaea ssp. minus and ssp. vitis-idaea var. 'Red Candy') were sequenced and de novo assembled into contig-level assemblies. The assemblies were scaffolded using the bilberry genome (Vaccinium myrtillus) to generate a chromosome-anchored reference genome consisting of 12 chromosomes each with a total length of 548.07 Mb [contig N50 = 1.17 Mb, BUSCO (C%) = 96.5%] for ssp. vitis-idaea and 518.70 Mb [contig N50 = 1.40 Mb, BUSCO (C%) = 96.9%] for ssp. minus. RNA-seq-based gene annotation identified 27,243 and 25,718 genes on the respective assembly, and transposable element detection methods found that 45.82 and 44.58% of the genome were repeats. Phylogenetic analysis confirmed that lingonberry was most closely related to bilberry and was more closely related to blueberries than cranberries. Estimates of past effective population size suggested a continuous decline over the past 1-3 MYA, possibly due to the impacts of repeated glacial cycles during the Pleistocene leading to frequent population fragmentation. The genomic resource created in this study can be used to identify industry-relevant genes (e.g. anthocyanin production), infer phylogeny, and call sequence-level variants (e.g. SNPs) in future research.

Differentially expressed genes of RNA-seq data are suggested on the intersections of normalization techniques

Data normalization is the critical step for the RNA-seq data analysis. Several techniques are suggested for the normalization of transcript reads in the samples. In this study, the differentially expressed genes (DEGs) are generated from the TCGA normalized laryngeal cancer data obtained using the TPM, FPKM, and DESeq2 techniques. The results showed that the reports of DEGs were different based on the normalization techniques. We suggested that the DEG intersections obtain the top transcripts from the normalized data in support of the pathway enrichment process.

Identification of a novel growth-associated promoter for biphasic expression of heterogenous proteins in Pichia pastoris

Pichia pastoris (P. pastoris) is one of the most popular cell factories for expressing exogenous proteins and producing useful chemicals. The alcohol oxidase 1 promoter (PAOX1) is the most commonly used strong promoter in P. pastoris and has the characteristic of biphasic expression. However, the inducer for PAOX1, methanol, has toxicity and poses risks in industrial settings. In the present study, analyzing transcriptomic data of cells collected at different stages of growth found that the formate dehydrogenase (FDH) gene ranked 4960th in relative expression among 5032 genes during the early logarithmic growth phase but rose to the 10th and 1st during the middle and late logarithmic growth phases, respectively, displaying a strict biphasic expression characteristic. The unique transcriptional regulatory profile of the FDH gene prompted us to investigate the properties of its promoter (PFDH800). Under single-copy conditions, when a green fluorescent protein variant was used as the expression target, the PFDH800 achieved 119% and 69% of the activity of the glyceraldehyde-3-phosphate dehydrogenase promoter and PAOX1, respectively. After increasing the copy number of the expression cassette in the strain to approximately four copies, the expression level of GFPuv driven by PFDH800 increased to approximately 2.5 times that of the strain containing GFPuv driven by a single copy of PAOX1. Our PFDH800-based expression system exhibited precise biphasic expression, ease of construction, minimal impact on normal cellular metabolism, and high strength. Therefore, it has the potential to serve as a new expression system to replace the PAOX1 promoter.IMPORTANCEThe alcohol oxidase 1 promoter (PAOX1) expression system has the characteristics of biphasic expression and high expression levels, making it the most widely used promoter in the yeast Pichia pastoris. However, PAOX1 requires methanol induction, which can be toxic and poses a fire hazard in large quantities. Our research has found that the activity of PFDH800 is closely related to the growth state of cells and can achieve biphasic expression without the need for an inducer. Compared to other reported non-methanol-induced biphasic expression systems, the system based on the PFDH800 offers several advantages, including high expression levels, simple construction, minimal impact on cellular metabolism, no need for an inducer, and the ability to fine-tune expression.

Wolfberry genome database: integrated genomic datasets for studying molecular biology

Wolfberry, also known as goji berry or Lycium barbarum, is a highly valued fruit with significant health benefits and nutritional value. For more efficient and comprehensive usage of published L. barbarum genomic data, we established the Wolfberry database. The utility of the Wolfberry Genome Database (WGDB) is highlighted through the Genome browser, which enables the user to explore the L. barbarum genome, browse specific chromosomes, and access gene sequences. Gene annotation features provide comprehensive information about gene functions, locations, expression profiles, pathway involvement, protein domains, and regulatory transcription factors. The transcriptome feature allows the user to explore gene expression patterns using transcripts per kilobase million (TPM) and fragments per kilobase per million mapped reads (FPKM) metrics. The Metabolism pathway page provides insights into metabolic pathways and the involvement of the selected genes. In addition to the database content, we also introduce six analysis tools developed for the WGDB. These tools offer functionalities for gene function prediction, nucleotide and amino acid BLAST analysis, protein domain analysis, GO annotation, and gene expression pattern analysis. The WGDB is freely accessible at https://cosbi7.ee.ncku.edu.tw/Wolfberry/. Overall, WGDB serves as a valuable resource for researchers interested in the genomics and transcriptomics of L. barbarum. Its user-friendly web interface and comprehensive data facilitate the exploration of gene functions, regulatory mechanisms, and metabolic pathways, ultimately contributing to a deeper understanding of wolfberry and its potential applications in agronomy and nutrition.