RNA-seq: from technology to biology

Molecular and physiological basis of heterosis in hybrid rice performance

Heterosis is often exploited to produce high-yielding crops with better performance than their inbred counterparts. Commercial rice breeding has made use of this phenomenon as well, primarily through the use of cytoplasmic male sterility (CMS) and environment-sensitive genic male sterility (EGMS). However, a limited understanding of the molecular and physiological basis of heterosis prevents researchers from harnessing the full potential of hybrid breeding. This review examines the various explanations and mechanisms of heterosis in rice, including evidence fitting the established theories of heterosis and the use of modern omics approaches to characterizing heterosis and heterosis-related traits. Overdominance was the most frequently cited mechanism behind yield-related traits and various molecular and physiological markers associated with heterosis were identified.

Integrated analysis of differential intra-chromosomal community interactions: A study of breast cancer

It is challenging to analyze the dynamics of intra-chromosomal interactions when considering multiple high-dimensional epigenetic datasets. A computational approach, differential network analysis in intra-chromosomal community interaction (DNAICI), was proposed here to elucidate these dynamics by integrating Hi-C data with other epigenetic data. DNAICI utilized a novel hyperparameter tuning method, for optimizing the network clustering, to identify valid intra-chromosomal community interactions at different resolutions. The approach was first trained on Hi-C data and other epigenetic data in an untreated and one hour estrogen (E2)-treated breast cancer cell line, MCF7, and uncovered two major types of valid intra-chromosomal community interactions (active/repressive) that resembles the properties of A/B compartments (or open/closed chromatin domains). It was further tested on the breast cancer cell line MCF7 and its corresponding tamoxifen-resistant (TR) derivative, MCF7TR, and identified 515 differentially interacting and expressed genes (DIEGs) within intra-chromosomal community interactions. In silico analysis of these DIEGs revealed that endocrine resistance is among the top biological pathways, suggesting an interacting/looping-mediated mechanism in regulating breast cancer tamoxifen resistance. This novel integrated network analysis approach offers a broad application in diverse biological systems for identifying a biological-context-specific differential community interaction.

DNA replication timing reveals genome-wide features of transcription and fragility

DNA replication in humans requires precise regulation to ensure accurate genome duplication and maintain genome integrity. A key indicator of this regulation is replication timing, which reflects the interplay between origin firing and fork dynamics. We present a high-resolution (1-kilobase) mathematical model that infers firing rate distributions from Repli-seq timing data across multiple cell lines, enabling a genome-wide comparison between predicted and observed replication. Notably, regions where the model and data diverge often overlap fragile sites and long genes, highlighting the influence of genomic architecture on replication dynamics. Conversely, regions of strong concordance are associated with open chromatin and active promoters, where elevated firing rates facilitate timely fork progression and reduce replication stress. In this work, we provide a valuable framework for exploring the structural interplay between replication timing, transcription, and chromatin organisation, offering insights into the mechanisms underlying replication stress and its implications for genome stability and disease.

Utilizing Nanopore direct RNA sequencing of blood from patients with sepsis for discovery of co- and post-transcriptional disease biomarkers

BACKGROUND

RNA sequencing of whole blood has been increasingly employed to find transcriptomic signatures of disease states. These studies traditionally utilize short-read sequencing of cDNA, missing important aspects of RNA expression such as differential isoform abundance and poly(A) tail length variation.

METHODS

We used Oxford Nanopore Technologies sequencing to sequence native mRNA extracted from whole blood from 12 patients with definite bacterial and viral sepsis and compared with results from matching Illumina short-read cDNA sequencing data. Additionally, we explored poly(A) tail length variation, novel transcript identification, and differential transcript usage.

RESULTS

The correlation of gene count data between Illumina cDNA- and Nanopore RNA-sequencing strongly depended on the choice of analysis pipeline; NanoCount for Nanopore and Kallisto for Illumina data yielded the highest mean Pearson's correlation of 0.927 at the gene level and 0.736 at the transcript isoform level. We identified 2 genes with differential polyadenylation, 9 genes with differential expression and 4 genes with differential transcript usage between bacterial and viral infection. Gene ontology gene set enrichment analysis of poly(A) tail length revealed enrichment of long tails in mRNA of genes involved in signaling and short tails in oxidoreductase molecular functions. Additionally, we detected 240 non-artifactual novel transcript isoforms.

CONCLUSIONS

Nanopore RNA- and Illumina cDNA-gene counts are strongly correlated, indicating that both platforms are suitable for discovery and validation of gene count biomarkers. Nanopore direct RNA-seq provides additional advantages by uncovering additional post- and co-transcriptional biomarkers, such as poly(A) tail length variation and transcript isoform usage.

Quantifying Transcriptome Turnover on Phylogenies by Modeling Gene Expression as a Binary Trait

Changes in gene expression are a key driver of phenotypic evolution, leading to a persistent interest in the evolution of transcriptomes. Traditionally, gene expression is modeled as a continuous trait, leaving qualitative transitions largely unexplored. In this paper, we detail the development of new Bayesian inference techniques to study the evolutionary turnover of organ-specific transcriptomes, which we define as instances where orthologous genes gain or lose expression in a particular organ. To test these techniques, we analyze the transcriptomes of 2 male reproductive organs, testes and accessory glands, across 11 species of the Drosophila melanogaster species group. We first discretize gene expression states by estimating the probability that each gene is expressed in each organ and species. We then define a phylogenetic model of correlated transcriptome evolution in 2 or more organs and fit it to the expression state data. Inferences under this model imply that many genes have gained and lost expression in each organ, and that the 2 organs experienced accelerated transcriptome turnover on different branches of the Drosophila phylogeny.

Transcriptomic analysis reveals molecular phenological changes during the flower-to-fruit transition in Vanilla planifolia Andrews (Orchidaceae)

BACKGROUND

The transition from flower to fruit, encompassing flower formation to fruit maturation, has been extensively studied in model plants such as Arabidopsis thaliana. However, the Orchidaceae family, including Vanilla planifolia, exhibits a unique phenomenon known as post-pollination syndrome (PPS), where pollination initiates ovule development but often leads to premature ovary drop. This phenomenon significantly impacts the yield and stability of V. planifolia crops. Understanding the molecular mechanisms underlying PPS is essential for improving crop production. This study explores transcriptomic and histological variations to identify key molecular and phenological changes in the ovary during the flower-to-fruit transition in V. planifolia.

RESULTS

The flower-to-fruit transition in Vanilla planifolia involves dynamic changes in gene expression and phenotypic events, which can be categorized into four distinct stages: (1) Pre-pollination: Ovary differentiation is characterized by the enrichment of nitrogen metabolism and photoperiod-responsive pathways. The upregulation of VpVRN5-like and VpNAC14-like suggests their roles in photoperiod-induced flowering and ovarian tissue differentiation in response to nitrate availability. (2) Pollination: Key events include nucellar filament branching and the functional enrichment of pathways associated with growth and responses to light intensity. The upregulation of VpMBS1-like indicates its involvement in regulating and adapting to high light conditions. (3) Post-pollination: This stage is marked by embryo sac formation and pollen tube elongation, with enrichment in auxin response pathways. The upregulation of VpIAA6-like and VpRALF27-like suggests their roles in auxin signaling during ovule development. (4) Fertilization: Seed development is associated with the enrichment of abiotic stress response pathways and carbohydrate transport. The upregulation of VpAAE3-like, VpPR1-like, and VpSWET12-like suggests functions in stress responses and sucrose transport, potentially linked to fungal interactions or symbiosis.

CONCLUSIONS

This study characterizes the molecular and phenological changes occurring during the flower-to-fruit transition in V. planifolia by integrating transcriptomic analysis with anatomical data on post-pollination syndrome. Based on functional predictions, this approach provides valuable insights into the mechanisms governing this transition in plants exhibiting PPS and identifies candidate genes for future experimental validation in V. planifolia.

CLINICAL TRIAL NUMBER

Not applicable.

Hepatic antioxidant capacity, immune response, and glycolysis of Tibetan sheep in response to dietary soluble protein levels

In recent years, the increasing cost of protein raw materials has significantly impacted feed expenses and presented challenges to the livestock industry. Ninety-two-month-old male Tibetan sheep (15.40±0.81 kg) were randomly divided into three groups based on protein levels in their diet: L group (12% protein), M group (14% protein), and H group (16% protein). The feeding experiment was performed for 100 days, including a 10-day adaption period. It was found that the liver cells of the M group exhibited a better uniform in cytoplasm. Additionally, group M sheep had higher levels of GSH-Px and T-AOC (P<0.05), as well as elevated IgM, IL-1β, IL-6, and SDH content compared to other groups (P<0.05). There were 577, 698, and 623 differentially expressed genes between groups H and L, groups H and M, and groups M and L, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the DEGs regulated the activities of 56 pathways. Six liver-metabolism-related DEGs, SOD2, SOD1, CD19, IGF1, HK2, and PFKFB3, were expressed differently among the three sheep groups. In summary, a 14% protein level in the diet improved the hepatic antioxidant capacity, immune function, and glycolysis in Tibetan sheep through modulating the expression of functional genes.

scRDiT: Generating Single-cell RNA-seq Data by Diffusion Transformers and Accelerating Sampling

Single-cell RNA sequencing (scRNA-seq) is a groundbreaking technology extensively utilized in biological research, facilitating the examination of gene expression at the individual cell level within a given tissue sample. While numerous tools have been developed for scRNA-seq data analysis, the challenge persists in capturing the distinct features of such data and replicating virtual datasets that share analogous statistical properties. Our study introduces a generative approach termed scRNA-seq Diffusion Transformer (scRDiT). This method generates virtual scRNA-seq data by leveraging a real dataset. The method is a neural network constructed based on Denoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers (DiTs). This involves subjecting Gaussian noises to the real dataset through iterative noise-adding steps and ultimately restoring the noises to form scRNA-seq samples. This scheme allows us to learn data features from actual scRNA-seq samples during model training. Our experiments, conducted on two distinct scRNA-seq datasets, demonstrate superior performance. Additionally, the model sampling process is expedited by incorporating Denoising Diffusion Implicit Models (DDIMs). scRDiT presents a unified methodology empowering users to train neural network models with their unique scRNA-seq datasets, enabling the generation of numerous high-quality scRNA-seq samples.

Review of microRNA detection workflows from liquid biopsy for disease diagnostics

MicroRNAs have emerged as effective biomarkers in disease diagnostics, particularly cancer, due to their role as regulatory sequences. More recently, microRNAs have been detected in liquid biopsies, which hold immense potential for early disease diagnostics. This review comprehensively analyses distinct liquid biopsy microRNA detection methods validated with clinical samples. Each step in the microRNA detection workflow, including sample collection, RNA isolation, processing, and detection of target microRNAs, has been thoroughly assessed. The review discusses the advantages and limitations of established and novel techniques in microRNA detection workflows, discussing their diagnostic capabilities and potential for future implementation at scale.

Transcription factor PagERF110 inhibits leaf development by direct regulating PagHB16 in poplar

Ethylene-responsive factor (ERF) family genes are crucial for plant growth and development. This study analyzed the functional role of the PagERF110 gene in leaf development of Populus alba×P. glandulosa. PagERF110 contains the AP2 conserved domain and exhibits transcriptional activation activity at its C-terminus. Overexpression of PagERF110 in transgenic poplar trees resulted in reduced leaf size, leaf area, and vein xylem thickness. Yeast two-hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) experiments confirmed that PagERF110 interacts with PagACD32.1. Transcriptome sequencing revealed that PagERF110 regulates the expression of key genes involved in leaf development. Furthermore, yeast one-hybrid (Y1H) assays, GUS staining, and ChIP experiments collectively confirmed that PagERF110 targets the expression of PagHB16. In summation, our findings demonstrate that PagERF110 functions as a negative regulator in poplar leaf development.

VarNMF: non-negative probabilistic factorization with source variation

MOTIVATION

Non-negative matrix factorization (NMF) is a powerful tool often applied to genomic data to identify non-negative latent components that constitute linearly mixed samples. It is useful when the observed signal combines contributions from multiple sources, such as cell types in bulk measurements of heterogeneous tissue. NMF accounts for two types of variation between samples - disparities in the proportions of sources and observation noise. However, in many settings, there is also a non-trivial variation between samples in the contribution of each source to the mixed data. This variation cannot be accurately modeled using the NMF framework.

RESULTS

We present VarNMF, a probabilistic extension of NMF that explicitly models this variation in source values. We show that by modeling sources as non-negative distributions, we can recover source variation directly from mixed samples without observing any of the sources directly. We apply VarNMF to a cell-free ChIP-seq dataset of two cancer cohorts and a healthy cohort, demonstrating that VarNMF provides a better estimation of the data distribution. Moreover, VarNMF extracts cancer-associated source distributions that decouple the tumor characteristics from the amount of tumor contribution, and identify patient-specific disease behaviors. This decomposition highlights the inter-tumor variability that is obscured in the mixed samples.

AVAILABILITY AND IMPLEMENTATION

Code is available at https://github.com/Nir-Friedman-Lab/VarNMF.

Advances in DNA/RNA Sequencing and Their Applications in Acute Myeloid Leukemia (AML)

Acute myeloid leukemia (AML) is an aggressive malignancy that poses significant challenges due to high rates of relapse and resistance to treatment, particularly in older populations. While therapeutic advances have been made, survival outcomes remain suboptimal. The evolution of DNA and RNA sequencing technologies, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and RNA sequencing (RNA-Seq), has significantly enhanced our understanding of AML at the molecular level. These technologies have led to the discovery of driver mutations and transcriptomic alterations critical for improving diagnosis, prognosis, and personalized therapy development. Furthermore, single-cell RNA sequencing (scRNA-Seq) has uncovered rare subpopulations of leukemia stem cells (LSCs) contributing to disease progression and relapse. However, widespread clinical integration of these tools remains limited by costs, data complexity, and ethical challenges. This review explores recent advancements in DNA/RNA sequencing in AML and highlights both the potential and limitations of these techniques in clinical practice.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Non-coding RNA notations, regulations and interactive resources

An increasing number of non-coding RNAs (ncRNAs) are found to have roles in gene expression and cellular regulations. However, there are still a large number of ncRNAs whose functions remain to be studied. Despite decades of research, the field continues to evolve, with each newly identified ncRNA undergoing processes such as biogenesis, identification, and functional annotation. Bioinformatics methodologies, alongside traditional biochemical experimental methods, have played an important role in advancing ncRNA research across various stages. Presently, over 50 types of ncRNAs have been characterized, each exhibiting diverse functions. However, there remains a need for standardization and integration of these ncRNAs within a unified framework. In response to this gap, this review traces the historical trajectory of ncRNA research and proposes a unified notation system. Additionally, we comprehensively elucidate the ncRNA interactome, detailing its associations with DNAs, RNAs, proteins, complexes, and chromatin. A web portal named ncRNA Hub ( https://bis.zju.edu.cn/nchub/ ) is also constructed to provide detailed notations of ncRNAs and share a collection of bioinformatics resources. This review aims to provide a broader perspective and standardized paradigm for advancing ncRNA research.

Hybrid Causal Feature Selection for Cancer Biomarker Identification From RNA-Seq Data

The discovery of cancer biomarkers helps to advance medical diagnosis and plays an important role in biomedical applications. Most of the existing data-driven methods identify biomarkers by ranking-based strategies, which generally return a subset or superset of the actual biomarkers, while some other causal-wise feature selection methods are based on Markov Blanket (MB) learning, facing the challenges of high-dimensionality & low-sample. In this work, we propose a novel hybrid causal feature selection method (called CAFES) to support large-scale cancer biomarker discovery from real RNA-seq data. Concretely, CAFES first uses minimal-redundancy & maximal-relevance strategy for dimensionality reduction that returns a set of candidate features. CAFES then learns the causal skeleton w.r.t. those features by CI tests and further obtains an appropriate superset of the MB of the target variable. Finally, CAFES learns the causal structure of this superset by the DAG-GNN algorithm and then obtains the MB of the target variable, which can be treated as the cancer biomarkers. We conduct experiments to evaluate the proposed method on two real well-known RNA-seq datasets that covering both binary and multi-class cases. We compare our method CAFES with seven recent methods including Semi-HITON-MB, STMB, BAMB, FBED, LCS-FS, EEMB, and EAMB. The results show that CAFES can identify dozens of cancer biomarkers, and of the discovered biomarkers can be verified by existing works that they are really directly related to the corresponding disease. An advantage of CAFES is that its Recall is significantly higher than those of all the counterparts, indicating that the continuous optimization (DAG-GNN) with the returned causal skeleton after feature selection (that can be treated as a conditional independence-based constraint to the optimization problem) is effective in cancer biomarkers identification under high-dimensional and low-sample RNA-seq data.

Transcriptome analysis and characteristics of drought resistance related genes in four varieties of foxtail millet [Setaria italica]

Foxtail millet [Setaria italica] plays a crucial role as a multigrain crop in agricultural production. However, due to future extreme weather conditions, drought remains the main abiotic stress that limits foxtail millet yield, it is highly significant to screen for drought-tolerant varieties throughout the entire growth period and identify the regulatory genes associated with drought resistance in foxtail millet breeding. We identified 217 foxtail millet seed resources for drought resistance during the maturity stage in the field, and subsequently categorized them into different levels of drought resistance. Two cultivars with extremely strong drought resistance during the maturity stage in the field, JKH4 (Chi 5422) and JKH6 (Chigu 26), as well as two cultivars with extremely weak drought resistance during the maturity stage in the field, JRK3 (17M1309) and JRK6 (Canggu 9), were selected for physiological comparison and transcriptome sequencing before and after drought treatment. Transcriptome analysis at the seedling stage revealed that JRK3 and JRK6 cultivar primarily regulated phenylpropanoid biosynthesis, MAPK signaling pathogen-plant, and plant hormone signal transduction pathway in response to drought stress. On the other hand, the fatty acid elongation pathway of JKH4 and JKH6 variety was found to be more significant. Furthermore, 22 drought resistance related genes were screened through transcriptome analysis of four foxtail millet varieties. These findings could offer valuable theoretical guidance for breeding foxtail millet with enhanced drought resistance and potentially facilitate the development of genetically engineered drought-resistant foxtail millet varieties.

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.

A comprehensive workflow for optimizing RNA-seq data analysis

BACKGROUND

Current RNA-seq analysis software for RNA-seq data tends to use similar parameters across different species without considering species-specific differences. However, the suitability and accuracy of these tools may vary when analyzing data from different species, such as humans, animals, plants, fungi, and bacteria. For most laboratory researchers lacking a background in information science, determining how to construct an analysis workflow that meets their specific needs from the array of complex analytical tools available poses a significant challenge.

RESULTS

By utilizing RNA-seq data from plants, animals, and fungi, it was observed that different analytical tools demonstrate some variations in performance when applied to different species. A comprehensive experiment was conducted specifically for analyzing plant pathogenic fungal data, focusing on differential gene analysis as the ultimate goal. In this study, 288 pipelines using different tools were applied to analyze five fungal RNA-seq datasets, and the performance of their results was evaluated based on simulation. This led to the establishment of a relatively universal and superior fungal RNA-seq analysis pipeline that can serve as a reference, and certain standards for selecting analysis tools were derived for reference. Additionally, we compared various tools for alternative splicing analysis. The results based on simulated data indicated that rMATS remained the optimal choice, although consideration could be given to supplementing with tools such as SpliceWiz.

CONCLUSION

The experimental results demonstrate that, in comparison to the default software parameter configurations, the analysis combination results after tuning can provide more accurate biological insights. It is beneficial to carefully select suitable analysis software based on the data, rather than indiscriminately choosing tools, in order to achieve high-quality analysis results more efficiently.

Application of RNA-Seq Technology for Screening Reproduction-Related Differentially Expressed Genes in Tibetan and Yorkshire Pig Ovarian Tissue

The purpose of this study was to explore and verify genes that regulate the reproductive traits of Tibetan pigs at the mRNA level. The ovarian tissues of Tibetan pigs (TPs) and Yorkshire pigs (YPs) were selected as research objects, and cDNA libraries of the ovarian tissue transcripts of Tibetan pigs and Yorkshire pigs were successfully constructed by the RNA-Seq technique. A total of 651 differentially expressed genes (DEGs) were screened, including 414 up-regulated genes and 237 down-regulated genes. Through GO and KEGG enrichment analysis, it was found that these differentially expressed genes were significantly enriched in cell process, reproductive process, reproduction, cell proliferation, binding, and catalytic activity, as well as oxidative phosphorylation, endocrine resistance, thyroid hormone, Notch, and other signal transduction pathways. Genes significantly enriched in pathways closely related to reproductive regulation were analyzed and selected, and the AR, CYP11A1, CYP17A1, INHBA, ARRB2, EGFR, ETS1, HSD17B1, IGF1R, MIF, SCARB1, and SMAD4 genes were identified as important candidate genes. Twelve differentially expressed genes related to reproduction were verified by RT-qPCR. The results showed that the expression of the AR, CYP17A1, EGFR, ETS1, IGF1R, and SMAD4 genes was significantly higher in Tibetan pigs than in Yorkshire pigs, while the expression of the CYP11A1, INHBA, ARRB2, HSD17B, MIF, and SCARB1 genes in Tibetan pigs was significantly lower than in Yorkshire pigs. The purpose of this study is to provide a theoretical basis for exploring the molecular mechanism of reproductive trait effect genes and the application of molecular breeding in Tibetan pigs.

A protocol for identifying universal reference genes within a genus based on RNA-Seq data: a case study of poplar stem gene expression

Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) plays a crucial role in relative gene expression analysis, and accurate normalization relies on suitable reference genes (RGs). In this study, a pipeline for identifying candidate RGs from publicly available stem-related RNA-Seq data of different Populus species under various developmental and abiotic stress conditions is presented. DESeq2's median of ratios yielded the smallest coefficient of variance (CV) values in a total of 292 RNA-Seq samples and was therefore chosen as the method for sample normalization. A total of 541 stably expressed genes were retrieved based on the CV values with a cutoff of 0.3. Universal gene-specific primer pairs were designed based on the consensus sequences of the orthologous genes of each Populus RG candidate. The expression levels of 12 candidate RGs and six reported RGs in stems under different abiotic stress conditions or in different Populus species were assessed by RT-qPCR. The expression stability of selected genes was further evaluated using ΔCt, geNorm, NormFinder, and BestKeeper. All candidate RGs were stably expressed in different experiments and conditions in Populus. A test dataset containing 117 RNA-Seq samples was then used to confirm the expression stability, six candidate RGs and three reported RGs met the requirement of CV ≤ 0.3. In summary, this study was to propose a systematic and optimized protocol for the identification of constitutively and stably expressed genes based on RNA-Seq data, and Potri.001G349400 (CNOT2) was identified as the best candidate RG suitable for gene expression studies in poplar stems.

Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach

Identifying key genes from a list of differentially expressed genes (DEGs) is a critical step in transcriptome analysis. However, current methods, including Gene Ontology analysis and manual annotation, essentially rely on existing knowledge, which is highly biased depending on the extent of the literature. As a result, understudied genes, some of which may be associated with important molecular mechanisms, are often ignored or remain obscure. To address this problem, we propose Clover, a data-driven scoring method to specifically highlight understudied genes. Clover aims to prioritize genes associated with important molecular mechanisms by integrating three metrics: the likelihood of appearing in the DEG list, tissue specificity, and number of publications. We applied Clover to Alzheimer's disease data and confirmed that it successfully detected known associated genes. Moreover, Clover effectively prioritized understudied but potentially druggable genes. Overall, our method offers a novel approach to gene characterization and has the potential to expand our understanding of gene functions. Clover is an open-source software written in Python3 and available on GitHub at https://github.com/G708/Clover.

Dissection of quantitative trait nucleotides and candidate genes associated with agronomic and yield-related traits under drought stress in rapeseed varieties: integration of genome-wide association study and transcriptomic analysis

Introduction

An important strategy to combat yield loss challenge is the development of varieties with increased tolerance to drought to maintain production. Improvement of crop yield under drought stress is critical to global food security.

Methods

In this study, we performed multiomics analysis in a collection of 119 diverse rapeseed (Brassica napus L.) varieties to dissect the genetic control of agronomic traits in two watering regimes [well-watered (WW) and drought stress (DS)] for 3 years. In the DS treatment, irrigation continued till the 50% pod development stage, whereas in the WW condition, it was performed throughout the whole growing season.

Results

The results of the genome-wide association study (GWAS) using 52,157 single-nucleotide polymorphisms (SNPs) revealed 1,281 SNPs associated with traits. Six stable SNPs showed sequence variation for flowering time between the two irrigation conditions across years. Three novel SNPs on chromosome C04 for plant weight were located within drought tolerance-related gene ABCG16, and their pleiotropically effects on seed weight per plant and seed yield were characterized. We identified the C02 peak as a novel signal for flowering time, harboring 52.77% of the associated SNPs. The 288-kbps LD decay distance analysis revealed 2,232 candidate genes (CGs) associated with traits. The CGs BIG1-D, CAND1, DRG3, PUP10, and PUP21 were involved in phytohormone signaling and pollen development with significant effects on seed number, seed weight, and grain yield in drought conditions. By integrating GWAS and RNA-seq, 215 promising CGs were associated with developmental process, reproductive processes, cell wall organization, and response to stress. GWAS and differentially expressed genes (DEGs) of leaf and seed in the yield contrasting accessions identified BIG1-D, CAND1, and DRG3 genes for yield variation.

Discussion

The results of our study provide insights into the genetic control of drought tolerance and the improvement of marker-assisted selection (MAS) for breeding high-yield and drought-tolerant varieties.

Identification of potential biomarkers for melanoma cancer (black tumor) using bioinformatics strategy: a study based on GEO and SRA datasets

Melanoma, a highly invasive type of skin cancer that penetrates the entire dermis layer, is associated with increased mortality rates. Excessive exposure of the skin to sunlight, specifically ultraviolet radiation, is the underlying cause of this malignant condition. The appearance of unique skin moles represents a visible clue, referred to as the "ugly duckling" sign, indicating the presence of melanoma and its association with cellular DNA damage. This research aims to explore potential biomarkers derived from microarray data, employing bioinformatics techniques and methodologies, for a thorough investigation of melanoma skin cancer. The microarray dataset for melanoma skin cancer was obtained from the GEO database, and thorough data analysis and quality control measures were performed to identify differentially expressed genes (DEGs). The top 14 highly expressed DEGs were identified, and their gene information and protein sequences were retrieved from the NCBI gene and protein database. These proteins were further analyzed for domain identification and network analysis. Gene expression analysis was conducted to visualize the upregulated and downregulated genes. Additionally, gene metabolite network analysis was carried out to understand the interactions between highly interconnected genes and regulatory transcripts. Molecular docking was employed to investigate the ligand-binding sites and visualize the three-dimensional structure of proteins. Our research unveiled a collection of genes with varying expression levels, some elevated and others reduced, which could function as promising biomarkers closely linked to the development and advancement of melanoma skin cancer. Through molecular docking analysis of the GINS2 protein, we identified two natural compounds (PubChem-156021169 and PubChem-60700) with potential as inhibitors against melanoma. This research has implications for early detection, treatment, and understanding the molecular basis of melanoma.

Maternal PM2.5 exposure is associated with preterm birth and gestational diabetes mellitus, and mitochondrial OXPHOS dysfunction in cord blood

Maternal exposure to fine particulate matter (PM2.5) is associated with adverse pregnancy and neonatal health outcomes. To explore the mechanism, we performed mRNA sequencing of neonatal cord blood. From an ongoing prospective cohort, Air Pollution on Pregnancy Outcome (APPO) study, 454 pregnant women from six centers between January 2021 and June 2022 were recruited. Individual PM2.5 exposure was calculated using a time-weighted average model. In the APPO study, age-matched cord blood samples from the High PM2.5 (˃15 ug/m3; n = 10) and Low PM2.5 (≤ 15 ug/m3; n = 30) groups were randomly selected for mRNA sequencing. After selecting genes with differential expression in the two groups (p-value < 0.05 and log2 fold change > 1.5), pathway enrichment analysis was performed, and the mitochondrial pathway was analyzed using MitoCarta3.0. The risk of preterm birth (PTB) increased with every 5 µg/m3 increase of PM2.5 in the second trimester (odds ratio 1.391, p = 0.019) after adjusting for confounding variables. The risk of gestational diabetes mellitus (GDM) increased in the second (odds ratio 1.238, p = 0.041) and third trimester (odds ratio 1.290, p = 0.029), and entire pregnancy (odds ratio 1.295, p = 0.029). The mRNA-sequencing of cord blood showed that genes related to mitochondrial activity (FAM210B, KRT1, FOXO4, TRIM58, and FBXO7) and PTB-related genes (ADIPOR1, YBX1, OPTN, NFkB1, HBG2) were upregulated in the High PM2.5 group. In addition, exposure to high PM2.5 affected mitochondrial oxidative phosphorylation (OXPHOS) and proteins in the electron transport chain, a subunit of OXPHOS. These results suggest that exposure to high PM2.5 during pregnancy may increase the risk of PTB and GDM, and dysregulate PTB-related genes. Alterations in mitochondrial OXPHOS by high PM2.5 exposure may occur not only in preterm infants but also in normal newborns. Further studies with larger sample sizes are required.

Sex diversity in the 21st century: Concepts, frameworks, and approaches for the future of neuroendocrinology

Sex is ubiquitous and variable throughout the animal kingdom. Historically, scientists have used reductionist methodologies that rely on a priori sex categorizations, in which two discrete sexes are inextricably linked with gamete type. However, this binarized operationalization does not adequately reflect the diversity of sex observed in nature. This is due, in part, to the fact that sex exists across many levels of biological analysis, including genetic, molecular, cellular, morphological, behavioral, and population levels. Furthermore, the biological mechanisms governing sex are embedded in complex networks that dynamically interact with other systems. To produce the most accurate and scientifically rigorous work examining sex in neuroendocrinology and to capture the full range of sex variability and diversity present in animal systems, we must critically assess the frameworks, experimental designs, and analytical methods used in our research. In this perspective piece, we first propose a new conceptual framework to guide the integrative study of sex. Then, we provide practical guidance on research approaches for studying sex-associated variables, including factors to consider in study design, selection of model organisms, experimental methodologies, and statistical analyses. We invite fellow scientists to conscientiously apply these modernized approaches to advance our biological understanding of sex and to encourage academically and socially responsible outcomes of our work. By expanding our conceptual frameworks and methodological approaches to the study of sex, we will gain insight into the unique ways that sex exists across levels of biological organization to produce the vast array of variability and diversity observed in nature.

Expanding the horizon of EV-RNAs: LncRNAs in EVs as biomarkers for disease pathways

Extracellular vesicles (EVs) are membrane-bound nanoparticles with different types of cargo released by cells and postulated to mediate functions such as intercellular communications. Recent studies have shown that long non-coding RNAs (lncRNAs) or their fragments are present as cargo within EVs. LncRNAs are a heterogeneous group of RNA species with a length exceeding 200 nucleotides with diverse functions in cells based on their localization. While lncRNAs are known for their important functions in cellular regulation, their presence and role in EVs have only recently been explored. While certain studies have observed EV-lncRNAs to be tissue-and disease-specific, it remains to be determined whether or not this is a global observation. Nonetheless, these molecules have demonstrated promising potential to serve as new diagnostic and prognostic biomarkers. In this review, we critically evaluate the role of EV-derived lncRNAs in several prevalent diseases, including cancer, cardiovascular diseases, and neurodegenerative diseases, with a specific focus on their role as biomarkers.

De novo assembly of Iron-Heart Cunninghamia lanceolata transcriptome and EST-SSR marker development for genetic diversity analysis

Iron-Heart Cunninghamia lanceolata, a wild relative of Chinese fir with valuable genetic and breeding traits, has been limited in genetic studies due to a lack of genomic resources and markers. In this study, we conducted transcriptome sequencing of Iron-Heart C. lanceolata leaves using Illumina NovaSeq 6000 and performed assembly and analysis. We obtained 45,326,576 clean reads and 115,501 unigenes. Comparative analysis in five functional databases resulted in successful annotation of 26,278 unigenes, with 6,693 unigenes annotated in all databases (5.79% of the total). UniProt and Pfam databases provided annotations for 22,673 and 18,315 unigenes, respectively. Gene Ontology analysis categorized 23,962 unigenes into three categories. KEGG database alignment annotated 10,195 unigenes, classifying them into five categories: metabolism, genetic information, biological systems, cellular processes, and environmental information processing. From the unigenes, we identified 5,645 SSRs, with dinucleotides repeats being the most common (41.47%). We observed variations in repeat numbers and base compositions, with the majority of markers ranging from 12 to 29 bp in length. We randomly selected 200 primer pairs and successfully amplified 15 pairs of polymorphic SSR primers, which effectively distinguished Chinese fir plants of different origins. This study provides insights into the genetic characteristics of Iron-Heart C. lanceolata and offers a foundation for future molecular marker development, breeding programs, genetic diversity analysis, and conservation strategies.

ChimeraTE: a pipeline to detect chimeric transcripts derived from genes and transposable elements

Transposable elements (TEs) produce structural variants and are considered an important source of genetic diversity. Notably, TE-gene fusion transcripts, i.e. chimeric transcripts, have been associated with adaptation in several species. However, the identification of these chimeras remains hindered due to the lack of detection tools at a transcriptome-wide scale, and to the reliance on a reference genome, even though different individuals/cells/strains have different TE insertions. Therefore, we developed ChimeraTE, a pipeline that uses paired-end RNA-seq reads to identify chimeric transcripts through two different modes. Mode 1 is the reference-guided approach that employs canonical genome alignment, and Mode 2 identifies chimeras derived from fixed or insertionally polymorphic TEs without any reference genome. We have validated both modes using RNA-seq data from four Drosophila melanogaster wild-type strains. We found ∼1.12% of all genes generating chimeric transcripts, most of them from TE-exonized sequences. Approximately ∼23% of all detected chimeras were absent from the reference genome, indicating that TEs belonging to chimeric transcripts may be recent, polymorphic insertions. ChimeraTE is the first pipeline able to automatically uncover chimeric transcripts without a reference genome, consisting of two running Modes that can be used as a tool to investigate the contribution of TEs to transcriptome plasticity.

Enhanced DNA Entropy-Driven Circuit by Locked Nucleic Acids and Simulation-Guided Localization

Signal amplification methods based on DNA molecular interactions are promising tools for detecting various biomarkers in low abundance. The entropy-driven circuit (EDC), as an enzyme-free signal amplification method, has been used in detecting and imaging a variety of biomarkers. The localization strategy can effectively increase the local concentration of the DNA reaction modules to improve the signal amplification effect. However, the localization strategy may also amplify the leak reaction of the EDC, and effective signal amplification can be limited by the unclear structure-function relationship. Herein, we utilized locked nucleic acid (LNA) modification to enhance the stability of the localized entropy-driven circuit (LEDC), which suppressed a 94.6% leak signal. The coarse-grained model molecular simulation was used to guide the structure design of the LEDC, and the influence of critical factors such as the localized distance and spacer length was analyzed at the molecular level to obtain the best reaction performance. The sensitivities of miR-21 and miR-141 detected by a simulation-guided optimal LEDC probe were 17.45 and 65 pM, 1345 and 521 times higher than free-EDC, respectively. The LEDC was further employed for the fluorescence imaging of miRNA in cancer cells, showing excellent specificity and sensitivity. This work utilizes LNA and molecular simulations to comprehensively improve the performance of a localized DNA signal amplification circuit, providing an advanced DNA probe design strategy for biosensing and imaging as well as valuable information for the designers of DNA-based probes.

Recent advancements in tumour microenvironment landscaping for target selection and response prediction in immune checkpoint therapies achieved through spatial protein multiplexing analysis

Immune checkpoint therapies have significantly advanced cancer treatment. Nevertheless, the high costs and potential adverse effects associated with these therapies highlight the need for better predictive biomarkers to identify patients who are most likely to benefit from treatment. Unfortunately, the existing biomarkers are insufficient to identify such patients. New high-dimensional spatial technologies have emerged as a valuable tool for discovering novel biomarkers by analysing multiple protein markers at a single-cell resolution in tissue samples. These technologies provide a more comprehensive map of tissue composition, cell functionality, and interactions between different cell types in the tumour microenvironment. In this review, we provide an overview of how spatial protein-based multiplexing technologies have fuelled biomarker discovery and advanced the field of immunotherapy. In particular, we will focus on how these technologies contributed to (i) characterise the tumour microenvironment, (ii) understand the role of tumour heterogeneity, (iii) study the interplay of the immune microenvironment and tumour progression, (iv) discover biomarkers for immune checkpoint therapies (v) suggest novel therapeutic strategies.

Transcriptomic and Proteomic Landscape of Sugarcane Response to Biotic and Abiotic Stressors

Sugarcane, a C₄ plant, provides most of the world's sugar, and a substantial amount of renewable bioenergy, due to its unique sugar-accumulating and feedstock properties. Brazil, India, China, and Thailand are the four largest sugarcane producers worldwide, and the crop has the potential to be grown in arid and semi-arid regions if its stress tolerance can be improved. Modern sugarcane cultivars which exhibit a greater extent of polyploidy and agronomically important traits, such as high sugar concentration, biomass production, and stress tolerance, are regulated by complex mechanisms. Molecular techniques have revolutionized our understanding of the interactions between genes, proteins, and metabolites, and have aided in the identification of the key regulators of diverse traits. This review discusses various molecular techniques for dissecting the mechanisms underlying the sugarcane response to biotic and abiotic stresses. The comprehensive characterization of sugarcane's response to various stresses will provide targets and resources for sugarcane crop improvement.

Local augmented graph neural network for multi-omics cancer prognosis prediction and analysis

Cancer prognosis prediction and analysis can help patients understand expected life and help clinicians provide correct therapeutic guidance. Thanks to the development of sequencing technology, multi-omics data, and biological networks have been used for cancer prognosis prediction. Besides, graph neural networks can simultaneously consider multi-omics features and molecular interactions in biological networks, becoming mainstream in cancer prognosis prediction and analysis. However, the limited number of neighboring genes in biological networks restricts the accuracy of graph neural networks. To solve this problem, a local augmented graph convolutional network named LAGProg is proposed in this paper for cancer prognosis prediction and analysis. The process follows: first, given a patient's multi-omics data features and biological network, the corresponding augmented conditional variational autoencoder generates features. Then, the generated augmented features and the original features are fed into a cancer prognosis prediction model to complete the cancer prognosis prediction task. The conditional variational autoencoder consists of two parts: encoder-decoder. In the encoding phase, an encoder learns the conditional distribution of the multi-omics data. As a generative model, a decoder takes the conditional distribution and the original feature as inputs to generate the enhanced features. The cancer prognosis prediction model consists of a two-layer graph convolutional neural network and a Cox proportional risk network. The Cox proportional risk network consists of fully connected layers. Extensive experiments on 15 real-world datasets from TCGA demonstrated the effectiveness and efficiency of the proposed method in predicting cancer prognosis. LAGProg improved the C-index values by an average of 8.5% over the state-of-the-art graph neural network method. Moreover, we confirmed that the local augmentation technique could enhance the model's ability to represent multi-omics features, improve the model's robustness to missing multi-omics features, and prevent the model's over-smoothing during training. Finally, based on genes identified through differential expression analysis, we discovered 13 prognostic markers highly associated with breast cancer, among which ten genes have been proved by literature review.

Differentially expressed genes in systemic sclerosis: Towards predictive medicine with new molecular tools for clinicians

Systemic sclerosis (SSc) is a rare and chronic autoimmune disease characterized by a pathogenic triad of immune dysregulation, vasculopathy, and progressive fibrosis. Clinical tools commonly used to assess patients, such as the modified Rodnan skin score, difference between limited or diffuse forms of skin involvement, presence of lung, heart or kidney involvement, or of various autoantibodies, are important prognostic factors, but still fail to reflect the large heterogeneity of the disease. SSc treatment options are diverse, ranging from conventional drugs to autologous hematopoietic stem cell transplantation, and predicting response is challenging. Genome-wide technologies, such as high throughput microarray analyses and RNA sequencing, allow accurate, unbiased, and broad assessment of alterations in expression levels of multiple genes. In recent years, many studies have shown robust changes in the gene expression profiles of SSc patients compared to healthy controls, mainly in skin tissues and peripheral blood cells. The objective analysis of molecular patterns in SSc is a powerful tool that can further classify SSc patients with similar clinical phenotypes and help predict response to therapy. In this review, we describe the journey from the first discovery of differentially expressed genes to the identification of enriched pathways and intrinsic subsets identified in SSc, using machine learning algorithms. Finally, we discuss the use of these new tools to predict the efficacy of various treatments, including stem cell transplantation. We suggest that the use of RNA gene expression-based classifications according to molecular subsets may bring us one step closer to precision medicine in Systemic Sclerosis.

Transcriptome Analysis of Differentially Expressed Genes Associated with Salt Stress in Cowpea (Vigna unguiculata L.) during the Early Vegetative Stage

Cowpea (Vigna unguiculata (L.), 2n = 22) is a tropical crop grown in arid and semiarid regions that is tolerant to abiotic stresses such as heat and drought. However, in these regions, salt in the soil is generally not eluted by rainwater, leading to salt stress for a variety of plant species. This study was conducted to identify genes related to salt stress using the comparative transcriptome analysis of cowpea germplasms with contrasting salt tolerance. Using the Illumina Novaseq 6000 platform, 1.1 billion high-quality short reads, with a total length of over 98.6 billion bp, were obtained from four cowpea germplasms. Of the differentially expressed genes identified for each salt tolerance type following RNA sequencing, 27 were shown to exhibit significant expression levels. These candidate genes were subsequently narrowed down using reference-sequencing analysis, and two salt stress-related genes (Vigun_02G076100 and Vigun_08G125100) with single-nucleotide polymorphism (SNP) variation were selected. Of the five SNPs identified in Vigun_02G076100, one that caused significant amino acid variation was identified, while all nucleotide variations in Vigun_08G125100 was classified as missing in the salt-resistant germplasms. The candidate genes and their variation, identified in this study provide, useful information for the development of molecular markers for cowpea breeding programs.

Assessing Outlier Probabilities in Transcriptomics Data When Evaluating a Classifier

Outliers in the training or test set used to fit and evaluate a classifier on transcriptomics data can considerably change the estimated performance of the model. Hence, an either too weak or a too optimistic accuracy is then reported and the estimated model performance cannot be reproduced on independent data. It is then also doubtful whether a classifier qualifies for clinical usage. We estimate classifier performances in simulated gene expression data with artificial outliers and in two real-world datasets. As a new approach, we use two outlier detection methods within a bootstrap procedure to estimate the outlier probability for each sample and evaluate classifiers before and after outlier removal by means of cross-validation. We found that the removal of outliers changed the classification performance notably. For the most part, removing outliers improved the classification results. Taking into account the fact that there are various, sometimes unclear reasons for a sample to be an outlier, we strongly advocate to always report the performance of a transcriptomics classifier with and without outliers in training and test data. This provides a more diverse picture of a classifier's performance and prevents reporting models that later turn out to be not applicable for clinical diagnoses.

Normalizing the Immune Macroenvironment via Debulking Surgery to Strengthen Tumor Nanovaccine Efficacy and Eliminate Metastasis

In tumor nanovaccines, nanocarriers enhance the delivery of tumor antigens to antigen-presenting cells (APCs), thereby ensuring the robust activation of tumor antigen-specific effector T-cells to kill tumor cells. Through employment of their high immunogenicity and nanosize, we have developed a "Plug-and-Display" delivery platform on the basis of bacterial outer membrane vesicles (OMVs) for tumor nanovaccines (NanoVac), which can rapidly display different tumor antigens and efficiently eliminate lung metastases of melanoma. In this study, we first upgraded the NanoVac to increase their antigen display efficiency. However, we found that the presence of a subcutaneous xenograft seriously hampered the efficiency of NanoVac to eliminate lung metastases, with the subcutaneous xenograft mimicking the primary tumor burden in clinical practice. The primary tumor secreted significant amounts of granulocyte colony-stimulating factor (G-CSF) and altered the epigenetic features of granulocyte monocyte precursor cells (GMPs) in the bone marrow, thus disrupting systemic immunity, particularly the function of APCs, and ultimately resulting in NanoVac failure to affect metastases. These changes in the systemic immune macroenvironment were plastic, and debulking surgery of primary tumor resection reversed the dysfunction of APCs and failure of NanoVac. These results demonstrate that, in addition to the formulation design of the tumor nanovaccines themselves, the systemic immune macroenvironment incapacitated by tumor development is another key factor that cannot be ignored to affect the efficiency of tumor nanovaccines, and the combination of primary tumor resection with NanoVac is a promising radical treatment for widely metastatic tumors.

Whole transcriptomic analysis of mesenchymal stem cells cultured in Nichoid micro-scaffolds

Mesenchymal stem cells (MSCs) are known to be ideal candidates for clinical applications where not only regenerative potential but also immunomodulation ability is fundamental. Over the last years, increasing efforts have been put into the design and fabrication of 3D synthetic niches, conceived to emulate the native tissue microenvironment and aiming at efficiently controlling the MSC phenotype in vitro. In this panorama, our group patented an engineered microstructured scaffold, called Nichoid. It is fabricated through two-photon polymerization, a technique enabling the creation of 3D structures with control of scaffold geometry at the cell level and spatial resolution beyond the diffraction limit, down to 100 nm. The Nichoid's capacity to maintain higher levels of stemness as compared to 2D substrates, with no need for adding exogenous soluble factors, has already been demonstrated in MSCs, neural precursors, and murine embryonic stem cells. In this work, we evaluated how three-dimensionality can influence the whole gene expression profile in rat MSCs. Our results show that at only 4 days from cell seeding, gene activation is affected in a significant way, since 654 genes appear to be differentially expressed (392 upregulated and 262 downregulated) between cells cultured in 3D Nichoids and in 2D controls. The functional enrichment analysis shows that differentially expressed genes are mainly enriched in pathways related to the actin cytoskeleton, extracellular matrix (ECM), and, in particular, cell adhesion molecules (CAMs), thus confirming the important role of cell morphology and adhesions in determining the MSC phenotype. In conclusion, our results suggest that the Nichoid, thanks to its exclusive architecture and 3D cell adhesion properties, is not only a useful tool for governing cell stemness but could also be a means for controlling immune-related MSC features specifically involved in cell migration.

Conservation and Divergence of the Trihelix Genes in Brassica and Expression Profiles of BnaTH Genes in Brassica napus under Abiotic Stresses

Trihelix (TH) proteins are a family of plant-specific transcription factors that play a role in light response and are extensively involved in plant growth and development, as well as in various stress responses. However, the function of TH genes in Brassica napus (B. napus) remains unclear, as does the evolution and differentiation pattern of TH genes in Brassica plants. Here, we identified a total of 455 TH genes in seven species, including six Brassica species and Arabidopsis, which were grouped into five clades, GT-1, GT-2, GTγ, SH4, and SIP1, each with 69, 142, 44, 55, and 145 members, respectively. The types and distributions of motifs of the TH proteins and the structures of the TH genes are conserved in the same subgroup, and some variations in certain amino acid residues occur in B. napus when inheriting motifs from Brassica rapa (B. rapa) and Brassica oleracea (B. oleracea). Collinearity analysis revealed that the massive expansion of TH genes in tetraploid species was attributed to the hetero-tetraploidization of diploid ancestors and gene duplication events within the tetraploid species. Comparative analysis of the membership numbers of five subgroups in different species revealed that the GT-2 and SIP1 genes underwent significant expansion during evolution, possibly to support the better adaptation of plants to their environments. The differential expression of the BnaTH genes under five stresses indicates that the BnaTH genes are involved in plant responses to stresses such as drought, cold, and heat. The presence of different stress-responsive cis-elements in the upstream promoter region of the genes indicated that BnaTH genes have the potential to cope with variable environments. Meanwhile, qRT-PCR analyses also confirmed that five TH genes respond to different abiotic stresses. Our results provide information and candidates for further studies on the role of TH genes in stress resistance of B. napus.

Dynamic Transcriptome Analysis Reveals Transcription Factors Involved in the Synthesis of Ethyl Acetate in Aroma-Producing Yeast

Ethyl acetate is an important flavor element that is a vital component of baijiu. To date, the transcription factors that can help identify the molecular mechanisms involved in the synthesis of ethyl acetate have not been studied. In the present study, we sequenced and assembled the Wickerhamomyces anomalus strain YF1503 transcriptomes to identify transcription factors. We identified 307 transcription factors in YF1503 using high-throughput RNA sequencing. Some transcription factors, such as C2H2, bHLH, MYB, and bZIP, were up-regulated, and these might play a role in ethyl acetate synthesis. According to the trend of ethyl acetate content, heat map results and STEM, twelve genes were selected for verification of expression levels using quantitative real-time PCR. This dynamic transcriptome analysis presents fundamental information on the transcription factors and pathways that are involved in the synthesis of ethyl acetate in aroma-producing yeast. Of significant interest is the discovery of the roles of various transcription factor genes in the synthesis of ethyl acetate.

Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data

Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.

Novel estrogen-responsive genes (ERGs) for the evaluation of estrogenic activity

Estrogen action is mediated by various genes, including estrogen-responsive genes (ERGs). ERGs have been used as reporter-genes and markers for gene expression. Gene expression profiling using a set of ERGs has been used to examine statistically reliable transcriptomic assays such as DNA microarray assays and RNA sequencing (RNA-seq). However, the quality of ERGs has not been extensively examined. Here, we obtained a set of 300 ERGs that were newly identified by six sets of RNA-seq data from estrogen-treated and control human breast cancer MCF-7 cells. The ERGs exhibited statistical stability, which was based on the coefficient of variation (CV) analysis, correlation analysis, and examination of the functional association with estrogen action using database searches. A set of the top 30 genes based on CV ranking were further evaluated quantitatively by RT-PCR and qualitatively by a functional analysis using the GO and KEGG databases and by a mechanistic analysis to classify ERα/β-dependent or ER-independent types of transcriptional regulation. The 30 ERGs were characterized according to (1) the enzymes, such as metabolic enzymes, proteases, and protein kinases, (2) the genes with specific cell functions, such as cell-signaling mediators, tumor-suppressors, and the roles in breast cancer, (3) the association with transcriptional regulation, and (4) estrogen-responsiveness. Therefore, the ERGs identified here represent various cell functions and cell signaling pathways, including estrogen signaling, and thus, may be useful to evaluate estrogenic activity.

TransMeta simultaneously assembles multisample RNA-seq reads

Assembling RNA-seq reads into full-length transcripts is crucial in transcriptomic studies and poses computational challenges. Here we present TransMeta, a simple and robust algorithm that simultaneously assembles RNA-seq reads from multiple samples. TransMeta is designed based on the newly introduced vector-weighted splicing graph model, which enables accurate reconstruction of the consensus transcriptome via incorporating a cosine similarity-based combing strategy and a newly designed label-setting path-searching strategy. Tests on both simulated and real data sets show that TransMeta consistently outperforms PsiCLASS, StringTie2 plus its merge mode, and Scallop plus TACO, the most popular tools, in terms of precision and recall under a wide range of coverage thresholds at the meta-assembly level. Additionally, TransMeta consistently shows superior performance at the individual sample level.

Impact of bisphenol-A on the spliceosome and meiosis of sperm in the testis of adolescent mice

BACKGROUND

Bisphenol-A (BPA) has estrogenic activity and adversely affects humans and animals' reproductive systems and functions. There has been a disagreement with the safety of BPA exposure at Tolerable daily intake (TDI) (0.05 mg/kg/d) value and non-observed adverse effect level (5 mg/kg/d). The current study investigated the effects of BPA exposure at various doses starting from Tolerable daily intake (0.05 mg/kg/d) to the lowest observed adverse effect level (50 mg/kg/d) on the testis development in male mice offspring. The BPA exposure lasted for 63 days from pregnancy day 0 of the dams to post-natal day (PND) 45 of the offspring.

RESULTS

The results showed that BPA exposure significantly increased testis (BPA ≥ 20 mg/kg/d) and serum (BPA ≥ 10 mg/kg/d) BPA contents of PND 45 mice. The spermatogenic cells became loose, and the lumen of seminiferous tubules enlarged when BPA exposure at 0.05 mg/kg/d TDI. BPA exposure at a low dose (0.05 mg/kg/d) significantly reduced the expression of Scp3 proteins and elevated sperm abnormality. The significant decrease in Scp3 suggested that BPA inhibits the transformation of spermatogonia into spermatozoa in the testis. The RNA-seq proved that the spliceosome was significantly inhibited in the testes of mice exposed to BPA. According to the RT-qPCR, BPA exposure significantly reduced the expression of Snrpc (BPA ≥ 20 mg/kg/d) and Hnrnpu (BPA ≥ 0.5 mg/kg/d).

CONCLUSIONS

This study indicated that long-term BPA exposure at Tolerable daily intake (0.05 mg/kg/d) is not safe because low-dose long-term exposure to BPA inhibits spermatogonial meiosis in mice testis impairs reproductive function in male offspring.

Opportunities and challenges of using high-sensitivity nanobiosensors to detect long noncoding RNAs: A preliminary review

The two types ofncRNAs, including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), are responsible for several biological processes within cells, such as the immune responses, cell growth and invasion, and regulation of the cell cycle. Rapidly expanding class of ncRNAs, lncRNAsinteract with other molecules to form chromatin-remodeling complexes. These potential hallmarks of diseases contribute to transcriptional and post-transcriptional regulation of several genes, possibly via cross-talk with other RNAs. Aberrant expression of lncRNAshas drawn increasing attention to the pathophysiology of different diseases, includingcancer and cardiovasculardiseases. Unfortunately, circulating lncRNAs are presented in the bloodstream at very low levels, making sensitive detection difficult. Currently, there are few methods for detecting these ncRNAs from which quantitative real-time-polymerase chain reaction (qRT-PCR) is the most routinely used technique. These techniqueslack sensitivity for intracellular detection of lncRNAs. Moreover, they are tedious and require a large sample size. Currently, nanotechnology has taken over the diagnostic field because of the tunable properties and modification opportunities. Furthermore, these conventional techniques can be merged with nanotechnology to improve detection sensitivity.This review highlights some of the most recent findings on nanotechnology-based methods and possible obstacles intheir application for moreaccurate sensing of lncRNAs.

Intellectual disability associated with craniofacial dysmorphism due to POLR3B mutation and defect in spliceosomal machinery

Background

Intellectual disability (ID) is a clinically important disease and a most prevalent neurodevelopmental disorder. The etiology and pathogenesis of ID are poorly recognized. Exome sequencing revealed a homozygous missense mutation in the POLR3B gene in a consanguineous family with three Intellectual disability with craniofacial anomalies patients. POLR3B gene encoding the second largest subunit of RNA polymerase III.

Methods

We performed RNA sequencing on blood samples to obtain insights into the biological pathways influenced by POLR3B mutation. We applied the results of our RNA-Seq analysis to several gene ontology programs such as ToppGene, Enrichr, KEGG.

Results

A significant decrease in expression of several spliceosomal RNAs, ribosomal proteins, and transcription factors was detected in the affected, compared to unaffected, family members.

Conclusions

We hypothesize that POLR3B mutation dysregulates the expression of some important transcription factors, ribosomal and spliceosomal genes, and impairments in protein synthesis and splicing mediated in part by transcription factors such as FOXC2 and GATA1 contribute to impaired neuronal function and concurrence of intellectual disability and craniofacial anomalies in our patients. Our study highlights the emerging role of the spliceosome and ribosomal proteins in intellectual disability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01237-5.

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Transcriptome Sequencing-Based Mining of Genes Associated With Pubertal Initiation in Dolang Sheep

Improving the fertility of sheep is an important goal in sheep breeding as it greatly increases the productivity. Dolang sheep is a typical representative breed of lamb in Xinjiang and is the main local sheep breed and meat source in the region. To explore the genes associated with the initiation of puberty in Dolang sheep, the hypothalamic tissues of Dolang sheep prepubertal, pubertal, and postpubertal periods were collected for RNA-seq analysis on the Illumina platform, generating 64.08 Gb clean reads. A total of 575, 166, and 648 differentially expressed genes (DEGs) were detected in prepuberty_vs._puberty, postpuberty_vs._prepuberty, and postpuberty_vs._puberty analyses, respectively. Based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, the related genes involved in the initiation of puberty in Dolang sheep were mined. Ten genes that have direct or indirect functions in the initiation of puberty in Dolang sheep were screened using the GO and KEGG results. Additionally, quantitative real-time PCR was used to verify the reliability of the RNA-Seq data. This study provided a new approach for revealing the mechanism of puberty initiation in sheep and provided a theoretical basis and candidate genes for the breeding of early-pubertal sheep by molecular techniques, and at the same time, it is also beneficial for the protection, development, and utilization of the fine genetic resources of Xinjiang local sheep.

Tiglon enables accurate transcriptome assembly via integrating mappings of different aligners

Full-length transcript reconstruction has a pivotal role in RNA-seq data analysis. In this research, we present a new genome-guided transcriptome assembly algorithm, namely Tiglon, which integrates multiple alignments of different mapping tools and builds the labeled splice graphs, followed by a label-based dynamic path-searching strategy to reconstruct the transcripts. We evaluate Tiglon on a simulated dataset and 12 real datasets under the Hisat2 and Star mappings. The results indicate that the integrating techniques of Tiglon exhibit great superiority over the state-of-the-art assemblers, including StringTie2 and Scallop, depending on Hisat2 alignments, Star alignments, or the merged alignments of both. Especially, Tiglon is significantly powerful in recovering lowly expressed transcripts.

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Tiglon is designed for integrating multiple alignments to assemble transcripts

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Integrating alignments of different aligners is helpful for transcriptome assembly

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Tiglon proposes a new graph model called the labeled splice graph

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Our experiments demonstrate that Tiglon outperforms the leading assemblers

Comparative proteomics and transcriptomics illustrate the allograft-induced stress response in the pearl oyster (Pinctada fucata martensii)

Implantation of a spherical nucleus into a recipient oyster is a critical step in artificial pearl production. However, the molecular mechanisms underlying the response of the pearl oyster to this operation are poorly understood. In this research, we used transcriptomic and proteomic analyses to examine allograft-induced changes in gene/protein expression patterns in Pinctada fucata martensii 12 h after nucleus implantation. Transcriptome analysis identified 688 differential expression genes (DEGs) (FDR<0.01 and |fold change) ＞ 2). Using a 1.2-fold increase or decrease in protein expression as a benchmark for differentially expressed proteins (DEPs), 108 DEPs were reliably quantified, including 71 up-regulated proteins (DUPs) and 37 down-regulated proteins (DDPs). Further analysis revealed that the GO terms, including "cellular process", "biological regulation" and "metabolic process" were considerably enriched. In addition, the transcriptomics analysis showed that "Neuroactive ligand-receptor interaction", "NF-kappa B signaling pathway", "MAPK signaling pathway", "PI3K-Akt signaling pathway', "Toll-like receptor signaling pathway", and "Notch signaling pathway" were significantly enriched in DEGs. The proteomics analysis showed that "ECM-receptor interaction", "Human papillomavirus infection", and "PI3K-Akt signaling pathway" were significantly enriched in DEPs. The results indicate that these functions could play an important role in response to pear oyster stress at nucleus implantation. To assess the potential relevance of quantitative information between mRNA and proteins, using Ward's hierarchical clustering analysis clustered the protein/gene expression patterns across the experimental and control samples into six groups. To investigate the biological processes associated with the protein in each cluster, we identified the significantly enriched GO terms and KEGG pathways in the proteins in each cluster. Gene set enrichment analysis (GSEA) was used to reveal the potential protein or transcription pathways associated with the response to nuclear implantation. Thus, the study of P. f. martensii is essential to enhance our understanding of the molecular mechanisms involved in pearl biosynthesis and the biology of bivalve molluscs.