A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging

Fantastic genes and where to find them expressed in CHO

The transcriptome of Chinese hamster ovary (CHO) cells plays a crucial role in determining cellular characteristics that are essential for biopharmaceutical applications. RNA-sequencing has been extensively used to profile gene expression patterns, aiming to gain a better understanding of intracellular behavior and mechanisms. Individual datasets, however, do not provide a comprehensive overview and characterization of the CHO cell's transcriptome, such that the fundamental structure of the transcriptome remains unknown. Using 15 RNA-sequencing datasets, encompassing almost 300 samples of various experimental setups, conditions and cell lines, we explore and classify the protein-coding transcriptome of CHO cells. Differences in cell line lineages are found to be the primary source of variation in transcribed genes. By employing a novel approach, we identified the core transcriptome that is ubiquitously expressed in all cell lines and culture conditions, as well as genes that remain entirely non-expressed. Additionally, we identified a set of genes that may be active or inactive depending on different conditions, which are linked to biological processes including translation as well as immune and stress response. Lastly, by integrating chromatin states derived from histone modifications, we provided additional context on the epigenetic level that supports our protein-coding gene classification. Our study offers a comprehensive insight into the CHO cell transcriptome and lays the foundation for future research into cellular adaptation to changing conditions and the development of phenotypes.

Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic data

Normalization is a critical step in quantitative analyses of biological processes. Recent works show that cross-platform integration and normalization enable machine learning (ML) training on RNA microarray and RNA-seq data, but no independent datasets were used in their studies. Therefore, it is unclear how to improve ML modelling performance on independent RNA array and RNA-seq based datasets. Inspired by the house-keeping genes that are commonly used in experimental biology, this study tests the hypothesis that non-differentially expressed genes (NDEG) may improve normalization of transcriptomic data and subsequently cross-platform modelling performance of ML models. Microarray and RNA-seq datasets of the TCGA breast cancer were used as independent training and test datasets, respectively, to classify the molecular subtypes of breast cancer. NDEG (p>0.85) and differentially expressed genes (DEG, p<0.05) were selected based on the p values of ANOVA analysis and used for subsequent data normalization and classification, respectively. Models trained based on data from one platform were used for testing on the other platform. Our data show that NDEG and DEG gene selection could effectively improve the model classification performance. Normalization methods based on parametric statistical analysis were inferior to those based on nonparametric statistics. In this study, the LOG_QN and LOG_QNZ normalization methods combined with the neural network classification model seem to achieve better performance. Therefore, NDEG-based normalization appears useful for cross-platform testing on completely independent datasets. However, more studies are required to examine whether NDEG-based normalization can improve ML classification performance in other datasets and other omic data types.

Harnessing de novo transcriptome sequencing to identify and characterize genes regulating carbohydrate biosynthesis pathways in Salvia guaranitica L

Introduction

Carbohydrate compounds serve multifaceted roles, from energy sources to stress protectants, found across diverse organisms including bacteria, fungi, and plants. Despite this broad importance, the molecular genetic framework underlying carbohydrate biosynthesis pathways, such as starch, sucrose, and glycolysis/gluconeogenesis in Salvia guaranitica, remains largely unexplored.

Methods

In this study, the Illumina-HiSeq 2500 platform was used to sequence the transcripts of S. guaranitica leaves, generating approximately 8.2 Gb of raw data. After filtering and removing adapter sequences, 38 million reads comprising 210 million high-quality nucleotide bases were obtained. De novo assembly resulted in 75,100 unigenes, which were annotated to establish a comprehensive database for investigating starch, sucrose, and glycolysis biosynthesis. Functional analyses of glucose-6-phosphate isomerase (SgGPI), trehalose-6-phosphate synthase/phosphatase (SgT6PS), and sucrose synthase (SgSUS) were performed using transgenic Arabidopsis thaliana.

Results

Among the unigenes, 410 were identified as putatively involved in these metabolic pathways, including 175 related to glycolysis/gluconeogenesis and 235 to starch and sucrose biosynthesis. Overexpression of SgGPI, SgT6PS, and SgSUS in transgenic A. thaliana enhanced leaf area, accelerated flower formation, and promoted overall growth compared to wild-type plants.

Discussion

These findings lay a foundation for understanding the roles of starch, sucrose, and glycolysis biosynthesis genes in S. guaranitica, offering insights into future metabolic engineering strategies for enhancing the production of valuable carbohydrate compounds in S. guaranitica or other plants.

Biomarkers for aging in Caenorhabditis elegans high throughput screening

Aging is characterized by a functional decline in organism fitness over time due to a complex combination of genetic and environmental factors [ 1-4]. With an increasing elderly population at risk of age-associated diseases, there is a pressing need for research dedicated to promoting health and longevity through anti-aging interventions. The roundworm Caenorhabditis elegans is an established model organism for aging studies due to its short life cycle, ease of culture, and conserved aging pathways. These benefits also make the worm well-suited for high-throughput screening (HTS) methods to study biomarkers of the molecular changes, cellular dysfunction, and physiological decline associated with aging. Within this review, we offer a summary of recent advances in HTS techniques to study biomarkers of aging in C. elegans.

Comprehensive transcriptomic meta-analysis unveils new responsive genes to methyl jasmonate and ethylene in Catharanthusroseus

In Catharanthus roseus, vital plant hormones, namely methyl jasmonate (MeJA) and ethylene, serve as abiotic triggers, playing a crucial role in stimulating the production of specific secondary compounds with anticancer properties. Understanding how plants react to various stresses, stimuli, and the pathways involved in biosynthesis holds significant promise. The application of stressors like ethylene and MeJA induces the plant's defense mechanisms, leading to increased secondary metabolite production. To delve into the essential transcriptomic processes linked to hormonal responses, this study employed an integrated approach combining RNA-Seq data meta-analysis and system biology methodologies. Furthermore, the validity of the meta-analysis findings was confirmed using RT-qPCR. Within the meta-analysis, 903 genes exhibited differential expression (DEGs) when comparing normal conditions to those of the treatment. Subsequent analysis, encompassing gene ontology, KEGG, TF, and motifs, revealed that these DEGs were actively engaged in multiple biological processes, particularly in responding to various stresses and stimuli. Additionally, these genes were notably enriched in diverse biosynthetic pathways, including those related to TIAs, housing valuable medicinal compounds found in this plant. Furthermore, by conducting co-expression network analysis, we identified hub genes within modules associated with stress response and the production of TIAs. Most genes linked to the biosynthesis pathway of TIAs clustered within three specific modules. Noteworthy hub genes, including Helicase ATP-binding domain, hbdA, and ALP1 genes within the blue, turquoise, and green module networks, are presumed to play a role in the TIAs pathway. These identified candidate genes hold potential for forthcoming genetic and metabolic engineering initiatives aimed at augmenting the production of secondary metabolites and medicinal compounds within C. roseus.

An automatic measurement method for the response of Caenorhabditis elegans to chemicals

BACKGROUND

Caenorhabditis elegans is a widely used model animal. Chemotaxis assay is one of the experiments that study the effects of different chemicals on nematodes. It is mainly used to study the effects of different chemicals on the perception behavior of nematodes. By conducting this experiment, not only can the neurotoxicity of chemicals be reflected, but also the impact of chemicals on physiological functions regulated by the nervous system, such as nematode feeding behavior and basic motor ability.

OBJECTIVE

The experiment of detecting the response of nematode to chemicals is also a common method of chemical toxicity testing based on nematode models. In the analysis of worm tendency behavior, manual operations are generally used. Manually processing a large number of worms under a microscope is very time-consuming and labor-intensive. The current quantitative methods for nematode chemotaxis experiments are not only time-consuming and labor-intensive, but also biased in experimental results due to differences in judgment standards among experimenters. The automatic and efficient quantification method for nematode chemotaxis experiments is a very important technical difficulty in the field of nematode experiments.

METHODS

Here, we have designed an automatic quantification method for nematode chemotaxis experiments by incorporating image acquisition and processing techniques into the nematode experiment.

RESULTS

The experimental results show that the Pearson correlation coefficient between manual and automatic counting results is 0.978.

CONCLUSION

This proves the effectiveness of our method. Applying the automatic measurement method to replace manual counting by the experimenter can improve work efficiency, and reduce errors in human counting operations.