Characterization of the yeast transcriptome

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.

Characterization of metalaxyl-induced notochord toxicity based on biochemical and transcriptomics in zebrafish (Danio rerio) model

Metalaxyl is an acylanilide systemic fungicide that is widely applied and can readily enter ecosystems through leaching and soil runoff. This research utilized zebrafish as a model organism to thoroughly investigate the detrimental impacts of environmentally relevant levels of metalaxyl on the development of the notochord in zebrafish embryos and to elucidate the underlying molecular mechanisms through transcriptomics, pharmacological intervention and molecular biological detection. The preliminary results demonstrated that metalaxyl induced significant modifications in the developmental parameters of zebrafish embryos. This study has also assessed the long-term consequences of metalaxyl exposure during the embryonic development of zebrafish. This study have demonstrated that zebrafish exposed to metalaxyl exhibit a range of abnormalities, including defects in notochord vacuole biogenesis, somite segmentation disorders, anomalous notochord curvatures, craniofacial cartilage deformities, and irregular chordacentra mineralisation. Through transcriptomic and bioinformatics analysis, it was found that most of the genes exhibiting differential expression were linked to oxidative stress. Furthermore, the evidence indicated that oxidative stress was present, as demonstrated by increased malondialdehyde (MDA) production and a decrease in antioxidant enzyme activity (CAT, SOD, GSH). Interestingly, the developmental dysfunction induced by metalaxyl was partially rescued by chlorogenic acid. Overall, metalaxyl disrupts notochord and skeletal formation in zebrafish embryos by modulating oxidative stress mediated by reactive oxygen species.

A Twist in Yeast: New Perspectives for Studying TDP-43 Proteinopathies in S. cerevisiae

TAR DNA-binding protein 43 kDa (TDP-43) proteinopathies are a group of neurodegenerative diseases (NDs) characterized by the abnormal accumulation of the TDP-43 protein in neurons and glial cells. These proteinopathies are associated with several NDs, including amyotrophic lateral sclerosis, frontotemporal lobar degeneration, and some forms of Alzheimer's disease. Yeast models have proven valuable in ND research due to their simplicity, genetic tractability, and the conservation of many cellular processes shared with higher eukaryotes. For several decades, Saccharomyces cerevisiae has been used as a model organism to study the behavior and toxicity of TDP-43, facilitating the identification of genes and pathways that either exacerbate or mitigate its toxic effects. This review will discuss evidence showing that yeast models of TDP-43 exhibit defects in proteostasis, mitochondrial function, autophagy, and RNA metabolism, which are key features of TDP-43-related NDs. Additionally, we will explore how modulating proteins involved in these processes reduce TDP-43 toxicity, aiding in restoring normal TDP-43 function or preventing its pathological aggregation. These findings highlight potential therapeutic targets for the treatment of TDP-43-related diseases.

Advancing Cartilage Tissue Engineering: A Review of 3D Bioprinting Approaches and Bioink Properties

Articular cartilage is crucial in human physiology, and its degeneration poses a significant public health challenge. While recent advancements in 3D bioprinting and tissue engineering show promise for cartilage regeneration, there remains a gap between research findings and clinical application. This review critically examines the mechanical and biological properties of hyaline cartilage, along with current 3D manufacturing methods and analysis techniques. Moreover, we provide a quantitative synthesis of bioink properties used in cartilage tissue engineering. After screening 181 initial works, 33 studies using extrusion bioprinting were analyzed and synthesized, presenting results that indicate the main materials, cells, and methods utilized for mechanical and biological evaluation. Altogether, this review motivates the standardization of mechanical analyses and biomaterial assessments of 3D bioprinted constructs to clarify their chondrogenic potential.

Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants

The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.

Beyond genome: Advanced omics progress of Panax ginseng

Ginseng is a perennial herb of the genus Panax in the family Araliaceae as one of the most important traditional medicine. Genomic studies of ginseng assist in the systematic discovery of genes related to bioactive ginsenosides biosynthesis and resistance to stress, which are of great significance in the conservation of genetic resources and variety improvement. The transcriptome reflects the difference and consistency of gene expression, and transcriptomics studies of ginseng assist in screening ginseng differentially expressed genes to further explore the powerful gene source of ginseng. Protein is the ultimate bearer of ginseng life activities, and proteomic studies of ginseng assist in exploring the biosynthesis and regulation of secondary metabolites like ginsenosides and the molecular mechanism of ginseng adversity adaptation at the overall level. In this review, we summarize the current status of ginseng research in genomics, transcriptomics and proteomics, respectively. We also discuss and look forward to the development of ginseng genome allele mapping, ginseng spatiotemporal, single-cell transcriptome, as well as ginseng post-translational modification proteome. We hope that this review will contribute to the in-depth study of ginseng and provide a reference for future analysis of ginseng from a systems biology perspective.

Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies

OBJECTIVE

Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades.

DESIGN

We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease.

RESULTS

Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues.

CONCLUSIONS

Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients' clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.

Estimating Transcriptome Diversity and Specialization in Capsicum annuum L

Chili pepper fruits of the genus Capsicum represent excellent experimental models to study the growth, development, and ripening processes in a non-climacteric species at the physiological, biochemical, and molecular levels. Fruit growth, development, and ripening involve a complex, harmonious, and finely controlled regulation of gene expression. The purpose of this study was to estimate the changes in transcriptome diversity and specialization, as well as gene specificities during fruit development in this crop, and to illustrate the advantages of estimating these parameters. To achieve these aims, we programmed and made publicly available an R package. In this study, we applied these methods to a set of 179 RNA-Seq libraries from a factorial experiment that includes 12 different genotypes at various stages of fruit development. We found that the diversity of the transcriptome decreases linearly from the flower to the mature fruit, while its specialization follows a complex and non-linear behavior during this process. Additionally, by defining sets of genes with different degrees of specialization and applying Gene Ontology enrichment analysis, we identified processes, functions, and components that play a central role in particular fruit development stages. In conclusion, the estimation of diversity, specialization, and specificity summarizes the global properties of the transcriptomes, providing insights that are difficult to achieve by other means.

Full-length transcriptome analysis of Ophioglossum vulgatum: effects of experimentally identified chloroplast gene clusters on expression and evolutionary patterns

Genes with similar or related functions in chloroplasts are often arranged in close proximity, forming clusters on chromosomes. These clusters are transcribed coordinated to facilitate the expression of genes with specific function. Our previous study revealed a significant negative correlation between the chloroplast gene expression level of the rare medicinal fern Ophioglossum vulgatum and its evolutionary rates as well as selection pressure. Therefore, in this study, we employed a combination of SMRT and Illumina sequencing technology to analyze the full-length transcriptome sequencing of O. vulgatum for the first time. In particular, we experimentally identified gene clusters based on transcriptome data and investigated the effects of chloroplast gene clustering on expression and evolutionary patterns. The results revealed that the total sequenced data volume of the full-length transcriptome of O. vulgatum amounted to 71,950,652,163 bp, and 110 chloroplast genes received transcript coverage. Nine different types of gene clusters were experimentally identified in their transcripts. The chloroplast cluster genes may cause a decrease in non-synonymous substitution rate and selection pressure, as well as a reduction in transversion rate, transition rate, and their ratio. While expression levels of chloroplast cluster genes in leaf, sporangium, and stem would be relatively elevated. The Mann-Whitney U test indicated statistically significant in the selection pressure, sporangia and leaves groups (P < 0.05). We have contributed novel full-length transcriptome data resources for ferns, presenting new evidence on the effects of chloroplast gene clustering on expression land evolutionary patterns, and offering new theoretical support for transgenic research through gene clustering.

Screening and identification of high yield tetramethylpyrazine strains in Nongxiangxing liquor Daqu and study on the mechanism of tetramethylpyrazine production

BACKGROUND

There are few reports on the breeding of high-yielding tetramethylpyrazine (TTMP) strains in strong-flavor Daqu. In addition, studies on the mechanism of TTMP production in strains are mostly based on common physiological and biochemical indicators, and there is no report on RNA level. Therefore, in this study, a strain with high production of TTMP was screened out from strong-flavor liquor, and transcriptome sequencing analysis was performed to analyze its key metabolic pathways and key genes, and to infer the mechanism of TTMP production in the strain.

RESULTS

In this study, a strain with a high yield of tetramethylpyrazine (TTMP) was screened out, and the yield was 29.83 μg mL-1 . The identified strain was Bacillus velezensis, which could increase the content of TTMP in liquor by about 88%. After transcriptome sequencing, a total of 1851 differentially expressed genes were screened, including 1055 up-regulated genes and 796 down-regulated genes. Three pathways related to the production of TTMP were identified by gene ontology (GO) annotation and COG annotation, including carbohydrate metabolism, cell movement and amino acid metabolism. The key genes of TTMP were analyzed, and the factors that might regulate the production of TTMP, such as the transfer of uracil phosphate ribose and glycosyltransferase, were obtained.

CONCLUSIONS

A strain of B. velezensis with high TTMP production was screened and identified in strong-flavor Daqu for the first time. The yield of TTMP was 29.83 μg mL-1 , which increased the TTMP content in liquor by 88%. The key metabolic pathways of TTMP production in the strain were obtained: carbohydrate metabolism, cell movement and amino acid metabolism, and the key regulatory genes of each pathway were found, which complemented the gap in gene level in the production regulation of the strain, and provided a theoretical basis for the subsequent study of TTMP in liquor. © 2023 Society of Chemical Industry.

Clinical implications of RAB13 expression in pan-cancer based on multi-databases integrative analysis

Worldwide, cancer is a huge burden, and each year sees an increase in its incidence. RAB (Ras-related in brain) 13 is crucial for a number of tumor types. But more research on RAB13's tumor-related mechanism is still required. This study's goal was to investigate RAB13's function in human pan-cancer, and we have also preliminarily explored the relevant mechanisms. To investigate the differential expression, survival prognosis, immunological checkpoints, and pathological stage of RAB13 in human pan-cancer, respectively, databases of TIMER2.0, GEPIA 2, and UALCAN were employed. CBioPortal database was used to analyze the mutation level, meanwhile, PPI network was constructed based on STRING website. The putative functions of RAB13 in immunological infiltration were investigated using single sample gene set enrichment analysis (ssGSEA). The mechanism of RAB13 in hepatocellular cancer was also briefly investigated by us using gene set enrichment analysis (GSEA). RAB13 was differentially expressed in a number of different cancers, including liver hepatocellular carcinoma (LIHC), stomach adenocarcinoma (STAD), etc. Additionally, RAB13 overexpression in LGG and LIHC is associated with a worse prognosis, including overall survival (OS) and disease-free survival (DFS). Then, we observed that early in BLCA, BRAC, CHOL, ESCA, HNSC, KICH, KIRC, LIHC, LUAD, LUSC, and STAD, the level of RAB13 expression was raised. Next, we found that "amplification" was the most common mutation in RAB13. The expression of SLC39A1, JTB, SSR2, SNAPIN, and RHOC was strongly positively linked with RAB13, according to a correlation study. RAB13 favorably regulated B cell, CD8 + T cell, CD4 + T cell, macrophage, neutrophil, and dendritic cell in LIHC, according to immune infiltration analysis. Immune checkpoint study revealed a positive correlation between RAB13 expression and PD1, PDL1, and CTLA4 in LIHC. According to GSEA, RAB13 is involved in a number of processes in LIHC, including MTORC1 signaling, MYC targets v1, G2M checkpoint, MITOTIC spindle, DNA repair, P53 pathway, glycolysis, PI3K-AKT-MTOR signaling, etc. RAB13 is a possible therapeutic target in LIHC and can be used as a prognostic marker.

Transcriptomic-based analysis to identify candidate genes for blue color rose breeding

Analysis of the flower color formation mechanism of 'Rhapsody in Blue' by BF and WF transcriptomes reveals that RhF3'H and RhGT74F2 play a key role in flower color formation. Rosa hybrida has colorful flowers and a high ornamental value. Although rose flowers have a wide range of colors, no blue roses exist in nature, and the reason for this is unclear. In this study, the blue-purple petals (BF) of the rose variety 'Rhapsody in Blue' and the white petals (WF) of its natural mutant were subjected to transcriptome analysis to find genes related to the formation of the blue-purple color. The results showed that the anthocyanin content was significantly higher in BF than in WF. A total of 1077 differentially expressed genes (DEGs) were detected by RNA-Seq analysis, of which 555 were up-regulated and 522 were down-regulated in the WF vs. BF petals. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of the DEGs revealed that a single gene up-regulated in BF was related to multiple metabolic pathways including metabolic process, cellular process, protein-containing complex, etc. Additionally, the transcript levels of most of the structural genes related to anthocyanin synthesis were significantly higher in BF than in WF. Selected genes were analyzed by qRT-PCR and the results were highly consistent with the RNA-Seq results. The functions of RhF3'H and RhGT74F2 were verified by transient overexpression analyses, and the results confirmed that both affect the accumulation of anthocyanins in 'Rhapsody in Blue'. We have obtained comprehensive transcriptome data for the rose variety 'Rhapsody in Blue'. Our results provide new insights into the mechanisms underlying rose color formation and even blue rose formation.

Dynamic metabolome profiling uncovers potential TOR signaling genes

Although the genetic code of the yeast Saccharomyces cerevisiae was sequenced 25 years ago, the characterization of the roles of genes within it is far from complete. The lack of a complete mapping of functions to genes hampers systematic understanding of the biology of the cell. The advent of high-throughput metabolomics offers a unique approach to uncovering gene function with an attractive combination of cost, robustness, and breadth of applicability. Here, we used flow-injection time-of-flight mass spectrometry to dynamically profile the metabolome of 164 loss-of-function mutants in TOR and receptor or receptor-like genes under a time course of rapamycin treatment, generating a dataset with >7000 metabolomics measurements. In order to provide a resource to the broader community, those data are made available for browsing through an interactive data visualization app hosted at https://rapamycin-yeast.ethz.ch. We demonstrate that dynamic metabolite responses to rapamycin are more informative than steady-state responses when recovering known regulators of TOR signaling, as well as identifying new ones. Deletion of a subset of the novel genes causes phenotypes and proteome responses to rapamycin that further implicate them in TOR signaling. We found that one of these genes, CFF1, was connected to the regulation of pyrimidine biosynthesis through URA10. These results demonstrate the efficacy of the approach for flagging novel potential TOR signaling-related genes and highlight the utility of dynamic perturbations when using functional metabolomics to deliver biological insight.

Application of omics technology in the research on edible fungi

Edible fungus is a large fungus distributed all over the world and used as food and medicine. But people's understanding of edible fungi is not as much as that of ordinary crops, so people have started a number of research on edible fungi in recent years. With the development of science and technology, omics technology has gradually walked into people's vision. Omics technology has high sensitivity and wide application range, which is favored by researchers. The application of omics technology to edible fungus research is a major breakthrough, which has transferred edible fungus research from artificial cultivation to basic research. Now omics technology in edible fungi has been flexibly combined with other research methods, involving multiple studies of edible fungus, such as genetic breeding, growth and development, stress resistance, and the use of special components in edible fungus as pharmaceutical additives. It is believed that in the future, the research of edible fungi will also be brought to a deeper level with the help of omics technology. This paper introduces the application progress of modern omics technology to the study on edible fungi and mentions the application prospect of edible fungi research with the constant development of omics technology, thereby providing ideas for the follow-up in-depth research on edible fungi.

Multi-omics reveals response mechanism of liver metabolism of hybrid sturgeon under ship noise stress

Underwater noise from ship engines can affect the metabolism and immune system of various fish species. Meanwhile, changes in the metabolic pathways in liver are important for fish to adapt to adverse environments. We used a combined multi-omics analysis to investigate the response mechanism of hybrid sturgeon to continuously played ship noise. A control group and a noise group (simulated ship noise: 12 h) were set up, and liver tissues were extracted for high-throughput transcriptome and metabolome sequencing. The results show that a total of 588 differentially expressed genes (DEGs) and 58 DEGs metabolites were detected. The joint analysis of transcriptome and metabolome showed that under noise stress, apoptosis and cell motility were intensified, DNA replication, RNA transcription and translation, and protein synthesis were inhibited, and lipid metabolism, nucleotide metabolism, and vitamin D3 metabolic pathways were also inhibited. Interestingly, the initiation of a partial immune responses ensured their normal immunity abilities. Moreover, material and energy requirements of the organism under noise stress were guaranteed by upregulation of carbohydrate and amino acid metabolic pathways.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Transcriptomic and Metabolomic Analysis of the Response of Quinoa Seedlings to Low Temperatures

Quinoa, a cool-weather high-altitude crop, is susceptible to low-temperature stress throughout its reproductive phase. Herein, we performed broadly targeted metabolic profiling of quinoa seedlings to explore the metabolites’ dynamics in response to low-temperature stress and transcriptome analysis to determine the underlying genetic mechanisms. Two variants, namely, Dian Quinoa 2324 and Dian Quinoa 281, were exposed to temperatures of −2, 5, and 22 °C. A total of 794 metabolites were detected; 52,845 genes, including 6628 novel genes, were annotated using UPLC-MS/MS analysis and the Illumina HiSeq system. Combined with morphological indicators to resolve the mechanism underlying quinoa seedling response to low-temperature stress, the molecular mechanisms of quinoa changed considerably based on temperature exposure. Soluble sugars heavily accumulated in plants with cold damage and changes in regulatory networks under freeze damage, such as the upregulation of α-linolenic acid metabolism and a reduction in energy substrates, may explain the spatial patterns of biosynthesis and accumulation of these metabolites. Genes that are actively expressed during cold responses, as revealed by co-expression analyses, may be involved in the regulation thereof. These results provide insights into the metabolic factors in quinoa under low-temperature stress and provide a reference for the screening of quinoa varieties resistant to low temperature.

Influence of maternal obesity on the multi-omics profiles of the maternal body, gestational tissue, and offspring

Epidemiological studies show that obesity during pregnancy affects more than half of the pregnancies in the developed countries and is associated with obstetric problems and poor outcomes. Obesity tends to increase the incidence of complications. Furthermore, the resulting offspring are also adversely affected. However, the molecular mechanisms of obesity leading to poor pregnancy outcomes remain unclear. Omics methods are used for genetic diagnosis and marker discovery. The aim of this review was to summarize the maternal and fetal pathophysiological alterations induced by gestational obesity,identified using multi-omics detection techniques, and to generalize the biological functions and potential mechanisms of the differentially expressed molecules.

Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research

In the age of big data an important question is how to ensure we make the most out of the resources we generate. In this review, we discuss the major methods used in Apicomplexan and Kinetoplastid research to produce big datasets and advance our understanding of Plasmodium, Toxoplasma, Cryptosporidium, Trypanosoma and Leishmania biology. We debate the benefits and limitations of the current technologies, and propose future advancements that may be key to improving our use of these techniques. Finally, we consider the difficulties the field faces when trying to make the most of the abundance of data that has already been, and will continue to be, generated.

Precision medicine journey through omics approach

It has been well established that understanding the underlying heterogeneity of numerous complex disease process needs new strategies that present in precision medicine for prediction, prevention and personalized treatment strategies. This approach must be tailored for each individual's unique omics that lead to personalized management of disease. The correlation between different omics data should be considered in precision medicine approach. The interaction provides a hypothesis which is called domino effect in the present minireview. Here we review the various potentials of omics data including genomics, transcriptomics, proteomics, metabolomics, pharmacogenomics. We comprehensively summarize the impact of omics data and its major role in precision medicine and provide a description about the domino effect on the pathophysiology of diseases. Each constituent of the omics data typically provides different information in associated with disease. Current research, although inadequate, clearly indicate that the information of omics data can be applicable in the concept of precision medicine. Integration of different omics data type in domino effect hypothesis can explain the causative changes of disease as it is discussed in the system biology too. While most existing studies investigate the omics data separately, data integration is needed on the horizon of precision medicine by using machine learning.

Oil plant genomes: current state of the science

Vegetable oils are an indispensable nutritional component of the human diet as well as important raw materials for a variety of industrial applications such as pharmaceuticals, cosmetics, oleochemicals, and biofuels. Oil plant genomes are highly diverse, and their genetic variation leads to a diversity in oil biosynthesis and accumulation along with agronomic traits. This review discusses plant oil biosynthetic pathways, current state of genome assembly, polyploidy and asymmetric evolution of genomes of oil plants and their wild relatives, and research progress of pan-genomics in oil plants. The availability of complete high-resolution genomes and pan-genomes has enabled the identification of structural variations in the genomes that are associated with the diversity of agronomic and environment fitness traits. These and future genomes also provide powerful tools to understand crop evolution and to harvest the rich natural variations to improve oil crops for enhanced productivity, oil quality, and adaptability to changing environments.

Data-driven identification of inherent features of eukaryotic stress-responsive genes

Living organisms are continuously challenged by changes in their environment that can propagate to stresses at the cellular level, such as rapid changes in osmolarity or oxygen tension. To survive these sudden changes, cells have developed stress-responsive mechanisms that tune cellular processes. The response of Saccharomyces cerevisiae to osmostress includes a massive reprogramming of gene expression. Identifying the inherent features of stress-responsive genes is of significant interest for understanding the basic principles underlying the rewiring of gene expression upon stress. Here, we generated a comprehensive catalog of osmostress-responsive genes from 5 independent RNA-seq experiments. We explored 30 features of yeast genes and found that 25 (83%) were distinct in osmostress-responsive genes. We then identified 13 non-redundant minimal osmostress gene traits and used statistical modeling to rank the most stress-predictive features. Intriguingly, the most relevant features of osmostress-responsive genes are the number of transcription factors targeting them and gene conservation. Using data on HeLa samples, we showed that the same features that define yeast osmostress-responsive genes can predict osmostress-responsive genes in humans, but with changes in the rank-ordering of feature-importance. Our study provides a holistic understanding of the basic principles of the regulation of stress-responsive gene expression across eukaryotes.

RNA-Seq Analysis of the Key Long Noncoding RNAs and mRNAs Related to the Regulation of Acute Heat Stress in Rainbow Trout

Simple Summary

At present, climate warming is a very serious environmental problem. A sudden and large increase or decrease in temperature is likely to cause stress response in animals. Rainbow trout is a kind of cultured cold-water fish, which is very sensitive to high temperature. Therefore, it is very vulnerable to heat waves during production. The current study found that the behavior, antioxidant capacity, and natural immune function of rainbow trout under acute heat stress were significantly enhanced in the early stages of stress response, but its anti-stress ability decreased with an increase in stress intensity and duration. Transcriptome sequencing and bioinformatics analysis showed that some non-coding RNAs could competitively bind to target genes, and jointly participate in metabolism, apoptosis, and the immune regulation of rainbow trout under stress environments. In conclusion, our study can lay a theoretical foundation for the breeding of heat-resistant rainbow trout varieties.

Abstract

As the global climate warms, more creatures are threatened by high temperatures, especially cold-water fish such as rainbow trout. Evidence has demonstrated that long noncoding RNAs (lncRNAs) play a pivotal role in regulating heat stress in animals, but we have little understanding of this regulatory mechanism. The present study aimed to identify potential key lncRNAs involved in regulating acute heat stress in rainbow trout. lncRNA and mRNA expression profiles of rainbow trout head kidney were analyzed via high-throughput RNA sequencing, which exhibited that 1256 lncRNAs (802 up-regulation, 454 down-regulation) and 604 mRNAs (353 up-regulation, 251 down-regulation) were differentially expressed. These differentially expressed genes were confirmed to be primarily associated with immune regulation, apoptosis, and metabolic process signaling pathways through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and coding-noncoding co-expression network analysis. These results suggested that 18 key lncRNA-mRNA pairs are essential in regulating acute heat stress in rainbow trout. Overall, these analyses showed the effects of heat stress on various physiological functions in rainbow trout at the transcriptome level, providing a theoretical basis for improving the production and breeding of rainbow trout and the selection of new heat-resistant varieties.

Modular, synthetic chromosomes as new tools for large scale engineering of metabolism

The construction of powerful cell factories requires intensive genetic engineering for the addition of new functionalities and the remodeling of native pathways and processes. The present study demonstrates the feasibility of extensive genome reprogramming using modular, specialized de novo-assembled neochromosomes in yeast. The in vivo assembly of linear and circular neochromosomes, carrying 20 native and 21 heterologous genes, enabled the first de novo production in a microbial cell factory of anthocyanins, plant compounds with a broad range pharmacological properties. Turned into exclusive expression platforms for heterologous and essential metabolic routes, the neochromosomes mimic native chromosomes regarding mitotic and genetic stability, copy number, harmlessness for the host and editability by CRISPR/Cas9. This study paves the way for future microbial cell factories with modular genomes in which core metabolic networks, localized on satellite, specialized neochromosomes can be swapped for alternative configurations and serve as landing pads for the addition of functionalities.

A Whole Transcriptome Analysis in Peripheral Blood Suggests That Energy Metabolism and Inflammation Are Involved in Major Depressive Disorder

INTRODUCTION

Previous studies on transcriptional profiles suggested dysregulation of multiple RNA species in major depressive disorder (MDD). However, the interaction between different types of RNA was neglected. Therefore, integration of different RNA species in transcriptome analysis would be helpful for interpreting the functional readout of the transcriptome in MDD.

METHODS

A whole transcriptome sequencing were performed on the peripheral blood of 15 patients with MDD and 15 matched healthy controls (HCs). The differential expression of miRNAs, lncRNAs, circRNAs, and mRNAs was examined between MDD and HCs using empirical analysis of digital gene expression data in R (edgeR). Weighted correlation network analysis (WGCNA) was used to identify RNA co-expression modules associated with MDD. A ceRNA network was constructed for interpretation of interactions between different RNA species. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to explore potential biological mechanisms associated with MDD.

RESULTS

Multiple RNAs and co-expression modules were identified to be significantly dysregulated in MDD compared to HCs. Based on the differential RNAs, a ceRNA network that were dysregulated in MDD were constructed. The pathway networks that related to oxidative phosphorylation and the chemokine signaling were found to be associated with MDD.

CONCLUSION

Our results suggested that the processes of energy metabolism and inflammation may be involved in the pathophysiology of MDD.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

The dedicated chaperones of eL43, Puf6 and Loc1, can also bind RPL43 mRNA and regulate the production of this ribosomal protein

The level of ribosome biogenesis is highly associated with cell growth rate. Because many ribosomal proteins have extraribosomal functions, overexpression or insufficient supply of these proteins may impair cellular growth. Therefore, the supply of ribosomal proteins is tightly controlled in response to rRNA syntheses and environmental stimuli. In our previous study, 2 RNA-binding proteins, Puf6 and Loc1, were identified as dedicated chaperones of the ribosomal protein eL43, with which they associate to maintain its protein level and proper loading. In this study, we demonstrate that Puf6 and Loc1 interact with RPL43 mRNA. Notably, Puf6 and Loc1 usually function as a dimeric complex to bind other mRNAs; however, in this instance, the individual proteins, but not the complex form, can bind RPL43 mRNA. Thus, Puf6 or Loc1 could bind RPL43 mRNA in loc1Δ or puf6Δ, respectively. The binding of Puf6 or Loc1 caused negative effects for eL43 production: decreased RNA stability and translation of RPL43A/B mRNA. The present results suggest that these dedicated chaperones control the protein levels of eL43 from the standpoint of stability and through regulating its production.

AI applications in functional genomics

We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.

Genetic associated complications of type 2 Diabetes Mellitus: a review

According to the International Diabetes Federation, the number of adults (age of 20-79) being diagnosed with Diabetes Mellitus (DM) have increased from 285 million in year 2009 to 463 million in year 2019 which comprises of 95% Type 2 DM patient (T2DM). Research have claimed that genetic predisposition could be one of the factors causing T2DM complications. In addition, T2DMcomplications cause an incremental risk to mortality. Therefore, this article aims to discuss some complications of T2DM in and their genetic association. The complications that are discussed in this article are diabetic nephropathy, diabetes induced cardiovascular disease, diabetic neuropathy, Diabetic Foot Ulcer (DFU) and Alzheimer's disease. According to the information obtained, genes associated with diabetic nephropathy (DN) are gene GABRR1 and ELMO1 that cause injury to glomerular. Replication of genes FRMD3, CARS and MYO16/IRS2 shown to have link with DN. The increase of gene THBS2, NGAL, PIP, TRAF6 polymorphism, ICAM-1 encoded for rs5498 polymorphism and C667T increase susceptibility towards DN in T2DM patient. Genes associated with cardiovascular diseases are Adiponectin gene (ACRP30) and Apolipoprotein E (APOE) polymorphism gene with ξ2 allele. Haptoglobin (Hp) 1-1 genotype and Mitochondria Superoxide Dismutase 2 (SOD2) plays a role in cardiovascular events. As for genes related to diabetic neuropathy, Janus Kinase (JAK), mutation of SCN9A and TRPA1 gene and destruction of miRNA contribute to pathogenesis of diabetic neuropathy among T2DM patients. Expression of cytokine IL-6, IL-10, miR-146a are found to cause diabetic neuropathy. Besides, A1a16Va1 gene polymorphism, an oxidative stress influence was found as one of the gene factors. Diabetic retinopathy (DR) is believed to have association with Monocyte Chemoattractant Protein-1 (MCP-1) and Insulin-like Growth Factor 1 (IGF1). Over-expression of gene ENPP1, IL-6 pro-inflammatory cytokine, ARHGAP22's protein rs3844492 polymorphism and TLR4 heterozygous genotype are contributing to significant pathophysiological process causing DR, while research found increases level of UCP1 gene protects retina cells from oxidative stress. Diabetic Foot Ulcer (DFU) is manifested by slowing in reepithelialisation of keratinocyte, persistence wound inflammation and healing impairment. Reepithelialisation disturbance was caused by E2F3 gene, reduction of Tacl gene encoded substance P causing persistence inflammation while expression of MMp-9 polymorphism contributes to healing impairment. A decrease in HIF-1a gene expression leads to increased risk of pathogenesis, while downregulation of TLR2 increases severity of wound in DFU patients. SNPs alleles has been shown to have significant association between the genetic dispositions of T2DM and Alzheimer's disease (AD). The progression of AD can be due to the change in DNA methylation of CLOCK gene, followed with worsening of AD by APOE4 gene due to dyslipidaemia condition in T2DM patients. Insulin resistance is also a factor that contributes to pathogenesis of AD.

Comparative Transcriptome Analysis Revealed Candidate Genes Potentially Related to Desiccation Sensitivity of Recalcitrant Quercus variabilis Seeds

Chinese cork oak (Quercus variabilis) is a widely distributed and highly valuable deciduous broadleaf tree from both ecological and economic perspectives. Seeds of this species are recalcitrant, i.e., sensitive to desiccation, which affects their storage and long-term preservation of germplasm. However, little is known about the underlying molecular mechanism of desiccation sensitivity of Q. variabilis seeds. In this study, the seeds were desiccated with silica gel for certain days as different treatments from 0 (Control) to 15 days (T15) with a gradient of 1 day. According to the seed germination percentage, four key stages (Control, T2, T4, and T11) were found. Then the transcriptomic profiles of these four stages were compared. A total of 4,405, 4,441, and 5,907 differentially expressed genes (DEGs) were identified in T2 vs. Control, T4 vs. Control, and T11 vs. Control, respectively. Among them, 2,219 DEGs were overlapped in the three comparison groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these DEGs were enriched into 124 pathways, such as "Plant hormone signal transduction" and "Glycerophospholipid metabolism". DEGs related to hormone biosynthesis and signal transduction (ZEP, YUC, PYR, ABI5, ERF1B, etc.), stress response proteins (LEA D-29, HSP70, etc.), and phospholipase D (PLD1) were detected during desiccation. These genes and their interactions may determine the desiccation sensitivity of seeds. In addition, group specific DEGs were also identified in T2 vs. Control (PP2C62, UNE12, etc.), T4 vs. Control (WRKY1-like, WAK10, etc.), and T11 vs. Control (IBH1, bZIP44, etc.), respectively. Finally, a possible work model was proposed to show the molecular regulation mechanism of desiccation sensitivity in Q. variabilis seeds. This is the first report on the molecular regulation mechanism of desiccation sensitivity of Q. variabilis seeds using RNA-Seq. The findings could make a great contribution to seed storage and long-term conservation of recalcitrant seeds in the future.

Non-Coding, RNAPII-Dependent Transcription at the Promoters of rRNA Genes Regulates Their Chromatin State in S. cerevisiae

Pervasive transcription is widespread in eukaryotes, generating large families of non-coding RNAs. Such pervasive transcription is a key player in the regulatory pathways controlling chromatin state and gene expression. Here, we describe long non-coding RNAs generated from the ribosomal RNA gene promoter called UPStream-initiating transcripts (UPS). In yeast, rDNA genes are organized in tandem repeats in at least two different chromatin states, either transcribed and largely depleted of nucleosomes (open) or assembled in regular arrays of nucleosomes (closed). The production of UPS transcripts by RNA Polymerase II from endogenous rDNA genes was initially documented in mutants defective for rRNA production by RNA polymerase I. We show here that UPS are produced in wild-type cells from closed rDNA genes but are hidden within the enormous production of rRNA. UPS levels are increased when rDNA chromatin states are modified at high temperatures or entering/leaving quiescence. We discuss their role in the regulation of rDNA chromatin states and rRNA production.

Omics analyses in keratoconus: from transcriptomics to proteomics

Purpose of Review

To summarize the recent advances in transcriptomics and proteomics studies of keratoconus using advanced genome-wide gene and protein expression profiling techniques.

Recent Findings

Second-generation sequencing including RNA sequencing has been widely used to characterize the genome-wide gene expression in corneal tissues or cells affected by keratoconus. Due to different sample types, sequencing platforms, and analysis pipeline, different lists of genes have been identified to be differentially expressed in KC-affected samples. Gene ontology and pathway/network analyses have indicated the involvement of genes related with extracellular matrix, WNT-signaling, TGFβ pathway, and NRF2-regulated network. High throughput proteomics studies using mass spectrometry have uncovered many KC-related protein molecules in pathways related with cytoskeleton, cell matrix, TGFβ signaling, and extracellular matrix remodeling, consistent with gene expression profiling.

Summary

Both transcriptomics and proteomics studies using genome-wide gene/protein expression profiling techniques have identified significant genes/proteins that may contribute to the pathogenesis of keratoconus. These molecules may be involved in functional categories related with extracellular matrix and TGFβ signaling. It is necessary to perform comprehensive gene/protein expression studies using larger sample size, same type of samples, up-to-date platform and bioinformatics tools.

Quantifying tagged mRNA export flux via nuclear pore complexes in single live cells

The mRNA export flux through nuclear pore complexes (NPC) changes under DNA manipulation and hence affects protein translation. However, monitoring the flux of a specific mRNA in single live cell is beyond reach of traditional techniques. We developed a fluorescence-based detection method for measuring the export flux of mRNA through NPC in single live cell using a snapshot image, which had been tested on exogenous genes' expression in HeLa cells, with transfection or infection, and endogenous genes' expression in yeast cells, during incubation and carbon catabolite repression. With its speediness, explicitness and noninvasiveness, we believe that it would be valuable in direct monitoring of gene behavior, and the understanding of gene regulation at a single cell level.

The yeast platform engineered for synthetic gRNA-landing pads enables multiple gene integrations by a single gRNA/Cas9 system

Saccharomyces cerevisiae is a versatile microbial platform to build synthetic metabolic pathways for production of diverse chemicals. To expedite the construction of complex metabolic pathways by multiplex CRISPR-Cas9 genome-edit, eight desirable intergenic loci, located adjacent to highly expressed genes selected from top 100 expressers, were identified and fully characterized for three criteria after integrating green fluorescent protein (GFP) gene - CRISPR-mediated GFP integration efficiency, expression competency assessed by levels of GFP fluorescence, and assessing growth rates of GFP integrated strains. Five best performing intergenic loci were selected to build a multiplex CRISPR platform, and a synthetic 23-bp DNA comprised of 20-bp synthetic DNA with a protospacer adjacent motif (PAM) was integrated into the five loci using CRISPR-Cas9 in a sequential manner. This process resulted in five different yeast strains harbouring 1-5 synthetic gRNA-binding sites in their genomes. Using these pre-engineered yeast strains, simultaneous integrations of 2-, 3-, 4-, or 5-genes to the targeted loci were demonstrated with efficiencies from 85% to 98% using beet pigment betalain (3-gene pathway), hygromycin and geneticin resistance markers. Integrations of the multiple, foreign genes in the targeted loci with 100% precision were validated by genotyping. Finally, we further developed the strain to have 6th synthetic gRNA-binding site, and the resulting yeast strain was used to generate a yeast strain producing a sesquiterpene lactone, kauniolide by simultaneous 6-gene integrations. This study demonstrates the effectiveness of a single gRNA-mediated CRISPR platform to build complex metabolic pathways in yeast.

Identification of suitable reference genes for studies of Syringa pinnatifolia Hemsl

Syringa pinnatifolia Hemsl. (Oleaceae) is a species of shrub with a limited distribution in China. Several chemical compounds with pharmacological effects have been isolated from S. pinnatifolia, including new lignans and sesquiterpenes. Studies of gene expression in this species require the identification of suitable reference genes that are stably expressed under different conditions and in different tissues. To identify candidate reference genes, here we used the geNorm, NormFinder, and BestKeeper algorithms to analyze the stability of 12 candidate genes. The geometric mean of the rankings generated with these algorithms was used to obtain a comprehensive ranking. TBP and PP2A were found to be appropriate reference genes for calli treated with different external stimuli, and TIP41 and TBP were found to be appropriate reference genes in differentiated tissues. When calli and differentiated tissues were considered together, TBP and TIP41 were found to be the most reliable reference genes. The selected genes were validated by analysis of HMGR expression in calli and differentiated tissues. This study is the first to screen candidate reference genes in the genus Syringa and could help guide future molecular studies in this genus.

Dynamic changes of transcriptome of fifth-instar spodoptera litura larvae in response to insecticide

Spodoptera litura is a major insect with a cosmopolitan distribution and strong resistance to multiple insecticides. Determining the molecular basis and key candidate genes of the insecticide resistance of S. litura may help in managing this insect. In this study, fifth-instar S. litura larvae were subjected to transcriptome analysis at 6, 12, 24, 48, and 72 h after feeding on an LC20 dose of avermectin. The result showed that genes responding to avermectin changed dynamically with different gene counts and resistance mechanisms at the fifth instar based on a metabolic pathway map. These responses included degrading the insecticide by a series of P450 and glutathione-S-transferase enzymes starting at the 12 h time point, with subsequent increases in the number of genes involved and shifts to TOLL and immune deficiency (IMD) pathways at 48 h after feeding the insecticide. Weighted correlation network analysis (WGCNA) determined a co-expression module related to the avermectin response at 12 and 24 h (r = 0.403, p = 0.0371; r = 0.436, p = 0.023), in which a hub gene (LOC111358940) related to metalloproteinase activity was identified. In addition, Analysis of the genes in the co-expression module further revealed that eight genes encoding UDP-glucuronosyltransferases were directly associated with insecticide response in S. litura. These results provide better understanding of the avermectin response mechanism of S. litura and may be useful in developing improved control strategies for this species.

SUPPLEMENTARY INFORMATION

The online version of this article (10.1007/s13205-021-02651-9) contains supplementary material, which is available to authorized users.

At the dawn of the transcriptomic medicine

Progress in genomic analytical technologies has improved our possibilities to obtain information regarding DNA, RNA, and their dynamic changes that occur over time or in response to specific challenges. This information describes the blueprint for cells, tissues, and organisms and has fundamental importance for all living organisms. This review focuses on the technological challenges to analyze the transcriptome and what is the impact of transcriptomics on precision medicine. The transcriptome is a term that covers all RNA present in cells and a substantial part of it will never be translated into protein but is nevertheless functional in determining cell phenotype. Recent developments in transcriptomics have challenged the fundamentals of the central dogma of biology by providing evidence of pervasive transcription of the genome. Such massive transcriptional activity is challenging the definition of a gene and especially the term "pseudogene" that has now been demonstrated in many examples to be both transcribed and translated. We also review the common sources of biomaterials for transcriptomics and justify the suitability of whole blood RNA as the current optimal analyte for clinical transcriptomics. At the end of the review, a brief overview of the clinical implications of transcriptomics in clinical trial design and clinical diagnosis is given. Finally, we introduce the transcriptome as a target for modern drug development as a tool for extending our capacity for precision medicine in multiple diseases.

The effect of acute heat stress on the innate immune function of rainbow trout based on the transcriptome

Heat stress is a condition in which the body's homeostasis is disturbed as a result of the rise in water temperature, resulting in the decline or even death of growth, immunity, and other functions. The mechanisms directing this response are not fully understood. To better characterize the effects of acute heat stress on the innate immune function of rainbow trout, we identified differentially regulated messenger RNA (mRNA) and non-coding RNA (ncRNA) in rainbow trout exposed to acute heat stress. Next-generation RNA sequencing and comprehensive bioinformatics analysis were conducted to characterize the transcriptome profiles, including mRNA, microRNA (miRNA), and long non-coding RNA (lncRNA). The head kidney of rainbow trout were exposed to acute heat stress at 22.5 °C for 24 h. A total of 2605 lncRNAs, 214 miRNAs, and 5608 mRNAs were identified as differentially regulated. Among these expressed genes differentially, 45 lncRNAs and 2 target genes, as well as 38 miRNAs and 14 target genes were significantly enriched in the innate immune response of rainbow trout. LncRNA is used as competitive endogenous RNA (ceRNA) to construct the ceRNA-miRNA-mRNA interaction network. Enrichment analysis of the Kyoto encyclopedia of genes and genomes (KEGG) of ceRNA, the differentially expressed genes related to the innate immune function of rainbow trout, were significantly enriched in the signaling pathway mediated by mitogen-activated protein kinase (MAPK). Overall, these analyses showed the effects of heat stress on the innate immune function in rainbow trout at the transcriptome level, providing a theoretical basis to improve the production and breeding of rainbow trout and the selection of new heat-resistant varieties.

Guidelines for Setting Up a mRNA Sequencing Experiment and Best Practices for Bioinformatic Data Analysis

RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analysis of transcriptomes. It uses high-throughput next-generation sequencing technologies and has revolutionized our understanding of the complexity and dynamics of whole transcriptomes.In this chapter, we recall the key developments in transcriptome analysis and dissect the different steps of the general workflow that can be run by users to design and perform a mRNA-seq experiment as well as to process mRNA-seq data obtained by the Illumina technology. The chapter proposes guidelines for completing a mRNA-seq study properly and makes available recommendations for best practices based on recent literature and on the latest developments in technology and algorithms. We also remark the large number of choices available (especially for bioinformatic data analysis) in front of which the scientist may be in trouble.In the last part of the chapter we discuss the new frontiers of single-cell RNA-seq and isoform sequencing by long read technology.

Advances in transcriptome analysis of human brain aging

Aging is associated with gradual deterioration of physiological and biochemical functions, including cognitive decline. Transcriptome profiling of brain samples from individuals of varying ages has identified the whole-transcriptome changes that underlie age-associated cognitive declines. In this review, we discuss transcriptome-based research on human brain aging performed by using microarray and RNA sequencing analyses. Overall, decreased synaptic function and increased immune function are prevalent in most regions of the aged brain. Age-associated gene expression changes are also cell dependent and region dependent and are affected by genotype. In addition, the transcriptome changes that occur during brain aging include different splicing events, intersample heterogeneity, and altered levels of various types of noncoding RNAs. Establishing transcriptome-based hallmarks of human brain aging will improve the understanding of cognitive aging and neurodegenerative diseases and eventually lead to interventions that delay or prevent brain aging.

Cracking of jujube fruits is associated with differential expression of metabolic genes

Cracks in the skin of jujube fruit reduce freshness and quality; thus, greater understanding of the molecular mechanism that underlies cracking is required to improve fruit production. In this study, we profiled genes that are differentially expressed between cracked and normal jujube fruits through RNA sequencing (RNA‐seq). We selectively confirmed differentially expressed genes (DEGs) using quantitative RT‐PCR. Among 1036 DEGs, 785 genes were up‐regulated and 251 genes were down‐regulated in cracked jujube fruits. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that some of these DEGs encode proteins involved in metabolic processes (including growth hormone and surface wax production) in cracked jujube fruits. In summary, we have identified differentially expressed metabolic genes between cracked and normal jujube fruits, which may serve as the basis for further studies of fruit quality control.

Expanding Role of Ubiquitin in Translational Control

The eukaryotic proteome has to be precisely regulated at multiple levels of gene expression, from transcription, translation, and degradation of RNA and protein to adjust to several cellular conditions. Particularly at the translational level, regulation is controlled by a variety of RNA binding proteins, translation and associated factors, numerous enzymes, and by post-translational modifications (PTM). Ubiquitination, a prominent PTM discovered as the signal for protein degradation, has newly emerged as a modulator of protein synthesis by controlling several processes in translation. Advances in proteomics and cryo-electron microscopy have identified ubiquitin modifications of several ribosomal proteins and provided numerous insights on how this modification affects ribosome structure and function. The variety of pathways and functions of translation controlled by ubiquitin are determined by the various enzymes involved in ubiquitin conjugation and removal, by the ubiquitin chain type used, by the target sites of ubiquitination, and by the physiologic signals triggering its accumulation. Current research is now elucidating multiple ubiquitin-mediated mechanisms of translational control, including ribosome biogenesis, ribosome degradation, ribosome-associated protein quality control (RQC), and redox control of translation by ubiquitin (RTU). This review discusses the central role of ubiquitin in modulating the dynamism of the cellular proteome and explores the molecular aspects responsible for the expanding puzzle of ubiquitin signals and functions in translation.

ChiTaRS 5.0: the comprehensive database of chimeric transcripts matched with druggable fusions and 3D chromatin maps

Chimeric RNA transcripts are formed when exons from two genes fuse together, often due to chromosomal translocations, transcriptional errors or trans-splicing effect. While these chimeric RNAs produce functional proteins only in certain cases, they play a significant role in disease phenotyping and progression. ChiTaRS 5.0 (http://chitars.md.biu.ac.il/) is the latest and most comprehensive chimeric transcript repository, with 111 582 annotated entries from eight species, including 23 167 known human cancer breakpoints. The database includes unique information correlating chimeric breakpoints with 3D chromatin contact maps, generated from public datasets of chromosome conformation capture techniques (Hi-C). In this update, we have added curated information on druggable fusion targets matched with chimeric breakpoints, which are applicable to precision medicine in cancers. The introduction of a new section that lists chimeric RNAs in various cell-lines is another salient feature. Finally, using text-mining techniques, novel chimeras in Alzheimer's disease, schizophrenia, dyslexia and other diseases were collected in ChiTaRS. Thus, this improved version is an extensive catalogue of chimeras from multiple species. It extends our understanding of the evolution of chimeric transcripts in eukaryotes and contributes to the analysis of 3D genome conformational changes and the functional role of chimeras in the etiopathogenesis of cancers and other complex diseases.

Puf6 and Loc1 Are the Dedicated Chaperones of Ribosomal Protein Rpl43 in Saccharomyces cerevisiae

Ribosomal proteins are highly expressed, and the quality of ribosomal proteins must be rigorously controlled to build up a functional ribosome. Rpl43, ribosomal protein large subunit 43, is located nearby the E-site of ribosomes. In our previous study, we found that Puf6, Loc1, and Rpl43 form a trimeric complex in Saccharomyces cerevisiae. Rpl43 protein levels are under-accumulated in the absence of PUF6 or LOC1. However, why the loss of Puf6 or Loc1 decreased the protein levels of Rpl43 remained unclear. In the present study, we further dissected the connections among these three proteins and found that the processing defects of pre-ribosomal RNA in puf6Δ and loc1Δ are similar to those of the mutant with depletion of Rpl43. The stability of newly synthesized Rpl43 protein decreased slightly in puf6Δ and significantly in loc1Δ. We also found that Puf6 and Loc1 could interact with nascent Rpl43 co-translationally via the N-terminus of Rpl43. While the association and dissociation of Rpl43 with karyopherins did not depend on Puf6 and Loc1, Puf6 and Loc1 interacted with nascent Rpl43 in collaboration. While the N-terminus of Puf6 contained nuclear localization signals for transport, the PUF (Pumilio) domain was essential to interaction with Loc1, Rpl43, and 60S subunits. The C-terminus of Loc1 is more important for interaction with Puf6 and Rpl43. In this study, we found that Puf6 and Loc1 are the dedicated chaperones of ribosomal protein Rpl43 and also analyzed the potential interaction domains among the three proteins. Correct formation of the Puf6, Loc1, and Rpl43 ternary complex is required to properly proceed to the next step in 60S biogenesis.

Integrated Epigenetics, Transcriptomics, and Metabolomics to Analyze the Mechanisms of Benzo[a]pyrene Neurotoxicity in the Hippocampus

Benzo[a]pyrene (B[a]P) is a common environmental pollutant that is neurotoxic to mammals, which can cause changes to hippocampal function and result in cognitive disorders. The mechanisms of B[a]P-induced impairments are complex .To date there have been no studies on the association of epigenetic, transcriptomic, and metabolomic changes with neurotoxicity after B[a]P exposure. In the present study, we investigated the global effect of B[a]P on DNA methylation patterns, noncoding RNAs (ncRNAs) expression, coding RNAs expression, and metabolites in the rat hippocampus. Male Sprague Dawley rats (SD rats) received daily gavage of B[a]P (2.0 mg/kg body weight [BW]) or corn oil for 7 weeks. Learning and memory ability was analyzed using the Morris water maze (MWM) test and change to cellular ultrastructure in the hippocampus was analyzed using electron microscope observation. Integrated analysis of epigenetics, transcriptomics, and metabolomics was conducted to investigate the effect of B[a]P exposure on the signaling and metabolic pathways. Our results suggest that B[a]P could lead to learning and memory deficits, likely as a result of epigenetic and transcriptomic changes that further affected the expression of CACNA1C, Tpo, etc. The changes in expression ultimately affecting LTP, tyrosine metabolism, and other important metabolic pathways.

A chemical kinetic basis for measuring translation initiation and elongation rates from ribosome profiling data

Analysis methods based on simulations and optimization have been previously developed to estimate relative translation rates from next-generation sequencing data. Translation involves molecules and chemical reactions, hence bioinformatics methods consistent with the laws of chemistry and physics are more likely to produce accurate results. Here, we derive simple equations based on chemical kinetic principles to measure the translation-initiation rate, transcriptome-wide elongation rate, and individual codon translation rates from ribosome profiling experiments. Our methods reproduce the known rates from ribosome profiles generated from detailed simulations of translation. By applying our methods to data from S. cerevisiae and mouse embryonic stem cells, we find that the extracted rates reproduce expected correlations with various molecular properties, and we also find that mouse embryonic stem cells have a global translation speed of 5.2 AA/s, in agreement with previous reports that used other approaches. Our analysis further reveals that a codon can exhibit up to 26-fold variability in its translation rate depending upon its context within a transcript. This broad distribution means that the average translation rate of a codon is not representative of the rate at which most instances of that codon are translated, and it suggests that translational regulation might be used by cells to a greater degree than previously thought.