A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome

Detecting differentially expressed genes from RNA-seq data using fuzzy clustering

A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clustering that artificially generates data with expression patterns similar to those of DEGs to identify genes that are highly likely to be classified into the same cluster as the initial cluster data. The proposed method is advantageous in that it does not perform any test. Furthermore, a certain level of accuracy can be maintained even when the sample size is biased, and we show that such a situation may improve the accuracy of the proposed method. We compared the proposed method with the conventional method using simulations. In the simulations, we changed the sample size and difference between the expression levels of group 1 and group 2 in the DEGs to obtain the desired accuracy of the proposed method. The results show that the proposed method is superior in all cases under the conditions simulated. We also show that the effect of the difference between group 1 and group 2 on the accuracy is more prominent when the sample size is biased.

Abnormal genes and pathways that drive muscle contracture from brachial plexus injuries: Towards machine learning approach

In order to clarify the pathways closely linked to denervated muscle contracture, this work uses IoMT-enabled healthcare stratergies to examine changes in gene expression patterns inside atrophic muscles following brachial plexus damage. The gene expression Omnibus (GEO) database searching was used to locate the dataset GSE137606, which is connected to brachial plexus injuries. Strict criteria (|logFC|≥2 & adj.p < 0.05) were used to extract differentially expressed genes (DEGs). To identify dysregulated activities and pathways in denervated muscles, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used. Hub genes were found using Cytoscape software's algorithms, which took into account parameters like as proximity, degree, and MNC. Their expression, enriched pathways, and correlations were then examined. The results showed that 316 DEGs were predominantly concentrated in muscle-related processes such as tissue formation and contraction pathways. Of these, 297 DEGs were highly expressed in denervated muscles, whereas 19 DEGs were weakly expressed. GSEA showed improvements in the contraction of striated and skeletal muscles. In addition, it was shown that in denervated muscles, Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1 were elevated hub genes with enriched pathways such adrenergic signaling and tight junction. These results point to possible therapeutic targets for denervated muscular contracture, including Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1. This highlights treatment options for this ailment which enhances the mental state of patient.

A reliable and quick method for screening alternative splicing variants for low-abundance genes

Alternative splicing (AS) is a universal phenomenon in eukaryotes, and it is still challenging to identify AS events. Several methods have been developed to identify AS events, such as expressed sequence tags (EST), microarrays and RNA-seq. However, EST has limitations in identifying low-abundance genes, while microarray and RNA-seq are high-throughput technologies, and PCR-based technology is needed for validation. To overcome the limitations of EST and shortcomings of high-throughput technologies, we established a method to identify AS events, especially for low-abundance genes, by reverse transcription (RT) PCR with gene-specific primers (GSPs) followed by nested PCR. This process includes two major steps: 1) the use of GSPs to amplify as long as the specific gene segment and 2) multiple rounds of nested PCR to screen the AS and confirm the unknown splicing variants. With this method, we successfully identified three new splicing variants, namely, GenBank Accession No. HM623886 for the bdnf gene (GenBank GeneID: 12064), GenBank Accession No. JF417977 for the trkc gene (GenBank GeneID: 18213) and GenBank Accession No. HM623888 for the glb-18 gene (GenBank GeneID: 172485). In addition to its reliability and simplicity, the method is also cost-effective and labor-intensive. In conclusion, we developed an RT-nested PCR method using gene-specific primers to efficiently identify known and novel AS variants. This approach overcomes the limitations of existing methods for detecting rare transcripts. By enabling the discovery of new isoforms, especially for low-abundance genes, this technique can aid research into aberrant splicing in disease. Future studies can apply this method to uncover AS variants involved in cancer, neurodegeneration, and other splicing-related disorders.

Comparison of RNA-Sequencing Methods for Degraded RNA

RNA sequencing (RNA-Seq) is a powerful technique and is increasingly being used in clinical research and drug development. Currently, several RNA-Seq methods have been developed. However, the relative advantage of each method for degraded RNA and low-input RNA, such as RNA samples collected in the field of clinical setting, has remained unknown. The Standard method of RNA-Seq captures mRNA by poly(A) capturing using Oligo dT beads, which is not suitable for degraded RNA. Here, we used three commercially available RNA-Seq library preparation kits (SMART-Seq, xGen Broad-range, and RamDA-Seq) using random primer instead of Oligo dT beads. To evaluate the performance of these methods, we compared the correlation, the number of detected expressing genes, and the expression levels with the Standard RNA-Seq method. Although the performance of RamDA-Seq was similar to that of Standard RNA-Seq, the performance for low-input RNA and degraded RNA has decreased. The performance of SMART-Seq was better than xGen and RamDA-Seq in low-input RNA and degraded RNA. Furthermore, the depletion of ribosomal RNA (rRNA) improved the performance of SMART-Seq and xGen due to increased expression levels. SMART-Seq with rRNA depletion has relative advantages for RNA-Seq using low-input and degraded RNA.

The Functional Relationship Between RNA Splicing and the Chromatin Landscape

Chromatin is a highly regulated and dynamic structure that has been shown to play an essential role in transcriptional and co-transcriptional regulation. In the context of RNA splicing, early evidence suggested a loose connection between the chromatin landscape and splicing. More recently, it has been shown that splicing occurs in a co-transcriptional manner, meaning that the splicing process occurs in the context of chromatin. Experimental and computational evidence have also shown that chromatin dynamics can influence the splicing process and vice versa. However, much of this evidence provides mainly correlative relationships between chromatin and splicing with just a few concrete examples providing defined molecular mechanisms by which these two processes are functionally related. Nevertheless, it is clear that chromatin and RNA splicing are tightly interconnected to one another. In this review, we highlight the current state of knowledge of the relationship between chromatin and splicing.

iFLAS: positive-unlabeled learning facilitates full-length transcriptome-based identification and functional exploration of alternatively spliced isoforms in maize

The advent of full-length transcriptome sequencing technologies has accelerated the discovery of novel splicing isoforms. However, existing alternative splicing (AS) tools are either tailored for short-read RNA-Seq data or designed for human and animal studies. The disparities in AS patterns between plants and animals still pose a challenge to the reliable identification and functional exploration of novel isoforms in plants. Here, we developed integrated full-length alternative splicing analysis (iFLAS), a plant-optimized AS toolkit that introduced a semi-supervised machine learning method known as positive-unlabeled (PU) learning to accurately identify novel isoforms. iFLAS also enables the investigation of AS functions from various perspectives, such as differential AS, poly(A) tail length, and allele-specific AS (ASAS) analyses. By applying iFLAS to three full-length transcriptome sequencing datasets, we systematically identified and functionally characterized maize (Zea mays) AS patterns. We found intron retention not only introduces premature termination codons, resulting in lower expression levels of isoforms, but may also regulate the length of 3'UTR and poly(A) tail, thereby affecting the functional differentiation of isoforms. Moreover, we observed distinct ASAS patterns in two genes within heterosis offspring, highlighting their potential value in breeding. These results underscore the broad applicability of iFLAS in plant full-length transcriptome-based AS research.

Lacticaseibacillus chiayiensis mediate intestinal microbiome and microbiota-derived metabolites regulating the growth and immunity of chicks

Emerging evidence confirms beneficial properties of probiotics in promoting growth and immunity of farmed chicken. However, the molecular mechanisms underlying the host-microbiome interactions mediated by probiotics are not fully understood. In this study, the internal mechanisms of Lacticaseibacillus chiayiensis-mediated host-microbiome interactions and to elucidate how it promotes host growth were investigated by additional supplementation with L. chiayiensis. We conducted experiments, including intestinal cytokines, digestive enzymes test, intestinal microbiome, metabolome and transcriptome analysis. The results showed that chickens fed L. chiayiensis exhibited higher body weight gain and digestive enzyme activity, and lower pro-inflammatory cytokines, compared to controls. Microbiota sequencing analysis showed that the gut microbiota structure was reshaped with L. chiayiensis supplementation. Specifically, Lactobacillus and Escherichia increased in abundance and Enterococcus, Lactococcus, Corynebacterium, Weissella and Gallicola decreased. In addition, the bacterial community diversity was significantly increased compared to controls. Metabolomic and transcriptomic analyses revealed that higher bile acids and N-acyl amides concentrations and lower carbohydrates concentrations in L. chiayiensis-fed chickens. Meanwhile, the expression of genes related to nutrient transport and absorption in the intestine was upregulated, which reflected the enhanced digestion and absorption of nutrients in chickens supplemented with L. chiayiensis. Moreover, supplementation of L. chiayiensis down-regulated genes involved in inflammation-related, mainly involved in NF-κB signaling pathway and MHC-II mediated antigen presentation process. Cumulatively, these findings highlight that host-microbiota crosstalk enhances the host growth phenotype in two ways: by enhancing bile acid metabolism and digestive enzyme activity, and reducing the occurrence of intestinal inflammation to promote nutrient absorption and maintain intestinal health. This provides a basis for the application of LAB as an alternative to antibiotics in animal husbandry.

Comparison of RNA-Seq and microarray in the prediction of protein expression and survival prediction

Gene expression profiling using RNA-sequencing (RNA-seq) and microarray technologies is widely used in cancer research to identify biomarkers for clinical endpoint prediction. We compared the performance of these two methods in predicting protein expression and clinical endpoints using The Cancer Genome Atlas (TCGA) datasets of lung cancer, colorectal cancer, renal cancer, breast cancer, endometrial cancer, and ovarian cancer. We calculated the correlation coefficients between gene expression measured by RNA-seq or microarray and protein expression measured by reverse phase protein array (RPPA). In addition, after selecting the top 103 survival-related genes, we compared the random forest survival prediction model performance across test platforms and cancer types. Both RNA-seq and microarray data were retrieved from TCGA dataset. Most genes showed similar correlation coefficients between RNA-seq and microarray, but 16 genes exhibited significant differences between the two methods. The BAX gene was recurrently found in colorectal cancer, renal cancer, and ovarian cancer, and the PIK3CA gene belonged to renal cancer and breast cancer. Furthermore, the survival prediction model using microarray was better than the RNA-seq model in colorectal cancer, renal cancer, and lung cancer, but the RNA-seq model was better in ovarian and endometrial cancer. Our results showed good correlation between mRNA levels and protein measured by RPPA. While RNA-seq and microarray performance were similar, some genes showed differences, and further clinical significance should be evaluated. Additionally, our survival prediction model results were controversial.

Integrative Analyses of Transcriptomics and Metabolomics in Immune Response of Leguminivora glycinivorella Mats to Beauveria bassiana Infection

This study utilized Beauveria bassiana to infect Leguminivora glycinivorella, analyzed the effects on the transcriptome and metabolome, and further investigated the antibacterial function of L. glycinivorella. We performed transcriptome and metabolome sequencing on the L. glycinivorella infected with B. bassiana and its control groups, and performed a joint analysis of transcriptome and metabolome results. Upon screening, 4560 differentially expressed genes were obtained in the transcriptome and 71 differentially expressed metabolites were obtained in the metabolome. On this basis, further integration of the use of transcriptomics and metabonomics combined an analysis of common enrichments of pathways of which there were three. They were glutathione S-transferase (GSTs) genes, heat shock protein (HSP) genes, and cytochrome P450 (CYP450) genes. These three pathways regulate the transport proteins, such as ppars, and thus affect the digestion and absorption of sugars and fats, thus regulating the development of pests. The above conclusion indicates that B. bassiana can affect the sugar metabolism, lipid metabolism, and amino acid metabolism pathways of L. glycinivorella, and can consume the necessary energy, protein, and lipids of L. glycinivorella. The research on the immune response mechanism of pests against pathogens can provide an important scientific basis and target for the development of immunosuppressants. This study laid an information foundation for the application of entomogenous fungi to control soybean borer at the molecular level.

A novel splice variant of goat CPT1a gene and their diverse mRNA expression profiles

The Alternative splicing (AS) of Carnitine palmitoyltransferase 1a (CPT1a) and their expression profiles had never been illuminated in goats until now. Herein, a novel splice transcript in the CPT1a gene that is predicted to result in the skipping of exons 6-19 (CPT1a-sv1) has been isolated in addition to the full-length transcript in goats. The result of RT-PCR showed that CPT1a-sv1 is 606 bp in length and consists of 6 exons. A novel exon 6 was consisted of partial exon 5 and partial exon 19, compared to that in CPT1a. RT-qPCR analysis showed that the expression patterns of CPT1a and CPT1a-sv1 are spatially different. In both kid and adult goats, the CPT1a transcript is strongly expressed in the liver, spleen, lung, kidney, and brain tissues. However, CPT1a-sv1 has a strong tissue-specific expression pattern, with moderate RNA levels in the liver and brain of kids, while highly expressed in the liver and minimally expressed in the brain of adults. We observed two transcripts to be involved in brain development. These findings improve our understanding of the function of the CPT1a gene in goats and provide information on the molecular mechanism of AS events.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

TRPM4 Drives Cerebral Edema by Switching to Alternative Splicing Isoform After Experimental Traumatic Brain Injury

Traumatic brain injury (TBI) affects persons of all ages and is recognized as a major cause of death and disability worldwide; it also brings heavy life burden to patients and their families. The treatment of those with secondary injury after TBI is still scarce, however. Alternative splicing (AS) is a crucial post-transcriptional regulatory mechanism associated with various physiological processes, while the contribution of AS in treatment after TBI is poorly illuminated. In this study, we performed and analyzed the transcriptome and proteome datasets of brain tissue at multiple time points in a controlled cortical impact (CCI) mouse model. We found that AS, as an independent change against the transcriptional level, is a novel mechanism linked to cerebral edema after TBI. Bioinformatics analysis further indicated that the transformation of splicing isoforms after TBI was related to cerebral edema. Accordingly, we found that the fourth exon of transient receptor potential channel melastatin 4 (Trpm4) abrogated skipping at 72 h after TBI, resulting in a frameshift of the encoded amino acid and an increase in the proportion of spliced isoforms. Using magnetic resonance imaging (MRI), we have shown the numbers of 3nEx isoforms of Trpm4 may be positively correlated with volume of cerebral edema. Thus alternative splicing of Trpm4 becomes a noteworthy mechanism of potential influence on edema. In summary, alternative splicing of Trpm4 may drive cerebral edema after TBI. Trpm4 is a potential therapeutic targeting cerebral edema in patients with TBI.

The mechanisms governing mouse embryonic palate mesenchymal cells' proliferation associated with atRA-induced cleft palate in mice: insights from integrated transcriptomic and metabolomic analyses

While exposure to high levels of all-trans retinoic acid (atRA) during pregnancy is known to suppress murine embryonic palate mesenchymal (MEPM) cells proliferation and to result in cleft palate (CP) development, the underlying mechanisms are poorly understood. Accordingly, this study was designed with the goal of clarifying the etiological basis for atRA-induced CP. A murine model of CP was established via the oral administration of atRA to pregnant mice on gestational day (GD) 10.5, after which transcriptomic and metabolomic analyses were performed with the goal of clarifying the critical genes and metabolites associated with CP development through an integrated multi-omics approach. MEPM cells proliferation was altered by atRA exposure as expected, contributing to CP incidence. In total, 110 genes were differentially expressed in the atRA treatment groups, suggesting that atRA may influence key biological processes including stimulus, adhesion, and signaling-related activities. In addition, 133 differentially abundant metabolites were identified including molecules associated with ABC transporters, protein digestion and absorption, mTOR signaling pathway, and the TCA cycle, suggesting a link between these mechanisms and CP. Overall, combined analyses of these transcriptomic and metabolomic results suggested that the MAPK, calcium, PI3K-Akt, Wnt, and mTOR signaling pathways are particularly important pathways enriched in the palatal cleft under conditions of atRA exposure. Together, these integrated transcriptomic and metabolomic approaches provided new evidence with respect to the mechanisms underlying altered MEPM cells proliferation and signal transduction associated with atRA-induced CP, revealing a possible link between oxidative stress and these pathological changes.

Transcriptomic Analysis of Mycobacterial Infected Macrophages Reveals a High MOI Specific Type I IFN Signaling

Macrophage (MΦ) infection models are important tools for studying host-mycobacterial interactions. Although the multiplicity of infection (MOI) is an important experimental variable, the selection of MOI in mycobacterial infection experiments is largely empirical, without reference to solid experimental data. To provide relevant data, we used RNA-seq to analyze the gene expression profiles of MΦs 4 or 24 h after infection with Mycobacterium marinum (M. m) at MOIs ranging from 0.1 to 50. Analysis of differentially expressed genes (DEGs) showed that different MOIs are linked to distinct transcriptomic changes and only 10% of DEGs were shared by MΦ infected at all MOIs. KEGG pathway enrichment analysis revealed that type I interferon (IFN)-related pathways were inoculant dose-dependent and enriched only at high MOIs, whereas TNF pathways were inoculant dose-independent and enriched at all MOIs. Protein-protein interaction (PPI) network alignment showed that different MOIs had distinct key node genes. By fluorescence-activated cell sorting and follow-up RT-PCR analysis, we could separate infected MΦs from uninfected MΦs and found phagocytosis of mycobacteria to be the determinant factor for type I IFN production. The distinct transcriptional regulation of RAW264.7 MΦ genes at different MOIs was also seen with Mycobacterium tuberculosis (M.tb) infections and primary MΦ infection models. In summary, transcriptional profiling of mycobacterial infected MΦs revealed that different MOIs activate distinct immune pathways and the type I IFN pathway is activated only at high MOIs. This study should provide guidance for selecting the MOI most appropriate for different research questions.

Embryonic Development of Parthenogenetic and Sexual Eggs in Lower Termites

Worldwide, termites are one of few social insects. In this research, the stages of embryonic development in the parthenogenetic and sexual eggs of Reticulitermes aculabialis and R. flaviceps were observed and described. In R. flaviceps, the egg development of the FF and FM groups happened during the early phases of development, whereas in R. aculabialis, this appeared mainly during the late phase of development. The variance in the number of micropyles between the R. flaviceps FF colony type and the R. aculabialis FF colony type was statistically significant. Five stages of egg development were found in both types of R. aculabialis but only the sexual eggs of R. flaviceps. In R. flaviceps, 86% of the parthenogenetic eggs stopped growing during the blastoderm development, with the yolk cell assembling frequently in the center of the egg. According to the results of the single-cell transcriptome sequencing, we investigated the egg-to-larval expression level of genes (pka, map2k1, mapk1/3, hgk, mkp, and pax6) and indicated that the levels of essential gene expression in RaFF were considerably higher than in RfFF (p < 0.05). We also discovered that the oocyte cleavage rate in the FF colony type was considerably lower in R. flaviceps compared to R. aculabialis, which gave rise to a smaller number of mature oocytes in R. flaviceps. During ovulation in both species, oocytes underwent activation and one or two cleavage events, but the development of unfertilized eggs ceased in R. flaviceps. It was shown that termite oocyte and embryonic development were heavily influenced by genes with significant expressions. Results from the databases KEGG, COG, and GO unigenes revealed the control of numerous biological processes. This study is the first to complete a database of parthenogenetic and sexual eggs of R. flaviceps and R. aculabialis.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq

The ability to analyze the transcriptomic and epigenomic states of individual single cells has in recent years transformed our ability to measure and understand biological processes. Recent advancements have focused on increasing sensitivity and throughput to provide richer and deeper biological insights at the cellular level. The next frontier is the development of multiomic methods capable of analyzing multiple features from the same cell, such as the simultaneous measurement of the transcriptome and the chromatin accessibility of candidate regulatory elements. In this chapter, we discuss and describe SHARE-seq (Simultaneous high-throughput ATAC, and RNA expression with sequencing) for carrying out simultaneous chromatin accessibility and transcriptome measurements in single cells, together with the experimental and analytical considerations for achieving optimal results.

Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.

A powerful global test for spliceQTL effects

Statistical methods to test for effects of single nucleotide polymorphisms (SNPs) on exon inclusion exist but often rely on testing of associations between multiple exon-SNP pairs, with sometimes subsequent summarization of results at the gene level. Such approaches require heavy multiple testing corrections and detect mostly events with large effect sizes. We propose here a test to find spliceQTL (splicing quantitative trait loci) effects that takes all exons and all SNPs into account simultaneously. For any chosen gene, this score-based test looks for an association between the set of exon expressions and the set of SNPs, via a random-effects model framework. It is efficient to compute and can be used if the number of SNPs is larger than the number of samples. In addition, the test is powerful in detecting effects that are relatively small for individual exon-SNP pairs but are observed for many pairs. Furthermore, test results are more often replicated across datasets than pairwise testing results. This makes our test more robust to exon-SNP pair-specific effects, which do not extend to multiple pairs within the same gene. We conclude that the test we propose here offers more power and better replicability in the search for spliceQTL effects.

The community ecology perspective of omics data

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract.

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis include many data types and file formats, and a wide variety of computational tools that can be applied alone or together as pipelines. This paper presents a review of the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. This review aims to address mainly the aspects involved in the differentially expressed gene (DEG) analysis from RNA sequencing data (RNA-seq), considering the computational methods. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.

Transcriptomic analysis reveals the key role of histone deacetylation via mediating different phytohormone signalings in fiber initiation of cotton

Background

Histone deacetylation is one of the most important epigenetic modifications and plays diverse roles in plant development. However, the detailed functions and mechanisms of histone deacetylation in fiber development of cotton are still unclear. HDAC inhibitors (HDACi) have been commonly used to study the molecular mechanism underlying histone deacetylation or to facilitate disease therapy in humans through hindering the histone deacetylase catalytic activity. Trichostatin A (TSA)—the most widely used HDACi has been extensively employed to determine the role of histone deacetylation on different developmental stages of plants.

Results

Through in vitro culture of ovules, we observed that exogenous application of TSA was able to inhibit the fiber initiation development. Subsequently, we performed a transcriptomic analysis to reveal the underlying mechanisms. The data showed that TSA treatment resulted in 4209 differentially expressed genes, which were mostly enriched in plant hormone signal transduction, phenylpropanoid biosynthesis, photosynthesis, and carbon metabolism pathways. The phytohormone signal transduction pathways harbor the most differentially expressed genes. Deeper studies showed that some genes promoting auxin, Gibberellic Acid (GA) signaling were down-regulated, while some genes facilitating Abscisic Acid (ABA) and inhibiting Jasmonic Acid (JA) signaling were up-regulated after the TSA treatments. Further analysis of plant hormone contents proved that TSA significantly promoted the accumulation of ABA, JA and GA₃.

Conclusions

Collectively, histone deacetylation can regulate some key genes involved in different phytohormone pathways, and consequently promoting the auxin, GA, and JA signaling, whereas repressing the ABA synthesis and signaling to improve the fiber cell initiation. Moreover, the genes associated with energy metabolism, phenylpropanoid, and glutathione metabolism were also regulated by histone deacetylation. The above results provided novel clues to illuminate the underlying mechanisms of epigenetic modifications as well as related different phytohormones in fiber cell differentiation, which is also very valuable for the molecular breeding of higher quality cotton.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13578-022-00840-4.

Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes

Background

RNA-seq has become a standard technology to quantify mRNA. The measured values usually vary by several orders of magnitude, and while the detection of differences at high values is statistically well grounded, the significance of the differences for rare mRNAs can be weakened by the presence of biological and technical noise.

Results

We have developed a method for cleaning RNA-seq data, which improves the detection of differentially expressed genes and specifically genes with low to moderate transcription. Using a data modeling approach, parameters of randomly distributed mRNA counts are identified and reads, most probably originating from technical noise, are removed. We demonstrate that the removal of this random component leads to the significant increase in the number of detected differentially expressed genes, more significant pvalues and no bias towards low-count genes.

Conclusion

Application of RNAdeNoise to our RNA-seq data on polysome profiling and several published RNA-seq datasets reveals its suitability for different organisms and sequencing technologies such as Illumina and BGI, shows improved detection of differentially expressed genes, and excludes the subjective setting of thresholds for minimal RNA counts. The program, RNA-seq data, resulted gene lists and examples of use are in the supplementary data and at https://github.com/Deyneko/RNAdeNoise.

SERBP1 affects the apoptotic level by regulating the expression and alternative splicing of cellular and metabolic process genes in HeLa cells

Background

RNA-binding proteins (RBPs) have important roles in orchestrating posttranscriptional regulation and modulating many tumorigenesis events. SERBP1 has been recognized as an important regulator in multiple cancers, while it remains unclear whether SERBP1-regulated gene expression at the transcriptome-wide level is significantly correlated with tumorigenesis.

Methods

We overexpressed SERBP1 in HeLa cells and explored whether SERBP1 overexpression (SERBP1-OE) affects the proliferation and apoptosis of HeLa cells. We analyzed the transcriptome-wide gene expression changes and alternative splicing changes mediated by SERBP1-OE using the transcriptome sequencing method (RNA-seq). RT-qPCR was conducted to assay SERBP1-regulated alternative splicing.

Results

SERBP1-OE induced the apoptosis of HeLa cells. The downregulated genes were strongly enriched in the cell proliferation and apoptosis pathways according to the GO analysis, including FOS, FOSB, PAK6 and RAB26. The genes undergoing at least one SERBP1-regulated alternative splicing event were enriched in transcriptional regulation, suggesting a mechanism of the regulation of gene expression, and in pyruvate and fatty acid metabolic processes critical for tumorigenesis events. The SERBP1-regulated alternative splicing of ME3, LPIN3, CROT, PDP1, SLC27A1 and ALKBH7 was validated by RT-qPCR analysis.

Conclusions

We for the first time demonstrated the cellular function and molecular targets of SERBP1 in HeLa cells at transcriptional and post-transcriptional levels. The SERBP1-regulated gene expression and alternative splicing networks revealed by this study provide important information for exploring the functional roles and regulatory mechanisms of SERBP1 in cancer development and progression.

Impact of adaptive filtering on power and false discovery rate in RNA-seq experiments

Background

In RNA-sequencing studies a large number of hypothesis tests are performed to compare the differential expression of genes between several conditions. Filtering has been proposed to remove candidate genes with a low expression level which may not be relevant and have little or no chance of showing a difference between conditions. This step may reduce the multiple testing burden and increase power.

Results

We show in a simulation study that filtering can lead to some increase in power for RNA-sequencing data, too aggressive filtering, however, can lead to a decline. No uniformly optimal filter in terms of power exists. Depending on the scenario different filters may be optimal. We propose an adaptive filtering strategy which selects one of several filters to maximise the number of rejections. No additional adjustment for multiplicity has to be included, but a rule has to be considered if the number of rejections is too small.

Conclusions

For a large range of simulation scenarios, the adaptive filter maximises the power while the simulated False Discovery Rate is bounded by the pre-defined significance level. Using the adaptive filter, it is not necessary to pre-specify a single individual filtering method optimised for a specific scenario.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04928-z.

Filtering ASVs/OTUs via mutual information-based microbiome network analysis

Microbial communities are widely studied using high-throughput sequencing techniques, such as 16S rRNA gene sequencing. These techniques have attracted biologists as they offer powerful tools to explore microbial communities and investigate their patterns of diversity in biological and biomedical samples at remarkable resolution. However, the accuracy of these methods can negatively affected by the presence of contamination. Several studies have recognized that contamination is a common problem in microbial studies and have offered promising computational and laboratory-based approaches to assess and remove contaminants. Here we propose a novel strategy, MI-based (mutual information based) filtering method, which uses information theoretic functionals and graph theory to identify and remove contaminants. We applied MI-based filtering method to a mock community data set and evaluated the amount of information loss due to filtering taxa. We also compared our method to commonly practice traditional filtering methods. In a mock community data set, MI-based filtering approach maintained the true bacteria in the community without significant loss of information. Our results indicate that MI-based filtering method effectively identifies and removes contaminants in microbial communities and hence it can be beneficial as a filtering method to microbiome studies. We believe our filtering method has two advantages over traditional filtering methods. First, it does not required an arbitrary choice of threshold and second, it is able to detect true taxa with low abundance.

Pathogenic variants detected by RNA sequencing in Cornelia de Lange syndrome

Recent studies suggest that transcript isoforms significantly overlap (approximately 60%) between brain tissue and Epstein-Barr virus-transformed lymphoblastoid cell lines (LCLs). Interestingly, 14 cohesion-related genes with variants that cause Cornelia de Lange Syndrome (CdLS) are highly expressed in the brain and LCLs. In this context, we first performed RNA sequencing of LCLs from 22 solved (with pathogenic variants) and 19 unsolved (with no confirmed variants) CdLS cases. Next, an RNA sequencing pipeline was developed using solved cases with two different methods: short variant analysis (for single-nucleotide and indel variants) and aberrant splicing detection analysis. Then, 19 unsolved cases were subsequently applied to our pipeline, and four pathogenic variants in NIPBL (one inframe deletion and three intronic variants) were newly identified. Two of three intronic variants were located at Alu elements in deep-intronic regions, creating cryptic exons. RNA sequencing with LCLs was useful for identifying hidden variants in exome-negative cases.

Transcriptomic analysis reveals crucial regulatory roles of immediate-early response genes and related signaling pathways in coronavirus infectious bronchitis virus infection

Coronavirus infection of cells differentially regulates the expression of host genes and their related pathways. In this study, we present the transcriptomic profile of cells infected with gammacoronavirus infectious bronchitis virus (IBV). In IBV-infected human non-small cell lung carcinoma cells (H1299 cells), a total of 1162 differentially expressed genes (DEGs), including 984 upregulated and 178 downregulated genes, was identified. These DEGs were mainly enriched in MAPK and Wnt signaling pathways, and 5 out of the 10 top upregulated genes in all transcripts were immediate-early response genes (IEGs). In addition, the induction of 11 transcripts was validated in IBV-infected H1299 and Vero cells by RT-qPCR. The accuracy, reliability and genericity of the transcriptomic data were demonstrated by functional characterization of these IEGs in cells infected with different coronaviruses in our previous publications. This study provides a reliable transcriptomic profile of host genes and pathways regulated by coronavirus infection.

Transcriptomics and Metabolomics Analysis of the Ovaries of High and Low Egg Production Chickens

Simple Summary

The ovarian tissues of different breeds of hens during egg production were investigated through transcriptomics and metabolomics to provide a more comprehensive understanding of the molecular mechanisms of the ovary during egg production. Four genes involved in egg production were predicted by the transcriptome, including P2RX1, INHBB, VIPR2, and FABP3, and several close metabolites associated with reproduction were identified in the metabolome, including 17α-hydroxyprogesterone, iloprost, spermidine and adenosine. Correlation analysis of specific differential genes and differential metabolites identified important gene-metabolite pairs VIPR2–Spermidine and P2RX1–Spermidine in the reproductive process.

Abstract

Egg production is a pivotal indicator for evaluating the fertility of poultry, and the ovary is an essential organ for egg production and plays an indispensable role in poultry production and reproduction. In order to investigate different aspects of egg production mechanisms in different poultry, in this study we performed a metabolomic analysis of the transcriptomic combination of the ovaries of two chicken breeds, the high-production Ninghai indigenous chickens and the low-production Wuliangshan black-boned chickens, to analyze the biosynthesis and potential key genes and metabolic pathways in the ovaries during egg production. We predicted four genes in the transcriptomic that are associated with egg production, namely P2RX1, INHBB, VIPR2, and FABP3, and identified three important pathways during egg production, “Calcium signaling pathway”, “Neuroactive ligand–receptor interaction” and “Cytokine–cytokine receptor interaction”, respectively. In the metabolomic 149 significantly differential metabolites were identified, 99 in the negative model and 50 in the positive model, of which 17α-hydroxyprogesterone, iloprost, spermidine, and adenosine are important metabolites involved in reproduction. By integrating transcriptomics and metabolomics, the correlation between specific differential genes and differential metabolites identified important gene-metabolite pairs “VIPR2-Spermidine” and “P2RX1-Spermidine” in egg production. In conclusion, these data provide a better understanding of the molecular differences between the ovaries of low- and high-production hens and provide a theoretical basis for further studies on the mechanics of poultry egg production.

Sofosbuvir induces gene expression for promoting cell proliferation and migration of hepatocellular carcinoma cells

Direct-acting antivirals (DAAs) have achieved a sustained virological response (SVR) rate of 95–99% in treating HCV. Several studies suggested that treatment with sofosbuvir (SOF), one type of DAAs, may be associated with increased risk of developing HCC. The aim of this study is to investigate the potential mechanisms of SOF on the development of HCC. OR-6 (harboring full-length genotype 1b HCV) and Huh 7.5.1 cells were used to examine the effects of SOF on cell proliferation and migration of HCC cells. SOF-upregulated genes in OR-6 cells were inspected using next generation sequencing (NGS)and the clinical significance of these candidate genes was analyzed using The Cancer Genome Atlas (TCGA) database. We found that SOF increased cell proliferation and cell migration in OR-6 and Huh 7.5.1 cells. Several SOF-upregulated genes screened from NGS were confirmed by real-time PCR in OR-6 cells. Among these genes, PHOSPHO2, KLHL23, TRIM39, TSNAX-DISC1 and RPP21 expression were significantly elevated in the tumor tissues compared with the non-tumor tissues of HCC according to TCGA database. High expression of PHOSPHO2 and RPP21 was associated with poor overall survival of HCC patients. Moreover, knockdown of PHOSPHO2-KLHL23, TSNAX-DISC1, TRIM39 and RPP21 diminished cell proliferation and migration increased by SOF in OR-6 and Huh 7.5.1 cells. In conclusion, SOF-upregulated genes promoted HCC cell proliferation and migration, which might be associated with the development of HCC.

Alternative splicing as a source of phenotypic diversity

A major goal of evolutionary genetics is to understand the genetic processes that give rise to phenotypic diversity in multicellular organisms. Alternative splicing generates multiple transcripts from a single gene, enriching the diversity of proteins and phenotypic traits. It is well established that alternative splicing contributes to key innovations over long evolutionary timescales, such as brain development in bilaterians. However, recent developments in long-read sequencing and the generation of high-quality genome assemblies for diverse organisms has facilitated comparisons of splicing profiles between closely related species, providing insights into how alternative splicing evolves over shorter timescales. Although most splicing variants are probably non-functional, alternative splicing is nonetheless emerging as a dynamic, evolutionarily labile process that can facilitate adaptation and contribute to species divergence.

The landscape of isoform switches in sepsis: a multicenter cohort study

Sepsis is caused by an uncontrolled inflammatory response, whose underlying mechanisms are not fully understood. It is well known that the majority of human genes can be expressed as alternative isoforms. While isoform switching is implicated in many diseases and is particularly prominent in cancer, it has never been reported in the context of sepsis. Patients presented to the emergency department of three tertiary care hospitals from January 2020 to December 2020 were enrolled. Clinical variables and genome-wide transcriptome of peripheral blood mononuclear cells (PBMC) were obtained. Isoform switching analysis were performed to identify significant isoform switches and relevant biological consequences. A total of 48 subjects with sepsis, involving 42 survivors and 6 non-survivors, admitted to the emergency department of three tertiary care hospitals were enrolled in this study. PBMCs were extracted for RNA sequencing (RNA-seq). Patients (n = 4) with mild stroke or acute coronary syndrome without infection were enrolled in this study as controls. The most frequent functional changes resulting from isoform switching were changes affecting the open reading frame, protein domains and intron retention. Many genes without differences in gene expression showed significant isoform switching. Many genes with significant isoform switches ([Formula: see text]> 0.1) were associated with higher mortality risk, including PIGS, CASP3, LITAF, HBB and RUVBL2. The study for the first time described the landscape of isoform switching in sepsis, including differentially expressed isoform fractions between patients with and without sepsis and survivors and nonsurvivors. The biological consequences of isoform switching, including protein domain loss, signal peptide gain, and intron retention, were identified.

The Long Read Transcriptome of Rice (Oryza sativa ssp. japonica var. Nipponbare) Reveals Novel Transcripts

BACKGROUND

High-throughput next-generation sequencing technologies offer a powerful approach to characterizing the transcriptomes of plants. Long read sequencing has been shown to support the discovery of novel isoforms of transcripts. This approach enables the generation of full-length sequences revealing splice variants that may be important in regulating gene action. Investigation of the diversity of transcripts in the rice transcriptome including splice variants was conducted using PacBio long-read sequence data to improve the annotation of the rice genome.

RESULTS

A cDNA library was prepared from RNA extracted from leaves, roots, seeds, inflorescences, and panicles of O. sativa ssp. japonica var Nipponbare and sequenced on a PacBio Sequel platform. This produced 346,190 non-redundant full-length non-chimeric reads (FLNC) resulting in 33,504 high-quality transcripts. Half of the transcripts were multi-exonic and entirely matched with the reference transcripts. However, 14,874 novel isoforms were also identified resulting predominantly from intron retention and at least one novel splice site. Intron retention was the prevalent alternative splicing event and exon skipping was the least observed. Of 73,659 splice junctions, 12,755 (17%) represented novel splice junctions with canonical and non-canonical intron boundaries. The complexity of the transcriptome was examined in detail for 19 starch synthesis-related genes, defining 276 spliced isoforms of which 94 splice variants were novel.

CONCLUSION

The data reveal the great complexity of the rice transcriptome. The novel transcripts provide new insights that may be a key input in future research to improve the annotation of the rice genome.

Multivalent interactions essential for lentiviral integrase function

A multimer of retroviral integrase (IN) synapses viral DNA ends within a stable intasome nucleoprotein complex for integration into a host cell genome. Reconstitution of the intasome from the maedi-visna virus (MVV), an ovine lentivirus, revealed a large assembly containing sixteen IN subunits1. Herein, we report cryo-EM structures of the lentiviral intasome prior to engagement of target DNA and following strand transfer, refined at 3.4 and 3.5 Å resolution, respectively. The structures elucidate details of the protein-protein and protein-DNA interfaces involved in lentiviral intasome formation. We show that the homomeric interfaces involved in IN hexadecamer formation and the α-helical configuration of the linker connecting the C-terminal and catalytic core domains are critical for MVV IN strand transfer activity in vitro and for virus infectivity. Single-molecule microscopy in conjunction with photobleaching reveals that the MVV intasome can bind a variable number, up to sixteen molecules, of the lentivirus-specific host factor LEDGF/p75. Concordantly, ablation of endogenous LEDGF/p75 results in gross redistribution of MVV integration sites in human and ovine cells. Our data confirm the importance of the expanded architecture observed in cryo-EM studies of lentiviral intasomes and suggest that this organization underlies multivalent interactions with chromatin for integration targeting to active genes.

Patterns of Crystallin Gene Expression in Differentiation State Specific Regions of the Embryonic Chicken Lens

Purpose

Transition from lens epithelial cells to lens fiber cell is accompanied by numerous changes in gene expression critical for lens transparency. We identify expression patterns of highly prevalent genes including ubiquitous and enzyme crystallins in the embryonic day 13 chicken lens.

Methods

Embryonic day 13 chicken lenses were dissected into central epithelial cell (EC), equatorial epithelial cell (EQ), cortical fiber cell (FP), and nuclear fiber cell (FC) compartments. Total RNA was prepared, subjected to high-throughput unidirectional mRNA sequencing, analyzed, mapped to the chicken genome, and functionally grouped.

Results

A total of 77,097 gene-specific transcripts covering 17,450 genes were expressed, of which 10,345 differed between two or more lens subregions. Ubiquitous crystallin gene expression increased from EC to EQ and was similar in FP and FC. Highly expressed crystallin genes fell into three coordinately expressed groups with R² ≥ 0.93: CRYAA, CRYBB2, CRYAB, and CRYBA2; CRYBB1, CRYBA4, CRYGN, ASL1, and ASL; and CRYBB3 and CRYBA1. The highly expressed transcription factors YBX1, YBX3, PNRC1, and BASP1 were coordinately expressed with the second group of crystallins (r² > 0.88).

Conclusions

Although it is well known that lens crystallin gene expression changes during the epithelial to fiber cell transition, these data identify for the first time three distinct patterns of expression for specific subsets of crystallin genes, each highly correlated with expression of specific transcription factors. The results provide a quantitative basis for designing functional experiments pinpointing the mechanisms governing the landscape of crystallin expression during fiber cell differentiation to attain lens transparency.

Alternative splicing diversifies the transcriptome and proteome of the rice blast fungus during host infection

Alternative splicing (AS) contributes to diversifying and regulating cellular responses to environmental conditions and developmental cues by differentially producing multiple mRNA and protein isoforms from a single gene. Previous studies on AS in pathogenic fungi focused on profiling AS isoforms under a limited number of conditions. We analysed AS profiles in the rice blast fungus Magnaporthe oryzae, a global threat to rice production, using high-quality transcriptome data representing its vegetative growth (mycelia) and multiple host infection stages. We identified 4,270 AS isoforms derived from 2,413 genes, including 499 genes presumably regulated by infection-specific AS. AS appears to increase during infection, with 32.7% of the AS isoforms being produced during infection but absent in mycelia. Analysis of the isoforms observed at each infection stage showed that 636 AS isoforms were more abundant than corresponding annotated mRNAs, especially after initial hyphal penetration into host cell. Many such dominant isoforms were predicted to encode regulatory proteins such as transcription factors and phospho-transferases. We also identified the genes encoding distinct proteins via AS and confirmed the translation of some isoforms via a proteomic analysis, suggesting potential AS-mediated neo-functionalization of some genes during infection. Comprehensive profiling of the pattern of genome-wide AS during multiple stages of rice-M. oryzae interaction established a foundational resource that will help investigate the role and regulation of AS during rice infection.

Genetic variants associated mRNA stability in lung

Background

Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs).

Results

Here, we presented a computational framework that takes advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3942 genes and 186,132 eQTLs for 4751 genes from 15,122,700 genetic variants for 13,476 genes on the autosomes, respectively. Interestingly, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08405-y.

DNA Methylation Malleability and Dysregulation in Cancer Progression: Understanding the Role of PARP1

Mammalian genomic DNA methylation represents a key epigenetic modification and its dynamic regulation that fine-tunes the gene expression of multiple pathways during development. It maintains the gene expression of one generation of cells; particularly, the mitotic inheritance of gene-expression patterns makes it the key governing mechanism of epigenetic change to the next generation of cells. Convincing evidence from recent discoveries suggests that the dynamic regulation of DNA methylation is accomplished by the enzymatic action of TET dioxygenase, which oxidizes the methyl group of cytosine and activates transcription. As a result of aberrant DNA modifications, genes are improperly activated or inhibited in the inappropriate cellular context, contributing to a plethora of inheritable diseases, including cancer. We outline recent advancements in understanding how DNA modifications contribute to tumor suppressor gene silencing or oncogenic-gene stimulation, as well as dysregulation of DNA methylation in cancer progression. In addition, we emphasize the function of PARP1 enzymatic activity or inhibition in the maintenance of DNA methylation dysregulation. In the context of cancer remediation, the impact of DNA methylation and PARP1 pharmacological inhibitors, and their relevance as a combination therapy are highlighted.

Transcriptome Sequencing and Screening of Anthocyanin-Related Genes in the Leaves of Acer truncatum Bunge

Acer truncatum Bunge is generally used as an ornamental tree because of its autumn leaves, although the viewing period is short-approximately 7-15 days. Color improvement of ornamental trees has consistently been an important research topic because color partially determines the value of the commodity; however, a lack of genomic data have limited the progress of molecular breeding research in this area. The purposes of this study were to obtain a transcriptome database for A. truncatum, screen anthocyanin biosynthesis-related genes, and reveal the mechanisms underlying leaf color transformation to provide a basis for increasing the viewing period or breeding cultivars that display red leaves throughout the growing season via gene regulation. In this study, although the use of an Illumina HiSeq 2000 platform and systematic bioinformatics analysis using both young and mature leaves as experimental materials, 233,912,882 clean reads were generated and 121,287 unique transcripts were retrieved. We selected 16 color-related genes (from the transcriptome results) for qRT-PCR to validate the results, and the expression trends of the selected genes were largely consistent with the transcriptome analysis results, with a consistency of 0.875. According to the results of the transcriptome analysis, the validation, and previous studies, we obtained sequences of genes related to anthocyanins, including CHS, CHI, ANS, UFGT, UGT75c1, DFR, BZ1, F3H, F3'H, LAR, ANR, FLS, and those of several transcription factors, including MYB1, BHLH, and WD40. Verifying specific regulation by one or several of these genes in the control of leaf color requires further research. The acquisition of transcriptomic information, especially information concerning anthocyanin biosynthesis-related genes and their base sequences, can provide a theoretical basis for the study of the molecular mechanisms determining changes in leaf color in Acer and is of great importance to the breeding of new cultivars.

Hidden RNA pairings counteract the "first-come, first-served" splicing principle to drive stochastic choice in Dscam1 splice variants

Drosophila melanogaster Dscam1 encodes 38,016 isoforms via mutually exclusive splicing; however, the regulatory mechanism behind this is not fully understood. Here, we found a set of hidden RNA secondary structures that balance the stochastic choice of Dscam1 splice variants (designated balancer RNA secondary structures). In vivo mutational analyses revealed the dual function of these balancer interactions in driving the stochastic choice of splice variants, through enhancement of the inclusion of distal exon 6s by cooperating with docking site–selector pairing to form a stronger multidomain pre-mRNA structure and through simultaneous repression of the inclusion of proximal exon 6s by antagonizing their docking site–selector pairings. Thus, we provide an elegant molecular model based on competition and cooperation between two sets of docking site–selector and balancer pairings, which counteracts the “first-come, first-served” principle. Our findings provide conceptual and mechanistic insight into the dynamics and functions of long-range RNA secondary structures.

Deciphering Cadmium (Cd) Tolerance in Newly Isolated Bacterial Strain, Ochrobactrum intermedium BB12, and Its Role in Alleviation of Cd Stress in Spinach Plant (Spinacia oleracea L.)

A cadmium (Cd)-tolerant bacterium Ochrobactrum intermedium BB12 was isolated from sewage waste collected from the municipal sewage dumping site of Bhopal, India. The bacterium showed multiple heavy metal tolerance ability and had the highest minimum inhibitory concentration of 150 mg L-1 of Cd. Growth kinetics, biosorption, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Fourier transform infrared (FTIR) spectroscopy studies on BB12 in the presence of Cd suggested biosorption as primary mode of interaction. SEM and TEM studies revealed surface deposition of Cd. FTIR spectra indicated nitrogen atom in exopolysaccharides secreted by BB12 to be the main site for Cd attachment. The potential of BB12 to alleviate the impact of Cd toxicity in spinach plants (Spinacia oleracea L.) var. F1-MULAYAM grown in the soil containing Cd at 25, 50, and 75 mg kg-1 was evaluated. Without bacterial inoculation, plants showed delayed germination, decrease in the chlorophyll content, and stunted growth at 50 and 75 mg kg-1 Cd content. Bacterial inoculation, however, resulted in the early germination, increased chlorophyll, and increase in shoot (28.33%) and root fresh weight (72.60%) at 50 mg kg-1 of Cd concentration after 75 days of sowing. Due to bacterial inoculation, elevated proline accumulation and lowered down superoxide dismutase (SOD) enzyme activity was observed in the Cd-stressed plants. The isolate BB12 was capable of alleviating Cd from the soil by biosorption as evident from significant reduction in the uptake/translocation and bioaccumulation of Cd in bacteria itself and in the plant parts of treated spinach. Potential PGP prospects and heavy metal bioremediation capability of BB12 can make the environmental application of the organism a promising approach to reduce Cd toxicity in the crops grown in metal-contaminated soils.

Comparative Transcriptome Analyses of Gayal (Bos frontalis), Yak (Bos grunniens), and Cattle (Bos taurus) Reveal the High-Altitude Adaptation

Gayal and yak are well adapted to their local high-altitude environments, yet the transcriptional regulation difference of the plateau environment among them remains obscure. Herein, cross-tissue and cross-species comparative transcriptome analyses were performed for the six hypoxia-sensitive tissues from gayal, yak, and cattle. Gene expression profiles for all single-copy orthologous genes showed tissue-specific expression patterns. By differential expression analysis, we identified 3,020 and 1,995 differentially expressed genes (DEGs) in at least one tissue of gayal vs. cattle and yak vs. cattle, respectively. Notably, we found that the adaptability of the gayal to the alpine canyon environment is highly similar to the yak living in the Qinghai-Tibet Plateau, such as promoting red blood cell development, angiogenesis, reducing blood coagulation, immune system activation, and energy metabolism shifts from fatty acid β-oxidation to glycolysis. By further analyzing the common and unique DEGs in the six tissues, we also found that numerous expressed regulatory genes related to these functions are unique in the gayal and yak, which may play important roles in adapting to the corresponding high-altitude environment. Combined with WGCNA analysis, we found that UQCRC1 and COX5A are the shared differentially expressed hub genes related to the energy supply of myocardial contraction in the heart-related modules of gayal and yak, and CAPS is a shared differential hub gene among the hub genes of the lung-related module, which is related to pulmonary artery smooth muscle contraction. Additionally, EDN3 is the unique differentially expressed hub gene related to the tracheal epithelium and pulmonary vasoconstriction in the lung of gayal. CHRM2 is a unique differentially expressed hub gene that was identified in the heart of yak, which has an important role in the autonomous regulation of the heart. These results provide a basis for further understanding the complex transcriptome expression pattern and the regulatory mechanism of high-altitude domestication of gayal and yak.

Identification and Quantification of Microexons Using Bulk and Single-Cell RNA-Seq Data

The analysis of RNA-seq has greatly improved the characterization and understanding of the transcriptome. In particular, RNA-seq experiments have extended catalogs of alternative splicing events. However, the analysis of RNAs-seq data for detection and quantification of microexons, extremely short exons of length up to 30 nt, require specialized computational workflows. Here, we describe MicroExonator, a reproducible computational workflow for microexon splicing analysis using bulk or single-cell RNA-seq data.

MYCN and MAX alterations in Wilms tumor and identification of novel N-MYC interaction partners as biomarker candidates

BACKGROUND

Wilms tumor (WT) is the most common renal tumor in childhood. Among others, MYCN copy number gain and MYCN P44L and MAX R60Q mutations have been identified in WT. MYCN encodes a transcription factor that requires dimerization with MAX to activate transcription of numerous target genes. MYCN gain has been associated with adverse prognosis in different childhood tumors including WT. The MYCN P44L and MAX R60Q mutations, located in either the transactivating or basic helix-loop-helix domain, respectively, are predicted to be damaging by different pathogenicity prediction tools, but the functional consequences remain to be characterized.

METHODS

We screened a large cohort of unselected WTs for MYCN and MAX alterations. Wild-type and mutant protein function were characterized biochemically, and we analyzed the N-MYC protein interactome by mass spectrometric analysis of N-MYC containing protein complexes.

RESULTS

Mutation screening revealed mutation frequencies of 3% for MYCN P44L and 0.9% for MAX R60Q that are associated with a higher risk of relapse. Biochemical characterization identified a reduced transcriptional activation potential for MAX R60Q, while the MYCN P44L mutation did not change activation potential or protein stability. The protein interactome of N-MYC-P44L was likewise not altered as shown by mass spectrometric analyses of purified N-MYC complexes. Nevertheless, we could identify a number of novel N-MYC partner proteins, e.g. PEG10, YEATS2, FOXK1, CBLL1 and MCRS1, whose expression is correlated with MYCN in WT samples and several of these are known for their own oncogenic potential.

CONCLUSIONS

The strongly elevated risk of relapse associated with mutant MYCN and MAX or elevated MYCN expression corroborates their role in WT oncogenesis. Together with the newly identified co-expressed interactors they expand the range of potential biomarkers for WT stratification and targeting, especially for high-risk WT.

Microbial signatures in the lower airways of mechanically ventilated COVID-19 patients associated with poor clinical outcome

Respiratory failure is associated with increased mortality in COVID-19 patients. There are no validated lower airway biomarkers to predict clinical outcome. We investigated whether bacterial respiratory infections were associated with poor clinical outcome of COVID-19 in a prospective, observational cohort of 589 critically ill adults, all of whom required mechanical ventilation. For a subset of 142 patients who underwent bronchoscopy, we quantified SARS-CoV-2 viral load, analysed the lower respiratory tract microbiome using metagenomics and metatranscriptomics and profiled the host immune response. Acquisition of a hospital-acquired respiratory pathogen was not associated with fatal outcome. Poor clinical outcome was associated with lower airway enrichment with an oral commensal (Mycoplasma salivarium). Increased SARS-CoV-2 abundance, low anti-SARS-CoV-2 antibody response and a distinct host transcriptome profile of the lower airways were most predictive of mortality. Our data provide evidence that secondary respiratory infections do not drive mortality in COVID-19 and clinical management strategies should prioritize reducing viral replication and maximizing host responses to SARS-CoV-2.

Evidence in disease and non-disease contexts that nonsense mutations cause altered splicing via motif disruption

Transcripts containing premature termination codons (PTCs) can be subject to nonsense-associated alternative splicing (NAS). Two models have been evoked to explain this, scanning and splice motif disruption. The latter postulates that exonic cis motifs, such as exonic splice enhancers (ESEs), are disrupted by nonsense mutations. We employ genome-wide transcriptomic and k-mer enrichment methods to scrutinize this model. First, we show that ESEs are prone to disruptive nonsense mutations owing to their purine richness and paucity of TGA, TAA and TAG. The motif model correctly predicts that NAS rates should be low (we estimate 5–30%) and approximately in line with estimates for the rate at which random point mutations disrupt splicing (8–20%). Further, we find that, as expected, NAS-associated PTCs are predictable from nucleotide-based machine learning approaches to predict splice disruption and, at least for pathogenic variants, are enriched in ESEs. Finally, we find that both in and out of frame mutations to TAA, TGA or TAG are associated with exon skipping. While a higher relative frequency of such skip-inducing mutations in-frame than out of frame lends some credence to the scanning model, these results reinforce the importance of considering splice motif modulation to understand the etiology of PTC-associated disease.

QKI-Regulated Alternative Splicing Events in Cervical Cancer: Pivotal Mechanism and Potential Therapeutic Strategy

QKI is a vital regulator in RNA splicing and maturation, but its role in cervical cancer (CC) is little known. In this study, we found that QKI is decreased in human CC, and overexpression of QKI inhibits HeLa cell proliferation and promotes the apoptosis of cancer cells. We identified hundreds of endogenous QKI-regulated alternative splicing events (ASEs) and differentially expressed genes (DEGs) in QKI-overexpressed HeLa cells by RNA-seq and selectively validated their expression by quantitative reverse-transcription polymerase chain reaction. The gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis showed that QKI-regulated ASEs and DEGs were closely related to cancer, apoptosis, and transcriptional regulatory functions. In short, QKI may affect the occurrence and development of CC by regulating gene expression through AS.

Next Generation Sequencing Technology in Lung Cancer Diagnosis

Simple Summary

Lung cancer is still one of the most commonly diagnosed and deadliest cancers in the world. Its diagnosis at an early stage is highly necessary and will improve the standard of care of this disease. The aim of this article is to review the importance and applications of next generation sequencing in lung cancer diagnosis. As observed in many studies, next generation sequencing has been proven as a very helpful tool in the early detection of different types of cancers, including lung cancer, and has been used in the clinic, mainly due to its many advantages, such as low cost, speed, efficacy, low quantity usage of biological samples, and diversity.

Abstract

Lung cancer is still one of the most commonly diagnosed cancers, and one of the deadliest. The high death rate is mainly due to the late stage of diagnosis and low response rate to therapy. Previous and ongoing research studies have tried to discover new reliable and useful cbiomarkers for the diagnosis and prognosis of lung cancer. Next generation sequencing has become an essential tool in cancer diagnosis, prognosis, and evaluation of the treatment response. This article aims to review the leading research and clinical applications in lung cancer diagnosis using next generation sequencing. In this scope, we identified the most relevant articles that present the successful use of next generation sequencing in identifying biomarkers for early diagnosis correlated to lung cancer diagnosis and treatment. This technique can be used to evaluate a high number of biomarkers in a short period of time and from small biological samples, which makes NGS the preferred technique to develop clinical tests for personalized medicine using liquid biopsy, the new trend in oncology.