Transcriptome Annotation Research Articles

Dynamic Landscapes of Long Noncoding RNAs During Early Root Development and Differentiation in Glycine max and Glycine soja.

Soybean (Glycine max) is an important crop for its nutritional value. Its wild relative, Glycine soja, provides a valuable genetic resource for improving soybean productivity. Root development and differentiation are essential for soybean plants to take up water and nutrients, store energy and anchor themselves. Long noncoding RNAs (lncRNAs) have been reported to play critical roles in various biological processes. However, the spatiotemporal landscape of lncRNAs during early root development and differentiation in soybeans is scarcely characterized. Using RNA sequencing and transcriptome assembly, we identified 1578 lncRNAs in G. max and 1454 in G. soja, spanning various root portions and time points. Differential expression analysis revealed 82 and 69 lncRNAs exhibiting spatiotemporally differential expression patterns in G. max and G. soja, respectively, indicating their involvement in the early stage of root architecture formation. By elucidating multiple competitive endogenous RNA (ceRNA) networks involving lncRNAs, microRNAs and protein-coding RNAs, we unveiled intricate regulatory mechanisms of lncRNA in early root development and differentiation. Our efforts significantly expand the transcriptome annotations of soybeans, unravel the dynamic landscapes of lncRNAs during early root development and differentiation, and provide valuable resources into the field of soybean root research.

TransAnnot—a fast transcriptome annotation pipeline

Abstract Summary The annotation of deeply sequenced, de novo assembled transcriptomes continues to be a challenge as some of the state-of-the-art tools are slow, difficult to install, and hard to use. We have tackled these issues with TransAnnot, a fast, automated transcriptome annotation pipeline that is easy to install and use. Leveraging the fast sequence searches provided by the MMseqs2 suite, TransAnnot offers one-step annotation of homologs from Swiss-Prot, gene ontology terms and orthogroups from eggNOG, and functional domains from Pfam. Users also have the option to annotate against custom databases. TransAnnot accepts sequencing reads (short and long), nucleotide sequences, or amino acid sequences as input for annotation. When benchmarked with test data sets of amino acid sequences, TransAnnot was 333, 284, and 18 times faster than comparable tools such as EnTAP, Trinotate and eggNOG-mapper respectively. Availability and implementation TransAnnot is free to use, open sourced under GPLv3, and is implemented in C ++ and Bash. Source code, documentation, and pre-compiled binaries are available at https://github.com/soedinglab/transannot. TransAnnot is also available via bioconda (https://anaconda.org/bioconda/transannot). Supplementary information Supplementary data are available at Bioinformatics online.

PSVII-1 Widespread allele-specific expression across tissues and cells of the chicken genome

Abstract Chickens are a valuable livestock species, providing an affordable and nutritious food source worldwide through eggs and meat. Additionally, chickens are extensively used in scientific research as model organisms in virology, immunology, epigenetics, development, and conservation biology. Considering this, there are increasing efforts to deepen our understanding of their genome complexity, such as efforts by the Functional Annotation of Animal Genomes (FAANG) Consortium. Toward this goal, we are working to contribute to the annotation of the chicken transcriptome. Allele-specific expression (ASE) is an imbalance of allelic gene expression often driven by genetic and epigenetic changes in cis-regulatory regions. We, therefore, aimed to determine allelic imbalances in gene expression across 20 tissues and cells to shed light on mechanisms regulating gene expression. Using replicate paired-end (PE) RNA-seq samples, we identified around 7 million variants across 20 tissues and cells (e.g., reproductive tissues, intestine tissues, muscle, and immune tissues and cells). We applied quality control measures and filtered out monoallelic and homozygous variants. After that, we calculated read counts per allele to determine allelic expression. We found 365,894 significant ASE SNPs and 11,530 ASE genes in at least one tissue or cell type (FDR ≤ 0.05). We discovered that ASE SNPs are widely distributed throughout the chicken genome, and ASE SNPs and ASE genes showed a varied distribution across tissues and cells. Primary macrophage exhibited the most abundant, while the pectoralis major (breast muscle) had the lowest ASE genes and ASE SNPs, ranging from 7,592 to 32,505 ASE SNPs and 773 to 2,387 ASE genes. Our findings reveal several important pathways affected by allelic imbalance of expression. These pathways included fatty acid biosynthesis and regulation, cell proliferation and differentiation, cell metabolism, adipocytokine signaling, proteolysis and autophagy, and focal adhesion. In summary, our study provides insight into relevant biological processes critical to chickens and can contribute to improving genome annotation, helping to expand the transcriptomic reference source.

Transcriptome Analysis of Multiple Plant Parts in the Woody Oil Tree Camellia drupifera Loureiro

Camellia drupifera is mainly used in forestry for its high-value industrial products; however, limited information is available on its transcriptome. This study aimed to construct a full-length transcriptome sequence based on the PacBio sequencing platform for various plant parts of C. drupifera, including flower buds, leaves, leaf buds, branches, the pericarp, and seed kernels. The transcriptomes were annotated with 23,207 genes, with 58 subgroups in the GO classification. The KEGG database revealed 10,407 genes involved in the metabolic pathway analysis, with 68,192 coding sequences, 3352 TF families, 48,541 SSRs, 1421 IncRNAs, and 2625 variable shears predicted. The transcriptomes of different parts were analyzed and compared. The majority of differentially expressed genes (DEGs) were found between the pericarp and seed kernels, followed by leaves and the pericarp with 5662 DEGs, and flower buds and leaf buds with 1616 DEGs. GO and KEGG enrichment analyses showed that KEGG differential genes were significant in microbial metabolism, carbon metabolism, and other functions. The data annotation and analysis of the full-length transcriptome and the comparative analysis between different plant parts provided a theoretical basis for studying gene function, metabolic pathway regulation, and gene expression analysis in KEGG.

Unveiling axolotl transcriptome for tissue regeneration with high-resolution annotation via long-read sequencing

Axolotls are known for their remarkable regeneration ability. Exploring their transcriptome provides insight into regenerative mechanisms. However, the current annotation of the axolotl transcriptome is limited, leaving the role of unannotated transcripts in regeneration unknown. To discourse this challenge, we exploited long-read sequencing technology, which enables direct observation of full-length RNA transcripts, greatly enhancing the coverage and accuracy of axolotl transcriptome annotation. By utilizing this method, we identified 222 novel gene loci and 4775 novel transcripts, which were quantified using short-read sequencing data. Through the inclusive analysis, we discovered novel homologs, potential functional proteins, noncoding RNAs, and alternative splicing events in key regeneration pathways. In particular, we identified novel transcripts with high protein-coding potential implicated in cell cycle regulation and musculoskeletal development, and regeneration were identified. Interestingly, alternative splice variants were also detected across diverse pathways critical to regeneration. This specifies that these novel transcripts potentially play vital roles underpinning the robust regenerative capacities of axolotls. Single-cell transcriptomic analysis further revealed these isoforms to predominantly exist in axolotl limb chondrocytes and mature tissue cell populations. Overall, the findings significantly advanced consideration of the axolotl transcriptome and provided a new perspective for understanding the mechanisms of regenerative abilities of axolotls.

The N6-methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma.

Comprehensive m6A epitranscriptome profiling of primary tumors remains largely uncharted. Here, we profiled the m6A epitranscriptome of 10 non-neoplastic lung (NL) tissues and 51 lung adenocarcinoma (LUAD) tumors, integrating the corresponding transcriptome, proteome and extensive clinical annotations. We identified distinct clusters and genes that were exclusively linked to disease progression through m6A modifications. In comparison with NL tissues, we identified 430 transcripts to be hypo-methylated and 222 to be hyper-methylated in tumors. Among these genes, EML4 emerged as a novel metastatic driver, displaying significant hyper-methylation in tumors. m6A modification promoted the translation of EML4, leading to its widespread overexpression in primary tumors. Functionally, EML4 modulated cytoskeleton dynamics through interacting with ARPC1A, enhancing lamellipodia formation, cellular motility, local invasion, and metastasis. Clinically, high EML4 protein abundance correlated with features of metastasis. METTL3 small molecule inhibitor markedly diminished both EML4 m6A and protein abundance, and efficiently suppressed lung metastases in vivo.

R2Dtool: integration and visualization of isoform-resolved RNA features.

Long-read RNA sequencing enables the mapping of RNA modifications, structures, and protein-interaction sites at the resolution of individual transcript isoforms. To understand the functions of these RNA features, it is critical to analyze them in the context of transcriptomic and genomic annotations, such as open reading frames and splice junctions. We have developed R2Dtool, a bioinformatics tool that integrates transcript-mapped information with transcript and genome annotations, allowing for the isoform-resolved analytics and graphical representation of RNA features in their genomic context. We illustrate R2Dtool's capability to integrate and expedite RNA feature analysis using epitranscriptomics data. R2Dtool facilitates the comprehensive analysis and interpretation of alternative transcript isoforms. R2Dtool is freely available under the MIT license at github.com/comprna/R2Dtool.

Accurate assembly of circular RNAs with TERRACE.

Circular RNA (circRNA) is a class of RNA molecules that forms a closed loop with their 5' and 3' ends covalently bonded. CircRNAs are known to be more stable than linear RNAs, have distinct properties and functions, and are promising biomarkers. Existing methods for assembling circRNAs heavily rely on the annotated transcriptomes, hence exhibiting unsatisfactory accuracy without a high-quality transcriptome. We present TERRACE, a new algorithm for full-length assembly of circRNAs from paired-end total RNA-seq data. TERRACE uses the splice graph as the underlying data structure that organizes the splicing and coverage information. We transform the problem of assembling circRNAs into finding paths that "bridge" the three fragments in the splice graph induced by back-spliced reads. We adopt a definition for optimal bridging paths and a dynamic programming algorithm to calculate such optimal paths. TERRACE features an efficient algorithm to detect back-spliced reads missed by RNA-seq aligners, contributing to its much-improved sensitivity. It also incorporates a new machine-learning approach trained to assign a confidence score to each assembled circRNA, which is shown to be superior to using abundance for scoring. On both simulations and biological data sets, TERRACE consistently outperforms existing methods by a large margin in sensitivity while achieving better or comparable precision. In particular, when the annotations are not provided, TERRACE assembles 123%-413% more correct circRNAs than state-of-the-art methods. TERRACE presents a significant advance in assembling full-length circRNAs from RNA-seq data, and we expect it to be widely used in future research on circRNAs.

Metabolic Engineering of Escherichia coli for Bioproduction of (R)-3-Hydroxybutyric Acid through a Three-Pronged Approach.

(R)-3-Hydroxybutyric acid (R-3HB) is an important chiral chemical with extensive applications in the agricultural, food, and chemical industries. The synthesis of R-3HB by microbial fermentation is of interest due to its remarkable stereoselectivity and economy. However, the low production of R-3HB failed to meet the needs of large-scale industrial production. In this study, an engineered strain for the efficient biosynthesis of R-3HB was constructed through a three-pronged approach encompassing biosynthetic pathway optimization, engineering of NADPH regenerators, and central metabolism regulation. The engineered strain Q5081 produced 75.7 g/L R-3HB, with a productivity of 1.26 g/L/h and a yield of 0.34 g/g glucose in fed-batch fermentation, showing the highest reported titer and productivity of R-3HB to date. We also performed transcriptome sequencing and annotation to illustrate the mechanism underlying the enhanced R-3HB production. The systematic metabolic engineering by a three-pronged approach demonstrated the feasibility of improving the biosynthesis, and the engineered strain Q5081 has the potential for widespread applications in the industrial production of R-3HB.

Nanopore guided annotation of transcriptome architectures.

The transcriptome of an organism denotes the full repertoire of encoded RNAs that may be expressed. This is critical to understanding the biology of an organism and for accurate transcriptomic and epitranscriptomic-based analyses. Annotating transcriptomes remains a complex task, particularly in small gene-dense organisms such as viruses which maximize their coding capacity through overlapping RNAs. To resolve this, we have developed a new software nanopore guided annotation of transcriptome architectures (NAGATA) which utilizes nanopore direct RNA sequencing (DRS) datasets to rapidly produce high-resolution transcriptome annotations for diverse viruses and other organisms.

Haplotype-resolved chromosome-level genome assembly of Huyou (Citrus changshanensis)

Huyou (Citrus changshanensis) is a significant citrus species that originated in Zhejiang Province, China, where it is also primarily cultivated. It is valued for its distinctive flavor and notable health benefits, owing to its high content of bioactive compounds like naringin and limonin. However, the absence of a high quality reference genome has limited the exploration of these health-promoting compounds in Huyou and hindered research into the mechanisms behind its medicinal properties. In this study, we present a phased chromosome-level genome assembly of Huyou. By combining PacBio and Hi-C sequencing, we generated a primary genome assembly and two haplotypes, comprising nine pseudo-chromosomes, with sizes of 339.91 Mb, 323.51 Mb, and 311.89 Mb, respectively. By integrating transcriptome data and annotations of homologous species, we identified a total of 29,775 protein-coding genes in the genome of Huyou. Additionally, we detected lots of structural variants between the two haplotypes. This represents the first reference genome of Huyou, providing a valuable resource for future studies on its agricultural characteristics and medicinal applications.

Functional annotation and meta-analysis of maize transcriptomes reveal genes involved in biotic and abiotic stress

BackgroundEnvironmental stress factors, such as biotic and abiotic stress, are becoming more common due to climate variability, significantly affecting global maize yield. Transcriptome profiling studies provide insights into the molecular mechanisms underlying stress response in maize, though the functions of many genes are still unknown. To enhance the functional annotation of maize-specific genes, MaizeGDB has outlined a data-driven approach with an emphasis on identifying genes and traits related to biotic and abiotic stress.ResultsWe mapped high-quality RNA-Seq expression reads from 24 different publicly available datasets (17 abiotic and seven biotic studies) generated from the B73 cultivar to the recent version of the reference genome B73 (B73v5) and deduced stress-related functional annotation of maize gene models. We conducted a robust meta-analysis of the transcriptome profiles from the datasets to identify maize loci responsive to stress, identifying 3,230 differentially expressed genes (DEGs): 2,555 DEGs regulated in response to abiotic stress, 408 DEGs regulated during biotic stress, and 267 common DEGs (co-DEGs) that overlap between abiotic and biotic stress. We discovered hub genes from network analyses, and among the hub genes of the co-DEGs we identified a putative NAC domain transcription factor superfamily protein (Zm00001eb369060) IDP275, which previously responded to herbivory and drought stress. IDP275 was up-regulated in our analysis in response to eight different abiotic and four different biotic stresses. A gene set enrichment and pathway analysis of hub genes of the co-DEGs revealed hormone-mediated signaling processes and phenylpropanoid biosynthesis pathways, respectively. Using phylostratigraphic analysis, we also demonstrated how abiotic and biotic stress genes differentially evolve to adapt to changing environments.ConclusionsThese results will help facilitate the functional annotation of multiple stress response gene models and annotation in maize. Data can be accessed and downloaded at the Maize Genetics and Genomics Database (MaizeGDB).

Transcriptomic changes behind Sparus aurata hepatic response to different aquaculture challenges: An RNA-seq study and multiomics integration.

Gilthead seabream (Sparus aurata) is an important species in Mediterranean aquaculture. Rapid intensification of its production and sub-optimal husbandry practices can cause stress, impairing overall fish performance and raising issues related to sustainability, animal welfare, and food safety. The advent of next-generation sequencing technologies has greatly revolutionized the study of fish stress biology, allowing a deeper understanding of the molecular stress responses. Here, we characterized for the first time, using RNA-seq, the different hepatic transcriptome responses of gilthead seabream to common aquaculture challenges, namely overcrowding, net handling, and hypoxia, further integrating them with the liver proteome and metabolome responses. After reference-guided transcriptome assembly, annotation, and differential gene expression analysis, 7, 343, and 654 genes were differentially expressed (adjusted p-value < 0.01, log2|fold-change| >1) in the fish from the overcrowding, net handling, and hypoxia challenged groups, respectively. Gene set enrichment analysis (FDR < 0.05) suggested a scenario of challenge-specific responses, that is, net handling induced ribosomal assembly stress, whereas hypoxia induced DNA replication stress in gilthead seabream hepatocytes, consistent with proteomics and metabolomics' results. However, both responses converged upon the downregulation of insulin growth factor signalling and induction of endoplasmic reticulum stress. These results demonstrate the high phenotypic plasticity of this species and its differential responses to distinct challenging environments at the transcriptomic level. Furthermore, it provides significant resources for characterizing and identifying potentially novel genes that are important for gilthead seabream resilience and aquaculture production efficiency with regard to fish welfare.

Abstract 4332: Isoform-level transcriptome-wide analyses identify extensive genetic mechanisms for breast cancer risk undetectable on the gene-level

Abstract Genome-wide association studies (GWAS) have identified over 170 genetic variants associated with breast cancer risk. Integrative methods, like colocalization and transcriptome-wide association studies (TWAS), can identify potential biological mediators for genetic associations but, to date, have only contextualized a fraction of GWAS loci. This reduced discovery is due to an overreliance on reference transcriptomic panels of total gene expression, which ignore widespread alternative splicing that gives rise to multiple transcript-isoforms of the same gene with differing effects on tumorigenesis and progression. However, integrating highly correlated transcript-isoforms with phenotypic associations from genome-wide association studies (GWAS) requires methodological innovations.Here, we first introduce isoTWAS, a multivariate, machine learning method to integrate genetics, isoform-level expression, and phenotypic associations in a step-wise testing framework, and present its advantages over traditional gene-level methods.We then apply isoTWAS to multiomic data from non-cancerous breast samples from the Genotype-Tissue Expression Project (GTEx) and breast cancer risk GWAS summary statistics from the Breast Cancer Association Consortium. In total, we generated strong predictive models for 70,816 isoforms across 12,103 genes and estimated isoform associations for overall and subtype-specific breast cancer risk. We observed 570 associated isoforms across 384 unique genes (compared to 141 associated genes out of 14,613 genes tested in a gene-level TWAS with the same GWAS) at FDR and FWER-corrected P &amp;lt; 0.05. Critically, isoTWAS prioritized isoform associations within 1 Megabase of 66 independent GWAS-significant loci compared to 22 from gene-level TWAS. Of these isoforms within 1 Megabase of a GWAS locus, 19 associated isoforms had a significantly associated risk variant within the gene body. Of note, we find a negative association between the ESR1 isoform ENST00000488573 and overall breast cancer risk (P = 3.35 x 10-9). Previous studies using breast tissue have not prioritized ESR1 expression as a potential mediator, despite strong SNP associations at this locus. We are currently updating transcriptomic annotations using long-read RNA-sequencing data to improve the precision and specificity of GTEx isoform-level quantifications. We will then expand our isoTWAS analysis to other related tissues (connective, adipose, and immune-related tissues) to identify causal susceptibility transcripts for overall and subtype-specific risk. Our study underscores the wealth of information remaining in bulk RNA-seq datasets that sophisticated methods like isoTWAS can leverage to identify genetic risk mechanisms for breast cancer that is missed with traditional gene-centric measurements and methods. Citation Format: Arjun Bhattacharya, Yung-Han Chang, Bogdan Pasaniuc, Sara Lindstroem. Isoform-level transcriptome-wide analyses identify extensive genetic mechanisms for breast cancer risk undetectable on the gene-level [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4332.

Txtools: an R package facilitating analysis of RNA modifications, structures, and interactions.

We present txtools, an R package that enables the processing, analysis, and visualization of RNA-seq data at the nucleotide-level resolution, seamlessly integrating alignments to the genome with transcriptomic representation. txtools' main inputs are BAM files and a transcriptome annotation, and the main output is a table, capturing mismatches, deletions, and the number of reads beginning and ending at each nucleotide in the transcriptomic space. txtools further facilitates downstream visualization and analyses. We showcase, using examples from the epitranscriptomic field, how a few calls to txtools functions can yield insightful and ready-to-publish results. txtools is of broad utility also in the context of structural mapping and RNA:protein interaction mapping. By providing a simple and intuitive framework, we believe that txtools will be a useful and convenient tool and pave the path for future discovery. txtools is available for installation from its GitHub repository at https://github.com/AngelCampos/txtools.

De novo transcriptome for Chiloscyllium griseum, a long-tail carpet shark of the Indian waters

Sharks have thrived in the oceans for 400 million years, experienced five extinctions and evolved into today’s apex predators. However, enormous genome size, poor karyotyping and limited tissue sampling options are the bottlenecks in shark research. Sharks of the family Orectolobiformes act as model species in transcriptome research with exceptionally high reproductive fecundity, catch prominence and oviparity. The present study illustrates a de novo transcriptome for an adult grey bamboo shark, Chiloscyllium griseum (Chondrichthyes; Hemiscyllidae) using paired-end RNA sequencing. Around 150 million short Illumina reads were obtained from five different tissues and assembled using the Trinity assembler. 70,647 hits on Uniprot by BLASTX was obtained after the transcriptome annotation. The data generated serve as a basis for transcriptome-based population genetic studies and open up new avenues in the field of comparative transcriptomics and conservation biology.

In-depth transcriptomic analysis of Anopheles gambiae hemocytes uncovers novel genes and the oenocytoid developmental lineage

BackgroundHemocytes are immune cells that patrol the mosquito hemocoel and mediate critical cellular defense responses against pathogens. However, despite their importance, a comprehensive transcriptome of these cells was lacking because they constitute a very small fraction of the total cells in the insect, limiting the study of hemocyte differentiation and immune function.ResultsIn this study, an in-depth hemocyte transcriptome was built by extensive bulk RNA sequencing and assembly of hemocyte RNAs from adult A. gambiae female mosquitoes, based on approximately 2.4 billion short Illumina and about 9.4 million long PacBio high-quality reads that mapped to the A. gambiae PEST genome (P4.14 version). A total of 34,939 transcripts were annotated including 4,020 transcripts from novel genes and 20,008 novel isoforms that result from extensive differential splicing of transcripts from previously annotated genes. Most hemocyte transcripts identified (89.8%) are protein-coding while 10.2% are non-coding RNAs. The number of transcripts identified in the novel hemocyte transcriptome is twice the number in the current annotation of the A. gambiae genome (P4.14 version). Furthermore, we were able to refine the analysis of a previously published single-cell transcriptome (scRNAseq) data set by using the novel hemocyte transcriptome as a reference to re-define the hemocyte clusters and determine the path of hemocyte differentiation. Unsupervised pseudo-temporal ordering using the Tools for Single Cell Analysis software uncovered a novel putative prohemocyte precursor cell type that gives rise to prohemocytes. Pseudo-temporal ordering with the Monocle 3 software, which analyses changes in gene expression during dynamic biological processes, determined that oenocytoids derive from prohemocytes, a cell population that also gives rise to the granulocyte lineage.ConclusionA high number of mRNA splice variants are expressed in hemocytes, and they may account for the plasticity required to mount efficient responses to many different pathogens. This study highlights the importance of a comprehensive set of reference transcripts to perform robust single-cell transcriptomic data analysis of cells present in low abundance. The detailed annotation of the hemocyte transcriptome will uncover new facets of hemocyte development and function in adult dipterans and is a valuable community resource for future studies on mosquito cellular immunity.

KSHV 3.0: a state-of-the-art annotation of the Kaposi's sarcoma-associated herpesvirus transcriptome using cross-platform sequencing.

Kaposi's sarcoma-associated herpesvirus (KSHV) is a large, oncogenic DNA virus belonging to the gammaherpesvirus subfamily. KSHV has been extensively studied with various high-throughput RNA-sequencing approaches to map the transcription start and end sites, the splice junctions, and the translation initiation sites. Despite these efforts, the comprehensive annotation of the viral transcriptome remains incomplete. In the present study, we generated a long-read sequencing data set of the lytic and latent KSHV transcriptome using native RNA and direct cDNA-sequencing methods. This was supplemented with Cap Analysis of Gene Expression sequencing based on a short-read platform. We also utilized data sets from previous publications for our analysis. As a result of this combined approach, we have identified a number of novel viral transcripts and RNA isoforms and have either corroborated or improved the annotation of previously identified viral RNA molecules, thereby notably enhancing our comprehension of the transcriptomic architecture of the KSHV genome. We also evaluated the coding capability of transcripts previously thought to be non-coding by integrating our data on the viral transcripts with translatomic information from other publications.IMPORTANCEDeciphering the viral transcriptome of Kaposi's sarcoma-associated herpesvirus is of great importance because we can gain insight into the molecular mechanism of viral replication and pathogenesis, which can help develop potential targets for antiviral interventions. Specifically, the identification of substantial transcriptional overlaps by this work suggests the existence of a genome-wide interference between transcriptional machineries. This finding indicates the presence of a novel regulatory layer, potentially controlling the expression of viral genes.

Mining raw plant transcriptomic data for new cyclopeptide alkaloids.

In recent years, genome and transcriptome mining have dramatically expanded the rate of discovering diverse natural products from bacteria and fungi. In plants, this approach is often more limited due to the lack of available annotated genomes and transcriptomes combined with a less consistent clustering of biosynthetic genes. The recently identified burpitide class of ribosomally synthesized and post-translationally modified peptide (RiPP) natural products offer a valuable opportunity for bioinformatics-guided discovery in plants due to their short biosynthetic pathways and gene encoded substrates. Using a high-throughput approach to assemble and analyze 700 publicly available raw transcriptomic data sets, we uncover the potential distribution of split burpitide precursor peptides in Streptophyta. Metabolomic analysis of target plants confirms our bioinformatic predictions of new cyclopeptide alkaloids from both known and new sources.

Unveiling the impact of tertiary lymphoid structures on immunotherapeutic responses of clear cell renal cell carcinoma.

Tertiary lymphoid structures (TLS) are organized aggregates of immune cells that form under pathological conditions. However, the predictive value of TLS in clear cell renal cell carcinoma (ccRCC) for immunotherapies remains unclear. We comprehensively assessed the implications for prognosis and immunological responses of the TLS spatial and maturation heterogeneity in 655 ccRCC patients. A higher proportion of early-TLS was found in peritumoral TLS, while intratumoral TLS mainly comprised secondary follicle-like TLS (SFL-TLS), indicating markedly better survival. Notably, presence of TLS, especially intratumoral TLS and SFL-TLS, significantly correlated with better survival and objective reflection rate for ccRCC patients receiving anti-Programmed Cell Death Protein-1 (PD-1)/Programmed Cell Death-Ligand-1 (PD-L1) immunotherapies. In peritumoral TLS cluster, primary follicle-like TLS, the proportion of tumor-associated macrophages, and Treg infiltration in the peritumoral regions increased prominently, suggesting an immunosuppressive tumor microenvironment. Interestingly, spatial transcriptome annotation and multispectral fluorescence showed that an abundance of mature plasma cells within mature TLS has the capacity to produce IgA and IgG, which demonstrate significantly higher objective response rates and a superior prognosis for ccRCC patients subjected to immunotherapy. In conclusion, this study revealed the implications of TLS spatial and maturation heterogeneity on the immunological status and clinical responses, allowing the improvement of precise immunotherapies of ccRCC.