Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification

CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning

Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficulties in effectively profiling circRNAs due to inherent limitations in circRNA sequencing efficiency. To address this gap, a deep learning model, CIRI-deep, is presented for comprehensive prediction of circRNA regulation on diverse types of RNA-seq data. CIRI-deep is trained on an extensive dataset of 25 million high-confidence circRNA regulation events and achieved high performances on both test and leave-out data, ensuring its accuracy in inferring differential events from RNA-seq data. It is demonstrated that CIRI-deep and its adapted version enable various circRNA analyses, including cluster- or region-specific circRNA detection, BSJ ratio map visualization, and trans and cis feature importance evaluation. Collectively, CIRI-deep's adaptability extends to all major types of RNA-seq datasets including single-cell and spatial transcriptomic data, which will undoubtedly broaden the horizons of circRNA research.

Enriched atlas of lncRNA and protein-coding genes for the GRCg7b chicken assembly and its functional annotation across 47 tissues

Gene atlases for livestock are steadily improving thanks to new genome assemblies and new expression data improving the gene annotation. However, gene content varies across databases due to differences in RNA sequencing data and bioinformatics pipelines, especially for long non-coding RNAs (lncRNAs) which have higher tissue and developmental specificity and are harder to consistently identify compared to protein coding genes (PCGs). As done previously in 2020 for chicken assemblies galgal5 and GRCg6a, we provide a new gene atlas, lncRNA-enriched, for the latest GRCg7b chicken assembly, integrating "NCBI RefSeq", "EMBL-EBI Ensembl/GENCODE" reference annotations and other resources such as FAANG and NONCODE. As a result, the number of PCGs increases from 18,022 (RefSeq) and 17,007 (Ensembl) to 24,102, and that of lncRNAs from 5789 (RefSeq) and 11,944 (Ensembl) to 44,428. Using 1400 public RNA-seq transcriptome representing 47 tissues, we provided expression evidence for 35,257 (79%) lncRNAs and 22,468 (93%) PCGs, supporting the relevance of this atlas. Further characterization including tissue-specificity, sex-differential expression and gene configurations are provided. We also identified conserved miRNA-hosting genes with human counterparts, suggesting common function. The annotated atlas is available at gega.sigenae.org.

Widespread stable noncanonical peptides identified by integrated analyses of ribosome profiling and ORF features

Studies have revealed dozens of functional peptides in putative 'noncoding' regions and raised the question of how many proteins are encoded by noncanonical open reading frames (ORFs). Here, we comprehensively annotate genome-wide translated ORFs across five eukaryotes (human, mouse, zebrafish, worm, and yeast) by analyzing ribosome profiling data. We develop a logistic regression model named PepScore based on ORF features (expected length, encoded domain, and conservation) to calculate the probability that the encoded peptide is stable in humans. Systematic ectopic expression validates PepScore and shows that stable complex-associating microproteins can be encoded in 5'/3' untranslated regions and overlapping coding regions of mRNAs besides annotated noncoding RNAs. Stable noncanonical proteins follow conventional rules and localize to different subcellular compartments. Inhibition of proteasomal/lysosomal degradation pathways can stabilize some peptides especially those with moderate PepScores, but cannot rescue the expression of short ones with low PepScores suggesting they are directly degraded by cellular proteases. The majority of human noncanonical peptides with high PepScores show longer lengths but low conservation across species/mammals, and hundreds contain trait-associated genetic variants. Our study presents a statistical framework to identify stable noncanonical peptides in the genome and provides a valuable resource for functional characterization of noncanonical translation during development and disease.

The hagfish genome and the evolution of vertebrates

As the only surviving lineages of jawless fishes, hagfishes and lampreys provide a crucial window into early vertebrate evolution1-3. Here we investigate the complex history, timing and functional role of genome-wide duplications4-7 and programmed DNA elimination8,9 in vertebrates in the light of a chromosome-scale genome sequence for the brown hagfish Eptatretus atami. Combining evidence from syntenic and phylogenetic analyses, we establish a comprehensive picture of vertebrate genome evolution, including an auto-tetraploidization (1RV) that predates the early Cambrian cyclostome-gnathostome split, followed by a mid-late Cambrian allo-tetraploidization (2RJV) in gnathostomes and a prolonged Cambrian-Ordovician hexaploidization (2RCY) in cyclostomes. Subsequently, hagfishes underwent extensive genomic changes, with chromosomal fusions accompanied by the loss of genes that are essential for organ systems (for example, genes involved in the development of eyes and in the proliferation of osteoclasts); these changes account, in part, for the simplification of the hagfish body plan1,2. Finally, we characterize programmed DNA elimination in hagfish, identifying protein-coding genes and repetitive elements that are deleted from somatic cell lineages during early development. The elimination of these germline-specific genes provides a mechanism for resolving genetic conflict between soma and germline by repressing germline and pluripotency functions, paralleling findings in lampreys10,11. Reconstruction of the early genomic history of vertebrates provides a framework for further investigations of the evolution of cyclostomes and jawed vertebrates.

Evolution and expression patterns of the neo-sex chromosomes of the crested ibis

Bird sex chromosomes play a unique role in sex-determination, and affect the sexual morphology and behavior of bird species. Core waterbirds, a major clade of birds, share the common characteristics of being sexually monomorphic and having lower levels of inter-sexual conflict, yet their sex chromosome evolution remains poorly understood. Here, by we analyse of a chromosome-level assembly of a female crested ibis (Nipponia nippon), a typical core waterbird. We identify neo-sex chromosomes resulting from fusion of microchromosomes with ancient sex chromosomes. These fusion events likely occurred following the divergence of Threskiornithidae and Ardeidae. The neo-W chromosome of the crested ibis exhibits the characteristics of slow degradation, which is reflected in its retention of abundant gametologous genes. Neo-W chromosome genes display an apparent ovary-biased gene expression, which is largely driven by genes that are retained on the crested ibis W chromosome but lost in other bird species. These results provide new insights into the evolutionary history and expression patterns for the sex chromosomes of bird species.

Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis

The plant genome produces an extremely large collection of long noncoding RNAs (lncRNAs) that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes. Here, we mapped the transcriptional heterogeneity of lncRNAs and their associated gene regulatory networks at single-cell resolution. We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing (scRNA-seq) datasets from juvenile Arabidopsis seedlings. We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers. We further demonstrated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity. In addition, we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs, and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs. The analysis results are available at the single-cell-based plant lncRNA atlas database (scPLAD; https://biobigdata.nju.edu.cn/scPLAD/). Overall, this work demonstrates the power of integrative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.

Tissue-specific and cis-regulatory changes underlie parallel, adaptive gene expression evolution in house mice

Changes in gene regulation have long been appreciated as a driving force of adaptive evolution, however the relative contributions of cis- and trans-acting changes to gene regulation over short evolutionary timescales remain unclear. Instances of recent, parallel phenotypic evolution provide an opportunity to assess whether parallel patterns are seen at the level of gene expression, and to assess the relative contribution of cis- and trans- changes to gene regulation in the early stages of divergence. Here, we studied gene expression in liver and brown adipose tissue in two wild-derived strains of house mice that independently adapted to cold, northern environments, and we compared them to a strain of house mice from a warm, tropical environment. To investigate gene regulatory evolution, we studied expression in parents and allele-specific expression in F1 hybrids of crosses between warm-adapted and cold-adapted strains. First, we found that the different cold-adapted mice showed both unique and shared changes in expression, but that the proportion of shared changes (i.e. parallelism) was greater than expected by chance. Second, we discovered that expression evolution occurred largely at tissue-specific and cis-regulated genes, and that these genes were over-represented in parallel cases of evolution. Finally, we integrated the expression data with scans for selection in natural populations and found substantial parallelism in the two northern populations for genes under selection. Furthermore, selection outliers were associated with cis-regulated genes more than expected by chance; cis-regulated genes under selection influenced phenotypes such as body size, immune functioning, and activity level. These results demonstrate that parallel patterns of gene expression in mice that have independently adapted to cold environments are driven largely by tissue-specific and cis-regulatory changes, providing insight into the mechanisms of adaptive gene regulatory evolution at the earliest stages of divergence.

The origin and structural evolution of de novo genes in Drosophila

Recent studies reveal that de novo gene origination from previously non-genic sequences is a common mechanism for gene innovation. These young genes provide an opportunity to study the structural and functional origins of proteins. Here, we combine high-quality base-level whole-genome alignments and computational structural modeling to study the origination, evolution, and protein structures of lineage-specific de novo genes. We identify 555 de novo gene candidates in D. melanogaster that originated within the Drosophilinae lineage. Sequence composition, evolutionary rates, and expression patterns indicate possible gradual functional or adaptive shifts with their gene ages. Surprisingly, we find little overall protein structural changes in candidates from the Drosophilinae lineage. We identify several candidates with potentially well-folded protein structures. Ancestral sequence reconstruction analysis reveals that most potentially well-folded candidates are often born well-folded. Single-cell RNA-seq analysis in testis shows that although most de novo gene candidates are enriched in spermatocytes, several young candidates are biased towards the early spermatogenesis stage, indicating potentially important but less emphasized roles of early germline cells in the de novo gene origination in testis. This study provides a systematic overview of the origin, evolution, and protein structural changes of Drosophilinae-specific de novo genes.

PancrESS - a meta-analysis resource for understanding cell-type specific expression in the human pancreas

BACKGROUND

The human pancreas is composed of specialized cell types producing hormones and enzymes critical to human health. These specialized functions are the result of cell type-specific transcriptional programs which manifest in cell-specific gene expression. Understanding these programs is essential to developing therapies for pancreatic disorders. Transcription in the human pancreas has been widely studied by single-cell RNA technologies, however the diversity of protocols and analysis methods hinders their interpretability in the aggregate.

RESULTS

In this work, we perform a meta-analysis of pancreatic single-cell RNA sequencing data. We present a database for reference transcriptome abundances and cell-type specificity metrics. This database facilitates the identification and definition of marker genes within the pancreas. Additionally, we introduce a versatile tool which is freely available as an R package, and should permit integration into existing workflows. Our tool accepts count data files generated by widely-used single-cell gene expression platforms in their original format, eliminating an additional pre-formatting step. Although we designed it to calculate expression specificity of pancreas cell types, our tool is agnostic to the biological source of count data, extending its applicability to other biological systems.

CONCLUSIONS

Our findings enhance the current understanding of expression specificity within the pancreas, surpassing previous work in terms of scope and detail. Furthermore, our database and tool enable researchers to perform similar calculations in diverse biological systems, expanding the applicability of marker gene identification and facilitating comparative analyses.

TE-TSS: an integrated data resource of human and mouse transposable element (TE)-derived transcription start site (TSS)

Transposable elements (TEs) are abundant in the genome and serve as crucial regulatory elements. Some TEs function as epigenetically regulated promoters, and these TE-derived transcription start sites (TSSs) play a crucial role in regulating genes associated with specific functions, such as cancer and embryogenesis. However, the lack of an accessible database that systematically gathers TE-derived TSS data is a current research gap. To address this, we established TE-TSS, an integrated data resource of human and mouse TE-derived TSSs (http://xozhanglab.com/TETSS). TE-TSS has compiled 2681 RNA sequencing datasets, spanning various tissues, cell lines and developmental stages. From these, we identified 5768 human TE-derived TSSs and 2797 mouse TE-derived TSSs, with 47% and 38% being experimentally validated, respectively. TE-TSS enables comprehensive exploration of TSS usage in diverse samples, providing insights into tissue-specific gene expression patterns and transcriptional regulatory elements. Furthermore, TE-TSS compares TE-derived TSS regions across 15 mammalian species, enhancing our understanding of their evolutionary and functional aspects. The establishment of TE-TSS facilitates further investigations into the roles of TEs in shaping the transcriptomic landscape and offers valuable resources for comprehending their involvement in diverse biological processes.

Comprehensive analyses of 435 goat transcriptomes provides insight into male reproduction

A systematic analysis of genes related to reproduction is crucial for obtaining a comprehensive understanding of the molecular mechanisms that underlie male reproductive traits in mammals. Here, we utilized 435 goat transcriptome datasets to unveil the testicular tissue-specific genes (TSGs), allele-specific expression (ASE) genes and their uncharacterized transcriptional features related to male goat reproduction. Results showed a total of 1790 TSGs were identified in goat testis, which was the most among all tissues. GO enrichment analyses suggested that testicular TSGs were mainly involved in spermatogenesis, multicellular organism development, spermatid development, and flagellated sperm motility. Subsequently, a total of 95 highly conserved TSGs (HCTSGs), 508 middle conserved TSGs (MCTSGs) and 42 no conserved TSGs (NCTSGs) were identified in goat testis. GO enrichment analyses suggested that the HCTSGs and MCTSGs has a more important association with male reproduction than NCTSGs. Additionally, we identified 644 ASE genes, including 88 tissue-specific ASE (TS-ASE) genes (e.g., FSIP2, TDRD9). GO enrichment analyses indicated that both ASE genes and TS-ASE genes were associated with goat male reproduction. Overall, this study revealed an extensive gene set involved in the regulation of male goat reproduction and their dynamic transcription patterns. Data reported here provide valuable insights for a further improvement of the economic benefits of goats as well as future treatments for male infertility.

Minimum entropy framework identifies a novel class of genomic functional elements and reveals regulatory mechanisms at human disease loci

We introduce CoRE-BED, a framework trained using 19 epigenomic features in 33 major cell and tissue types to predict cell-type-specific regulatory function. CoRE-BED identifies nine functional classes de-novo, capturing both known and new regulatory categories. Notably, we describe a previously undercharacterized class that we term Development Associated Elements (DAEs), which are highly enriched in cell types with elevated regenerative potential and distinguished by the dual presence of either H3K4me2 and H3K9ac (an epigenetic signature associated with kinetochore assembly) or H3K79me3 and H4K20me1 (a signature associated with transcriptional pause release). Unlike bivalent promoters, which represent a transitory state between active and silenced promoters, DAEs transition directly to or from a non-functional state during stem cell differentiation and are proximal to highly expressed genes. CoRE-BED's interpretability facilitates causal inference and functional prioritization. Across 70 complex traits, distal insulators account for the largest mean proportion of SNP heritability (~49%) captured by the GWAS. Collectively, our results demonstrate the value of exploring non-conventional ways of regulatory classification that enrich for trait heritability, to complement existing approaches for cis-regulatory prediction.

Comparison of Alternative Splicing Landscapes Revealed by Long-Read Sequencing in Hepatocyte-Derived HepG2 and Huh7 Cultured Cells and Human Liver Tissue

The long-read RNA sequencing developed by Oxford Nanopore Technologies provides a direct quantification of transcript isoforms, thereby making it possible to present alternative splicing (AS) profiles as arrays of single splice variants with different abundances. Additionally, AS profiles can be presented as arrays of genes characterized by the degree of alternative splicing (the DAS-the number of detected splice variants per gene). Here, we successfully utilized the DAS to reveal biological pathways influenced by the alterations in AS in human liver tissue and the hepatocyte-derived malignant cell lines HepG2 and Huh7, thus employing the mathematical algorithm of gene set enrichment analysis. Furthermore, analysis of the AS profiles as abundances of single splice variants by using the graded tissue specificity index τ provided the selection of the groups of genes expressing particular splice variants specifically in liver tissue, HepG2 cells, and Huh7 cells. The majority of these splice variants were translated into proteins products and appeal to be in focus regarding further insights into the mechanisms underlying cell malignization. The used metrics are intrinsically suitable for transcriptome-wide AS profiling using long-read sequencing.

Evolution of high-molecular-mass hyaluronic acid is associated with subterranean lifestyle

Hyaluronic acid is a major component of extracellular matrix which plays an important role in development, cellular response to injury and inflammation, cell migration, and cancer. The naked mole-rat (Heterocephalus glaber) contains abundant high-molecular-mass hyaluronic acid in its tissues, which contributes to this species' cancer resistance and possibly to its longevity. Here we report that abundant high-molecular-mass hyaluronic acid is found in a wide range of subterranean mammalian species, but not in phylogenetically related aboveground species. These subterranean mammalian species accumulate abundant high-molecular-mass hyaluronic acid by regulating the expression of genes involved in hyaluronic acid degradation and synthesis and contain unique mutations in these genes. The abundant high-molecular-mass hyaluronic acid may benefit the adaptation to subterranean environment by increasing skin elasticity and protecting from oxidative stress due to hypoxic conditions. Our work suggests that high-molecular-mass hyaluronic acid has evolved with subterranean lifestyle.

TAGADA: a scalable pipeline to improve genome annotations with RNA-seq data

Genome annotation plays a crucial role in providing comprehensive catalog of genes and transcripts for a particular species. As research projects generate new transcriptome data worldwide, integrating this information into existing annotations becomes essential. However, most bioinformatics pipelines are limited in their ability to effectively and consistently update annotations using new RNA-seq data. Here we introduce TAGADA, an RNA-seq pipeline for Transcripts And Genes Assembly, Deconvolution, and Analysis. Given a genomic sequence, a reference annotation and RNA-seq reads, TAGADA enhances existing gene models by generating an improved annotation. It also computes expression values for both the reference and novel annotation, identifies long non-coding transcripts (lncRNAs), and provides a comprehensive quality control report. Developed using Nextflow DSL2, TAGADA offers user-friendly functionalities and ensures reproducibility across different computing platforms through its containerized environment. In this study, we demonstrate the efficacy of TAGADA using RNA-seq data from the GENE-SWiTCH project alongside chicken and pig genome annotations as references. Results indicate that TAGADA can substantially increase the number of annotated transcripts by approximately [Formula: see text] in these species. Furthermore, we illustrate how TAGADA can integrate Illumina NovaSeq short reads with PacBio Iso-Seq long reads, showcasing its versatility. TAGADA is available at github.com/FAANG/analysis-TAGADA.

Subgenome dominance shapes novel gene evolution in the decaploid pitcher plant Nepenthes gracilis

Subgenome dominance after whole-genome duplication generates distinction in gene number and expression at the level of chromosome sets, but it remains unclear how this process may be involved in evolutionary novelty. Here we generated a chromosome-scale genome assembly of the Asian pitcher plant Nepenthes gracilis to analyse how its novel traits (dioecy and carnivorous pitcher leaves) are linked to genomic evolution. We found a decaploid karyotype and a clear indication of subgenome dominance. A male-linked and pericentromerically located region on the putative sex chromosome was identified in a recessive subgenome and was found to harbour three transcription factors involved in flower and pollen development, including a likely neofunctionalized LEAFY duplicate. Transcriptomic and syntenic analyses of carnivory-related genes suggested that the paleopolyploidization events seeded genes that subsequently formed tandem clusters in recessive subgenomes with specific expression in the digestive zone of the pitcher, where specialized cells digest prey and absorb derived nutrients. A genome-scale analysis suggested that subgenome dominance likely contributed to evolutionary innovation by permitting recessive subgenomes to diversify functions of novel tissue-specific duplicates. Our results provide insight into how polyploidy can give rise to novel traits in divergent and successful high-ploidy lineages.

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1-3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.

Single-cell dynamics of liver development in postnatal pigs

The postnatal development of the liver, an essential organ for metabolism and immunity, remains poorly characterized at the single-cell resolution. Here, we generated single-nucleus and single-cell transcriptomes of 84,824 pig liver cells at four postnatal time points: day 30, 42, 150, and 730. We uncovered 23 cell types, including three rare cell types: plasmacytoid dendritic cells, CAVIN3+IGF2+ endothelial cells, and EBF1+ fibroblasts. The latter two were verified by multiplex immunohistochemistry. Trajectory and gene regulatory analyses revealed 33 genes that encode transcription factors associated with hepatocyte development and function, including NFIL3 involved in regulating hepatic metabolism. We characterized the spatiotemporal heterogeneity of liver endothelial cells, identified and validated leucine zipper protein 2 (LUZP2) as a novel adult liver sinusoidal endothelial cell-specific transcription factor. Lymphoid cells (NK and T cells) governed the immune system of the pig liver since day 30. Furthermore, we identified a cluster of tissue-resident NK cells, which displayed virus defense functions, maintained proliferative features at day 730, and manifested a higher conservative transcription factor expression pattern in humans than in mouse liver. Our study presents the most comprehensive postnatal liver development single-cell atlas and demonstrates the metabolic and immune changes across the four age stages.

Sex-biased gene expression across mammalian organ development and evolution

Sexually dimorphic traits are common among mammals and are specified during development through the deployment of sex-specific genetic programs. Because little is known about these programs, we investigated them using a resource of gene expression profiles in males and females throughout the development of five organs in five mammals (human, mouse, rat, rabbit, and opossum) and a bird (chicken). We found that sex-biased gene expression varied considerably across organs and species and was often cell-type specific. Sex differences increased abruptly around sexual maturity instead of increasing gradually during organ development. Finally, sex-biased gene expression evolved rapidly at the gene level, with differences between organs in the evolutionary mechanisms used, but more slowly at the cellular level, with the same cell types being sexually dimorphic across species.

Identifying promoter sequence architectures via a chunking-based algorithm using non-negative matrix factorisation

Core promoters are stretches of DNA at the beginning of genes that contain information that facilitates the binding of transcription initiation complexes. Different functional subsets of genes have core promoters with distinct architectures and characteristic motifs. Some of these motifs inform the selection of transcription start sites (TSS). By discovering motifs with fixed distances from known TSS positions, we could in principle classify promoters into different functional groups. Due to the variability and overlap of architectures, promoter classification is a difficult task that requires new approaches. In this study, we present a new method based on non-negative matrix factorisation (NMF) and the associated software called seqArchR that clusters promoter sequences based on their motifs at near-fixed distances from a reference point, such as TSS. When combined with experimental data from CAGE, seqArchR can efficiently identify TSS-directing motifs, including known ones like TATA, DPE, and nucleosome positioning signal, as well as novel lineage-specific motifs and the function of genes associated with them. By using seqArchR on developmental time courses, we reveal how relative use of promoter architectures changes over time with stage-specific expression. seqArchR is a powerful tool for initial genome-wide classification and functional characterisation of promoters. Its use cases are more general: it can also be used to discover any motifs at near-fixed distances from a reference point, even if they are present in only a small subset of sequences.

Comparative single-cell transcriptomic analysis of primate brains highlights human-specific regulatory evolution

Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic innovations remain poorly understood, in part because of the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single-cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell type-specific gene co-expression networks for comparative analysis. Although orthologue expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3,383 out of 14,131), which are also associated with multiple brain disorders. To assess the functional significance of gene expression differences in an evolutionary context, we evaluate changes in network connectivity across meta-analytic co-expression networks from 19 animals. We find that a subset of these genes has deeply conserved co-expression across all non-human animals, and strongly divergent co-expression relationships in humans (139 out of 3,383, <1% of primate orthologues). Genes with human-specific cellular expression and co-expression profiles (such as NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function.

Genomic and Transcriptomic Analyses Illuminates Unique Traits of Elusive Night Flowering Jasmine Parijat (Nyctanthes arbor-tristis)

The night-flowering Jasmine, Nyctanthes arbor-tristis also known as Parijat, is a perennial woody shrub belonging to the family of Oleaceae. It is popular for its fragrant flowers that bloom in the night and is a potent source of secondary metabolites. However, knowledge about its genome and the expression of genes regulating flowering or secondary metabolite accumulation is lacking. In this study, we generated whole genome sequencing data to assemble the first de novo assembly of Parijat and use it for comparative genomics and demographic history reconstruction. The temporal dynamics of effective population size (Ne ) experienced a positive influence of colder climates suggesting the switch to night flowering may have provided an evolutionary advantage. We employed multi-tissue transcriptome sequencing of floral stages/parts to obtain insights into the transcriptional regulation of nocturnal flower development and the production of volatiles/metabolites. Tissue-specific transcripts for mature flowers revealed key players in circadian regulation and flower development, including the auxin pathway and cell wall modifying genes. Furthermore, we identified tissue-specific transcripts responsible for producing numerous secondary metabolites, mainly terpenoids and carotenoids. The diversity and specificity of Terpene Synthase (TPS) and CCDs (Carotenoid Cleavage Deoxygenases) mediate the bio-synthesis of specialised metabolites in Parijat. Our study establishes Parijat as a novel non-model species to understand the molecular mechanisms of nocturnal blooming and secondary metabolite production.

The Soybean Expression Atlas v2: A comprehensive database of over 5000 RNA-seq samples

Soybean is a crucial crop worldwide, used as a source of food, feed, and industrial products due to its high protein and oil content. Previously, the rapid accumulation of soybean RNA-seq data in public databases and the computational challenges of processing raw RNA-seq data motivated us to develop the Soybean Expression Atlas, a gene expression database of over a thousand RNA-seq samples. Over the past few years, our database has allowed researchers to explore the expression profiles of important gene families, discover genes associated with agronomic traits, and understand the transcriptional dynamics of cellular processes. Here, we present the Soybean Expression Atlas v2, an updated version of our database with a fourfold increase in the number of samples, featuring transcript- and gene-level transcript abundance matrices for 5481 publicly available RNA-seq samples. New features in our database include the availability of transcript-level abundance estimates and equivalence classes to explore differential transcript usage, abundance estimates in bias-corrected counts to increase the accuracy of differential gene expression analyses, a new web interface with improved data visualization and user experience, and a reproducible and scalable pipeline available as an R package. The Soybean Expression Atlas v2 is available at https://soyatlas.venanciogroup.uenf.br/, and it will accelerate soybean research, empowering researchers with high-quality and easily accessible gene expression data.

A tumor-infiltrating immune cells-related pseudogenes signature based on machine-learning predicts outcomes and immunotherapy responses in ovarian cancer

Previous researches have provided evidence for the significant involvement of pseudogenes in immune-related functions across different types of cancer. However, the mechanisms by which pseudogenes regulate immunity in ovarian cancer (OV) and their potential impact on clinical outcomes remain unclear. To address this gap in knowledge, our study utilized a novel computational framework to analyze a total of 491 samples from three public datasets. We employed a combination of 10 machine-learning algorithms to construct a signature known as the tumor-infiltrating immune cells-related pseudogenes signature (TIICPS). The TIICPS, consisting of 12 pseudogenes, demonstrated independent prognostic value for overall survival, surpassing conventional clinical traits, 62 published signatures, and TP53 and BRCA mutation status in three cohorts. Patients with low TIICPS exhibited heightened immune-related pathways, intricate genomic alterations, substantial immune infiltration, and greater potential for immunotherapy efficacy. Consequently, TIICPS holds promise as a predictive tool for prognosis and immunotherapy response in ovarian cancer.

Transcription factors KANADI 1, MYB DOMAIN PROTEIN 44, and PHYTOCHROME INTERACTING FACTOR 4 regulate long intergenic noncoding RNAs expressed in Arabidopsis roots

Thousands of long intergenic noncoding RNAs (lincRNAs) have been identified in plant genomes. While some lincRNAs have been characterized as important regulators in different biological processes, little is known about the transcriptional regulation for most plant lincRNAs. Through the integration of 8 annotation resources, we defined 6,599 high-confidence lincRNA loci in Arabidopsis (Arabidopsis thaliana). For lincRNAs belonging to different evolutionary age categories, we identified major differences in sequence and chromatin features, as well as in the level of conservation and purifying selection acting during evolution. Spatiotemporal gene expression profiles combined with transcription factor (TF) chromatin immunoprecipitation (ChIP) data were used to construct a TF-lincRNA regulatory network containing 2,659 lincRNAs and 15,686 interactions. We found that properties characterizing lincRNA expression, conservation, and regulation differ between plants and animals. Experimental validation confirmed the role of 3 TFs, KANADI 1, MYB DOMAIN PROTEIN 44, and PHYTOCHROME INTERACTING FACTOR 4, as key regulators controlling root-specific lincRNA expression, demonstrating the predictive power of our network. Furthermore, we identified 58 lincRNAs, regulated by these TFs, showing strong root cell type-specific expression or chromatin accessibility, which are linked with genome-wide association studies genetic associations related to root system development and growth. The multilevel genome-wide characterization covering chromatin state information, promoter conservation, and chromatin immunoprecipitation-based TF binding, for all detectable lincRNAs across 769 expression samples, permits rapidly defining the biological context and relevance of Arabidopsis lincRNAs through regulatory networks.

Transcriptomic cytoarchitecture reveals principles of human neocortex organization

Variation in cytoarchitecture is the basis for the histological definition of cortical areas. We used single cell transcriptomics and performed cellular characterization of the human cortex to better understand cortical areal specialization. Single-nucleus RNA-sequencing of 8 areas spanning cortical structural variation showed a highly consistent cellular makeup for 24 cell subclasses. However, proportions of excitatory neuron subclasses varied substantially, likely reflecting differences in connectivity across primary sensorimotor and association cortices. Laminar organization of astrocytes and oligodendrocytes also differed across areas. Primary visual cortex showed characteristic organization with major changes in the excitatory to inhibitory neuron ratio, expansion of layer 4 excitatory neurons, and specialized inhibitory neurons. These results lay the groundwork for a refined cellular and molecular characterization of human cortical cytoarchitecture and areal specialization.

Identifying quantitatively differential chromosomal compartmentalization changes and their biological significance from Hi-C data using DARIC

BACKGROUND

Chromosomal compartmentalization plays a critical role in maintaining proper transcriptional programs in cell differentiation and oncogenesis. However, currently the prevalent method for comparative analysis of compartmentalization landscapes between different cell types is limited to the qualitative switched compartments.

RESULTS

To identify genomic regions with quantitatively differential compartmentalization changes from genome-wide chromatin conformation data like Hi-C, we developed a computational framework named DARIC. DARIC includes three modules: compartmentalization quantification, normalization, and differential analysis. Comparing DARIC with the conventional compartment switching analysis reveals substantial regions characterized by quantitatively significant compartmentalization changes without switching. These changes are accompanied by changes in gene expression, chromatin accessibility, H3K27ac intensity, as well as the interactions with nuclear lamina proteins and nuclear positioning, highlighting the functional importance of such quantitative changes in gene regulation. We applied DARIC to dissect the quantitative compartmentalization changes during human cardiomyocyte differentiation and identified two distinct mechanisms for gene activation based on the association with compartmentalization changes. Using the quantitative compartmentalization measurement module from DARIC, we further dissected the compartment variability landscape in the human genome by analyzing a compendium of 32 Hi-C datasets from 4DN. We discovered an interesting correlation between compartmentalization variability and sub-compartments.

CONCLUSIONS

DARIC is a useful tool for analyzing quantitative compartmentalization changes and mining novel biological insights from increasing Hi-C data. Our results demonstrate the functional significance of quantitative compartmentalization changes in gene regulation, and provide new insights into the relationship between compartmentalization variability and sub-compartments in the human genome.

Integrative Genomic Analyses Identify LncRNA Regulatory Networks across Pediatric Leukemias and Solid Tumors

Long noncoding RNAs (lncRNA) play an important role in gene regulation and contribute to tumorigenesis. While pan-cancer studies of lncRNA expression have been performed for adult malignancies, the lncRNA landscape across pediatric cancers remains largely uncharted. Here, we curated RNA sequencing data for 1,044 pediatric leukemia and extracranial solid tumors and integrated paired tumor whole genome sequencing and epigenetic data in relevant cell line models to explore lncRNA expression, regulation, and association with cancer. A total of 2,657 lncRNAs were robustly expressed across six pediatric cancers, including 1,142 exhibiting histotype-elevated expression. DNA copy number alterations contributed to lncRNA dysregulation at a proportion comparable to protein coding genes. Application of a multidimensional framework to identify and prioritize lncRNAs impacting gene networks revealed that lncRNAs dysregulated in pediatric cancer are associated with proliferation, metabolism, and DNA damage hallmarks. Analysis of upstream regulation via cell type-specific transcription factors further implicated distinct histotype-elevated and developmental lncRNAs. Integration of these analyses prioritized lncRNAs for experimental validation, and silencing of TBX2-AS1, the top-prioritized neuroblastoma-specific lncRNA, resulted in significant growth inhibition of neuroblastoma cells, confirming the computational predictions. Taken together, these data provide a comprehensive characterization of lncRNA regulation and function in pediatric cancers and pave the way for future mechanistic studies.

SIGNIFICANCE

Comprehensive characterization of lncRNAs in pediatric cancer leads to the identification of highly expressed lncRNAs across childhood cancers, annotation of lncRNAs showing histotype-specific elevated expression, and prediction of lncRNA gene regulatory networks.

Deep multiple-instance learning accurately predicts gene haploinsufficiency and deletion pathogenicity

Copy number losses (deletions) are a major contributor to the etiology of severe genetic disorders. Although haploinsufficient genes play a critical role in deletion pathogenicity, current methods for deletion pathogenicity prediction fail to integrate multiple lines of evidence for haploinsufficiency at the gene level, limiting their power to pinpoint deleterious deletions associated with genetic disorders. Here we introduce DosaCNV, a deep multiple-instance learning framework that, for the first time, models deletion pathogenicity jointly with gene haploinsufficiency. By integrating over 30 gene-level features potentially predictive of haploinsufficiency, DosaCNV shows unmatched performance in prioritizing pathogenic deletions associated with a broad spectrum of genetic disorders. Furthermore, DosaCNV outperforms existing methods in predicting gene haploinsufficiency even though it is not trained on known haploinsufficient genes. Finally, DosaCNV leverages a state-of-the-art technique to quantify the contributions of individual gene-level features to haploinsufficiency, allowing for human-understandable explanations of model predictions. Altogether, DosaCNV is a powerful computational tool for both fundamental and translational research.

Controlling Antigen Fate in Therapeutic Cancer Vaccines by Targeting Dendritic Cell Receptors

Antigen-presenting cells (APCs) orchestrate immune responses and are therefore of interest for the targeted delivery of therapeutic vaccines. Dendritic cells (DCs) are professional APCs that excel in presentation of exogenous antigens toward CD4+ T helper cells, as well as cytotoxic CD8+ T cells. DCs are highly heterogeneous and can be divided into subpopulations that differ in abundance, function, and phenotype, such as differential expression of endocytic receptor molecules. It is firmly established that targeting antigens to DC receptors enhances the efficacy of therapeutic vaccines. While most studies emphasize the importance of targeting a specific DC subset, we argue that the differential intracellular routing downstream of the targeted receptors within the DC subset should also be considered. Here, we review the mouse and human receptors studied as target for therapeutic vaccines, focusing on antibody and ligand conjugates and how their targeting affects antigen presentation. We aim to delineate how targeting distinct receptors affects antigen presentation and vaccine efficacy, which will guide target selection for future therapeutic vaccine development.

Integrative time-serial networks for genome-wide lncRNA-mRNA interactions reveal interferon-inducible antiviral and T-cell receptor regulations against PRRSV infection

Porcine reproductive and respiratory syndrome virus (PRRSV) infection severely affects the swine industry each year. Although the host mechanisms against PRRSV infection have been identified in key target tissues through whole transcriptome sequencing, specific molecular regulators have not been elucidated. Long non-coding RNA (lncRNA) expression is highly specific and could thus be used to effectively identify PRRSV-specific candidates. Here, we identified novel lncRNAs in lungs, bronchial lymph nodes, and tonsils after PRRSV infection and constructed phenotype-based integrative co-expression networks using time-series differentially expressed (DE) lncRNAs and mRNAs. After the analyses, a total of 309 lncRNA-mRNA interactions were identified. During early host innate signalling, interferon-inducible and interferon genes were positively regulated by specific lncRNA. Moreover, T-cell receptor genes in lung adaptive immune signalling were negatively regulated by specific lncRNA. Collectively, our findings provide insights into the genome-wide lncRNA-mRNA interactions and dynamic regulation of lncRNA-mediated mechanisms against PRRSV infection.

An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data

BACKGROUND

The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed to predict human essential genes from population genomic data. While the existing methods are highly predictive of essential genes of long length, they have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome.

RESULTS

Motivated by the premise that population and functional genomic data may provide complementary evidence for gene essentiality, here we present an evolution-based deep learning model, DeepLOF, to predict essential genes in an unsupervised manner. Unlike previous population genetic methods, DeepLOF utilizes a novel deep learning framework to integrate both population and functional genomic data, allowing us to pinpoint short essential genes that can hardly be predicted from population genomic data alone. Compared with previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Notably, at a false positive rate of 5%, DeepLOF detects 50% more ClinGen haploinsufficient genes than previous methods. Furthermore, DeepLOF discovers 109 novel essential genes that are too short to be identified by previous methods.

CONCLUSION

The predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes.

JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom

Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

Large-scale across species transcriptomic analysis identifies genetic selection signatures associated with longevity in mammals

Lifespan varies significantly among mammals, with more than 100-fold difference between the shortest and longest living species. This natural difference may uncover the evolutionary forces and molecular features that define longevity. To understand the relationship between gene expression variation and longevity, we conducted a comparative transcriptomics analysis of liver, kidney, and brain tissues of 103 mammalian species. We found that few genes exhibit common expression patterns with longevity in the three organs analyzed. However, pathways related to translation fidelity, such as nonsense-mediated decay and eukaryotic translation elongation, correlated with longevity across mammals. Analyses of selection pressure found that selection intensity related to the direction of longevity-correlated genes is inconsistent across organs. Furthermore, expression of methionine restriction-related genes correlated with longevity and was under strong selection in long-lived mammals, suggesting that a common strategy is utilized by natural selection and artificial intervention to control lifespan. Our results indicate that lifespan regulation via gene expression is driven through polygenic and indirect natural selection.

Development of Nanopore sequencing-based full-length transcriptome database toward functional genome annotation of the Pacific oyster, Crassostrea gigas

The Pacific oyster (Crassostrea gigas) is a widely cultivated shellfish in the world, while its transcriptome diversity remains less unexplored due to the limitation of short reads. In this study, we used Oxford Nanopore sequencing to develop the full-length transcriptome database of C. gigas. We identified 77,920 full-length transcripts from 21,523 genes, and uncovered 9668 alternative splicing events and 87,468 alternative polyadenylation sites. Notably, a total of 16,721 novel transcripts were annotated in this work. Furthermore, integrative analysis of 25 publicly available RNA-seq datasets revealed the transcriptome diversity involved in post-transcriptional regulation in C. gigas. We further developed a Drupal based webserver, Cgtdb, which can be used for transcriptome visualization, sequence alignment, and functional genome annotation analyses. This work provides valuable resources and a useful tool for integrative analysis of various transcriptome datasets in C. gigas, which will serve as an essential reference for functional annotation of the oyster genome.

Analysis and Prediction of Pathogen Nucleic Acid Specificity for Toll-like Receptors in Vertebrates

Identification of key sequence, expression and function related features of nucleic acid-sensing host proteins is of fundamental importance to understand the dynamics of pathogen-specific host responses. To meet this objective, we considered toll-like receptors (TLRs), a representative class of membrane-bound sensor proteins, from 17 vertebrate species covering mammals, birds, reptiles, amphibians, and fishes in this comparative study. We identified the molecular signatures of host TLRs that are responsible for sensing pathogen nucleic acids or other pathogen-associated molecular patterns (PAMPs), and potentially play important roles in host defence mechanism. Interestingly, our findings reveal that such host-specific features are directly related to the strand (single or double) specificity of nucleic acid from pathogens. However, during host-pathogen interactions, such features were unable to explain the pathogenic PAMP (i.e., DNA, RNA or other) selectivity, suggesting a more complex mechanism. Using these features, we developed a number of machine learning models, of which Random Forest achieved a high performance (94.57% accuracy) to predict strand specificity of TLRs from protein-derived features. We applied the trained model to propose strand specificity of some previously uncharacterized distinct fish-specific novel TLRs (TLR18, TLR23, TLR24, TLR25, TLR27).

Artificial intelligence learning landscape of triple-negative breast cancer uncovers new opportunities for enhancing outcomes and immunotherapy responses

Triple-negative breast cancer (TNBC) is a relatively aggressive breast cancer subtype due to tumor relapse, drug resistance, and multi-organ metastatic properties. Identifying reliable biomarkers to predict prognosis and precisely guide TNBC immunotherapy is still an unmet clinical need. To address this issue, we successfully constructed a novel 25 machine learning (ML) algorithms-based immune infiltrating cell (IIC) associated signature of TNBC (MLIIC), achieved by multiple transcriptome data of purified immune cells, TNBC cell lines, and TNBC entities. The TSI index was employed to determine IIC-RNAs that were accompanied by an expression pattern of upregulation in immune cells and downregulation in TNBC cells. LassoLR, Boruta, Xgboost, SVM, RF, and Pamr were utilized for further obtaining the optimal IIC-RNAs. Following univariate Cox regression analysis, LassoCox, CoxBoost, and RSF were utilized for the dimensionality reduction of IIC-RNAs from a prognostic perspective. RSF, Ranger, ObliqueRSF, Rpart, CoxPH, SurvivalSVM, CoxBoost, GlmBoost, SuperPC, StepwiseCox, Enet, LassoCox, CForest, Akritas, BlackBoost, PlsRcox, SurvReg, GBM, and CTree were used for determining the most potent MLIIC signature. Consequently, this MLIIC signature was correlated significantly with survival status validated by four independent TNBC cohorts. Also, the MLIIC signature had a superior predictive capability for TNBC prognosis, compared with 148 previously reported signatures. In addition, MLIIC signature scores developed by immunofluorescent staining of tissue arrays from TNBC patients showed a substantial prognostic value. In TNBC immunotherapy, the low MLIIC profile demonstrated significant immune-responsive efficacy in a dataset of multiple cancer types. MLIIC signature could also predict m6A epigenetic regulation which controls T cell homeostasis. Therefore, this well-established MLIIC signature is a robust predictive indicator for TNBC prognosis and the benefit of immunotherapy, thus providing an efficient tool for combating TNBC.

The genome of the Wollemi pine, a critically endangered "living fossil" unchanged since the Cretaceous, reveals extensive ancient transposon activity

We present the genome of the living fossil, Wollemia nobilis, a southern hemisphere conifer morphologically unchanged since the Cretaceous. Presumed extinct until rediscovery in 1994, the Wollemi pine is critically endangered with less than 60 wild adults threatened by intensifying bushfires in the Blue Mountains of Australia. The 12 Gb genome is among the most contiguous large plant genomes assembled, with extremely low heterozygosity and unusual abundance of DNA transposons. Reduced representation and genome re-sequencing of individuals confirms a relictual population since the last major glacial/drying period in Australia, 120 ky BP. Small RNA and methylome sequencing reveal conservation of ancient silencing mechanisms despite the presence of thousands of active and abundant transposons, including some transferred horizontally to conifers from arthropods in the Jurassic. A retrotransposon burst 8-6 my BP coincided with population decline, possibly as an adaptation enhancing epigenetic diversity. Wollemia, like other conifers, is susceptible to Phytophthora, and a suite of defense genes, similar to those in loblolly pine, are targeted for silencing by sRNAs in leaves. The genome provides insight into the earliest seed plants, while enabling conservation efforts.

Comparative Analysis Reveals Different Evolutionary Fates and Biological Functions in Wheat Duplicated Genes (Triticum aestivum L.)

Wheat (Triticum aestivum L.) is a staple food crop that provides 20% of total human calorie consumption. Gene duplication has been considered to play an important role in evolution by providing new genetic resources. However, the evolutionary fates and biological functions of the duplicated genes in wheat remain to be elucidated. In this study, the resulting data showed that the duplicated genes evolved faster with shorter gene lengths, higher codon usage bias, lower expression levels, and higher tissue specificity when compared to non-duplicated genes. Our analysis further revealed functions of duplicated genes in various biological processes with significant enrichment to environmental stresses. In addition, duplicated genes derived from dispersed, proximal, tandem, transposed, and whole-genome duplication differed in abundance, evolutionary rate, gene compactness, expression pattern, and genetic diversity. Tandem and proximal duplicates experienced stronger selective pressure and showed a more compact gene structure with diverse expression profiles than other duplication modes. Moreover, genes derived from different duplication modes showed an asymmetrical evolutionary pattern for wheat A, B, and D subgenomes. Several candidate duplication hotspots associated with wheat domestication or polyploidization were characterized as potential targets for wheat molecular breeding. Our comprehensive analysis revealed the evolutionary trajectory of duplicated genes and laid the foundation for future functional studies on wheat.

Comparative Genomics and Transcriptomics Analyses Reveal Divergent Plant Biomass-Degrading Strategies in Fungi

Plant biomass is one of the most abundant renewable carbon sources, which holds great potential for replacing current fossil-based production of fuels and chemicals. In nature, fungi can efficiently degrade plant polysaccharides by secreting a broad range of carbohydrate-active enzymes (CAZymes), such as cellulases, hemicellulases, and pectinases. Due to the crucial role of plant biomass-degrading (PBD) CAZymes in fungal growth and related biotechnology applications, investigation of their genomic diversity and transcriptional dynamics has attracted increasing attention. In this project, we systematically compared the genome content of PBD CAZymes in six taxonomically distant species, Aspergillus niger, Aspergillus nidulans, Penicillium subrubescens, Trichoderma reesei, Phanerochaete chrysosporium, and Dichomitus squalens, as well as their transcriptome profiles during growth on nine monosaccharides. Considerable genomic variation and remarkable transcriptomic diversity of CAZymes were identified, implying the preferred carbon source of these fungi and their different methods of transcription regulation. In addition, the specific carbon utilization ability inferred from genomics and transcriptomics was compared with fungal growth profiles on corresponding sugars, to improve our understanding of the conversion process. This study enhances our understanding of genomic and transcriptomic diversity of fungal plant polysaccharide-degrading enzymes and provides new insights into designing enzyme mixtures and metabolic engineering of fungi for related industrial applications.

Uncovering the candidate genes related to sheep body weight using multi-trait genome-wide association analysis

In sheep, body weight is an economically important trait. This study sought to map genetic loci related to weaning weight and yearling weight. To this end, a single-trait and multi-trait genome-wide association study (GWAS) was performed using a high-density 600 K single nucleotide polymorphism (SNP) chip. The results showed that 43 and 56 SNPs were significantly associated with weaning weight and yearling weight, respectively. A region associated with both weaning and yearling traits (OARX: 6.74-7.04 Mb) was identified, suggesting that the same genes could play a role in regulating both these traits. This region was found to contain three genes (TBL1X, SHROOM2 and GPR143). The most significant SNP was Affx-281066395, located at 6.94 Mb (p = 1.70 × 10-17), corresponding to the SHROOM2 gene. We also identified 93 novel SNPs elated to sheep weight using multi-trait GWAS analysis. A new genomic region (OAR10: 76.04-77.23 Mb) with 22 significant SNPs were discovered. Combining transcriptomic data from multiple tissues and genomic data in sheep, we found the HINT1, ASB11 and GPR143 genes may involve in sheep body weight. So, multi-omic anlaysis is a valuable strategy identifying candidate genes related to body weight.

Strong Purifying Selection in Haploid Tissue-Specific Genes of Scots Pine Supports the Masking Theory

The masking theory states that genes expressed in a haploid stage will be under more efficient selection. In contrast, selection will be less efficient in genes expressed in a diploid stage, where the fitness effects of recessive deleterious or beneficial mutations can be hidden from selection in heterozygous form. This difference can influence several evolutionary processes such as the maintenance of genetic variation, adaptation rate, and genetic load. Masking theory expectations have been confirmed in single-cell haploid and diploid organisms. However, in multicellular organisms, such as plants, the effects of haploid selection are not clear-cut. In plants, the great majority of studies indicating haploid selection have been carried out using male haploid tissues in angiosperms. Hence, evidence in these systems is confounded with the effects of sexual selection and intraspecific competition. Evidence from other plant groups is scarce, and results show no support for the masking theory. Here, we have used a gymnosperm Scots pine megagametophyte, a maternally derived seed haploid tissue, and four diploid tissues to test the strength of purifying selection on a set of genes with tissue-specific expression. By using targeted resequencing data of those genes, we obtained estimates of genetic diversity, the site frequency spectrum of 0-fold and 4-fold sites, and inferred the distribution of fitness effects of new mutations in haploid and diploid tissue-specific genes. Our results show that purifying selection is stronger for tissue-specific genes expressed in the haploid megagametophyte tissue and that this signal of strong selection is not an artifact driven by high expression levels.

5mC DNA methylation modification-mediated regulation in tissue functional differentiation and important flavor substance synthesis of tea plant (Camellia sinensis L.)

In plants, 5mC DNA methylation is an important and conserved epistatic mark involving genomic stability, gene transcriptional regulation, developmental regulation, abiotic stress response, metabolite synthesis, etc. However, the roles of 5mC DNA methylation modification (5mC methylation) in tea plant growth and development (in pre-harvest processing) and flavor substance synthesis in pre- and post-harvest processing are unknown. We therefore conducted a comprehensive methylation analysis of four key pre-harvest tissues (root, leaf, flower, and fruit) and two processed leaves during oolong tea post-harvest processing. We found that differential 5mC methylation among four key tissues is closely related to tissue functional differentiation and that genes expressed tissue-specifically, responsible for tissue-specific functions, maintain relatively low 5mC methylation levels relative to non-tissue-specifically expressed genes. Importantly, hypomethylation modifications of CsAlaDC and TS/GS genes in roots provided the molecular basis for the dominant synthesis of theanine in roots. In addition, integration of 5mC DNA methylationomics, metabolomics, and transcriptomics of post-harvest leaves revealed that content changes in flavor metabolites during oolong tea processing were closely associated with transcription level changes in corresponding metabolite synthesis genes, and changes in transcript levels of these important synthesis genes were strictly regulated by 5mC methylation. We further report that some key genes during processing are regulated by 5mC methylation, which can effectively explain the content changes of important aroma metabolites, including α-farnesene, nerolidol, lipids, and taste substances such as catechins. Our results not only highlight the key roles of 5mC methylation in important flavor substance synthesis in pre- and post-harvest processing, but also provide epimutation-related gene targets for future improvement of tea quality or breeding of whole-tissue high-theanine varieties.

Proteomics analysis of plasma from middle-aged adults identifies protein markers of dementia risk in later life

A diverse set of biological processes have been implicated in the pathophysiology of Alzheimer's disease (AD) and related dementias. However, there is limited understanding of the peripheral biological mechanisms relevant in the earliest phases of the disease. Here, we used a large-scale proteomics platform to examine the association of 4877 plasma proteins with 25-year dementia risk in 10,981 middle-aged adults. We found 32 dementia-associated plasma proteins that were involved in proteostasis, immunity, synaptic function, and extracellular matrix organization. We then replicated the association between 15 of these proteins and clinically relevant neurocognitive outcomes in two independent cohorts. We demonstrated that 12 of these 32 dementia-associated proteins were associated with cerebrospinal fluid (CSF) biomarkers of AD, neurodegeneration, or neuroinflammation. We found that eight of these candidate protein markers were abnormally expressed in human postmortem brain tissue from patients with AD, although some of the proteins that were most strongly associated with dementia risk, such as GDF15, were not detected in these brain tissue samples. Using network analyses, we found a protein signature for dementia risk that was characterized by dysregulation of specific immune and proteostasis/autophagy pathways in adults in midlife ~20 years before dementia onset, as well as abnormal coagulation and complement signaling ~10 years before dementia onset. Bidirectional two-sample Mendelian randomization genetically validated nine of our candidate proteins as markers of AD in midlife and inferred causality of SERPINA3 in AD pathogenesis. Last, we prioritized a set of candidate markers for AD and dementia risk prediction in midlife.

A protein-coding gene expression atlas from the brain of pregnant and non-pregnant goats

Background: The brain is an extraordinarily complex organ with multiple anatomical structures involved in highly specialized functions related with behavior and physiological homeostasis. Our goal was to build an atlas of protein-coding gene expression in the goat brain by sequencing the transcriptomes of 12 brain regions in seven female Murciano-Granadina goats, from which three of them were 1-month pregnant. Results: Between 14,889 (cerebellar hemisphere) and 15,592 (pineal gland) protein-coding genes were expressed in goat brain regions, and most of them displayed ubiquitous or broad patterns of expression across tissues. Principal component analysis and hierarchical clustering based on the patterns of mRNA expression revealed that samples from certain brain regions tend to group according to their position in the anterior-posterior axis of the neural tube, i.e., hindbrain (pons and medulla oblongata), midbrain (rostral colliculus) and forebrain (frontal neocortex, olfactory bulb, hypothalamus, and hippocampus). Exceptions to this observation were cerebellum and glandular tissues (pineal gland and hypophysis), which showed highly divergent mRNA expression profiles. Differential expression analysis between pregnant and non-pregnant goats revealed moderate changes of mRNA expression in the frontal neocortex, hippocampus, adenohypophysis and pons, and very dramatic changes in the olfactory bulb. Many genes showing differential expression in this organ are related to olfactory function and behavior in humans. Conclusion: With the exception of cerebellum and glandular tissues, there is a relationship between the cellular origin of sampled regions along the anterior-posterior axis of the neural tube and their mRNA expression patterns in the goat adult brain. Gestation induces substantial changes in the mRNA expression of the olfactory bulb, a finding consistent with the key role of this anatomical structure on the development of maternal behavior.

Proteins with amino acid repeats constitute a rapidly evolvable and human-specific essentialome

Protein products of essential genes, indispensable for organismal survival, are highly conserved and bring about fundamental functions. Interestingly, proteins that contain amino acid homorepeats that tend to evolve rapidly are enriched in eukaryotic essentialomes. Why are proteins with hypermutable homorepeats enriched in conserved and functionally vital essential proteins? We solve this functional versus evolutionary paradox by demonstrating that human essential proteins with homorepeats bring about crosstalk across biological processes through high interactability and have distinct regulatory functions affecting expansive global regulation. Importantly, essential proteins with homorepeats rapidly diverge with the amino acid substitutions frequently affecting functional sites, likely facilitating rapid adaptability. Strikingly, essential proteins with homorepeats influence human-specific embryonic and brain development, implying that the presence of homorepeats could contribute to the emergence of human-specific processes. Thus, we propose that homorepeat-containing essential proteins affecting species-specific traits can be potential intervention targets across pathologies, including cancers and neurological disorders.

The genome of the glasshouse plant noble rhubarb (Rheum nobile) provides a window into alpine adaptation

Glasshouse plants are species that trap warmth via specialized morphology and physiology, mimicking a human glasshouse. In the Himalayan alpine region, the highly specialized glasshouse morphology has independently evolved in distinct lineages to adapt to intensive UV radiation and low temperature. Here we demonstrate that the glasshouse structure - specialized cauline leaves - is highly effective in absorbing UV light but transmitting visible and infrared light, creating an optimal microclimate for the development of reproductive organs. We reveal that this glasshouse syndrome has evolved at least three times independently in the rhubarb genus Rheum. We report the genome sequence of the flagship glasshouse plant Rheum nobile and identify key genetic network modules in association with the morphological transition to specialized glasshouse leaves, including active secondary cell wall biogenesis, upregulated cuticular cutin biosynthesis, and suppression of photosynthesis and terpenoid biosynthesis. The distinct cell wall organization and cuticle development might be important for the specialized optical property of glasshouse leaves. We also find that the expansion of LTRs has likely played an important role in noble rhubarb adaptation to high elevation environments. Our study will enable additional comparative analyses to identify the genetic basis underlying the convergent occurrence of glasshouse syndrome.

Profiling of Chromatin Accessibility in Pigs across Multiple Tissues and Developmental Stages

The study of chromatin accessibility across tissues and developmental stages is essential for elucidating the transcriptional regulation of various phenotypes and biological processes. However, the chromatin accessibility profiles of multiple tissues in newborn pigs and across porcine liver development remain poorly investigated. Here, we used ATAC-seq and rRNA-depleted RNA-seq to profile open chromatin maps and transcriptional features of heart, kidney, liver, lung, skeletal muscle, and spleen in newborn pigs and porcine liver tissue in the suckling and adult stages, respectively. Specifically, by analyzing a union set of protein-coding genes (PCGs) and two types of transcripts (lncRNAs and TUCPs), we obtained a comprehensive annotation of consensus ATAC-seq peaks for each tissue and developmental stage. As expected, the PCGs with tissue-specific accessible promoters had active transcription and were relevant to tissue-specific functions. In addition, other non-coding tissue-specific peaks were involved in both physical activity and the morphogenesis of neonatal tissues. We also characterized stage-specific peaks and observed a close association between dynamic chromatin accessibility and hepatic function transition during liver postnatal development. Overall, this study expands our current understanding of epigenetic regulation in mammalian tissues and organ development, which can benefit both economic trait improvement and improve the biomedical usage of pigs.

Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection

Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling of multiple target genes may improve performance, even under linearity assumptions. To address this problem, we propose two novel approaches that cast the GRN reconstruction problem as a blend between regularized multivariate regression and graphical models that combine the L2,1-norm with classical regularization techniques. We used data and networks from the DREAM5 challenge to show that the proposed models provide consistently good performance in comparison to contenders whose performance varies with data sets from simulation and experiments from model unicellular organisms Escherichia coli and Saccharomyces cerevisiae. Since the models' formulation facilitates the prediction of master regulators, we also used the resulting findings to identify master regulators over all data sets as well as their plasticity across different environments. Our results demonstrate that the identified master regulators are in line with experimental evidence from the model bacterium E. coli. Together, our study demonstrates that simultaneous modelling of several target genes results in improved inference of GRNs and can be used as an alternative in different applications.