HRT Atlas v1.0 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets

Comprehensive transcriptomic analysis of long non-coding RNAs in bovine ovarian follicles and early embryos

Long non-coding RNAs (lncRNAs) have been the subject of numerous studies over the past decade. First thought to come from aberrant transcriptional events, lncRNAs are now considered a crucial component of the genome with roles in multiple cellular functions. However, the functional annotation and characterization of bovine lncRNAs during early development remain limited. In this comprehensive analysis, we review lncRNAs expression in bovine ovarian follicles and early embryos, based on a unique database comprising 468 microarray hybridizations from a single platform designed to target 7,724 lncRNA transcripts, of which 5,272 are intergenic (lincRNA), 958 are intronic, and 1,524 are antisense (lncNAT). Compared to translated mRNA, lncRNAs have been shown to be more tissue-specific and expressed in low copy numbers. This analysis revealed that protein-coding genes and lncRNAs are both expressed more in oocytes. Differences between the oocyte and the 2-cell embryo are also more apparent in terms of lncRNAs than mRNAs. Co-expression network analysis using WGCNA generated 25 modules with differing proportions of lncRNAs. The modules exhibiting a higher proportion of lncRNAs were found to be associated with fewer annotated mRNAs and housekeeping functions. Functional annotation of co-expressed mRNAs allowed attribution of lncRNAs to a wide array of key cellular events such as meiosis, translation initiation, immune response, and mitochondrial related functions. We thus provide evidence that lncRNAs play diverse physiological roles that are tissue-specific and associated with key cellular functions alongside mRNAs in bovine ovarian follicles and early embryos. This contributes to add lncRNAs as active molecules in the complex regulatory networks driving folliculogenesis, oogenesis and early embryogenesis all of which are necessary for reproductive success.

TimeTalk uses single-cell RNA-seq datasets to decipher cell-cell communication during early embryo development

Early embryonic development is a dynamic process that relies on proper cell-cell communication to form a correctly patterned embryo. Early embryo development-related ligand-receptor pairs (eLRs) have been shown to guide cell fate decisions and morphogenesis. However, the scope of eLRs and their influence on early embryo development remain elusive. Here, we developed a computational framework named TimeTalk from integrated public time-course mouse scRNA-seq datasets to decipher the secret of eLRs. Extensive validations and analyses were performed to ensure the involvement of identified eLRs in early embryo development. Process analysis identified that eLRs could be divided into six temporal windows corresponding to sequential events in the early embryo development process. With the interpolation strategy, TimeTalk is powerful in revealing paracrine settings and studying cell-cell communication during early embryo development. Furthermore, by using TimeTalk in the blastocyst and blastoid models, we found that the blastoid models share the core communication pathways with the epiblast and primitive endoderm lineages in the blastocysts. This result suggests that TimeTalk has transferability to other bio-dynamic processes. We also curated eLRs recognized by TimeTalk, which may provide valuable clues for understanding early embryo development and relevant disorders.

Quantifying common and distinct information in single-cell multimodal data with Tilted Canonical Correlation Analysis

Multimodal single-cell technologies profile multiple modalities for each cell simultaneously, enabling a more thorough characterization of cell populations. Existing dimension-reduction methods for multimodal data capture the "union of information," producing a lower-dimensional embedding that combines the information across modalities. While these tools are useful, we focus on a fundamentally different task of separating and quantifying the information among cells that is shared between the two modalities as well as unique to only one modality. Hence, we develop Tilted Canonical Correlation Analysis (Tilted-CCA), a method that decomposes a paired multimodal dataset into three lower-dimensional embeddings-one embedding captures the "intersection of information," representing the geometric relations among the cells that is common to both modalities, while the remaining two embeddings capture the "distinct information for a modality," representing the modality-specific geometric relations. We analyze single-cell multimodal datasets sequencing RNA along surface antibodies (i.e., CITE-seq) as well as RNA alongside chromatin accessibility (i.e., 10x) for blood cells and developing neurons via Tilted-CCA. These analyses show that Tilted-CCA enables meaningful visualization and quantification of the cross-modal information. Finally, Tilted-CCA's framework allows us to perform two specific downstream analyses. First, for single-cell datasets that simultaneously profile transcriptome and surface antibody markers, we show that Tilted-CCA helps design the target antibody panel to complement the transcriptome best. Second, for developmental single-cell datasets that simultaneously profile transcriptome and chromatin accessibility, we show that Tilted-CCA helps identify development-informative genes and distinguish between transient versus terminal cell types.

Transcription factors organize into functional groups on the linear genome and in 3D chromatin

Transcription factors (TFs) and their binding sites have evolved to interact cooperatively or competitively with each other. Here we examine in detail, across multiple cell lines, such cooperation or competition among TFs both in sequential and spatial proximity (using chromatin conformation capture assays), considering in vivo binding data as well as TF binding motifs in DNA. We ascertain significantly co-occurring ("attractive") or avoiding ("repulsive") TF pairs using robust randomized models that retain the essential characteristics of the experimental data. Across human cell lines TFs organize into two groups, with intra-group attraction and inter-group repulsion. This is true for both sequential and spatial proximity, and for both in vivo binding and sequence motifs. Attractive TF pairs exhibit significantly more physical interactions suggesting an underlying mechanism. The two TF groups differ significantly in their genomic and network properties, as well in their function-while one group regulates housekeeping function, the other potentially regulates lineage-specific functions, that are disrupted in cancer. Weaker binding sites tend to occur in spatially interacting regions of the genome. Our results suggest that a complex pattern of spatial cooperativity of TFs and chromatin has evolved with the genome to support housekeeping and lineage-specific functions.

Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster

The volume of ribonucleic acid (RNA)-seq data has increased exponentially, providing numerous new insights into various biological processes. However, due to significant practical challenges, such as data heterogeneity, it is still difficult to ensure the quality of these data when integrated. Although some quality control methods have been developed, sample consistency is rarely considered and these methods are susceptible to artificial factors. Here, we developed MassiveQC, an unsupervised machine learning-based approach, to automatically download and filter large-scale high-throughput data. In addition to the read quality used in other tools, MassiveQC also uses the alignment and expression quality as model features. Meanwhile, it is user-friendly since the cutoff is generated from self-reporting and is applicable to multimodal data. To explore its value, we applied MassiveQC to Drosophila RNA-seq data and generated a comprehensive transcriptome atlas across 28 tissues from embryogenesis to adulthood. We systematically characterized fly gene expression dynamics and found that genes with high expression dynamics were likely to be evolutionarily young and expressed at late developmental stages, exhibiting high nonsynonymous substitution rates and low phenotypic severity, and they were involved in simple regulatory programs. We also discovered that human and Drosophila had strong positive correlations in gene expression in orthologous organs, revealing the great potential of the Drosophila system for studying human development and disease.

Low RNA stability signifies increased post-transcriptional regulation of cell identity genes

Cell identity genes are distinct from other genes with respect to the epigenetic mechanisms to activate their transcription, e.g. by super-enhancers and broad H3K4me3 domains. However, it remains unclear whether their post-transcriptional regulation is also unique. We performed a systematic analysis of transcriptome-wide RNA stability in nine cell types and found that unstable transcripts were enriched in cell identity-related pathways while stable transcripts were enriched in housekeeping pathways. Joint analyses of RNA stability and chromatin state revealed significant enrichment of super-enhancers and broad H3K4me3 domains at the gene loci of unstable transcripts. Intriguingly, the RNA m6A methyltransferase, METTL3, preferentially binds to chromatin at super-enhancers, broad H3K4me3 domains and their associated genes. METTL3 binding intensity is positively correlated with RNA m6A methylation and negatively correlated with RNA stability of cell identity genes, probably due to co-transcriptional m6A modifications promoting RNA decay. Nanopore direct RNA-sequencing showed that METTL3 knockdown has a stronger effect on RNA m6A and mRNA stability for cell identity genes. Our data suggest a run-and-brake model, where cell identity genes undergo both frequent transcription and fast RNA decay to achieve precise regulation of RNA expression.

Characterizing the landscape of gene expression variance in humans

Gene expression variance has been linked to organismal function and fitness but remains a commonly neglected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease.

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.

Safety and feasibility of the gene transfer of hemopexin for conditions with increased free heme

Heme is a fundamental molecule for several biological processes, but when released in the extracellular space such as in hemolytic diseases, it can be toxic to cells and tissues. Hemopexin (HPX) is a circulating protein responsible for removing free heme from the circulation, whose levels can be severely depleted in conditions such as sickle cell diseases. Accordingly, increasing HPX levels represents an attractive strategy to mitigate the deleterious effects of heme in these conditions. Gene transfer of liver-produced proteins with adeno-associated virus (AAV) has been shown to be an effective and safety strategy in animal and human studies mainly in hemophilia. Here, we report the feasibility of increasing HPX levels using an AAV8 vector expressing human HPX (hHPX). C57Bl mice were injected with escalating doses of our vector, and expression was assessed by enzyme immunoassay (ELISA), Western blot, and quantitative polymerase chain reaction (qPCR). In addition, the biological activity of transgenic hHPX was confirmed using two different models of heme challenge consisting of serial heme injections or phenylhydrazine-induced hemolysis. Sustained expression of hHPX was confirmed for up to 26 weeks in plasma. Expression was dose-dependent and not associated with clinical signs of toxicity. hHPX levels were significantly reduced by heme infusions and phenylhydrazine-induced hemolysis. No clinical toxicity or laboratory signs of liver damage were observed in preliminary short-term heme challenge studies. Our results confirm that long-term expression of hHPX is feasible and safe in mice, even in the presence of heme overload. Additional studies are needed to explore the effect of transgenic HPX protein in animal models of chronic hemolysis.

scHiMe: predicting single-cell DNA methylation levels based on single-cell Hi-C data

Recently a biochemistry experiment named methyl-3C was developed to simultaneously capture the chromosomal conformations and DNA methylation levels on individual single cells. However, the number of data sets generated from this experiment is still small in the scientific community compared with the greater amount of single-cell Hi-C data generated from separate single cells. Therefore, a computational tool to predict single-cell methylation levels based on single-cell Hi-C data on the same individual cells is needed. We developed a graph transformer named scHiMe to accurately predict the base-pair-specific (bp-specific) methylation levels based on both single-cell Hi-C data and DNA nucleotide sequences. We benchmarked scHiMe for predicting the bp-specific methylation levels on all of the promoters of the human genome, all of the promoter regions together with the corresponding first exon and intron regions, and random regions on the whole genome. Our evaluation showed a high consistency between the predicted and methyl-3C-detected methylation levels. Moreover, the predicted DNA methylation levels resulted in accurate classifications of cells into different cell types, which indicated that our algorithm successfully captured the cell-to-cell variability in the single-cell Hi-C data. scHiMe is freely available at http://dna.cs.miami.edu/scHiMe/.

Mathematical modeling identifies LAG3 and HAVCR2 as biomarkers of T cell exhaustion in melanoma

Cytotoxic T lymphocytes (CTLs) control tumors via lysis of antigen-presenting targets or through secretion of cytokines such as interferon-γ (IFNG), which inhibit tumor cell proliferation. Improved understanding of CTL interactions within solid tumors will aid the development of immunotherapeutic strategies against cancer. In this study, we take a systems biology approach to compare the importance of cytolytic versus IFNG-mediated cytostatic effects in a murine melanoma model (B16F10) and to dissect the contribution of immune checkpoints HAVCR2, LAG3, and PDCD1/CD274 to CTL exhaustion. We integrated multimodal data to inform an ordinary differential equation (ODE) model of CTL activities inside the tumor. Our model predicted that CTL cytotoxicity played only a minor role in tumor control relative to the cytostatic effects of IFNG. Furthermore, our analysis revealed that within B16F10 melanomas HAVCR2 and LAG3 better characterize the development of a dysfunctional CTL phenotype than does the PDCD1/CD274 axis.

Single-cell analysis of shared signatures and transcriptional diversity during zebrafish development

During development, animals generate distinct cell populations with specific identities, functions, and morphologies. We mapped transcriptionally distinct populations across 489,686 cells from 62 stages during wild-type zebrafish embryogenesis and early larval development (3-120 hours post-fertilization). Using these data, we identified the limited catalog of gene expression programs reused across multiple tissues and their cell-type-specific adaptations. We also determined the duration each transcriptional state is present during development and suggest new long-term cycling populations. Focused analyses of non-skeletal muscle and the endoderm identified transcriptional profiles of understudied cell types and subpopulations, including the pneumatic duct, individual intestinal smooth muscle layers, spatially distinct pericyte subpopulations, and homologs of recently discovered human best4+ enterocytes. The transcriptional regulators of these populations remain unknown, so we reconstructed gene expression trajectories to suggest candidates. To enable additional discoveries, we make this comprehensive transcriptional atlas of early zebrafish development available through our website, Daniocell.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

ProInfer: An interpretable protein inference tool leveraging on biological networks

In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.

The RNA-Seq data analysis shows how the ontogenesis defines aging

This paper presents a global statistical analysis of the RNA-Seq results of the entire Mus musculus genome. We explain aging by a gradual redistribution of limited resources between two major tasks of the organism: its self-sustenance based on the function of the housekeeping gene group (HG) and functional differentiation provided by the integrative gene group (IntG). All known disorders associated with aging are the result of a deficiency in the repair processes provided by the cellular infrastructure. Understanding exactly how this deficiency arises is our primary goal. Analysis of RNA production data of 35,630 genes, from which 5,101 were identified as HG genes, showed that RNA production levels in the HG and IntG genes had statistically significant differences (p-value <0.0001) throughout the entire observation period. In the reproductive period of life, which has the lowest actual mortality risk for Mus musculus, changes in the age dynamics of RNA production occur. The statistically significant dynamics of the decrease of RNA production in the HG group in contrast to the IntG group was determined (p-value = 0.0045). The trend toward significant shift in the HG/IntG ratio occurs after the end of the reproductive period, coinciding with the beginning of the mortality rate increase in Mus musculus indirectly supports our hypothesis. The results demonstrate a different orientation of the impact of ontogenesis regulatory mechanisms on the groups of genes representing cell infrastructures and their organismal functions, making the chosen direction promising for further research and understanding the mechanisms of aging.

A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases

The nuclear factor NF-kB is the master transcription factor in the inflammatory process by modulating the expression of pro-inflammatory genes. However, an additional level of complexity is the ability to promote the transcriptional activation of post-transcriptional modulators of gene expression as non-coding RNA (i.e., miRNAs). While NF-kB’s role in inflammation-associated gene expression has been extensively investigated, the interplay between NF-kB and genes coding for miRNAs still deserves investigation. To identify miRNAs with potential NF-kB binding sites in their transcription start site, we predicted miRNA promoters by an in silico analysis using the PROmiRNA software, which allowed us to score the genomic region’s propensity to be miRNA cis-regulatory elements. A list of 722 human miRNAs was generated, of which 399 were expressed in at least one tissue involved in the inflammatory processes. The selection of “high-confidence” hairpins in miRbase identified 68 mature miRNAs, most of them previously identified as inflammamiRs. The identification of targeted pathways/diseases highlighted their involvement in the most common age-related diseases. Overall, our results reinforce the hypothesis that persistent activation of NF-kB could unbalance the transcription of specific inflammamiRNAs. The identification of such miRNAs could be of diagnostic/prognostic/therapeutic relevance for the most common inflammatory-related and age-related diseases.

Molecular Detection and Identification of Plant-Associated Lactiplantibacillus plantarum

Lactiplantibacillus plantarum is a lactic acid bacterium often isolated from a wide variety of niches. Its ubiquity can be explained by a large, flexible genome that helps it adapt to different habitats. The consequence of this is great strain diversity, which may make their identification difficult. Accordingly, this review provides an overview of molecular techniques, both culture-dependent, and culture-independent, currently used to detect and identify L. plantarum. Some of the techniques described can also be applied to the analysis of other lactic acid bacteria.

Deciphering Hierarchical Chromatin Domains and Preference of Genomic Position Forming Boundaries in Single Mouse Embryonic Stem Cells

The exploration of single-cell 3D genome maps reveals that chromatin domains are indeed physical structures presenting in single cells, and domain boundaries vary from cell to cell. However, systematic analysis of the association between regulatory factor binding and elements and the formation of chromatin domains in single cells has not yet emerged. To this end, a hierarchical chromatin domain structure identification algorithm (named as HiCS) is first developed from individual single-cell Hi-C maps, with superior performance in both accuracy and efficiency. The results suggest that in addition to the known CTCF-cohesin complex, Polycomb, TrxG, pluripotent protein families, and other multiple factors also contribute to shaping chromatin domain boundaries in single embryonic stem cells. Different cooperation patterns of these regulatory factors drive genomic position categories with differential preferences forming boundaries, and the most extensive six types of retrotransposons are differentially distributed in these genomic position categories with preferential localization. The above results suggest that these different retrotransposons within genomic regions interplay with regulatory factors navigating the preference of genomic positions forming boundaries, driving the formation of higher-order chromatin structures, and thus regulating cell functions in single mouse embryonic stem cells.

Cell Proteomic Footprinting: Advances in the Quality of Cellular and Cell-Derived Cancer Vaccines

In omics sciences, many compounds are measured simultaneously in a sample in a single run. Such analytical performance opens up prospects for improving cellular cancer vaccines and other cell-based immunotherapeutics. This article provides an overview of proteomics technology, known as cell proteomic footprinting. The molecular phenotype of cells is highly variable, and their antigenic profile is affected by many factors, including cell isolation from the tissue, cell cultivation conditions, and storage procedures. This makes the therapeutic properties of cells, including those used in vaccines, unpredictable. Cell proteomic footprinting makes it possible to obtain controlled cell products. Namely, this technology facilitates the cell authentication and quality control of cells regarding their molecular phenotype, which is directly connected with the antigenic properties of cell products. Protocols for cell proteomic footprinting with their crucial moments, footprint processing, and recommendations for the implementation of this technology are described in this paper. The provided footprints in this paper and program source code for their processing contribute to the fast implementation of this technology in the development and manufacturing of cell-based immunotherapeutics.

DNA methylation changes and increased mRNA expression of coagulation proteins, factor V and thrombomodulin in Fuchs endothelial corneal dystrophy

Late-onset Fuchs endothelial corneal dystrophy (FECD) is a disease affecting the corneal endothelium (CE), associated with a cytosine-thymine-guanine repeat expansion at the CTG18.1 locus in the transcription factor 4 (TCF4) gene. It is unknown whether CTG18.1 expansions affect global methylation including TCF4 gene in CE or whether global CE methylation changes at advanced age. Using genome-wide DNA methylation array, we investigated methylation in CE from FECD patients with CTG18.1 expansions and studied the methylation in healthy CE at different ages. The most revealing DNA methylation findings were analyzed by gene expression and protein analysis. 3488 CpGs had significantly altered methylation pattern in FECD though no substantial changes were found in TCF4. The most hypermethylated site was in a predicted promoter of aquaporin 1 (AQP1) gene, and the most hypomethylated site was in a predicted promoter of coagulation factor V (F5 for gene, FV for protein). In FECD, AQP1 mRNA expression was variable, while F5 gene expression showed a ~ 23-fold increase. FV protein was present in both healthy and affected CE. Further gene expression analysis of coagulation factors interacting with FV revealed a ~ 34-fold increase of thrombomodulin (THBD). THBD protein was detected only in CE from FECD patients. Additionally, we observed an age-dependent hypomethylation in elderly healthy CE.Thus, tissue-specific genome-wide and gene-specific methylation changes associated with altered gene expression were discovered in FECD. TCF4 pathological methylation in FECD because of CTG18.1 expansion was ruled out.

Exploring the Anti-Inflammatory Effect of Inulin by Integrating Transcriptomic and Proteomic Analyses in a Murine Macrophage Cell Model

Inulin is a natural polysaccharide classified as a soluble fiber with demonstrated prebiotic activity. Prebiotics can reduce intestinal and systemic inflammation through modulation of the gut microflora and their metabolites. Additionally, extensive research is illuminating the role of macrophages in the interaction between gut microbiota and many systemic inflammatory diseases. In this study, the anti-inflammatory properties of inulin were evaluated using a murine macrophage cell model (RAW 264.7) of inflammation, and the immunomodulatory mechanism was investigated using omics technologies. The cells underwent comprehensive transcriptomic and proteomic analyses to identify the mechanisms responsible for the observed anti-inflammatory phenotype. Functional analyses of these omics results revealed two potential mechanisms that may lead to an overall reduction in cytokine and chemokine transcription: the inhibition of the NF-κB signaling pathway, leading to the downregulation of proinflammatory factors such as COX2, and the promotion of the phase II defense protein Hmox1 via the Nrf2 pathway. This study provides promising targets for research on immune modulation by dietary fibers and offers new strategies for the design of functional ingredients, foods, and nutraceutical products, which could ultimately lead to personalized nutrition and improved consumer health.

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.

Preclinical species gene expression database: Development and meta-analysis

The evaluation of toxicity in preclinical species is important for identifying potential safety liabilities of experimental medicines. Toxicology studies provide translational insight into potential adverse clinical findings, but data interpretation may be limited due to our understanding of cross-species biological differences. With the recent technological advances in sequencing and analyzing omics data, gene expression data can be used to predict cross species biological differences and improve experimental design and toxicology data interpretation. However, interpreting the translational significance of toxicogenomics analyses can pose a challenge due to the lack of comprehensive preclinical gene expression datasets. In this work, we performed RNA-sequencing across four preclinical species/strains widely used for safety assessment (CD1 mouse, Sprague Dawley rat, Beagle dog, and Cynomolgus monkey) in ∼50 relevant tissues/organs to establish a comprehensive preclinical gene expression body atlas for both males and females. In addition, we performed a meta-analysis across the large dataset to highlight species and tissue differences that may be relevant for drug safety analyses. Further, we made these databases available to the scientific community. This multi-species, tissue-, and sex-specific transcriptomic database should serve as a valuable resource to enable informed safety decision-making not only during drug development, but also in a variety of disciplines that use these preclinical species.

GAGAM v1.2: An Improvement on Peak Labeling and Genomic Annotated Gene Activity Matrix Construction

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) is rapidly becoming a powerful technology for assessing the epigenetic landscape of thousands of cells. However, the sparsity of the resulting data poses significant challenges to their interpretability and informativeness. Different computational methods are available, proposing ways to generate significant features from accessibility data and process them to obtain meaningful results. Foremost among them is the peak calling, which interprets the raw scATAC-seq data generating the peaks as features. However, scATAC-seq data are not trivially comparable with single-cell RNA sequencing (scRNA-seq) data, an increasingly pressing challenge since the necessity of multimodal experiments integration. For this reason, this study wants to improve the concept of the Gene Activity Matrix (GAM), which links the accessibility data to the genes, by proposing an improved version of the Genomic-Annotated Gene Activity Matrix (GAGAM) concept. Specifically, this paper presents GAGAM v1.2, a new and better version of GAGAM v1.0. GAGAM aims to label the peaks and link them to the genes through functional annotation of the whole genome. Using genes as features in scATAC-seq datasets makes different datasets comparable and allows linking gene accessibility and expression. This link is crucial for gene regulation understanding and fundamental for the increasing impact of multi-omics data. Results confirm that our method performs better than the previous GAMs and shows a preliminary comparison with scRNA-seq data.

Expression Profile of Housekeeping Genes and Tissue-Specific Genes in Multiple Tissues of Pigs

Pigs have become an ideal model system for human disease research and development and an important farm animal that provides a valuable source of nutrition. To profile the all-sided gene expression and their biological functions across multiple tissues, we conducted a comprehensive analysis of gene expression on a large scale around the side of housekeeping genes (HKGs), tissue specific genes (TSGs), and the co-expressed genes in 14 various tissues. In this study, we identified 2351 HKGs and 3018 TSGs across tissues, among which 4 HKGs (COX1, UBB, OAZ1/NPFF) exhibited low variation and high expression levels, and 31 particular TSGs (e.g., PDC, FKBP6, STAT2, and COL1A1) were exclusively expressed in several tissues, including endocrine brain, ovaries, livers, backfat, jejunum, kidneys, lungs, and longissimus dorsi muscles. We also obtained 17 modules with 230 hub genes (HUBGs) by weighted gene co-expression network analysis. On the other hand, HKGs functions were enriched in the signaling pathways of the ribosome, spliceosome, thermogenesis, oxidative phosphorylation, and nucleocytoplasmic transport, which have been highly suggested to involve in the basic biological tissue activities. While TSGs were highly enriched in the signaling pathways that were involved in specific physiological processes, such as the ovarian steroidogenesis pathway in ovaries and the renin-angiotensin system pathway in kidneys. Collectively, these stable, specifical, and co-expressed genes provided useful information for the investigation of the molecular mechanism for an understanding of the genetic and biological processes of complex traits in pigs.

Aging is a Side Effect of the Ontogenesis Program of Multicellular Organisms

The review presents a brief outline of the current state of the main theoretical approaches to the aging problem. The works of authors, supporting the theory of "accumulation of errors" and theories stating the presence of a hypothetical "aging program" in all multicellular organisms are reviewed. The role of apoptosis and its connection with phenoptosis, as well as the theory of "hyperfunction" are analyzed. Our own approach to this problem is presented, in which aging is explained by the redistribution of limited resources between the two main aims of the organism: its self-sufficiency, based on the function of the housekeeping genes (HG) group, and functional specialization, provided by the integrative genes (IntG) group. Agreeing with the inseparable connection between aging and the ontogenesis program, the main role in the aging mechanisms is assigned to the redistribution of resources from the HG self-sufficiency genes to the IntGs necessary for the operation of all specialized functions of the organism as a whole. The growing imbalance between HGs and IntGs with age, suggests that switching of cellular resources in favor of IntGs is a side effect of ontogenesis program implementation and the main reason for aging, inherent in the nature of genome functioning under conditions of highly integrated multicellularity. The hypothesis of functional subdivision of the genome also points to the leading role of slow-dividing and postmitotic cells, as the most sensitive to reduction of repair levels, for triggering and realization of the aging process.

Identification of Suitable Reference Genes for Lowlanders Exposed to High Altitude and Ladakhi Highlanders

Sharma, Vandana, Rajeev Varshney, and Niroj Kumar Sethy. Identification of suitable reference genes for lowlanders exposed to high altitude and Ladakhi highlanders. High Alt Med Biol. 23:319-329, 2022. Background: Identifying a stable and reliable reference gene (RG) is a prerequisite for the unbiased and unambiguous analysis of gene expression data. It has become evident that conventionally used housekeeping genes such as beta-actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and peptidylprolyl Isomerase A (PPIA) exhibit varied expression patterns under hypoxia. Hence, the identification of stable RGs for humans exposed to hypobaric hypoxia can enhance the accuracy of gene expression studies by limiting the negligent use of random housekeeping genes. Methods: Using TaqMan™ array-based quantitative real-time quantitative polymerase chain reaction, we evaluated the expression of 32 commonly used human RGs among lowlanders at Delhi (altitude 216 m, SL), lowlanders at Leh (altitude 3,524 m) after 1 day (HA-D1) and 7 days (HA-D7), as well as indigenous Ladakhi highlanders at the same altitude. The expression stability of the RGs was evaluated using geNorm, NormFinder, BestKeeper, Delta CT method, and RefFinder algorithms. Results: Our studies identify TATA-box binding protein (TBP), proteasome 26S subunit, ATPase 4 (PSMC4), and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ) as the most stable human RGs for normalizing human gene expression under hypobaric hypoxia. In addition, we report the combination of TBP and cyclin-dependent kinase inhibitor 1B (CDKN1B) as the most stable RG for studying lowlander gene expression during high-altitude exposure. In contrast, RPL30 and 18S exhibited maximum variation across study groups and were identified as the least stable RGs.

scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data

MOTIVATION

Technological advances have enabled us to profile single-cell multi-omics data from the same cells, providing us with an unprecedented opportunity to understand the cellular phenotype and links to its genotype. The available protocols and multi-omics datasets [including parallel single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data profiled from the same cell] are growing increasingly. However, such data are highly sparse and tend to have high level of noise, making data analysis challenging. The methods that integrate the multi-omics data can potentially improve the capacity of revealing the cellular heterogeneity.

RESULTS

We propose an adaptively weighted multi-view learning (scAWMV) method for the integrative analysis of parallel scRNA-seq and scATAC-seq data profiled from the same cell. scAWMV considers both the difference in importance across different modalities in multi-omics data and the biological connection of the features in the scRNA-seq and scATAC-seq data. It generates biologically meaningful low-dimensional representations for the transcriptomic and epigenomic profiles via unsupervised learning. Application to four real datasets demonstrates that our framework scAWMV is an efficient method to dissect cellular heterogeneity for single-cell multi-omics data.

AVAILABILITY AND IMPLEMENTATION

The software and datasets are available at https://github.com/pengchengzeng/scAWMV.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The co-evolution of the genome and epigenome in colorectal cancer

Colorectal malignancies are a leading cause of cancer-related death1 and have undergone extensive genomic study2,3. However, DNA mutations alone do not fully explain malignant transformation4-7. Here we investigate the co-evolution of the genome and epigenome of colorectal tumours at single-clone resolution using spatial multi-omic profiling of individual glands. We collected 1,370 samples from 30 primary cancers and 8 concomitant adenomas and generated 1,207 chromatin accessibility profiles, 527 whole genomes and 297 whole transcriptomes. We found positive selection for DNA mutations in chromatin modifier genes and recurrent somatic chromatin accessibility alterations, including in regulatory regions of cancer driver genes that were otherwise devoid of genetic mutations. Genome-wide alterations in accessibility for transcription factor binding involved CTCF, downregulation of interferon and increased accessibility for SOX and HOX transcription factor families, suggesting the involvement of developmental genes during tumourigenesis. Somatic chromatin accessibility alterations were heritable and distinguished adenomas from cancers. Mutational signature analysis showed that the epigenome in turn influences the accumulation of DNA mutations. This study provides a map of genetic and epigenetic tumour heterogeneity, with fundamental implications for understanding colorectal cancer biology.

H3K18 lactylation marks tissue-specific active enhancers

Background

Histone lactylation has been recently described as a novel histone post-translational modification linking cellular metabolism to epigenetic regulation.

Results

Given the expected relevance of this modification and current limited knowledge of its function, we generate genome-wide datasets of H3K18la distribution in various in vitro and in vivo samples, including mouse embryonic stem cells, macrophages, adipocytes, and mouse and human skeletal muscle. We compare them to profiles of well-established histone modifications and gene expression patterns. Supervised and unsupervised bioinformatics analysis shows that global H3K18la distribution resembles H3K27ac, although we also find notable differences. H3K18la marks active CpG island-containing promoters of highly expressed genes across most tissues assessed, including many housekeeping genes, and positively correlates with H3K27ac and H3K4me3 as well as with gene expression. In addition, H3K18la is enriched at active enhancers that lie in proximity to genes that are functionally important for the respective tissue.

Conclusions

Overall, our data suggests that H3K18la is not only a marker for active promoters, but also a mark of tissue specific active enhancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02775-y.

D2 Plot, a Matrix of DNA Density and Distance to Periphery, Reveals Functional Genome Regions

The execution of biological activities inside space-limited cell nuclei requires sophisticated organization. Current studies on the 3D genome focus on chromatin interactions and local structures, e.g., topologically associating domains (TADs). In this study, two global physical properties: DNA density and distance to nuclear periphery (DisTP), are introduced and a 2D matrix, D2 plot, is constructed for mapping genetic and epigenetic markers. Distinct patterns of functional markers on the D2 plot, indicating its ability to compartmentalize functional genome regions, are observed. Furthermore, enrichments of transcription-related markers are concatenated into a cross-species transcriptional activation model, where the nucleus is divided into four areas: active, intermediate, repress and histone, and repress and repeat. Based on the trajectories of the genomic regions on D2 plot, the constantly active and newly activated genes are successfully identified during olfactory sensory neuron maturation. The analysis reveals that the D2 plot effectively categorizes functional regions and provides a universal and transcription-related measurement for the 3D genome.

The transcriptional coactivator RUVBL2 regulates Pol II clustering with diverse transcription factors

RNA polymerase II (Pol II) apparatuses are compartmentalized into transcriptional clusters. Whether protein factors control these clusters remains unknown. In this study, we find that the ATPase-associated with diverse cellular activities (AAA + ) ATPase RUVBL2 co-occupies promoters with Pol II and various transcription factors. RUVBL2 interacts with unphosphorylated Pol II in chromatin to promote RPB1 carboxy-terminal domain (CTD) clustering and transcription initiation. Rapid depletion of RUVBL2 leads to a decrease in the number of Pol II clusters and inhibits nascent RNA synthesis, and tethering RUVBL2 to an active promoter enhances Pol II clustering at the promoter. We also identify target genes that are directly linked to the RUVBL2-Pol II axis. Many of these genes are hallmarks of cancers and encode proteins with diverse cellular functions. Our results demonstrate an emerging activity for RUVBL2 in regulating Pol II cluster formation in the nucleus.

Identification of Human Global, Tissue and Within-Tissue Cell-Specific Stably Expressed Genes at Single-Cell Resolution

Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.

Celda: a Bayesian model to perform co-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and to elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform co-clustering of genes into transcriptional modules and cells into subpopulations. Celda can quantify the probabilistic contribution of each gene to each module, each module to each cell population and each cell population to each sample. In a peripheral blood mononuclear cell dataset, Celda identified a subpopulation of proliferating T cells and a plasma cell which were missed by two other common single-cell workflows. Celda also identified transcriptional modules that could be used to characterize unique and shared biological programs across cell types. Finally, Celda outperformed other approaches for clustering genes into modules on simulated data. Celda presents a novel method for characterizing transcriptional programs and cellular heterogeneity in scRNA-seq data.

Evaluation of the mechanisms of heme-induced tissue factor activation: Contribution of innate immune pathways

Hemolytic diseases such as Sickle Cell Disease (SCD) are characterized by a natural propensity for both arterial and venous thrombosis. The ability of heme to induce tissue factor (TF) activation has been shown both in animal models of SCD, and in human endothelial cells and monocytes. Moreover, it was recently demonstrated that heme can induce coagulation activation in the whole blood of healthy volunteers in a TF-dependent fashion. Herein, we aim to further explore the cellular mechanisms by which heme induces TF-coagulation activation, using human mononuclear cells, which have been shown to be relevant to in vivo hemostasis. TF mRNA expression was evaluated by qPCR and TF procoagulant activity was evaluated using a 2-stage assay based on the generation of activated factor X (FXa). Heme was capable of inducing both TF expression and activation in a TLR4-dependent pathway. This activity was further amplified after TNF-α-priming. Our results provide additional details on the mechanisms by which heme is involved in the pathogenesis of hypercoagulability in hemolytic diseases.

Transcriptome innovations in primates revealed by single-molecule long-read sequencing

Transcriptomic diversity greatly contributes to the fundamentals of disease, lineage-specific biology, and environmental adaptation. However, much of the actual isoform repertoire contributing to shaping primate evolution remains unknown. Here, we combined deep long- and short-read sequencing complemented with mass spectrometry proteomics in a panel of lymphoblastoid cell lines (LCLs) from human, three other great apes, and rhesus macaque, producing the largest full-length isoform catalog in primates to date. Around half of the captured isoforms are not annotated in their reference genomes, significantly expanding the gene models in primates. Furthermore, our comparative analyses unveil hundreds of transcriptomic innovations and isoform usage changes related to immune function and immunological disorders. The confluence of these evolutionary innovations with signals of positive selection and their limited impact in the proteome points to changes in alternative splicing in genes involved in immune response as an important target of recent regulatory divergence in primates.

Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis

Single-cell RNA sequencing (scRNA-seq) can determine gene expression in numerous individual cells simultaneously, promoting progress in the biomedical sciences. However, scRNA-seq data are high-dimensional with substantial technical noise, including dropouts. During analysis of scRNA-seq data, such noise engenders a statistical problem known as the curse of dimensionality (COD). Based on high-dimensional statistics, we herein formulate a noise reduction method, RECODE (resolution of the curse of dimensionality), for high-dimensional data with random sampling noise. We show that RECODE consistently resolves COD in relevant scRNA-seq data with unique molecular identifiers. RECODE does not involve dimension reduction and recovers expression values for all genes, including lowly expressed genes, realizing precise delineation of cell fate transitions and identification of rare cells with all gene information. Compared with representative imputation methods, RECODE employs different principles and exhibits superior overall performance in cell-clustering, expression value recovery, and single-cell-level analysis. The RECODE algorithm is parameter-free, data-driven, deterministic, and high-speed, and its applicability can be predicted based on the variance normalization performance. We propose RECODE as a powerful strategy for preprocessing noisy high-dimensional data.

Postmitotic differentiation of human monocytes requires cohesin-structured chromatin

Cohesin is a major structural component of mammalian genomes and is required to maintain loop structures. While acute depletion in short-term culture models suggests a limited importance of cohesin for steady-state transcriptional circuits, long-term studies are hampered by essential functions of cohesin during replication. Here, we study genome architecture in a postmitotic differentiation setting, the differentiation of human blood monocytes (MO). We profile and compare epigenetic, transcriptome and 3D conformation landscapes during MO differentiation (either into dendritic cells or macrophages) across the genome and detect numerous architectural changes, ranging from higher level compartments down to chromatin loops. Changes in loop structures correlate with cohesin-binding, as well as epigenetic and transcriptional changes during differentiation. Functional studies show that the siRNA-mediated depletion of cohesin (and to a lesser extent also CTCF) markedly disturbs loop structures and dysregulates genes and enhancers that are primarily regulated during normal MO differentiation. In addition, gene activation programs in cohesin-depleted MO-derived macrophages are disturbed. Our findings implicate an essential function of cohesin in controlling long-term, differentiation- and activation-associated gene expression programs.

How chromatin structure and gene accessibility changes during monocyte differentiation is not clearly defined. Here the authors characterize the chromatin changes during macrophage or dendritic cell maturation from monocytes and the dependence of this upon cohesin and CTCF.

Mononucleotide A-repeats may Play a Regulatory Role in Endothermic Housekeeping Genes

Background:

Coding and non-coding short tandem repeats (STRs) facilitate a great diversity of phenotypic traits. The imbalance of mononucleotide A-repeats around transcription start sites (TSSs) was found in 3 mammals: H. sapiens, M. musculus, and R. norvegicus.

Principal Findings:

We found that the imbalance pattern originated in some vertebrates. A similar pattern was observed in mammals and birds, but not in amphibians and reptiles. We proposed that the enriched A-repeats upstream of TSSs is a novel hallmark of endotherms or warm-blooded animals. Gene ontology analysis indicates that the primary function of upstream A-repeats involves metabolism, cellular transportation, and sensory perception (smell and chemical stimulus) through housekeeping genes.

Conclusions:

Upstream A-repeats may play a regulatory role in the metabolic process of endothermic animals.

EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.

Single-cell analysis reveals X upregulation is not global in pre-gastrulation embryos

In mammals, transcriptional inactivation of one X chromosome in female compensates for the dosage of X-linked gene expression between the sexes. Additionally, it is believed that the upregulation of active X chromosome in male and female balances the dosage of X-linked gene expression relative to autosomal genes, as proposed by Ohno. However, the existence of X chromosome upregulation (XCU) remains controversial. Here, we have profiled gene-wise dynamics of XCU in pre-gastrulation mouse embryos at single-cell level and found that XCU is dynamically linked with X chromosome inactivation (XCI); however, XCU is not global like XCI. Moreover, we show that upregulated genes are enriched with activating marks and have enhanced burst frequency. Finally, our In-silico model predicts that recruitment probabilities of activating factors and a surge of these factors upon X-inactivation trigger XCU. Altogether, our study provides significant insight into the gene-wise dynamics and mechanistic basis of XCU during early development and extends support for Ohno’s hypothesis.

•

X-upregulation coincides with X chromosome inactivation in pre-gastrulation embryos

•

X-upregulation is not chromosome-wide like X-inactivation

•

Upregulated genes have enhanced burst frequency and are enriched with activating marks

•

A surge of activating factors on X-inactivation triggers X-upregulation

De Novo Assembly of 20 Chicken Genomes Reveals the Undetectable Phenomenon for Thousands of Core Genes on Microchromosomes and Subtelomeric Regions

The gene numbers and evolutionary rates of birds were assumed to be much lower than those of mammals, which is in sharp contrast to the huge species number and morphological diversity of birds. It is, therefore, necessary to construct a complete avian genome and analyze its evolution. We constructed a chicken pan-genome from 20 de novo assembled genomes with high sequencing depth, and identified 1,335 protein-coding genes and 3,011 long noncoding RNAs not found in GRCg6a. The majority of these novel genes were detected across most individuals of the examined transcriptomes but were seldomly measured in each of the DNA sequencing data regardless of Illumina or PacBio technology. Furthermore, different from previous pan-genome models, most of these novel genes were overrepresented on chromosomal subtelomeric regions and microchromosomes, surrounded by extremely high proportions of tandem repeats, which strongly blocks DNA sequencing. These hidden genes were proved to be shared by all chicken genomes, included many housekeeping genes, and enriched in immune pathways. Comparative genomics revealed the novel genes had 3-fold elevated substitution rates than known ones, updating the knowledge about evolutionary rates in birds. Our study provides a framework for constructing a better chicken genome, which will contribute toward the understanding of avian evolution and the improvement of poultry breeding.

Germinal centre-driven maturation of B cell response to mRNA vaccination

Germinal centres (GC) are lymphoid structures in which B cells acquire affinity-enhancing somatic hypermutations (SHM), with surviving clones differentiating into memory B cells (MBCs) and long-lived bone marrow plasma cells1-5 (BMPCs). SARS-CoV-2 mRNA vaccination induces a persistent GC response that lasts for at least six months in humans6-8. The fate of responding GC B cells as well as the functional consequences of such persistence remain unknown. Here, we detected SARS-CoV-2 spike protein-specific MBCs in 42 individuals who had received two doses of the SARS-CoV-2 mRNA vaccine BNT162b2 six month earlier. Spike-specific IgG-secreting BMPCs were detected in 9 out of 11 participants. Using a combined approach of sequencing the B cell receptors of responding blood plasmablasts and MBCs, lymph node GC B cells and plasma cells and BMPCs from eight individuals and expression of the corresponding monoclonal antibodies, we tracked the evolution of 1,540 spike-specific B cell clones. On average, early blood spike-specific plasmablasts exhibited the lowest SHM frequencies. By contrast, SHM frequencies of spike-specific GC B cells increased by 3.5-fold within six months after vaccination. Spike-specific MBCs and BMPCs accumulated high levels of SHM, which corresponded with enhanced anti-spike antibody avidity in blood and enhanced affinity as well as neutralization capacity of BMPC-derived monoclonal antibodies. We report how the notable persistence of the GC reaction induced by SARS-CoV-2 mRNA vaccination in humans culminates in affinity-matured long-term antibody responses that potently neutralize the virus.

Transcriptomics and network analysis highlight potential pathways in the pathogenesis of pterygium

Pterygium is a common ocular surface condition frequently associated with irritative symptoms. The precise identity of its critical triggers as well as the hierarchical relationship between all the elements involved in the pathogenesis of this disease are not yet elucidated. Meta-analysis of gene expression studies represents a novel strategy capable of identifying key pathogenic mediators and therapeutic targets in complex diseases. Samples from nine patients were collected during surgery after photo documentation and clinical characterization of pterygia. Gene expression experiments were performed using Human Clariom D Assay gene chip. Differential gene expression analysis between active and atrophic pterygia was performed using limma package after adjusting variables by age. In addition, a meta-analysis was performed including recent gene expression studies available at the Gene Expression Omnibus public repository. Two databases including samples from adults with pterygium and controls fulfilled our inclusion criteria. Meta-analysis was performed using the Rank Production algorithm of the RankProd package. Gene set analysis was performed using ClueGO and the transcription factor regulatory network prediction was performed using appropriate bioinformatics tools. Finally, miRNA-mRNA regulatory network was reconstructed using up-regulated genes identified in the gene set analysis from the meta-analysis and their interacting miRNAs from the Brazilian cohort expression data. The meta-analysis identified 154 up-regulated and 58 down-regulated genes. A gene set analysis with the top up-regulated genes evidenced an overrepresentation of pathways associated with remodeling of extracellular matrix. Other pathways represented in the network included formation of cornified envelopes and unsaturated fatty acid metabolic processes. The miRNA-mRNA target prediction network, also reconstructed based on the set of up-regulated genes presented in the gene ontology and biological pathways network, showed that 17 target genes were negatively correlated with their interacting miRNAs from the Brazilian cohort expression data. Once again, the main identified cluster involved extracellular matrix remodeling mechanisms, while the second cluster involved formation of cornified envelope, establishment of skin barrier and unsaturated fatty acid metabolic process. Differential expression comparing active pterygium with atrophic pterygium using data generated from the Brazilian cohort identified differentially expressed genes between the two forms of presentation of this condition. Our results reveal differentially expressed genes not only in pterygium, but also in active pterygium when compared to the atrophic ones. New insights in relation to pterygium's pathophysiology are suggested.

The Vulnerability of the Developing Brain: Analysis of Highly Expressed Genes in Infant C57BL/6 Mouse Hippocampus in Relation to Phenotypic Annotation Derived From Mutational Studies

The hippocampus has been shown to have a major role in learning and memory, but also to participate in the regulation of emotions. However, its specific role(s) in memory is still unclear. Hippocampal damage or dysfunction mainly results in memory issues, especially in the declarative memory but, in animal studies, has also shown to lead to hyperactivity and difficulty in inhibiting responses previously taught. The brain structure is affected in neuropathological disorders, such as Alzheimer's, epilepsy, and schizophrenia, and also by depression and stress. The hippocampus structure is far from mature at birth and undergoes substantial development throughout infant and juvenile life. The aim of this study was to survey genes highly expressed throughout the postnatal period in mouse hippocampus and which have also been linked to an abnormal phenotype through mutational studies to achieve a greater understanding about hippocampal functions during postnatal development. Publicly available gene expression data from C57BL/6 mouse hippocampus was analyzed; from a total of 5 time points (at postnatal day 1, 10, 15, 21, and 30), 547 genes highly expressed in all of these time points were selected for analysis. Highly expressed genes are considered to be of potential biological importance and appear to be multifunctional, and hence any dysfunction in such a gene will most likely have a large impact on the development of abilities during the postnatal and juvenile period. Phenotypic annotation data downloaded from Mouse Genomic Informatics database were analyzed for these genes, and the results showed that many of them are important for proper embryo development and infant survival, proper growth, and increase in body size, as well as for voluntary movement functions, motor coordination, and balance. The results also indicated an association with seizures that have primarily been characterized by uncontrolled motor activity and the development of proper grooming abilities. The complete list of genes and their phenotypic annotation data have been compiled in a file for easy access.

Misexpression of genes lacking CpG islands drives degenerative changes during aging

Cellular aging is characterized by disruption of the nuclear lamina and its associated heterochromatin. How these structural changes within the nucleus contribute to age-related degeneration of the organism is unclear. Genes lacking CpG islands (CGI− genes) generally associate with heterochromatin when they are inactive. Here, we show that the expression of these genes is globally activated in aged cells and tissues. This CGI− gene misexpression is a common feature of normal and pathological aging in mice and humans. We report evidence that CGI− gene up-regulation is directly responsible for age-related physiological deterioration, notably for increased secretion of inflammatory mediators.

A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation

Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.

Germinal centre-driven maturation of B cell response to SARS-CoV-2 vaccination

Germinal centres (GC) are lymphoid structures where vaccine-responding B cells acquire affinity-enhancing somatic hypermutations (SHM), with surviving clones differentiating into memory B cells (MBCs) and long-lived bone marrow plasma cells (BMPCs) 1-4 . Induction of the latter is a hallmark of durable immunity after vaccination 5 . SARS-CoV-2 mRNA vaccination induces a robust GC response in humans 6-8 , but the maturation dynamics of GC B cells and propagation of their progeny throughout the B cell diaspora have not been elucidated. Here we show that anti-SARS-CoV-2 spike (S)-binding GC B cells were detectable in draining lymph nodes for at least six months in 10 out of 15 individuals who had received two doses of BNT162b2, a SARS-CoV-2 mRNA vaccine. Six months after vaccination, circulating S-binding MBCs were detected in all participants (n=42) and S-specific IgG-secreting BMPCs were detected in 9 out of 11 participants. Using a combined approach of single-cell RNA sequencing of responding blood and lymph node B cells from eight participants and expression of the corresponding monoclonal antibodies, we tracked the evolution of 1540 S-specific B cell clones. SHM accumulated along the B cell differentiation trajectory, with early blood plasmablasts showing the lowest frequencies, followed by MBCs and lymph node plasma cells whose SHM largely overlapped with GC B cells. By three months after vaccination, the frequency of SHM within GC B cells had doubled. Strikingly, S + BMPCs detected six months after vaccination accumulated the highest level of SHM, corresponding with significantly enhanced anti-S polyclonal antibody avidity in blood at that time point. This study documents the induction of affinity-matured BMPCs after two doses of SARS-CoV-2 mRNA vaccination in humans, providing a foundation for the sustained high efficacy observed with these vaccines.