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

GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

3D chromatin architecture, BRD4, and Mediator have distinct roles in regulating genome-wide transcriptional bursting and gene network

Discontinuous transcription is evolutionarily conserved and a fundamental feature of gene regulation; yet, the exact mechanisms underlying transcriptional bursting are unresolved. Analyses of bursting transcriptome-wide have focused on the role of cis-regulatory elements, but other factors that regulate this process remain elusive. We applied mathematical modeling to single-cell RNA sequencing data to infer bursting dynamics transcriptome-wide under multiple conditions to identify possible molecular mechanisms. We found that Mediator complex subunit 26 (MED26) primarily regulates frequency, MYC regulates burst size, while cohesin and Bromodomain-containing protein 4 (BRD4) can modulate both. Despite comparable effects on RNA levels among these perturbations, acute depletion of MED26 had the most profound impact on the entire gene regulatory network, acting downstream of chromatin spatial architecture and without affecting TATA box-binding protein (TBP) recruitment. These results indicate that later steps in the initiation of transcriptional bursts are primary nodes for integrating gene networks in single cells.

Functional characteristics and computational model of abundant hyperactive loci in the human genome

Enhancers and promoters are classically considered to be bound by a small set of TFs in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1,003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected 5 distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.

Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry

The rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling.

Directly selecting cell-type marker genes for single-cell clustering analyses

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.

The mouse multi-organ proteome from infancy to adulthood

The early-life organ development and maturation shape the fundamental blueprint for later-life phenotype. However, a multi-organ proteome atlas from infancy to adulthood is currently not available. Herein, we present a comprehensive proteomic analysis of ten mouse organs (brain, heart, lung, liver, kidney, spleen, stomach, intestine, muscle and skin) at three crucial developmental stages (1-, 4- and 8-weeks after birth) acquired using data-independent acquisition mass spectrometry. We detect and quantify 11,533 protein groups across the ten organs and obtain 115 age-related differentially expressed protein groups that are co-expressed in all organs from infancy to adulthood. We find that spliceosome proteins prevalently play crucial regulatory roles in the early-life development of multiple organs, and detect organ-specific expression patterns and sexual dimorphism. This multi-organ proteome atlas provides a fundamental resource for understanding the molecular mechanisms underlying early-life organ development and maturation.

Extensive long-range polycomb interactions and weak compartmentalization are hallmarks of human neuronal 3D genome

Chromatin architecture regulates gene expression and shapes cellular identity, particularly in neuronal cells. Specifically, polycomb group (PcG) proteins enable establishment and maintenance of neuronal cell type by reorganizing chromatin into repressive domains that limit the expression of fate-determining genes and sustain distinct gene expression patterns in neurons. Here, we map the 3D genome architecture in neuronal and non-neuronal cells isolated from the Wernicke's area of four human brains and comprehensively analyze neuron-specific aspects of chromatin organization. We find that genome segregation into active and inactive compartments is greatly reduced in neurons compared to other brain cells. Furthermore, neuronal Hi-C maps reveal strong long-range interactions, forming a specific network of PcG-mediated contacts in neurons that is nearly absent in other brain cells. These interacting loci contain developmental transcription factors with repressed expression in neurons and other mature brain cells. But only in neurons, they are rich in bivalent promoters occupied by H3K4me3 histone modification together with H3K27me3, which points to a possible functional role of PcG contacts in neurons. Importantly, other layers of chromatin organization also exhibit a distinct structure in neurons, characterized by an increase in short-range interactions and a decrease in long-range ones.

Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems

Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.

A computational approach for deciphering the interactions between proximal and distal gene regulators in GC B-cell response

Delineating the intricate interplay between promoter-proximal and -distal regulators is crucial for understanding the function of transcriptional mediator complexes implicated in the regulation of gene expression. The present study aimed to develop a computational method for accurately modeling the spatial proximal and distal regulatory interactions. Our method combined regression-based models to identify key regulators through gene expression prediction and a graph-embedding approach to detect coregulated genes. This approach enabled a detailed investigation of the gene regulatory mechanisms for germinal center B cells, accompanied by dramatic rearrangements of the genome structure. We found that while the promoter-proximal regulatory elements were the principal regulators of gene expression, the distal regulators fine-tuned transcription. Moreover, our approach unveiled the presence of modular regulators, such as cofactors and proximal/distal transcription factors, which were co-expressed with their target genes. Some of these modules exhibited abnormal expression patterns in lymphoma. These findings suggest that the dysregulation of interactions between transcriptional and architectural factors is associated with chromatin reorganization failure, which may increase the risk of malignancy. Therefore, our computational approach helps decipher the transcriptional cis-regulatory code spatially interacting.

Stratifying TAD boundaries pinpoints focal genomic regions of regulation, damage, and repair

Advances in chromatin mapping have exposed the complex chromatin hierarchical organization in mammals, including topologically associating domains (TADs) and their substructures, yet the functional implications of this hierarchy in gene regulation and disease progression are not fully elucidated. Our study delves into the phenomenon of shared TAD boundaries, which are pivotal in maintaining the hierarchical chromatin structure and regulating gene activity. By integrating high-resolution Hi-C data, chromatin accessibility, and DNA double-strand breaks (DSBs) data from various cell lines, we systematically explore the complex regulatory landscape at high-level TAD boundaries. Our findings indicate that these boundaries are not only key architectural elements but also vibrant hubs, enriched with functionally crucial genes and complex transcription factor binding site-clustered regions. Moreover, they exhibit a pronounced enrichment of DSBs, suggesting a nuanced interplay between transcriptional regulation and genomic stability. Our research provides novel insights into the intricate relationship between the 3D genome structure, gene regulation, and DNA repair mechanisms, highlighting the role of shared TAD boundaries in maintaining genomic integrity and resilience against perturbations. The implications of our findings extend to understanding the complexities of genomic diseases and open new avenues for therapeutic interventions targeting the structural and functional integrity of TAD boundaries.

Effects of chemical in vitro activation versus fragmentation on human ovarian tissue and follicle growth in culture

STUDY QUESTION

What is the effect of the chemical in vitro activation (cIVA) protocol compared with fragmentation only (Frag, also known as mechanical IVA) on gene expression, follicle activation and growth in human ovarian tissue in vitro?

SUMMARY ANSWER

Although histological assessment shows that cIVA significantly increases follicle survival and growth compared to Frag, both protocols stimulate extensive and nearly identical transcriptomic changes in cultured tissue compared to freshly collected ovarian tissue, including marked changes in energy metabolism and inflammatory responses.

WHAT IS KNOWN ALREADY

Treatments based on cIVA of the phosphatase and tensin homolog (PTEN)-phosphatidylinositol 3-kinase (PI3K) pathway in ovarian tissue followed by auto-transplantation have been administered to patients with refractory premature ovarian insufficiency (POI) and resulted in live births. However, comparable effects with mere tissue fragmentation have been shown, questioning the added value of chemical stimulation that could potentially activate oncogenic responses.

STUDY DESIGN SIZE DURATION

Fifty-nine ovarian cortical biopsies were obtained from consenting women undergoing elective caesarean section (C-section). The samples were fragmented for culture studies. Half of the fragments were exposed to bpV (HOpic)+740Y-P (Frag+cIVA group) during the first 24 h of culture, while the other half were cultured with medium only (Frag group). Subsequently, both groups were cultured with medium only for an additional 6 days. Tissue and media samples were collected for histological, transcriptomic, steroid hormone, and cytokine/chemokine analyses at various time points.

PARTICIPANTS/MATERIALS SETTING METHODS

Effects on follicles were evaluated by counting and scoring serial sections stained with hematoxylin and eosin before and after the 7-day culture. Follicle function was assessed by quantification of steroids by ultra-performance liquid chromatography tandem-mass spectrometry at different time points. Cytokines and chemokines were measured by multiplex assay. Transcriptomic effects were measured by RNA-sequencing (RNA-seq) of the tissue after the initial 24-h culture. Selected differentially expressed genes (DEGs) were validated by quantitative PCR and immunofluorescence in cultured ovarian tissue as well as in KGN cell (human ovarian granulosa-like tumor cell line) culture experiments.

MAIN RESULTS AND THE ROLE OF CHANCE

Compared to the Frag group, the Frag+cIVA group exhibited a significantly higher follicle survival rate, increased numbers of secondary follicles, and larger follicle sizes. Additionally, the tissue in the Frag+cIVA group produced less dehydroepiandrosterone compared to Frag. Cytokine measurement showed a strong inflammatory response at the start of the culture in both groups. The RNA-seq data revealed modest differences between the Frag+cIVA and Frag groups, with only 164 DEGs identified using a relaxed cut-off of false discovery rate (FDR) <0.1. Apart from the expected PI3K-protein kinase B (Akt) pathway, cIVA also regulated pathways related to hypoxia, cytokines, and inflammation. In comparison to freshly collected ovarian tissue, gene expression in general was markedly affected in both the Frag+cIVA and Frag groups, with a total of 3119 and 2900 DEGs identified (FDR < 0.001), respectively. The top enriched gene sets in both groups included several pathways known to modulate follicle growth such as mammalian target of rapamycin (mTOR)C1 signaling. Significant changes compared to fresh tissue were also observed in the expression of genes encoding for steroidogenesis enzymes and classical granulosa cell markers in both groups. Intriguingly, we discovered a profound upregulation of genes related to glycolysis and its upstream regulator in both Frag and Frag+cIVA groups, and these changes were further boosted by the cIVA treatment. Cell culture experiments confirmed glycolysis-related genes as direct targets of the cIVA drugs. In conclusion, cIVA enhances follicle growth, as expected, but the mechanisms may be more complex than PI3K-Akt-mTOR alone, and the impact on function and quality of the follicles after the culture period remains an open question.

LARGE SCALE DATA

Data were deposited in the GEO data base, accession number GSE234765. The code for sequencing analysis can be found in https://github.com/tialiv/IVA_project.

LIMITATIONS REASONS FOR CAUTION

Similar to the published IVA protocols, the first steps in our study were performed in an in vitro culture model where the ovarian tissue was isolated from the regulation of hypothalamic-pituitary-ovarian axis. Further in vivo experiments will be needed, for example in xeno-transplantation models, to explore the long-term impacts of the discovered effects. The tissue collected from patients undergoing C-section may not be comparable to tissue of patients with POI.

WIDER IMPLICATIONS OF THE FINDINGS

The general impact of fragmentation and short (24 h) in vitro culture on gene expression in ovarian tissue far exceeded the effects of cIVA. Yet, follicle growth was stimulated by cIVA, which may suggest effects on specific cell populations that may be diluted in bulk RNA-seq. Nevertheless, we confirmed the impact of cIVA on glycolysis using a cell culture model, suggesting impacts on cellular signaling beyond the PI3K pathway. The profound changes in inflammation and glycolysis following fragmentation and culture could contribute to follicle activation and loss in ovarian tissue culture, as well as in clinical applications, such as fertility preservation by ovarian tissue auto-transplantation.

STUDY FUNDING/COMPETING INTERESTS

This study was funded by research grants from European Union's Horizon 2020 Research and Innovation Programme (Project ERIN No. 952516, FREIA No. 825100), Swedish Research Council VR (2020-02132), StratRegen funding from Karolinska Institutet, KI-China Scholarship Council (CSC) Programme and the Natural Science Foundation of Hunan (2022JJ40782). International Iberian Nanotechnology Laboratory Research was funded by the European Union's H2020 Project Sinfonia (857253) and SbDToolBox (NORTE-01-0145-FEDER-000047), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund. No competing interests are declared.

Transcriptomic signatures of classical monocytes reveal pro-inflammatory modules and heterogeneity in polyarticular juvenile idiopathic arthritis

Introduction

Polyarticular juvenile idiopathic arthritis (pJIA) is a childhood-onset autoimmune disease. Immune cells contribute to persistent inflammation observed in pJIA. Despite the crucial role of monocytes in arthritis, the precise involvement of classical monocytes in the pathogenesis of pJIA remains uncertain. Here, we aimed to uncover the transcriptomic patterns of classical monocytes in pJIA, focusing on their involvement in disease mechanism and heterogeneity.

Methods

A total of 17 healthy subjects and 18 premenopausal women with pJIA according to ILAR criteria were included. Classical monocytes were isolated, and RNA sequencing was performed. Differential expression analysis was used to compare pJIA patients and healthy control group. Differentially expressed genes (DEGs) were identified, and gene set enrichment analysis (GSEA) was performed. Using unsupervised learning approach, patients were clustered in two groups based on their similarities at transcriptomic level. Subsequently, these clusters underwent a comparative analysis to reveal differences at the transcriptomic level.

Results

We identified 440 DEGs in pJIA patients of which 360 were upregulated and 80 downregulated. GSEA highlighted TNF-α and IFN-γ response. Importantly, this analysis not only detected genes targeted by pJIA therapy but also identified new modulators of immuno-inflammation. PLAUR, IL1B, IL6, CDKN1A, PIM1, and ICAM1 were pointed as drivers of chronic hyperinflammation. Unsupervised learning approach revealed two clusters within pJIA, each exhibiting varying inflammation levels.

Conclusion

These findings indicate the pivotal role of immuno-inflammation driven by classical monocytes in pJIA and reveals the existence of two subclusters within pJIA, regardless the positivity of rheumatoid factor and anti-CCP, paving the way to precision medicine.

Identifying the stability of housekeeping genes to be used for the quantitative real-time PCR normalization in retinal tissue of streptozotocin-induced diabetic rats

AIM

To investigate the stability of the seven housekeeping genes: beta-actin (ActB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 18s ribosomal unit 5 (18s), cyclophilin A (CycA), hypoxanthine-guanine phosphoribosyl transferase (HPRT), ribosomal protein large P0 (36B4) and terminal uridylyl transferase 1 (U6) in the diabetic retinal tissue of rat model.

METHODS

The expression of these seven genes in rat retinal tissues was determined using real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) in two groups; normal control rats and streptozotocin-induced diabetic rats. The stability analysis of gene expression was investigated using geNorm, NormFinder, BestKeeper, and comparative delta-Ct (ΔCt) algorithms.

RESULTS

The 36B4 gene was stably expressed in the retinal tissues of normal control animals; however, it was less stable in diabetic retinas. The 18s gene was expressed consistently in both normal control and diabetic rats' retinal tissue. That this gene was the best reference for data normalisation in RT-qPCR studies that used the retinal tissue of streptozotocin-induced diabetic rats. Furthermore, there was no ideal gene stably expressed for use in all experimental settings.

CONCLUSION

Identifying relevant genes is a need for achieving RT-qPCR validity and reliability and must be appropriately achieved based on a specific experimental setting.

Analysis of AlphaMissense data in different protein groups and structural context

Single amino acid substitutions can profoundly affect protein folding, dynamics, and function. The ability to discern between benign and pathogenic substitutions is pivotal for therapeutic interventions and research directions. Given the limitations in experimental examination of these variants, AlphaMissense has emerged as a promising predictor of the pathogenicity of missense variants. Since heterogenous performance on different types of proteins can be expected, we assessed the efficacy of AlphaMissense across several protein groups (e.g. soluble, transmembrane, and mitochondrial proteins) and regions (e.g. intramembrane, membrane interacting, and high confidence AlphaFold segments) using ClinVar data for validation. Our comprehensive evaluation showed that AlphaMissense delivers outstanding performance, with MCC scores predominantly between 0.6 and 0.74. We observed low performance on disordered datasets and ClinVar data related to the CFTR ABC protein. However, a superior performance was shown when benchmarked against the high quality CFTR2 database. Our results with CFTR emphasizes AlphaMissense's potential in pinpointing functional hot spots, with its performance likely surpassing benchmarks calculated from ClinVar and ProteinGym datasets.

Age-Invariant Genes: Multi-Tissue Identification and Characterization of Murine Reference Genes

Studies of the aging transcriptome focus on genes that change with age. But what can we learn from age-invariant genes-those that remain unchanged throughout the aging process? These genes also have a practical application: they serve as reference genes (often called housekeeping genes) in expression studies. Reference genes have mostly been identified and validated in young organisms, and no systematic investigation has been done across the lifespan. Here, we build upon a common pipeline for identifying reference genes in RNA-seq datasets to identify age-invariant genes across seventeen C57BL/6 mouse tissues (brain, lung, bone marrow, muscle, white blood cells, heart, small intestine, kidney, liver, pancreas, skin, brown, gonadal, marrow, and subcutaneous adipose tissue) spanning 1 to 21+ months of age. We identify 9 pan-tissue age-invariant genes and many tissue-specific age-invariant genes. These genes are stable across the lifespan and are validated in independent bulk RNA-seq datasets and RT-qPCR. We find age-invariant genes have shorter transcripts on average and are enriched for CpG islands. Interestingly, pathway enrichment analysis for age-invariant genes identifies an overrepresentation of molecular functions associated with some, but not all, hallmarks of aging. Thus, though hallmarks of aging typically involve changes in cell maintenance mechanisms, select genes associated with these hallmarks resist fluctuations in expression with age. Finally, our analysis concludes no classical reference gene is appropriate for aging studies in all tissues. Instead, we provide tissue-specific and pan-tissue genes for assays utilizing reference gene normalization (i.e., RT-qPCR) that can be applied to animals across the lifespan.

Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation

BACKGROUND

Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging.

RESULTS

Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters.

CONCLUSION

Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

Online application for the diagnosis of atherosclerosis by six genes

BACKGROUND

Atherosclerosis (AS) is a primary contributor to cardiovascular disease, leading to significant global mortality rates. Developing effective diagnostic indicators and models for AS holds the potential to substantially reduce the fatalities and disabilities associated with cardiovascular disease. Blood sample analysis has emerged as a promising avenue for facilitating diagnosis and assessing disease prognosis. Nonetheless, it lacks an accurate model or tool for AS diagnosis. Hence, the principal objective of this study is to develop a convenient, simple, and accurate model for the early detection of AS.

METHODS

We downloaded the expression data of blood samples from GEO databases. By dividing the mean values of housekeeping genes (meanHGs) and applying the comBat function, we aimed to reduce the batch effect. After separating the datasets into training, evaluation, and testing sets, we applied differential expression analyses (DEA) between AS and control samples from the training dataset. Then, a gradient-boosting model was used to evaluate the importance of genes and identify the hub genes. Using different machine learning algorithms, we constructed a prediction model with the highest accuracy in the testing dataset. Finally, we make the machine learning models publicly accessible by shiny app construction.

RESULTS

Seven datasets (GSE9874, GSE12288, GSE20129, GSE23746, GSE27034, GSE90074, and GSE202625), including 403 samples with AS and 325 healthy subjects, were obtained by comprehensive searching and filtering by specific requirements. The batch effect was successfully removed by dividing the meanHGs and applying the comBat function. 331 genes were found to be related to atherosclerosis by the DEA analysis between AS and health samples. The top 6 genes with the highest importance values from the gradient boosting model were identified. Out of the seven machine learning algorithms tested, the random forest model exhibited the most impressive performance in the testing datasets, achieving an accuracy exceeding 0.8. While the batch effect reduction analysis in our study could have contributed to the increased accuracy values, our comparison results further highlight the superiority of our model over the genes provided in published studies. This underscores the effectiveness of our approach in delivering superior predictive performance. The machine-learning models were then uploaded to the Shiny app's server, making it easy for users to distinguish AS samples from normal samples.

CONCLUSIONS

A prognostic Shiny application, built upon six potential atherosclerosis-associated genes, has been developed, offering an accurate diagnosis of atherosclerosis.

SWI/SNF-dependent genes are defined by their chromatin landscape

SWI/SNF complexes are evolutionarily conserved, ATP-dependent chromatin remodeling machines. Here, we characterize the features of SWI/SNF-dependent genes using BRM014, an inhibitor of the ATPase activity of the complexes. We find that SWI/SNF activity is required to maintain chromatin accessibility and nucleosome occupancy for most enhancers but not for most promoters. SWI/SNF activity is needed for expression of genes with low to medium levels of expression that have promoters with (1) low chromatin accessibility, (2) low levels of active histone marks, (3) high H3K4me1/H3K4me3 ratio, (4) low nucleosomal phasing, and (5) enrichment in TATA-box motifs. These promoters are mostly occupied by the canonical Brahma-related gene 1/Brahma-associated factor (BAF) complex. These genes are surrounded by SWI/SNF-dependent enhancers and mainly encode signal transduction, developmental, and cell identity genes (with almost no housekeeping genes). Machine-learning models trained with different chromatin characteristics of promoters and their surrounding regulatory regions indicate that the chromatin landscape is a determinant for establishing SWI/SNF dependency.

Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics

The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN predictions with experimental perturbation assays, which provides insights into the generalization capabilities within the studied loci but offers a limited perspective of what drives their predictions. Moreover, existing model explainability tools focus mainly on motif analysis, which becomes complex when interpreting longer sequences. Here we introduce CREME, an in silico perturbation toolkit that interrogates large-scale DNNs to uncover rules of gene regulation that it learns. Using CREME, we investigate Enformer, a prominent DNN in gene expression prediction, revealing cis-regulatory elements (CREs) that directly enhance or silence target genes. We explore the intricate complexity of higher-order CRE interactions, the relationship between CRE distance from transcription start sites on gene expression, as well as the biochemical features of enhancers and silencers learned by Enformer. Moreover, we demonstrate the flexibility of CREME to efficiently uncover a higher-resolution view of functional sequence elements within CREs. This work demonstrates how CREME can be employed to translate the powerful predictions of large-scale DNNs to study open questions in gene regulation.

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

BACKGROUND

Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes.

RESULTS

We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression.

CONCLUSIONS

eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

Reference genes for qPCR expression in black tiger shrimp, Penaeus monodon

BACKGROUND

Gene expression profiling via qPCR is an essential tool for unraveling the intricate molecular mechanisms underlying growth and development. Identifying and validating the most appropriate reference genes is essential for qPCR experiments. Nevertheless, there exists a deficiency in a thorough assessment of reference genes concerning the expression of the genes in the research in the context of the growth and development of the Black Tiger Shrimp, P. monodon. This popular marine crustacean is extensively raised for human consumption. In this study, we assessed the expression stability of seven reference genes (ACTB, 18S, EF-1α, AK, PK, cox1, and CLTC) in adult tissues (hepatopancreas, gills, and stomach) of small and large polymorphs of P. monodon.

METHODS AND RESULTS

The stability of gene expressions was assessed utilizing NormFinder, BestKeeper, and geNorm, and a comprehensive ranking of these genes was conducted through the online tool RefFinder. In the overall ranking, 18S and CLTC emerged as the most stable genes in the hepatopancreas and stomach, while CLTC and AK exhibited significant statistical reliability in the gills of adult P. monodon. The validation of these identified stable genes was carried out using a growth-associated gene, insr-1.

CONCLUSION

The results indicated that 18S and CLTC stand out as the most versatile reference genes for conducting qPCR analysis focused on the growth of P. monodon. This study represents the first comprehensive exploration that identifies and assesses reference genes for qPCR analysis in P. monodon, providing valuable tools for research involving similar crustaceans.

Near-cognate tRNAs increase the efficiency and precision of pseudouridine-mediated readthrough of premature termination codons

Programmable RNA pseudouridylation has emerged as a new type of RNA base editor to suppress premature termination codons (PTCs) that can lead to truncated and nonfunctional proteins. However, current methods to correct disease-associated PTCs suffer from low efficiency and limited precision. Here we develop RESTART v3, which uses near-cognate tRNAs to improve the readthrough efficiency of pseudouridine-modified PTCs. We show an average of ~5-fold (range: 2.1- to 9.5-fold) higher editing efficiency than RESTART v2 in cultured cells and achieve functional PTC readthrough in disease cell models of cystic fibrosis and Hurler syndrome. Furthermore, RESTART v3 enables accurate incorporation of the original amino acid for nearly half of the PTC sites, considering the naturally occurring frequencies of sense-to-nonsense codons, without affecting normal termination codons. Although off-target sites were detected, we did not observe changes to the coding information or the expression level of transcripts, and the overall natural tRNA abundance remained constant.

eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

Background

Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes.

Results

We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression.

Conclusions

eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

Optimization of FFPE preparation and identification of gene attributes associated with RNA degradation

Formalin-fixed paraffin-embedded (FFPE) tissues are widely available specimens for clinical studies. However, RNA degradation in FFPE tissues often restricts their utility. In this study, we determined optimal FFPE preparation conditions, including tissue ischemia at 4°C (<48 h) or 25°C for a short time (0.5 h), 48-h fixation at 25°C and sampling from FFPE scrolls instead of sections. Notably, we observed an increase in intronic reads and a significant change in gene rank based on expression level in the FFPE as opposed to fresh-frozen (FF) samples. Additionally, we found that more reads were mapped to genes associated with chemical stimulus in FFPE samples. Furthermore, we demonstrated that more degraded genes in FFPE samples were enriched in genes with short transcripts and high free energy. Besides, we found 40 housekeeping genes exhibited stable expression in FF and FFPE samples across various tissues. Moreover, our study showed that FFPE samples yielded comparable results to FF samples in dimensionality reduction and pathway analyses between case and control samples. Our study established the optimal conditions for FFPE preparation and identified gene attributes associated with degradation, which would provide useful clues for the utility of FFPE tissues in clinical practice and research.

SinCMat: A single-cell-based method for predicting functional maturation transcription factors

A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.

Epigenetic characterization of housekeeping core promoters and their importance in tumor suppression

In this research, we elucidate the presence of around 11,000 housekeeping cis-regulatory elements (HK-CREs) and describe their main characteristics. Besides the trivial promoters of housekeeping genes, most HK-CREs reside in promoter regions and are involved in a broader role beyond housekeeping gene regulation. HK-CREs are conserved regions rich in unmethylated CpG sites. Their distribution highly correlates with that of protein-coding genes, and they interact with many genes over long distances. We observed reduced activity of a subset of HK-CREs in diverse cancer subtypes due to aberrant methylation, particularly those located in chromosome 19 and associated with zinc finger genes. Further analysis of samples from 17 cancer subtypes showed a significantly increased survival probability of patients with higher expression of these genes, suggesting them as housekeeping tumor suppressor genes. Overall, our work unravels the presence of housekeeping CREs indispensable for the maintenance and stability of cells.

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

Evidence of steady-state fibroblast subtypes in the normal human breast as cells-of-origin for perturbed-state fibroblasts in breast cancer

BACKGROUND

Human breast cancer most frequently originates within a well-defined anatomical structure referred to as the terminal duct lobular unit (TDLU). This structure is endowed with its very own lobular fibroblasts representing one out of two steady-state fibroblast subtypes-the other being interlobular fibroblasts. While cancer-associated fibroblasts (CAFs) are increasingly appreciated as covering a spectrum of perturbed states, we lack a coherent understanding of their relationship-if any-with the steady-state fibroblast subtypes. To address this, we here established two autologous CAF lines representing inflammatory CAFs (iCAFs) and myofibroblast CAFs (myCAFs) and compared them with already established interlobular- and lobular fibroblasts with respect to their origin and impact on tumor formation.

METHODS

Primary breast tumor-derived CAFs were transduced to express human telomerase reverse transcriptase (hTERT) and sorted into CD105low and CD105high populations using fluorescence-activated cell sorting (FACS). The two populations were tested for differentiation similarities to iCAF and myCAF states through transcriptome-wide RNA-Sequencing (RNA-Seq) including comparison to an available iCAF-myCAF cell state atlas. Inference of origin in interlobular and lobular fibroblasts relied on RNA-Seq profiles, immunocytochemistry and growth characteristics. Osteogenic differentiation and bone formation assays in culture and in vivo were employed to gauge for origin in bone marrow-derived mesenchymal stem cells (bMSCs). Functional characteristics were assessed with respect to contractility in culture and interaction with tumor cells in mouse xenografts. The cells' gene expression signatures were tested for association with clinical outcome of breast cancer patients using survival data from The Cancer Genome Atlas database.

RESULTS

We demonstrate that iCAFs have properties in common with interlobular fibroblasts while myCAFs and lobular fibroblasts are related. None of the CAFs qualify as bMSCs as revealed by lack of critical performance in bone formation assays. Functionally, myCAFs and lobular fibroblasts are almost equally tumor promoting as opposed to iCAFs and interlobular fibroblasts. A myCAF gene signature is found to associate with poor breast cancer-specific survival.

CONCLUSIONS

We propose that iCAFs and myCAFs originate in interlobular and lobular fibroblasts, respectively, and more importantly, that the tumor-promoting properties of lobular fibroblasts render the TDLU an epicenter for breast cancer evolution.

The transcriptional activator Klf5 recruits p300-mediated H3K27ac for maintaining trophoblast stem cell pluripotency

The effective proliferation and differentiation of trophoblast stem cells (TSCs) is indispensable for the development of the placenta, which is the key to maintaining normal fetal growth during pregnancy. Kruppel-like factor 5 (Klf5) is implicated in the activation of pluripotency gene expression in embryonic stem cells (ESCs), yet its function in TSCs is poorly understood. Here, we showed that Klf5 knockdown resulted in the downregulation of core TSC-specific genes, consequently causing rapid differentiation of TSCs. Consistently, Klf5-depleted embryos lost the ability to establish TSCs in vitro. At the molecular level, Klf5 preferentially occupied the proximal promoter regions and maintained an open chromatin architecture of key TSC-specific genes. Deprivation of Klf5 impaired the enrichment of p300, a major histone acetyl transferase of H3 lysine 27 acetylation (H3K27ac), and further reduced the occupancy of H3K27ac at promoter regions, leading to decreased transcriptional activity of TSC pluripotency genes. Thus, our findings highlight a novel mechanism of Klf5 in regulating the self-renewal and differentiation of TSCs and provide a reference for understanding placental development and improving pregnancy rates.

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.

3D Enhancer-promoter networks provide predictive features for gene expression and coregulation in early embryonic lineages

Mammalian embryogenesis commences with two pivotal and binary cell fate decisions that give rise to three essential lineages: the trophectoderm, the epiblast and the primitive endoderm. Although key signaling pathways and transcription factors that control these early embryonic decisions have been identified, the non-coding regulatory elements through which transcriptional regulators enact these fates remain understudied. Here, we characterize, at a genome-wide scale, enhancer activity and 3D connectivity in embryo-derived stem cell lines that represent each of the early developmental fates. We observe extensive enhancer remodeling and fine-scale 3D chromatin rewiring among the three lineages, which strongly associate with transcriptional changes, although distinct groups of genes are irresponsive to topological changes. In each lineage, a high degree of connectivity, or 'hubness', positively correlates with levels of gene expression and enriches for cell-type specific and essential genes. Genes within 3D hubs also show a significantly stronger probability of coregulation across lineages compared to genes in linear proximity or within the same contact domains. By incorporating 3D chromatin features, we build a predictive model for transcriptional regulation (3D-HiChAT) that outperforms models using only 1D promoter or proximal variables to predict levels and cell-type specificity of gene expression. Using 3D-HiChAT, we identify, in silico, candidate functional enhancers and hubs in each cell lineage, and with CRISPRi experiments, we validate several enhancers that control gene expression in their respective lineages. Our study identifies 3D regulatory hubs associated with the earliest mammalian lineages and describes their relationship to gene expression and cell identity, providing a framework to comprehensively understand lineage-specific transcriptional behaviors.

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 h 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 identify unexpected long-term cycling populations. Focused clustering and transcriptional trajectory analyses of non-skeletal muscle and endoderm identified transcriptional profiles and candidate transcriptional regulators of understudied cell types and subpopulations, including the pneumatic duct, individual intestinal smooth muscle layers, spatially distinct pericyte subpopulations, and recently discovered best4+ cells. To enable additional discoveries, we make this comprehensive transcriptional atlas of early zebrafish development available through our website, Daniocell.

A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation

BACKGROUND

The coordinated transcriptional regulation of activated T-cells is based on a complex dynamic behavior of signaling networks. Given an external stimulus, T-cell gene expression is characterized by impulse and sustained patterns over the course. Here, we analyze the temporal pattern of activation across different T-cell populations to develop consensus gene signatures for T-cell activation.

RESULTS

Here, we identify and verify general biomarker signatures robustly evaluating T-cell activation in a time-resolved manner. We identify time-resolved gene expression profiles comprising 521 genes of up to 10 disjunct time points during activation and different polarization conditions. The gene signatures include central transcriptional regulators of T-cell activation, representing successive waves as well as sustained patterns of induction. They cover sustained repressed, intermediate, and late response expression rates across multiple T-cell populations, thus defining consensus biomarker signatures for T-cell activation. In addition, intermediate and late response activation signatures in CAR T-cell infusion products are correlated to immune effector cell-associated neurotoxicity syndrome.

CONCLUSION

This study is the first to describe temporally resolved gene expression patterns across T-cell populations. These biomarker signatures are a valuable source, e.g., monitoring transcriptional changes during T-cell activation with a reasonable number of genes, annotating T-cell states in single-cell transcriptome studies, or assessing dysregulated functions of human T-cell immunity.

GCparagon: evaluating and correcting GC biases in cell-free DNA at the fragment level

Analyses of cell-free DNA (cfDNA) are increasingly being employed for various diagnostic and research applications. Many technologies aim to increase resolution, e.g. for detecting early-stage cancer or minimal residual disease. However, these efforts may be confounded by inherent base composition biases of cfDNA, specifically the over - and underrepresentation of guanine (G) and cytosine (C) sequences. Currently, there is no universally applicable tool to correct these effects on sequencing read-level data. Here, we present GCparagon, a two-stage algorithm for computing and correcting GC biases in cfDNA samples. In the initial step, length and GC base count parameters are determined. Here, our algorithm minimizes the inclusion of known problematic genomic regions, such as low-mappability regions, in its calculations. In the second step, GCparagon computes weights counterbalancing the distortion of cfDNA attributes (correction matrix). These fragment weights are added to a binary alignment map (BAM) file as alignment tags for individual reads. The GC correction matrix or the tagged BAM file can be used for downstream analyses. Parallel computing allows for a GC bias estimation below 1 min. We demonstrate that GCparagon vastly improves the analysis of regulatory regions, which frequently show specific GC composition patterns and will contribute to standardized cfDNA applications.

The human genome contains over a million autonomous exons

Mammalian mRNA and lncRNA exons are often small compared to introns. The exon definition model predicts that exons splice autonomously, dependent on proximal exon sequence features, explaining their delineation within large introns. This model has not been examined on a genome-wide scale, however, leaving open the question of how often mRNA and lncRNA exons are autonomous. It is also unknown how frequently such exons can arise by chance. Here, we directly assayed large fragments (500-1000 bp) of the human genome by exon trapping, which detects exons spliced into a heterologous transgene, here designed with a large intron context. We define the trapped exons as "autonomous." We obtained ∼1.25 million trapped exons, including most known mRNA and well-annotated lncRNA internal exons, demonstrating that human exons are predominantly autonomous. mRNA exons are trapped with the highest efficiency. Nearly a million of the trapped exons are unannotated, most located in intergenic regions and antisense to mRNA, with depletion from the forward strand of introns. These exons are not conserved, suggesting they are nonfunctional and arose from random mutations. They are nonetheless highly enriched with known splicing promoting sequence features that delineate known exons. Novel autonomous exons are more numerous than annotated lncRNA exons, and computational models also indicate they will occur with similar frequency in any randomly generated sequence. These results show that most human coding exons splice autonomously, and provide an explanation for the existence of many unconserved lncRNAs, as well as a new annotation and inclusion levels of spliceable loci in the human genome.

Characterization of human transcription factor function and patterns of gene regulation in HepG2 cells

Transcription factors (TFs) are trans-acting proteins that bind cis-regulatory elements (CREs) in DNA to control gene expression. Here, we analyzed the genomic localization profiles of 529 sequence-specific TFs and 151 cofactors and chromatin regulators in the human cancer cell line HepG2, for a total of 680 broadly termed DNA-associated proteins (DAPs). We used this deep collection to model each TF's impact on gene expression, and identified a cohort of 26 candidate transcriptional repressors. We examine high occupancy target (HOT) sites in the context of three-dimensional genome organization and show biased motif placement in distal-promoter connections involving HOT sites. We also found a substantial number of closed chromatin regions with multiple DAPs bound, and explored their properties, finding that a MAFF/MAFK TF pair correlates with transcriptional repression. Altogether, these analyses provide novel insights into the regulatory logic of the human cell line HepG2 genome and show the usefulness of large genomic analyses for elucidation of individual TF functions.

Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells

In metazoans, both transcription initiation and the escape of RNA polymerase (RNAP) from promoter-proximal pausing are key rate-limiting steps in gene expression. These processes play out at physically proximal sites on the DNA template and appear to influence one another through steric interactions. Here, we examine the dynamics of these processes using a combination of statistical modeling, simulation, and analysis of real nascent RNA sequencing data. We develop a simple probabilistic model that jointly describes the kinetics of transcription initiation, pause-escape, and elongation, and the generation of nascent RNA sequencing read counts under steady-state conditions. We then extend this initial model to allow for variability across cells in promoter-proximal pause site locations and steric hindrance of transcription initiation from paused RNAPs. In an extensive series of simulations, we show that this model enables accurate estimation of initiation and pause-escape rates. Furthermore, we show by simulation and analysis of real data that pause-escape is often strongly rate-limiting and that steric hindrance can dramatically reduce initiation rates. Our modeling framework is applicable to a variety of inference problems, and our software for estimation and simulation is freely available.

A novel chromatin-remodeling complex variant, dcPBAF, is involved in maintaining transcription in differentiated neurons

The Polybromo-associated BAF (BRG1- or BRM-associated factors) (PBAF) chromatin-remodeling complex is essential for transcription in mammalian cells. In this study, we describe a novel variant of the PBAF complex from differentiated neuronal cells, called dcPBAF, that differs from the canonical PBAF existing in proliferating neuroblasts. We describe that in differentiated adult neurons, a specific subunit of PBAF, PHF10, is replaced by a PHF10 isoform that lacks N- and C-terminal domains (called PHF10D). In addition, dcPBAF does not contain the canonical BRD7 subunit. dcPBAF binds promoters of the actively transcribed neuron-specific and housekeeping genes in terminally differentiated neurons of adult mice. Furthermore, in differentiated human neuronal cells, PHF10D-containing dcPBAF maintains a high transcriptional level at several neuron-specific genes.

Stable internal reference genes for quantitative RT-PCR analyses in Rhipicephalus microplus during embryogenesis

Studies on the transcriptional control of gene expression are crucial to understand changes in organism's physiological or cellular conditions. To obtain reliable data on mRNA amounts and the estimation of gene expression levels, it is crucial to normalize the target gene with one or more internal reference gene(s). However, the use of constitutive genes as reference genes is controversial, as their expression patterns are sometimes more complex than previously thought. In various arthropod vectors, including ticks, several constitutive genes have been identified by studying gene expression in different tissues and life stages. The cattle tick Rhipicephalus microplus is a major vector for several pathogens and is widely distributed in tropical and subtropical regions globally. Tick developmental physiology is an essential aspect of research, particularly embryogenesis, where many important developmental events occur, thus the identification of stable reference genes is essential for the interpretation of reliable gene expression data. This study aimed to identify and select R. microplus housekeeping genes and evaluate their stability during embryogenesis. Reference genes used as internal control in molecular assays were selected based on previous studies. These genes were screened by quantitative PCR (qPCR) and tested for gene expression stability during embryogenesis. Results demonstrated that the relative stability of reference genes varied at different time points during the embryogenesis. The GeNorm tool showed that elongation factor 1α (Elf1a) and ribosomal protein L4 (Rpl4) were the most stable genes, while H3 histone family 3A (Hist3A) and ribosomal protein S18 (RpS18) were the least stable. The NormFinder tool showed that Rpl4 was the most stable gene, while the ranking of Elf1a was intermediate in all tested conditions. The BestKeeper tool showed that Rpl4 and cyclophilin A (CycA) were the more and less stable genes, respectively. These data collectively demonstrate that Rpl4, Elf1a, and GAPDH are suitable internal controls for normalizing qPCR during R. microplus embryogenesis. These genes were consistently identified as the most stable in various analysis methods employed in this study. Thus, findings presented in this study offer valuable information for the study of gene expression during embryogenesis in R. microplus.

TrACES of time: Transcriptomic analyses for the contextualization of evidential stains - Identification of RNA markers for estimating time-of-day of bloodstain deposition

Obtaining forensically relevant information beyond who deposited a biological stain on how and under which circumstances it was deposited is a question of increasing importance in forensic molecular biology. In the past few years, several studies have been produced on the potential of gene expression analysis to deliver relevant contextualizing information, e.g. on nature and condition of a stain as well as aspects of stain deposition timing. However, previous attempts to predict the time-of-day of sample deposition were all based on and thus limited by previously described diurnal oscillators. Herein, we newly approached this goal by applying current sequencing technologies and statistical methods to identify novel candidate markers for forensic time-of-day predictions from whole transcriptome analyses. To this purpose, we collected whole blood samples from ten individuals at eight different time points throughout the day, performed whole transcriptome sequencing and applied biostatistical algorithms to identify 81 mRNA markers with significantly differential expression as candidates to predict the time of day. In addition, we performed qPCR analysis to assess the characteristics of a subset of 13 candidate predictors in dried and aged blood stains. While we demonstrated the general possibility of using the selected candidate markers to predict time-of-day of sample deposition, we also observed notable variation between different donors and storage conditions, highlighting the relevance of employing accurate quantification methods in combination with robust normalization procedures.This study's results are foundational and may be built upon when developing a targeted assay for time-of-day predictions from forensic blood samples in the future.

Cross-species imputation and comparison of single-cell transcriptomic profiles

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue

We define and identify a new class of control genes for next-generation sequencing called total RNA expression genes (TREGs), which correlate with total RNA abundance in cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single-cell RNA sequencing data, allowing the estimation of total amount of RNA when restricted to quantifying a limited number of genes. We demonstrate our method in postmortem human brain using multiplex single-molecule fluorescent in situ hybridization and compare candidate TREGs against classic housekeeping genes. We identify AKT3 as a top TREG across five brain regions.

SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle

RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time‐dependent kinetic rates during dynamic processes. Here, we present SLAM‐Drop‐seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol‐fixed cells with droplet‐based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene‐specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust‐cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM‐Drop‐seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single‐cell gene expression dynamics in continuous biological processes.

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.