Gene expression variability across cells and species shapes innate immunity

The role of innate immunity triggered by HPV infection in promoting cervical lesions

Innate immunity is the immune system that organisms possess from birth. It is primarily responsible for the rapid, nonspecific recognition of pathogens when they invade, activating the host's immune response to eliminate. Cervical cancer is one of the most well-known tumors caused by human papillomavirus (HPV) infection. As the first line of defense against pathogens, innate immunity plays a crucial role in the response to HPV invasion, and there has been significant research in this area in recent years. The findings suggest that innate immune responses not only contribute to the clearance of HPV but may also facilitate the spread of the virus and the carcinogenic transformation of cervical epithelial cells. In this review, we comprehensively examine the activation of innate immune responses during HPV infection, the mechanisms by which HPV evades these immune defenses, and the role of innate immunity in promoting cervical intraepithelial neoplasia. Additionally, we explore the characteristics of innate immune responses within the tumor microenvironment of cervical cancer. Furthermore, we summarize recent advances in understanding the various mechanisms by which innate immune responses can be activated, with a focus on potential therapeutic implications. By reviewing the latest research, this article aims to provide valuable insights and stimulate further investigation into the role of innate immunity in HPV-associated cervical lesions, potentially leading to more effective strategies for prevention and treatment in the future.

An evolutionarily conserved constellation of functional cis-elements programs the virus-responsive fate of the human (epi)genome

Human health depends on perplexing defensive cellular responses against microbial pathogens like Viruses. Despite the major effort undertaken, the (epi)genomic mechanisms that human cells utilize to tailor defensive gene expression programs against microbial attacks have remained inadequately understood, mainly due to a significant lack of recording of the in vivo functional cis-regulatory modules (CRMs) of the human genome. Here, we introduce the virus-responsive fate of the human (epi)genome as characterized in naïve and infected cells by functional genomics, computational biology, DNA evolution, and DNA Grammar and Syntax investigations. We discovered that multitudes of novel functional virus-responsive CRMs (vrCRMs) compose typical enhancers (tEs), super-enhancers (SEs), repetitive-DNA enhancers (rDEs), and stand-alone functional genomic stretches that grant human cells regulatory underpinnings for layering basal immunity and eliminating illogical/harmful defensive responses under homeostasis, yet stimulating virus-responsive genes and transposable elements (TEs) upon infection. Moreover, extensive epigenomic reprogramming of previously unknown SE landscapes marks the transition from naïve to antiviral human cell states and involves the functions of the antimicrobial transcription factors (TFs), including interferon response factor 3 (IRF3) and nuclear factor-κB (NF-κB), as well as coactivators and transcriptional apparatus, along with intensive modifications/alterations in histone marks and chromatin accessibility. Considering the polyphyletic evolutionary fingerprints of the composite DNA sequences of the vrCRMs assessed by TFs-STARR-seq, ranging from the animal to microbial kingdoms, the conserved features of antimicrobial TFs and chromatin complexes, and their pluripotent stimulus-induced activation, these findings shed light on how mammalian (epi)genomes evolved their functions to interpret the exogenous stress inflicted and program defensive transcriptional responses against microbial agents. Crucially, many known human short variants, e.g. single-nucleotide polymorphisms (SNPs), insertions, deletions etc., and quantitative trait loci (QTLs) linked to autoimmune diseases, such as multiple sclerosis (MS), systemic lupus erythematosus (SLE), Crohn's disease (CD) etc., were mapped within or vastly proximal (±2.5 kb) to the novel in vivo functional SEs and vrCRMs discovered, thus underscoring the impact of their (mal)functions on human physiology and disease development. Hence, we delved into the virus-responsive fate of the human (epi)genome and illuminated its architecture, function, evolutionary origins, and its significance for cellular homeostasis. These results allow us to chart the "Human hyper-Atlas of virus-infection", an integrated "molecular in silico" encyclopedia situated in the UCSC Genome Browser that benefits our mechanistic understanding of human infectious/(auto)immune diseases development and can facilitate the generation of in vivo preclinical animal models, drug design, and evolution of therapeutic applications.

NF-κB signaling directs a program of transient amplifications at innate immune response genes

The cellular response to pathogens involves an intricate response directed by key innate immune signaling pathways which is characterized by cell-to-cell heterogeneity. How this heterogeneity is established and regulated remains unclear. We describe a program of transient site-specific gains (TSSG) producing extrachromosomal DNA (ecDNA) of immune-related genes in response to innate immune signaling. Activation of NF-κB drives TSSG of the interferon receptor gene cluster through inducible recruitment of the transcription factor RelA and the pre-replication complex member MCM2 to an epigenetically regulated TSSG control element. Targeted recruitment of RelA or p300 are sufficient to induce TSSG formation. RelA and MCM2 specify a program of TSSG for at least six and as many as 179 regions enriched in innate immune response genes. Identification of this program reveals regulated production of ecDNA as a mechanism of heterogeneity in the host response.

Transcriptome Complexity Disentangled: A Regulatory Molecules Approach

Transcription factors (TFs) and microRNAs (miRNAs) are fundamental regulators of gene expression, cell state, and biological processes. This study investigated whether a small subset of TFs and miRNAs could accurately predict genome-wide gene expression. We analyzed 8895 samples across 31 cancer types from The Cancer Genome Atlas and identified 28 miRNA and 28 TF clusters using unsupervised learning. Medoids of these clusters could differentiate tissues of origin with 92.8% accuracy, demonstrating their biological relevance. We developed Tissue-Agnostic and Tissue-Aware models to predict 20,000 gene expressions using the 56 selected medoid miRNAs and TFs. The Tissue-Aware model attained an R2 of 0.70 by incorporating tissue-specific information. Despite measuring only 1/400th of the transcriptome, the prediction accuracy was comparable to that achieved by the 1000 landmark genes. This suggests the transcriptome has an intrinsically low-dimensional structure that can be captured by a few regulatory molecules. Our approach could enable cheaper transcriptome assays and analysis of low-quality samples. It also provides insights into genes that are heavily regulated by miRNAs/TFs versus alternative mechanisms. However, model transportability was impacted by dataset discrepancies, especially in miRNA distribution. Overall, this study demonstrates the potential of a biology-guided approach for robust transcriptome representation.

CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data

With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on the dynamics of single cells in response to perturbations is of great significance for understanding the functions and behaviors of living organisms. However, the acquisition of post-perturbation cellular states via biological experiments is frequently cost-prohibitive. Predicting the single-cell perturbation responses poses a critical challenge in the field of computational biology. In this work, we propose a novel deep learning method called coupled variational autoencoders (CoupleVAE), devised to predict the postperturbation single-cell RNA-Seq data. CoupleVAE is composed of two coupled VAEs connected by a coupler, initially extracting latent features for controlled and perturbed cells via two encoders, subsequently engaging in mutual translation within the latent space through two nonlinear mappings via a coupler, and ultimately generating controlled and perturbed data by two separate decoders to process the encoded and translated features. CoupleVAE facilitates a more intricate state transformation of single cells within the latent space. Experiments in three real datasets on infection, stimulation and cross-species prediction show that CoupleVAE surpasses the existing comparative models in effectively predicting single-cell RNA-seq data for perturbed cells, achieving superior accuracy.

Global chromatin reorganization and regulation of genes with specific evolutionary ages during differentiation and cancer

Cancer cells are highly plastic, favoring adaptation to changing conditions. Genes related to basic cellular processes evolved in ancient species, while more specialized genes appeared later with multicellularity (metazoan genes) or even after mammals evolved. Transcriptomic analyses have shown that ancient genes are up-regulated in cancer, while metazoan-origin genes are inactivated. Despite the importance of these observations, the underlying mechanisms remain unexplored. Here, we study local and global epigenomic mechanisms that may regulate genes from specific evolutionary periods. Using evolutionary gene age data, we characterize the epigenomic landscape, gene expression regulation, and chromatin organization in several cell types: human embryonic stem cells, normal primary B-cells, primary chronic lymphocytic leukemia malignant B-cells, and primary colorectal cancer samples. We identify topological changes in chromatin organization during differentiation observing patterns in Polycomb repression and RNA polymerase II pausing, which are reversed during oncogenesis. Beyond the non-random organization of genes and chromatin features in the 3D epigenome, we suggest that these patterns lead to preferential interactions among ancient, intermediate, and recent genes, mediated by RNA polymerase II, Polycomb, and the lamina, respectively. Our findings shed light on gene regulation according to evolutionary age and suggest this organization changes across differentiation and oncogenesis.

Single-cell and Spatial Transcriptomics Illuminate Bat Immunity and Barrier Tissue Evolution

Bats have adapted to pathogens through diverse mechanisms, including increased resistance-rapid pathogen elimination, and tolerance-limiting tissue damage following infection. In the Egyptian fruit bat (an important model in comparative immunology), several mechanisms conferring disease tolerance were discovered, but mechanisms underpinning resistance remain poorly understood. Previous studies on other species suggested that the elevated basal expression of innate immune genes may lead to increased resistance to infection. Here, we test whether such transcriptional patterns occur in Egyptian fruit bat tissues through single-cell and spatial transcriptomics of gut, lung, and blood cells, comparing gene expression between bat, mouse, and human. Despite numerous recent loss and expansion events of interferons in the bat genome, interferon expression and induction are remarkably similar to that of mouse. In contrast, central complement system genes are highly and uniquely expressed in key regions in bat lung and gut epithelium, unlike in human and mouse. Interestingly, the unique expression of these genes in the bat gut is strongest in the crypt, where developmental expression programs are highly conserved. The complement system genes also evolve rapidly in their coding sequences across the bat lineage. Finally, the bat complement system displays strong hemolytic activity. Together, these results indicate a distinctive transcriptional divergence of the complement system, which may be linked to bat resistance, and highlight the intricate evolutionary landscape of bat immunity.

Comparative Genomics and Epigenomics of Transcriptional Regulation

Transcriptional regulation in response to diverse physiological cues involves complicated biological processes. Recent initiatives that leverage whole genome sequencing and annotation of regulatory elements significantly contribute to our understanding of transcriptional gene regulation. Advances in the data sets available for comparative genomics and epigenomics can identify evolutionarily constrained regulatory variants and shed light on noncoding elements that influence transcription in different tissues and developmental stages across species. Most epigenomic data, however, are generated from healthy subjects at specific developmental stages. To bridge the genotype-phenotype gap, future research should focus on generating multidimensional epigenomic data under diverse physiological conditions. Farm animal species offer advantages in terms of feasibility, cost, and experimental design for such integrative analyses in comparison to humans. Deep learning modeling and cutting-edge technologies in sequencing and functional screening and validation also provide great promise for better understanding transcriptional regulation in this dynamic field.

Positive Selection on Mammalian Immune Genes-Effects of Gene Function and Selective Constraint

Genome-wide analyses of various taxa have repeatedly shown that immune genes are important targets of positive selection. However, little is known about what factors determine which immune genes are under positive selection. To address this question, we here focus on the mammalian immune system and investigate the importance of gene function and other factors such as gene expression, protein-protein interactions, and overall selective constraint as determinants of positive selection. We compiled a list of >1,100 immune genes that were divided into six functional categories and analyzed using data from rodents. Genes encoding proteins that are in direct interactions with pathogens, such as pattern recognition receptors (PRRs), are often expected to be key targets of positive selection. We found that categories containing cytokines, cytokine receptors, and other cell surface proteins involved in, for example, cell-cell interactions were at least as important targets as PRRs, with three times higher rate of positive selection than nonimmune genes. The higher rate of positive selection on cytokines and cell surface proteins was partly an effect of these categories having lower selective constraint. Nonetheless, cytokines had a higher rate of positive selection than nonimmune genes even at a given level of selective constraint, indicating that gene function per se can also be a determinant of positive selection. These results have broad implications for understanding the causes of positive selection on immune genes, specifically the relative importance of host-pathogen coevolution versus other processes.

Establishing a Mandibular Osteosarcoma Model in SD Rats Using Tissue Block Transplantation

BACKGROUND/AIM

To investigate the feasibility of establishing a mandibular osteosarcoma model in Sprague-Dawley (SD) rats using tissue block transplantation, providing a foundational model for osteosarcoma research.

MATERIALS AND METHODS

Fourteen male SD rats, 3 weeks old and SPF grade, were randomly divided into a control group (n=4) and a mandibular osteosarcoma group (n=10). Using tissue block transplantation, UMR106 cell-induced tumor tissues were transplanted subcutaneously into the left mandibular marrow cavity of the SD rats. Observations included behavioral changes, weight variations, tumor growth, and tumor formation rate. Bone changes were monitored via micro-CT scanning, and histological analysis was conducted using HE staining.

RESULTS

Two weeks post-transplantation, the mandibular osteosarcoma group exhibited significant left facial swelling, malocclusion, eating difficulties, and weight loss compared to the control group. The tumor formation rate was 80% (8/10). Micro-CT scans indicated significant bone destruction in the osteosarcoma group. HE staining revealed high cellular atypia and pathological mitoses in both subcutaneous and mandibular osteosarcoma cells, with no notable abnormalities in lung tissues.

CONCLUSION

Tissue block transplantation is a viable method to establish a mandibular osteosarcoma model in SD rats. This method is simple, with a high tumor formation rate, providing an ideal animal model for mandibular osteosarcoma research.

scPerb: Predict single-cell perturbation via style transfer-based variational autoencoder

INTRODUCTION

Traditional methods for obtaining cellular responses after perturbation are usually labor-intensive and costly, especially when working with multiple different experimental conditions. Therefore, accurate prediction of cellular responses to perturbations is of great importance in computational biology. Existing methodologies, such as graph-based approaches, vector arithmetic, and neural networks, either mix perturbation-related variances with cell-type-specific patterns or implicitly distinguish them within black-box models.

OBJECTIVES

This study aims to introduce and demonstrate a novel framework, scPerb, which explicitly extracts perturbation-related variances and transfers them from unperturbed to perturbed cells to accurately predict the effect of perturbation in single-cell level.

METHODS

scPerb utilizes a style transfer strategy by incorporating a style encoder into the architecture of a variational autoencoder. The style encoder captures the differences in latent representations between unperturbed and perturbed cells, enabling accurate prediction of post-perturbation gene expression data.

RESULTS

Comprehensive comparisons with existing methods demonstrate that scPerb delivers improved performance and higher accuracy in predicting cellular responses to perturbations. Notably, scPerb outperforms other methods across multiple datasets, achieving superior R2 values of 0.98, 0.98, and 0.96 on three benchmarking datasets.

CONCLUSION

scPerb offers a significant advancement in predicting cellular responses by effectively separating and transferring perturbation-related variances. This framework not only enhances prediction accuracy but also provides a robust tool for computational biology, addressing the limitations of current methodologies.

Method of moments framework for differential expression analysis of single-cell RNA sequencing data

Differential expression analysis of single-cell RNA sequencing (scRNA-seq) data is central for characterizing how experimental factors affect the distribution of gene expression. However, distinguishing between biological and technical sources of cell-cell variability and assessing the statistical significance of quantitative comparisons between cell groups remain challenging. We introduce Memento, a tool for robust and efficient differential analysis of mean expression, variability, and gene correlation from scRNA-seq data, scalable to millions of cells and thousands of samples. We applied Memento to 70,000 tracheal epithelial cells to identify interferon-responsive genes, 160,000 CRISPR-Cas9 perturbed T cells to reconstruct gene-regulatory networks, 1.2 million peripheral blood mononuclear cells (PBMCs) to map cell-type-specific quantitative trait loci (QTLs), and the 50-million-cell CELLxGENE Discover corpus to compare arbitrary cell groups. In all cases, Memento identified more significant and reproducible differences in mean expression compared with existing methods. It also identified differences in variability and gene correlation that suggest distinct transcriptional regulation mechanisms imparted by perturbations.

Evidence for compensatory evolution within pleiotropic regulatory elements

Pleiotropy, measured as expression breadth across tissues, is one of the best predictors for protein sequence and expression conservation. In this study, we investigated its effect on the evolution of cis-regulatory elements (CREs). To this end, we carefully reanalyzed the Epigenomics Roadmap data for nine fetal tissues, assigning a measure of pleiotropic degree to nearly half a million CREs. To assess the functional conservation of CREs, we generated ATAC-seq and RNA-seq data from humans and macaques. We found that more pleiotropic CREs exhibit greater conservation in accessibility, and the mRNA expression levels of the associated genes are more conserved. This trend of higher conservation for higher degrees of pleiotropy persists when analyzing the transcription factor binding repertoire. In contrast, simple DNA sequence conservation of orthologous sites between species tends to be even lower for pleiotropic CREs than for species-specific CREs. Combining various lines of evidence, we propose that the lack of sequence conservation in functionally conserved pleiotropic CREs is owing to within-element compensatory evolution. In summary, our findings suggest that pleiotropy is also a good predictor for the functional conservation of CREs, even though this is not reflected in the sequence conservation of pleiotropic CREs.

Global chromatin reorganization and regulation of genes with specific evolutionary ages during differentiation and cancer

Cancer cells are highly plastic, allowing them to adapt to changing conditions. Genes related to basic cellular processes evolved in ancient species, while more specialized genes appeared later with multicellularity (metazoan genes) or even after mammals evolved. Transcriptomic analyses have shown that ancient genes are up-regulated in cancer, while metazoan-origin genes are inactivated. Despite the importance of these observations, the underlying mechanisms remain unexplored. Here, we study local and global epigenomic mechanisms that may regulate genes from specific evolutionary periods. Using evolutionary gene age data, we characterize the epigenomic landscape, gene expression regulation, and chromatin organization in three cell types: human embryonic stem cells, normal B-cells, and primary cells from Chronic Lymphocytic Leukemia, a B-cell malignancy. We identify topological changes in chromatin organization during differentiation observing patterns in Polycomb repression and RNA Polymerase II pausing, which are reversed during oncogenesis. Beyond the non-random organization of genes and chromatin features in the 3D epigenome, we suggest that these patterns lead to preferential interactions among ancient, intermediate, and recent genes, mediated by RNA Polymerase II, Polycomb, and the lamina, respectively. Our findings shed light on gene regulation according to evolutionary age and suggest this organization changes across differentiation and oncogenesis.

Correlation of disulfidptosis and periodontitis: New insights and clinical significance

OBJECTIVES

This study aims to investigate and predict the therapeutic agents associated with disulfidptosis in periodontitis.

DESIGN

The dataset GSE10334 was downloaded from the Gene Expression Omnibus (GEO) database and used to train a least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) algorithm to identify genes associated with disulfidptosis in periodontitis. GSE16134 validation sets, polymerase chain reaction (PCR), and gingival immunofluorescence were used to verify the results.Single-gene Gene Set Enrichment Analysis (GSEA) was performed to explore the potential mechanisms and functions of the characterized genes. Immune infiltration and correlation analyses were performed, and competing endogenous RNA (ceRNA) networks were constructed. Effective therapeutic drugs were then predicted using the DGIdb database, and molecular docking was used to validate binding affinity.

RESULTS

Six genes (SLC7A11, SLC3A2, RPN1, NCKAP1, LRPPRC, and NDUFS1) associated with disulfidptosis in periodontitis were obtained. Validation results from external datasets and experiments were consistent with the screening results. Single-gene GSEA analysis was mainly enriched for antigen presentation and immune-related pathways and functions.Immune infiltration and correlation analyses revealed significant regulatory relationships between these genes and plasma cells, resting dendritic cell, and activated NK cells. The ceRNA network was visualized. And ME-344, NV-128, and RILUZOLE, which have good affinity to target genes, were identified as promising agents for the treatment of periodontitis.

CONCLUSIONS

SLC7A11, SLC3A2, RPN1, NCKAP1, LRPPRC, and NDUFS1 are targets associated with disulfidptosis in periodontitis, and ME-344, NV-128, and RILUZOLE are promising agents for the treatment of periodontitis.

Single-cell RNA sequencing illuminates the ontogeny, conservation and diversification of cartilaginous and bony fish lymphocytes

Elucidating cellular architecture and cell-type evolution across species is central to understanding immune system function and susceptibility to disease. Adaptive immunity is a shared trait of the common ancestor of cartilaginous and bony fishes. However, evolutionary features of lymphocytes in these two jawed vertebrates remain unclear. Here, we present a single-cell RNA sequencing atlas of immune cells from cartilaginous (white-spotted bamboo shark) and bony (zebrafish and Chinese tongue sole) fishes. Cross-species comparisons show that the same cell types across different species exhibit similar transcriptional profiles. In the bamboo shark, we identify a phagocytic B cell population expressing several pattern recognition receptors, as well as a T cell sub-cluster co-expressing both T and B cell markers. In contrast to a division by function in the bony fishes, we show close linkage and poor functional specialization among lymphocytes in the cartilaginous fish. Our cross-species single-cell comparison presents a resource for uncovering the origin and evolution of the gnathostome immune system.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

BACKGROUND

Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals.

RESULTS

We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh's method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl .

CONCLUSIONS

The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Interactions between immune cell types facilitate the evolution of immune traits

An essential prerequisite for evolution by natural selection is variation among individuals in traits that affect fitness1. The ability of a system to produce selectable variation, known as evolvability2, thus markedly affects the rate of evolution. Although the immune system is among the fastest-evolving components in mammals3, the sources of variation in immune traits remain largely unknown4,5. Here we show that an important determinant of the immune system's evolvability is its organization into interacting modules represented by different immune cell types. By profiling immune cell variation in bone marrow of 54 genetically diverse mouse strains from the Collaborative Cross6, we found that variation in immune cell frequencies is polygenic and that many associated genes are involved in homeostatic balance through cell-intrinsic functions of proliferation, migration and cell death. However, we also found genes associated with the frequency of a particular cell type that are expressed in a different cell type, exerting their effect in what we term cyto-trans. The vertebrate evolutionary record shows that genes associated in cyto-trans have faced weaker negative selection, thus increasing the robustness and hence evolvability2,7,8 of the immune system. This phenomenon is similarly observable in human blood. Our findings suggest that interactions between different components of the immune system provide a phenotypic space in which mutations can produce variation with little detriment, underscoring the role of modularity in the evolution of complex systems9.

Induction of apoptosis by double-stranded RNA was present in the last common ancestor of cnidarian and bilaterian animals

Apoptosis, a major form of programmed cell death, is an essential component of host defense against invading intracellular pathogens. Viruses encode inhibitors of apoptosis to evade host responses during infection, and to support their own replication and survival. Therefore, hosts and their viruses are entangled in a constant evolutionary arms race to control apoptosis. Until now, apoptosis in the context of the antiviral immune system has been almost exclusively studied in vertebrates. This limited phyletic sampling makes it impossible to determine whether a similar mechanism existed in the last common ancestor of animals. Here, we established assays to probe apoptosis in the sea anemone Nematostella vectensis, a model species of Cnidaria, a phylum that diverged approximately 600 million years ago from the rest of animals. We show that polyinosinic:polycytidylic acid (poly I:C), a synthetic long double-stranded RNA mimicking viral RNA and a primary ligand for the vertebrate RLR melanoma differentiation-associated protein 5 (MDA5), is sufficient to induce apoptosis in N. vectensis. Furthermore, at the transcriptomic level, apoptosis related genes are significantly enriched upon poly(I:C) exposure in N. vectensis as well as bilaterian invertebrates. Our phylogenetic analysis of caspase family genes in N. vectensis reveals conservation of all four caspase genes involved in apoptosis in mammals and revealed a cnidarian-specific caspase gene which was strongly upregulated. Altogether, our findings suggest that apoptosis in response to a viral challenge is a functionally conserved mechanism that can be traced back to the last common ancestor of Bilateria and Cnidaria.

Assessing immune phenotypes using simple proxy measures: promise and limitations

The study of immune phenotypes in wild animals is beset by numerous methodological challenges, with assessment of detailed aspects of phenotype difficult to impossible. This constrains the ability of disease ecologists and ecoimmunologists to describe immune variation and evaluate hypotheses explaining said variation. The development of simple approaches that allow characterization of immune variation across many populations and species would be a significant advance. Here we explore whether serum protein concentrations and coarse-grained white blood cell profiles, immune quantities that can easily be assayed in many species, can predict, and therefore serve as proxies for, lymphocyte composition properties. We do this in rewilded laboratory mice, which combine the benefits of immune phenotyping of lab mice with the natural context and immune variation found in the wild. We find that easily assayed immune quantities are largely ineffective as predictors of lymphocyte composition, either on their own or with other covariates. Immunoglobulin G (IgG) concentration and neutrophil-lymphocyte ratio show the most promise as indicators of other immune traits, but their explanatory power is limited. Our results prescribe caution in inferring immune phenotypes beyond what is directly measured, but they do also highlight some potential paths forward for the development of proxy measures employable by ecoimmunologists.

Kernel-based testing for single-cell differential analysis

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism

MOTIVATION

With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution.

RESULTS

Here, we present scPRAM, a method for predicting perturbation responses in single-cell gene expression based on attention mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations.

AVAILABILITY AND IMPLEMENTATION

https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.

Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns

BACKGROUND

Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms.

RESULTS

We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the TF. We demonstrate that similarity between tissues based on our discovered genes corresponds to other types of tissue similarity. The genes affected by complex TF regulation, and their modelled TFs, were highly enriched for pharmacogenomic functions, while the TFs themselves were also enriched in several cancer and metabolic pathways. Additionally, genes that appear in multiple clusters are enriched for regulation of immune system while tissue clusters include cluster-specific genes that are enriched for biological functions and diseases previously associated with the tissues forming the cluster. Finally, our atlas exposes multilevel regulation across multiple tissues, where TFs regulate other TFs through the two tested mechanisms.

CONCLUSIONS

Our tissue-specific atlas provides hierarchical tissue-specific trans genetic regulations that can be further studied for association with human phenotypes.

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data

Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.

Establishing Primary and Stable Cell Lines from Frozen Wing Biopsies for Cellular, Physiological, and Genetic Studies in Bats

Bats stand out among mammalian species for their exceptional traits, including the capacity to navigate through flight and echolocation, conserve energy through torpor/hibernation, harbor a multitude of viruses, exhibit resistance to disease, survive harsh environmental conditions, and demonstrate exceptional longevity compared to other mammals of similar size. In vivo studies of bats can be challenging for several reasons such as ability to locate and capture them in their natural environments, limited accessibility, low sample size, environmental variation, long lifespans, slow reproductive rates, zoonotic disease risks, species protection, and ethical concerns. Thus, establishing alternative laboratory models is crucial for investigating the diverse physiological adaptations observed in bats. Obtaining quality cells from tissues is a critical first step for successful primary cell derivation. However, it is often impractical to collect fresh tissue and process the samples immediately for cell culture due to the resources required for isolating and expanding cells. As a result, frozen tissue is typically the starting resource for bat primary cell derivation. Yet, cells in frozen tissue are usually damaged and represent low integrity and viability. As a result, isolating primary cells from frozen tissues poses a significant challenge. Herein, we present a successfully developed protocol for isolating primary dermal fibroblasts from frozen bat wing biopsies. This protocol marks a significant milestone, as this the first protocol specially focused on fibroblasts isolation from bat frozen tissue. We also describe methods for primary cell characterization, genetic manipulation of primary cells through lentivirus transduction, and the development of stable cell lines. Basic Protocol 1: Bat wing biopsy collection and preservation Support Protocol 1: Blood collection from bat- venipuncture Basic Protocol 2: Isolation of primary fibroblasts from adult bat frozen wing biopsy Support Protocol 2: Maintenance of primary fibroblasts Support Protocol 3: Cell banking and thawing of primary fibroblasts Support Protocol 4: Growth curve and doubling time Support Protocol 5: Lentiviral transduction of bat primary fibroblasts Basic Protocol 3: Bat stable fibroblasts cell lines development Support Protocol 6: Bat fibroblasts validation by immunofluorescence staining Support Protocol 7: Chromosome counting.

Species-specific NLRP3 regulation and its role in CNS autoinflammatory diseases

The NLRP3 inflammasome is essential for caspase-1 activation and the release of interleukin (IL)-1β, IL-18, and gasdermin-D in myeloid cells. However, research on species-specific NLRP3's physiological impact is limited. We engineer mice with the human NLRP3 gene, driven by either the human or mouse promoter, via syntenic replacement at the mouse Nlrp3 locus. Both promoters facilitate hNLRP3 expression in myeloid cells, but the mouse promoter responds more robustly to LPS. Investigating the disease impact of differential NLRP3 regulation, we introduce the D305N gain-of-function mutation into both humanized lines. Chronic inflammation is evident with both promoters; however, CNS outcomes vary significantly. Despite poor response to LPS, the human promoter results in D305N-associated aseptic meningitis, mirroring human pathology. The mouse promoter, although leading to increased CNS expression post-LPS, does not induce meningitis in D305N mutants. Therefore, human-like NLRP3 expression may be crucial for accurate modeling of its role in disease pathogenesis.

Unraveling IFN-I response dynamics and TNF crosstalk in the pathophysiology of systemic lupus erythematosus

Introduction

The innate immune system serves the crucial first line of defense against a wide variety of potential threats, during which the production of pro-inflammatory cytokines IFN-I and TNFα are key. This astonishing power to fight invaders, however, comes at the cost of risking IFN-I-related pathologies, such as observed during autoimmune diseases, during which IFN-I and TNFα response dynamics are dysregulated. Therefore, these response dynamics must be tightly regulated, and precisely matched with the potential threat. This regulation is currently far from understood.

Methods

Using droplet-based microfluidics and ODE modeling, we studied the fundamentals of single-cell decision-making upon TLR signaling in human primary immune cells (n = 23). Next, using biologicals used for treating autoimmune diseases [i.e., anti-TNFα, and JAK inhibitors], we unraveled the crosstalk between IFN-I and TNFα signaling dynamics. Finally, we studied primary immune cells isolated from SLE patients (n = 8) to provide insights into SLE pathophysiology.

Results

single-cell IFN-I and TNFα response dynamics display remarkable differences, yet both being highly heterogeneous. Blocking TNFα signaling increases the percentage of IFN-I-producing cells, while blocking IFN-I signaling decreases the percentage of TNFα-producing cells. Single-cell decision-making in SLE patients is dysregulated, pointing towards a dysregulated crosstalk between IFN-I and TNFα response dynamics.

Discussion

We provide a solid droplet-based microfluidic platform to study inherent immune secretory behaviors, substantiated by ODE modeling, which can challenge the conceptualization within and between different immune signaling systems. These insights will build towards an improved fundamental understanding on single-cell decision-making in health and disease.

Viral afterlife: SARS-CoV-2 as a reservoir of immunomimetic peptides that reassemble into proinflammatory supramolecular complexes

It is unclear how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leads to the strong but ineffective inflammatory response that characterizes severe Coronavirus disease 2019 (COVID-19), with amplified immune activation in diverse cell types, including cells without angiotensin-converting enzyme 2 receptors necessary for infection. Proteolytic degradation of SARS-CoV-2 virions is a milestone in host viral clearance, but the impact of remnant viral peptide fragments from high viral loads is not known. Here, we examine the inflammatory capacity of fragmented viral components from the perspective of supramolecular self-organization in the infected host environment. Interestingly, a machine learning analysis to SARS-CoV-2 proteome reveals sequence motifs that mimic host antimicrobial peptides (xenoAMPs), especially highly cationic human cathelicidin LL-37 capable of augmenting inflammation. Such xenoAMPs are strongly enriched in SARS-CoV-2 relative to low-pathogenicity coronaviruses. Moreover, xenoAMPs from SARS-CoV-2 but not low-pathogenicity homologs assemble double-stranded RNA (dsRNA) into nanocrystalline complexes with lattice constants commensurate with the steric size of Toll-like receptor (TLR)-3 and therefore capable of multivalent binding. Such complexes amplify cytokine secretion in diverse uninfected cell types in culture (epithelial cells, endothelial cells, keratinocytes, monocytes, and macrophages), similar to cathelicidin's role in rheumatoid arthritis and lupus. The induced transcriptome matches well with the global gene expression pattern in COVID-19, despite using <0.3% of the viral proteome. Delivery of these complexes to uninfected mice boosts plasma interleukin-6 and CXCL1 levels as observed in COVID-19 patients.

Representing and extracting knowledge from single-cell data

Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.

Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO among the most influential genes composing the latent space for integration. Subsequently, during the alignment of latent spaces, each composed of species-specific genes, it is possible to identify functional annotations of genes missing from public orthology databases. We evaluated our approach with four different single-cell sequencing datasets focusing on cell-type prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms related methods working without prior knowledge when predicting unseen cell types based on other species' data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.

The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics

Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.

Learning single-cell perturbation responses using neural optimal transport

Understanding and predicting molecular responses in single cells upon chemical, genetic or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of input convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by mapping these unpaired distributions. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein-imaging technology. Further, we illustrate that CellOT generalizes well on unseen settings by (1) predicting the scRNA-seq responses of holdout patients with lupus exposed to interferon-β and patients with glioblastoma to panobinostat; (2) inferring lipopolysaccharide responses across different species; and (3) modeling the hematopoietic developmental trajectories of different subpopulations.

Single-cell gene set scoring with nearest neighbor graph smoothed data (gssnng)

Summary

Gene set scoring (or enrichment) is a common dimension reduction task in bioinformatics that can be focused on the differences between groups or at the single sample level. Gene sets can represent biological functions, molecular pathways, cell identities, and more. Gene set scores are context dependent values that are useful for interpreting biological changes following experiments or perturbations. Single sample scoring produces a set of scores, one for each member of a group, which can be analyzed with statistical models that can include additional clinically important factors such as gender or age. However, the sparsity and technical noise of single-cell expression measures create difficulties for these methods, which were originally designed for bulk expression profiling (microarrays, RNAseq). This can be greatly remedied by first applying a smoothing transformation that shares gene measure information within transcriptomic neighborhoods. In this work, we use the nearest neighbor graph of cells for matrix smoothing to produce high quality gene set scores on a per-cell, per-group, level which is useful for visualization and statistical analysis.

Availability and implementation

The gssnng software is available using the python package index (PyPI) and works with Scanpy AnnData objects. It can be installed using "pip install gssnng." More information and demo notebooks: see https://github.com/IlyaLab/gssnng.

Neuroimmune Mechanisms of Opioid Use Disorder and Recovery: Translatability to Human Studies, and Future Research Directions

Opioid use disorder (OUD) is a major current cause of morbidity and mortality. Long-term exposure to short-acting opioids (MOP-r agonists such as heroin or fentanyl) results in complex pathophysiological changes to neuroimmune and neuroinflammatory functions, affected in part by peripheral mechanisms (e.g., cytokines in blood), and by neuroendocrine systems such as the hypothalamic-pituitary-adrenal (HPA) stress axis. There are important findings from preclinical models, but their role in the trajectory and outcomes of OUD in humans is not well understood. The goal of this narrative review is to examine available data on immune and inflammatory functions in persons with OUD, and to identify major areas for future research. Peripheral blood biomarker studies revealed a pro-inflammatory state in persons with OUD in withdrawal or early abstinence, consistent with available postmortem brain studies (which show glial activation) and diffusion tensor imaging studies (indicating white matter disruptions), with gradual abstinence-associated recovery. The mechanistic roles of these neuroimmune and neuroinflammatory changes in the trajectory of OUD (including recovery and medication management) cannot be examined practically with postmortem data. Collection of longitudinal data in larger-scale human cohorts would allow examination of these mechanisms associated with OUD stage and progression. Given the heterogeneity in presentation of OUD, a precision medicine approach integrating multi-omic peripheral biomarkers and comprehensive phenotyping, including neuroimaging, can be beneficial in risk stratification, and individually optimized selection of interventions for individuals who will benefit, and assessments under refractory therapy.

Homogeneous Space Construction and Projection for Single-Cell Expression Prediction Based on Deep Learning

Predicting cellular responses to perturbations is an unsolved problem in biology. Traditional approaches assume that different cell types respond similarly to perturbations. However, this assumption does not take into account the context of genome interactions in different cell types, which leads to compromised prediction quality. More recently, deep learning models used to discover gene-gene relationships can yield more accurate predictions of cellular responses. The huge difference in biological information between different cell types makes it difficult for deep learning models to encode data into a continuous low-dimensional feature space, which means that the features captured by the latent space may not be continuous. Therefore, the mapping relationship between the two conditional spaces learned by the model can only be applied where the real reference data resides, leading to the wrong mapping of the predicted target cells because they are not in the same domain as the reference data. In this paper, we propose an information-navigated variational autoencoder (INVAE), a deep neural network for cell perturbation response prediction. INVAE filters out information that is not conducive to predictive performance. For the remaining information, INVAE constructs a homogeneous space of control conditions, and finds the mapping relationship between the control condition space and the perturbation condition space. By embedding the target unit into the control space and then mapping it to the perturbation space, we can predict the perturbed state of the target unit. Comparing our proposed method with other three state-of-the-art methods on three real datasets, experimental results show that INVAE outperforms existing methods in cell state prediction after perturbation. Furthermore, we demonstrate that filtering out useless information not only improves prediction accuracy but also reveals similarities in how genes in different cell types are regulated following perturbation.

Comparison of antiviral responses in two bat species reveals conserved and divergent innate immune pathways

Bats host a range of disease-causing viruses without displaying clinical symptoms. The mechanisms behind this are a continuous source of interest. Here, we studied the antiviral response in the Egyptian fruit bat and Kuhl's pipistrelle, representing two subordinal clades. We profiled the antiviral response in fibroblasts using RNA sequencing and compared bat with primate and rodent responses. Both bats upregulate similar genes; however, a subset of these genes is transcriptionally divergent between them. These divergent genes also evolve rapidly in sequence, have specific promoter architectures, and are associated with programs underlying tolerance and resistance. Finally, we characterized antiviral genes that expanded in bats, with duplicates diverging in sequence and expression. Our study reveals a largely conserved antiviral program across bats and points to a set of genes that rapidly evolve through multiple mechanisms. These can contribute to bat adaptation to viral infection and provide directions to understanding the mechanisms behind it.

Generative modeling of single-cell gene expression for dose-dependent chemical perturbations

Single-cell sequencing reveals the heterogeneity of cellular response to chemical perturbations. However, testing all relevant combinations of cell types, chemicals, and doses is a daunting task. A deep generative learning formalism called variational autoencoders (VAEs) has been effective in predicting single-cell gene expression perturbations for single doses. Here, we introduce single-cell variational inference of dose-response (scVIDR), a VAE-based model that predicts both single-dose and multiple-dose cellular responses better than existing models. We show that scVIDR can predict dose-dependent gene expression across mouse hepatocytes, human blood cells, and cancer cell lines. We biologically interpret the latent space of scVIDR using a regression model and use scVIDR to order individual cells based on their sensitivity to chemical perturbation by assigning each cell a "pseudo-dose" value. We envision that scVIDR can help reduce the need for repeated animal testing across tissues, chemicals, and doses.

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.

Variability of the innate immune response is globally constrained by transcriptional bursting

Transcription of almost all mammalian genes occurs in stochastic bursts, however the fundamental control mechanisms that allow appropriate single-cell responses remain unresolved. Here we utilise single cell genomics data and stochastic models of transcription to perform global analysis of the toll-like receptor (TLR)-induced gene expression variability. Based on analysis of more than 2000 TLR-response genes across multiple experimental conditions we demonstrate that the single-cell, gene-by-gene expression variability can be empirically described by a linear function of the population mean. We show that response heterogeneity of individual genes can be characterised by the slope of the mean-variance line, which captures how cells respond to stimulus and provides insight into evolutionary differences between species. We further demonstrate that linear relationships theoretically determine the underlying transcriptional bursting kinetics, revealing different regulatory modes of TLR response heterogeneity. Stochastic modelling of temporal scRNA-seq count distributions demonstrates that increased response variability is associated with larger and more frequent transcriptional bursts, which emerge via increased complexity of transcriptional regulatory networks between genes and different species. Overall, we provide a methodology relying on inference of empirical mean-variance relationships from single cell data and new insights into control of innate immune response variability.

Mapping interindividual dynamics of innate immune response at single-cell resolution

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution.

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution

Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.

Network models of chromatin structure

Increasing numbers of datasets and experimental assays that capture the organization of chromatin inside the nucleus warrant an effort to develop tools to visualize and analyze these structures. Alongside polymer physics or constraint-based modeling, network theory approaches to describe 3D epigenome organization have gained in popularity. Representing genomic regions as nodes in a network enables visualization of 1D epigenomics datasets in the context of chromatin structure maps, while network theory metrics can be used to describe 3D epigenome organization and dynamics. In this review, we summarize the most salient applications of network theory to the study of chromatin contact maps, demonstrating its potential in revealing epigenomic patterns and relating them to cellular phenotypes.

Predicting cellular responses to complex perturbations in high-throughput screens

Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.

Promoter evolution of mammalian gene duplicates

BACKGROUND

Gene duplication is thought to be a central process in evolution to gain new functions. The factors that dictate gene retention following duplication as well paralog gene divergence in sequence, expression and function have been extensively studied. However, relatively little is known about the evolution of promoter regions of gene duplicates and how they influence gene duplicate divergence. Here, we focus on promoters of paralog genes, comparing their similarity in sequence, in the sets of transcription factors (TFs) that bind them, and in their overall promoter architecture.

RESULTS

We observe that promoters of recent duplications display higher sequence similarity between them and that sequence similarity rapidly declines between promoters of more ancient paralogs. In contrast, similarity in cis-regulation, as measured by the set of TFs that bind promoters of both paralogs, does not simply decrease with time from duplication and is instead related to promoter architecture-paralogs with CpG Islands (CGIs) in their promoters share a greater fraction of TFs, while CGI-less paralogs are more divergent in their TF binding set. Focusing on recent duplication events and partitioning them by their duplication mechanism enables us to uncover promoter properties associated with gene retention, as well as to characterize the evolution of promoters of newly born genes: In recent retrotransposition-mediated duplications, we observe asymmetry in cis-regulation of paralog pairs: Retrocopy genes are lowly expressed and their promoters are bound by fewer TFs and are depleted of CGIs, in comparison with the original gene copy. Furthermore, looking at recent segmental duplication regions in primates enable us to compare successful retentions versus loss of duplicates, showing that duplicate retention is associated with fewer TFs and with CGI-less promoter architecture.

CONCLUSIONS

In this work, we profiled promoters of gene duplicates and their inter-paralog divergence. We also studied how their characteristics are associated with duplication time and duplication mechanism, as well as with the fate of these duplicates. These results underline the importance of cis-regulatory mechanisms in shaping the evolution of new genes and their fate following duplication.

Gene expression variability across cells and species shapes the relationship between renal resident macrophages and infiltrated macrophages

BACKGROUND

Two main subclasses of macrophages are found in almost all solid tissues: embryo-derived resident tissue macrophages and bone marrow-derived infiltrated macrophages. These macrophage subtypes show transcriptional and functional divergence, and the programs that have shaped the evolution of renal macrophages and related signaling pathways remain poorly understood. To clarify these processes, we performed data analysis based on single-cell transcriptional profiling of renal tissue-resident and infiltrated macrophages in human, mouse and rat.

RESULTS

In this study, we (i) characterized the transcriptional divergence among species and (ii) illustrated variability in expression among cells of each subtype and (iii) compared the gene regulation network and (iv) ligand-receptor pairs in human and mouse. Using single-cell transcriptomics, we mapped the promoter architecture during homeostasis.

CONCLUSIONS

Transcriptionally divergent genes, such as the differentially TF-encoding genes expressed in resident and infiltrated macrophages across the three species, vary among cells and include distinct promoter structures. The gene regulatory network in infiltrated macrophages shows comparatively better species-wide consistency than resident macrophages. The conserved transcriptional gene regulatory network in infiltrated macrophages among species is uniquely enriched in pathways related to kinases, and TFs associated with largely conserved regulons among species are uniquely enriched in kinase-related pathways.

Unraveling the potential human health risks from used disposable face mask-derived micro/nanoplastics during the COVID-19 pandemic scenario: A critical review

With the global spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), disposable face masks (DFMs) have caused negative environmental impacts. DFMs will release microplastics (MPs) and nanoplastics (NPs) during environmental degradation. However, few studies reveal the release process of MPs/NPs from masks in the natural environment. This review presents the current knowledge on the abiotic and biotic degradation of DFMs. Though MPs and NPs have raised serious concerns about their potentially detrimental effects on human health, little attention was paid to their impacts on human health from DFM-derived MPs and NPs. The potential toxicity of mask-derived MPs/NPs, such as gastrointestinal toxicity, pneumotoxicity, neurotoxicity, hepatotoxicity, reproductive and transgenerational toxicity, and the underlying mechanism will be discussed in the present study. MPs/NPs serve as carriers of toxic chemicals and pathogens, leading to their bioaccumulation and adverse effects of biomagnification by food chains. Given human experiments are facing ethical issues and animal studies cannot completely reveal human characteristics, advanced human organoids will provide promising models for MP/NP risk assessment. Moreover, in-depth investigations are required to identify the release of MPs/NPs from discarded face masks and characterize their transportation through the food chains. More importantly, innovative approaches and eco-friendly strategies are urgently demanded to reduce DFM-derived MP/NP pollution.

m6A RNA methylation impairs gene expression variability and reproductive thermotolerance in Arabidopsis

Heat-imposed crop failure is often attributed to reduced thermotolerance of floral tissues; however, the underlying mechanism remains unknown. Here, we demonstrate that m⁶A RNA methylation increases in Arabidopsis flowers and negatively regulates gene expression variability. Stochastic gene expression provides flexibility to cope with environmental stresses. We find that reduced transcriptional fluctuation is associated with compromised activation of heat-responsive genes. Moreover, disruption of an RNA demethylase AtALKBH10B leads to lower gene expression variability, suppression of heat-activated genes, and strong reduction of plant fertility. Our work proposes a novel role for RNA methylation in the bet-hedging strategy of heat stress response.

SRSF6 balances mitochondrial-driven innate immune outcomes through alternative splicing of BAX

To mount a protective response to infection while preventing hyperinflammation, gene expression in innate immune cells must be tightly regulated. Despite the importance of pre-mRNA splicing in shaping the proteome, its role in balancing immune outcomes remains understudied. Transcriptomic analysis of murine macrophage cell lines identified Serine/Arginine Rich Splicing factor 6 (SRSF6) as a gatekeeper of mitochondrial homeostasis. SRSF6-dependent orchestration of mitochondrial health is directed in large part by alternative splicing of the pro-apoptosis pore-forming protein BAX. Loss of SRSF6 promotes accumulation of BAX-κ, a variant that sensitizes macrophages to undergo cell death and triggers upregulation of interferon stimulated genes through cGAS sensing of cytosolic mitochondrial DNA. Upon pathogen sensing, macrophages regulate SRSF6 expression to control the liberation of immunogenic mtDNA and adjust the threshold for entry into programmed cell death. This work defines BAX alternative splicing by SRSF6 as a critical node not only in mitochondrial homeostasis but also in the macrophage's response to pathogens.

Antimicrobial Peptides and Biomarkers Induced by Ultraviolet Irradiation Have the Potential to Reduce Endodontic Inflammation and Facilitate Tissue Healing

Background: Ultraviolet (UV) irradiation can modulate host immune responses and this approach is a novel application for treating endodontic infections and inflammation in root canals. Methods: A dataset of UV-induced molecules was compiled from a literature search. A subset of this dataset was used to calculate expression log2 ratios of endodontic tissue molecules from HEPM cells and gingival fibroblasts after 255, 405, and 255/405 nm UV irradiation. Both datasets were analyzed using ingenuity pathway analysis (IPA, Qiagen, Germantown, MD, USA). Statistical significance was calculated using Fisher’s exact test and z-scores were calculated for IPA comparison analysis. Results: The dataset of 32 UV-induced molecules contained 9 antimicrobial peptides, 10 cytokines, 6 growth factors, 3 enzymes, 2 transmembrane receptors, and 2 transcription regulators. These molecules were in the IPA canonical pathway annotations for the wound healing signaling pathway (9/32, p = 3.22 × 10⁻¹¹) and communication between immune cells (6/32, p = 8.74 × 10⁻¹¹). In the IPA disease and function annotations, the 32 molecules were associated with an antimicrobial response, cell-to-cell signaling and interaction, cellular movement, hematological system development and function, immune cell trafficking, and inflammatory response. In IPA comparison analysis of the 13 molecules, the predicted activation or inhibition of pathways depended upon the cell type exposed, the wavelength of the UV irradiation used, and the time after exposure. Conclusions: UV irradiation activates and inhibits cellular pathways and immune functions. These results suggested that UV irradiation can activate innate and adaptive immune responses, which may supplement endodontic procedures to reduce infection, inflammation, and pain and assist tissues to heal.

Comparative studies of genomic and epigenetic factors influencing transcriptional variation in two insect species

Different genes show different levels of expression variability. For example, highly expressed genes tend to exhibit less expression variability. Genes whose promoters have TATA box and initiator motifs tend to have increased expression variability. On the other hand, DNA methylation of transcriptional units, or gene body DNA methylation, is associated with reduced gene expression variability in many species. Interestingly, some insect lineages, most notably Diptera including the canonical model insect Drosophila melanogaster, have lost DNA methylation. Therefore, it is of interest to determine whether genomic features similarly influence gene expression variability in lineages with and without DNA methylation. We analyzed recently generated large-scale data sets in D. melanogaster and honey bee (Apis mellifera) to investigate these questions. Our analysis shows that increased gene expression levels are consistently associated with reduced expression variability in both species, while the presence of TATA box is consistently associated with increased gene expression variability. In contrast, initiator motifs and gene lengths have weak effects limited to some data sets. Importantly, we show that a sequence characteristics indicative of gene body DNA methylation is strongly and negatively associate with gene expression variability in honey bees, while it shows no such association in D. melanogaster. These results suggest the evolutionary loss of DNA methylation in some insect lineages has reshaped the molecular mechanisms concerning the regulation of gene expression variability.