Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses

PbImpute: Precise Zero Discrimination and Balanced Imputation in Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology for elucidating cellular heterogeneity at unprecedented resolution. However, technical limitations such as limited sequencing depth and mRNA capture efficiency often result in zero counts, commonly referred to as "dropout zeros" in scRNA-seq data. These zeros pose significant challenges to downstream analysis, as they can distort the interpretation of cellular transcriptomes. While numerous computational methods have been developed to address this challenge, existing approaches frequently suffer from either insufficient imputation of zeros (under-imputation) or excessive modification of zeros (over-imputation). Here, we propose a precisely balanced imputation (PbImpute) method designed to achieve optimal equilibrium between dropout recovery and biological zero preservation in scRNA-seq data. PbImpute employs a multistage approach: (1) Initial discrimination between technical dropouts and biological zeros through parameter optimization of a new zero-inflated negative binomial (ZINB) distribution model, followed by initial imputation; (2) Application of a uniquely designed static repair algorithm to enhance data fidelity; (3) Secondary dropout identification based on gene expression frequency and partition-specific coefficient of variation; (4) Graph-embedding neural network-based imputation; and (5) Implementation of a uniquely designed dynamic repair mechanism to mitigate over-imputation effects. PbImpute distinguishes itself by uniquely integrating ZINB modeling with static and dynamic repair. This advantageous combined approach achieves a balance between over- and under-imputation, while simultaneously preserving true biological zeros and reducing signal distortion. Comprehensive evaluation using both simulated and real scRNA-seq data sets demonstrated that PbImpute achieves superior performance (F1 Score = 0.88 at 83% dropout rate, ARI = 0.78 on PBMC) in discriminating between technical dropouts and biological zeros compared to state-of-the-art methods. The method significantly improves gene-gene and cell-cell correlation structures, enhances differential expression analysis sensitivity, optimizes clustering resolution and dimensional reduction visualization, and facilitates more accurate trajectory inference. Ablation studies confirmed the essential contribution of both the imputation and repair modules to the method's performance. The code is available at https://github.com/WyBioTeam/PbImpute. By enhancing the accuracy of scRNA-seq data imputation, PbImpute can improve the identification of cell subpopulations and the detection of differentially expressed genes, thereby facilitating more precise analyses of cellular heterogeneity and advancing disease research.

Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics

With the continuous evolution of single-cell RNA sequencing technology, it has become feasible to reconstruct cell development processes using computational methods. Trajectory inference is a crucial downstream analytical task that provides valuable insights into understanding cell cycle and differentiation. During cell development, cells exhibit both stable and transition states, which makes it challenging to accurately identify these cells. To address this challenge, we propose a novel single-cell trajectory inference method using fuzzy clustering, named scFCTI. By introducing fuzzy clustering and quantifying cell uncertainty, scFCTI can identify transition cells within unstable cell states. Moreover, scFCTI can obtain refined cell classification by characterizing different cell stages, which gain more accurate single-cell trajectory reconstruction containing transition paths. To validate the effectiveness of scFCTI, we conduct experiments on five real datasets and four different structure simulation datasets, comparing them with several state-of-the-art trajectory inference methods. The results demonstrate that scFCTI outperforms these methods by successfully identifying unstable cell clusters and obtaining more accurate cell paths with transition states. Especially the experimental results demonstrate that scFCTI can reconstruct the cell trajectory more precisely.

Multi-locus CRISPRi targeting with a single truncated guide RNA

A critical goal in functional genomics is evaluating which non-coding elements contribute to gene expression, cellular function, and disease. Functional characterization remains a challenge due to the abundance and complexity of candidate elements. Here, we develop a CRISPRi-based approach for multi-locus screening of putative transcription factor binding sites with a single truncated guide. A truncated guide with hundreds of sequence match sites can reliably disrupt enhancer activity, which expands the targeting scope of CRISPRi while maintaining repressive efficacy. We screen over 13,000 possible CTCF binding sites with 24 guides at 10 nucleotides in spacer length. These truncated guides direct CRISPRi-mediated deposition of repressive H3K9me3 marks and disrupt transcription factor binding at most sequence match target sites. This approach can be a valuable screening step for testing transcription factor binding motifs or other repeated genomic sequences and is easily implemented with existing tools.

CPARI: a novel approach combining cell partitioning with absolute and relative imputation to address dropout in single-cell RNA-seq data

A key challenge in analyzing single-cell RNA sequencing data is the large number of false zeros, known as "dropout zeros", which are caused by technical limitations such as shallow sequencing depth or inefficient mRNA capture. To address this challenge, we propose a novel imputation model called CPARI, which combines cell partitioning with our designed absolute and relative imputation methods. Initially, CPARI employs a new approach to select highly variable genes and constructs an average consensus matrix using C-mean fuzzy clustering-based blockchain technology to obtain results at different resolutions. Hierarchical clustering is then applied to further refine these blocks, resulting in well-defined cellular partitions. Subsequently, CPARI identifies dropout events and determines the imputation positions of these identified zeros. An autoencoder is trained within each cellular block to learn gene features and reconstruct data. Our uniquely defined absolute imputation technique is first applied to the identified positions, followed by our relative imputation technique to address remaining dropout zeros, ensuring that both global consistency and local variation are maintained. Through comprehensive analyses conducted on simulated and real scRNA-seq datasets, including quantitative assessment, differential expression analysis, cell clustering, cell trajectory inference, robustness evaluation, and large-scale data imputation, CPARI demonstrates superior performance compared to 12 other art-of-state imputation models. Additionally, ablation experiments further confirm the significance and necessity of both the cell partitioning and relative imputation components of CPARI. Notably, CPARI as a new denoising approach could distinguish between real biological zeros and dropout zeros and minimize false positives, and maximize the accuracy of imputation.

Single-cell stimulus-response gene expression trajectories reveal the stimulus specificities of dynamic responses by single macrophages

Macrophages induce the expression of hundreds of genes in response to immune threats. However, current technology limits our ability to capture single-cell inducible gene expression dynamics. Here, we generated high-resolution time series single-cell RNA sequencing (scRNA-seq) data from mouse macrophages responding to six stimuli, and imputed ensembles of real-time single-cell gene expression trajectories (scGETs). We found that dynamic information contained in scGETs substantially contributes to macrophage stimulus-response specificity (SRS). Dynamic information also identified correlations between immune response genes, indicating biological coordination. Furthermore, we showed that the microenvironmental context of polarizing cytokines profoundly affects scGETs, such that measuring response dynamics offered clearer discrimination of the polarization state of individual macrophage cells than single time-point measurements. Our findings highlight the important contribution of dynamic information contained in the single-cell expression responses of immune genes in characterizing the SRS and functional states of macrophages.

Carbohydrate-Lectin Interactions Reprogram Dendritic Cells to Promote Type 1 Anti-Tumor Immunity

Cancer vaccine development is inhibited by a lack of strategies for directing dendritic cell (DC) induction of effective tumor-specific cellular immunity. Pathogen engagement of DC lectins and toll-like receptors (TLRs) is thought to shape immunity by directing T cell function. Controlling downstream responses, however, remains a major challenge. A critical goal in advancing vaccine development involves the identification of receptors that drive type 1 cellular immunity. The immune system monitors cells for aberrant glycosylation (a sign of a foreign entity), but potent activation occurs when a second signal, such as single-stranded RNA or lipopolysaccharide, is present to activate TLR signaling. To exploit dual signaling, we engineered a glycan-costumed virus-like particle (VLP) vaccine that displays a DC-SIGN-selective aryl mannose ligand and encapsulates TLR7 agonists. These VLPs deliver programmable peptide antigens to induce robust DC activation and type 1 cellular immunity. In contrast, VLPs lacking this critical DC-SIGN ligand promoted DC-mediated humoral immunity, offering limited tumor control. Vaccination with glycan-costumed VLPs generated tumor antigen-specific Th1 CD4+ and CD8+ T cells that infiltrated solid tumors, significantly inhibiting tumor growth in a murine melanoma model. The tailored VLPs also afforded protection against the reintroduction of tumor cells. Thus, DC lectin-driven immune reprogramming, combined with the modular programmability of VLP platforms, provides a promising framework for directing cellular immunity to advance cancer immunotherapies and vaccines.

A systematic comparison of computational methods for expression forecasting

Expression forecasting methods use machine learning models to predict how a cell will alter its transcriptome upon perturbation. Such methods are enticing because they promise to answer pressing questions in fields ranging from developmental genetics to cell fate engineering and because they are a fast, cheap, and accessible complement to the corresponding experiments. However, the absolute and relative accuracy of these methods is poorly characterized, limiting their informed use, their improvement, and the interpretation of their predictions. To address these issues, we created a benchmarking platform that combines a panel of 11 large-scale perturbation datasets with an expression forecasting software engine that encompasses or interfaces to a wide variety of methods. We used our platform to systematically assess methods, parameters, and sources of auxiliary data, finding that performance strongly depends on the choice of metric, and especially for simple metrics like mean squared error, it is uncommon for expression forecasting methods to out-perform simple baselines. Our platform will serve as a resource to improve methods and to identify contexts in which expression forecasting can succeed.

scVSC: Deep Variational Subspace Clustering for Single-Cell Transcriptome Data

Single-cell RNA sequencing (scRNA-seq) is a potent advancement for analyzing gene expression at the individual cell level, allowing for the identification of cellular heterogeneity and subpopulations. However, it suffers from technical limitations that result in sparse and heterogeneous data. Here, we propose scVSC, an unsupervised clustering algorithm built on deep representation neural networks. The method incorporates the variational inference into the subspace model, which imposes regularization constraints on the latent space and further prevents overfitting. In a series of experiments across multiple datasets, scVSC outperforms existing state-of-the-art unsupervised and semi-supervised clustering tools regarding clustering accuracy and running efficiency. Moreover, the study indicates that scVSC could visually reveal the state of trajectory differentiation, accurately identify differentially expressed genes, and further discover biologically critical pathways.

Identification of TIMP1-induced dysregulation of epithelial-mesenchymal transition as a key pathway in inflammatory bowel disease and small intestinal neuroendocrine tumors shared pathogenesis

Inflammatory Bowel Disease (IBD) is believed to be a risk factor for Small Intestinal Neuroendocrine Tumors (SI-NET) development; however, the molecular relationship between IBD and SI-NET has yet to be elucidated. In this study, we use a systems biology approach to uncover such relationships. We identified a more similar transcriptomic-wide expression pattern between Crohn's Disease (CD) and SI-NET whereas a higher proportion of overlapping dysregulated genes between Ulcerative Colitis (UC) and SI-NET. Enrichment analysis indicates that extracellular matrix remodeling, particularly in epithelial-mesenchymal transition and intestinal fibrosis mediated by TIMP1, is the most significantly dysregulated pathway among upregulated genes shared between both IBD subtypes and SI-NET. However, this remodeling occurs through distinct regulatory molecular mechanisms unique to each IBD subtype. Specifically, myofibroblast activation in CD and SI-NET is mediated through IL-6 and ciliary-dependent signaling pathways. Contrarily, in UC and SI-NET, this phenomenon is mainly regulated through immune cells like macrophages and the NCAM signaling pathway, a potential gut-brain axis in the context of these two diseases. In both IBD and SI-NET, intestinal fibrosis resulted in significant metabolic reprogramming of fatty acid and glucose to an inflammatory- and cancer-inducing state. This altered metabolic state, revealed through enrichment analysis of downregulated genes, showed dysfunctions in oxidative phosphorylation, gluconeogenesis, and glycogenesis, indicating a shift towards glycolysis. Also known as the Warburg effect, this glycolytic switch, in return, exacerbates fibrosis. Corresponding to enrichment analysis results, network construction and subsequent topological analysis pinpointed 7 protein complexes, 17 hub genes, 11 microRNA, and 1 transcription factor related to extracellular matrix accumulation and metabolic reprogramming that are candidate biomarkers in both IBD and SI-NET. Together, these biological pathways and candidate biomarkers may serve as potential therapeutic targets for these diseases.

Transcription Factor Activity Regulating Macrophage Heterogeneity during Skin Wound Healing

Monocytes and macrophages (Mos/Mϕs) play diverse roles in wound healing by adopting a spectrum of functional phenotypes; however, the regulation of such heterogeneity remains poorly defined. We enhanced our previously published Bayesian inference TF activity model, incorporating both single-cell RNA sequencing and single-cell ATAC sequencing data to infer transcription factor (TF) activity in Mos/Mϕs during skin wound healing. We found that wound Mos/Mϕs clustered into early-stage Mos/Mϕs, late-stage Mϕs, and APCs, and that each cluster showed differential chromatin accessibility and differential predicted TF activity that did not always correlate with mRNA or protein expression. Network analysis revealed two highly connected large communities involving a total of 19 TFs, highlighting TF cooperation in regulating wound Mos/Mϕs. This analysis also revealed a small community populated by NR4A1 and NFKB1, supporting a proinflammatory link between these TFs. Importantly, we validated a proinflammatory role for NR4A1 activity during wound healing, showing that Nr4a1 knockout mice exhibit decreased inflammatory gene expression in early-stage wound Mos/Mϕs, along with delayed wound re-epithelialization and impaired granulation tissue formation. In summary, our study provides insight into TF activity that regulates Mo/Mϕ heterogeneity during wound healing and provides a rational basis for targeting Mo/Mϕ TF networks to alter phenotypes and improve healing.

Dynamical and combinatorial coding by MAPK p38 and NFκB in the inflammatory response of macrophages

Macrophages sense pathogens and orchestrate specific immune responses. Stimulus specificity is thought to be achieved through combinatorial and dynamical coding by signaling pathways. While NFκB dynamics are known to encode stimulus information, dynamical coding in other signaling pathways and their combinatorial coordination remain unclear. Here, we established live-cell microscopy to investigate how NFκB and p38 dynamics interface in stimulated macrophages. Information theory and machine learning revealed that p38 dynamics distinguish cytokine TNF from pathogen-associated molecular patterns and high doses from low, but contributed little to information-rich NFκB dynamics when both pathways are considered. This suggests that immune response genes benefit from decoding immune signaling dynamics or combinatorics, but not both. We found that the heterogeneity of the two pathways is surprisingly uncorrelated. Mathematical modeling revealed potential sources of uncorrelated heterogeneity in the branched pathway network topology and predicted it to drive gene expression variability. Indeed, genes dependent on both p38 and NFκB showed high scRNAseq variability and bimodality. These results identify combinatorial signaling as a mechanism to restrict NFκB-AND-p38-responsive inflammatory cytokine expression to few cells.

Diagnostic value of cytokines in severe childhood Mycoplasma pneumoniae pneumonia combined with Adenovirus infection

BACKGROUND

To explore the alterations of inflammatory markers and immune-related cytokines in children infected with Mycoplasma pneumoniae (MP) combined with Adenovirus (ADV).

METHODS

The study population consisted of 201 children with MPP, and they were grouped according to whether they were coinfected with ADV infection and critically ill. Additionally, comparative analyses were performed. The diagnostic value of different indicators and combined indicators for SMPP combined with ADV was assessed using ROC curves.

RESULTS

There was no difference between group A1 and group A2, group B1 and group B2 in terms of age, gender, duration of hospitalisation and fever. The levels of calcitoninogen(PCT), lactate dehydrogenase concentration(LDH), interleukin(IL)-6, IL-8, IL-10, IL-4, IL-12P70, and IFN-γ in group A were higher than group B. The severe group (A1, B1) was significantly higher than the mild group (A2, B2) in terms of D-dimer, CRP, PCT, LDH, IL-6, IL-8, IL-10, IL-17a and number of patients with pleural effusion, solid lung changes. Among the individual indexes of D-dimer, CRP, N%,LDH, and PCT, the AUC of the combined test was 0.977, which was higher than that of the individual indicators. Among IL-6, IL-8, IL-10, and IL-17a, the AUC of the combined assay was 0.802, which was higher than that of the individual indicators.

CONCLUSION

MP combined with ADV infection was associated with increased expression levels of IL-6, IL-8, IL-10, IL-4, IL-12P70, IFN-γ, and LDH. IL-6, IL-8, IL-10, IL-17a, LDH, PCT, CRP, and D-dimer could be used as predictors of SMPP and the combined test can improve the diagnostic value.

Immune response stability to the SARS-CoV-2 mRNA vaccine booster is influenced by differential splicing of HLA genes

Many molecular mechanisms that lead to the host antibody response to COVID-19 vaccines remain largely unknown. In this study, we used serum antibody detection combined with whole blood RNA-based transcriptome analysis to investigate variability in vaccine response in healthy recipients of a booster (third) dose schedule of the mRNA BNT162b2 vaccine against COVID-19. The cohort was divided into two groups: (1) low-stable individuals, with antibody concentration anti-SARS-CoV IgG S1 below 0.4 percentile at 180 days after boosting vaccination; and (2) high-stable individuals, with antibody values greater than 0.6 percentile of the range in the same period (median 9525 [185-80,000] AU/mL). Differential gene expression, expressed single nucleotide variants and insertions/deletions, differential splicing events, and allelic imbalance were explored to broaden our understanding of the immune response sustenance. Our analysis revealed a differential expression of genes with immunological functions in individuals with low antibody titers, compared to those with higher antibody titers, underscoring the fundamental importance of the innate immune response for boosting immunity. Our findings also provide new insights into the determinants of the immune response variability to the SARS-CoV-2 mRNA vaccine booster, highlighting the significance of differential splicing regulatory mechanisms, mainly concerning HLA alleles, in delineating vaccine immunogenicity.

scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference

Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.

Glycan-costumed virus-like particles promote type 1 anti-tumor immunity

Cancer vaccine development is inhibited by a lack of strategies for directing dendritic cell (DC) induction of effective tumor-specific cellular immunity. Pathogen engagement of DC lectins and toll-like receptors (TLRs) shapes immunity by directing T cell function. Strategies to activate specific DC signaling pathways via targeted receptor engagement are crucial to unlocking type 1 cellular immunity. Here, we engineered a glycan-costumed virus-like particle (VLP) vaccine that delivers programmable peptide antigens to induce tumor-specific cellular immunity in vivo. VLPs encapsulating TLR7 agonists and decorated with a selective mannose-derived ligand for the lectin DC-SIGN induced robust DC activation and type 1 cellular immunity, whereas VLPs lacking this key DC-SIGN ligand failed to promote DC-mediated immunity. Vaccination with glycan-costumed VLPs generated tumor antigen-specific Th1 CD4+ and CD8+ T cells that infiltrated solid tumors, inhibiting tumor growth in a murine melanoma model. Thus, VLPs employing lectin-driven immune reprogramming provide a framework for advancing cancer immunotherapies.

Dictionary of immune responses to cytokines at single-cell resolution

Cytokines mediate cell-cell communication in the immune system and represent important therapeutic targets1-3. A myriad of studies have highlighted their central role in immune function4-13, yet we lack a global view of the cellular responses of each immune cell type to each cytokine. To address this gap, we created the Immune Dictionary, a compendium of single-cell transcriptomic profiles of more than 17 immune cell types in response to each of 86 cytokines (>1,400 cytokine-cell type combinations) in mouse lymph nodes in vivo. A cytokine-centric view of the dictionary revealed that most cytokines induce highly cell-type-specific responses. For example, the inflammatory cytokine interleukin-1β induces distinct gene programmes in almost every cell type. A cell-type-centric view of the dictionary identified more than 66 cytokine-driven cellular polarization states across immune cell types, including previously uncharacterized states such as an interleukin-18-induced polyfunctional natural killer cell state. Based on this dictionary, we developed companion software, Immune Response Enrichment Analysis, for assessing cytokine activities and immune cell polarization from gene expression data, and applied it to reveal cytokine networks in tumours following immune checkpoint blockade therapy. Our dictionary generates new hypotheses for cytokine functions, illuminates pleiotropic effects of cytokines, expands our knowledge of activation states of each immune cell type, and provides a framework to deduce the roles of specific cytokines and cell-cell communication networks in any immune response.

Genome-wide DNase I-hypersensitive site assay reveals distinct genomic distributions and functional features of open chromatin in autopolyploid sugarcane

The characterization of cis-regulatory DNA elements (CREs) is essential for deciphering the regulation of gene expression in eukaryotes. Although there have been endeavors to identify CREs in plants, the properties of CREs in polyploid genomes are still largely unknown. Here, we conducted the genome-wide identification of DNase I-hypersensitive sites (DHSs) in leaf and stem tissues of the auto-octoploid species Saccharum officinarum. We revealed that DHSs showed highly similar distributions in the genomes of these two S. officinarum tissues. Notably, we observed that approximately 74% of DHSs were located in distal intergenic regions, suggesting considerable differences in the abundance of distal CREs between S. officinarum and other plants. Leaf- and stem-dependent transcriptional regulatory networks were also developed by mining the binding motifs of transcription factors (TFs) from tissue-specific DHSs. Four TEOSINTE BRANCHED 1, CYCLOIDEA, and PCF1 (TCP) TFs (TCP2, TCP4, TCP7, and TCP14) and two ethylene-responsive factors (ERFs) (ERF109 and ERF03) showed strong causal connections with short binding distances from each other, pointing to their possible roles in the regulatory networks of leaf and stem development. Through functional validation in transiently transgenic protoplasts, we isolate a set of tissue-specific promoters. Overall, the DHS maps presented here offer a global view of the potential transcriptional regulatory elements in polyploid sugarcane and can be expected to serve as a valuable resource for both transcriptional network elucidation and genome editing in sugarcane breeding.

Dexamethasone impairs the expression of antimicrobial mediators in lipopolysaccharide-activated primary macrophages by inhibiting both expression and function of interferon β

Glucocorticoids potently inhibit expression of many inflammatory mediators, and have been widely used to treat both acute and chronic inflammatory diseases for more than seventy years. However, they can have several unwanted effects, amongst which immunosuppression is one of the most common. Here we used microarrays and proteomic approaches to characterise the effect of dexamethasone (a synthetic glucocorticoid) on the responses of primary mouse macrophages to a potent pro-inflammatory agonist, lipopolysaccharide (LPS). Gene ontology analysis revealed that dexamethasone strongly impaired the lipopolysaccharide-induced antimicrobial response, which is thought to be driven by an autocrine feedback loop involving the type I interferon IFNβ. Indeed, dexamethasone strongly and dose-dependently inhibited the expression of IFNβ by LPS-activated macrophages. Unbiased proteomic data also revealed an inhibitory effect of dexamethasone on the IFNβ-dependent program of gene expression, with strong down-regulation of several interferon-induced antimicrobial factors. Surprisingly, dexamethasone also inhibited the expression of several antimicrobial genes in response to direct stimulation of macrophages with IFNβ. We tested a number of hypotheses based on previous publications, but found that no single mechanism could account for more than a small fraction of the broad suppressive impact of dexamethasone on macrophage type I interferon signaling, underlining the complexity of this pathway. Preliminary experiments indicated that dexamethasone exerted similar inhibitory effects on primary human monocyte-derived or alveolar macrophages.

Long-Read High-Throughput Sequencing (HTS) Revealed That the Sf-Rhabdovirus X+ Genome Contains a 3.7 kb Internal Duplication

We previously reported a novel rhabdovirus produced from the Spodoptera frugiperda Sf9 cell line, designated as Sf-rhabdovirus X+ since it contained a unique accessory gene X. The Sf-rhabdovirus X+ genome sequence was generated using Sanger sequencing and short-read high-throughput sequencing (HTS). In this study, we have used long-read HTS technologies, PacBio's single-molecule real-time sequencing and Oxford's Nanopore RNA direct sequencing, to analyze the parent Sf9 cell line transcriptome and the virus RNA produced from an X+ cell clone, respectively. A unique 3.7 kb duplication was identified in the L gene between nucleotide position 8523 and 8524, preceded by a GA dinucleotide insertion. This duplication contained a partial G gene, the complete X gene, and a partial L gene, which extended from nucleotide positions 4767-8523 in the X+ virus. Thus, the X+ genome length is 17,361 nucleotides, and we have re-designated the virus as Sf-rhabdovirus X+3.7. The 3.7 kb duplication was found in all Sf9 cell clones producing the X+ variant virus. Furthermore, the Sf-rhabdovirus X+3.7 genome was stable at passage 30, which was the highest passage tested. These results highlight the importance of combining short-read and long-read technologies for accurately sequencing virus genomes using HTS.

Long Noncoding RNA U90926 Is Induced in Activated Macrophages, Is Protective in Endotoxic Shock, and Encodes a Novel Secreted Protein

Thousands of long noncoding RNAs are encoded in mammalian genomes, yet most remain uncharacterized. In this study, we functionally characterized a mouse long noncoding RNA named U90926. Analysis of U90926 RNA levels revealed minimal expression across multiple tissues at steady state. However, the expression of this gene was highly induced in macrophages and dendritic cells by TLR activation, in a p38 MAPK- and MyD88-dependent manner. To study the function of U90926, we generated U90926-deficient (U9-KO) mice. Surprisingly, we found minimal effects of U90926 deficiency in cultured macrophages. Given the lack of macrophage-intrinsic effect, we investigated the subcellular localization of U90926 transcript and its protein-coding potential. We found that U90926 RNA localizes to the cytosol, associates with ribosomes, and contains an open reading frame that encodes a novel glycosylated protein (termed U9-ORF), which is secreted from the cell. An in vivo model of endotoxic shock revealed that, in comparison with wild type mice, U9-KO mice exhibited increased sickness responses and mortality. Mechanistically, serum levels of IL-6 were elevated in U9-KO mice, and IL-6 neutralization improved endotoxemia outcomes in U9-KO mice. Taken together, these results suggest that U90926 expression is protective during endotoxic shock, potentially mediated by the paracrine and/or endocrine actions of the novel U9-ORF protein secreted by activated myeloid cells.

Systematically characterizing the roles of E3-ligase family members in inflammatory responses with massively parallel Perturb-seq

E3 ligases regulate key processes, but many of their roles remain unknown. Using Perturb-seq, we interrogated the function of 1,130 E3 ligases, partners and substrates in the inflammatory response in primary dendritic cells (DCs). Dozens impacted the balance of DC1, DC2, migratory DC and macrophage states and a gradient of DC maturation. Family members grouped into co-functional modules that were enriched for physical interactions and impacted specific programs through substrate transcription factors. E3s and their adaptors co-regulated the same processes, but partnered with different substrate recognition adaptors to impact distinct aspects of the DC life cycle. Genetic interactions were more prevalent within than between modules, and a deep learning model, comβVAE, predicts the outcome of new combinations by leveraging modularity. The E3 regulatory network was associated with heritable variation and aberrant gene expression in immune cells in human inflammatory diseases. Our study provides a general approach to dissect gene function.

A transcription factor atlas of directed differentiation

Transcription factors (TFs) regulate gene programs, thereby controlling diverse cellular processes and cell states. To comprehensively understand TFs and the programs they control, we created a barcoded library of all annotated human TF splice isoforms (>3,500) and applied it to build a TF Atlas charting expression profiles of human embryonic stem cells (hESCs) overexpressing each TF at single-cell resolution. We mapped TF-induced expression profiles to reference cell types and validated candidate TFs for generation of diverse cell types, spanning all three germ layers and trophoblasts. Targeted screens with subsets of the library allowed us to create a tailored cellular disease model and integrate mRNA expression and chromatin accessibility data to identify downstream regulators. Finally, we characterized the effects of combinatorial TF overexpression by developing and validating a strategy for predicting combinations of TFs that produce target expression profiles matching reference cell types to accelerate cellular engineering efforts.

Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks

Gene regulatory networks ensure that important genes are expressed at precise levels. When gene expression is sufficiently perturbed, it can lead to disease. To understand how gene expression disruptions percolate through a network, we must first map connections between regulatory genes and their downstream targets. However, we lack comprehensive knowledge of the upstream regulators of most genes. Here, we developed an approach for systematic discovery of upstream regulators of critical immune factors-IL2RA, IL-2 and CTLA4-in primary human T cells. Then, we mapped the network of the target genes of these regulators and putative cis-regulatory elements using CRISPR perturbations, RNA-seq and ATAC-seq. These regulators form densely interconnected networks with extensive feedback loops. Furthermore, this network is enriched for immune-associated disease variants and genes. These results provide insight into how immune-associated disease genes are regulated in T cells and broader principles about the structure of human gene regulatory networks.

Epigenomics of conventional type-I dendritic cells depicted preferential control of TLR9 versus TLR3 response by NCoR1 through differential IRF3 activation

Tight control of gene regulation in dendritic cells (DCs) is important to mount pathogen specific immune responses. Apart from transcription factor binding, dynamic regulation of enhancer activity through global transcriptional repressors like Nuclear Receptor Co-repressor 1 (NCoR1) plays a major role in fine-tuning of DC responses. However, how NCoR1 regulates enhancer activity and gene expression in individual or multiple Toll-like receptor (TLR) activation in DCs is largely unknown. In this study, we did a comprehensive epigenomic analysis of murine conventional type-I DCs (cDC1) across different TLR ligation conditions. We profiled gene expression changes along with H3K27ac active enhancers and NCoR1 binding in the TLR9, TLR3 and combined TLR9 + TLR3 activated cDC1. We observed spatio-temporal activity of TLR9 and TLR3 specific enhancers regulating signal specific target genes. Interestingly, we found that NCoR1 differentially controls the TLR9 and TLR3-specific responses. NCoR1 depletion specifically enhanced TLR9 responses as evident from increased enhancer activity as well as TLR9-specific gene expression, whereas TLR3-mediated antiviral response genes were negatively regulated. We validated that NCoR1 KD cDC1 showed significantly decreased TLR3 specific antiviral responses through decreased IRF3 activation. In addition, decreased IRF3 binding was observed at selected ISGs leading to their decreased expression upon NCoR1 depletion. Consequently, the NCoR1 depleted cDC1 showed reduced Sendai Virus (SeV) clearance and cytotoxic potential of CD8+ T cells upon TLR3 activation. NCoR1 directly controls the majority of these TLR specific enhancer activity and the gene expression. Overall, for the first time, we revealed NCoR1 mediates transcriptional control towards TLR9 as compared to TLR3 in cDC1.

TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering

Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages.

Single-cell transcriptomic and chromatin accessibility analyses of dairy cattle peripheral blood mononuclear cells and their responses to lipopolysaccharide

Background

Gram-negative bacteria are important pathogens in cattle, causing severe infectious diseases, including mastitis. Lipopolysaccharides (LPS) are components of the outer membrane of Gram-negative bacteria and crucial mediators of chronic inflammation in cattle. LPS modulations of bovine immune responses have been studied before. However, the single-cell transcriptomic and chromatin accessibility analyses of bovine peripheral blood mononuclear cells (PBMCs) and their responses to LPS stimulation were never reported.

Results

We performed single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) in bovine PBMCs before and after LPS treatment and demonstrated that seven major cell types, which included CD4 T cells, CD8 T cells, and B cells, monocytes, natural killer cells, innate lymphoid cells, and dendritic cells. Bioinformatic analyses indicated that LPS could increase PBMC cell cycle progression, cellular differentiation, and chromatin accessibility. Gene analyses further showed significant changes in differential expression, transcription factor binding site, gene ontology, and regulatory interactions during the PBMC responses to LPS. Consistent with the findings of previous studies, LPS induced activation of monocytes and dendritic cells, likely through their upregulated TLR4 receptor. NF-κB was observed to be activated by LPS and an increased transcription of an array of pro-inflammatory cytokines, in agreement that NF-κB is an LPS-responsive regulator of innate immune responses. In addition, by integrating LPS-induced differentially expressed genes (DEGs) with large-scale GWAS of 45 complex traits in Holstein, we detected trait-relevant cell types. We found that selected DEGs were significantly associated with immune-relevant health, milk production, and body conformation traits.

Conclusion

This study provided the first scRNAseq and scATAC-seq data for cattle PBMCs and their responses to the LPS stimulation to the best of our knowledge. These results should also serve as valuable resources for the future study of the bovine immune system and open the door for discoveries about immune cell roles in complex traits like mastitis at single-cell resolution.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08562-0.

Functional Hallmarks of Healthy Macrophage Responses: Their Regulatory Basis and Disease Relevance

Macrophages are first responders for the immune system. In this role, they have both effector functions for neutralizing pathogens and sentinel functions for alerting other immune cells of diverse pathologic threats, thereby initiating and coordinating a multipronged immune response. Macrophages are distributed throughout the body-they circulate in the blood, line the mucosal membranes, reside within organs, and survey the connective tissue. Several reviews have summarized their diverse roles in different physiological scenarios and in the initiation or amplification of different pathologies. In this review, we propose that both the effector and the sentinel functions of healthy macrophages rely on three hallmark properties: response specificity, context dependence, and stimulus memory. When these hallmark properties are diminished, the macrophage's biological functions are impaired, which in turn results in increased risk for immune dysregulation, manifested by immune deficiency or autoimmunity. We review the evidence and the molecular mechanisms supporting these functional hallmarks.

Loss of Ripk3 attenuated neutrophil accumulation in a lipopolysaccharide-induced zebrafish inflammatory model

Neutrophils are important effector cells during inflammation, which play complex roles. Therefore, investigating the regulation of neutrophil accumulation during inflammation might provide targets for treating related diseases. In the present study, we generated a ripk3-deficient zebrafish line to study the roles of Ripk3 in neutrophil-related inflammation. The homeostatic hematopoiesis and cytokine expression of the ripk3-deficient larvae were unaltered. The ripk3-deficient larvae with caudal fin fold injury exhibited similar neutrophil enrichment with wild-type larvae, suggesting that Ripk3 is not essential for non-infectious inflammatory responses. When challenged with lipopolysaccharide (LPS), the ripk3-deficient larvae showed significantly less neutrophil accumulation in the injection site and differential expression of several key cytokines. Ripk3 inhibitors could also attenuate neutrophil accumulation in wild-type larvae, indicating that Ripk3 could serve as a candidate target for inflammation treatment. In summary, our study indicated that Ripk3 has an essential role in LPS-induced inflammatory responses. It was suggested that the ripk3-deficient zebrafish might be applied in developing infectious disease models, while Ripk3 also has potential as an inflammation-treatment target.

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network

Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify putative cell types. However, due to noise impacts, high dimensionality and pervasive dropout events, clustering analysis of scRNA-seq data remains a computational challenge. Here, we propose a new deep structural clustering method for scRNA-seq data, named scDSC, which integrate the structural information into deep clustering of single cells. The proposed scDSC consists of a Zero-Inflated Negative Binomial (ZINB) model-based autoencoder, a graph neural network (GNN) module and a mutual-supervised module. To learn the data representation from the sparse and zero-inflated scRNA-seq data, we add a ZINB model to the basic autoencoder. The GNN module is introduced to capture the structural information among cells. By joining the ZINB-based autoencoder with the GNN module, the model transfers the data representation learned by autoencoder to the corresponding GNN layer. Furthermore, we adopt a mutual supervised strategy to unify these two different deep neural architectures and to guide the clustering task. Extensive experimental results on six real scRNA-seq datasets demonstrate that scDSC outperforms state-of-the-art methods in terms of clustering accuracy and scalability. Our method scDSC is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/scDSC.

Protection against severe infant lower respiratory tract infections by immune training: Mechanistic studies

BACKGROUND

Results from recent clinical studies suggest potential efficacy of immune training (IT)-based approaches for protection against severe lower respiratory tract infections in infants, but underlying mechanisms are unclear.

OBJECTIVE

We used systems-level analyses to elucidate IT mechanisms in infants in a clinical trial setting.

METHODS

Pre- and posttreatment peripheral blood mononuclear cells from a placebo-controlled trial in which winter treatment with the IT agent OM85 reduced infant respiratory infection frequency and/or duration were stimulated for 24 hours with the virus/bacteria mimics polyinosinic:polycytidylic acid/lipopolysaccharide. Transcriptomic profiling via RNA sequencing, pathway and upstream regulator analyses, and systems-level gene coexpression network analyses were used sequentially to elucidate and compare responses in treatment and placebo groups.

RESULTS

In contrast to subtle changes in antivirus-associated polyinosinic:polycytidylic acid response profiles, the bacterial lipopolysaccharide-triggered gene coexpression network responses exhibited OM85 treatment-associated upregulation of IFN signaling. This was accompanied by network rewiring resulting in increased coordination of TLR4 expression with IFN pathway-associated genes (especially master regulator IRF7); segregation of TNF and IFN-γ (which potentially synergize to exaggerate inflammatory sequelae) into separate expression modules; and reduced size/complexity of the main proinflammatory network module (containing, eg, IL-1,IL-6, and CCL3). Finally, we observed a reduced capacity for lipopolysaccharide-induced inflammatory cytokine (eg, IL-6 and TNF) production in the OM85 group.

CONCLUSION

These changes are consistent with treatment-induced enhancement of bacterial pathogen detection/clearance capabilities concomitant with enhanced capacity to regulate ensuing inflammatory response intensity and duration. We posit that IT agents exemplified by OM85 potentially protect against severe lower respiratory tract infections in infants principally by effects on innate immune responses targeting the bacterial components of the mixed respiratory viral/bacterial infections that are characteristic of this age group.

Transcriptome-wide changes in gene expression, splicing, and lncRNAs in response to a live attenuated dengue virus vaccine

The tetravalent dengue vaccine candidate, TAK-003, induces a functional antibody response, but the titers of antibodies against the four serotypes of the dengue virus (DENV) can vary. Here, through a transcriptomic analysis on whole blood collected from recipients of a two-dose schedule of TAK-003, we examine gene expression, splicing, and transcript isoform-level changes for both protein-coding and noncoding genes to broaden our understanding of the immune response. Our analysis reveals a dynamic pattern of vaccine-associated regulation of long noncoding RNAs (lncRNAs), differential splicing of interferon-stimulated gene exons, and gene expression changes related to multiple signaling pathways that detect viral infection. Co-expression networks isolate immune cell-type-related and interferon-response modules that represent specific biological processes that correlate with more robust antibody responses. These data provide insights into the early determinants of the variable immune response to the vaccine, highlighting the significance of splicing and isoform-level gene regulatory mechanisms in defining vaccine immunogenicity.

Parasympathetic neural activity and the reciprocal regulation of innate antiviral and inflammatory genes in the human immune system

The vagus nerve mediates parasympathetic nervous system control of peripheral physiological processes including cardiovascular activity and immune response. In mice, tonic vagal activation down-regulates inflammation via nicotinic acetylcholine receptor-mediated inhibition of the pro-inflammatory transcription factor NF-κB in monocyte/macrophages. Because Type I interferon and pro-inflammatory genes are regulated reciprocally at the level of transcription factor activation and cell differentiation, we hypothesized that vagal activity would up-regulate Type I interferon response genes concurrently with inflammatory downregulation in human immune cells. We mapped empirical individual differences in the circulating leukocyte transcriptome and vagal activity indexed by high frequency (0.15-0.40 Hz) heart rate variability (HF-HRV) in 380 participants in the Midlife in the US study. Here we show that promoter-based bioinformatics analyses linked greater HF-HRV to reduced NF-κB activity and increased activity of IRF transcription factors involved in Type I interferon response (independent of β-antagonists, BMI, smoking, heavy alcohol consumption, and demographic factors). Transcript origin analyses implicated myeloid lineage immune cells as targets, representing per-cell alterations in gene transcription as HF-HRV was not associated with differential prevalence of leukocyte subsets. These findings support the concept of parasympathetic inhibition of pro-inflammatory gene expression in humans and up-regulation of Type I interferons that could augment host defense against viral infections.

Immunomodulatory and anti-inflammatory effects of Phellinus linteus mycelium

BACKGROUND

The present study extensively aimed to evaluate the underlying mechanism of the immunomodulatory and anti-inflammatory effects of Phellinus linteus mycelium (PLM).

METHODS

To assess whether PLM influences the production of markers related to inflammation, Lipopolysaccharide (LPS)-stimulated RAW264.7 cells were treated with PLM (50, 100, 200, and 500 μg/mL). Splenocyte, thymus, peritoneal exudate cells (PEC), and peripheral blood mononuclear cells (PBMC) were isolated from the Balb/c mice treated with Korean red ginseng or PLM once a day for 5 weeks. Moreover, all mice except normal mice were stimulated with 10% proteose peptone (PP) treated 3 days before the sacrifice and 2% starch treated 2 days before the sacrifice. Subsequently, the cytotropic substance was evaluated by using flow cytometry analysis and ELISA assay.

RESULTS

PLM200 treatment significantly suppressed the production of nitric oxide (NO) and prostaglandin E2 (PGE2) and inhibited the release of proinflammatory cytokines such as interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α dose-dependently in the LPS-stimulated RAW264.7 cells. PLM200 supplementation showed a significant increase in IL-2, IL-12, and interferon (IFN)-γ production and upregulated the ratio of IFN-γ (T-helper type 1, Th1) to IL-4 (T-helper type 2, Th2) in splenocytes. After PLM200 treatment, the significant elevation of CD4+CD25+, CD4+&CD8+, and CD4+CD69+ treatment were detected in thymus. Moreover, CD4+ and CD4+CD69+ in PBMC and CD69+ in PEC were also shown in a significant increase.

CONCLUSIONS

Taken together, these results showed an immunomodulatory effect of PLM about an elevated INF-γ/IL4 ratio, as an index of Th1/Th2, as well as the anti-inflammatory effect in the LPS-stimulated RAW264.7 cells. Therefore, our findings demonstrate that PLM possesses immunostimulatory and anti-inflammatory effects.

Aseptic and septic prosthetic joint loosening: Impact of biomaterial wear on immune cell function, inflammation, and infection

The success of total joint replacements has led to consistent growth in the use of arthroplasty in progressively younger patients. However, more than 10 percent of patients require revision surgeries due to implant failure caused by osteolytic loosening. These failures are classified as either aseptic or septic and are associated with the presence of particulate wear debris generated by mechanical action between implant components. Aseptic loosening results from chronic inflammation caused by activation of resident immune cells in contact with implant wear debris. In contrast, septic loosening is defined by the presence of chronic infection at the implant site. However, recent findings suggest that subclinical biofilms may be overlooked when evaluating the cause of implant failure, leading to a misdiagnosis of aseptic loosening. Many of the inflammatory pathways contributing to periprosthetic joint infections are also involved in bone remodeling and resorption. In particular, wear debris is increasingly implicated in the inhibition of the innate and adaptive immune response to resolve an infection or prevent hematogenous spread. This review examines the interconnectivity of wear particle- and infection-associated mechanisms of implant loosening, as well as biomaterials-based strategies to combat infection-related osteolysis.

Stimulus-specific responses in innate immunity: Multilayered regulatory circuits

Immune sentinel cells initiate immune responses to pathogens and tissue injury and are capable of producing highly stimulus-specific responses. Insight into the mechanisms underlying such specificity has come from the identification of regulatory factors and biochemical pathways, as well as the definition of signaling circuits that enable combinatorial and temporal coding of information. Here, we review the multi-layered molecular mechanisms that underlie stimulus-specific gene expression in macrophages. We categorize components of inflammatory and anti-pathogenic signaling pathways into five layers of regulatory control and discuss unifying mechanisms determining signaling characteristics at each layer. In this context, we review mechanisms that enable combinatorial and temporal encoding of information, identify recurring regulatory motifs and principles, and present strategies for integrating experimental and computational approaches toward the understanding of signaling specificity in innate immunity.

Baseline pro-inflammatory gene expression in whole blood is related to adverse long-term outcomes after transcatheter aortic valve replacement: a case control study

BACKGROUND

Age-associated inflammation and immune system dysfunction have been implicated as mechanisms that increase risk for adverse long-term procedural outcomes in older adults. The purpose of this study was to investigate relationships between baseline inflammatory and innate antiviral gene expression and outcomes after transcatheter aortic valve replacement (TAVR) in older adults with severe aortic stenosis.

METHODS

We performed a retrospective case-control study comparing pre-procedural pro-inflammatory and Type 1 interferon (IFN) gene expression in 48 controls with favorable outcomes (alive 1 year after TAVR with improved quality of life [QoL]) versus 48 individuals with unfavorable outcomes (dead by 1 year or alive at 1 year but with reduced QoL). Gene expression was evaluated in whole blood via (1) pre-defined composite scores of 19 inflammation-associated genes and 34 Type I IFN response genes, and (2) pro-inflammatory and antiviral transcription factor activity inferred from promotor based bioinformatics analyses of genes showing > 25% difference in average expression levels across groups. All analyses were adjusted for age, gender, body mass index, diabetes, immunosuppression, cardiovascular disease (CVD), and frailty.

RESULTS

Relative to controls, those with unfavorable outcomes demonstrated higher expression of the pro-inflammatory gene composite prior to TAVR (p < 0.01) and bioinformatic indicators of elevated Nuclear Factor kB (p < 0.001) and Activator Protein 1 (p < 0.001) transcription factor activity, but no significant differences in Type I IFN-related gene expression.

CONCLUSIONS

These results demonstrate that a pro-inflammatory state prior to TAVR, independent of CVD severity and frailty status, is associated with worse long-term procedural outcomes.

Long tracks of homozygosity predict the severity of alcohol use disorders in an American Indian population

Runs of homozygosity (ROH) arise when an individual inherits two copies of the same haplotype segment. While ROH are ubiquitous across human populations, Native populations-with shared parental ancestry arising from isolation and endogamy-can carry a substantial enrichment for ROH. We have been investigating genetic and environmental risk factors for alcohol use disorders (AUD) in a group of American Indians (AI) who have higher rates of AUD than the general U. S. population. Here we explore whether ROH might be associated with incidence and severity of AUD in this admixed AI population (n = 742) that live on geographically contiguous reservations, using low-coverage whole genome sequences. We have found that the genomic regions in the ROH that were identified in this population had significantly elevated American Indian heritage compared with the rest of the genome. Increased ROH abundance and ROH burden are likely risk factors for AUD severity in this AI population, especially in those diagnosed with severe and moderate AUD. The association between ROH and AUD was mostly driven by ROH of moderate lengths between 1 and 2 Mb. An ROH island on chromosome 1p32.3 and a rare ROH pool on chromosome 3p12.3 were found to be significantly associated with AUD severity. They contain genes involved in lipid metabolism, oxidative stress and inflammatory responses; and OSBPL9 was found to reside on the consensus part of the ROH island. These data demonstrate that ROH are associated with risk for AUD severity in this AI population.

Time will tell: The temporal code of immune threats

Activation of NF-κB is a common downstream consequence of inflammatory stimulation, yet it achieves stimulus-specific transcriptional responses. In this issue of Immunity, Adelaja et al. use single-cell imaging and computational approaches to understand temporal features of NF-κB dynamics that transmit information about immune threats.

FASCICLIN-LIKE 18 Is a New Player Regulating Root Elongation in Arabidopsis thaliana

The plasticity of root development represents a key trait that enables plants to adapt to diverse environmental cues. The pattern of cell wall deposition, alongside other parameters, affects the extent, and direction of root growth. In this study, we report that FASCICLIN-LIKE ARABINOGALACTAN PROTEIN 18 (FLA18) plays a role during root elongation in Arabidopsis thaliana. Using root-specific co-expression analysis, we identified FLA18 to be co-expressed with a sub-set of genes required for root elongation. FLA18 encodes for a putative extra-cellular arabinogalactan protein from the FLA-gene family. Two independent T-DNA insertion lines, named fla18-1 and fla18-2, display short and swollen lateral roots (LRs) when grown on sensitizing condition of high-sucrose containing medium. Unlike fla4/salt overly sensitive 5 (sos5), previously shown to display short and swollen primary root (PR) and LRs under these conditions, the PR of the fla18 mutants is slightly longer compared to the wild-type. Overexpression of the FLA18 CDS complemented the fla18 root phenotype. Genetic interaction between either of the fla18 alleles and sos5 reveals a more severe perturbation of anisotropic growth in both PR and LRs, as compared to the single mutants and the wild-type under restrictive conditions of high sucrose or high-salt containing medium. Additionally, under salt-stress conditions, fla18sos5 had a small, chlorotic shoot phenotype, that was not observed in any of the single mutants or the wild type. As previously shown for sos5, the fla18-1 and fla18-1sos5 root-elongation phenotype is suppressed by abscisic acid (ABA) and display hypersensitivity to the ABA synthesis inhibitor, Fluridon. Last, similar to other cell wall mutants, fla18 root elongation is hypersensitive to the cellulose synthase inhibitor, Isoxaben. Altogether, the presented data assign a new role for FLA18 in the regulation of root elongation. Future studies of the unique vs. redundant roles of FLA proteins during root elongation is anticipated to shed a new light on the regulation of root architecture during plant adaptation to different growth conditions.

A Comprehensive Map of the Monocyte-Derived Dendritic Cell Transcriptional Network Engaged upon Innate Sensing of HIV

Transcriptional programming of the innate immune response is pivotal for host protection. However, the transcriptional mechanisms that link pathogen sensing with innate activation remain poorly under-stood. During HIV-1 infection, human dendritic cells (DCs) can detect the virus through an innate sensing pathway, leading to antiviral interferon and DC maturation. Here, we develop an iterative experimental and computational approach to map the HIV-1 innate response circuitry in monocyte-derived DCs (MDDCs). By integrating genome-wide chromatin accessibility with expression kinetics, we infer a gene regulatory network that links 542 transcription factors with 21,862 target genes. We observe that an interferon response is required, yet insufficient, to drive MDDC maturation and identify PRDM1 and RARA as essential regulators of the interferon response and MDDC maturation, respectively. Our work provides a resource for interrogation of regulators of HIV replication and innate immunity, highlighting complexity and cooperativity in the regulatory circuit controlling the response to infection.

Pathogen sensing leads to host transcriptional reprogramming to protect against infection. However, it is unclear how transcription factor activity is coordinated across the genome. Johnson et al. integrate chromatin accessibility and gene expression data to infer and validate a gene regulatory network that directs the innate immune response to HIV.

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.

Advances in multiplexed techniques for the detection and quantification of microRNAs

MicroRNA detection is currently a crucial analytical chemistry challenge: almost 2000 papers were referenced in PubMed in 2018 and 2019 for the keywords "miRNA detection method". MicroRNAs are potential biomarkers for multiple diseases including cancers, neurodegenerative and cardiovascular diseases. Since miRNAs are stably released in bodily fluids, they are of prime interest for the development of non-invasive diagnosis methods, such as liquid biopsies. Their detection is however challenging, as high levels of sensitivity, specificity and robustness are required. The analysis also needs to be quantitative, since the aim is to detect miRNA concentration changes. Moreover, a high multiplexing capability is also of crucial importance, since the clinical potential of miRNAs probably lays in our ability to perform parallel mapping of multiple miRNA concentrations and recognize typical disease signature from this profile. A plethora of biochemical innovative detection methods have been reported recently and some of them provide new solutions to the problem of sensitive multiplex detection. In this review, we propose to analyze in particular the new developments in multiplexed approaches to miRNA detection. The main aspects of these methods (including sensitivity and specificity) will be analyzed, with a particular focus on the demonstrated multiplexing capability and potential of each of these methods.

A road map from single-cell transcriptome to patient classification for the immune response to trauma

Immune dysfunction is an important factor driving mortality and adverse outcomes after trauma but remains poorly understood, especially at the cellular level. To deconvolute the trauma-induced immune response, we applied single-cell RNA sequencing to circulating and bone marrow mononuclear cells in injured mice and circulating mononuclear cells in trauma patients. In mice, the greatest changes in gene expression were seen in monocytes across both compartments. After systemic injury, the gene expression pattern of monocytes markedly deviated from steady state with corresponding changes in critical transcription factors, which can be traced back to myeloid progenitors. These changes were largely recapitulated in the human single-cell analysis. We generalized the major changes in human CD14⁺ monocytes into 6 signatures, which further defined 2 trauma patient subtypes (SG1 vs. SG2) identified in the whole-blood leukocyte transcriptome in the initial 12 hours after injury. Compared with SG2, SG1 patients exhibited delayed recovery, more severe organ dysfunction, and a higher incidence of infection and noninfectious complications. The 2 patient subtypes were also recapitulated in burn and sepsis patients, revealing a shared pattern of immune response across critical illness. Our data will be broadly useful to further explore the immune response to inflammatory diseases and critical illness.

SARS-CoV-2 morbidity and mortality in racial/ethnic minority populations: A window into the stress related inflammatory basis of health disparities?

Health disparity related to race/ethnicity has been cited as “the most serious and shameful health care issue of our time”(Peterson et al., 2018). A portion of the now recognized disproportionate impact of the COVID-19 pandemic among Black, Indigenous and People of Color (BIPOC) communities is attributable to social determinants such as socioeconomic status (SES), physical living situation, health care access, and the psychosocial factors associated with socioenvironmental circumstances such as bias, victimization, trauma and toxic stress as well as structural factors that reduce the capacity to practice physical distancing (Agurs-Collins et al., 2019).

In this paper, we hypothesize that, prior to the COVID-19 pandemic, disproportionate socio-economic and environmental stressors in the BIPOC population promoted heightened stress-associated neurobiological activity (Stress-NbA). This chronic elevation in Stress-NbA results in down-stream complications of chronic stress including underactivation of anti-viral type I IFN pathway genes. This results in an increase in susceptibility to viral diseases, including coronavirus illnesses. Additionally, Stress-NbA chronically potentiates systemic inflammation (from hematopoietic system activation with myelopoiesis) increasing the prevalence of metabolic syndrome (MetS) and setting the stage for stress-related chronic non-communicable diseases (NCDs).

This process was propelled by overactivation of immune cell gene expression in the nuclear factor κ-light-chain-enhancer of activated B cells (NF-kB) activation pathway and underactivation of gene expression in the anti-viral type I interferon (IFN) pathway. The higher prevalence of MetS and NCDs in minority populations turned out to be predictive of the elevated risk they would face in the presence of a highly contagious viral pandemic. The stress-related generation of a chronic non-pathogen associated molecular pattern (non-PAMP) immunoactivation state led to decreased viral immune defense and increased susceptibility to SARS-CoV-2 infection with increased risk of severe illness induced by cytokine storm syndrome (CSS).

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There is a disproportionate impact of the COVID-19 pandemic among Black, Indigenous and People of Color (BIPOC) communities.

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Prior to the COVID-19 pandemic and during the pandemic, existing disproportionate structural, socio-economic and environmental stressors in the BIPOC communities may have resulted in heightened stress-associated neurobiological activity (Stress-NbA).

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In this paper, we hypothesize that a combination of chronic elevation of stress-NbA, systemic inflammation, stress response and immune response related factors aligned against BIPIC communities are potential drivers of excess morbidity and mortality.

lncRNA-miRNA-mRNA interaction network for colorectal cancer; An in silico analysis

BACKGROUND

Colorectal cancer (CRC) is one of the most frequent and diagnosed diseases. Accumulating evidences showed that mRNAs and noncoding RNAs play important regulatory roles in tumorigenesis. Identification and determining the relationship between them can help diagnosis and treatment of cancer.

METHODS

Here we analyzed three microarray datasets; GSE110715, GSE32323 and GSE21510, to identify differentially expressed lncRNAs and mRNAs in CRC. The adjusted p-value ≤0.05 was considered statistically significant. Gene set enrichment analysis was carried out using DAVID tool. The miRCancer database was searched to obtain differentially expressed miRNAs in colorectal cancer, and the miRDB database was used to attain the targets of the obtained miRNAs. To predict the lncRNA-miRNA interactions we used DIANA-LncBase v2 and RegRNA 2.0. Finally the lncRNA-miRNA-mRNA-signaling pathway network was constructed using Cytoscape v3.1.

RESULTS

By analyzing the three datasets, a total of 21 mRNAs (15 up- and 6 down-regulated) and 24 lncRNAs (18 up- and 6 down-regulated) were identified as common differentially expressed genes between CRC tumor and marginal tissues. Nevertheless, the constructed lncRNA-miRNA-mRNA-signaling pathway network revealed a convergence on 6 lncRNAs (3 up- and 3 downregulated), 7 mRNAs (2 up- and 5 downregulated) and 6 miRNAs (3 up- and 3 downregulated). We found that dysregulation of lncRNAs such as PCBP1-AS1, UCA1 and SNHG16 could sequester several miRNAs such as hsa-miR-582-5p and hsa-miR-198 and promote the proliferation, invasion and drug resistance of colorectal cancer cells.

CONCLUSIONS

We introduced a set of lncRNAs, mRNAs and miRNAs differentially expressed in CRC which might be considered for further experimental research as potential biomarkers of CRC development.

Changes in H3K27ac at Gene Regulatory Regions in Porcine Alveolar Macrophages Following LPS or PolyIC Exposure

Changes in chromatin structure, especially in histone modifications (HMs), linked with chromatin accessibility for transcription machinery, are considered to play significant roles in transcriptional regulation. Alveolar macrophages (AM) are important immune cells for protection against pulmonary pathogens, and must readily respond to bacteria and viruses that enter the airways. Mechanism(s) controlling AM innate response to different pathogen-associated molecular patterns (PAMPs) are not well defined in pigs. By combining RNA sequencing (RNA-seq) with chromatin immunoprecipitation and sequencing (ChIP-seq) for four histone marks (H3K4me3, H3K4me1, H3K27ac and H3K27me3), we established a chromatin state map for AM stimulated with two different PAMPs, lipopolysaccharide (LPS) and Poly(I:C), and investigated the potential effect of identified histone modifications on transcription factor binding motif (TFBM) prediction and RNA abundance changes in these AM. The integrative analysis suggests that the differential gene expression between non-stimulated and stimulated AM is significantly associated with changes in the H3K27ac level at active regulatory regions. Although global changes in chromatin states were minor after stimulation, we detected chromatin state changes for differentially expressed genes involved in the TLR4, TLR3 and RIG-I signaling pathways. We found that regions marked by H3K27ac genome-wide were enriched for TFBMs of TF that are involved in the inflammatory response. We further documented that TF whose expression was induced by these stimuli had TFBMs enriched within H3K27ac-marked regions whose chromatin state changed by these same stimuli. Given that the dramatic transcriptomic changes and minor chromatin state changes occurred in response to both stimuli, we conclude that regulatory elements (i.e. active promoters) that contain transcription factor binding motifs were already active/poised in AM for immediate inflammatory response to PAMPs. In summary, our data provides the first chromatin state map of porcine AM in response to bacterial and viral PAMPs, contributing to the Functional Annotation of Animal Genomes (FAANG) project, and demonstrates the role of HMs, especially H3K27ac, in regulating transcription in AM in response to LPS and Poly(I:C).

Coupled scRNA-Seq and Intracellular Protein Activity Reveal an Immunosuppressive Role of TREM2 in Cancer

Cell function and activity are regulated through integration of signaling, epigenetic, transcriptional, and metabolic pathways. Here, we introduce INs-seq, an integrated technology for massively parallel recording of single-cell RNA sequencing (scRNA-seq) and intracellular protein activity. We demonstrate the broad utility of INs-seq for discovering new immune subsets by profiling different intracellular signatures of immune signaling, transcription factor combinations, and metabolic activity. Comprehensive mapping of Arginase 1-expressing cells within tumor models, a metabolic immune signature of suppressive activity, discovers novel Arg1⁺ Trem2⁺ regulatory myeloid (Mreg) cells and identifies markers, metabolic activity, and pathways associated with these cells. Genetic ablation of Trem2 in mice inhibits accumulation of intra-tumoral Mreg cells, leading to a marked decrease in dysfunctional CD8⁺ T cells and reduced tumor growth. This study establishes INs-seq as a broadly applicable technology for elucidating integrated transcriptional and intra-cellular maps and identifies the molecular signature of myeloid suppressive cells in tumors.

Genome-Wide Characterization of DNase I-Hypersensitive Sites and Cold Response Regulatory Landscapes in Grasses

Deep sequencing of DNase-I treated chromatin (DNase-seq) can be used to identify DNase I-hypersensitive sites (DHSs) and facilitates genome-scale mining of de novo cis-regulatory DNA elements. Here, we adapted DNase-seq to generate genome-wide maps of DHSs using control and cold-treated leaf, stem, and root tissues of three widely studied grass species: Brachypodium distachyon, foxtail millet (Setaria italica), and sorghum (Sorghum bicolor). Functional validation demonstrated that 12 of 15 DHSs drove reporter gene expression in transiently transgenic B. distachyon protoplasts. DHSs under both normal and cold treatment substantially differed among tissues and species. Intriguingly, the putative DHS-derived transcription factors (TFs) are largely colocated among tissues and species and include 17 ubiquitous motifs covering all grass taxa and all tissues examined in this study. This feature allowed us to reconstruct a regulatory network that responds to cold stress. Ethylene-responsive TFs SHINE3, ERF2, and ERF9 occurred frequently in cold feedback loops in the tissues examined, pointing to their possible roles in the regulatory network. Overall, we provide experimental annotation of 322,713 DHSs and 93 derived cold-response TF binding motifs in multiple grasses, which could serve as a valuable resource for elucidating the transcriptional networks that function in the cold-stress response and other physiological processes.