Compendium of Immune Signatures Identifies Conserved and Species-Specific Biology in Response to Inflammation

Two distinct subpopulations of human stem-like memory T cells exhibit complementary roles in self-renewal and clonal longevity

T stem cell-like memory cells (TSCM cells) are considered to be essential for the maintenance of immune memory. The TSCM population has been shown to have the key properties of a stem cell population: multipotency, self-renewal and clonal longevity. Here we show that no single population has all these stem cell properties, instead the properties are distributed. We show that the human TSCM population consists of two distinct cell subpopulations which can be distinguished by the level of their CD95 expression (CD95int and CD95hi). Crucially, using long-term in vivo labelling of human volunteers, we establish that these are distinct populations rather than transient states of the same population. These two subpopulations have different functional profiles ex vivo, different transcriptional patterns, and different tissue distributions. They also have significantly different TREC content indicating different division histories and we find that the frequency of CD95hi TSCM increases with age. Most importantly, CD95hi and CD95int TSCM cells also have very different dynamics in vivo with CD95hi cells showing considerably higher proliferation but significantly reduced clonal longevity compared with CD95int TSCM. While both TSCM subpopulations exhibit considerable multipotency, no single population of TSCM cells has both the properties of self-renewal and clonal longevity. Instead, the "stemness" of the TSCM population is generated by the complementary dynamic properties of the two subpopulations: CD95int TSCM which have the property of clonal longevity and CD95hi TSCM which have the properties of expansion and self-renewal. We suggest that together, these two populations function as a stem cell population.

Systematic review and mega-analysis of the peripheral blood transcriptome in depression implicates dysregulation of lymphoid cells and histones

Background

Depression has been associated with transcriptomic changes in peripheral blood. However, the contribution of specific immune cell subsets or pathways remains unclear, and findings have been variable across previous studies, which have not tended to account for sample cellular composition.

Methods

We performed a systematic review of peripheral blood transcriptome studies in depression. For the five datasets meeting criteria (total N=6,011), we performed harmonized reprocessing and cell-composition-adjusted differential gene and transcript analyses, followed by a bias- and inflation-adjusted weighted Z-score mega-analysis. We investigated the biological pathways and cell subsets implicated by the results. We also performed a sex-stratified gene network mega-analysis using consensus weighted gene co-expression network analysis (WGCNA).

Results

Few genes showed robust differential gene expression (DGE) in depression. Depression was reproducibly associated with decreases in replication-dependent histones, and with a decrease in oxidative phosphorylation pathways in females only. Cell source analyses implicated lymphoid cells (T cells and NK cells) as likely contributors to the depression differential expression signature. WGCNA mega-analysis revealed multiple consensus modules associated with depression, with a PUF60-related module upregulated in both female and male depression in sex-stratified analyses. Two genes predicted to be causally relevant to depression by transcriptome-wide association studies (GPX4 and GYPE) showed significant DGE.

Conclusions

These results are convergent with immunogenetic evidence implicating lymphoid cell dysregulation in depression, while also highlighting histone alterations as a key molecular signature in depression. They also indicate the importance of large-scale datasets for biomarker discovery in the context of heterogeneous disorders like depression.

Comprehensive analysis of immune subtype characterization on identification of potential cells and drugs to predict response to immune checkpoint inhibitors for hepatocellular carcinoma

Immunosubtyping enables the segregation of immune responders from non-responders. However, numerous studies failed to focus on the integration of cellular heterogeneity and immunophenotyping in the prediction of hepatocellular carcinoma (HCC) patients' response to immune checkpoint inhibitors (ICIs). We categorized HCC patients into various immune subtypes based on feature scores linked to ICI response. Single-cell sequencing technology was to investigate the cellular heterogeneity of different immune subtypes and acquire significant ICI response-associated cells. Candidate drugs were identified using a blend of various drug databases and network approaches. HCC patients were divided into two distinct immune subtypes based on characterization scores of 151 immune-related gene sets. Patients in both subtypes showed varying overall survival, immunity levels, biological activities, and TP53 mutation rates. Subtype 1-related natural killer cells showed a positive correlation with immune-promoting scores but a negative correlation with immune-suppressing scores. Notably, docetaxel sensitivity in HCC patients rose as the levels of subtype 1-related natural killer cells increased. Our study demonstrated that immune subtypes have cellular heterogeneity in predicting response to ICIs. A combination of subtype 1-associated natural killer cells and docetaxel may offer new hope for ICI treatment in HCC.

Integrative In-Silico Analysis of Retroperitoneal Tumors in Colorectal Surgery: Advancements and Implications

Retroperitoneal tumors pose significant challenges in colorectal surgery due to their complex anatomical location, aggressive behavior, and heterogeneous nature. Traditional diagnostic and treatment methods often fall short in effectively managing these tumors. This study leverages advanced in-silico methodologies to perform a comprehensive analysis of retroperitoneal tumors associated with colorectal conditions. By integrating computational modeling and cutting-edge bioinformatics tools, we aim to enhance the understanding of tumor biology, improve diagnostic precision, and optimize surgical outcomes. Our integrative approach combines transcriptomic, and proteomic data from publicly available databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Transcriptomic analysis reveals differentially expressed genes (DEGs) that serve as potential biomarkers for early diagnosis and prognosis. Proteomic analysis highlights critical protein interaction networks and pathways involved in tumorigenesis and metastasis. Our integrative approach identifies key DEGs and constructs protein-protein interaction (PPI) networks to pinpoint critical regulatory genes, such as VWF, PF4, ITGA2B, CXCL8, and GP9, that may serve as potential biomarkers or therapeutic targets. Functional enrichment analysis reveals significant pathways involved in tumorigenesis, including cell proliferation, immune response, and DNA repair. Additionally, immune cell infiltration analysis using the CIBERSORT algorithm demonstrates an immunosuppressive tumor microenvironment characterized by increased regulatory T cells (Tregs) and M2 macrophages, which could contribute to tumor immune evasion.Future studies should focus on clinical validation of these findings and the expansion of computational models to include diverse patient populations. Through these efforts, we aim to revolutionize the management of retroperitoneal tumors in colorectal surgery, ultimately improving patient care and survival rates.

Maintenance DNA methylation is required for induced regulatory T cell reparative function following viral pneumonia

FOXP3+ natural regulatory T cells (nTregs) promote resolution of inflammation and repair of epithelial damage following viral pneumonia-induced lung injury, thus representing a cellular therapy for patients with acute respiratory distress syndrome (ARDS). Whether in vitro induced Tregs (iTregs), which can be rapidly generated in substantial numbers from conventional T cells, also promote lung recovery is unknown. nTregs require specific DNA methylation patterns maintained by the epigenetic regulator, ubiquitin-like with PHD and RING finger domains 1 (UHRF1). Here, we tested whether iTregs promote recovery following viral pneumonia and whether iTregs require UHRF1 for their pro-recovery function. We found that adoptive transfer of iTregs to mice with influenza virus pneumonia promotes lung recovery and that loss of UHRF1-mediated maintenance DNA methylation in iTregs leads to reduced engraftment and a delayed repair response. Transcriptional and DNA methylation profiling of adoptively transferred UHRF1-deficient iTregs that had trafficked to influenza-injured lungs demonstrated transcriptional instability with gain of effector T cell lineage-defining transcription factors. Strategies to promote the stability of iTregs could be leveraged to further augment their pro-recovery function during viral pneumonia and other causes of ARDS.

Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection

Numerous studies have identified similarities in blood transcriptomic signatures of tuberculosis (TB) phenotypes between mice and humans, including type 1 interferon production and innate immune cell activation. However, murine infection pathophysiology is distinct from human disease. We hypothesized that this is partly due to differences in the relative importance of biological pathways across species. To address this animal-to-human gap, we applied a systems modeling framework, Translatable Components Regression, to identify the axes of variation in the preclinical data most relevant to human TB disease state. Among the pathways our cross-species model pinpointed as highly predictive of human TB phenotype was the infection-induced unfolded protein response. To validate this mechanism, we confirmed that this cellular stress pathway modulates immune functions in Mycobacterium tuberculosis-infected mouse macrophages. Our work demonstrates how systems-level computational models enhance the value of animal studies for elucidating complex human pathophysiology.

E2F1-induced autocrine IL-6 inflammatory loop mediates cancer-immune crosstalk that predicts T cell phenotype switching and therapeutic responsiveness

Melanoma is a metastatic, drug-refractory cancer with the ability to evade immunosurveillance. Cancer immune evasion involves interaction between tumor intrinsic properties and the microenvironment. The transcription factor E2F1 is a key driver of tumor evolution and metastasis. To explore E2F1's role in immune regulation in presence of aggressive melanoma cells, we established a coculture system and utilized transcriptome and cytokine arrays combined with bioinformatics and structural modeling. We identified an E2F1-dependent gene regulatory network with IL6 as a central hub. E2F1-induced IL-6 secretion unleashes an autocrine inflammatory feedback loop driving invasiveness and epithelial-to-mesenchymal transition. IL-6-activated STAT3 physically interacts with E2F1 and cooperatively enhances IL-6 expression by binding to an E2F1-STAT3-responsive promoter element. The E2F1-STAT3/IL-6 axis strongly modulates the immune niche and generates a crosstalk with CD4+ cells resulting in transcriptional changes of immunoregulatory genes in melanoma and immune cells that is indicative of an inflammatory and immunosuppressive environment. Clinical data from TCGA demonstrated that elevated E2F1, STAT3, and IL-6 correlate with infiltration of Th2, while simultaneously blocking Th1 in primary and metastatic melanomas. Strikingly, E2F1 depletion reduces the secretion of typical type-2 cytokines thereby launching a Th2-to-Th1 phenotype shift towards an antitumor immune response. The impact of activated E2F1-STAT3/IL-6 axis on melanoma-immune cell communication and its prognostic/therapeutic value was validated by mathematical modeling. This study addresses important molecular aspects of the tumor-associated microenvironment in modulating immune responses, and will contribute significantly to the improvement of future cancer therapies.

Stratified analysis identifies HIF-2α as a therapeutic target for highly immune-infiltrated melanomas

While immune-checkpoint blockade (ICB) has revolutionized treatment of metastatic melanoma over the last decade, the identification of broadly applicable robust biomarkers has been challenging, driven in large part by the heterogeneity of ICB regimens and patient and tumor characteristics. To disentangle these features, we performed a standardized meta-analysis of eight cohorts of patients treated with anti-PD-1 (n=290), anti-CTLA-4 (n=175), and combination anti-PD-1/anti-CTLA-4 (n=51) with RNA sequencing of pre-treatment tumor and clinical annotations. Stratifying by immune-high vs -low tumors, we found that surprisingly, high immune infiltrate was a biomarker for response to combination ICB, but not anti-PD-1 alone. Additionally, hypoxia-related signatures were associated with non-response to anti-PD-1, but only amongst immune infiltrate-high melanomas. In a cohort of scRNA-seq of patients with metastatic melanoma, hypoxia also correlated with immunosuppression and changes in tumor-stromal communication in the tumor microenvironment (TME). Clinically actionable targets of hypoxia signaling were also uniquely expressed across different cell types. We focused on one such target, HIF-2α, which was specifically upregulated in endothelial cells and fibroblasts but not in immune cells or tumor cells. HIF-2α inhibition, in combination with anti-PD-1, enhanced tumor growth control in pre-clinical models, but only in a more immune-infiltrated melanoma model. Our work demonstrates how careful stratification by clinical and molecular characteristics can be leveraged to derive meaningful biological insights and lead to the rational discovery of novel clinical targets for combination therapy.

Bioinformatics analysis-based mining of potential markers for inflammatory bowel disease and their immune relevance

Background

The incidence of inflammatory bowel disease (IBD) is increasing every year and is characterized by a prolonged course, frequent relapses, difficulty in curing, and a lack of more efficacious therapeutic biomarkers. The aim of this study was to find key core genes as therapeutic biomarkers for IBD.

Methods

GSE75214 in Gene Expression Omnibus (GEO) was used as the experimental set. The genes in the top 25% of standard deviation of all samples in the experimental set were subjected to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, least absolute shrinkage and selection operator (LASSO) logistic regression was used to further screen the central genes. Finally, the validity of hub genes was verified on GEO dataset GSE179285 using "BiocManager" R package.

Results

Twelve well-preserved modules were identified in the experimental set using the WGCNA method. Among them, five modules significantly associated with IBD were screened as clinically significant modules, and four candidate genes were screened from these five modules. Then TIMP1, GUCA2B, and HIF1A were screened as hub genes. These hub genes successfully distinguished tumor samples from healthy tissues by artificial neural network algorithm in an independent test set with an area under the working characteristic curve of 0.946 for the subjects.

Conclusions

IBD differentially expressed gene (DEGs) are involved in immunoregulatory processes. TIMP1, GUCA2B, and HIF1A, as core genes of IBD, have the potential to be therapeutic targets for patients with IBD, and our findings may provide a new outlook on the future treatment of IBD.

Denoiseit: denoising gene expression data using rank based isolation trees

BACKGROUND

Selecting informative genes or eliminating uninformative ones before any downstream gene expression analysis is a standard task with great impact on the results. A carefully curated gene set significantly enhances the likelihood of identifying meaningful biomarkers.

METHOD

In contrast to the conventional forward gene search methods that focus on selecting highly informative genes, we propose a backward search method, DenoiseIt, that aims to remove potential outlier genes yielding a robust gene set with reduced noise. The gene set constructed by DenoiseIt is expected to capture biologically significant genes while pruning irrelevant ones to the greatest extent possible. Therefore, it also enhances the quality of downstream comparative gene expression analysis. DenoiseIt utilizes non-negative matrix factorization in conjunction with isolation forests to identify outlier rank features and remove their associated genes.

RESULTS

DenoiseIt was applied to both bulk and single-cell RNA-seq data collected from TCGA and a COVID-19 cohort to show that it proficiently identified and removed genes exhibiting expression anomalies confined to specific samples rather than a known group. DenoiseIt also showed to reduce the level of technical noise while preserving a higher proportion of biologically relevant genes compared to existing methods. The DenoiseIt Software is publicly available on GitHub at https://github.com/cobi-git/DenoiseIt.

Thymic stromal lymphopoietin induces IL-4/IL-13 from T cells to promote sebum secretion and adipose loss

BACKGROUND

The cytokine TSLP promotes type 2 immune responses and can induce adipose loss by stimulating lipid loss from the skin through sebum secretion by sebaceous glands, which enhances the skin barrier. However, the mechanism by which TSLP upregulates sebaceous gland function is unknown.

OBJECTIVES

This study investigated the mechanism by which TSLP stimulates sebum secretion and adipose loss.

METHODS

RNA-sequencing analysis was performed on sebaceous glands isolated by laser capture microdissection and single-cell RNA-sequencing analysis was performed on sorted skin T cells. Sebocyte function was analyzed by histological analysis and sebum secretion in vivo and by measuring lipogenesis and proliferation in vitro.

RESULTS

This study found that TSLP sequentially stimulated the expression of lipogenesis genes followed by cell death genes in sebaceous glands to induce holocrine secretion of sebum. TSLP did not affect sebaceous gland activity directly. Rather, single-cell RNA-sequencing revealed that TSLP recruited distinct T-cell clusters that produce IL-4 and IL-13, which were necessary for TSLP-induced adipose loss and sebum secretion. Moreover, IL-13 was sufficient to cause sebum secretion and adipose loss in vivo and to induce lipogenesis and proliferation of a human sebocyte cell line in vitro.

CONCLUSIONS

This study proposes that TSLP stimulates T cells to deliver IL-4 and IL-13 to sebaceous glands, which enhances sebaceous gland function, turnover, and subsequent adipose loss.

Identifying cell type-specific transcription factor-mediated activity immune modules reveal implications for immunotherapy and molecular classification of pan-cancer

Systematic investigation of tumor-infiltrating immune (TII) cells is important to the development of immunotherapies, and the clinical response prediction in cancers. There exists complex transcriptional regulation within TII cells, and different immune cell types display specific regulation patterns. To dissect transcriptional regulation in TII cells, we first integrated the gene expression profiles from single-cell datasets, and proposed a computational pipeline to identify TII cell type-specific transcription factor (TF) mediated activity immune modules (TF-AIMs). Our analysis revealed key TFs, such as BACH2 and NFKB1 play important roles in B and NK cells, respectively. We also found some of these TF-AIMs may contribute to tumor pathogenesis. Based on TII cell type-specific TF-AIMs, we identified eight CD8+ T cell subtypes. In particular, we found the PD1 + CD8+ T cell subset and its specific TF-AIMs associated with immunotherapy response. Furthermore, the TII cell type-specific TF-AIMs displayed the potential to be used as predictive markers for immunotherapy response of cancer patients. At the pan-cancer level, we also identified and characterized six molecular subtypes across 9680 samples based on the activation status of TII cell type-specific TF-AIMs. Finally, we constructed a user-friendly web interface CellTF-AIMs (http://bio-bigdata.hrbmu.edu.cn/CellTF-AIMs/) for exploring transcriptional regulatory pattern in various TII cell types. Our study provides valuable implications and a rich resource for understanding the mechanisms involved in cancer microenvironment and immunotherapy.

A multiomic atlas of the aging hippocampus reveals molecular changes in response to environmental enrichment

Aging involves the deterioration of organismal function, leading to the emergence of multiple pathologies. Environmental stimuli, including lifestyle, can influence the trajectory of this process and may be used as tools in the pursuit of healthy aging. To evaluate the role of epigenetic mechanisms in this context, we have generated bulk tissue and single cell multi-omic maps of the male mouse dorsal hippocampus in young and old animals exposed to environmental stimulation in the form of enriched environments. We present a molecular atlas of the aging process, highlighting two distinct axes, related to inflammation and to the dysregulation of mRNA metabolism, at the functional RNA and protein level. Additionally, we report the alteration of heterochromatin domains, including the loss of bivalent chromatin and the uncovering of a heterochromatin-switch phenomenon whereby constitutive heterochromatin loss is partially mitigated through gains in facultative heterochromatin. Notably, we observed the multi-omic reversal of a great number of aging-associated alterations in the context of environmental enrichment, which was particularly linked to glial and oligodendrocyte pathways. In conclusion, our work describes the epigenomic landscape of environmental stimulation in the context of aging and reveals how lifestyle intervention can lead to the multi-layered reversal of aging-associated decline.

Single-cell analysis of CD4+ tissue residency memory cells (TRMs) in adult atopic dermatitis: A new potential mechanism

The pathophysiology of atopic dermatitis (AD) is complex. CD4+ T cells play an essential role in the development of lesions in AD. However, the underlying mechanism remains unclear. In the present study, we investigated the differentially expressed genes (DEGs) between adult AD lesioned and non-lesioned skin using two datasets from the Gene Expression Omnibus (GEO) database. 62 DEGs were shown to be related to cytokine response. Compared to non-lesioned skin, lesioned skin showed immune infiltration with increased numbers of activated natural killer (NK) cells and CD4+ T memory cells (p < 0.01). We then identified 13 hub genes with a strong association with CD4+ T cells using weighted correlation network analysis. Single-cell analysis of AD detected a novel CD4+ T subcluster, CD4+ tissue residency memory cells (TRMs), which were verified through immunohistochemistry (IHC) to be increased in the dermal area of AD. The significant relationship between CD4+ TRM and AD was assessed through further analyses. FOXO1 and SBNO2, two of the 13 hub genes, were characteristically expressed in the CD4+ TRM, but down-regulated in IFN-γ/TNF-α-induced HaCaT cells, as shown using quantitative polymerase chain reaction (qPCR). Moreover, SBNO2 expression was associated with increased Th1 infiltration in AD (p < 0.05). In addition, genes filtered using Mendelian randomization were positively correlated with CD4+ TRM and were highly expressed in IFN-γ/TNF-α-induced HaCaT cells, as determined using qPCR and western blotting. Collectively, our results revealed that the newly identified CD4+ TRM may be involved in the pathogenesis of adult AD.

Comparative proteomic profiling of the ovine and human PBMC inflammatory response

Understanding the cellular and molecular mechanisms of inflammation requires robust animal models. Sheep are commonly used in immune-related studies, yet the validity of sheep as animal models for immune and inflammatory diseases remains to be established. This cross-species comparative study analyzed the in vitro inflammatory response of ovine (oPBMCs) and human PBMCs (hPBMCs) using mass spectrometry, profiling the proteome of the secretome and whole cell lysate. Of the entire cell lysate proteome (oPBMCs: 4217, hPBMCs: 4574 proteins) 47.8% and in the secretome proteome (oPBMCs: 1913, hPBMCs: 1375 proteins) 32.8% were orthologous between species, among them 32 orthologous CD antigens, indicating the presence of six immune cell subsets. Following inflammatory stimulation, 71 proteins in oPBMCs and 176 in hPBMCs showed differential abundance, with only 7 overlapping. Network and Gene Ontology analyses identified 16 shared inflammatory-related terms and 17 canonical pathways with similar activation/inhibition patterns in both species, demonstrating significant conservation in specific immune and inflammatory responses. However, ovine PMBCs also contained a unique WC1+γδ T-cell subset, not detected in hPBMCs. Furthermore, differences in the activation/inhibition trends of seven canonical pathways and the sets of DAPs between sheep and humans, emphasize the need to consider interspecies differences in translational studies and inflammation research.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Nano(bio)Materials Do Not Affect Macrophage Phenotype-A Study Conducted by the REFINE Project

Macrophages are well known for their involvement in the biocompatibility, as well as biodistribution, of nano(bio)materials. Although there are a number of rodent cell lines, they may not fully recapitulate primary cell responses, particularly those of human cells. Isolation of tissue-resident macrophages from humans is difficult and may result in insufficient cells with which to determine the possible interaction with nano(bio)materials. Isolation of primary human monocytes and differentiation to monocyte-derived macrophages may provide a useful tool with which to further study these interactions. To that end, we developed a standard operating procedure for this differentiation, as part of the Regulatory Science Framework for Nano(bio)material-based Medical Products and Devices (REFINE) project, and used it to measure the secretion of bioactive molecules from M1 and M2 differentiated monocytes in response to model nano(bio)materials, following an initial assessment of pyrogenic contamination, which may confound potential observations. The SOP was deployed in two partner institutions with broadly similar results. The work presented here shows the utility of this assay but highlights the relevance of donor variability in responses to nano(bio)materials. Whilst donor variability can provide some logistical challenges to the application of such assays, this variability is much closer to the heterogeneous cells that are present in vivo, compared to homogeneous non-human cell lines.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Diving into drug-screening: zebrafish embryos as an in vivo platform for antimicrobial drug discovery and assessment

The rise of multidrug-resistant bacteria underlines the need for innovative treatments, yet the introduction of new drugs has stagnated despite numerous antimicrobial discoveries. A major hurdle is a poor correlation between promising in vitro data and in vivo efficacy in animal models, which is essential for clinical development. Early in vivo testing is hindered by the expense and complexity of existing animal models. Therefore, there is a pressing need for cost-effective, rapid preclinical models with high translational value. To overcome these challenges, zebrafish embryos have emerged as an attractive model for infectious disease studies, offering advantages such as ethical alignment, rapid development, ease of maintenance, and genetic manipulability. The zebrafish embryo infection model, involving microinjection or immersion of pathogens and potential antibiotic hit compounds, provides a promising solution for early-stage drug screening. It offers a cost-effective and rapid means of assessing the efficacy, toxicity and mechanism of action of compounds in a whole-organism context. This review discusses the experimental design of this model, but also its benefits and challenges. Additionally, it highlights recently identified compounds in the zebrafish embryo infection model and discusses the relevance of the model in predicting the compound's clinical potential.

GS-TCGA: Gene Set-Based Analysis of The Cancer Genome Atlas

Most tools for analyzing large gene expression datasets, including The Cancer Genome Atlas (TCGA), have focused on analyzing the expression of individual genes or inference of the abundance of specific cell types from whole transcriptome information. While these methods provide useful insights, they can overlook crucial process-based information that may enhance our understanding of cancer biology. In this study, we describe three novel tools incorporated into an online resource; gene set-based analysis of The Cancer Genome Atlas (GS-TCGA). GS-TCGA is designed to enable user-friendly exploration of TCGA data using gene set-based analysis, leveraging gene sets from the Molecular Signatures Database. GS-TCGA includes three unique tools: GS-Surv determines the association between the expression of gene sets and survival in human cancers. Co-correlative gene set enrichment analysis (CC-GSEA) utilizes interpatient heterogeneity in cancer gene expression to infer functions of specific genes based on GSEA of coregulated genes in TCGA. GS-Corr utilizes interpatient heterogeneity in cancer gene expression profiles to identify genes coregulated with the expression of specific gene sets in TCGA. Users are also able to upload custom gene sets for analysis with each tool. These tools empower researchers to perform survival analysis linked to gene set expression, explore the functional implications of gene coexpression, and identify potential gene regulatory mechanisms.

Breast Cancer Immunity: It is TIME for the Next Chapter

Our ability to interrogate the tumor immune microenvironment (TIME) at an ever-increasing granularity has uncovered critical determinants of disease progression. Not only do we now have a better understanding of the immune response in breast cancer, but it is becoming possible to leverage key mechanisms to effectively combat this disease. Almost every component of the immune system plays a role in enabling or inhibiting breast tumor growth. Building on early seminal work showing the involvement of T cells and macrophages in controlling breast cancer progression and metastasis, single-cell genomics and spatial proteomics approaches have recently expanded our view of the TIME. In this article, we provide a detailed description of the immune response against breast cancer and examine its heterogeneity in disease subtypes. We discuss preclinical models that enable dissecting the mechanisms responsible for tumor clearance or immune evasion and draw parallels and distinctions between human disease and murine counterparts. Last, as the cancer immunology field is moving toward the analysis of the TIME at the cellular and spatial levels, we highlight key studies that revealed previously unappreciated complexity in breast cancer using these technologies. Taken together, this article summarizes what is known in breast cancer immunology through the lens of translational research and identifies future directions to improve clinical outcomes.

Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics

Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels. Spatial monoomics technology has evolved from monoomics to spatial multiomics. Moreover, the spatial resolution, high-throughput detection capability, capture efficiency, and compatibility with various sample types of omics technology have considerably advanced. Despite the technological advances in this field, data analysis frameworks have stagnated. Current challenges include incomplete spatial monoomics data analysis pipeline, overly complex data analysis tasks, and few established spatial multiomics data analysis strategies. In this review, we systematically summarize recent developments of various spatial monoomics techniques and improvements in related data analysis pipeline. On the basis of the spatial multiomics technology, we propose a data integration strategy with cross-platform, cross-slice, and cross-modality. We summarize the potential applications of spatial monoomics technology, aiming to provide researchers and clinicians with a better understanding of how such applications have advanced. Spatial multiomics technology is expected to substantially impact biology and precision medicine through measurements of cellular tissue structures and the extraction of biomolecular features.

Identification of key biomarkers of the glomerulus in focal segmental glomerulosclerosis and their relationship with immune cell infiltration based on WGCNA and the LASSO algorithm

OBJECTIVE

The aim of our study was to identify key biomarkers of glomeruli in focal glomerulosclerosis (FSGS) and analyze their relationship with the infiltration of immune cells.

METHODS

The expression profiles (GSE108109 and GSE200828) were obtained from the GEO database. The differentially expressed genes (DEGs) were filtered and analyzed by gene set enrichment analysis (GSEA). MCODE module was constructed. Weighted gene coexpression network analysis (WGCNA) was performed to obtain the core gene modules. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes. ROC curves were employed to explore their diagnostic accuracy. Transcription factor prediction of the key biomarkers was performed using the Cytoscape plugin IRegulon. The analysis of the infiltration of 28 immune cells and their correlation with the key biomarkers were performed.

RESULTS

A total of 1474 DEGs were identified. Their functions were mostly related to immune-related diseases and signaling pathways. MCODE identified five modules. The turquoise module of WGCNA had significant relevance to the glomerulus in FSGS. TGFB1 and NOTCH1 were identified as potential key glomerular biomarkers in FSGS. Eighteen transcription factors were obtained from the two hub genes. Immune infiltration showed significant correlations with T cells. The results of immune cell infiltration and their relationship with key biomarkers implied that NOTCH1 and TGFB1 were enhanced in immune-related pathways.

CONCLUSION

TGFB1 and NOTCH1 may be strongly correlated with the pathogenesis of the glomerulus in FSGS and are new candidate key biomarkers. T-cell infiltration plays an essential role in the FSGS lesion process.

Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival

The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. To investigate global interactions in MM, we combined a known protein interaction network with a large clinically annotated MM dataset. We hypothesized that an unbiased network analysis method based on large-scale similarities in gene expression, copy number aberration, and protein interactions may provide novel biological insights. Applying a novel measure of network robustness, Ollivier-Ricci Curvature, we examined patterns in the RNA-Seq gene expression and CNA data and how they relate to clinical outcomes. Hierarchical clustering using ORC differentiated high-risk subtypes with low progression free survival. Differential gene expression analysis defined 118 genes with significantly aberrant expression. These genes, while not previously associated with MM, were associated with DNA repair, apoptosis, and the immune system. Univariate analysis identified 8/118 to be prognostic genes; all associated with the immune system. A network topology analysis identified both hub and bridge genes which connect known genes of biological significance of MM. Taken together, gene interaction network analysis in MM uses a novel method of global assessment to demonstrate complex immune dysregulation associated with shorter survival.

Exploration of the shared genetic biomarkers in Alzheimer's disease and chronic kidney disease using integrated bioinformatics analysis

In order to investigate the potential link between Alzheimer's disease (AD) and chronic kidney disease (CKD), we conducted a comprehensive analysis using a bioinformatics approach. We downloaded AD and CKD datasets from the Gene Expression Omnibus database and analyzed differentially expressed genes and weighted gene co-expression networks to identify candidate genes for AD and CKD. We used a combination of the least absolute shrinkage and selection operator and random forest algorithms to select the shared genes. Subsequently, we shared genes and performed an immune infiltration analysis to investigate the association between different immune cell types and shared genes. Finally, we elucidated the relationship between the expression levels of the shared genes in disease samples and cells using single-cell analysis. Our analysis identified 150 candidate genes that may be primarily involved in immune inflammatory responses and energy metabolism pathways. We found that JunD Proto-Oncogene, ALF transcription elongation factor 1, and ZFP36 Ring Finger Protein Like 1 were the best co-diagnostic markers for AD and CKD based on the results of Least Absolute Shrinkage Selection Operator analysis and the random forest algorithm. Based on the results of immune infiltration analysis, macrophages and T-cells play a significant role in the progression of AD and CKD. Our scRNA-sequencing data showed that the 3 shared genes in AD were significantly expressed in astrocytes, excitatory neurons, oligodendrocytes, and MAIT cells. The 3 shared genes in CKD were significantly expressed in oligodendrocytes, neutrophils, fibroblasts, astrocytes, and T-cells. JunD Proto-Oncogene, ALF transcription elongation factor 1, and ZFP36 Ring Finger Protein Like 1 genes are the best diagnostic markers for AD and CKD.

Allometrically scaling tissue forces drive pathological foreign-body responses to implants via Rac2-activated myeloid cells

Small animals do not replicate the severity of the human foreign-body response (FBR) to implants. Here we show that the FBR can be driven by forces generated at the implant surface that, owing to allometric scaling, increase exponentially with body size. We found that the human FBR is mediated by immune-cell-specific RAC2 mechanotransduction signalling, independently of the chemistry and mechanical properties of the implant, and that a pathological FBR that is human-like at the molecular, cellular and tissue levels can be induced in mice via the application of human-tissue-scale forces through a vibrating silicone implant. FBRs to such elevated extrinsic forces in the mice were also mediated by the activation of Rac2 signalling in a subpopulation of mechanoresponsive myeloid cells, which could be substantially reduced via the pharmacological or genetic inhibition of Rac2. Our findings provide an explanation for the stark differences in FBRs observed in small animals and humans, and have implications for the design and safety of implantable devices.

Delving into gene-set multiplex networks facilitated by a k-nearest neighbor-based measure of similarity

Gene sets are functional units for living cells. Previously, limited studies investigated the complex relations among gene sets, but documents about their altering patterns across biological conditions still need to be prepared. In this study, we adopted and modified a classical k-nearest neighbor-based association function to detect inter-gene-set similarities. Based on this method, we built multiplex networks of gene sets for the first time; these networks contain layers of gene sets corresponding to different populations of cells. The context-based multiplex networks can capture meaningful biological variation and have considerable differences from knowledge-based networks of gene sets built on Jaccard similarity, as demonstrated in this study. Furthermore, at the scale of individual gene sets, the structural coefficients of gene sets (multiplex PageRank centrality, clustering coefficient, and participation coefficient) disclose the diversity of gene sets from the perspective of structural properties and make it easier to identify unique gene sets. In gene set enrichment analysis (GSEA), each gene set is treated independently, and its contextual and relational attributes are ignored. The structural coefficients of gene sets can supplement GSEA with information about the overall picture of gene sets, promoting the constructive reorganization of the enriched terms and helping researchers better prioritize and select gene sets.

Neoantigen-specific CD4+ tumor-infiltrating lymphocytes are potent effectors identified within adoptive cell therapy products for metastatic melanoma patients

BACKGROUND

Adoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TILs) is a promising immunotherapeutic approach for patients with advanced solid tumors. While numerous advances have been made, the contribution of neoantigen-specific CD4+T cells within TIL infusion products remains underexplored and therefore offers a significant opportunity for progress.

METHODS

We analyzed infused TIL products from metastatic melanoma patients previously treated with ACT for the presence of neoantigen-specific T cells. TILs were enriched on reactivity to neoantigen peptides derived and prioritized from patient sample-directed mutanome analysis. Enriched TILs were further investigated to establish the clonal neoantigen response with respect to function, transcriptomics, and persistence following ACT.

RESULTS

We discovered that neoantigen-specific TIL clones were predominantly CD4+ T cells and were present in both therapeutic responders and non-responders. CD4+ TIL demonstrated an effector T cell response with cytotoxicity toward autologous tumor in a major histocompatibility complex class II-dependent manner. These results were validated by paired TCR and single cell RNA sequencing, which elucidated transcriptomic profiles distinct to neoantigen-specific CD4+ TIL.

CONCLUSIONS

Despite methods which often focus on CD8+T cells, our study supports the importance of prospective identification of neoantigen-specific CD4+ T cells within TIL products as they are a potent source of tumor-specific effectors. We further advocate for the inclusion of neoantigen-specific CD4+ TIL in future ACT protocols as a strategy to improve antitumor immunity.

Whole-genome bisulfite sequencing identified the key role of the Src family tyrosine kinases and related genes in systemic lupus erythematosus

BACKGROUND

As a multisystemic autoimmune illness, the basic mechanisms behind the pathophysiology of systemic lupus erythematosus (SLE) remain poorly understood.

OBJECTIVE

We aimed to investigate the possible significance of SLE's DNA methylation and gain further insight into potential SLE-related biomarkers and therapeutic targets.

METHODS

We used whole genome bisulfite sequencing (WGBS) method to analyze DNA methylation in 4 SLE patients and 4 healthy people.

RESULTS

702 differentially methylated regions (DMRs) were identified, and 480 DMR-associated genes were annotated. We found the majority of the DMR-associated elements were enriched in repeat and gene bodies. The top 10 hub genes identified were LCK, FYB, PTK2B, LYN, CTNNB1, MAPK1, GNAQ, PRKCA, ABL1, and CD247. Compared to the control group, LCK and PTK2B had considerably decreased levels of mRNA expression in the SLE group. Receiver operating characteristic (ROC) curve suggested that LCK and PTK2B may be potential candidate biomarkers to predict SLE.

CONCLUSIONS

Our study improved comprehension of the DNA methylation patterns of SLE and identified potential biomarkers and therapeutic targets for SLE.

Alternative splicing impacts microRNA regulation within coding regions

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation. To check this hypothesis, we studied the role of miRNA target sites in both coding and non-coding regions using six cancer data sets from The Cancer Genome Atlas (TCGA) and Parkinson's disease data from PPMI. First, we predicted miRNA target sites on mRNAs from their sequence using TarPmiR. To check whether alternative splicing interferes with this regulation, we trained linear regression models to predict miRNA expression from transcript expression. Using nested models, we compared the predictive power of transcripts with miRNA target sites in the coding regions to that of transcripts without target sites. Models containing transcripts with target sites perform significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.

Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high-dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Here, we propose an interpretable deep learning method called moETM to perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder and employs multiple linear decoders to learn the multi-omics signatures. moETM demonstrates superior performance compared with six state-of-the-art methods on seven publicly available datasets. By applying moETM to the scRNA + scATAC data, we identified sequence motifs corresponding to the transcription factors regulating immune gene signatures. Applying moETM to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omics biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan-Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored.

A pipeline for the retrieval and extraction of domain-specific information with application to COVID-19 immune signatures

BACKGROUND

The accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific information and extract this information. This is especially challenging when the information itself resides beyond titles or abstracts. For emerging science, with a limited set of known papers of interest and an incomplete information model, this is of pressing concern. A timely example in retrospect is the identification of immune signatures (coherent sets of biomarkers) driving differential SARS-CoV-2 infection outcomes.

IMPLEMENTATION

We built a classifier to identify papers containing domain-specific information from the document embeddings of the title and abstract. To train this classifier with limited data, we developed an iterative process leveraging pre-trained SPECTER document embeddings, SVM classifiers and web-enabled expert review to iteratively augment the training set. This training set was then used to create a classifier to identify papers containing domain-specific information. Finally, information was extracted from these papers through a semi-automated system that directly solicited the paper authors to respond via a web-based form.

RESULTS

We demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. The type of immune signature (e.g., gene expression vs. other types of profiling) was also identified with a positive predictive value of 74%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate.

CONCLUSIONS

Our results demonstrate the efficacy of using a SVM classifier with document embeddings of the title and abstract, to retrieve papers with domain-specific information, even when that information is rarely present in the abstract. Targeted author engagement based on classifier predictions offers a promising pathway to build a semi-structured representation of such information. Through this approach, partially automated literature mining can help rapidly create semi-structured knowledge repositories for automatic analysis of emerging health threats.

Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system

BACKGROUND

Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system.

METHODS

We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets.

RESULTS

MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons.

CONCLUSIONS

Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

Integrative dissection of gene regulatory elements at base resolution

Although vast numbers of putative gene regulatory elements have been cataloged, the sequence motifs and individual bases that underlie their functions remain largely unknown. Here, we combine epigenetic perturbations, base editing, and deep learning to dissect regulatory sequences within the exemplar immune locus encoding CD69. We converge on a ∼170 base interval within a differentially accessible and acetylated enhancer critical for CD69 induction in stimulated Jurkat T cells. Individual C-to-T base edits within the interval markedly reduce element accessibility and acetylation, with corresponding reduction of CD69 expression. The most potent base edits may be explained by their effect on regulatory interactions between the transcriptional activators GATA3 and TAL1 and the repressor BHLHE40. Systematic analysis suggests that the interplay between GATA3 and BHLHE40 plays a general role in rapid T cell transcriptional responses. Our study provides a framework for parsing regulatory elements in their endogenous chromatin contexts and identifying operative artificial variants.

Multiparametric magnetic resonance imaging discerns glioblastoma immune microenvironmental heterogeneity

RATIONALE AND OBJECTIVE

Poor clinical outcomes for patients with glioblastoma are in part due to dysfunction of the tumor-immune microenvironment. An imaging approach able to characterize immune microenvironmental signatures could provide a framework for biologically based patient stratification and response assessment. We hypothesized spatially distinct gene expression networks can be distinguished by multiparametric Magnetic Resonance Imaging (MRI) phenotypes.

MATERIALS AND METHODS

Patients with newly diagnosed glioblastoma underwent image-guided tissue sampling allowing for co-registration of MRI metrics with gene expression profiles. MRI phenotypes based on gadolinium contrast enhancing lesion (CEL) and non-enhancing lesion (NCEL) regions were subdivided based on imaging parameters (relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC)). Gene set enrichment analysis and immune cell type abundance was estimated using CIBERSORT methodology. Significance thresholds were set at a p-value cutoff 0.005 and an FDR q-value cutoff of 0.1.

RESULTS

Thirteen patients (eight men, five women, mean age 58 ± 11 years) provided 30 tissue samples (16 CEL and 14 NCEL). Six non-neoplastic gliosis samples differentiated astrocyte repair from tumor associated gene expression. MRI phenotypes displayed extensive transcriptional variance reflecting biological networks, including multiple immune pathways. CEL regions demonstrated higher immunologic signature expression than NCEL, while NCEL regions demonstrated stronger immune signature expression levels than gliotic non-tumor brain. Incorporation of rCBV and ADC metrics identified sample clusters with differing immune microenvironmental signatures.

CONCLUSION

Taken together, our study demonstrates that MRI phenotypes provide an approach for non-invasively characterizing tumoral and immune microenvironmental glioblastoma gene expression networks.

Pleiotropic loci for cannabis use disorder severity in multi-ancestry high-risk populations

Cannabis use disorder (CUD) is common and has in part a genetic basis. The risk factors underlying its development likely involve multiple genes that are polygenetic and interact with each other and the environment to ultimately lead to the disorder. Co-morbidity and genetic correlations have been identified between CUD and other disorders and traits in select populations primarily of European descent. If two or more traits, such as CUD and another disorder, are affected by the same genetic locus, they are said to be pleiotropic. The present study aimed to identify specific pleiotropic loci for the severity level of CUD in three high-risk population cohorts: American Indians (AI), Mexican Americans (MA), and European Americans (EA). Using a previously developed computational method based on a machine learning technique, we leveraged the entire GWAS catalog and identified 114, 119, and 165 potentially pleiotropic variants for CUD severity in AI, MA, and EA respectively. Ten pleiotropic loci were shared between the cohorts although the exact variants from each cohort differed. While majority of the pleiotropic genes were distinct in each cohort, they converged on numerous enriched biological pathways. The gene ontology terms associated with the pleiotropic genes were predominately related to synaptic functions and neurodevelopment. Notable pathways included Wnt/β-catenin signaling, lipoprotein assembly, response to UV radiation, and components of the complement system. The pleiotropic genes were the most significantly differentially expressed in frontal cortex and coronary artery, up-regulated in adipose tissue, and down-regulated in testis, prostate, and ovary. They were significantly up-regulated in most brain tissues but were down-regulated in the cerebellum and hypothalamus. Our study is the first to attempt a large-scale pleiotropy detection scan for CUD severity. Our findings suggest that the different population cohorts may have distinct genetic factors for CUD, however they share pleiotropic genes from underlying pathways related to Alzheimer's disease, neuroplasticity, immune response, and reproductive endocrine systems.

Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures

The advent of single-cell multi-omics sequencing technology makes it possible for re-searchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human bone marrow mononuclear cells (BMMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes

Background: Breast cancer (BRCA) is regarded as a lethal and aggressive cancer with increasing morbidity and mortality worldwide. cGAS-STING signaling regulates the crosstalk between tumor cells and immune cells in the tumor microenvironment (TME), emerging as an important DNA-damage mechanism. However, cGAS-STING-related genes (CSRGs) have rarely been investigated for their prognostic value in breast cancer patients.

Methods: Our study aimed to construct a risk model to predict the survival and prognosis of breast cancer patients. We obtained 1087 breast cancer samples and 179 normal breast tissue samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) database, 35 immune-related differentially expression genes (DEGs) from cGAS-STING-related genes were systematically assessed. The Cox regression was applied for further selection, and 11 prognostic-related DEGs were used to develop a machine learning-based risk assessment and prognostic model.

Results: We successfully developed a risk model to predict the prognostic value of breast cancer patients and its performance acquired effective validation. The results derived from Kaplan-Meier analysis revealed that the low-risk score patients had better overall survival (OS). The nomogram that integrated the risk score and clinical information was established and had good validity in predicting the overall survival of breast cancer patients. Significant correlations were observed between the risk score and tumor-infiltrating immune cells, immune checkpoints and the response to immunotherapy. The cGAS-STING-related genes risk score was also relevant to a series of clinic prognostic indicators such as tumor staging, molecular subtype, tumor recurrence, and drug therapeutic sensibility in breast cancer patients.

Conclusion: cGAS-STING-related genes risk model provides a new credible risk stratification method to improve the clinical prognostic assessment for breast cancer.

Warmer ambient air temperatures reduce nasal turbinate and brain infection, but increase lung inflammation in the K18-hACE2 mouse model of COVID-19

Warmer climatic conditions have been associated with fewer COVID-19 cases. Herein we infected K18-hACE2 mice housed at the standard animal house temperature of ∼22 °C, or at ∼31 °C, which is considered to be thermoneutral for mice. On day 2 post infection, RNA-Seq analyses showed no significant differential gene expression lung in lungs of mice housed at the two temperatures, with almost identical viral loads and type I interferon responses. There was also no significant difference in viral loads in lungs on day 5, but RNA-Seq and histology analyses showed clearly elevated inflammatory signatures and infiltrates. Thermoneutrality thus promoted lung inflammation. On day 2 post infection mice housed at 31 °C showed reduced viral loads in nasal turbinates, consistent with increased mucociliary clearance at the warmer ambient temperature. These mice also had reduced virus levels in the brain, and an ensuing amelioration of weight loss and a delay in mortality. Warmer air temperatures may thus reduce infection of the upper respiratory track and the olfactory epithelium, resulting in reduced brain infection. Potential relevance for anosmia and neurological sequelae in COVID-19 patients is discussed.

Neuroimmune transcriptome changes in patient brains of psychiatric and neurological disorders

Neuroinflammation has been implicated in multiple brain disorders but the extent and the magnitude of change in immune-related genes (IRGs) across distinct brain disorders has not been directly compared. In this study, 1275 IRGs were curated and their expression changes investigated in 2467 postmortem brains of controls and patients with six major brain disorders, including schizophrenia (SCZ), bipolar disorder (BD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD). There were 865 IRGs present across all microarray and RNA-seq datasets. More than 60% of the IRGs had significantly altered expression in at least one of the six disorders. The differentially expressed immune-related genes (dIRGs) shared across disorders were mainly related to innate immunity. Moreover, sex, tissue, and putative cell type were systematically evaluated for immune alterations in different neuropsychiatric disorders. Co-expression networks revealed that transcripts of the neuroimmune systems interacted with neuronal-systems, both of which contribute to the pathology of brain disorders. However, only a few genes with expression changes were also identified as containing risk variants in genome-wide association studies. The transcriptome alterations at gene and network levels may clarify the immune-related pathophysiology and help to better define neuropsychiatric and neurological disorders.

Technology meets TILs: Deciphering T cell function in the -omics era

T cells are at the center of cancer immunology because of their ability to recognize mutations in tumor cells and directly mediate cancer cell killing. Immunotherapies to rejuvenate exhausted T cell responses have transformed the clinical management of several malignancies. In parallel, the development of novel multidimensional analysis platforms, such as single-cell RNA sequencing and high-dimensional flow cytometry, has yielded unprecedented insights into immune cell biology. This convergence has revealed substantial heterogeneity of tumor-infiltrating immune cells in single tumors, across tumor types, and among individuals with cancer. Here we discuss the opportunities and challenges of studying the complex tumor microenvironment with -omics technologies that generate vast amounts of data, highlighting the opportunities and limitations of these technologies with a particular focus on interpreting high-dimensional studies of CD8+ T cells in the tumor microenvironment.

Separate and combined effects of advanced age and obesity on mammary adipose inflammation, immunosuppression and tumor progression in mouse models of triple negative breast cancer

Introduction

Advanced age and obesity are independent risk and progression factors for triple negative breast cancer (TNBC), which presents significant public health concerns for the aging population and its increasing burden of obesity. Due to parallels between advanced age- and obesityrelated biology, particularly adipose inflammation, we hypothesized that advanced age and obesity each accelerate mammary tumor growth through convergent, and likely interactive, mechanisms.

Methods

To test this hypothesis, we orthotopically transplanted murine syngeneic TNBC cells into the mammary glands of young normoweight control (7 months), young diet-induced obese (DIO), aged normoweight control (17 months), and aged DIO female C57BL/6J mice.

Results

Here we report accelerated tumor growth in aged control and young DIO mice, compared with young controls. Transcriptional analyses revealed, with a few exceptions, overlapping patterns of mammary tumor inflammation and tumor immunosuppression in aged control mice and young DIO mice, relative to young controls. Moreover, aged control and young DIO tumors, compared with young controls, had reduced abundance ofcytotoxic CD8 T cells. Finally, DIO in advanced age exacerbated mammary tumor growth, inflammation and tumor immunosuppression.

Discussion

These findings demonstrate commonalities in the mechanisms driving TNBC in aged and obese mice, relative to young normoweight controls. Moreover, we found that advanced age and DIO interact to accelerate mammary tumor progression. Given the US population is getting older and more obese, age- and obesity-related biological differences will need to be considered when developing mechanism-based strategies for preventing or controlling breast cancer.

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.

Diagnosis and prognosis prediction model for digestive system tumors based on immunologic gene sets

According to 2020 global cancer statistics, digestive system tumors (DST) are ranked first in both incidence and mortality. This study systematically investigated the immunologic gene set (IGS) to discover effective diagnostic and prognostic biomarkers. Gene set variation (GSVA) analysis was used to calculate enrichment scores for 4,872 IGSs in patients with digestive system tumors. Using the machine learning algorithm XGBoost to build a classifier that distinguishes between normal samples and cancer samples, it shows high specificity and sensitivity on both the validation set and the overall dataset (area under the receptor operating characteristic curve [AUC]: validation set = 0.993, overall dataset = 0.999). IGS-based digestive system tumor subtypes (IGTS) were constructed using a consistent clustering approach. A risk prediction model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. DST is divided into three subtypes: subtype 1 has the best prognosis, subtype 3 is the second, and subtype 2 is the worst. The prognosis model constructed using nine gene sets can effectively predict prognosis. Prognostic models were significantly associated with tumor mutational burden (TMB), tumor immune microenvironment (TIME), immune checkpoints, and somatic mutations. A composite nomogram was constructed based on the risk score and the patient's clinical information, with a well-fitted calibration curve (AUC = 0.762). We further confirmed the reliability and validity of the diagnostic and prognostic models using other cohorts from the Gene Expression Omnibus database. We identified diagnostic and prognostic models based on IGS that provide a strong basis for early diagnosis and effective treatment of digestive system tumors.

Chikungunya patient transcriptional signatures faithfully recapitulated in a C57BL/6J mouse model

Introduction

An adult wild-type C57BL/6J mouse model of chikungunya virus (CHIKV) infection and disease has been extensively used to study the alphaviral arthritic immunopathology and to evaluate new interventions. How well mouse models recapitulate the gene expression profiles seen in humans remains controversial.

Methods

Herein we perform a comparative transcriptomics analysis using RNA-Seq datasets from the C57BL/6J CHIKV mouse model with datasets obtained from adults and children acutely infected with CHIKV.

Results

Despite sampling quite different tissues, peripheral blood from humans and feet from mice, gene expression profiles were quite similar, with an overlap of up to ≈50% for up-regulated single copy orthologue differentially expressed genes. Furthermore, high levels of significant concordance between mouse and human were seen for immune pathways and signatures, which were dominated by interferons, T cells and monocyte/macrophages. Importantly, predicted responses to a series of anti-inflammatory drug and biologic treatments also showed cogent similarities between species.

Discussion

Comparative transcriptomics and subsequent pathway analysis provides a detailed picture of how a given model recapitulates human gene expression. Using this method, we show that the C57BL/6J CHIKV mouse model provides a reliable and representative system in which to study CHIKV immunopathology and evaluate new treatments.

Human variation in population-wide gene expression data predicts gene perturbation phenotype

Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.

Association of body mass index and inflammatory dietary pattern with breast cancer pathologic and genomic immunophenotype in the nurses' health study

BACKGROUND

Breast tumor immune infiltration is clearly associated with improved treatment response and outcomes in breast cancer. However, modifiable patient factors associated with breast cancer immune infiltrates are poorly understood. The Nurses' Health Study (NHS) offers a unique cohort to study immune gene expression in tumor and adjacent normal breast tissue, immune cell-specific immunohistochemistry (IHC), and patient exposures. We evaluated the association of body mass index (BMI) change since age 18, physical activity, and the empirical dietary inflammatory pattern (EDIP) score, all implicated in systemic inflammation, with immune cell-specific expression scores.

METHODS

This population-based, prospective observational study evaluated 882 NHS and NHSII participants diagnosed with invasive breast cancer with detailed exposure and gene expression data. Of these, 262 women (training cohort) had breast tumor IHC for four classic immune cell markers (CD8, CD4, CD20, and CD163). Four immune cell-specific scores were derived via lasso regression using 105 published immune expression signatures' association with IHC. In the remaining 620 patient evaluation cohort, we evaluated association of each immune cell-specific score as outcomes, with BMI change since age 18, physical activity, and EDIP score as predictors, using multivariable-adjusted linear regression.

RESULTS

Among women with paired expression/IHC data from breast tumor tissue, we identified robust correlation between novel immune cell-specific expression scores and IHC. BMI change since age 18 was positively associated with CD4+ (β = 0.16; p = 0.009), and CD163 novel immune scores (β = 0.14; p = 0.04) in multivariable analyses. In other words, for each 10 unit (kg/m2) increase in BMI, the percentage of cells positive for CD4 and CD163 increased 1.6% and 1.4%, respectively. Neither physical activity nor EDIP was significantly associated with any immune cell-specific expression score in multivariable analyses.

CONCLUSIONS

BMI change since age 18 was positively associated with novel CD4+ and CD163+ cell scores in breast cancer, supporting further study of the effect of modifiable factors like weight gain on the immune microenvironment.

Clonal germinal center B cells function as a niche for T-cell lymphoma

Angioimmunoblastic T-cell lymphoma (AITL) is proposed to be initiated by age-related clonal hematopoiesis (ACH) with TET2 mutations, whereas the G17V RHOA mutation in immature cells with TET2 mutations promotes the development of T follicular helper (TFH)-like tumor cells. Here, we investigated the mechanism by which TET2-mutant immune cells enable AITL development using mouse models and human samples. Among the 2 mouse models, mice lacking Tet2 in all the blood cells (Mx-Cre × Tet2flox/flox × G17V RHOA transgenic mice) spontaneously developed AITL for approximately up to a year, while mice lacking Tet2 only in the T cells (Cd4-Cre × Tet2flox/flox × G17V RHOA transgenic mice) did not. Therefore, Tet2-deficient immune cells function as a niche for AITL development. Single-cell RNA-sequencing (scRNA-seq) of >50 000 cells from mouse and human AITL samples revealed significant expansion of aberrant B cells, exhibiting properties of activating light zone (LZ)-like and proliferative dark zone (DZ)-like germinal center B (GCB) cells. The GCB cells in AITL clonally evolved with recurrent mutations in genes related to core histones. In silico network analysis using scRNA-seq data identified Cd40-Cd40lg as a possible mediator of GCB and tumor cell cluster interactions. Treatment of AITL model mice with anti-Cd40lg inhibitory antibody prolonged survival. The genes expressed in aberrantly expanded GCB cells in murine tumors were also broadly expressed in the B-lineage cells of TET2-mutant human AITL. Therefore, ACH-derived GCB cells could undergo independent clonal evolution and support the tumorigenesis in AITL via the CD40-CD40LG axis.