Decoding the Body Language of Immunity: Tackling the Immune System at the Organism Level

InTiCAR: Network-based identification of significant inter-tissue communicators for autoimmune diseases

Inter-tissue communicators (ITCs) are intricate and essential aspects of our body, as they are the keepers of homeostatic equilibrium. It is no surprise that the dysregulation of the exchange between tissues are at the core of various disorders. Among such conditions, autoimmune diseases (AIDs) refer to a collection of pathological conditions where the miscommunication drives the immune system to mistakenly attack one's own body. Due to their myriad and diverse pathophysiologies, AIDs cannot be easily diagnosed or treated, and continuous efforts are required to seek for potential diagnostic markers or therapeutic targets. The identification of ITCs with significant involvement in the disease states is therefore crucial. Here, we present InTiCAR, Inter-Tissue Communicators for Autoimmune diseases by Random walk with restart, which is a network exploration-based analysis method that suggests disease-specific ITCs based on prior knowledge of disease genes, without the need for the external expression data. We first show that distinct ITC profile s can be acquired for various diseases by InTiCAR. We further illustrate that, for autoimmune diseases (AIDs) specifically, the disease-specific ITCs outperform disease genes in diagnosing patients using the UK Biobank plasma proteome dataset. Also, through CMap LINCS dataset, we find that high perturbation on the AIDs genes can be observed by the disease-specific ITCs. Our results provide and highlight unique perspectives on biological network analysis by focusing on the entities of extracellular communications.

Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective

Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations.

IntiCom-DB: A Manually Curated Database of Inter-Tissue Communication Molecules and Their Communication Routes

Inter-tissue communication (ITC) is critical for maintaining the physiological functions of multiple tissues and is closely related to the onset and development of various complex diseases. Nevertheless, there is no well-organized data resource for known ITC molecules with explicit ITC routes from source tissues to target tissues. To address this issue, in this work, we manually reviewed nearly 190,000 publications and identified 1408 experimentally supported ITC entries in which the ITC molecules, their communication routes, and their functional annotations were included. To facilitate our work, these curated ITC entries were incorporated into a user-friendly database named IntiCom-DB. This database also enables visualization of the expression abundances of ITC proteins and their interaction partners. Finally, bioinformatics analyses on these data revealed common biological characteristics of the ITC molecules. For example, tissue specificity scores of ITC molecules at the protein level are often higher than those at the mRNA level in the target tissues. Moreover, the ITC molecules and their interaction partners are more abundant in both the source tissues and the target tissues. IntiCom-DB is freely available as an online database. As the first comprehensive database of ITC molecules with explicit ITC routes to the best of our knowledge, we hope that IntiCom-DB will benefit future ITC-related studies.

SARS-CoV-2 infection of kidney tissues from severe COVID-19 patients

BACKGROUND

Coronavirus disease 2019 (COVID-19) caused by infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests diverse clinical pathologies involving multiple organs. While the respiratory tract is the primary SARS-CoV-2 target, acute kidney injury is common in COVID-19 patients, displaying as acute tubular necrosis (ATN) resulting from focal epithelial necrosis and eosinophilia, glomerulosclerosis, and autolysis of renal tubular cells. However, whether any renal cells are infected by SARS-CoV-2 and the mechanism involved in the COVID-19 kidney pathology remain unclear.

METHODS

Kidney tissues obtained at autopsy from four severe COVID-19 patients and one healthy subject were examined by hematoxylin and eosin staining. Indirect immunofluorescent antibody assay was performed to detect SARS-CoV-2 spike protein S1 and nonstructural protein 8 (NSP8) together with markers of different kidney cell types and immune cells to identify the infected cells.

RESULTS

Renal parenchyma showed tissue injury comprised of ATN and glomerulosclerosis. Positive staining of S1 protein was observed in renal parenchymal and tubular epithelial cells. Evidence of viral infection was also observed in innate monocytes/macrophages and NK cells. Positive staining of NSP8, which is essential for viral RNA synthesis and replication, was confirmed in renal parenchymal cells, indicating the presence of active viral replication in the kidney.

CONCLUSIONS

In fatal COVID-19 kidneys, there are SARS-CoV-2 infection, minimally infiltrated innate immune cells, and evidence of viral replication, which could contribute to tissue damage in the form of ATN and glomerulosclerosis.

Understanding the constitutive presentation of MHC class I immunopeptidomes in primary tissues

Understanding the molecular principles that govern the composition of the MHC-I immunopeptidome across different primary tissues is fundamentally important to predict how T cells respond in different contexts in vivo. Here, we performed a global analysis of the MHC-I immunopeptidome from 29 to 19 primary human and mouse tissues, respectively. First, we observed that different HLA-A, HLA-B, and HLA-C allotypes do not contribute evenly to the global composition of the MHC-I immunopeptidome across multiple human tissues. Second, we found that tissue-specific and housekeeping MHC-I peptides share very distinct properties. Third, we discovered that proteins that are evolutionarily hyperconserved represent the primary source of the MHC-I immunopeptidome at the organism-wide scale. Fourth, we uncovered new components of the antigen processing and presentation network, including the carboxypeptidases CPE, CNDP1/2, and CPVL. Together, this study opens up new avenues toward a system-wide understanding of antigen presentation in vivo across mammalian species.

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Tissue-specific and housekeeping MHC class I peptides share distinct properties

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HLA-A, HLA-B, and HLA-C allotypes contribute very unevenly to the pool of class I peptides

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MHC-I immunopeptidomes are represented by evolutionarily conserved proteins

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An extended antigen processing and presentation pathway is uncovered

The strength and form of natural selection on transcript abundance in the wild

Gene transcription variation is known to contribute to disease susceptibility and adaptation, but we currently know very little about how contemporary natural selection shapes transcript abundance. Here, we propose a novel analytical framework to quantify the strength and form of ongoing natural selection at the transcriptome level in a wild vertebrate. We estimated selection on transcript abundance in a cohort of a wild salmonid fish (Salmo trutta) affected by an extracellular myxozoan parasite (Tetracapsuloides bryosalmonae) through mark–recapture field sampling and the integration of RNA‐sequencing with classical regression‐based selection analysis. We show, based on fin transcriptomes of the host, that infection by the parasite and subsequent host survival is linked to upregulation of mitotic cell cycle process. We also detect a widespread signal of disruptive selection on transcripts linked to host immune defence, host–pathogen interactions, cellular repair and maintenance. Our results provide insights into how selection can be measured at the transcriptome level to dissect the molecular mechanisms of contemporary evolution driven by climate change and emerging anthropogenic threats. We anticipate that the approach described here will enable critical information on the molecular processes and targets of natural selection to be obtained in real time.

see also the Perspective by Matthew P. Josephson and James K. Bull.