SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.

Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.

LEF1 isoforms regulate cellular senescence and aging.

The study of aging and its mechanisms, such as cellular senescence, has provided valuable insights into age-related pathologies, thus contributing to their prevention and treatment. The current abundance of high-throughput data combined with the surge of robust analysis algorithms has facilitated novel ways of identifying underlying pathways that may drive these pathologies. For the purpose of identifying key regulators of lung aging, we performed comparative analyses of transcriptional profiles of aged versus young human subjects and mice, focusing on the common age-related changes in the transcriptional regulation in lung macrophages, T cells, and B immune cells. Importantly, we validated our findings in cell culture assays and human lung samples. Our analysis identified lymphoid enhancer binding factor 1 (LEF1) as an important age-associated regulator of gene expression in all three cell types across different tissues and species. Follow-up experiments showed that the differential expression of long and short LEF1 isoforms is a key regulatory mechanism of cellular senescence. Further examination of lung tissue from patients with idiopathic pulmonary fibrosis, an age-related disease with strong ties to cellular senescence, revealed a stark dysregulation of LEF1. Collectively, our results suggest that LEF1 is a key factor of aging, and its differential regulation is associated with human and murine cellular senescence.

Antigen-driven T cell-macrophage interactions mediate the interface between innate and adaptive immunity in histidyl-tRNA synthetase-induced myositis.

Introduction: Previous work in humans has demonstrated that both innate and adaptive immune signaling pathways contribute to the pathogenesis of idiopathic inflammatory myopathy (IIM), a systemic autoimmune disease targeting muscle as well as extra-muscular organs. To better define interactive signaling networks in IIM, we characterized the cellular phenotype and transcriptomic profiles of muscle-infiltrating cells in our established murine model of histidyl-tRNA synthetase (HRS)-induced myositis. Methods: Myositis was induced in wild type (WT) and various congenic/mutant strains of C57BL/6 mice through intramuscular immunization with recombinant HRS. Histopathological, immunohistochemical, flow cytometric, and transcriptomic assessments were used to characterize the functional relationship between muscle-infiltrating cell populations in these strains lacking different components of innate and/or adaptive immune signaling. Results: RAG1 KO mice developed markedly reduced muscle inflammation relative to WT mice, demonstrating a key requirement for T cells in driving HRS-induced myositis. While the reduction of mononuclear cell infiltrates in CD4-Cre.MyD88fl/fl conditional knockout mice and OT-II TCR transgenic mice highlighted roles for both innate and TCR-mediated/adaptive immune signaling in T cells, diminished inflammation in Lyz2-Cre.MyD88fl/fl conditional knockout mice underscored the importance of macrophage/myeloid cell populations in supporting T cell infiltration. Single cell RNA sequencing-based clustering of muscle-infiltrating subpopulations and associated pathway analyses showed that perturbations of T cell signaling/function alter the distribution and phenotype of macrophages, fibroblasts, and other non-lymphoid cell populations contributing to HRS-induced myositis. Discussion: Overall, HRS-induced myositis reflects the complex interplay between multiple cell types that collectively drive a TH1-predominant, pro-inflammatory tissue phenotype requiring antigen-mediated activation of both MyD88- and TCR-dependent T cell signaling pathways.

Transcriptional changes of the aging lung.

Aging is a natural process associated with declined organ function and higher susceptibility to developing chronic diseases. A systemic single-cell type-based study provides a unique opportunity to understand the mechanisms behind age-related pathologies. Here, we use single-cell gene expression analysis comparing healthy young and aged human lungs from nonsmoker donors to investigate age-related transcriptional changes. Our data suggest that aging has a heterogenous effect on lung cells, as some populations are more transcriptionally dynamic while others remain stable in aged individuals. We found that monocytes and alveolar macrophages were the most transcriptionally affected populations. These changes were related to inflammation and regulation of the immune response. Additionally, we calculated the LungAge score, which reveals the diversity of lung cell types during aging. Changes in DNA damage repair, fatty acid metabolism, and inflammation are essential for age prediction. Finally, we quantified the senescence score in aged lungs and found that the more biased cells toward senescence are immune and progenitor cells. Our study provides a comprehensive and systemic analysis of the molecular signatures of lung aging. Our LungAge signature can be used to predict molecular signatures of physiological aging and to detect common signatures of age-related lung diseases.

Imaging Features of Autoimmune Disease-Related Interstitial Lung Diseases.

Interstitial lung diseases (ILDs) associated with autoimmune diseases show characteristic signs of imaging. Radiologic signs are also used in the identification of ILDs with features suggestive of autoimmune disease that do not meet the criteria for a specific autoimmune disease. Radiologists play a key role in identifying these signs and assessing their relevance as part of multidisciplinary team discussions. A radiologist may be the first health care professional to pick up signs of autoimmune disease in a patient referred for assessment of ILD or with suspicion for ILD. Multidisciplinary team discussion of imaging findings observed during follow-up may inform a change in diagnosis or identify progression, with implications for a patient's treatment regimen. This article describes the imaging features of autoimmune disease-related ILDs and the role of radiologists in assessing their relevance.

LEF1 isoforms regulate cellular senescence and aging.

Background: The study of aging and its mechanisms, such as cellular senescence, has provided valuable insights into age-related pathologies, thus contributing to their prevention and treatment. The current abundance of high throughput data combined with the surge of robust analysis algorithms has facilitated novel ways of identifying underlying pathways that may drive these pathologies. Methods: With the focus on identifying key regulators of lung aging, we performed comparative analyses of transcriptional profiles of aged versus young human subjects and mice, focusing on the common age-related changes in the transcriptional regulation in lung macrophages, T cells, and B immune cells. Importantly, we validated our findings in cell culture assays and human lung samples. Results: We identified Lymphoid Enhancer Binding Factor 1 (LEF1) as an important age-associated regulator of gene expression in all three cell types across different tissues and species. Follow-up experiments showed that the differential expression of long and short LEF1 isoforms is a key regulatory mechanism of cellular senescence. Further examination of lung tissue from patients with Idiopathic Pulmonary Fibrosis (IPF), an age-related disease with strong ties to cellular senescence, we demonstrated a stark dysregulation of LEF1. Conclusions: Collectively, our results suggest that the LEF1 is a key factor of aging, and its differential regulation is associated with human and murine cellular senescence.

Cell Type-Specific Biomarkers of Systemic Sclerosis Disease Severity Capture Cell-Intrinsic and Cell-Extrinsic Circuits.

OBJECTIVE: Systemic sclerosis (SSc) is a multifactorial autoimmune fibrotic disorder involving complex rewiring of cell-intrinsic and cell-extrinsic signaling coexpression networks involving a range of cell types. However, the rewired circuits as well as corresponding cell-cell interactions remain poorly understood. To address this, we used a predictive machine learning framework to analyze single-cell RNA-sequencing data from 24 SSc patients across the severity spectrum as quantified by the modified Rodnan skin score (MRSS). METHODS: We used a least absolute shrinkage and selection operator (LASSO)-based predictive machine learning approach on the single-cell RNA-sequencing data set to identify predictive biomarkers of SSc severity, both across and within cell types. The use of L1 regularization helps prevent overfitting on high-dimensional data. Correlation network analyses were coupled to the LASSO model to identify cell-intrinsic and cell-extrinsic co-correlates of the identified biomarkers of SSc severity. RESULTS: We found that the uncovered cell type-specific predictive biomarkers of MRSS included previously implicated genes in fibroblast and myeloid cell subsets (e.g., SFPR2+ fibroblasts and monocytes), as well as novel gene biomarkers of MRSS, especially in keratinocytes. Correlation network analyses revealed novel cross-talk between immune pathways and implicated keratinocytes in addition to fibroblast and myeloid cells as key cell types involved in SSc pathogenesis. We then validated the uncovered association of key gene expression and protein markers in keratinocytes, KRT6A and S100A8, with SSc skin disease severity. CONCLUSION: Our global systems analyses reveal previously uncharacterized cell-intrinsic and cell-extrinsic signaling coexpression networks underlying SSc severity that involve keratinocytes, myeloid cells, and fibroblasts.

Treg Cells Promote the SREBP1-Dependent Metabolic Fitness of Tumor-Promoting Macrophages via Repression of CD8+ T Cell-Derived Interferon-γ.

Regulatory T (Treg) cells are crucial for immune homeostasis, but they also contribute to tumor immune evasion by promoting a suppressive tumor microenvironment (TME). Mice with Treg cell-restricted Neuropilin-1 deficiency show tumor resistance while maintaining peripheral immune homeostasis, thereby providing a controlled system to interrogate the impact of intratumoral Treg cells on the TME. Using this and other genetic models, we showed that Treg cells shaped the transcriptional landscape across multiple tumor-infiltrating immune cell types. Treg cells suppressed CD8+ T cell secretion of interferon-γ (IFNγ), which would otherwise block the activation of sterol regulatory element-binding protein 1 (SREBP1)-mediated fatty acid synthesis in immunosuppressive (M2-like) tumor-associated macrophages (TAMs). Thus, Treg cells indirectly but selectively sustained M2-like TAM metabolic fitness, mitochondrial integrity, and survival. SREBP1 inhibition augmented the efficacy of immune checkpoint blockade, suggesting that targeting Treg cells or their modulation of lipid metabolism in M2-like TAMs could improve cancer immunotherapy.

Gene-level association analysis of systemic sclerosis: A comparison of African-Americans and White populations.

Gene-level analysis of ImmunoChip or genome-wide association studies (GWAS) data has not been previously reported for systemic sclerosis (SSc, scleroderma). The objective of this study was to analyze genetic susceptibility loci in SSc at the gene level and to determine if the detected associations were shared in African-American and White populations, using data from ImmunoChip and GWAS genotyping studies. The White sample included 1833 cases and 3466 controls (956 cases and 2741 controls from the US and 877 cases and 725 controls from Spain) and the African American sample, 291 cases and 260 controls. In both Whites and African Americans, we performed a gene-level analysis that integrates association statistics in a gene possibly harboring multiple SNPs with weak effect on disease risk, using Versatile Gene-based Association Study (VEGAS) software. The SNP-level analysis was performed using PLINK v.1.07. We identified 4 novel candidate genes (STAT1, FCGR2C, NIPSNAP3B, and SCT) significantly associated and 4 genes (SERBP1, PINX1, TMEM175 and EXOC2) suggestively associated with SSc in the gene level analysis in White patients. As an exploratory analysis we compared the results on Whites with those from African Americans. Of previously established susceptibility genes identified in Whites, only TNFAIP3 was significant at the nominal level (p = 6.13x10-3) in African Americans in the gene-level analysis of the ImmunoChip data. Among the top suggestive novel genes identified in Whites based on the ImmunoChip data, FCGR2C and PINX1 were only nominally significant in African Americans (p = 0.016 and p = 0.028, respectively), while among the top novel genes identified in the gene-level analysis in African Americans, UNC5C (p = 5.57x10-4) and CLEC16A (p = 0.0463) were also nominally significant in Whites. We also present the gene-level analysis of SSc clinical and autoantibody phenotypes among Whites. Our findings need to be validated by independent studies, particularly due to the limited sample size of African Americans.

14-3-3z sequesters cytosolic T-bet, upregulating IL-13 levels in TC2 and CD8+ lymphocytes from patients with scleroderma.

BACKGROUND: IL-13-producing CD8+ T cells have been implicated in the pathogenesis of type 2-driven inflammatory human conditions. We have shown that CD8+IL-13+ cells play a critical role in cutaneous fibrosis, the most characteristic feature of systemic sclerosis (SSc; scleroderma). However, the molecular mechanisms underlying production of IL-13 and other type 2 cytokines by CD8+ T cells remain unclear. OBJECTIVE: We sought to establish the molecular basis of IL-13 overproduction by CD8+ T cells from patients with SSc, focusing on T-bet modulation of GATA-3 activity, which we showed to underlie IL-13 overproduction in CD8+IL-13+ cells from patients with SSc. METHODS: Biochemical and biophysical methods were used to determine the expression and association of T-bet, GATA-3, and regulatory factors in CD8+ T cells isolated from the blood and lesional skin of patients with SSc with severe skin thickening. Chromatin immunoprecipitation analysis determined GATA-3 binding to the IL-13 promoter. ImageStream analysis and confocal microscopy visualized the subcellular localization of T-bet and GATA-3. Transcript levels were decreased by small interfering RNAs. RESULTS: Interaction of T-bet with the adaptor protein 14-3-3z in the cytosol of CD8+ T cells from patients with SSc reduces T-bet translocation into the nucleus and its ability to associate with GATA-3, allowing more GATA-3 to bind to the IL-13 promoter and inducing IL-13 upregulation. Strikingly, we show that this mechanism is also found during type 2 polarization of CD8+ T cells (TC2) from healthy donors. CONCLUSIONS: We identified a novel molecular mechanism underlying type 2 cytokine production by CD8+ T cells, revealing a more complete picture of the complex pathway leading to SSc disease pathogenesis.

A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis.

BACKGROUND: Systemic sclerosis (SSc) is a multi-organ autoimmune disease characterized by skin fibrosis. Internal organ involvement is heterogeneous. It is unknown whether disease mechanisms are common across all involved affected tissues or if each manifestation has a distinct underlying pathology. METHODS: We used consensus clustering to compare gene expression profiles of biopsies from four SSc-affected tissues (skin, lung, esophagus, and peripheral blood) from patients with SSc, and the related conditions pulmonary fibrosis (PF) and pulmonary arterial hypertension, and derived a consensus disease-associate signature across all tissues. We used this signature to query tissue-specific functional genomic networks. We performed novel network analyses to contrast the skin and lung microenvironments and to assess the functional role of the inflammatory and fibrotic genes in each organ. Lastly, we tested the expression of macrophage activation state-associated gene sets for enrichment in skin and lung using a Wilcoxon rank sum test. RESULTS: We identified a common pathogenic gene expression signature-an immune-fibrotic axis-indicative of pro-fibrotic macrophages (MØs) in multiple tissues (skin, lung, esophagus, and peripheral blood mononuclear cells) affected by SSc. While the co-expression of these genes is common to all tissues, the functional consequences of this upregulation differ by organ. We used this disease-associated signature to query tissue-specific functional genomic networks to identify common and tissue-specific pathologies of SSc and related conditions. In contrast to skin, in the lung-specific functional network we identify a distinct lung-resident MØ signature associated with lipid stimulation and alternative activation. In keeping with our network results, we find distinct MØ alternative activation transcriptional programs in SSc-associated PF lung and in the skin of patients with an "inflammatory" SSc gene expression signature. CONCLUSIONS: Our results suggest that the innate immune system is central to SSc disease processes but that subtle distinctions exist between tissues. Our approach provides a framework for examining molecular signatures of disease in fibrosis and autoimmune diseases and for leveraging publicly available data to understand common and tissue-specific disease processes in complex human diseases.