Unraveling the molecular architecture of autoimmune thyroid diseases at spatial resolution.

Autoimmune thyroid diseases (AITD) such as Graves' disease (GD) or Hashimoto's thyroiditis (HT) are organ-specific diseases that involve complex interactions between distinct components of thyroid tissue. Here, we use spatial transcriptomics to explore the molecular architecture, heterogeneity and location of different cells present in the thyroid tissue, including thyroid follicular cells (TFCs), stromal cells such as fibroblasts, endothelial cells, and thyroid infiltrating lymphocytes. We identify damaged antigen-presenting TFCs with upregulated CD74 and MIF expression in thyroid samples from AITD patients. Furthermore, we discern two main fibroblast subpopulations in the connective tissue including ADIRF+ myofibroblasts, mainly enriched in GD, and inflammatory fibroblasts, enriched in HT patients. We also demonstrate an increase of fenestrated PLVAP+ vessels in AITD, especially in GD. Our data unveil stromal and thyroid epithelial cell subpopulations that could play a role in the pathogenesis of AITD.

Technologies to Study Genetics and Molecular Pathways.

Over the last few decades, the study of congenital heart disease (CHD) has benefited from various model systems and the development of molecular biological techniques enabling the analysis of single gene as well as global effects. In this chapter, we first describe different models including CHD patients and their families, animal models ranging from invertebrates to mammals, and various cell culture systems. Moreover, techniques to experimentally manipulate these models are discussed. Second, we introduce cardiac phenotyping technologies comprising the analysis of mouse and cell culture models, live imaging of cardiogenesis, and histological methods for fixed hearts. Finally, the most important and latest molecular biotechniques are described. These include genotyping technologies, different applications of next-generation sequencing, and the analysis of transcriptome, epigenome, proteome, and metabolome. In summary, the models and technologies presented in this chapter are essential to study the function and development of the heart and to understand the molecular pathways underlying CHD.

T Cell Homeostasis Disturbances in a Cohort of Long-Term Elite Controllers of HIV Infection.

Elite controllers (ECs) are people living with HIV (PLWH) able to control HIV replication without antiretroviral therapy and have been proposed as a model of a functional HIV cure. Much evidence suggests that this spontaneous control of HIV has a cost in terms of T cell homeostasis alterations. We performed a deep phenotypic study to obtain insight into T cell homeostasis disturbances in ECs maintaining long-term virologic and immunologic control of HIV (long-term elite controllers; LTECs). Forty-seven PLWH were included: 22 LTECs, 15 non-controllers under successful antiretroviral therapy (onART), and 10 non-controllers not receiving ART (offART). Twenty uninfected participants (UCs) were included as a reference. T cell homeostasis was analyzed by spectral flow cytometry and data were analyzed using dimensionality reduction and clustering using R software v3.3.2. Dimensionality reduction and clustering yielded 57 and 54 different CD4 and CD8 T cell clusters, respectively. The offART group showed the highest perturbation of T cell homeostasis, with 18 CD4 clusters and 15 CD8 clusters significantly different from those of UCs. Most of these alterations were reverted in the onART group. Interestingly, LTECs presented several disturbances of T cell homeostasis with 15 CD4 clusters and 13 CD8 clusters different from UC. Moreover, there was a specific profile of T cell homeostasis alterations associated with LTECs, characterized by increases in clusters of naïve T cells, increases in clusters of non-senescent effector CD8 cells, and increases in clusters of central memory CD4 cells. These results demonstrate that, compared to ART-mediated control of HIV, the spontaneous control of HIV is associated with several disturbances in CD4 and CD8 T cell homeostasis. These alterations could be related to the existence of a potent and efficient virus-specific T cell response, and to the ability to halt disease progression by maintaining an adequate pool of CD4 T cells.

A specific natural killer cells phenotypic signature associated to long term elite control of HIV infection.

Elite controllers (ECs) are an exceptional group of people living with HIV (PLWH) that control HIV replication without therapy. Among the mechanisms involved in this ability, natural killer (NK)-cells have recently gained much attention. We performed an in-deep phenotypic analysis of NK-cells to search for surrogate markers associated with the long term spontaneous control of HIV. Forty-seven PLWH (22 long-term EC [PLWH-long-term elite controllers (LTECs)], 15 noncontrollers receiving antiretroviral treatment [ART] [PLWH-onART], and 10 noncontrollers cART-naïve [PLWH-offART]), and 20 uninfected controls were included. NK-cells homeostasis was analyzed by spectral flow cytometry using a panel of 15 different markers. Data were analyzed using FCSExpress and R software for unsupervised multidimensional analysis. Six different subsets of NK-cells were defined on the basis of CD16 and CD56 expression, and the multidimensional analysis revealed the existence of 68 different NK-cells clusters based on the expression levels of the 15 different markers. PLWH-offART presented the highest disturbance of NK-cells homeostasis and this was not completely restored by long-term ART. Interestingly, long term spontaneous control of HIV (PLWH-LTEC group) was associated with a specific profile of NK-cells homeostasis disturbance, characterized by an increase of CD16dimCD56dim subset when compared to uninfected controls (UC) group and also to offART and onART groups (p < 0.0001 for the global comparison), an increase of clusters C16 and C26 when compared to UC and onART groups (adjusted p-value < 0.05 for both comparisons), and a decrease of clusters C10 and C20 when compared to all the other groups (adjusted p-value < 0.05 for all comparisons). These findings may provide clues to elucidate markers of innate immunity with a relevant role in the long-term control of HIV.

Deep generative models in single-cell omics.

Deep Generative Models (DGMs) are becoming instrumental for inferring probability distributions inherent to complex processes, such as most questions in biomedical research. For many years, there was a lack of mathematical methods that would allow this inference in the scarce data scenario of biomedical research. The advent of single-cell omics has finally made square the so-called "skinny matrix", allowing to apply mathematical methods already extensively used in other areas. Moreover, it is now possible to integrate data at different molecular levels in thousands or even millions of samples, thanks to the number of single-cell atlases being collaboratively generated. Additionally, DGMs have proven useful in other frequent tasks in single-cell analysis pipelines, from dimensionality reduction, cell type annotation to RNA velocity inference. In spite of its promise, DGMs need to be used with caution in biomedical research, paying special attention to its use to answer the right questions and the definition of appropriate error metrics and validation check points that confirm not only its correct use but also its relevance. All in all, DGMs provide an exciting tool that opens a bright future for the integrative analysis of single-cell -omics to understand health and disease.

SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks.

SUMMARY: Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available state-of-the art tools. AVAILABILITY AND IMPLEMENTATION: The R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS.

Apolipoprotein E-ε2 and Resistance to Atherosclerosis in Midlife: The PESA Observational Study.

BACKGROUND: APOE is a known genetic contributor to cardiovascular disease, but the differential role APOE alleles play in subclinical atherosclerosis remains unclear. METHODS: The PESA (Progression of Early Subclinical Atherosclerosis) is an observational cohort study that recruited 4184 middle-aged asymptomatic individuals to be screened for cardiovascular risk and multiterritorial subclinical atherosclerosis. Participants were APOE-genotyped, and omics data were additionally evaluated. RESULTS: In the PESA study, the frequencies for APOE -ε2, -ε3, and -ε4 alleles were 0.060, 0.844, and 0.096, respectively. This study included a subcohort of 3887 participants (45.8±4.3 years of age; 62% males). As expected, APOE-ε4 carriers were at the highest risk for cardiovascular disease and had significantly greater odds of having subclinical atherosclerosis compared with ε3/ε3 carriers, which was mainly explained by their higher levels of low-density lipoprotein (LDL)-cholesterol. In turn, APOE-ε2 carriers were at the lowest risk for cardiovascular disease and had significantly lower odds of having subclinical atherosclerosis in several vascular territories (carotids: 0.62 [95% CI, 0.47-0.81]; P=0.00043; femorals: 0.60 [0.47-0.78]; P=9.96×10-5; coronaries: 0.53 [0.39-0.74]; P=0.00013; and increased PESA score: 0.58 [0.48-0.71]; P=3.16×10-8). This APOE-ε2 atheroprotective effect was mostly independent of the associated lower LDL-cholesterol levels and other cardiovascular risk factors. The protection conferred by the ε2 allele was greater with age (50-54 years: 0.49 [95% CI, 0.32-0.73]; P=0.00045), and normal (<150 mg/dL) levels of triglycerides (0.54 [0.44-0.66]; P=4.70×10-9 versus 0.90 [0.57-1.43]; P=0.67 if ≥150 mg/dL). Omics analysis revealed an enrichment of several canonical pathways associated with anti-inflammatory mechanisms together with the modulation of erythrocyte homeostasis, coagulation, and complement activation in ε2 carriers that might play a relevant role in the ε2's atheroprotective effect. CONCLUSIONS: This work sheds light on the role of APOE in cardiovascular disease development with important therapeutic and prevention implications on cardiovascular health, especially in early midlife. REGISTRATION: URL: https://www.clinicaltrials.gov: NCT01410318.

Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks.

Identifying the most relevant variables or features in massive datasets for dimensionality reduction can lead to improved and more informative display, faster computation times, and more explainable models of complex systems. Despite significant advances and available algorithms, this task generally remains challenging, especially in unsupervised settings. In this work, we propose a method that constructs correlation networks using all intervening variables and then selects the most informative ones based on network bootstrapping. The method can be applied in both supervised and unsupervised scenarios. We demonstrate its functionality by applying Uniform Manifold Approximation and Projection for dimensionality reduction to several high-dimensional biological datasets, derived from 4D live imaging recordings of hundreds of morpho-kinetic variables, describing the dynamics of thousands of individual leukocytes at sites of prominent inflammation. We compare our method with other standard ones in the field, such as Principal Component Analysis and Elastic Net, showing that it outperforms them. The proposed method can be employed in a wide range of applications, encompassing data analysis and machine learning.

Cardiovascular toxicity of checkpoint inhibitors: review of associated toxicity and design of the Spanish Immunotherapy Registry of Cardiovascular Toxicity

Immune checkpoint inhibitors (ICI) have changed the prognosis of many tumors. However, concerning associated cardiotoxicity has been reported. Little is known about the real-life incidence-specific surveillance protocols or the translational correlation between the underlying mechanisms and the clinical presentation of ICI-induced cardiotoxicity. The lack of data from prospective studies led us to review the current knowledge and to present the creation of the Spanish Immunotherapy Registry of Cardiovascular Toxicity (SIR-CVT), a prospective registry of patients receiving ICI that aims to examine the role of hsa-miR-Chr8:96, (a specific serum biomarker of myocarditis) in the early diagnosis of ICI-induced myocarditis. An exhaustive prospective cardiac imaging study will be performed before and during the first 12 months of treatment. The correlation between clinical, imaging, and immunologic parameters may improve our understanding of ICI-induced cardiotoxicity and enable simpler surveillance protocols. We assess ICI-induced cardiovascular toxicity and describe the rationale of the SIR-CVT (AU)

Incongruence between transcriptional and vascular pathophysiological cell states.

The Notch pathway is a major regulator of endothelial transcriptional specification. Targeting the Notch receptors or Delta-like ligand 4 (Dll4) dysregulates angiogenesis. Here, by analyzing single and compound genetic mutants for all Notch signaling members, we find significant differences in the way ligands and receptors regulate liver vascular homeostasis. Loss of Notch receptors caused endothelial hypermitogenic cell-cycle arrest and senescence. Conversely, Dll4 loss triggered a strong Myc-driven transcriptional switch inducing endothelial proliferation and the tip-cell state. Myc loss suppressed the induction of angiogenesis in the absence of Dll4, without preventing the vascular enlargement and organ pathology. Similarly, inhibition of other pro-angiogenic pathways, including MAPK/ERK and mTOR, had no effect on the vascular expansion induced by Dll4 loss; however, anti-VEGFA treatment prevented it without fully suppressing the transcriptional and metabolic programs. This study shows incongruence between single-cell transcriptional states, vascular phenotypes and related pathophysiology. Our findings also suggest that the vascular structure abnormalization, rather than neoplasms, causes the reported anti-Dll4 antibody toxicity.

ISG20L2: an RNA nuclease regulating T cell activation.

ISG20L2, a 3' to 5' exoribonuclease previously associated with ribosome biogenesis, is identified here in activated T cells as an enzyme with a preferential affinity for uridylated miRNA substrates. This enzyme is upregulated in T lymphocytes upon TCR and IFN type I stimulation and appears to be involved in regulating T cell function. ISG20L2 silencing leads to an increased basal expression of CD69 and induces greater IL2 secretion. However, ISG20L2 absence impairs CD25 upregulation, CD3 synaptic accumulation and MTOC translocation towards the antigen-presenting cell during immune synapsis. Remarkably, ISG20L2 controls the expression of immunoregulatory molecules, such as AHR, NKG2D, CTLA-4, CD137, TIM-3, PD-L1 or PD-1, which show increased levels in ISG20L2 knockout T cells. The dysregulation observed in these key molecules for T cell responses support a role for this exonuclease as a novel RNA-based regulator of T cell function.

Subclinical atherosclerosis and accelerated epigenetic age mediated by inflammation: a multi-omics study.

AIMS: Epigenetic age is emerging as a personalized and accurate predictor of biological age. The aim of this article is to assess the association of subclinical atherosclerosis with accelerated epigenetic age and to investigate the underlying mechanisms mediating this association. METHODS AND RESULTS: Whole blood methylomics, transcriptomics, and plasma proteomics were obtained for 391 participants of the Progression of Early Subclinical Atherosclerosis study. Epigenetic age was calculated from methylomics data for each participant. Its divergence from chronological age is termed epigenetic age acceleration. Subclinical atherosclerosis burden was estimated by multi-territory 2D/3D vascular ultrasound and by coronary artery calcification. In healthy individuals, the presence, extension, and progression of subclinical atherosclerosis were associated with a significant acceleration of the Grim epigenetic age, a predictor of health and lifespan, regardless of traditional cardiovascular risk factors. Individuals with an accelerated Grim epigenetic age were characterized by an increased systemic inflammation and associated with a score of low-grade, chronic inflammation. Mediation analysis using transcriptomics and proteomics data revealed key pro-inflammatory pathways (IL6, Inflammasome, and IL10) and genes (IL1B, OSM, TLR5, and CD14) mediating the association between subclinical atherosclerosis and epigenetic age acceleration. CONCLUSION: The presence, extension, and progression of subclinical atherosclerosis in middle-aged asymptomatic individuals are associated with an acceleration in the Grim epigenetic age. Mediation analysis using transcriptomics and proteomics data suggests a key role of systemic inflammation in this association, reinforcing the relevance of interventions on inflammation to prevent cardiovascular disease.

Cardiovascular toxicity of checkpoint inhibitors: review of associated toxicity and design of the Spanish Immunotherapy Registry of Cardiovascular Toxicity.

Immune checkpoint inhibitors (ICI) have changed the prognosis of many tumors. However, concerning associated cardiotoxicity has been reported. Little is known about the real-life incidence-specific surveillance protocols or the translational correlation between the underlying mechanisms and the clinical presentation of ICI-induced cardiotoxicity. The lack of data from prospective studies led us to review the current knowledge and to present the creation of the Spanish Immunotherapy Registry of Cardiovascular Toxicity (SIR-CVT), a prospective registry of patients receiving ICI that aims to examine the role of hsa-miR-Chr8:96, (a specific serum biomarker of myocarditis) in the early diagnosis of ICI-induced myocarditis. An exhaustive prospective cardiac imaging study will be performed before and during the first 12 months of treatment. The correlation between clinical, imaging, and immunologic parameters may improve our understanding of ICI-induced cardiotoxicity and enable simpler surveillance protocols. We assess ICI-induced cardiovascular toxicity and describe the rationale of the SIR-CVT.

γ-Linolenic acid in maternal milk drives cardiac metabolic maturation.

Birth presents a metabolic challenge to cardiomyocytes as they reshape fuel preference from glucose to fatty acids for postnatal energy production1,2. This adaptation is triggered in part by post-partum environmental changes3, but the molecules orchestrating cardiomyocyte maturation remain unknown. Here we show that this transition is coordinated by maternally supplied γ-linolenic acid (GLA), an 18:3 omega-6 fatty acid enriched in the maternal milk. GLA binds and activates retinoid X receptors4 (RXRs), ligand-regulated transcription factors that are expressed in cardiomyocytes from embryonic stages. Multifaceted genome-wide analysis revealed that the lack of RXR in embryonic cardiomyocytes caused an aberrant chromatin landscape that prevented the induction of an RXR-dependent gene expression signature controlling mitochondrial fatty acid homeostasis. The ensuing defective metabolic transition featured blunted mitochondrial lipid-derived energy production and enhanced glucose consumption, leading to perinatal cardiac dysfunction and death. Finally, GLA supplementation induced RXR-dependent expression of the mitochondrial fatty acid homeostasis signature in cardiomyocytes, both in vitro and in vivo. Thus, our study identifies the GLA-RXR axis as a key transcriptional regulatory mechanism underlying the maternal control of perinatal cardiac metabolism.

Low-affinity CTCF binding drives transcriptional regulation whereas high-affinity binding encompasses architectural functions.

CTCF is a DNA-binding protein which plays critical roles in chromatin structure organization and transcriptional regulation; however, little is known about the functional determinants of different CTCF-binding sites (CBS). Using a conditional mouse model, we have identified one set of CBSs that are lost upon CTCF depletion (lost CBSs) and another set that persists (retained CBSs). Retained CBSs are more similar to the consensus CTCF-binding sequence and usually span tandem CTCF peaks. Lost CBSs are enriched at enhancers and promoters and associate with active chromatin marks and higher transcriptional activity. In contrast, retained CBSs are enriched at TAD and loop boundaries. Integration of ChIP-seq and RNA-seq data has revealed that retained CBSs are located at the boundaries between distinct chromatin states, acting as chromatin barriers. Our results provide evidence that transient, lost CBSs are involved in transcriptional regulation, whereas retained CBSs are critical for establishing higher-order chromatin architecture.