scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning.

Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.

In vitro evolution and whole genome analysis to study chemotherapy drug resistance in haploid human cells.

In vitro evolution and whole genome analysis has proven to be a powerful method for studying the mechanism of action of small molecules in many haploid microbes but has generally not been applied to human cell lines in part because their diploid state complicates the identification of variants that confer drug resistance. To determine if haploid human cells could be used in MOA studies, we evolved resistance to five different anticancer drugs (doxorubicin, gemcitabine, etoposide, topotecan, and paclitaxel) using a near-haploid cell line (HAP1) and then analyzed the genomes of the drug resistant clones, developing a bioinformatic pipeline that involved filtering for high frequency alleles predicted to change protein sequence, or alleles which appeared in the same gene for multiple independent selections with the same compound. Applying the filter to sequences from 28 drug resistant clones identified a set of 21 genes which was strongly enriched for known resistance genes or known drug targets (TOP1, TOP2A, DCK, WDR33, SLCO3A1). In addition, some lines carried structural variants that encompassed additional known resistance genes (ABCB1, WWOX and RRM1). Gene expression knockdown and knockout experiments of 10 validation targets showed a high degree of specificity and accuracy in our calls and demonstrates that the same drug resistance mechanisms found in diverse clinical samples can be evolved, discovered and studied in an isogenic background.

Induction of a memory-like CD4+ T-cell phenotype by airway smooth muscle cells.

In asthma, CD4+ T-cell interaction with airway smooth muscle (ASM) may enhance its contractile properties and promote its proliferation. However, less is known about the effects of this interaction on T cells. To explore the consequences of interaction of CD4+ T cells with ASM we placed the cells in co-culture and analyzed the phenotypic and functional changes in the T cells. Effector status as well as cytokine expression was assessed by flow cytometry. An increase in CD45RA-CD45RO+ memory T cells was observed after co-culture; however, these cells were not more responsive to CD3/28 restimulation. A reduction in mitochondrial coupling and an increase in the production of mitochondrial reactive oxygen species by CD4+ T cells post-restimulation suggested altered mitochondrial metabolism after co-culture. RNA sequencing analysis of the T cells revealed characteristic downregulation of effector T-cell-associated genes, but a lack of upregulation of memory T-cell-associated genes. The results of this study demonstrate that ASM cells can induce a phenotypic shift in CD4+ T cells into memory-like T cells but with reduced capacity for activation.

Regulation of long non-coding RNA expression by aryl hydrocarbon receptor activation.

The aryl hydrocarbon receptor (AhR) is a cytosolic transcription factor that can be activated by endogenous or xenobiotic ligands. Upon activation, the AhR translocates to the nucleus, dimerizes with the AhR nuclear translator (ARNT), and binds to specific DNA sequences called xenobiotic response elements (XRE) to promote target gene transcription, including cytochrome P450 (e.g., CYP1A1) expression. In addition to mRNA, the AhR may also regulate long non-coding RNA (lncRNA) expression. lncRNA are transcripts more than 200 nucleotides in length that do not encode a protein. Herein, we tested whether AhR activation regulates the expression of lncRNA in response to benzo[a]pyrene (B[a]P) using RNA sequencing (RNA-seq). We found that many lncRNA (e.g., SATB1-AS1, MIR4290HG, AC008969.1, LINC01533, VIPR1-AS1) and protein-coding RNA (e.g., CYP1A1, BX005266.2, AQP3, BTG2, DCX, and AhRR) were differentially expressed (DE) in A549 cells treated with B[a]P; many of these genes were dependent on AhR expression including CYP1A1, CYP1B1 and TiPARP. GO analyses indicated that DE protein-coding RNAs in A549WT cells are associated with distinct molecular functions compared to A549KO cells. KEGG analyses showed the hsa01100 pathway was associated with DE lncRNA only in A549WT cells. A549KO cells treated with B[a]P exhibited a distinct set of differentially-regulated lncRNA including upregulation of HOTAIR. We further confirmed that despite AhR activation in A549WT cells, B[a]P did not alter the expression of many well-characterized lncRNA including NEAT1, HOTTIP, SOX2OT, MALAT1, H19, and Linc00673. Thus, there is control over select lncRNA expression in A549 cells exposed to B[a]P, a finding which could yield insight into the molecular function of the AhR.

Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope.

As the sophistication of machine learning force fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze and assess the practical performance of MLFFs. To go beyond average error metrics and into a complete picture of a model's applicability and limitations, we developed FFAST (force field analysis software and tools): a cross-platform software package designed to gain detailed insights into a model's performance and limitations, complete with an easy-to-use graphical user interface. The software allows the user to gauge the performance of any molecular force field,─such as popular state-of-the-art MLFF models, ─ on various popular data set types, providing general prediction error overviews, outlier detection mechanisms, atom-projected errors, and more. It has a 3D visualizer to find and picture problematic configurations, atoms, or clusters in a large data set. In this paper, the example of the MACE and NequIP models is used on two data sets of interest [stachyose and docosahexaenoic acid (DHA)]─to illustrate the use cases of the software. With this, it was found that carbons and oxygens involved in or near glycosidic bonds inside the stachyose molecule present increased prediction errors. In addition, prediction errors on DHA rise as the molecule folds, especially for the carboxylic group at the edge of the molecule. We emphasize the need for a systematic assessment of MLFF models for ensuring their successful application to the study of dynamics of molecules and materials.

Differences in the Tumor Microenvironment of EBV-Associated Gastric Cancers Revealed Using Single-Cell Transcriptome Analysis.

Epstein-Barr virus (EBV) is a gamma-herpesvirus associated with nearly 10% of gastric cancers (GCs). These EBV-associated GCs (EBVaGCs) are molecularly, histopathologically, and clinically distinct from EBV-negative GCs (EBVnGCs). While viral genes in EBVaGCs contribute to the carcinogenesis process, viral proteins also represent foreign antigens that could trigger enhanced immune responses compared to EBVnGCs. Despite prior investigations of the EBVaGC tumor microenvironment (TME), the cellular composition has not been thoroughly explored. In this study, cellular subpopulations overrepresented in EBVaGCs were identified and molecularly characterized. Genes consistently expressed across both bulk tumor and single-cell RNA sequencing data were highlighted, with the expression across the identified cellular subpopulations analyzed. As expected, based on existing histopathological analysis, EBVaGC is characterized by abundant lymphocytic infiltration of the stroma. Our molecular analysis identified three unique immune cell subpopulations in EBVaGC: T and B cells expressing high levels of proliferation markers and B cells expressing T cell features. The proliferating T cell cluster also expressed markers of follicular T helper cells. Overall, EBVaGC also exhibited unique features indicative of a higher inflammatory response. These substantial differences within the TME suggest that further detailed exploration of the cellular composition of EBVaGCs is needed, which may identify cellular subpopulations and phenotypes associated with patient outcomes.

Receptor-mediated effects of Δ9 -tetrahydrocannabinol and cannabidiol on the inflammatory response of alveolar macrophages.

Δ9 -Tetrahydrocannabinol (Δ9 -THC) and cannabidiol (CBD) are cannabinoids found in Cannabis sativa. While research supports cannabinoids reduce inflammation, the consensus surrounding receptor(s)-mediated effects has yet to be established. Here, we investigated the receptor-mediated properties of Δ9 -THC and CBD on alveolar macrophages, an important pulmonary immune cell in direct contact with cannabinoids inhaled by cannabis smokers. MH-S cells, a mouse alveolar macrophage cell line, were exposed to Δ9 -THC and CBD, with and without lipopolysaccharide (LPS). Outcomes included RNA-sequencing and cytokine analysis. Δ9 -THC and CBD alone did not affect the basal transcriptional response of MH-S cells. In response to LPS, Δ9 -THC and CBD significantly reduced the expression of numerous proinflammatory cytokines including tumor necrosis factor-alpha, interleukin (IL)-1ß and IL-6, an effect that was dependent on CB2 . The anti-inflammatory effects of CBD but not Δ9 -THC were mediated through a reduction in signaling through nuclear factor-kappa B and extracellular signal-regulated protein kinase 1/2. These results suggest that CBD and Δ9 -THC have potent immunomodulatory properties in alveolar macrophages, a cell type important in immune homeostasis in the lungs. Further investigation into the effects of cannabinoids on lung immune cells could lead to the identification of therapies that may ameliorate conditions characterized by inflammation.

DOT1L regulates chamber-specific transcriptional networks during cardiogenesis and mediates postnatal cell cycle withdrawal.

Mechanisms by which specific histone modifications regulate distinct gene networks remain little understood. We investigated how H3K79me2, a modification catalyzed by DOT1L and previously considered a general transcriptional activation mark, regulates gene expression during cardiogenesis. Embryonic cardiomyocyte ablation of Dot1l revealed that H3K79me2 does not act as a general transcriptional activator, but rather regulates highly specific transcriptional networks at two critical cardiogenic junctures: embryonic cardiogenesis, where it was particularly important for left ventricle-specific genes, and postnatal cardiomyocyte cell cycle withdrawal, with Dot1L mutants having more mononuclear cardiomyocytes and prolonged cardiomyocyte cell cycle activity. Mechanistic analyses revealed that H3K79me2 in two distinct domains, gene bodies and regulatory elements, synergized to promote expression of genes activated by DOT1L. Surprisingly, H3K79me2 in specific regulatory elements also contributed to silencing genes usually not expressed in cardiomyocytes. These results reveal mechanisms by which DOT1L successively regulates left ventricle specification and cardiomyocyte cell cycle withdrawal.

Helminth-induced reprogramming of the stem cell compartment inhibits type 2 immunity.

Enteric helminths form intimate physical connections with the intestinal epithelium, yet their ability to directly alter epithelial stem cell fate has not been resolved. Here we demonstrate that infection of mice with the parasite Heligmosomoides polygyrus bakeri (Hpb) reprograms the intestinal epithelium into a fetal-like state marked by the emergence of Clusterin-expressing revival stem cells (revSCs). Organoid-based studies using parasite-derived excretory-secretory products reveal that Hpb-mediated revSC generation occurs independently of host-derived immune signals and inhibits type 2 cytokine-driven differentiation of secretory epithelial lineages that promote their expulsion. Reciprocally, type 2 cytokine signals limit revSC differentiation and, consequently, Hpb fitness, indicating that helminths compete with their host for control of the intestinal stem cell compartment to promote continuation of their life cycle.

TLR4 is a regulator of trained immunity in a murine model of Duchenne muscular dystrophy.

Dysregulation of the balance between pro-inflammatory and anti-inflammatory macrophages has a key function in the pathogenesis of Duchenne muscular dystrophy (DMD), a fatal genetic disease. We postulate that an evolutionarily ancient protective mechanism against infection, known as trained immunity, drives pathological inflammation in DMD. Here we show that bone marrow-derived macrophages from a murine model of DMD (mdx) exhibit cardinal features of trained immunity, consisting of transcriptional hyperresponsiveness associated with metabolic and epigenetic remodeling. The hyperresponsive phenotype is transmissible by bone marrow transplantation to previously healthy mice and persists for up to 11 weeks post-transplant. Mechanistically, training is induced by muscle extract in vitro. The functional and epigenetic changes in bone marrow-derived macrophages from dystrophic mice are TLR4-dependent. Adoptive transfer experiments further support the TLR4-dependence of trained macrophages homing to damaged muscles from the bone marrow. Collectively, this suggests that a TLR4-regulated, memory-like capacity of innate immunity induced at the level of the bone marrow promotes dysregulated inflammation in DMD.

Systematic analysis of naturally occurring insertions and deletions that alter transcription factor spacing identifies tolerant and sensitive transcription factor pairs.

Regulation of gene expression requires the combinatorial binding of sequence-specific transcription factors (TFs) at promoters and enhancers. Prior studies showed that alterations in the spacing between TF binding sites can influence promoter and enhancer activity. However, the relative importance of TF spacing alterations resulting from naturally occurring insertions and deletions (InDels) has not been systematically analyzed. To address this question, we first characterized the genome-wide spacing relationships of 73 TFs in human K562 cells as determined by ChIP-seq (chromatin immunoprecipitation sequencing). We found a dominant pattern of a relaxed range of spacing between collaborative factors, including 45 TFs exclusively exhibiting relaxed spacing with their binding partners. Next, we exploited millions of InDels provided by genetically diverse mouse strains and human individuals to investigate the effects of altered spacing on TF binding and local histone acetylation. These analyses suggested that spacing alterations resulting from naturally occurring InDels are generally tolerated in comparison to genetic variants directly affecting TF binding sites. To experimentally validate this prediction, we introduced synthetic spacing alterations between PU.1 and C/EBPß binding sites at six endogenous genomic loci in a macrophage cell line. Remarkably, collaborative binding of PU.1 and C/EBPß at these locations tolerated changes in spacing ranging from 5 bp increase to >30 bp decrease. Collectively, these findings have implications for understanding mechanisms underlying enhancer selection and for the interpretation of non-coding genetic variation.

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning.

The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability of the MLFF model for predictive molecular simulations. However, most atomistic reference datasets are inhomogeneously distributed across configurational space (CS), and thus, choosing the training set randomly or according to the probability distribution of the data leads to models whose accuracy is mainly defined by the most common close-to-equilibrium configurations in the reference data. In this work, we combine unsupervised and supervised ML methods to bypass the inherent bias of the data for common configurations, effectively widening the applicability range of the MLFF to the fullest capabilities of the dataset. To achieve this goal, we first cluster the CS into subregions similar in terms of geometry and energetics. We iteratively test a given MLFF performance on each subregion and fill the training set of the model with the representatives of the most inaccurate parts of the CS. The proposed approach has been applied to a set of small organic molecules and alanine tetrapeptide, demonstrating an up to twofold decrease in the root mean squared errors for force predictions on non-equilibrium geometries of these molecules. Furthermore, our ML models demonstrate superior stability over the default training approaches, allowing reliable study of processes involving highly out-of-equilibrium molecular configurations. These results hold for both kernel-based methods (sGDML and GAP/SOAP models) and deep neural networks (SchNet model).

Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules.

Dynamics of flexible molecules are often determined by an interplay between local chemical bond fluctuations and conformational changes driven by long-range electrostatics and van der Waals interactions. This interplay between interactions yields complex potential-energy surfaces (PESs) with multiple minima and transition paths between them. In this work, we assess the performance of the state-of-the-art Machine Learning (ML) models, namely, sGDML, SchNet, Gaussian Approximation Potentials/Smooth Overlap of Atomic Positions (GAPs/SOAPs), and Behler-Parrinello neural networks, for reproducing such PESs, while using limited amounts of reference data. As a benchmark, we use the cis to trans thermal relaxation in an azobenzene molecule, where at least three different transition mechanisms should be considered. Although GAP/SOAP, SchNet, and sGDML models can globally achieve a chemical accuracy of 1 kcal mol-1 with fewer than 1000 training points, predictions greatly depend on the ML method used and on the local region of the PES being sampled. Within a given ML method, large differences can be found between predictions of close-to-equilibrium and transition regions, as well as for different transition mechanisms. We identify key challenges that the ML models face mainly due to the intrinsic limitations of commonly used atom-based descriptors. All in all, our results suggest switching from learning the entire PES within a single model to using multiple local models with optimized descriptors, training sets, and architectures for different parts of the complex PES.

A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19.

Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated with hyperinflammation. The presence of HLH was described in severely ill patients during the H1N1 influenza epidemic, presenting with pulmonary vascular thrombosis. We tested the hypothesis that genes causing primary HLH regulate pathways linking pulmonary thromboembolism to the presence of SARS-CoV-2 using novel network-informed computational algorithms. This approach led to the identification of Neutrophils Extracellular Traps (NETs) as plausible mediators of vascular thrombosis in severe COVID-19 in children and adults. Taken together, the network-informed analysis led us to propose the following model: the release of NETs in response to inflammatory signals acting in concert with SARS-CoV-2 damage the endothelium and direct platelet-activation promoting abnormal coagulation leading to serious complications of COVID-19. The underlying hypothesis is that genetic and/or environmental conditions that favor the release of NETs may predispose individuals to thrombotic complications of COVID-19 due to an increase risk of abnormal coagulation. This would be a common pathogenic mechanism in conditions including autoimmune/infectious diseases, hematologic and metabolic disorders.

Angiotensin-converting enzyme 2 expression in COPD and IPF fibroblasts: the forgotten cell in COVID-19.

The COVID-19 pandemic is associated with severe pneumonia and acute respiratory distress syndrome leading to death in susceptible individuals. For those who recover, post-COVID-19 complications may include development of pulmonary fibrosis. Factors contributing to disease severity or development of complications are not known. Using computational analysis with experimental data, we report that idiopathic pulmonary fibrosis (IPF)- and chronic obstructive pulmonary disease (COPD)-derived lung fibroblasts express higher levels of angiotensin-converting enzyme 2 (ACE2), the receptor for SARS-CoV-2 entry and part of the renin-angiotensin system that is antifibrotic and anti-inflammatory. In preclinical models, we found that chronic exposure to cigarette smoke, a risk factor for both COPD and IPF and potentially for SARS-CoV-2 infection, significantly increased pulmonary ACE2 protein expression. Further studies are needed to understand the functional implications of ACE2 on lung fibroblasts, a cell type that thus far has received relatively little attention in the context of COVID-19.

Infiltrating Hematogenous Macrophages Aggregate Around ß-Amyloid Plaques in an Age- and Sex-Dependent Manner in a Mouse Model of Alzheimer Disease.

ß-Amyloid (Aß) plaques can trigger chronic inflammation in the cellular environment that recruits infiltrating macrophages during the course of Alzheimer disease (AD). Activated macrophages release pro-inflammatory cytokines that increase neurotoxicity associated with AD. A major impediment to investigating neuroinflammation involving macrophage activity is the inability to discriminate resident microglial macrophages (mMϕ) from hematogenous macrophages (hMϕ), as they are morphologically and phenotypically similar when activated. To distinguish between mMϕ and hMϕ and to determine their respective roles in chronic inflammation associated with the progression of amyloidosis, we used lys-EGFP-ki transgenic mice that express enhanced green fluorescent protein in hMϕ, but not in mMϕ. These mice were crossed with 5XFAD mice. The offspring demonstrated robust AD pathology and enabled visual discrimination of mMϕ from hMϕ. Mutant mice demonstrated robust increases in Aß1-42, area of Aß plaques, gliosis and deficits in spatial learning by age 5 months. The time-course of Aß accumulation, paralleled by the accumulation of hMϕ around Aß plaques, was more robust in female compared with male mice and preceded behavioral changes. Thus, the accumulation of infiltrating hMϕ around Aß plaques was age- and sex-dependent and preceded cognitive impairment.

Differential Effects of Human Adenovirus E1A Protein Isoforms on Aerobic Glycolysis in A549 Human Lung Epithelial Cells.

Viruses alter a multitude of host-cell processes to create a more optimal environment for viral replication. This includes altering metabolism to provide adequate substrates and energy required for replication. Typically, viral infections induce a metabolic phenotype resembling the Warburg effect, with an upregulation of glycolysis and a concurrent decrease in cellular respiration. Human adenovirus (HAdV) has been observed to induce the Warburg effect, which can be partially attributed to the adenovirus protein early region 4, open reading frame 1 (E4orf1). E4orf1 regulates a multitude of host-cell processes to benefit viral replication and can influence cellular metabolism through the transcription factor avian myelocytomatosis viral oncogene homolog (MYC). However, E4orf1 does not explain the full extent of Warburg-like HAdV metabolic reprogramming, especially the accompanying decrease in cellular respiration. The HAdV protein early region 1A (E1A) also modulates the function of the infected cell to promote viral replication. E1A can interact with a wide variety of host-cell proteins, some of which have been shown to interact with metabolic enzymes independently of an interaction with E1A. To determine if the HAdV E1A proteins are responsible for reprogramming cell metabolism, we measured the extracellular acidification rate and oxygen consumption rate of A549 human lung epithelial cells with constitutive endogenous expression of either of the two major E1A isoforms. This was followed by the characterization of transcript levels for genes involved in glycolysis and cellular respiration, and related metabolic pathways. Cells expressing the 13S encoded E1A isoform had drastically increased baseline glycolysis and lower maximal cellular respiration than cells expressing the 12S encoded E1A isoform. Cells expressing the 13S encoded E1A isoform exhibited upregulated expression of glycolysis genes and downregulated expression of cellular respiration genes. However, tricarboxylic acid cycle genes were upregulated, resembling anaplerotic metabolism employed by certain cancers. Upregulation of glycolysis and tricarboxylic acid cycle genes was also apparent in IMR-90 human primary lung fibroblast cells infected with a HAdV-5 mutant virus that expressed the 13S, but not the 12S encoded E1A isoform. In conclusion, it appears that the two major isoforms of E1A differentially influence cellular glycolysis and oxidative phosphorylation and this is at least partially due to the altered regulation of mRNA expression for the genes in these pathways.

Transcriptomic and epigenetic mechanisms underlying myeloid diversity in the lung.

The lung is inhabited by resident alveolar and interstitial macrophages as well as monocytic cells that survey lung tissues. Each cell type plays distinct functional roles under homeostatic and inflammatory conditions, but mechanisms establishing their molecular identities and functional potential remain poorly understood. In the present study, systematic evaluation of transcriptomes and open chromatin of alveolar macrophages (AMs), interstitial macrophages (IMs) and lung monocytes from two mouse strains enabled inference of common and cell-specific transcriptional regulators. We provide evidence that these factors drive selection of regulatory landscapes that specify distinct phenotypes of AMs and IMs and entrain qualitatively different responses to toll-like receptor 4 signaling in vivo. These studies reveal a striking divergence in a fundamental innate immune response pathway in AMs and establish a framework for further understanding macrophage diversity in the lung.

Transcriptome clarifies mechanisms of lesion genesis versus progression in models of Ccm3 cerebral cavernous malformations.

Cerebral cavernous malformations (CCMs) are dilated capillaries causing epilepsy and stroke. Inheritance of a heterozygous mutation in CCM3/PDCD10 is responsible for the most aggressive familial form of the disease. Here we studied the differences and commonalities between the transcriptomes of microdissected lesional neurovascular units (NVUs) from acute and chronic in vivo Ccm3/Pdcd10ECKO mice, and cultured brain microvascular endothelial cells (BMECs) Ccm3/Pdcd10ECKO.We identified 2409 differentially expressed genes (DEGs) in acute and 2962 in chronic in vivo NVUs compared to microdissected brain capillaries, as well as 121 in in vitro BMECs with and without Ccm3/Pdcd10 loss (fold change ≥ |2.0|; p < 0.05, false discovery rate corrected). A functional clustered dendrogram generated using the Euclidean distance showed that the DEGs identified only in acute in vivo NVUs were clustered in cellular proliferation gene ontology functions. The DEGs only identified in chronic in vivo NVUs were clustered in inflammation and immune response, permeability, and adhesion functions. In addition, 1225 DEGs were only identified in the in vivo NVUs but not in vitro BMECs, and these clustered within neuronal and glial functions. One miRNA mmu-miR-3472a was differentially expressed (FC = - 5.98; p = 0.07, FDR corrected) in the serum of Ccm3/Pdcd10+/- when compared to wild type mice, and this was functionally related as a putative target to Cand2 (cullin associated and neddylation dissociated 2), a DEG in acute and chronic lesional NVUs and in vitro BMECs. Our results suggest that the acute model is characterized by cell proliferation, while the chronic model showed inflammatory, adhesion and permeability processes. In addition, we highlight the importance of extra-endothelial structures in CCM disease, and potential role of circulating miRNAs as biomarkers of disease, interacting with DEGs. The extensive DEGs library of each model will serve as a validation tool for potential mechanistic, biomarker, and therapeutic targets.

Comprehensive transcriptome analysis of cerebral cavernous malformation across multiple species and genotypes.

The purpose of this study was to determine important genes, functions, and networks contributing to the pathobiology of cerebral cavernous malformation (CCM) from transcriptomic analyses across 3 species and 2 disease genotypes. Sequencing of RNA from laser microdissected neurovascular units of 5 human surgically resected CCM lesions, mouse brain microvascular endothelial cells, Caenorhabditis elegans with induced Ccm gene loss, and their respective controls provided differentially expressed genes (DEGs). DEGs from mouse and C. elegans were annotated into human homologous genes. Cross-comparisons of DEGs between species and genotypes, as well as network and gene ontology (GO) enrichment analyses, were performed. Among hundreds of DEGs identified in each model, common genes and 1 GO term (GO:0051656, establishment of organelle localization) were commonly identified across the different species and genotypes. In addition, 24 GO functions were present in 4 of 5 models and were related to cell-to-cell adhesion, neutrophil-mediated immunity, ion transmembrane transporter activity, and responses to oxidative stress. We have provided a comprehensive transcriptome library of CCM disease across species and for the first time to our knowledge in Ccm1/Krit1 versus Ccm3/Pdcd10 genotypes. We have provided examples of how results can be used in hypothesis generation or mechanistic confirmatory studies.