Analysis of Genetically Diverse Macrophages Reveals Local and Domain-wide Mechanisms that Control Transcription Factor Binding and Function.

Non-coding genetic variation is a major driver of phenotypic diversity and allows the investigation of mechanisms that control gene expression. Here, we systematically investigated the effects of >50 million variations from five strains of mice on mRNA, nascent transcription, transcription start sites, and transcription factor binding in resting and activated macrophages. We observed substantial differences associated with distinct molecular pathways. Evaluating genetic variation provided evidence for roles of ∼100 TFs in shaping lineage-determining factor binding. Unexpectedly, a substantial fraction of strain-specific factor binding could not be explained by local mutations. Integration of genomic features with chromatin interaction data provided evidence for hundreds of connected cis-regulatory domains associated with differences in transcription factor binding and gene expression. This system and the >250 datasets establish a substantial new resource for investigation of how genetic variation affects cellular phenotypes.

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.

Environment drives selection and function of enhancers controlling tissue-specific macrophage identities.

Macrophages reside in essentially all tissues of the body and play key roles in innate and adaptive immune responses. Distinct populations of tissue macrophages also acquire context-specific functions that are important for normal tissue homeostasis. To investigate mechanisms responsible for tissue-specific functions, we analyzed the transcriptomes and enhancer landscapes of brain microglia and resident macrophages of the peritoneal cavity. In addition, we exploited natural genetic variation as a genome-wide "mutagenesis" strategy to identify DNA recognition motifs for transcription factors that promote common or subset-specific binding of the macrophage lineage-determining factor PU.1. We find that distinct tissue environments drive divergent programs of gene expression by differentially activating a common enhancer repertoire and by inducing the expression of divergent secondary transcription factors that collaborate with PU.1 to establish tissue-specific enhancers. These findings provide insights into molecular mechanisms by which tissue environment influences macrophage phenotypes that are likely to be broadly applicable to other cell types.

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.

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.

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.

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.

Functional and Structural Mimicry of Cellular Protein Kinase A Anchoring Proteins by a Viral Oncoprotein.

The oncoproteins of the small DNA tumor viruses interact with a plethora of cellular regulators to commandeer control of the infected cell. During infection, adenovirus E1A deregulates cAMP signalling and repurposes it for activation of viral gene expression. We show that E1A structurally and functionally mimics a cellular A-kinase anchoring protein (AKAP). E1A interacts with and relocalizes protein kinase A (PKA) to the nucleus, likely to virus replication centres, via an interaction with the regulatory subunits of PKA. Binding to PKA requires the N-terminus of E1A, which bears striking similarity to the amphipathic α-helical domain present in cellular AKAPs. E1A also targets the same docking-dimerization domain of PKA normally bound by cellular AKAPs. In addition, the AKAP like motif within E1A could restore PKA interaction to a cellular AKAP in which its normal interaction motif was deleted. During infection, E1A successfully competes with endogenous cellular AKAPs for PKA interaction. E1A's role as a viral AKAP contributes to viral transcription, protein expression and progeny production. These data establish HAdV E1A as the first known viral AKAP. This represents a unique example of viral subversion of a crucial cellular regulatory pathway via structural mimicry of the PKA interaction domain of cellular AKAPs.

Adenovirus E1A recruits the human Paf1 complex to enhance transcriptional elongation.

UNLABELLED: During infection by human adenovirus (HAdV), the proteins encoded by the early region 1A (E1A) gene bind and appropriate components of the cellular transcriptional machinery to activate the transcription of viral early genes. Previously, we identified roles for the human Bre1 (hBre1) and hPaf1 complexes in E1A-mediated transcriptional activation of HAdV early genes. Here we show that E1A binds hBre1 directly and that this complex targets the hPaf1 complex via the Rtf1 subunit. Depletion of hPaf1 reduces E1A-dependent activation of transcription from the E2e, E3, and E4 viral transcription units, and this does not result from a reduced ability of RNA polymerase II to be recruited to the promoter-proximal regions of these genes. In contrast, depletion of hPaf1 reduces the occupancy of RNA polymerase II across these transcription units. This is accompanied by reductions in the level of H3K36 trimethylation, a posttranslational histone modification associated with efficient transcriptional elongation, and the number of full-length transcripts from these genes. Together, these results indicate that E1A uses hBre1 to recruit the hPaf1 complex in order to optimally activate viral early transcription by enhancing transcriptional elongation. IMPORTANCE: This work provides the mechanism by which the hPaf1 complex contributes to E1A-dependent activation of early gene transcription. The work also demonstrates that E1A induces gene expression by stimulating transcriptional elongation, in addition to its better-characterized effects on transcriptional initiation.

Viral retasking of hBre1/RNF20 to recruit hPaf1 for transcriptional activation.

Upon infection, human adenovirus (HAdV) must activate the expression of its early genes to reprogram the cellular environment to support virus replication. This activation is orchestrated in large part by the first HAdV gene expressed during infection, early region 1A (E1A). E1A binds and appropriates components of the cellular transcriptional machinery to modulate cellular gene transcription and activate viral early genes transcription. Previously, we identified hBre1/RNF20 as a target for E1A. The interaction between E1A and hBre1 antagonizes the innate antiviral response by blocking H2B monoubiquitination, a chromatin modification necessary for the interferon (IFN) response. Here, we describe a second distinct role for the interaction of E1A with hBre1 in transcriptional activation of HAdV early genes. Furthermore, we show that E1A changes the function of hBre1 from a ubiquitin ligase involved in substrate selection to a scaffold which recruits hPaf1 as a means to stimulate transcription and transcription-coupled histone modifications. By using hBre1 to recruit hPaf1, E1A is able to optimally activate viral early transcription and begin the cycle of viral replication. The ability of E1A to target hBre1 to simultaneously repress cellular IFN dependent transcription while activating viral transcription, represents an elegant example of the incredible economy of action accomplished by a viral regulatory protein through a single protein interaction.

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.

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.

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.

Cellular GCN5 is a novel regulator of human adenovirus E1A-conserved region 3 transactivation.

The largest isoform of adenovirus early region 1A (E1A) contains a unique region termed conserved region 3 (CR3). This region activates viral gene expression by recruiting cellular transcription machinery to the early viral promoters. Recent studies have suggested that there is an optimal level of E1A-dependent transactivation required by human adenovirus (hAd) during infection and that this may be achieved via functional cross talk between the N termini of E1A and CR3. The N terminus of E1A binds GCN5, a cellular lysine acetyltransferase (KAT). We have identified a second independent interaction of E1A with GCN5 that is mediated by CR3, which requires residues 178 to 188 in hAd5 E1A. GCN5 was recruited to the viral genome during infection in an E1A-dependent manner, and this required both GCN5 interaction sites on E1A. Ectopic expression of GCN5 repressed transactivation by both E1A CR3 and full-length E1A. In contrast, RNA interference (RNAi) depletion of GCN5 or treatment with the KAT inhibitor cyclopentylidene-[4-(4'-chlorophenyl)thiazol-2-yl]hydrazone (CPTH2) resulted in increased E1A CR3 transactivation. Moreover, activation of the adenovirus E4 promoter by E1A was increased during infection of homozygous GCN5 KAT-defective (hat/hat) mouse embryonic fibroblasts (MEFs) compared to wild-type control MEFs. Enhanced histone H3 K9/K14 acetylation at the viral E4 promoter required the newly identified binding site for GCN5 within CR3 and correlated with repression and reduced occupancy by phosphorylated RNA polymerase II. Treatment with CPTH2 during infection also reduced virus yield. These data identify GCN5 as a new negative regulator of transactivation by E1A and suggest that its KAT activity is required for optimal virus replication.

Characterization of the 55-residue protein encoded by the 9S E1A mRNA of species C adenovirus.

Early region 1A (E1A) of human adenovirus (HAdV) has been the focus of over 30 years of investigation and is required for the oncogenic capacity of HAdV in rodents. Alternative splicing of the E1A transcript generates mRNAs encoding multiple E1A proteins. The 55-residue (55R) E1A protein, which is encoded by the 9S mRNA, is particularly interesting due to the unique properties it displays relative to all other E1A isoforms. 55R E1A does not contain any of the conserved regions (CRs) present in the other E1A isoforms. The C-terminal region of the 55R E1A protein contains a unique sequence compared to all other E1A isoforms, which results from a frameshift generated by alternative splicing. The 55R E1A protein is thought to be produced preferentially at the late stages of infection. Here we report the first study to directly investigate the function of the species C HAdV 55R E1A protein during infection. Polyclonal rabbit antibodies (Abs) have been generated that are capable of immunoprecipitating HAdV-2 55R E1A. These Abs can also detect HAdV-2 55R E1A by immunoblotting and indirect immunofluorescence assay. These studies indicate that 55R E1A is expressed late and is localized to the cytoplasm and to the nucleus. 55R E1A was able to activate the expression of viral genes during infection and could also promote productive replication of species C HAdV. 55R E1A was also found to interact with the S8 component of the proteasome, and knockdown of S8 was detrimental to viral replication dependent on 55R E1A.

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.

The C-terminal region of E1A: a molecular tool for cellular cartography.

The adenovirus E1A proteins function via protein-protein interactions. By making many connections with the cellular protein network, individual modules of this virally encoded hub reprogram numerous aspects of cell function and behavior. Although many of these interactions have been thoroughly studied, those mediated by the C-terminal region of E1A are less well understood. This review focuses on how this region of E1A affects cell cycle progression, apoptosis, senescence, transformation, and conversion of cells to an epithelial state through interactions with CTBP1/2, DYRK1A/B, FOXK1/2, and importin-α. Furthermore, novel potential pathways that the C-terminus of E1A influences through these connections with the cellular interaction network are discussed.