A TREM2-activating antibody with a blood-brain barrier transport vehicle enhances microglial metabolism in Alzheimer's disease models.

Loss-of-function variants of TREM2 are associated with increased risk of Alzheimer's disease (AD), suggesting that activation of this innate immune receptor may be a useful therapeutic strategy. Here we describe a high-affinity human TREM2-activating antibody engineered with a monovalent transferrin receptor (TfR) binding site, termed antibody transport vehicle (ATV), to facilitate blood-brain barrier transcytosis. Upon peripheral delivery in mice, ATV:TREM2 showed improved brain biodistribution and enhanced signaling compared to a standard anti-TREM2 antibody. In human induced pluripotent stem cell (iPSC)-derived microglia, ATV:TREM2 induced proliferation and improved mitochondrial metabolism. Single-cell RNA sequencing and morphometry revealed that ATV:TREM2 shifted microglia to metabolically responsive states, which were distinct from those induced by amyloid pathology. In an AD mouse model, ATV:TREM2 boosted brain microglial activity and glucose metabolism. Thus, ATV:TREM2 represents a promising approach to improve microglial function and treat brain hypometabolism found in patients with AD.

Novel App knock-in mouse model shows key features of amyloid pathology and reveals profound metabolic dysregulation of microglia.

BACKGROUND: Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain. METHODS: We engineered a novel App knock-in mouse model (AppSAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-ß pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous AppSAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aß content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays. RESULTS: Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aß content. The AppSAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged. DISCUSSION: Our findings demonstrate that fibrillar Aß in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology.

The Immunoglobulin Superfamily Receptome Defines Cancer-Relevant Networks Associated with Clinical Outcome.

Cell surface receptors and their interactions play a central role in physiological and pathological signaling. Despite its clinical relevance, the immunoglobulin superfamily (IgSF) remains uncharacterized and underrepresented in databases. Here, we present a systematic extracellular protein map, the IgSF interactome. Using a high-throughput technology to interrogate most single transmembrane receptors for binding to 445 IgSF proteins, we identify over 500 interactions, 82% previously undocumented, and confirm more than 60 receptor-ligand pairs using orthogonal assays. Our study reveals a map of cell-type-specific interactions and the landscape of dysregulated receptor-ligand crosstalk in cancer, including selective loss of function for tumor-associated mutations. Furthermore, investigation of the IgSF interactome in a large cohort of cancer patients identifies interacting protein signatures associated with clinical outcome. The IgSF interactome represents an important resource to fuel biological discoveries and a framework for understanding the functional organization of the surfaceome during homeostasis and disease, ultimately informing therapeutic development.

Brain delivery and activity of a lysosomal enzyme using a blood-brain barrier transport vehicle in mice.

Most lysosomal storage diseases (LSDs) involve progressive central nervous system (CNS) impairment, resulting from deficiency of a lysosomal enzyme. Treatment of neuronopathic LSDs remains a considerable challenge, as approved intravenously administered enzyme therapies are ineffective in modifying CNS disease because they do not effectively cross the blood-brain barrier (BBB). We describe a therapeutic platform for increasing the brain exposure of enzyme replacement therapies. The enzyme transport vehicle (ETV) is a lysosomal enzyme fused to an Fc domain that has been engineered to bind to the transferrin receptor, which facilitates receptor-mediated transcytosis across the BBB. We demonstrate that ETV fusions containing iduronate 2-sulfatase (ETV:IDS), the lysosomal enzyme deficient in mucopolysaccharidosis type II, exhibited high intrinsic activity and degraded accumulated substrates in both IDS-deficient cell and in vivo models. ETV substantially improved brain delivery of IDS in a preclinical model of disease, enabling enhanced cellular distribution to neurons, astrocytes, and microglia throughout the brain. Improved brain exposure for ETV:IDS translated to a reduction in accumulated substrates in these CNS cell types and peripheral tissues and resulted in a complete correction of downstream disease-relevant pathologies in the brain, including secondary accumulation of lysosomal lipids, perturbed gene expression, neuroinflammation, and neuroaxonal damage. These data highlight the therapeutic potential of the ETV platform for LSDs and provide preclinical proof of concept for TV-enabled therapeutics to treat CNS diseases more broadly.

TREM2 Regulates Microglial Cholesterol Metabolism upon Chronic Phagocytic Challenge.

Loss-of-function (LOF) variants of TREM2, an immune receptor expressed in microglia, increase Alzheimer's disease risk. TREM2 senses lipids and mediates myelin phagocytosis, but its role in microglial lipid metabolism is unknown. Combining chronic demyelination paradigms and cell sorting with RNA sequencing and lipidomics, we find that wild-type microglia acquire a disease-associated transcriptional state, while TREM2-deficient microglia remain largely homeostatic, leading to neuronal damage. TREM2-deficient microglia phagocytose myelin debris but fail to clear myelin cholesterol, resulting in cholesteryl ester (CE) accumulation. CE increase is also observed in APOE-deficient glial cells, reflecting impaired brain cholesterol transport. This finding replicates in myelin-treated TREM2-deficient murine macrophages and human iPSC-derived microglia, where it is rescued by an ACAT1 inhibitor and LXR agonist. Our studies identify TREM2 as a key transcriptional regulator of cholesterol transport and metabolism under conditions of chronic myelin phagocytic activity, as TREM2 LOF causes pathogenic lipid accumulation in microglia.

Transposable element expression in tumors is associated with immune infiltration and increased antigenicity.

Profound global loss of DNA methylation is a hallmark of many cancers. One potential consequence of this is the reactivation of transposable elements (TEs) which could stimulate the immune system via cell-intrinsic antiviral responses. Here, we develop REdiscoverTE, a computational method for quantifying genome-wide TE expression in RNA sequencing data. Using The Cancer Genome Atlas database, we observe increased expression of over 400 TE subfamilies, of which 262 appear to result from a proximal loss of DNA methylation. The most recurrent TEs are among the evolutionarily youngest in the genome, predominantly expressed from intergenic loci, and associated with antiviral or DNA damage responses. Treatment of glioblastoma cells with a demethylation agent results in both increased TE expression and de novo presentation of TE-derived peptides on MHC class I molecules. Therapeutic reactivation of tumor-specific TEs may synergize with immunotherapy by inducing inflammation and the display of potentially immunogenic neoantigens.

Germline genetic polymorphisms influence tumor gene expression and immune cell infiltration.

Cancer immunotherapy has emerged as an effective therapy in a variety of cancers. However, a key challenge in the field is that only a subset of patients who receive immunotherapy exhibit durable response. It has been hypothesized that host genetics influences the inherent immune profiles of patients and may underlie their differential response to immunotherapy. Herein, we systematically determined the association of common germline genetic variants with gene expression and immune cell infiltration of the tumor. We identified 64,094 expression quantitative trait loci (eQTLs) that associated with 18,210 genes (eGenes) across 24 human cancers. Overall, eGenes were enriched for their being involved in immune processes, suggesting that expression of immune genes can be shaped by hereditary genetic variants. We identified the endoplasmic reticulum aminopeptidase 2 (ERAP2) gene as a pan-cancer type eGene whose expression levels stratified overall survival in a subset of patients with bladder cancer receiving anti-PD-L1 (atezolizumab) therapy. Finally, we identified 103 gene signature QTLs (gsQTLs) that were associated with predicted immune cell abundance within the tumor microenvironment. Our findings highlight the impact of germline SNPs on cancer-immune phenotypes and response to therapy; and these analyses provide a resource for integration of germline genetics as a component of personalized cancer immunotherapy.

Testosterone is an endogenous regulator of BAFF and splenic B cell number.

Testosterone deficiency in men is associated with increased risk for autoimmunity and increased B cell numbers through unknown mechanisms. Here we show that testosterone regulates the cytokine BAFF, an essential survival factor for B cells. Male mice lacking the androgen receptor have increased splenic B cell numbers, serum BAFF levels and splenic Baff mRNA. Testosterone deficiency by castration causes expansion of BAFF-producing fibroblastic reticular cells (FRCs) in spleen, which may be coupled to lower splenic noradrenaline levels in castrated males, as an α-adrenergic agonist decreases splenic FRC number in vitro. Antibody-mediated blockade of the BAFF receptor or treatment with the neurotoxin 6-hydroxydopamine revert the increased splenic B cell numbers induced by castration. Among healthy men, serum BAFF levels are higher in men with low testosterone. Our study uncovers a previously unrecognized regulation of BAFF by testosterone and raises important questions about BAFF in testosterone-mediated protection against autoimmunity.

Tumor suppressor BAP1 is essential for thymic development and proliferative responses of T lymphocytes.

Loss of function of the nuclear deubiquitinating enzyme BRCA1-associated protein-1 (BAP1) is associated with a wide spectrum of cancers. We report that tamoxifen-induced BAP1 deletion in adult mice resulted in severe thymic atrophy. BAP1 was critical for T cell development at several stages. In the thymus, BAP1 was required for progression through the pre-T cell receptor checkpoint. Peripheral T cells lacking BAP1 demonstrated a defect in homeostatic and antigen-driven expansion. Deletion of BAP1 resulted in suppression of E2F target genes and defects in cell cycle progression, which was dependent on the catalytic activity of BAP1, but did not require its interaction with host cell factor-1 (HCF-1). Loss of BAP1 led to increased monoubiquitination of histone H2A at Lys119 (H2AK119ub) throughout the T cell lineage, in particular in immature thymocytes, but did not alter trimethylation of histone H3 at Lys27 (H3K27me3). Deletion of BAP1 also abrogated B cell development in the bone marrow. Our findings uncover a nonredundant function for BAP1 in maintaining the lymphoid lineage.

TGFß attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells.

Therapeutic antibodies that block the programmed death-1 (PD-1)-programmed death-ligand 1 (PD-L1) pathway can induce robust and durable responses in patients with various cancers, including metastatic urothelial cancer. However, these responses only occur in a subset of patients. Elucidating the determinants of response and resistance is key to improving outcomes and developing new treatment strategies. Here we examined tumours from a large cohort of patients with metastatic urothelial cancer who were treated with an anti-PD-L1 agent (atezolizumab) and identified major determinants of clinical outcome. Response to treatment was associated with CD8+ T-effector cell phenotype and, to an even greater extent, high neoantigen or tumour mutation burden. Lack of response was associated with a signature of transforming growth factor ß (TGFß) signalling in fibroblasts. This occurred particularly in patients with tumours, which showed exclusion of CD8+ T cells from the tumour parenchyma that were instead found in the fibroblast- and collagen-rich peritumoural stroma; a common phenotype among patients with metastatic urothelial cancer. Using a mouse model that recapitulates this immune-excluded phenotype, we found that therapeutic co-administration of TGFß-blocking and anti-PD-L1 antibodies reduced TGFß signalling in stromal cells, facilitated T-cell penetration into the centre of tumours, and provoked vigorous anti-tumour immunity and tumour regression. Integration of these three independent biological features provides the best basis for understanding patient outcome in this setting and suggests that TGFß shapes the tumour microenvironment to restrain anti-tumour immunity by restricting T-cell infiltration.

Tumour and host cell PD-L1 is required to mediate suppression of anti-tumour immunity in mice.

Expression of PD-L1, the ligand for T-cell inhibitory receptor PD-1, is one key immunosuppressive mechanism by which cancer avoids eradication by the immune system. Therapeutic use of blocking antibodies to PD-L1 or its receptor PD-1 has produced unparalleled, durable clinical responses, with highest likelihood of response seen in patients whose tumour or immune cells express PD-L1 before therapy. The significance of PD-L1 expression in each cell type has emerged as a central and controversial unknown in the clinical development of immunotherapeutics. Using genetic deletion in preclinical mouse models, here we show that PD-L1 from disparate cellular sources, including tumour cells, myeloid or other immune cells can similarly modulate the degree of cytotoxic T-cell function and activity in the tumour microenvironment. PD-L1 expression in both the host and tumour compartment contribute to immune suppression in a non-redundant fashion, suggesting that both sources could be predictive of sensitivity to therapeutic agents targeting the PD-L1/PD-1 axis.

Reproducible pharmacogenomic profiling of cancer cell line panels.

The use of large-scale genomic and drug response screening of cancer cell lines depends crucially on the reproducibility of results. Here we consider two previously published screens, plus a later critique of these studies. Using independent data, we show that consistency is achievable, and provide a systematic description of the best laboratory and analysis practices for future studies.

Musashi-2 controls cell fate, lineage bias, and TGF-ß signaling in HSCs.

Hematopoietic stem cells (HSCs) are maintained through the regulation of symmetric and asymmetric cell division. We report that conditional ablation of the RNA-binding protein Msi2 results in a failure of HSC maintenance and engraftment caused by a loss of quiescence and increased commitment divisions. Contrary to previous studies, we found that these phenotypes were independent of Numb. Global transcriptome profiling and RNA target analysis uncovered Msi2 interactions at multiple nodes within pathways that govern RNA translation, stem cell function, and TGF-ß signaling. Msi2-null HSCs are insensitive to TGF-ß-mediated expansion and have decreased signaling output, resulting in a loss of myeloid-restricted HSCs and myeloid reconstitution. Thus, Msi2 is an important regulator of the HSC translatome and balances HSC homeostasis and lineage bias.

Ubiquitously transcribed genes use alternative polyadenylation to achieve tissue-specific expression.

More than half of human genes use alternative cleavage and polyadenylation (ApA) to generate mRNA transcripts that differ in the lengths of their 3' untranslated regions (UTRs), thus altering the post-transcriptional fate of the message and likely the protein output. The extent of 3' UTR variation across tissues and the functional role of ApA remain poorly understood. We developed a sequencing method called 3'-seq to quantitatively map the 3' ends of the transcriptome of diverse human tissues and isogenic transformation systems. We found that cell type-specific gene expression is accomplished by two complementary programs. Tissue-restricted genes tend to have single 3' UTRs, whereas a majority of ubiquitously transcribed genes generate multiple 3' UTRs. During transformation and differentiation, single-UTR genes change their mRNA abundance levels, while multi-UTR genes mostly change 3' UTR isoform ratios to achieve tissue specificity. However, both regulation programs target genes that function in the same pathways and processes that characterize the new cell type. Instead of finding global shifts in 3' UTR length during transformation and differentiation, we identify tissue-specific groups of multi-UTR genes that change their 3' UTR ratios; these changes in 3' UTR length are largely independent from changes in mRNA abundance. Finally, tissue-specific usage of ApA sites appears to be a mechanism for changing the landscape targetable by ubiquitously expressed microRNAs.

Genome-wide identification of miRNA targets by PAR-CLIP.

miRNAs are short (20-23 nt) RNAs that are loaded into proteins of the Argonaute (AGO) family and guide them to partially complementary target sites on mRNAs, resulting in mRNA destabilization and/or translational repression. It is estimated that about 60% of the mammalian genes are potentially regulated by miRNAs, and therefore methods for experimental miRNA target determination have become valuable tools for the characterization of posttranscriptional gene regulation. Here we present a step-by-step protocol and guidelines for the computational analysis for the large-scale identification of miRNA target sites in cultured cells by photoactivatable ribonucleoside enhanced crosslinking and immunoprecipitation (PAR-CLIP) of AGO proteins.

A predictive model of the oxygen and heme regulatory network in yeast.

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included.

Learning regulatory programs that accurately predict differential expression with MEDUSA.

Inferring gene regulatory networks from high-throughput genomic data is one of the central problems in computational biology. In this paper, we describe a predictive modeling approach for studying regulatory networks, based on a machine learning algorithm called MEDUSA. MEDUSA integrates promoter sequence, mRNA expression, and transcription factor occupancy data to learn gene regulatory programs that predict the differential expression of target genes. Instead of using clustering or correlation of expression profiles to infer regulatory relationships, MEDUSA determines condition-specific regulators and discovers regulatory motifs that mediate the regulation of target genes. In this way, MEDUSA meaningfully models biological mechanisms of transcriptional regulation. MEDUSA solves the problem of predicting the differential (up/down) expression of target genes by using boosting, a technique from statistical learning, which helps to avoid overfitting as the algorithm searches through the high-dimensional space of potential regulators and sequence motifs. Experimental results demonstrate that MEDUSA achieves high prediction accuracy on held-out experiments (test data), that is, data not seen in training. We also present context-specific analysis of MEDUSA regulatory programs for DNA damage and hypoxia, demonstrating that MEDUSA identifies key regulators and motifs in these processes. A central challenge in the field is the difficulty of validating reverse-engineered networks in the absence of a gold standard. Our approach of learning regulatory programs provides at least a partial solution for the problem: MEDUSA's prediction accuracy on held-out data gives a concrete and statistically sound way to validate how well the algorithm performs. With MEDUSA, statistical validation becomes a prerequisite for hypothesis generation and network building rather than a secondary consideration.