Biology subjects

Kellis, M.

Publications and source records attributed to Kellis, M..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Programmed ribosomal frameshifting during PLEKHM2 mRNA decoding generates a constitutively active mediator of kinesin-1-dependent lysosome transport

Programmed ribosomal frameshifting is a process where a proportion of ribosomes change their reading frame on an mRNA1, rephasing the ribosome relative to the mRNA. While frameshifting is commonly employed by viruses2, very few phylogenetically conserved examples are known in nuclear encoded genes and some of the evidence is controversial3,4. Here we report a +1 frameshifting event during decoding of the human gene PLEKHM25. This frameshifting occurs at the sequence UCC_UUU_CGG, which is conserved in vertebrates and is similar to an influenza virus sequence that frameshifts with similar efficiency6,7. The new C-terminal domain generated by this frameshift forms an -helix, which relieves PLEKHM2 from autoinhibition and allows it to move to the tips of cells via association with kinesin-1 without requiring activation by ARL8. Reintroducing both the canonically-translated and frameshifted protein are necessary to restore normal contractile function of PLEKHM2-knockout cardiomyocytes, demonstrating the necessity of frameshifting for normal cardiac activity.

molecular biology↗

bioRxiv2024

Epigenetic control of metabolic identity across cell types

BackgroundConstraint-based network modeling is a powerful genomic-scale approach for analyzing cellular metabolism, capturing metabolic variations across tissues and cell types, and defining the metabolic identity essential for identifying disease-associated transcriptional states. ResultsUsing RNA-seq and epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC), we reconstructed metabolic networks for 1,555 samples spanning 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell-type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression. DiscussionThis integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.

systems biology↗

bioRxiv2024

Mature and migratory dendritic cells promote immune infiltration and response to anti-PD-1 checkpoint blockade in metastatic melanoma

Immune checkpoint inhibitors (ICIs) have heralded a remarkable shift in cancer care, significantly extending survival for advanced cancer patients. However, despite their remarkable clinical successes, a substantial majority of patients fail to achieve a lasting response to ICIs. To address this challenge and gain insights into the complex landscape of the tumor microenvironment (TME), we conducted an extensive analysis using single-cell RNA sequencing (scRNA; [~]216K cells across 39 samples) and single-nucleus transposase-accessible chromatin sequencing (snATAC; [~]43K cells from 15 samples) in a metastatic melanoma cohort. This systematic approach delineates 14 distinct cell types and 55 cell subtypes, including the identification of 15 transcriptional hallmarks of malignant cells. Through correlation analysis of cell subtype proportions, we unveiled six distinct clusters associated with varying tumor responses. Particularly intriguing was the identification of the mature DC enriched in immunoregulatory molecules (mregDC) subtype exhibiting correlations with naive T and B cells, forming an anti-tumor program that underscores the importance of multiple immune cell types in mediating anti-tumor immunity. Notably, we found that mregDC abundance represents a good prognostic predictor of progression-free survival (PFS) in the context of ICI treatment, and when combined with the TCF7+/- CD8 T cell ratio, it reliably predicts patient PFS across treatments beyond ICI. We validated our findings using an independent cohort of 274 ICI-treated melanoma samples analyzed using tissue-level expression. We next compared mregDCs and conventional dendritic cell types 1 and 2 (cDC1 and cDC2) using transcriptome signature, differentiation trajectory, interactome, cytokine milieu, and epigenome landscape analyses. This comparative analysis shed light on the unique attributes of mregDCs within the TME. Finally, we investigated cell type/subtype-specific genes, pathways, immune response enrichment, and ligand-receptor interactions closely associated with the proportions of mregDCs within the TME. These molecular and cellular insights, with their critical roles in enhancing the immune response against cancer, offer valuable prospects for predicting the efficacy of ICI regimens, and potentially guiding the selection of rational combinatorial therapies.

cancer biology↗

bioRxiv2024

TDC-2: Multimodal Foundation for Therapeutic Science

Drug discovery AI datasets and benchmarks have not traditionally included single-cell analysis biomarkers. While benchmarking efforts in single-cell analysis have recently released collections of single-cell tasks, they have yet to comprehensively release datasets, models, and benchmarks that integrate a broad range of therapeutic discovery tasks with cell-type-specific biomarkers. Therapeutics Commons (TDC-2) presents datasets, tools, models, and benchmarks integrating cell-type-specific contextual features with ML tasks across therapeutics. We present four tasks for contextual learning at single-cell resolution: drug-target nomination, genetic perturbation response prediction, chemical perturbation response prediction, and protein-peptide interaction prediction. We introduce datasets, models, and benchmarks for these four tasks. Finally, we detail the advancements and challenges in machine learning and biology that drove the implementation of TDC-2 and how they are reflected in its architecture, datasets and benchmarks, and foundation model tooling.

bioinformatics↗

bioRxiv2024

Specific oncogene activation of the cell of origin in mucosal melanoma

Mucosal melanoma (MM) is a deadly cancer derived from mucosal melanocytes. To test the consequences of MM genetics, we developed a zebrafish model in which all melanocytes experienced CCND1 expression and loss of PTEN and TP53. Surprisingly, melanoma only developed from melanocytes lining internal organs, analogous to the location of patient MM. We found that zebrafish MMs had a unique chromatin landscape from cutaneous melanoma. Internal melanocytes could be labeled using a MM-specific transcriptional enhancer. Normal zebrafish internal melanocytes shared a gene expression signature with MMs. Patient and zebrafish MMs have increased migratory neural crest gene and decreased antigen presentation gene expression, consistent with the increased metastatic behavior and decreased immunotherapy sensitivity of MM. Our work suggests the cell state of the originating melanocyte influences the behavior of derived melanomas. Our animal model phenotypically and transcriptionally mimics patient tumors, allowing this model to be used for MM therapeutic discovery.

cancer biology↗

bioRxiv2024

Biophysical characterization of high-confidence, small human proteins

Significant efforts have been made to characterize the biophysical properties of proteins. Small proteins have received less attention because their annotation has historically been less reliable. However, recent improvements in sequencing, proteomics, and bioinformatics techniques have led to the high-confidence annotation of small open reading frames (smORFs) that encode for functional proteins, producing smORF-encoded proteins (SEPs). SEPs have been found to perform critical functions in several species, including humans. While significant efforts have been made to annotate SEPs, less attention has been given to the biophysical properties of these proteins. We characterized the distributions of predicted and curated biophysical properties, including sequence composition, structure, localization, function, and disease association of a conservative list of previously identified human SEPs. We found significant differences between SEPs and both larger proteins and control sets. Additionally, we provide an example of how our characterization of biophysical properties can contribute to distinguishing protein-coding smORFs from non-coding ones in otherwise ambiguous cases. Why it MattersThe identification of proteins smaller than 100 amino acids has lagged being that of larger proteins in part because their small size makes proteomic and genomic approaches more challenging. However, small proteins can perform significant functions. There are now enough proteins with high confidence of function to enable meaningful comparison with larger proteins, and to determine whether putative small proteins might be identified, based on their biophysical properties. As an example, we found that small proteins often contain transmembrane helices but are relatively devoid of beta sheets, which helped to support the annotation of a putative protein as functional. We also identified a wide range of biological functions and localization for small proteins, including many with disease associations.

biophysics↗

bioRxiv2024

The critical role of isomiRs in accurate differential expression analysis of miRNA-seq data

MicroRNAs (miRNAs) are crucial for the regulation of gene expression and are promising biomarkers and therapeutic targets. miRNA isoforms (isomiRs) differ in their start/end offsets, which can impact the target gene selection and non-canonical function of the miRNA species. In addition, isomiRs frequently differ in their expression patterns from their parent miRNAs, yet their roles and tissue-specific responses are currently understudied, leading to their typical omission in miRNA research. Here, we evaluate the expression differences of isomiRs across conditions and their impact on standard miRNA-seq quantification results. We analyze 28 public miRNA-seq datasets, showing significant expression pattern differences between the isomiRs and their corresponding reference miRNAs, leading to misinterpretation of differential expression signals for both. As a case study, we generate a new dataset assessing isomiR abundance under hypoxia in human endothelial cells between the nuclear and cytosolic compartments. The results suggest that isomiRs are dramatically altered in their nuclear localization in response to hypoxia, indicating a potential non-canonical effect of the species, which would be missed without isomiR-aware analysis. Our results call for a comprehensive re-evaluation of the miRNA-seq analysis practices.

bioinformatics↗

bioRxiv2024

Deep learning modeling of ribosome profiling reveals regulatory underpinnings of translatome and interprets disease variants

Gene expression involves transcription and translation. Despite large datasets and increasingly powerful methods devoted to calculating genetic variants effects on transcription, discrepancy between mRNA and protein levels hinders the systematic interpretation of the regulatory effects of disease-associated variants. Accurate models of the sequence determinants of translation are needed to close this gap and to interpret disease-associated variants that act on translation. Here, we present Translatomer, a multimodal transformer framework that predicts cell-type-specific translation from mRNA expression and gene sequence. We train Translatomer on 33 tissues and cell lines, and show that the inclusion of sequence substantially improves the prediction of ribosome profiling signal, indicating that Translatomer captures sequence-dependent translational regulatory information. Translatomer achieves accuracies of 0.72 to 0.80 for de novo prediction of cell-type-specific ribosome profiling. We develop an in silico mutagenesis tool to estimate mutational effects on translation and demonstrate that variants associated with translation regulation are evolutionarily constrained, both within the human population and across species. Notably, we identify cell-type-specific translational regulatory mechanisms independent of eQTLs for 3,041 non-coding and synonymous variants associated with complex diseases, including Alzheimers disease, schizophrenia, and congenital heart disease. Translatomer accurately models the genetic underpinnings of translation, bridging the gap between mRNA and protein levels, and providing valuable mechanistic insights toward mapping "missing regulation" in disease genetics.

genomics↗

bioRxiv2024

Disrupting CD38-driven T cell dysfunction restores sensitivity to cancer immunotherapy

A central problem in cancer immunotherapy with immune checkpoint blockade (ICB) is the development of resistance, which affects 50% of patients with metastatic melanoma1,2. T cell exhaustion, resulting from chronic antigen exposure in the tumour microenvironment, is a major driver of ICB resistance3. Here, we show that CD38, an ecto-enzyme involved in nicotinamide adenine dinucleotide (NAD+) catabolism, is highly expressed in exhausted CD8+ T cells in melanoma and is associated with ICB resistance. Tumour-derived CD38hiCD8+ T cells are dysfunctional, characterised by impaired proliferative capacity, effector function, and dysregulated mitochondrial bioenergetics. Genetic and pharmacological blockade of CD38 in murine and patient-derived organotypic tumour models (MDOTS/PDOTS) enhanced tumour immunity and overcame ICB resistance. Mechanistically, disrupting CD38 activity in T cells restored cellular NAD+ pools, improved mitochondrial function, increased proliferation, augmented effector function, and restored ICB sensitivity. Taken together, these data demonstrate a role for the CD38-NAD+ axis in promoting T cell exhaustion and ICB resistance, and establish the efficacy of CD38 directed therapeutic strategies to overcome ICB resistance using clinically relevant, patient-derived 3D tumour models.

cancer biology↗

bioRxiv2024

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.

bioinformatics↗

bioRxiv2023

Contextualized Networks Reveal Heterogeneous Transcriptomic Regulation in Tumors at Sample-Specific Resolution

Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory networks (GRNs) from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, limiting GRNs to cluster-level analyses that ignore intra-cluster heterogeneity. We propose to move beyond coarse analyses of pre-defined subgroups by using contextualized learning, a multi-task learning paradigm that uses multi-view contexts including phenotypic, molecular, and environmental information to infer personalized models. With sample-specific contexts, contextualization enables sample-specific models and even generalizes at test time to predict network models for entirely unseen contexts. We unify three network model classes (Correlation, Markov, Neighborhood Selection) and estimate context-specific GRNs for 7997 tumors across 25 tumor types, using copy number and driver mutation profiles, tumor microenvironment, and patient demographics as model context. Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology by providing a structured view of expression dynamics at sample-specific resolution, explaining known biomarkers in terms of network-mediated effects and leading to novel subtypings that improve survival prognosis. We provide a SKLearn-style Python package https://contextualized.ml for learning and analyzing contextualized models, as well as interactive plotting tools for pan-cancer data exploration at https://github.com/cnellington/CancerContextualized. Significance StatementNetwork estimation is essential for understanding the structure and function of biological systems, but current statistical approaches fail to capture inter-subject heterogeneity or cross-modality information flow, both of which are needed for understanding complex phenotypes and pathologies. We introduce contextualized network inference, leveraging multi-view contextual metadata to capture similarities and differences among heterogeneous observations during network estimation. Sharing information across contexts enables inference at sample-specific resolution, thus quantifying variation between subjects and revealing context-specific network rewiring. Applied to tumor-specific transcriptional network inference using clinical, molecular, and multi-omic data, contextualized networks improve accuracy, generalize to unseen cancer types, and discover novel prognostic tumor subtypes. By tailoring disease models to each sample, contextualized networks promise to enable precision medicine at unprecedented resolution.

bioinformatics↗

bioRxiv2023

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 element-gene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancer-promoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

genomics↗

bioRxiv2023

Integrating single-cell RNA-seq datasets with substantial batch effects

Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard part of the analysis, with conditional variational autoencoders (cVAE) being among the most popular approaches. Increasingly, researchers are asking to map cells across challenging cases such as cross-organs, species, or organoids and primary tissue, as well as different scRNA-seq protocols, including single-cell and single-nuclei. Current computational methods struggle to harmonize datasets with such substantial differences, driven by technical or biological variation. Here, we propose to address these challenges for the popular cVAE-based approaches by introducing and comparing a series of regularization constraints. The two commonly used strategies for increasing batch correction in cVAEs, that is Kullback-Leibler divergence (KL) regularization strength tuning and adversarial learning, suffer from substantial loss of biological information. Therefore, we adapt, implement, and assess alternative regularization strategies for cVAEs and investigate how they improve batch effect removal or better preserve biological variation, enabling us to propose an optimal cVAE-based integration strategy for complex systems. We show that using a VampPrior instead of the commonly used Gaussian prior not only improves the preservation of biological variation but also unexpectedly batch correction. Moreover, we show that our implementation of cycle-consistency loss leads to significantly better biological preservation than adversarial learning implemented in the previously proposed GLUE model. Additionally, we do not recommend relying only on the KL regularization strength tuning for increasing batch correction, as it removes both biological and batch information without discriminating between the two. Based on our findings, we propose a new model that combines VampPrior and cycle-consistency loss. We show that using it for datasets with substantial batch effects improves downstream interpretation of cell states and biological conditions. To ease the use of the newly proposed model, we make it available in the scvi-tools package as an external model named sysVI. Moreover, in the future, these regularization techniques could be added to other established cVAE-based models to improve the integration of datasets with substantial batch effects.

bioinformatics↗

bioRxiv2023

A single-cell atlas of ABCA7 loss-of-function reveals lipid disruptions, mitochondrial dysfunction and DNA damage in neurons

Loss-of-function (LoF) variants in the lipid transporter ABCA7 significantly increase Alzheimers disease risk (odds ratio {approx} 2), yet the underlying pathogenic mechanisms and specific neural cell types affected remain unclear. To investigate this, we generated a single-nucleus RNA sequencing atlas of 36 human postmortem prefrontal cortex samples, including 12 carriers of ABCA7 LoF variants and 24 matched non-carriers. ABCA7 LoF variants were associated with transcriptional changes across all major neural cell types. Excitatory neurons, which expressed the highest levels of ABCA7, showed significant alterations in oxidative phosphorylation, lipid metabolism, DNA damage responses, and synaptic signaling pathways. ABCA7 LoF-associated transcriptional changes in neurons were similarly perturbed in carriers of the common AD missense variant ABCA7 p.Ala1527Gly (n = 240 controls, 135 carriers) predicted by molecular dynamic simulations to disrupt ABCA7 structure -, indicating that findings from our study may extend to large portions of the at-risk population. Human induced pluripotent stem cell (iPSC)-derived neurons carrying ABCA7 LoF variants closely recapitulated the transcriptional changes observed in human postmortem neurons. Biochemical experiments further demonstrated that ABCA7 LoF disrupts mitochondrial membrane potential via regulated uncoupling, increases oxidative stress, and alters phospholipid homeostasis in neurons, notably elevating saturated phosphatidylcholine levels. Supplementation with CDP-choline to enhance de novo phosphatidylcholine synthesis effectively reversed these transcriptional changes, restored mitochondrial uncoupling, and reduced oxidative stress. Additionally, CDP-choline normalized amyloid-{beta} secretion and alleviated neuronal hyperexcitability in ABCA7 LoF neurons. This study provides a detailed transcriptomic profile of ABCA7 LoF-induced changes and highlights phosphatidylcholine metabolism as a key driver in ABCA7-induced risk. Our findings suggest a promising therapeutic approach that may benefit a large proportion of individuals at increased risk for Alzheimers disease.

neuroscience↗

bioRxiv2023

Engineered 3D Immuno-Glial-Neurovascular Human Brain Model

Patient-specific, human-based cellular models integrating a biomimetic blood-brain barrier (BBB), immune, and myelinated neuron components are critically needed to enable accelerated, translationally relevant discovery of neurological disease mechanisms and interventions. By engineering a novel brain-mimicking 3D hydrogel and co-culturing all six major brain cell types derived from patient iPSCs, we have constructed, characterized, and utilized a multicellular integrated brain (miBrain) immuno-glial-neurovascular model with in vivo-like hallmarks inclusive of neuronal activity, functional connectivity, barrier function, myelin-producing oligodendrocyte engagement with neurons, multicellular interactions, and transcriptomic profiles. We implemented the model to study Alzheimers Disease pathologies associated with APOE4 genetic risk. APOE4 miBrains differentially exhibit amyloid aggregation, tau phosphorylation, and astrocytic GFAP. Unlike the co-emergent fate specification of glia and neurons in organoids, miBrains integrate independently differentiated cell types, a feature we harnessed to identify that APOE4 in astrocytes promotes neuronal tau pathogenesis and dysregulation through crosstalk with microglia.

neuroscience↗

bioRxiv2023

Integrative Multi-omic Profiling of Two Human Decedents Receiving Pig Heart Xenografts Reveals Strong Perturbations in Early Immune-Cell and Cellular Metabolism Responses.

BackgroundRecent advances in xenotransplantation in living and decedent humans using pig xenografts have laid promising groundwork towards future emergency use and first in human trials. Major obstacles remain though, including a lack of knowledge of the genetic incompatibilities between pig donors and human recipients which may led to harmful immune responses against the xenograft or dysregulation of normal physiology. In 2022 two pig heart xenografts were transplanted into two brain-dead human decedents with a minimized immunosuppression regime, primarily to evaluate onset of hyper-acute antibody mediated rejection and sustained xenograft function over 3 days. MethodsWe performed multi-omic profiling to assess the dynamic interactions between the pig and human genomes in the first two pig heart-xenografts transplants into human decedents. To assess global and specific biological changes that may correlate with immune-related outcomes and xenograft function, we generated transcriptomic, lipidomic, proteomic and metabolomics datasets, across blood and tissue samples collected every 6 hours over the 3-day procedures. ResultsSingle-cell datasets in the 3-day pig xenograft-decedent models show dynamic immune activation processes. We observe specific scRNA-seq, snRNA-seq and geospatial transcriptomic changes of early immune-activation leading to pronounced downstream T-cell activity and hallmarks of early antibody mediated rejection (AbMR) and/or ischemia reperfusion injury (IRI) in the first xenograft recipient. Using longitudinal multiomic integrative analyses from blood in addition to antigen presentation pathway enrichment, we also observe in the first xeno-heart recipient significant cellular metabolism and liver damage pathway changes that correlate with profound physiological dysfunction whereas, these signals are not present in the other xenograft recipient. ConclusionsSingle-cell and multiomics approaches reveal fundamental insights into early molecular immune responses indicative of IRI and/or early AbMR in the first human decedent, which was not evident in the conventional histological evaluations.

systems biology↗

bioRxiv2023

Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing

Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to map circuits at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNA-seq, and 4,130 retrogradely labeled cells and 2,914 transsynaptically labeled cells in situ. We found that the transcriptomic identities of rabies virus-infected cells can be robustly identified using both single-cell RNA-seq and in situ sequencing. By associating gene expression with connectivity inferred from barcode sequencing, we distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.

neuroscience↗

bioRxiv2022

An Autoimmune Transcriptional Circuit Driving Foxp3+ Regulatory T cell Dysfunction

Autoimmune diseases, among the most common disorders of young adults, are mediated by genetic and environmental factors. While CD4+Foxp3+ regulatory T cells (Tregs) play a central role in preventing autoimmunity, the molecular mechanism underlying their dysfunction is unknown. Here, we performed comprehensive transcriptomic and epigenomic profiling of Tregs in the autoimmune disease multiple sclerosis (MS) to identify central transcriptional programs regulating human autoimmunity. We discovered that upregulation of a primate-specific short PRDM1 isoform (PRDM1-S) induces SGK1 independent from evolutionally conserved long PRDM1, leading to destabilization of Foxp3 and Treg dysfunction. This aberrant PRDM1-S/SGK1 axis is shared among other autoimmune diseases. Furthermore, by chromatin landscape profiling in MS Tregs we identified a PRDM1-S specific cis-regulatory element associated with enriched binding of AP-1/IRF transcription factors. Our study identifies evolutionally emerged PRDM1-S and epigenetic priming of AP-1/IRF as key drivers of pathogenic Treg programs leading to human autoimmune disease.

immunology↗