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2021 ◽  
Vol 118 (49) ◽  
pp. e2026668118
Author(s):  
Donghwa Kim ◽  
Alina Tokmakova ◽  
Lauren K. Lujan ◽  
Hannah R. Strzelinski ◽  
Nicholas Kim ◽  
...  

G protein–coupled receptors display multifunctional signaling, offering the potential for agonist structures to promote conformational selectivity for biased outputs. For β2-adrenergic receptors (β2AR), unbiased agonists stabilize conformation(s) that evoke coupling to Gαs (cyclic adenosine monophosphate [cAMP] production/human airway smooth muscle [HASM] cell relaxation) and β-arrestin engagement, the latter acting to quench Gαs signaling, contributing to receptor desensitization/tachyphylaxis. We screened a 40-million-compound scaffold ranking library, revealing unanticipated agonists with dihydroimidazolyl-butyl-cyclic urea scaffolds. The S-stereoisomer of compound C1 shows no detectable β-arrestin engagement/signaling by four methods. However, C1-S retained Gαs signaling—a divergence of the outputs favorable for treating asthma. Functional studies with two models confirmed the biasing: β2AR-mediated cAMP signaling underwent desensitization to the unbiased agonist albuterol but not to C1-S, and desensitization of HASM cell relaxation was observed with albuterol but not with C1-S. These HASM results indicate biologically pertinent biasing of C1-S, in the context of the relevant physiologic response, in the human cell type of interest. Thus, C1-S was apparently strongly biased away from β-arrestin, in contrast to albuterol and C5-S. C1-S structural modeling and simulations revealed binding differences compared with unbiased epinephrine at transmembrane (TM) segments 3,5,6,7 and ECL2. C1-S (R2 = cyclohexane) was repositioned in the pocket such that it lost a TM6 interaction and gained a TM7 interaction compared with the analogous unbiased C5-S (R2 = benzene group), which appears to contribute to C1-S biasing away from β-arrestin. Thus, an agnostic large chemical-space library identified agonists with receptor interactions that resulted in relevant signal splitting of β2AR actions favorable for treating obstructive lung disease.


Author(s):  
Maria Mircea ◽  
Stefan Semrau

On its path from a fertilized egg to one of the many cell types in a multicellular organism, a cell turns the blank canvas of its early embryonic state into a molecular profile fine-tuned to achieve a vital organismal function. This remarkable transformation emerges from the interplay between dynamically changing external signals, the cell's internal, variable state, and tremendously complex molecular machinery; we are only beginning to understand. Recently developed single-cell omics techniques have started to provide an unprecedented, comprehensive view of the molecular changes during cell-type specification and promise to reveal the underlying gene regulatory mechanism. The exponentially increasing amount of quantitative molecular data being created at the moment is slated to inform predictive, mathematical models. Such models can suggest novel ways to manipulate cell types experimentally, which has important biomedical applications. This review is meant to give the reader a starting point to participate in this exciting phase of molecular developmental biology. We first introduce some of the principal molecular players involved in cell-type specification and discuss the important organizing ability of biomolecular condensates, which has been discovered recently. We then review some of the most important single-cell omics methods and relevant findings they produced. We devote special attention to the dynamics of the molecular changes and discuss methods to measure them, most importantly lineage tracing. Finally, we introduce a conceptual framework that connects all molecular agents in a mathematical model and helps us make sense of the experimental data.


Author(s):  
Antonia Malinova ◽  
Lisa Veghini ◽  
Francisco X. Real ◽  
Vincenzo Corbo

Infidelity to cell fate occurs when differentiated cells lose their original identity and either revert to a more multipotent state or transdifferentiate into a different cell type, either within the same embryonic lineage or in an entirely different one. Whilst in certain circumstances, such as in wound repair, this process is beneficial, it can be hijacked by cancer cells to drive disease initiation and progression. Cell phenotype switching has been shown to also serve as a mechanism of drug resistance in some epithelial cancers. In pancreatic ductal adenocarcinoma (PDAC), the role of lineage infidelity and phenotype switching is still unclear. Two consensus molecular subtypes of PDAC have been proposed that mainly reflect the existence of cell lineages with different degrees of fidelity to pancreatic endodermal precursors. Indeed, the classical subtype of PDAC is characterised by the expression of endodermal lineage specifying transcription factors, while the more aggressive basal-like/squamous subtype is defined by epigenetic downregulation of endodermal genes and alterations in chromatin modifiers. Here, we summarise the current knowledge of mechanisms (genetic and epigenetic) of cell fate switching in PDAC and discuss how pancreatic organoids might help increase our understanding of both cell-intrinsic and cell-extrinsic factors governing lineage infidelity during the distinct phases of PDAC evolution.


2021 ◽  
pp. gr.275944.121
Author(s):  
William Gao ◽  
Carlos J Gallardo-Dodd ◽  
Claudia Kutter

The correlation between codon and anticodon pools influences the efficiency of translation, but whether differences exist in these pools across individual cells is unknown. We determined that codon usage and amino acid demand are highly stable across different cell types using available mouse and human single-cell RNA sequencing atlases. After demonstrating the robustness of ATAC-seq measurements for the analysis of tRNA gene usage, we quantified anticodon usage and amino acid supply in both mouse and human single-cell ATAC-seq atlases. We found that tRNA gene usage is overall coordinated across cell types, except in neurons, which clustered separately from other cell types. Integration of these datasets revealed a strong and statistically significant correlation between amino acid supply and demand across almost all cell types. Neurons have an enhanced translation efficiency over other cell types, driven by an increased supply of tRNAAla (AGC) anticodons. This results in faster decoding of the Ala-GCC codon, as determined by cell type-specific ribosome profiling, suggesting that the reduction of tRNAAla (AGC) anticodon pools may be implicated in neurological pathologies. This study, the first such examination of codon usage, anticodon usage, and translation efficiency resolved at the cell type level with single-cell information, identifies a conserved landscape of translation elongation across mammalian cellular diversity and evolution.


2021 ◽  
Author(s):  
Logan Brase ◽  
Shih-Feng You ◽  
Jorge L Del-Aguila ◽  
Yaoyi Dai ◽  
Brenna C Novotny ◽  
...  

Background Alzheimer disease (AD) has substantial genetic, molecular, and cellular heterogeneity associated with its etiology. Much of our current understanding of the main AD molecular events associated with the amyloid hypothesis (APP, PSEN1 and PSEN2) and neuroimmune modulation (TREM2 and MS4A) is based on genetic studies including GWAS. However, the functional genes, downstream transcriptional ramifications, and the cell-type-specific effects of many GWAS loci remain poorly understood. Understanding these effects can point us to the cellular processes involved in AD and uncover potential therapeutic targets. Methods We applied a genetic-based approach to our sample selection; our cohort included carriers of AD pathogenic mutations (APP, PSEN1), risk variants in TREM2, and the resilience variant (rs1582763) in the MS4A cluster associated with cerebrospinal fluid (CSF) soluble TREM2 levels. We performed single-nucleus RNA-sequencing (snRNA-seq) of 1,102,459 nuclei from the human parietal cortex of these carriers. Following initial unbiased clustering and cell-type annotation, we performed deep subclustering analysis per cell type to identify unique cellular transcriptional states associated with these genetic variants. We identified differentially expressed genes between cell states and genetic variant carriers/controls, and performed differential cell proportion analyses to determine key differences among these carriers. We analyzed sequencing data from human dorsolateral prefrontal cortex and mouse models to replicate the enrichment of unique cell states in genetic variant carriers. Finally, we leveraged these cell-state differential expression results to link genes in AD GWAS loci to their functional cell types. Findings We identified cell-specific expression states influenced by AD genetic factors for neurons and glia. Autosomal dominant AD (ADAD) brains exhibited unique transcriptional states in all cell types. TREM2 variant carrier brains were also enriched for specific microglia and oligodendrocyte subpopulations. Carriers of the resilience MS4A variant were enriched for an altered activated-microglia expression state. We mapped AD GWAS genes to their potential functional cell types, and some, including PLCG2 and SORL1, were expressed in a broader range of brain cell types than previously reported. Interpretation AD pathogenic, risk, or resilience variants are sufficient to alter the transcriptional and cellular landscape of human brains. Overall, our results suggest that the genetic architecture contributes to the cortical cellular heterogeneity associated with disease status, which is a critical factor to consider when designing drug trials and selecting the treatment program for AD patients. Our findings suggest that integrating genetic and single-cell molecular data facilitates our understanding of the heterogeneity of pathways, biological processes and cell types modulated by genetic risk factors for AD. Funding US National Institutes of Health, Hope Center, Archer foundation, Alzheimer Association, CZI.


2021 ◽  
Author(s):  
Mickaël Mendez ◽  
Jayson Harshbarger ◽  
Michael M. Hoffman

Background: Identifying key transcriptional features, such as genes or transcripts, involved in cellular differentiation remains a challenging problem. Current methods for identifying key transcriptional features predominantly rely on pairwise comparisons among different cell types. These methods also identify long lists of differentially expressed transcriptional features. Combining the results from many such pairwise comparisons to find the transcriptional features specific only to one cell type is not straightforward. Thus, one must have a principled method for amalgamating pairwise cell type comparisons that makes full use of prior knowledge about the developmental relationships between cell types. Method: We developed Cell Lineage Analysis (CLA), a computational method which identifies transcriptional features with expression patterns that discriminate cell types, incorporating Cell Ontology knowledge on the relationship between different cell types. CLA uses random forest classification with a stratified bootstrap to increase the accuracy of binary classifiers when each cell type have a different number of samples. Regularized random forest results in a classifier that selects few but important transcriptional features. For each cell type pair, CLA runs multiple instances of regularized random forest and reports the transcriptional features consistently selected. CLA not only discriminates individual cell types but can also discriminate lineages of cell types related in the developmental hierarchy. Results: We applied CLA to Functional Annotation of the Mammalian Genome 5 (FANTOM5) data and identified discriminative transcription factor and long non-coding RNA (lncRNA) genes for 71 human cell types. With capped analysis of gene expression (CAGE) data, CLA identified individual cell-type–specific alternative promoters for cell surface markers. Compared to random forest with a standard bootstrap approach, CLA's stratified bootstrap approach improved the accuracy of gene expression classification models for more than 95% of 2060 cell type pairs examined. Applied on 10X Genomics single-cell RNA-seq data for CD14+ monocytes and FCGR3A+ monocytes, CLA selected only 13 discriminative genes. These genes included the top 9 out of 370 significantly differentially expressed genes obtained from conventional differential expression analysis methods. Discussion: Our CLA method combines tools to simplify the interpretation of transcriptome datasets from many cell types. It automates the identification of the most differentially expressed genes for each cell type pairs CLA's lineage score allows easy identification of the best transcriptional markers for each cell type and lineage in both bulk and single-cell transcriptomic data. Availability: CLA is available at https://cla.hoffmanlab.org. We deposited the version of the CLA source with which we ran our experiments at https://doi.org/10.5281/zenodo.3630670. We deposited other analysis code and results at https://doi.org/10.5281/zenodo.5735636.


2021 ◽  
Author(s):  
Yunhee Jeong ◽  
Reka Toth ◽  
Marlene Ganslmeier ◽  
Kersten Breuer ◽  
Christoph Plass ◽  
...  

DNA methylation sequencing is becoming increasingly popular, yielding genome-wide methylome data at single-base pair resolution through the novel cost- and labor-optimized protocols. It has tremendous potential for cell-type heterogeneity analysis, particularly in tumors, due to intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, their systematic evaluation has not been performed so far. Here, we thoroughly review and evaluate five previously published deconvolution methods: Bayesian epiallele detection (BED), PRISM, csmFinder + coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation. Accordingly, we individually assessed the performance of each step and demonstrated the impact of the former step upon the performance of the following one. In conclusion, we demonstrate the best method showing the highest accuracy in different samples, and infer factors affecting cell-type deconvolution performance according to the number of cell types in the mixture. We found that cell-type deconvolution performance is influenced by different factors according to the number of components in the mixture. Whereas selecting similar genomic regions to DMRs generally contributed to increasing the performance in bi-component mixtures, the uniformity of cell-type distribution showed a high correlation with the performance in five cell-type bulk analyses.


Author(s):  
Alanna G. Spiteri ◽  
Claire L. Wishart ◽  
Roger Pamphlett ◽  
Giuseppe Locatelli ◽  
Nicholas J. C. King

AbstractIn neurological diseases, the actions of microglia, the resident myeloid cells of the CNS parenchyma, may diverge from, or intersect with, those of recruited monocytes to drive immune-mediated pathology. However, defining the precise roles of each cell type has historically been impeded by the lack of discriminating markers and experimental systems capable of accurately identifying them. Our ability to distinguish microglia from monocytes in neuroinflammation has advanced with single-cell technologies, new markers and drugs that identify and deplete them, respectively. Nevertheless, the focus of individual studies on particular cell types, diseases or experimental approaches has limited our ability to connect phenotype and function more widely and across diverse CNS pathologies. Here, we critically review, tabulate and integrate the disease-specific functions and immune profiles of microglia and monocytes to provide a comprehensive atlas of myeloid responses in viral encephalitis, demyelination, neurodegeneration and ischemic injury. In emphasizing the differential roles of microglia and monocytes in the severe neuroinflammatory disease of viral encephalitis, we connect inflammatory pathways common to equally incapacitating diseases with less severe inflammation. We examine these findings in the context of human studies and highlight the benefits and inherent limitations of animal models that may impede or facilitate clinical translation. This enables us to highlight common and contrasting, non-redundant and often opposing roles of microglia and monocytes in disease that could be targeted therapeutically.


2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Chao Li ◽  
Adilson Fonseca Teixeira ◽  
Hong-Jian Zhu ◽  
Peter ten Dijke

AbstractTo identify novel cancer therapies, the tumor microenvironment (TME) has received a lot of attention in recent years in particular with the advent of clinical successes achieved by targeting immune checkpoint inhibitors (ICIs). The TME consists of multiple cell types that are embedded in the extracellular matrix (ECM), including immune cells, endothelial cells and cancer associated fibroblasts (CAFs), which communicate with cancer cells and each other during tumor progression. CAFs are a dominant and heterogeneous cell type within the TME with a pivotal role in controlling cancer cell invasion and metastasis, immune evasion, angiogenesis and chemotherapy resistance. CAFs mediate their effects in part by remodeling the ECM and by secreting soluble factors and extracellular vesicles. Exosomes are a subtype of extracellular vesicles (EVs), which contain various biomolecules such as nucleic acids, lipids, and proteins. The biomolecules in exosomes can be transmitted from one to another cell, and thereby affect the behavior of the receiving cell. As exosomes are also present in circulation, their contents can also be explored as biomarkers for the diagnosis and prognosis of cancer patients. In this review, we concentrate on the role of CAFs-derived exosomes in the communication between CAFs and cancer cells and other cells of the TME. First, we introduce the multiple roles of CAFs in tumorigenesis. Thereafter, we discuss the ways CAFs communicate with cancer cells and interplay with other cells of the TME, and focus in particular on the role of exosomes. Then, we elaborate on the mechanisms by which CAFs-derived exosomes contribute to cancer progression, as well as and the clinical impact of exosomes. We conclude by discussing aspects of exosomes that deserve further investigation, including emerging insights into making treatment with immune checkpoint inhibitor blockade more efficient.


2021 ◽  
Vol 15 ◽  
Author(s):  
Jacob H. Hines

Oligodendrocytes are multifunctional central nervous system (CNS) glia that are essential for neural function in gnathostomes. The evolutionary origins and specializations of the oligodendrocyte cell type are among the many remaining mysteries in glial biology and neuroscience. The role of oligodendrocytes as CNS myelinating glia is well established, but recent studies demonstrate that oligodendrocytes also participate in several myelin-independent aspects of CNS development, function, and maintenance. Furthermore, many recent studies have collectively advanced our understanding of myelin plasticity, and it is now clear that experience-dependent adaptations to myelination are an additional form of neural plasticity. These observations beg the questions of when and for which functions the ancestral oligodendrocyte cell type emerged, when primitive oligodendrocytes evolved new functionalities, and the genetic changes responsible for these evolutionary innovations. Here, I review recent findings and propose working models addressing the origins and evolution of the oligodendrocyte cell type and adaptive myelination. The core gene regulatory network (GRN) specifying the oligodendrocyte cell type is also reviewed as a means to probe the existence of oligodendrocytes in basal vertebrates and chordate invertebrates.


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