Multiplexed RNA-FISH-guided Laser Capture Microdissection RNA Sequencing Improves Breast Cancer Molecular Subtyping, Prognostic Classification, and Predicts Response to Antibody Drug Conjugates.

On a retrospective cohort of 1,082 FFPE breast tumors, we demonstrated the analytical validity of a test using multiplexed RNA-FISH-guided laser capture microdissection (LCM) coupled with RNA-sequencing (mFISHseq), which showed 93% accuracy compared to immunohistochemistry. The combination of these technologies makes strides in i) precisely assessing tumor heterogeneity, ii) obtaining pure tumor samples using LCM to ensure accurate biomarker expression and multigene testing, and iii) providing thorough and granular data from whole transcriptome profiling. We also constructed a 293-gene intrinsic subtype classifier that performed equivalent to the research based PAM50 and AIMS classifiers. By combining three molecular classifiers for consensus subtyping, mFISHseq alleviated single sample discordance, provided near perfect concordance with other classifiers (κ > 0.85), and reclassified 30% of samples into different subtypes with prognostic implications. We also use a consensus approach to combine information from 4 multigene prognostic classifiers and clinical risk to characterize high, low, and ultra-low risk patients that relapse early (< 5 years), late (> 10 years), and rarely, respectively. Lastly, to identify potential patient subpopulations that may be responsive to treatments like antibody drug-conjugates (ADC), we curated a list of 92 genes and 110 gene signatures to interrogate their association with molecular subtype and overall survival. Many genes and gene signatures related to ADC processing (e.g., antigen/payload targets, endocytosis, and lysosome activity) were independent predictors of overall survival in multivariate Cox regression models, thus highlighting potential ADC treatment-responsive subgroups. To test this hypothesis, we constructed a unique 19-feature classifier using multivariate logistic regression with elastic net that predicted response to trastuzumab emtansine (T-DM1; AUC = 0.96) better than either ERBB2 mRNA or Her2 IHC alone in the T-DM1 arm of the I-SPY2 trial. This test was deployed in a research-use only format on 26 patients and revealed clinical insights into patient selection for novel therapies like ADCs and immunotherapies and de-escalation of adjuvant chemotherapy.

Arabinose- and xylose-modified analogs of 2',3'-cGAMP act as STING agonists.

Stimulator of interferon genes (STING) agonists are promising candidates for vaccine adjuvants and antitumor immune stimulants. The most potent natural agonist of STING, 2',3'-cyclic GMP-AMP (2',3'-cGAMP), is subject to nuclease-mediated inherent metabolic instability, thereby placing limits on its clinical efficacy. Here, we report on a new class of chemically synthesized sugar-modified analogs of 2',3'-cGAMP containing arabinose and xylose sugar derivatives that bind mouse and human STING alleles with high affinity. The co-crystal structures demonstrate that such analogs act as 2',3'-cGAMP mimetics that induce the "closed" conformation of human STING. These analogs show significant resistance to hydrolysis mediated by ENPP1 and increased stability in human serum, while retaining similar potency as 2',3'-cGAMP at inducing IFN-ß secretion from human THP1 cells. The arabinose- and xylose-modified 2',3'-cGAMP analogs open a new strategy for overcoming the inherent nuclease-mediated vulnerability of natural ribose cyclic nucleotides, with the additional benefit of high translational potential as cancer therapeutics and vaccine adjuvants.

Discovery of SARS-CoV-2 antiviral synergy between remdesivir and approved drugs in human lung cells.

SARS coronavirus 2 (SARS-CoV-2) has caused an ongoing global pandemic with significant mortality and morbidity. At this time, the only FDA-approved therapeutic for COVID-19 is remdesivir, a broad-spectrum antiviral nucleoside analog. Efficacy is only moderate, and improved treatment strategies are urgently needed. To accomplish this goal, we devised a strategy to identify compounds that act synergistically with remdesivir in preventing SARS-CoV-2 replication. We conducted combinatorial high-throughput screening in the presence of submaximal remdesivir concentrations, using a human lung epithelial cell line infected with a clinical isolate of SARS-CoV-2. This identified 20 approved drugs that act synergistically with remdesivir, many with favorable pharmacokinetic and safety profiles. Strongest effects were observed with established antivirals, Hepatitis C virus nonstructural protein 5A (HCV NS5A) inhibitors velpatasvir and elbasvir. Combination with their partner drugs sofosbuvir and grazoprevir further increased efficacy, increasing remdesivir's apparent potency > 25-fold. We report that HCV NS5A inhibitors act on the SARS-CoV-2 exonuclease proofreader, providing a possible explanation for the synergy observed with nucleoside analog remdesivir. FDA-approved Hepatitis C therapeutics Epclusa® (velpatasvir/sofosbuvir) and Zepatier® (elbasvir/grazoprevir) could be further optimized to achieve potency and pharmacokinetic properties that support clinical evaluation in combination with remdesivir.

Heat-inactivated modified vaccinia virus Ankara boosts Th1 cellular and humoral immunity as a vaccine adjuvant.

Protein or peptide-based subunit vaccines have generated excitement and renewed interest in combating human cancer or COVID-19 outbreak. One major concern for subunit vaccine application is the weak immune responses induced by protein or peptides. Developing novel and effective vaccine adjuvants are critical for the success of subunit vaccines. Here we explored the potential of heat-inactivated MVA (heat-iMVA) as a vaccine adjuvant. Heat-iMVA dramatically enhances T cell responses and antibodies responses, mainly toward Th1 immune responses when combined with protein or peptide-based immunogen. The adjuvant effect of Heat-iMVA is stronger than live MVA and is dependent on the cGAS/STING-mediated cytosolic DNA-sensing pathway. In a therapeutic vaccination model based on tumor neoantigen peptide vaccine, Heat-iMVA significantly extended the survival and delayed tumor growth. When combined with SARS-CoV-2 spike protein, Heat-iMVA induced more robust spike-specific antibody production and more potent neutralization antibodies. Our results support that Heat-iMVA can be developed as a safe and potent vaccine adjuvant for subunit vaccines against cancer or SARS-CoV-2.

Detection of infiltrating fibroblasts by single-cell transcriptomics in human kidney allografts.

We tested the hypothesis that single-cell RNA-sequencing (scRNA-seq) analysis of human kidney allograft biopsies will reveal distinct cell types and states and yield insights to decipher the complex heterogeneity of alloimmune injury. We selected 3 biopsies of kidney cortex from 3 individuals for scRNA-seq and processed them fresh using an identical protocol on the 10x Chromium platform; (i) HK: native kidney biopsy from a living donor, (ii) AK1: allograft kidney with transplant glomerulopathy, tubulointerstitial fibrosis, and worsening graft function, and (iii) AK2: allograft kidney after successful treatment of active antibody-mediated rejection. We did not study T-cell-mediated rejections. We generated 7217 high-quality single cell transcriptomes. Taking advantage of the recipient-donor sex mismatches revealed by X and Y chromosome autosomal gene expression, we determined that in AK1 with fibrosis, 42 months after transplantation, more than half of the kidney allograft fibroblasts were recipient-derived and therefore likely migratory and graft infiltrative, whereas in AK2 without fibrosis, 84 months after transplantation, most fibroblasts were donor-organ-derived. Furthermore, AK1 was enriched for tubular progenitor cells overexpressing profibrotic extracellular matrix genes. AK2, eight months after successful treatment of rejection, contained plasmablast cells with high expression of immunoglobulins, endothelial cell elaboration of T cell chemoattractant cytokines, and persistent presence of cytotoxic T cells. In addition to these key findings, our analysis revealed unique cell types and states in the kidney. Altogether, single-cell transcriptomics yielded novel mechanistic insights, which could pave the way for individualizing the care of transplant recipients.

Dynamic genome-wide gene expression and immune cell composition in the developing human placenta.

Despite the central role of the placenta in supporting a pregnancy, relatively little is known about transcriptomic and immune-cell changes that occur across gestation. To generate a reference gene expression map of first (T1), second (T2) and third (T3) trimester human placenta, and assess differences in transcriptome in maternal versus fetal side tissues sections of full-term placenta, we performed RNA-Seq analysis on 64 biopsy samples from 18 placentas across all three gestations. We identified 1120 differentially expressed genes in placenta tissues from T1 and T3 samples using a generalized linear model within DESeq2. In total, 411 and 709 genes were positively associated with T1 and T3 placenta, respectively. Comparison of the top 200 differentially expressed genes in the T1 placenta with T3 showed that most of the top enriched biological processes were related to cell division and proliferation. T1 and T2 tissues shared expression of fibroblast-specific COL6A2, HGF, and SPP1 genes. In T3 samples, the expression of genes relating to vasculature development and regulation were highly enriched. Monocytes and NK cell population increased in T3 compared to T1 and T2, whereas Hofbauer cell proportion expanded significantly in T2 and then decreased in T3 samples. There were no significant gene expression differences in the maternal vs. fetal side in T3 placentas. Gene expression patterns shift temporally across trimesters but not spatially across the placenta, at least at the resolution of the biopsy samples. The genes and gene set we identified here represent a valuable resource for studying pathology in pregnancy-related disorders.

Combination of antiviral drugs inhibits SARS-CoV-2 polymerase and exonuclease and demonstrates COVID-19 therapeutic potential in viral cell culture.

SARS-CoV-2 has an exonuclease-based proofreader, which removes nucleotide inhibitors such as Remdesivir that are incorporated into the viral RNA during replication, reducing the efficacy of these drugs for treating COVID-19. Combinations of inhibitors of both the viral RNA-dependent RNA polymerase and the exonuclease could overcome this deficiency. Here we report the identification of hepatitis C virus NS5A inhibitors Pibrentasvir and Ombitasvir as SARS-CoV-2 exonuclease inhibitors. In the presence of Pibrentasvir, RNAs terminated with the active forms of the prodrugs Sofosbuvir, Remdesivir, Favipiravir, Molnupiravir and AT-527 were largely protected from excision by the exonuclease, while in the absence of Pibrentasvir, there was rapid excision. Due to its unique structure, Tenofovir-terminated RNA was highly resistant to exonuclease excision even in the absence of Pibrentasvir. Viral cell culture studies also demonstrate significant synergy using this combination strategy. This study supports the use of combination drugs that inhibit both the SARS-CoV-2 polymerase and exonuclease for effective COVID-19 treatment.

Topology preserving stratification of tissue neoplasticity using Deep Neural Maps and microRNA signatures.

BACKGROUND: Accurate cancer classification is essential for correct treatment selection and better prognostication. microRNAs (miRNAs) are small RNA molecules that negatively regulate gene expression, and their dyresgulation is a common disease mechanism in many cancers. Through a clearer understanding of miRNA dysregulation in cancer, improved mechanistic knowledge and better treatments can be sought. RESULTS: We present a topology-preserving deep learning framework to study miRNA dysregulation in cancer. Our study comprises miRNA expression profiles from 3685 cancer and non-cancer tissue samples and hierarchical annotations on organ and neoplasticity status. Using unsupervised learning, a two-dimensional topological map is trained to cluster similar tissue samples. Labelled samples are used after training to identify clustering accuracy in terms of tissue-of-origin and neoplasticity status. In addition, an approach using activation gradients is developed to determine the attention of the networks to miRNAs that drive the clustering. Using this deep learning framework, we classify the neoplasticity status of held-out test samples with an accuracy of 91.07%, the tissue-of-origin with 86.36%, and combined neoplasticity status and tissue-of-origin with an accuracy of 84.28%. The topological maps display the ability of miRNAs to recognize tissue types and neoplasticity status. Importantly, when our approach identifies samples that do not cluster well with their respective classes, activation gradients provide further insight in cancer subtypes or grades. CONCLUSIONS: An unsupervised deep learning approach is developed for cancer classification and interpretation. This work provides an intuitive approach for understanding molecular properties of cancer and has significant potential for cancer classification and treatment selection.

Serum MicroRNA Transcriptomics and Acute Rejection or Recurrent Hepatitis C Virus in Human Liver Allograft Recipients: A Pilot Study.

BACKGROUND: Acute rejection (AR) and recurrent hepatitis C virus (R-HCV) are significant complications in liver allograft recipients. Noninvasive diagnosis of intragraft pathologies may improve their management. METHODS: We performed small RNA sequencing and microRNA (miRNA) microarray profiling of RNA from sera matched to liver allograft biopsies from patients with nonimmune, nonviral (NINV) native liver disease. Absolute levels of informative miRNAs in 91 sera matched to 91 liver allograft biopsies were quantified using customized real-time quantitative PCR (RT-qPCR) assays: 30 biopsy-matched sera from 26 unique NINV patients and 61 biopsy-matched sera from 41 unique R-HCV patients. The association between biopsy diagnosis and miRNA abundance was analyzed by logistic regression and calculating the area under the receiver operating characteristic curve. RESULTS: Nine miRNAs-miR-22, miR-34a, miR-122, miR-148a, miR-192, miR-193b, miR-194, miR-210, and miR-885-5p-were identified by both sRNA-seq and TLDA to be associated with NINV-AR. Logistic regression analysis of absolute levels of miRNAs and goodness-of-fit of predictors identified a linear combination of miR-34a + miR-210 (P < 0.0001) as the best statistical model and miR-122 + miR-210 (P < 0.0001) as the best model that included miR-122. A different linear combination of miR-34a + miR-210 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft inflammation, and miR-34a + miR-122 (P < 0.0001) was the best model for discriminating NINV-AR from R-HCV with intragraft fibrosis. CONCLUSIONS: Circulating levels of miRNAs, quantified using customized RT-qPCR assays, may offer a rapid and noninvasive means of diagnosing AR in human liver allografts and for discriminating AR from intragraft inflammation or fibrosis due to R-HCV.

Discriminating Neoplastic from Nonneoplastic Tissues Using an miRNA-Based Deep Cancer Classifier.

Next-generation sequencing has enabled the collection of large biological data sets, allowing novel molecular-based classification methods to be developed for increased understanding of disease. miRNAs are small regulatory RNA molecules that can be quantified using next-generation sequencing and are excellent classificatory markers. Herein, a deep cancer classifier (DCC) was adapted to differentiate neoplastic from nonneoplastic samples using comprehensive miRNA expression profiles from 1031 human breast and skin tissue samples. The classifier was fine-tuned and evaluated using 750 neoplastic and 281 nonneoplastic breast and skin tissue samples. Performance of the DCC was compared with two machine-learning classifiers: support vector machine and random forests. In addition, performance of feature extraction through the DCC was also compared with a developed feature selection algorithm, cancer specificity. The DCC had the highest performance of area under the receiver operating curve and high performance in both sensitivity and specificity, unlike machine-learning and feature selection models, which often performed well in one metric compared with the other. In particular, deep learning had noticeable advantages with highly heterogeneous data sets. In addition, our cancer specificity algorithm identified candidate biomarkers for differentiating neoplastic and nonneoplastic tissue samples (eg, miR-144 and miR-375 in breast cancer and miR-375 and miR-451 in skin cancer).

The E3 ubiquitin ligase RNF10 modifies 40S ribosomal subunits of ribosomes compromised in translation.

Reversible monoubiquitination of small subunit ribosomal proteins RPS2/uS5 and RPS3/uS3 has been noted to occur on ribosomes involved in ZNF598-dependent mRNA surveillance. Subsequent deubiquitination of RPS2 and RPS3 by USP10 is critical for recycling of stalled ribosomes in a process known as ribosome-associated quality control. Here, we identify and characterize the RPS2- and RPS3-specific E3 ligase Really Interesting New Gene (RING) finger protein 10 (RNF10) and its role in translation. Overexpression of RNF10 increases 40S ribosomal subunit degradation similarly to the knockout of USP10. Although a substantial fraction of RNF10-mediated RPS2 and RPS3 monoubiquitination results from ZNF598-dependent sensing of collided ribosomes, ZNF598-independent impairment of translation initiation and elongation also contributes to RPS2 and RPS3 monoubiquitination. RNF10 photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) identifies crosslinked mRNAs, tRNAs, and 18S rRNAs, indicating recruitment of RNF10 to ribosomes stalled in translation. These impeded ribosomes are tagged by ubiquitin at their 40S subunit for subsequent programmed degradation unless rescued by USP10.

Inducible and reversible inhibition of miRNA-mediated gene repression in vivo.

Although virtually all gene networks are predicted to be controlled by miRNAs, the contribution of this important layer of gene regulation to tissue homeostasis in adult animals remains unclear. Gain and loss-of-function experiments have provided key insights into the specific function of individual miRNAs, but effective genetic tools to study the functional consequences of global inhibition of miRNA activity in vivo are lacking. Here we report the generation and characterization of a genetically engineered mouse strain in which miRNA-mediated gene repression can be reversibly inhibited without affecting miRNA biogenesis or abundance. We demonstrate the usefulness of this strategy by investigating the consequences of acute inhibition of miRNA function in adult animals. We find that different tissues and organs respond differently to global loss of miRNA function. While miRNA-mediated gene repression is essential for the homeostasis of the heart and the skeletal muscle, it is largely dispensable in the majority of other organs. Even in tissues where it is not required for homeostasis, such as the intestine and hematopoietic system, miRNA activity can become essential during regeneration following acute injury. These data support a model where many metazoan tissues primarily rely on miRNA function to respond to potentially pathogenic events.

Convergence of mammalian RQC and C-end rule proteolytic pathways via alanine tailing.

Incompletely synthesized nascent chains obstructing large ribosomal subunits are targeted for degradation by ribosome-associated quality control (RQC). In bacterial RQC, RqcH marks the nascent chains with C-terminal alanine (Ala) tails that are directly recognized by proteasome-like proteases, whereas in eukaryotes, RqcH orthologs (Rqc2/NEMF [nuclear export mediator factor]) assist the Ltn1/Listerin E3 ligase in nascent chain ubiquitylation. Here, we study RQC-mediated proteolytic targeting of ribosome stalling products in mammalian cells. We show that mammalian NEMF has an additional, Listerin-independent proteolytic role, which, as in bacteria, is mediated by tRNA-Ala binding and Ala tailing. However, in mammalian cells Ala tails signal proteolysis indirectly, through a pathway that recognizes C-terminal degrons; we identify the CRL2KLHDC10 E3 ligase complex and the novel C-end rule E3, Pirh2/Rchy1, as bona fide RQC pathway components that directly bind to Ala-tailed ribosome stalling products and target them for degradation. As Listerin mutation causes neurodegeneration in mice, functionally redundant E3s may likewise be implicated in molecular mechanisms of neurodegeneration.

Longitudinal profiling of circulating miRNA during cardiac allograft rejection: a proof-of-concept study.

AIMS: Allograft rejection following heart transplantation (HTx) is a serious complication even in the era of modern immunosuppressive regimens and causes up to a third of early deaths after HTx. Allograft rejection is mediated by a cascade of immune mechanisms leading to acute cellular rejection (ACR) and/or antibody-mediated rejection (AMR). The gold standard for monitoring allograft rejection is invasive endomyocardial biopsy that exposes patients to complications. Little is known about the potential of circulating miRNAs as biomarkers to detect cardiac allograft rejection. We here present a systematic analysis of circulating miRNAs as biomarkers and predictors for allograft rejection after HTx using next-generation small RNA sequencing. METHODS AND RESULTS: We used next-generation small RNA sequencing to investigate circulating miRNAs among HTx recipients (10 healthy controls, 10 heart failure patients, 13 ACR, and 10 AMR). MiRNA profiling was performed at different time points before, during, and after resolution of the rejection episode. We found three miRNAs with significantly increased serum levels in patients with biopsy-proven cardiac rejection when compared with patients without rejection: hsa-miR-139-5p, hsa-miR-151a-5p, and hsa-miR-186-5p. We identified miRNAs that may serve as potential predictors for the subsequent development of ACR: hsa-miR-29c-3p (ACR) and hsa-miR-486-5p (AMR). Overall, hsa-miR-486-5p was most strongly associated with acute rejection episodes. CONCLUSIONS: Monitoring cardiac allograft rejection using circulating miRNAs might represent an alternative strategy to invasive endomyocardial biopsy.

Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining.

Lung neuroendocrine neoplasms (NENs) can be challenging to classify due to subtle histologic differences between pathological types. MicroRNAs (miRNAs) are small RNA molecules that are valuable markers in many neoplastic diseases. To evaluate miRNAs as classificatory markers for lung NENs, we generated comprehensive miRNA expression profiles from 14 typical carcinoid (TC), 15 atypical carcinoid (AC), 11 small cell lung carcinoma (SCLC), and 15 large cell neuroendocrine carcinoma (LCNEC) samples, through barcoded small RNA sequencing. Following sequence annotation and data preprocessing, we randomly assigned these profiles to discovery and validation sets. Through high expression analyses, we found that miR-21 and -375 are abundant in all lung NENs, and that miR-21/miR-375 expression ratios are significantly lower in carcinoids (TC and AC) than in neuroendocrine carcinomas (NECs; SCLC and LCNEC). Subsequently, we ranked and selected miRNAs for use in miRNA-based classification, to discriminate carcinoids from NECs. Using miR-18a and -155 expression, our classifier discriminated these groups in discovery and validation sets, with 93% and 100% accuracy. We also identified miR-17, -103, and -127, and miR-301a, -106b, and -25, as candidate markers for discriminating TC from AC, and SCLC from LCNEC, respectively. However, these promising findings require external validation due to sample size.

Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining.

Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomical sites was proposed by experts, with the requirement of improved molecular data integration. Here, we compiled 378 miRNA expression profiles to examine NEN classification through comprehensive miRNA profiling and data mining. Following data preprocessing, our final study cohort included 221 NEN and 114 non-NEN samples, representing 15 NEN pathological types and 5 site-matched non-NEN control groups. Unsupervised hierarchical clustering of miRNA expression profiles clearly separated NENs from non-NENs. Comparative analyses showed that miR-375 and miR-7 expression is substantially higher in NEN cases than non-NEN controls. Correlation analyses showed that NENs from diverse anatomical sites have convergent miRNA expression programs, likely reflecting morphological and functional similarities. Using machine learning approaches, we identified 17 miRNAs to discriminate 15 NEN pathological types and subsequently constructed a multilayer classifier, correctly identifying 217 (98%) of 221 samples and overturning one histological diagnosis. Through our research, we have identified common and type-specific miRNA tissue markers and constructed an accurate miRNA-based classifier, advancing our understanding of NEN diversity.

Statistical Assessment of Depth Normalization for Small RNA Sequencing.

PURPOSE: Methods for depth normalization have been assessed primarily with simulated data or cell-line-mixture data. There is a pressing need for benchmark data enabling a more realistic and objective assessment, especially in the context of small RNA sequencing. METHODS: We collected a unique pair of microRNA sequencing data sets for the same set of tumor samples; one data set was collected with and the other without uniform handling and balanced design. The former provided a benchmark for evaluating evidence of differential expression and the latter served as a test bed for normalization. Next, we developed a data perturbation algorithm to simulate additional data set pairs. Last, we assembled a set of computational tools to visualize and quantify the assessment. RESULTS: We validated the quality of the benchmark data and showed the need for normalization of the test data. For illustration, we applied the data and tools to assess the performance of 9 existing normalization methods. Among them, trimmed mean of M-values was a better scaling method, whereas the median and the upper quartiles were consistently the worst performers; one variation of remove unwanted variation had the best chance of capturing true positives but at the cost of increased false positives. In general, these methods were, at best, moderately helpful when the level of differential expression was extensive and asymmetric. CONCLUSION: Our study (1) provides the much-needed benchmark data and computational tools for assessing depth normalization, (2) shows the dependence of normalization performance on the underlying pattern of differential expression, and (3) calls for continued research efforts to develop more effective normalization methods.

Single-cell transcriptome analysis of human skin identifies novel fibroblast subpopulation and enrichment of immune subsets in atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is a prevalent inflammatory skin disease with a complex pathogenesis involving immune cell and epidermal abnormalities. Despite whole tissue biopsy studies that have advanced the mechanistic understanding of AD, single cell-based molecular alterations are largely unknown. OBJECTIVE: Our aims were to construct a detailed, high-resolution atlas of cell populations and assess variability in cell composition and cell-specific gene expression in the skin of patients with AD versus in controls. METHODS: We performed single-cell RNA sequencing on skin biopsy specimens from 5 patients with AD (4 lesional samples and 5 nonlesional samples) and 7 healthy control subjects, using 10× Genomics. RESULTS: We created transcriptomic profiles for 39,042 AD (lesional and nonlesional) and healthy skin cells. Fibroblasts demonstrated a novel COL6A5+COL18A1+ subpopulation that was unique to lesional AD and expressed CCL2 and CCL19 cytokines. A corresponding LAMP3+ dendritic cell (DC) population that expressed the CCL19 receptor CCR7 was also unique to AD lesions, illustrating a potential role for fibroblast signaling to immune cells. The lesional AD samples were characterized by expansion of inflammatory DCs (CD1A+FCER1A+) and tissue-resident memory T cells (CD69+CD103+). The frequencies of type 2 (IL13+)/type 22 (IL22+) T cells were higher than those of type 1 (IFNG+) in lesional AD, whereas this ratio was slightly diminished in nonlesional AD and further diminished in controls. CONCLUSION: AD lesions were characterized by expanded type 2/type 22 T cells and inflammatory DCs, and by a unique inflammatory fibroblast that may interact with immune cells to regulate lymphoid cell organization and type 2 inflammation.

Chromosome 19 microRNA cluster enhances cell reprogramming by inhibiting epithelial-to-mesenchymal transition.

During implantation, cytotrophoblasts undergo epithelial-to-mesenchymal transition (EMT) as they differentiate into invasive extravillous trophoblasts (EVTs). The primate-specific microRNA cluster on chromosome 19 (C19MC) is exclusively expressed in the placenta, embryonic stem cells and certain cancers however, its role in EMT gene regulation is unknown. In situ hybridization for miR-517a/c, a C19MC cistron microRNA, in first trimester human placentas displayed strong expression in villous trophoblasts and a gradual decrease from proximal to distal cell columns as cytotrophoblasts differentiate into invasive EVTs. To investigate the role of C19MC in the regulation of EMT genes, we employed the CRISPR/dCas9 Synergistic Activation Mediator (SAM) system, which induced robust transcriptional activation of the entire C19MC cistron and resulted in suppression of EMT associated genes. Exposure of human iPSCs to hypoxia or differentiation of iPSCs into either cytotrophoblast-stem-like cells or EVT-like cells under hypoxia reduced C19MC expression and increased EMT genes. Furthermore, transcriptional activation of the C19MC cistron induced the expression of OCT4 and FGF4 and accelerated cellular reprogramming. This study establishes the CRISPR/dCas9 SAM as a powerful tool that enables activation of the entire C19MC cistron and uncovers its novel role in suppressing EMT genes critical for maintaining the epithelial cytotrophoblasts stem cell phenotype.

The G3BP1-Family-USP10 Deubiquitinase Complex Rescues Ubiquitinated 40S Subunits of Ribosomes Stalled in Translation from Lysosomal Degradation.

Ribosome-associated quality control (RQC) purges aberrant mRNAs and nascent polypeptides in a multi-step molecular process initiated by the E3 ligase ZNF598 through sensing of ribosomes collided at aberrant mRNAs and monoubiquitination of distinct small ribosomal subunit proteins. We show that G3BP1-family-USP10 complexes are required for deubiquitination of RPS2, RPS3, and RPS10 to rescue modified 40S subunits from programmed degradation. Knockout of USP10 or G3BP1 family proteins increased lysosomal ribosomal degradation and perturbed ribosomal subunit stoichiometry, both of which were rescued by a single K214R substitution of RPS3. While the majority of RPS2 and RPS3 monoubiquitination resulted from ZNF598-dependent sensing of ribosome collisions initiating RQC, another minor pathway contributed to their monoubiquitination. G3BP1 family proteins have long been considered RNA-binding proteins, however, our results identified 40S subunits and associated mRNAs as their predominant targets, a feature shared by stress granules to which G3BP1 family proteins localize under stress.