Expression Of Gene Cluster Research Articles

Actin networks modulate heterogeneous NF-κB dynamics in response to TNFα.

The canonical NF-κB transcription factor RELA is a master regulator of immune and stress responses and is upregulated in pancreatic ductal adenocardinoma (PDAC) tumours. In this study, we characterised previously unexplored endogenous RELA-GFP dynamics in PDAC cell lines through live single-cell imaging. Our observations revealed that TNFα stimulation induces rapid, sustained, and non-oscillatory nuclear translocation of RELA. Through Bayesian analysis of single-cell datasets with variation in nuclear RELA, we predicted that RELA heterogeneity in PDAC cell lines is dependent on F-actin dynamics. RNA-seq analysis identified distinct clusters of RELA-regulated gene expression in PDAC cells, including TNFα-induced RELA upregulation of the actin regulators NUAK2 and ARHGAP31. Further, siRNA-mediated depletion of ARHGAP31 and NUAK2 altered TNFα-stimulated nuclear RELA dynamics in PDAC cells, establishing a novel negative feedback loop that regulates RELA activation by TNFα. Additionally, we characterised the NF-κB pathway in PDAC cells, identifying how NF-κB/IκB proteins genetically and physically interact with RELA in the absence or presence of TNFα. Taken together, we provide computational and experimental support for interdependence between the F-actin network and the NF-κB pathway with RELA translocation dynamics in PDAC.

NRBP1 promotes malignant phenotypes of glioblastoma by regulating PI3K/Akt activation.

Glioblastoma (GBM) is the most aggressive of intracranial gliomas. Despite the maximal treatment intervention, the median survival rate is still about 14-16 months. Nuclear receptor-binding protein 1 (NRBP1) has a potential growth-promoting role on biology function of cells. In this study, we investigated whether NRBP1 promotes GBM malignant phenotypes and the potential mechanisms. The correlation between NRBP1 and glioma grade, prognosis in TCGA/CGGA databases and our clinical data were analyzed. Next, we conducted knockout and overexpression of NRBP1 on GBM cells to verify that NRBP1 promoted cell proliferation, invasion, and migration invitro and invivo. Finally, we detected the impact of NRBP1 on PI3K/Akt signaling pathway and EMT. There was a correlation between elevated NRBP1 expression and advanced stage glioma, as well as decreased overall and disease-free survival. The suppression of proliferation, invasion, and migration of tumor cells was observed upon NRBP1 knockout, and invitro studies also demonstrated the induction of apoptotic cell death. Whereas, its overexpression is associated with high multiplication rate, migration, invasion, and apoptotic escape. GO enrichment and KEGG analysis revealed that NRBP1 regulated differentially expressed gene clusters are involved in PI3K/Akt signaling pathway, as well as EMT mediated by this pathway. Moreover, the effects of NRBP1 knockdown and overexpression on GBM were mitigated by MK-2206 and SC79, both of which respectively function as an inhibitor and an activator of the PI3K/Akt signaling pathway. Similarly, the suppression of NRBP1 led to a decrease in tumor growth, whereas its overexpression promoted tumor growth in a mouse model. This study shows that NRBP1 promotes malignant phenotypes in GBM by activating PI3K/Akt pathway. Hence, it can function as both a predictive indicator and a new target for therapies in GBM treatment.

Construction of an expression platform for fungal secondary metabolite biosynthesis in Penicillium crustosum

Filamentous fungi are prolific producers of bioactive natural products and play a vital role in drug discovery. Yet, their potential cannot be fully exploited since many biosynthetic genes are silent or cryptic under laboratory culture conditions. Several strategies have been applied to activate these genes, with heterologous expression as one of the most promising approaches. However, successful expression and identification of new products are often hindered by host-dependent factors, such as low gene targeting efficiencies, a high metabolite background, or a lack of selection markers. To overcome these challenges, we have constructed a Penicillium crustosum expression host in a pyrG deficient strain by combining the split-marker strategy and CRISPR-Cas9 technology. Deletion of ligD and pcribo improved gene targeting efficiencies and enabled the use of an additional selection marker in P. crustosum. Furthermore, we reduced the secondary metabolite background by inactivation of two highly expressed gene clusters and abolished the formation of the reactive ortho-quinone methide. Finally, we replaced the P. crustosum pigment gene pcr4401 with the commonly used Aspergillus nidulans wA expression site for convenient use of constructs originally designed for A. nidulans in our P. crustosum host strain. As proof of concept, we successfully expressed a single polyketide synthase gene and an entire gene cluster at the P. crustosum wA locus. Resulting transformants were easily detected by their albino phenotype. With this study, we provide a highly efficient platform for heterologous expression of fungal genes.Key pointsConstruction of a highly efficient Penicillium crustosum heterologous expression hostReduction of secondary metabolite background by genetic dereplication strategyIntegration of wA site to provide an alternative host besides Aspergillus nidulansGraphical

Deletions of conserved extracytoplasmic function sigma factors-encoding genes in Streptomyces have a major impact on secondary metabolism

BackgroundEthanol shock significantly affects expression of over 1200 genes in Streptomyces venezuelae NRRL B-65,442, including those involved in secondary metabolite biosynthesis and a cryptic gene pepX, which encodes a 19-amino acid peptide with an unknown function.ResultsTo establish a possible correlation between the PepX peptide and secondary metabolism in S. venezuelae, its gene was deleted, followed by analyses of the transcriptome and secondary metabolome of the mutant. Although the secondary metabolome of the pepX mutant was not strongly affected, pepX deletion, similar to ethanol shock, mostly resulted in downregulated expression of secondary metabolite biosynthesis gene clusters (BGCs). At the same time, there was a reverse correlation between the expression of certain extracytoplasmic function sigma factors (ECFs) and several BGCs. Individual deletions of three selected ECF-coding genes conserved in Streptomyces that were upregulated upon both pepX deletion and ethanol shock, had a profound positive effect on the expression of BGCs, which also correlated with the overproduction of specific secondary metabolites. Deletion of one such ECF-coding gene in a marine sponge-derived Streptomyces sp. also significantly altered the secondary metabolite profile, suggesting an important role of this ECF in the regulation of secondary metabolism.ConclusionsThese findings pave the way for the activation or upregulation of BGCs in Streptomyces bacteria harboring genes for ECFs homologous to those identified in this study, hereby assisting in the discovery of novel bioactive secondary metabolites.

A CRISPR-Cas9-Mediated Large-Fragment Assembly Method for Cloning Genomes and Biosynthetic Gene Cluster.

The ability to clone large DNA fragments from genomes is valuable for both basic and applied research, such as the construction of synthetic genomes, and the expression of biosynthetic gene clusters (BGCs) for natural product discovery. Here, we report a fast and efficient platform for the direct capture of genome DNAs, by combining CRISPR and Gibson assembly. We demonstrate this method with the ability of cloning large DNA fragments ranging from 30 to 77 kb from various host genomes, achieving a near 100% cloning fidelity for DNA fragments below 50 kb. We next demonstrate this method by the cloning of a 40 kb fragment from Streptomyces ceruleus A3(2), which is rich in BGCs for natural products; and used this method cloning the 40 kb fengycin synthetic gene cluster from B. subtilis 168, encoding for a class of peptides with bioactivity. This method provides efficient and simple opportunities for assembling large DNA constructs from distant sources.

Stimulating Novel and Bioactive Metabolite Production by Cocultivation of Two Fungi─Aspergillus oryzae and Epicoccum dendrobii.

Fungi produce various bioactive secondary metabolites (SMs) as protective and weaponized tools to enhance survival in shared ecological niches. By mimicking a competitive ecosystem, cocultivation has been proven to be particularly successful in stimulating SM discovery. Here, we reported the identification of four novel metabolites, epiclactones A and B, epioxochromane and aoergostane, from the coculture of two biotechnologically important strains, Aspergillus oryzae and Epicoccum dendrobii. Transcriptome and metabolome analyses revealed widespread silent gene activation during fungal-fungal interaction. The majority of differentially expressed gene clusters were summarized for both strains. Based on these highly activated biosynthetic pathways, we suggested that a bidirectional chemical defense occurred under cocultivation. E. dendrobii enhanced the production of the spore inhibitor, fumigermin. Moreover, A. oryzae highly accumulated the antifungal agent kojic acid with a yield of up to 1.10 g/L. This study provides an excellent example for the discovery of hidden natural products by cocultivation.

Genome-Driven Discovery of Antiviral Atralabdans A-C from the Soil-Dwelling Streptomyces atratus.

Heterologous expression of an atr terpenoid gene cluster derived from Streptomyces atratus Gö66 in S. albus J1074 led to the discovery of three novel labdane diterpenoids featuring an unprecedented 6/6/5-fused tricyclic skeleton, designated as atralabdans A-C (1-3), along with a known compound, labdanmycin A. Compounds 1-3 were identified through extensive spectroscopic analysis, including NMR calculations with DP4+ probability analysis, and a comparative assessment of experimental and theoretical electronic circular dichroism (ECD) spectra. A plausible biosynthetic pathway for these compounds was proposed. Compounds 1-3 exhibited inhibitory activity against the human neurotropic coxsackievirus B3 (CVB3); 1 was the most potent, surpassing the positive control ribavirin with a higher therapeutic index.

Benzoxazinoids Biosynthetic Gene Cluster Identification and Expression Analysis in Maize under Biotic and Abiotic Stresses.

Benzoxazinoids (BXs) are unique bioactive metabolites with protective and allelopathic properties in maize in response to diverse stresses. The production of BXs involves the fine regulations of BXs biosynthetic gene cluster (BGC). However, little is known about whether and how the expression pattern of BGC members is impacted by biotic and abiotic stresses. Here, maize BGC was systemically investigated and 26 BGC gene members were identified on seven chromosomes, for which Bin 4.00-4.01/4.03-4.04/7.02 were the most enriched regions. All BX proteins were clearly divided into three classes and seven subclasses, and ten conserved motifs were further identified among these proteins. These proteins were localized in the subcellular compartments of chloroplast, endoplasmic reticulum, or cytoplasmic, where their catalytic activities were specifically executed. Three independent RNA-sequencing (RNA-Seq) analyses revealed that the expression profiles of the majority of BGC gene members were distinctly affected by multiple treatments, including light spectral quality, low-temperature, 24-epibrassinolide induction, and Asian corn borer infestation. Thirteen differentially expressed genes (DEGs) with high and specific expression levels were commonly detected among three RNA-Seq, as core conserved BGC members for regulating BXs biosynthesis under multiple abiotic/biotic stimulates. Moreover, the quantitative real-time PCR (qRT-PCR) verified that six core conserved genes in BGC were significantly differentially expressed in leaves of seedlings upon four treatments, which caused significant increases in 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) content under darkness and wound treatments, whereas a clear decrease in DIMBOA content was observed under low-temperature treatment. In conclusion, the changes in BX metabolites in maize were regulated by BGC gene members in multiple stress presences. Therefore, the identification of key genes associated with BX accumulation under biotic/abiotic stresses will provide valuable gene resources for breeding maize varieties with enhanced capability to adapt to environmental stresses.

GeneSurfer Enables Transcriptome-wide Exploration and Functional Annotation of Gene Co-expression Modules in 3D Spatial Transcriptomics Data.

Gene co-expression provides crucial insights into biological functions, however, there is a lack of exploratory analysis tools for localized gene co-expression in large-scale datasets. We present GeneSurfer, an interactive interface designed to explore localized transcriptome-wide gene co-expression patterns in the 3D spatial domain. Key features of GeneSurfer include transcriptome-wide gene filtering and gene clustering based on spatial local co-expression within transcriptomically similar cells, multi-slice 3D rendering of average expression of gene clusters, and on-the-fly Gene Ontology term annotation of co-expressed gene sets. Additionally, GeneSurfer offers multiple linked views for investigating individual genes or gene co-expression in the spatial domain at each exploration stage. Demonstrating its utility with both spatial transcriptomics and single-cell RNA sequencing data from the Allen Brain Cell Atlas, GeneSurfer effectively identifies and annotates localized transcriptome-wide co-expression, providing biological insights and facilitating hypothesis generation and validation.

Deciphering the Nuclear Role of Heme Oxygenase-1 in Prostate Cancer: Transcriptional Reprogramming and Neuroendocrine Differentiation

PURPOSE This study probes the role of Heme Oxygenase-1 (HO-1) and its nuclear interactors in prostate cancer (PCa). Previous results, demonstrated the anti-tumoral role of HO-1 in PCa and its nuclear translocation, hinting at its potential regulatory roles within the nucleus. Thus, we postulate that HO-1, alongside its interactors, can reprogram PCa cells to a less aggressive phenotype. METHODS From the 11 HO-1 nuclear interacting proteins previously identified, 4 were transcription factors Using ChIP Atlas we evaluated the binding affinity of these factors to their downstream gene targets. Gene ontology and KEGG pathway analyses were then performed to associate these targets with specific biological processes and pathologies. Additionally, RNA-seq data from patient-derived xenografts (PDXs) at MD Anderson Cancer Center (MDA PCa PDXs) were analyzed to evaluate the clinical relevance of these genes in the context of PCa heterogeneity. These PDXs offer a clinically relevant model due to their representation of the disease's heterogeneity. We scrutinized the expression of the gene clusters of interest in PDXs, including their association with neuroendocrine (NE) phenotypes. RESULTS The transcription factors among the HO-1 interactors previously described, were found to bind genes enriched in processes related to histone modification, acetylation, and transcription termination, implicating their role in gene expression regulation. KEGG pathways linked these genes to neurodegenerative diseases (including Parkinson's, Huntington's, and Alzheimer's disease), mirroring the neuroendocrine (NE) phenotype in advanced PCa, which typically emerges in advanced stages and is characterized by the transdifferentiating of epithelial cells into NE cells lacking androgen receptor expression. The unsupervised clustering of gene expression from PDXs showed that high expression of these genes is associated with an AR-negative NE histopathology, suggesting a potential for these interactors in prostate cancer subtyping. CONCLUSION HO-1 nuclear interactors, especially transcription factors, appear to influence gene transcription associated with NE differentiation and cancer aggressiveness. The therapeutic relevance of modulating HO-1's interaction with its nuclear partners warrants further investigation to ascertain its role in mitigating the NE of PCa.

Identification of shared disease marker genes and underlying mechanisms between rheumatoid arthritis and Crohn disease through bioinformatics analysis.

As chronic autoimmune inflammatory diseases, rheumatoid arthritis (RA) and Crohn disease (CD) are closely associated and display a significant positive correlation. However, the underlying mechanisms and disease markers responsible for their cooccurrence remain unknown and have not been systematically studied. Therefore, this study aimed to identify key molecules and pathways commonly involved in both RA and CD through bioinformatic analysis of public sequencing databases. Datasets for RA and CD were downloaded from the GEO database. Overlapping genes were identified using weighted gene co-expression network analysis and differential analysis crossover, and enrichment analysis was conducted for these genes. Protein-protein interaction networks were then constructed using these overlapping genes to identify hub genes. Expression validation and receiver operating characteristic curve validation were performed for these hub genes using different datasets. Additionally, the immune cell correlation, single-cell expression cluster, and the immune cell expression cluster of the core gene were analyzed. Furthermore, upstream shared microRNAs (miRNA) were predicted and a miRNA-gene network was constructed. Finally, drug candidates were analyzed and predicted. These core genes were found to be positively correlated with multiple immune cells that are infiltrated by the disease. Analysis of gene expression clusters revealed that these genes were mostly associated with inflammatory and immune responses. The miRNA-genes network analysis suggested that hsa-miR-31-5p may play an important role in the common mechanism of RA and CD. Finally, tamibarotene, retinoic acid, and benzo[a]pyrene were identified as potential treatment options for patients with both RA and CD. This bioinformatics study has identified ITGB2, LCP2, and PLEK as key diagnostic genes in patients with both RA and CD. The study has further confirmed that inflammation and immune response play a central role in the development of both RA and CD. Interestingly, the study has highlighted hsa-miR-31-5p as a potential key player in the common mechanism of both diseases, representing a new direction in research and a potential therapeutic target. These shared genes, potential mechanisms, and regulatory networks offer new opportunities for further research and may provide hope for future treatment of patients with both RA and CD.

Enhanced catabolism of glycine betaine and derivatives provides improved osmotic stress protection in Methylorubrum extorquens PA1.

Integration of metabolites into the overall metabolic network of a cell requires careful coordination dependent upon the ultimate usage of the metabolite. Different stoichiometric needs, and thus pathway fluxes, must exist for compounds destined for diverse uses, such as carbon sources, nitrogen sources, or stress-protective agents. Herein, we expand upon our previous work that highlighted the nature of glycine betaine (GB) metabolism in Methylobacteria to examine the utilization of GB-derivative compounds dimethylglycine (DMG) and sarcosine into Methylorubrum extorquens in different metabolic capacities, including as sole nitrogen and/or carbon sources. We isolated gain-of-function mutations that allowed M. extorquens PA1 to utilize dimethylglycine as a carbon source and dimethylglycine and sarcosine as nitrogen source. Characterization of mutants demonstrated selection for variants of the AraC-like regulator Mext_3735 that confer constitutive expression of the GB metabolic gene cluster, allowing direct utilization of the downstream GB derivatives. Finally, among the distinct isolates examined, we found that catabolism of the osmoprotectant used for selection (GB or dimethylglycine) enhanced osmotic stress resistance provided in the presence of that particular osmolyte. Thus, access to the carbon and nitrogen and osmoprotective effects of GB and DMG are made readily accessible through adaptive mutations. In M. extorquens PA1, the limitations to exploiting this group of compounds appear to exist predominantly at the levels of gene regulation and functional activity, rather than being constrained by transport or toxicity.IMPORTANCEOsmotic stress is a common challenge for bacteria colonizing the phyllosphere, where glycine betaine (GB) can be found as a prevalent osmoprotectant. Though Methylorubrum extorquens PA1 cannot use GB or its demethylation products, dimethylglycine (DMG) and sarcosine, as a sole carbon source, utilization is highly selectable via single nucleotide changes for both GB and DMG growth. The innate inability to use these compounds is due to limited flux through steps in the pathway and regulatory constraints. Herein, the characterization of the transcriptional regulator, Mext_3735 (GbdR), expands our understanding of the various roles in which GB derivatives can be used in M. extorquens PA1. Interestingly, increased catabolism of GB and derivatives does not interfere with, but rather improves, the ability of cells to thrive under increased salt stress conditions, suggesting that metabolic flux improves stress tolerance rather than providing a distinct tension between uses.

Cancer-associated fibroblasts (CAFs) gene signatures predict outcomes in breast and prostate tumor patients

BackgroundOver the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed.MethodsRNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm.ResultsData obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts.ConclusionWe identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients.

Получение гетерологичных продуцентов рамнолипидов для промышленного производства высокоэффективного биосурфактанта

Rhamnolipids are promising microbial glycolipid biosurfactants that can be used as preparations for soil bioremediation in oil pollution. The expression of the rhamnolipid biosynthesis gene cluster in the main producer, Pseudomonas aeruginosa, is regulated by quorum sensing signals (QS). Complex biosynthesis regulation does not allow achieving high product yields. The production of heterologous rhamnolipid producers is a promising way to overcome the complex QS-dependent regulation of rhamnolipid biosynthesis, and also avoids the use of opportunistic Pseudomonas aeruginosa for the industrial production of rhamnolipids. Heterologous Escherichia coli BLWT-based producers with a transformant E14 productivity of 3.40±0.96 g/L were obtained on the basis of the plasmid vector pAl2–T with a construct from the native promoter and rhlAB genes, and Pseudomonas viridiflava AlC1223-based ones with the productivity of transformants P5, P6, P8 332.00±0.10, 200.00±0.10 and 180.00±0.10 g/L, respectively. The presence of rhlAB genes in the expression vector allows the obtaining monorhamnolipids for further use as an agent for the solubilization of soil hydrophobic pollutant (e. g. petroleum products), which will intensify bioremediation processes.

Unveiling Nature’s Secrets: Activating Silent Biosynthetic Genes Clusters in Fungi and Bacteria

Microorganisms such as fungi and bacteria are prosperous producers of secondary metabolites; moreover, they are home to a large number of silent biosynthetic gene clusters, but they are all poorly expressed. Thus, these resultant compounds remain cryptic or unknown. However, since these cluster products have many bioactivities, stimulating their production and gaining access to their related structures are considered a top priority. Here, we summarize the three strategies that have been the topic of current trends. Regulating of genetic investigation, heterogeneous and refactoring cluster expression, as well as elicitor and coculture screening are the strategies utilized to unlock the expression of biosynthetic gene clusters (BGCs). These all help to triggers the manufacturing of innovative metabolites having various functions.

Generation of New β-Conglycinin-Deficient Soybean Lines by Editing the lincRNA lincCG1 Using the CRISPR/Cas9 System.

Soybean β-conglycinin is a major allergen that adversely affects the nutritional properties of soybean. Soybean deficient in β-conglycinin is associated with low allergenicity and high nutritional value. Long intergenic noncoding RNAs (lincRNAs) regulate gene expression and are considered important regulators of essential biological processes. Despite increasing knowledge of the functions of lincRNAs, relatively little is known about the effects of lincRNAs on the accumulation of soybean β-conglycinin. The current study presents the identification of a lincRNA lincCG1 that was mapped to the intergenic noncoding region of the β-conglycinin α-subunit locus. The full-length lincCG1 sequence was cloned and found to regulate the expression of soybean seed storage protein (SSP) genes via both cis- and trans-acting regulatory mechanisms. Loss-of-function lincCG1 mutations generated using the clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system led to the deficiency of the allergenic α'-, α-, and β-subunits of soybean β-conglycinin as well as higher content of proteins, sulfur-containing amino acids, and free arginine. The dominant null allele LincCG1, and consequently, the β-conglycinin-deficient phenotype associated with the lincCG1-gene-edited line was stably inherited by the progenies in a Mendelian fashion. The dominant null allele LincCG1 may therefore be exploited for engineering/developing novel hypoallergenic soybean varieties. Furthermore, Cas9-free and β-conglycinin-deficient homozygous mutant lines were obtained in the T1 generation. This study is the first to employ the CRISPR/Cas9 technology for editing a lincRNA gene associated with the soybean allergenic protein β-conglycinin. Moreover, this study reveals that lincCG1 plays a crucial role in regulating the expression of the β-conglycinin subunit gene cluster, besides highlighting the efficiency of employing the CRISPR/Cas9 system for modulating lincRNAs, and thereby regulating soybean seed components.

Human SARS-CoV-2 challenge uncovers local and systemic response dynamics

The COVID-19 pandemic is an ongoing global health threat, yet our understanding of the dynamics of early cellular responses to this disease remains limited1. Here in our SARS-CoV-2 human challenge study, we used single-cell multi-omics profiling of nasopharyngeal swabs and blood to temporally resolve abortive, transient and sustained infections in seronegative individuals challenged with pre-Alpha SARS-CoV-2. Our analyses revealed rapid changes in cell-type proportions and dozens of highly dynamic cellular response states in epithelial and immune cells associated with specific time points and infection status. We observed that the interferon response in blood preceded the nasopharyngeal response. Moreover, nasopharyngeal immune infiltration occurred early in samples from individuals with only transient infection and later in samples from individuals with sustained infection. High expression of HLA-DQA2 before inoculation was associated with preventing sustained infection. Ciliated cells showed multiple immune responses and were most permissive for viral replication, whereas nasopharyngeal T cells and macrophages were infected non-productively. We resolved 54 T cell states, including acutely activated T cells that clonally expanded while carrying convergent SARS-CoV-2 motifs. Our new computational pipeline Cell2TCR identifies activated antigen-responding T cells based on a gene expression signature and clusters these into clonotype groups and motifs. Overall, our detailed time series data can serve as a Rosetta stone for epithelial and immune cell responses and reveals early dynamic responses associated with protection against infection.

MDB-106. MEDULLOBLASTOMA TUMOR MICROENVIRONMENTS SHOW INTRA- AND INTER-TUMORAL HETEROGENEITY ON SPATIAL TRANSCRIPTOMICS

Abstract BACKGROUND Medulloblastoma defines a heterogeneous set of neuroepithelial neoplasms of the posterior fossa. Four groups of medulloblastoma are recognized: wingless (WNT), sonic hedgehog (SHH), group 3, and group 4. The tumor microenvironment (TME) influences tumor progression and response to therapy and has emerged as a growing target for the study of novel therapeutic approaches. METHODS We show spatial gene expression architecture through 10X Visium Spatial Sequencing on 16 formalin-fixed paraffin-embedded (FFPE) samples representing all molecular groups of medulloblastoma. Spatial transcriptomics data is correlated with clinical parameters, including M-stage at diagnosis, tumor histological classification, and outcomes data including survival and relapse status. RESULTS Spatial sequencing demonstrates both intra- and inter-tumoral TME heterogeneity across molecular subgroups. Unsupervised clustering and uniform manifold approximate projection illustrate clusters of distinct spatial gene expression representing cell states from different components of the TME, including tumor-associated astrocytes (TAA), macrophages (TAM), and vascular endothelium. Medulloblastoma cells constitute the majority of cells and express marker genes representing different stages of neuronal differentiation and cell cycle. Pathway analysis reveals the enrichment of pathways associated with cell motility (RHO GTPase, MET, MAPK signaling), heat shock response, growth factor signaling, and adaptive immune response (TLR, MHC-II signaling). In addition, molecular pathways known in medulloblastoma, including NF-kB and TP53 regulatory pathways, were also enriched. Neighborhood-enrichment analysis reveals the co-localization of TAMs and TAAs in close spatial relationship with mitotic progenitor-like medulloblastoma cells. Additionally, these two cell types constitute a greater proportion of the TME in patients at relapse compared to initial diagnosis. CONCLUSION Spatial transcriptomics enables new insight into the heterogeneity of the medulloblastoma TME. Notably, our analysis elucidates the spatial association of TAMs and TAAs with proliferating tumor cells and an increased abundance of these cell types following relapse. These initial results support the role of TAMs and TAAs in medulloblastoma progression.

Control of a gene transfer agent cluster in Caulobacter crescentus by transcriptional activation and anti-termination

Gene Transfer Agents (GTAs) are phage-like particles that cannot self-multiply and be infectious. Caulobacter crescentus, a bacterium best known as a model organism to study bacterial cell biology and cell cycle regulation, has recently been demonstrated to produce bona fide GTA particles (CcGTA). Since C. crescentus ultimately die to release GTA particles, the production of GTA particles must be tightly regulated and integrated with the host physiology to prevent a collapse in cell population. Two direct activators of the CcGTA biosynthetic gene cluster, GafY and GafZ, have been identified, however, it is unknown how GafYZ controls transcription or how they coordinate gene expression of the CcGTA gene cluster with other accessory genes elsewhere on the genome for complete CcGTA production. Here, we show that the CcGTA gene cluster is transcriptionally co-activated by GafY, integration host factor (IHF), and by GafZ-mediated transcription anti-termination. We present evidence that GafZ is a transcription anti-terminator that likely forms an anti-termination complex with RNA polymerase, NusA, NusG, and NusE to bypass transcription terminators within the 14 kb CcGTA cluster. Overall, we reveal a two-tier regulation that coordinates the synthesis of GTA particles in C. crescentus.

Tumor origin identification through machine learning and gene expression profiling.

e13597 Background: Accurate identification of tumor origin is crucial for effective diagnosis and treatment, particularly in cases of metastatic tumors. Interpretable machine learning models have displayed significant potential in addressing this problem when imaging and immunohistochemistry (IHC) examinations are ineffective. In this study, we developed a panel using gene expression profiles from various tumors and constructed a robust machine learning model for precise identification of tumor origin. Methods: RNA sequencing (RNA-seq) data of 9462 tumor samples originating from 21 different organs were collected from The Cancer Genome Atlas (TCGA). We conducted feature engineering through unsupervised clustering and differential gene expression analysis, selecting a refined panel of 164 genes from a pool of over 60,000 identifiers. Subsequently, a machine learning classifier grounded in Logistic regression (LR) was trained on 9462 samples with the constructed 164-gene panel. To enhance the adaptability of our model to this task, 10-fold cross-validation was employed in the multi-class mode. Two independent test sets, the Primary tumor set (PT, n=3420, including 19 tumor types) and the Metastatic tumor set (MTP, n=100, all originating from the prostate and spread to bone, liver, etc.), were established with samples from Gene Expression Omnibus (GEO) and other published studies. Notably, all samples underwent FPKM normalization and there was no overlap between training and testing samples. Model performance was assessed using accuracy, specificity, and sensitivity metrics. Results: The 164-gene expression panel achieved a cross-validation accuracy of 96.73%. In the assessment of the PT test set, our model achieved an overall 99.62% specificity, 92.31% accuracy and 89.79% sensitivity, exhibiting performance comparable to similar models in other studies. Additionally, our model accurately traced 91% of metastatic tumors in the MTP test set to the prostate, surpassing previous lines of work by a large margin. Conclusions: Gene expression patterns reveal organ-specific characteristics that could be used to identify tumor origin. The combination of a condensed yet comprehensive gene expression panel with a robust machine learning model serves as a promising tool for tumor diagnosis. Ongoing correlative studies aim to extend predictions from tissue organs to cancer subtypes.