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Regulating the regulator: Phosphorylation-mediated regulation of the RNAi effector protein Argonaute
No full textArgonaute (Ago) proteins play a central role in post-transcriptional gene regulation through RNA interference (RNAi). In this process, Ago binds to a small interfering RNA (siRNA) or a microRNA (miRNA) and uses it as a guide to target messenger RNAs containing regions of complementarity and down-regulates production of their corresponding proteins. It was previously shown that the kinase CK1α phosphorylates a cluster of residues in the eukaryotic insertion (EI) of Ago, leading to the alleviation of miRNA-mediated repression through an undetermined mechanism. We show that binding of miRNA-loaded human Argonaute-2 (hAgo2) to target RNA with complementarity to the seed and 3’ supplemental regions of the miRNA primes the EI for hierarchical phosphorylation by CK1α. The added negative charges electrostatically promote target release, freeing hAgo2 to seek out additional targets once it is dephosphorylated. The high conservation of potential phosphosites in the EI suggests that such a regulatory strategy may be a shared mechanism for regulating miRNA-mediated repression
The genetic and epigenetic landscape of the Arabidopsis centromeres
No full textThe centromeres of eukaryotic chromosomes assemble the multiprotein kinetochore complex and thereby position attachment to the spindle microtubules, allowing chromosome segregation during cell division. The key function of the centromere is to load nucleosomes containing the CENTROMERE SPECIFIC HISTONE H3 (CENH3) histone variant [also known as centromere protein A (CENPA)], which directs kinetochore formation. Despite their conserved function during chromosome segregation, centromeres show radically diverse organization between species at the sequence level, ranging from single nucleosomes to megabase-scale satellite repeat arrays, which is termed the centromere paradox. Centromeric satellite repeats are variable in sequence composition and length when compared between species and show a high capacity for evolutionary change, both at the levels of primary sequence and array position along the chromosome. However, the genetic and epigenetic features that contribute to centromere function and evolution are incompletely understood, in part because of the challenges of centromere sequence assembly and functional genomics of highly repetitive sequences. New long-read DNA sequencing technologies can now resolve these complex repeat arrays, revealing insights into centromere architecture and chromatin organization
Prostate tumor-induced stromal reprogramming generates Tenascin C that promotes prostate cancer metastasis through YAP/TAZ inhibition
No full textMetastatic prostate cancer (PCa) in bone induces bone-forming lesions that enhance PCa progression. How tumor-induced bone formation enhances PCa progression is not known. We have previously shown that PCa-induced bone originates from endothelial cells (ECs) that have undergone endothelial-to-osteoblast (EC-to-OSB) transition by tumor-secreted bone morphogenetic protein 4 (BMP4). Here, we show that EC-to-OSB transition leads to changes in the tumor microenvironment that increases the metastatic potential of PCa cells. We found that conditioned medium (CM) from EC-OSB hybrid cells increases the migration, invasion, and survival of PC3-mm2 and C4-2B4 PCa cells. Quantitative mass spectrometry (Isobaric Tags for Relative and Absolute Quantitation) identified Tenascin C (TNC) as one of the major proteins secreted from EC-OSB hybrid cells. TNC expression in tumor-induced OSBs was confirmed by immunohistochemistry of MDA PCa-118b xenograft and human bone metastasis specimens. Mechanistically, BMP4 increases TNC expression in EC-OSB cells through the Smad1-Notch/Hey1 pathway. How TNC promotes PCa metastasis was next interrogated by in vitro and in vivo studies. In vitro studies showed that a TNC-neutralizing antibody inhibits EC-OSB-CM-mediated PCa cell migration and survival. TNC knockdown decreased, while the addition of recombinant TNC or TNC overexpression increased migration and anchorage-independent growth of PC3 or C4-2b cells. When injected orthotopically, PC3-mm2-shTNC clones decreased metastasis to bone, while C4-2b-TNC-overexpressing cells increased metastasis to lymph nodes. TNC enhances PCa cell migration through α5β1 integrin-mediated YAP/TAZ inhibition. These studies elucidate that tumor-induced stromal reprogramming generates TNC that enhances PCa metastasis and suggest that TNC may be a target for PCa therapy
Association of TP53 mutation status and GATA6 amplification with clinical outcome of pancreatic cancer.
No full text e16224 Background: Recent advances in pancreatic adenocarcinoma (PDAC) research unveiled that molecular subtypes reflect cancer prognosis and chemosensitivity. Here, we examined the possible use of genomic profiling of PDAC in the clinic by assessing retrospective clinical outcomes and treatment responsiveness based on genetic alterations. Methods: All patients treated for PDAC with Next-Generation Sequencing (NGS) data available between 2014 to 2020 at Northwell Health Cancer Institute were included in a retrospective analysis. Patients were subdivided into resectable and unresectable cancer. Genetic findings frequently reported in NGS were used to compare progression-free survival (PFS) and overall survival (OS) within subgroups. Survival probability was compared using Peto-Peto’s modified survival estimate followed by pairwise comparisons using Peto-Peto’s modified survival estimate. Family-wise error rate was adjusted using Benjamini & Hochberg method. Results: A total 115 patients were qualified for the evaluation. In all cases of PDAC, TP53 mutation (n = 89) was associated with poor OS compared to the wild-type TP53 gene (n = 19) (median OS 20.2 months, 95% CI 10.2 to 39.7, vs. 41.1 months, 95% CI 20.9 to 81.0, HR 1.98, p = 0.028). In unresectable PDAC, tumors with GATA6 amplification (n = 11) were associated with a significantly better OS over patients whose tumors harbored a TP53 mutation (n = 57) (median OS 22.9 months, 95% CI 9.6 to 54.5, vs. 10.0 months, 95% CI 4.2 to 23.8, HR 0.48, p = 0.048) . Within the TP53 mutation group, FOLFIRINOX (n = 21) did not show improved OS compare to Gem/NabP (n = 30) (mean OS 13.8 months, 95% CI 6.8 to 28.2, vs. 8.5 months, 95% CI 4.17 to 17.4, HR 0.84, p = 0.25). Other genetic alterations were not associated with OS. There was no difference in PFS in all PDACs. Conclusions: Our retrospective analysis showed that genetic changes in TP53 and GATA6 were significantly associated with the clinical outcome for PDAC. Mutation of TP53 was associated with poor OS in general. However, in unresectable PDAC, GATA6 amplification was associated with better clinical outcome than tumors with TP53 mutation. In contrary to general belief, FOLFIRINOX did not result in better OS than Gem/NabP. </jats:p
Expression Atlas update: gene and protein expression in multiple species
Get PDFThe EMBL-EBI Expression Atlas is an added value knowledge base that enables researchers to answer the question of where (tissue, organism part, developmental stage, cell type) and under which conditions (disease, treatment, gender, etc) a gene or protein of interest is expressed. Expression Atlas brings together data from >4500 expression studies from >65 different species, across different conditions and tissues. It makes these data freely available in an easy to visualise form, after expert curation to accurately represent the intended experimental design, re-analysed via standardised pipelines that rely on open-source community developed tools. Each study's metadata are annotated using ontologies. The data are re-analyzed with the aim of reproducing the original conclusions of the underlying experiments. Expression Atlas is currently divided into Bulk Expression Atlas and Single Cell Expression Atlas. Expression Atlas contains data from differential studies (microarray and bulk RNA-Seq) and baseline studies (bulk RNA-Seq and proteomics), whereas Single Cell Expression Atlas is currently dedicated to Single Cell RNA-Sequencing (scRNA-Seq) studies. The resource has been in continuous development since 2009 and it is available at https://www.ebi.ac.uk/gxa
Democratizing long-read genome assembly.
No full textDe novo assembled genomes serve as the backbone for modern genomics. In an article in this issue of Cell Systems, Ekim et al. present the mdBG assembler that can assemble genomes 100-fold faster than previous methods, including a human genome in under 10 min, which unlocks pan-genomics for many species
A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.
Get PDFDetecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity
PIN and CCCH Zn-finger domains coordinate RNA targeting in ZC3H12 family endoribonucleases.
Get PDFThe CCCH-type zinc finger (ZnF) containing ZC3H12 ribonucleases are crucial in post-transcriptional immune homoeostasis with ZC3H12A being the only structurally studied member of the family. In this study, we present a structural-biochemical characterization of ZC3H12C, which is linked with chronic immune disorders like psoriasis. We established that the RNA substrate is cooperatively recognized by the PIN and ZnF domains of ZC3H12C and analyzed the crystal structure of ZC3H12C bound to a single-stranded RNA substrate. The RNA engages in hydrogen-bonded contacts and stacking interactions with the PIN and ZnF domains simultaneously. The ZC3H12 ZnF shows unprecedented structural features not previously observed in any member of the CCCH-ZnF family and utilizes stacking interactions via a unique combination of spatially conserved aromatic residues to align the target transcript in a bent conformation onto the ZnF scaffold. Further comparative structural analysis of ZC3H12 CCCH-ZnF suggests that a trinucleotide sequence is recognized by ZC3H12 ZnF in target RNA. Our work not only describes the initial structure-biochemical study on ZC3H12C, but also provides the first molecular insight into RNA recognition by a ZC3H12 family member. Finally, our work points to an evolutionary code for RNA recognition adopted by CCCH-type ZnF proteins
Chromosome-level genome assembly of a regenerable maize inbred line A188.
Get PDFBACKGROUND
The maize inbred line A188 is an attractive model for elucidation of gene function and improvement due to its high embryogenic capacity and many contrasting traits to the first maize reference genome, B73, and other elite lines. The lack of a genome assembly of A188 limits its use as a model for functional studies.
RESULTS
Here, we present a chromosome-level genome assembly of A188 using long reads and optical maps. Comparison of A188 with B73 using both whole-genome alignments and read depths from sequencing reads identify approximately 1.1 Gb of syntenic sequences as well as extensive structural variation, including a 1.8-Mb duplication containing the Gametophyte factor1 locus for unilateral cross-incompatibility, and six inversions of 0.7 Mb or greater. Increased copy number of carotenoid cleavage dioxygenase 1 (ccd1) in A188 is associated with elevated expression during seed development. High ccd1 expression in seeds together with low expression of yellow endosperm 1 (y1) reduces carotenoid accumulation, accounting for the white seed phenotype of A188. Furthermore, transcriptome and epigenome analyses reveal enhanced expression of defense pathways and altered DNA methylation patterns of the embryonic callus.
CONCLUSIONS
The A188 genome assembly provides a high-resolution sequence for a complex genome species and a foundational resource for analyses of genome variation and gene function in maize. The genome, in comparison to B73, contains extensive intra-species structural variations and other genetic differences. Expression and network analyses identify discrete profiles for embryonic callus and other tissues
A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity.
Get PDFT cell receptor (TCR) antigen-specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide-major histocompatibility complex) interactions and a neural network-based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen-specific interactions for basic immunological research and clinical immune monitoring