Explore the vast range of titles available.

The SARS-CoV-2 RNA-dependent RNA polymerase coordinates viral RNA synthesis as part of an assembly known as the replication–transcription complex (RTC) 1 . Accordingly, the RTC is a target for clinically approved antiviral nucleoside analogues, including remdesivir 2 . Faithful synthesis of viral RNAs by the RTC requires recognition of the correct nucleotide triphosphate (NTP) for incorporation into the nascent RNA. To be effective inhibitors, antiviral nucleoside analogues must compete with the natural NTPs for incorporation. How the SARS-CoV-2 RTC discriminates between the natural NTPs, and how antiviral nucleoside analogues compete, has not been discerned in detail. Here, we use cryogenic-electron microscopy to visualize the RTC bound to each of the natural NTPs in states poised for incorporation. Furthermore, we investigate the RTC with the active metabolite of remdesivir, remdesivir triphosphate (RDV-TP), highlighting the structural basis for the selective incorporation of RDV-TP over its natural counterpart adenosine triphosphate 3 , 4 . Our results explain the suite of interactions required for NTP recognition, informing the rational design of antivirals. Our analysis also yields insights into nucleotide recognition by the nsp12 NiRAN (nidovirus RdRp-associated nucleotidyltransferase), an enigmatic catalytic domain essential for viral propagation 5 . The NiRAN selectively binds guanosine triphosphate, strengthening proposals for the role of this domain in the formation of the 5′ RNA cap 6 . Cryo-EM is used to visualize the SARS-CoV-2 RTC bound to each of the natural NTPs as well as remdesivir triphosphate (RDV-TP) in states poised for incorporation, explaining the interactions required for NTP recognition and RDV-TP selectivity.

In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also tested a greedy hill climbing algorithm and observed similar results as the optimal algorithm.

Cereal endosperm represents 60% of the calories consumed by human beings worldwide. In addition, cereals also serve as the primary feedstock for livestock. However, the regulatory mechanism of cereal endosperm and seed development is largely unknown. Polycomb complex has been shown to play a key role in the regulation of endosperm development in Arabidopsis, but its role in cereal endosperm development remains obscure. Additionally, the enzyme activities of the polycomb complexes have not been demonstrated in plants. Here we purified the rice OsFIE2-polycomb complex using tandem affinity purification and demonstrated its specific H3 methyltransferase activity. We found that the OsFIE2 gene product was responsible for H3K27me3 production specifically in vivo. Genetic studies showed that a reduction of OsFIE2 expression led to smaller seeds, partially filled seeds, and partial loss of seed dormancy. Gene expression and proteomics analyses found that the starch synthesis rate limiting step enzyme and multiple storage proteins are down-regulated in OsFIE2 reduction lines. Genome wide ChIP-Seq data analysis shows that H3K27me3 is associated with many genes in the young seeds. The H3K27me3 modification and gene expression in a key helix-loop-helix transcription factor is shown to be regulated by OsFIE2. Our results suggest that OsFIE2-polycomb complex positively regulates rice endosperm development and grain filling via a mechanism highly different from that in Arabidopsis.

Chromatin immunoprecipitation coupled with high throughput DNA Sequencing (ChIP-Seq) has emerged as a powerful tool for genome wide profiling of the binding sites of proteins associated with DNA such as histones and transcription factors. However, no peak calling program has gained consensus acceptance by the scientific community as the preferred tool for ChIP-Seq data analysis. Analyzing the large data sets generated by ChIP-Seq studies remains highly challenging for most molecular biology laboratories.Here we profile H3K27me3 enrichment sites in rice young endosperm using the ChIP-Seq approach and analyze the data using four peak calling algorithms (FindPeaks, PeakSeq, USeq, and MACS). Comparison of the four algorithms reveals that these programs produce very different peaks in terms of peak size, number, and position relative to genes. We verify the peak predictions using ChIP-PCR to evaluate the accuracy of peak prediction of the four algorithms. We discuss the approach of each algorithm and compare similarities and differences in the results. Despite their differences in the peaks identified, all of the programs reach similar conclusions about the effect of H3K27me3 on gene expression. Its presence either upstream or downstream of a gene is predominately associated with repression of the gene. Additionally, GO analysis finds that a substantially higher ratio of genes associated with H3K27me3 were involved in multicellular organism development, signal transduction, response to external and endogenous stimuli, and secondary metabolic pathways than the rest of the rice genome.

Backtracking, the reverse motion of the transcriptase enzyme on the nucleic acid template, is a universal regulatory feature of transcription in cellular organisms but its role in viruses is not established. Here we present evidence that backtracking extends into the viral realm, where backtracking by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA-dependent RNA polymerase (RdRp) may aid viral transcription and replication. Structures of SARS-CoV-2 RdRp bound to the essential nsp13 helicase and RNA suggested the helicase facilitates backtracking. We use cryo-electron microscopy, RNA–protein cross-linking, and unbiased molecular dynamics simulations to characterize SARS-CoV-2 RdRp backtracking. The results establish that the single-stranded 3′ segment of the product RNA generated by backtracking extrudes through the RdRp nucleoside triphosphate (NTP) entry tunnel, that a mismatched nucleotide at the product RNA 3′ end frays and enters the NTP entry tunnel to initiate backtracking, and that nsp13 stimulates RdRp backtracking. Backtracking may aid proofreading, a crucial process for SARS-CoV-2 resistance against antivirals.

The SARS-CoV-2 nonstructural proteins coordinate genome replication and gene expression. Structural analyses revealed the basis for coupling of the essential nsp13 helicase with the RNA-dependent RNA polymerase (RdRp) where the holo-RdRp and RNA substrate (the replication–transcription complex or RTC) associated with two copies of nsp13 (nsp13 2 –RTC). One copy of nsp13 interacts with the template-RNA in an opposing polarity to the RdRp and is envisaged to drive the RdRp backward on the RNA template (backtracking), prompting questions as to how the RdRp can efficiently synthesize RNA in the presence of nsp13. Here we use cryogenic-electron microscopy and molecular dynamics simulations to analyze the nsp13 2 –RTC, revealing four distinct conformational states of the helicases. The results indicate a mechanism for the nsp13 2 –RTC to turn backtracking on and off, using an allosteric mechanism to switch between RNA synthesis or backtracking in response to stimuli at the RdRp active site. In their complex, the SARS-CoV-2 nsp13 helicase and RNA polymerase would translocate on RNA in opposite directions. Cryo-EM and MD simulations resolve this conundrum, suggesting an allosteric mechanism to turn the helicase on and off.

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8 + T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.

Sarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes. To explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME). A multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important. The outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI.