Architecture and nucleic acids recognition mechanism of the THO complex, an mRNP assembly factor.

The THO complex is a key factor in co-transcriptional formation of export-competent messenger ribonucleoprotein particles, yet its structure and mechanism of chromatin recruitment remain unknown. In yeast, this complex has been described as a heterotetramer (Tho2, Hpr1, Mft1, and Thp2) that interacts with Tex1 and mRNA export factors Sub2 and Yra1 to form the TRanscription EXport (TREX) complex. In this study, we purified yeast THO and found Tex1 to be part of its core. We determined the three-dimensional structures of five-subunit THO complex by electron microscopy and located the positions of Tex1, Hpr1, and Tho2 C-terminus using various labelling techniques. In the case of Tex1, a ß-propeller protein, we have generated an atomic model which docks into the corresponding part of the THO complex envelope. Furthermore, we show that THO directly interacts with nucleic acids through the unfolded C-terminal region of Tho2, whose removal reduces THO recruitment to active chromatin leading to mRNA biogenesis defects. In summary, this study describes the THO architecture, the structural basis for its chromatin targeting, and highlights the importance of unfolded regions of eukaryotic proteins.

OSPred Tool: A Digital Health Aid for Rapid Predictive Analysis of Correlations Between Early End Points and Overall Survival in Non-Small-Cell Lung Cancer Clinical Trials.

PURPOSE: Overall survival (OS) is the gold standard end point for establishing clinical benefits in phase III oncology trials. However, these trials are associated with low success rates, largely driven by failure to meet the primary end point. Surrogate end points such as progression-free survival (PFS) are increasingly being used as indicators of biologic drug activity and to inform early go/no-go decisions in oncology drug development. We developed OSPred, a digital health aid that combines actual clinical data and machine intelligence approaches to visualize correlation trends between early (PFS-based) and late (OS) end points and provide support for shared decision making in the drug development pipeline. METHODS: OSPred is based on a trial-level data set of 81 reports (35 anticancer drugs with various mechanisms of action; 156 observations) in non-small-cell lung cancer (NSCLC). OSPred was developed using R Shiny, with packages ggplot2, metafor, boot, dplyr, and mvtnorm, to analyze and visualize correlation results and predict OS hazard ratio (HR OS) on the basis of user-inputted PFS-based data, namely, HR PFS, or the odds ratio of PFS at 4 (OR PFS4) or 6 (OR PFS6) months. RESULTS: The three main features of the tool are as follows: prediction of HR OS on the basis of user-inputted early end point values; visualization of comparisons of the user's investigational drug with other drugs in the NSCLC setting, including by specific MoA; and creation of a probability density chart, providing point prediction and CIs for HR OS. A working version of the tool for download is linked. CONCLUSION: The OSPred tool offers interactive visualization of clinical trial end point correlations with reference to a large pool of historical NSCLC studies. Its focused capability has the potential to digitally transform and accelerate data-driven decision making as part of the drug development process.

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit.

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

SAEgnal: A Predictive Assessment Framework for Optimizing Safety Profiles in Immuno-Oncology Combination Trials.

Combination therapies are an emerging drug development strategy in cancer, particularly in the immunooncology (IO) space. Many combination studies do not meet their safety objectives due to serious adverse events (SAEs). Prediction of SAEs based on evidence from single and combination studies would be highly beneficial. To address the emerging challenge of optimizing the safety and efficacy of combination studies, we have assembled a novel oncology clinical trial data set with 329 trials, 685 arms (279 unique treatment arms), including 200 combinations, 79 mono arms, and 59 curated adverse event categories in the setting of non-small cell lung cancer (NSCLC). We integrated the database with an analytical framework: SAEgnal. Using SAEgnal, we have investigated the difference in the risk of 39 adverse event types between combination and monotherapy arms across a subset of 34 combination trials. We observed different risk profiles between combination and monotherapies; interestingly, while the risk of elevated AST/ALT is lower in combination arms (in 1/8 trials, p-value < 0.05), it is higher for bleeding (7/8 trials, p-value < 0.05). We envisage that the SAEgnal framework would enable rapid predictive analytics of SAEs in oncology and accelerate drug development in oncology.

Correlation Between Early Endpoints and Overall Survival in Non-Small-Cell Lung Cancer: A Trial-Level Meta-Analysis.

Early endpoints, such as progression-free survival (PFS), are increasingly used as surrogates for overall survival (OS) to accelerate approval of novel oncology agents. Compiling trial-level data from randomized controlled trials (RCTs) could help to develop a predictive framework to ascertain correlation trends between treatment effects for early and late endpoints. Through trial-level correlation and random-effects meta-regression analysis, we assessed the relationship between hazard ratio (HR) OS and (1) HR PFS and (2) odds ratio (OR) PFS at 4 and 6 months, stratified according to the mechanism of action of the investigational product. Using multiple source databases, we compiled a data set including 81 phase II-IV RCTs (35 drugs and 156 observations) of patients with non-small-cell lung cancer. Low-to-moderate correlations were generally observed between treatment effects for early endpoints (based on PFS) and HR OS across trials of agents with different mechanisms of action. Moderate correlations were seen between treatment effects for HR PFS and HR OS across all trials, and in the programmed cell death-1/programmed cell death ligand-1 and epidermal growth factor receptor trial subsets. Although these results constitute an important step, caution is advised, as there are some limitations to our evaluation, and an additional patient-level analysis would be needed to establish true surrogacy.

OrfX, a Nucleomodulin Required for Listeria monocytogenes Virulence.

Listeria monocytogenes is a bacterial pathogen causing severe foodborne infections in humans and animals. Listeria can enter into host cells and survive and multiply therein, due to an arsenal of virulence determinants encoded in different loci on the chromosome. Several key Listeria virulence genes are clustered in Listeria pathogenicity island 1. This important locus also contains orfX (lmo0206), a gene of unknown function. Here, we found that OrfX is a small, secreted protein whose expression is positively regulated by PrfA, the major transcriptional activator of Listeria virulence genes. We provide evidence that OrfX is a virulence factor that dampens the oxidative response of infected macrophages, which contributes to intracellular survival of bacteria. OrfX is targeted to the nucleus and interacts with the regulatory protein RybP. We show that in macrophages, the expression of OrfX decreases the level of RybP, which controls cellular infection. Collectively, these data reveal that Listeria targets RybP and evades macrophage oxidative stress for efficient infection. Altogether, OrfX is after LntA, the second virulence factor acting directly in the nucleus.IMPORTANCEListeria monocytogenes is a model bacterium that has been successfully used over the last 30 years to refine our understanding of the molecular, cellular, and tissular mechanisms of microbial pathogenesis. The major virulence factors of pathogenic Listeria species are located on a single chromosomal locus. Here, we report that the last gene of this locus encodes a small secreted nucleomodulin, OrfX, that is required for bacterial survival within macrophages and in the infected host. This work demonstrates that the production of OrfX contributes to limiting the host innate immune response by dampening the oxidative response of macrophages. We also identify a target of OrfX, RybP, which is an essential pleiotropic regulatory protein of the cell, and uncover its role in host defense. Our data reinforce the view that the secretion of nucleomodulins is an important strategy used by microbial pathogens to promote infection.