Dynamic conformation: Marching toward circular RNA function and application.

Circular RNA is a group of covalently closed, single-stranded transcripts with unique biogenesis, stability, and conformation that play distinct roles in modulating cellular functions and also possess a great potential for developing circular RNA-based therapies. Importantly, due to its circular conformation, circular RNA generates distinct intramolecular base pairing that is different from the linear transcript. In this perspective, we review how circular RNA conformation can affect its turnover and modes of action, as well as what factors can modulate circular RNA conformation. We also discuss how understanding circular RNA conformation can facilitate learning about their functions as well as the remaining technological issues to further address their conformation. These efforts will ultimately inform the design of circular RNA-based platforms for biomedical applications.

A peroxiredoxin-P38 MAPK scaffold increases MAPK activity by MAP3K-independent mechanisms.

Peroxiredoxins (Prdxs) utilize reversibly oxidized cysteine residues to reduce peroxides and promote H2O2 signal transduction, including H2O2-induced activation of P38 MAPK. Prdxs form H2O2-induced disulfide complexes with many proteins, including multiple kinases involved in P38 MAPK signaling. Here, we show that a genetically encoded fusion between a Prdx and P38 MAPK is sufficient to hyperactivate the kinase in yeast and human cells by a mechanism that does not require the H2O2-sensing cysteine of the Prdx. We demonstrate that a P38-Prdx fusion protein compensates for loss of the yeast scaffold protein Mcs4 and MAP3K activity, driving yeast into mitosis. Based on our findings, we propose that the H2O2-induced formation of Prdx-MAPK disulfide complexes provides an alternative scaffold and signaling platform for MAPKK-MAPK signaling. The demonstration that formation of a complex with a Prdx is sufficient to modify the activity of a kinase has broad implications for peroxide-based signal transduction in eukaryotes.

Haplotype-resolved genome assembly and implementation of VitExpress, an open interactive transcriptomic platform for grapevine.

Haplotype-resolved genome assemblies were produced for Chasselas and Ugni Blanc, two heterozygous Vitis vinifera cultivars by combining high-fidelity long-read sequencing and high-throughput chromosome conformation capture (Hi-C). The telomere-to-telomere full coverage of the chromosomes allowed us to assemble separately the two haplo-genomes of both cultivars and revealed structural variations between the two haplotypes of a given cultivar. The deletions/insertions, inversions, translocations, and duplications provide insight into the evolutionary history and parental relationship among grape varieties. Integration of de novo single long-read sequencing of full-length transcript isoforms (Iso-Seq) yielded a highly improved genome annotation. Given its higher contiguity, and the robustness of the IsoSeq-based annotation, the Chasselas assembly meets the standard to become the annotated reference genome for V. vinifera. Building on these resources, we developed VitExpress, an open interactive transcriptomic platform, that provides a genome browser and integrated web tools for expression profiling, and a set of statistical tools (StatTools) for the identification of highly correlated genes. Implementation of the correlation finder tool for MybA1, a major regulator of the anthocyanin pathway, identified candidate genes associated with anthocyanin metabolism, whose expression patterns were experimentally validated as discriminating between black and white grapes. These resources and innovative tools for mining genome-related data are anticipated to foster advances in several areas of grapevine research.

Platform-independent estimation of human physiological time from single blood samples.

Abundant epidemiological evidence links circadian rhythms to human health, from heart disease to neurodegeneration. Accurate determination of an individual's circadian phase is critical for precision diagnostics and personalized timing of therapeutic interventions. To date, however, we still lack an assay for physiological time that is accurate, minimally burdensome to the patient, and readily generalizable to new data. Here, we present TimeMachine, an algorithm to predict the human circadian phase using gene expression in peripheral blood mononuclear cells from a single blood draw. Once trained on data from a single study, we validated the trained predictor against four independent datasets with distinct experimental protocols and assay platforms, demonstrating that it can be applied generalizably. Importantly, TimeMachine predicted circadian time with a median absolute error ranging from 1.65 to 2.7 h, regardless of systematic differences in experimental protocol and assay platform, without renormalizing the data or retraining the predictor. This feature enables it to be flexibly applied to both new samples and existing data without limitations on the transcriptomic profiling technology (microarray, RNAseq). We benchmark TimeMachine against competing approaches and identify the algorithmic features that contribute to its performance.

Interaction structure constrains the emergence of conventions in group communication.

Real-world communication frequently requires language producers to address more than one comprehender at once, yet most psycholinguistic research focuses on one-on-one communication. As the audience size grows, interlocutors face new challenges that do not arise in dyads. They must consider multiple perspectives and weigh multiple sources of feedback to build shared understanding. Here, we ask which properties of the group's interaction structure facilitate successful communication. We used a repeated reference game paradigm in which directors instructed between one and five matchers to choose specific targets out of a set of abstract figures. Across 313 games (N = 1,319 participants), we manipulated several key constraints on the group's interaction, including the amount of feedback that matchers could give to directors and the availability of peer interaction between matchers. Across groups of different sizes and interaction constraints, describers produced increasingly efficient utterances and matchers made increasingly accurate selections. Critically, however, we found that smaller groups and groups with less-constrained interaction structures ("thick channels") showed stronger convergence to group-specific conventions than large groups with constrained interaction structures ("thin channels"), which struggled with convention formation. Overall, these results shed light on the core structural factors that enable communication to thrive in larger groups.

Understanding proteome quantification in an interactive learning module on Google Cloud Platform.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on protein quantification in an interactive format that uses appropriate cloud resources for data access and analyses. Quantitative proteomics is a rapidly growing discipline due to the cutting-edge technologies of high resolution mass spectrometry. There are many data types to consider for proteome quantification including data dependent acquisition, data independent acquisition, multiplexing with Tandem Mass Tag reporter ions, spectral counts, and more. As part of the NIH NIGMS Sandbox effort, we developed a learning module to introduce students to mass spectrometry terminology, normalization methods, statistical designs, and basics of R programming. By utilizing the Google Cloud environment, the learning module is easily accessible without the need for complex installation procedures. The proteome quantification module demonstrates the analysis using a provided TMT10plex data set using MS3 reporter ion intensity quantitative values in a Jupyter notebook with an R kernel. The learning module begins with the raw intensities, performs normalization, and differential abundance analysis using limma models, and is designed for researchers with a basic understanding of mass spectrometry and R programming language. Learners walk away with a better understanding of how to navigate Google Cloud Platform for proteomic research, and with the basics of mass spectrometry data analysis at the command line. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Whole-genome bisulfite sequencing data analysis learning module on Google Cloud Platform.

This study describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module is designed to facilitate interactive learning of whole-genome bisulfite sequencing (WGBS) data analysis utilizing cloud-based tools in Google Cloud Platform, such as Cloud Storage, Vertex AI notebooks and Google Batch. WGBS is a powerful technique that can provide comprehensive insights into DNA methylation patterns at single cytosine resolution, essential for understanding epigenetic regulation across the genome. The designed learning module first provides step-by-step tutorials that guide learners through two main stages of WGBS data analysis, preprocessing and the identification of differentially methylated regions. And then, it provides a streamlined workflow and demonstrates how to effectively use it for large datasets given the power of cloud infrastructure. The integration of these interconnected submodules progressively deepens the user's understanding of the WGBS analysis process along with the use of cloud resources. Through this module, we can enhance the accessibility and adoption of cloud computing in epigenomic research, speeding up the advancements in the related field and beyond. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities.

In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction.

Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.

A cross-species approach using an in vivo evaluation platform in mice demonstrates that sequence variation in human RABEP2 modulates ischemic stroke outcomes.

Ischemic stroke, caused by vessel blockage, results in cerebral infarction, the death of brain tissue. Previously, quantitative trait locus (QTL) mapping of cerebral infarct volume and collateral vessel number identified a single, strong genetic locus regulating both phenotypes. Additional studies identified RAB GTPase-binding effector protein 2 (Rabep2) as the casual gene. However, there is yet no evidence that variation in the human ortholog of this gene plays any role in ischemic stroke outcomes. We established an in vivo evaluation platform in mice by using adeno-associated virus (AAV) gene replacement and verified that both mouse and human RABEP2 rescue the mouse Rabep2 knockout ischemic stroke volume and collateral vessel phenotypes. Importantly, this cross-species complementation enabled us to experimentally investigate the functional effects of coding sequence variation in human RABEP2. We chose four coding variants from the human population that are predicted by multiple in silico algorithms to be damaging to RABEP2 function. In vitro and in vivo analyses verify that all four led to decreased collateral vessel connections and increased infarct volume. Thus, there are naturally occurring loss-of-function alleles. This cross-species approach will expand the number of targets for therapeutics development for ischemic stroke.

ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions.

Cracking the entangling code of protein-ligand interaction (PLI) is of great importance to structure-based drug design and discovery. Different physical and biochemical representations can be used to describe PLI such as energy terms and interaction fingerprints, which can be analyzed by machine learning (ML) algorithms to create ML-based scoring functions (MLSFs). Here, we propose the ML-based PLI capturer (ML-PLIC), a web platform that automatically characterizes PLI and generates MLSFs to identify the potential binders of a specific protein target through virtual screening (VS). ML-PLIC comprises five modules, including Docking for ligand docking, Descriptors for PLI generation, Modeling for MLSF training, Screening for VS and Pipeline for the integration of the aforementioned functions. We validated the MLSFs constructed by ML-PLIC in three benchmark datasets (Directory of Useful Decoys-Enhanced, Active as Decoys and TocoDecoy), demonstrating accuracy outperforming traditional docking tools and competitive performance to the deep learning-based SF, and provided a case study of the Serine/threonine-protein kinase WEE1 in which MLSFs were developed by using the ML-based VS pipeline in ML-PLIC. Underpinning the latest version of ML-PLIC is a powerful platform that incorporates physical and biological knowledge about PLI, leveraging PLI characterization and MLSF generation into the design of structure-based VS pipeline. The ML-PLIC web platform is now freely available at http://cadd.zju.edu.cn/plic/.

Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM.

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.

Benchmarking multi-platform sequencing technologies for human genome assembly.

Genome assembly is a computational technique that involves piecing together deoxyribonucleic acid (DNA) fragments generated by sequencing technologies to create a comprehensive and precise representation of the entire genome. Generating a high-quality human reference genome is a crucial prerequisite for comprehending human biology, and it is also vital for downstream genomic variation analysis. Many efforts have been made over the past few decades to create a complete and gapless reference genome for humans by using a diverse range of advanced sequencing technologies. Several available tools are aimed at enhancing the quality of haploid and diploid human genome assemblies, which include contig assembly, polishing of contig errors, scaffolding and variant phasing. Selecting the appropriate tools and technologies remains a daunting task despite several studies have investigated the pros and cons of different assembly strategies. The goal of this paper was to benchmark various strategies for human genome assembly by combining sequencing technologies and tools on two publicly available samples (NA12878 and NA24385) from Genome in a Bottle. We then compared their performances in terms of continuity, accuracy, completeness, variant calling and phasing. We observed that PacBio HiFi long-reads are the optimal choice for generating an assembly with low base errors. On the other hand, we were able to produce the most continuous contigs with Oxford Nanopore long-reads, but they may require further polishing to improve on quality. We recommend using short-reads rather than long-reads themselves to improve the base accuracy of contigs from Oxford Nanopore long-reads. Hi-C is the best choice for chromosome-level scaffolding because it can capture the longest-range DNA connectedness compared to 10× linked-reads and Bionano optical maps. However, a combination of multiple technologies can be used to further improve the quality and completeness of genome assembly. For diploid assembly, hifiasm is the best tool for human diploid genome assembly using PacBio HiFi and Hi-C data. Looking to the future, we expect that further advancements in human diploid assemblers will leverage the power of PacBio HiFi reads and other technologies with long-range DNA connectedness to enable the generation of high-quality, chromosome-level and haplotype-resolved human genome assemblies.

Digital Intervention for behaviouR changE and Chronic disease prevenTION (DIRECTION): Study protocol for a randomized controlled trial of a web-based platform integrating nutrition, physical activity, and mindfulness for individuals with obesity.

Excess body weight, suboptimal diet, physical inactivity, alcohol consumption, sleep disruption, and elevated stress are modifiable risk factors associated with the development of chronic diseases. Digital behavioural interventions targeting these factors have shown promise in improving health and reducing chronic disease risk. The Digital Intervention for behaviouR changE and Chronic disease prevenTION (DIRECTION) study is a parallel group, two-arm, randomized controlled trial evaluating the effects of adding healthcare professional guidance and peer support via group-based sessions to a web-based wellness platform (experimental group, n = 90) compared to a self-guided use of the platform (active control group, n = 90) among individuals with a body mass index (BMI) of 30 to <35 kg/m2 and aged 40-65 years. Obesity is defined by a high BMI. The web-based wellness platform employed in this study is My Viva Plan (MVP)®, which holistically integrates nutrition, physical activity, and mindfulness programs. Over 16 weeks, the experimental group uses the web-based wellness platform daily and engages in weekly online support group sessions. The active control group exclusively uses the web-based wellness platform daily. Assessments are conducted at baseline and weeks 8 and 16. The primary outcome is between-group difference in weight loss (kg) at week 16, and secondary outcomes are BMI, percent weight change, proportion of participants achieving 5% or more weight loss, dietary intake, physical activity, alcohol consumption, sleep, and stress across the study. A web-based wellness platform may be a scalable approach to promote behavioural changes that positively impact health. This study will inform the development and implementation of interventions using web-based wellness platforms and personalized digital interventions to improve health outcomes and reduce chronic disease risk among individuals with obesity.

Protocol for a pilot study: Feasibility of a web-based platform to improve nutrition, mindfulness, and physical function in people living with Post COVID-19 condition (BLEND).

Individuals with Post COVID-19 condition (PCC), or long COVID, experience symptoms such as fatigue, muscle weakness, and psychological distress, including anxiety, depression, or sleep disorders that persist after recovery from COVID-19. These ongoing symptoms significantly compromise quality of life and diminish functional capacity and independence. Multimodal digital interventions targeting behavioural factors such as nutrition and mindfulness have shown promise in improving health outcomes of people with chronic health conditions and may be beneficial for those with PCC. The BLEND study (weB-based pLatform to improve nutrition, mindfulnEss, and physical function, in patients with loNg COVID) study is an 8-week pilot randomized controlled trial evaluating the feasibility of a digital wellness platform compared to usual care among individuals with PCC. The web-based wellness platform employed in this study, My Viva Plan (MVP)®, integrates a holistic, multicomponent approach to promote wellness. The intervention group receives access to the digital health platform for 8 weeks with encouragement for frequent interactions to improve dietary intake and mindfulness. The control group receives general content focusing on improvements in dietary intake and mindfulness. Assessments are conducted at baseline and week 8. The primary outcome is the feasibility of platform use. Secondary and exploratory outcomes include a between-group comparison of changes in body composition, nutritional status, quality of life, mindfulness, physical activity, and physical performance after 8 weeks. Findings of this study will inform the development of effective web-based wellness programs tailored for individuals with PCC to promote sustainable behavioural changes and improved health outcomes.

Study protocol for a single-arm pilot trial investigating the feasibility of a multimodal digital technology for managing metabolic syndrome in patients with chronic obstructive pulmonary disease.

Individuals diagnosed with Chronic Obstructive Pulmonary Disease (COPD) are exposed to an increased risk of metabolic syndrome (MetS), which negatively affects their health outcomes and quality of life. Lifestyle interventions have shown promise in managing MetS. This study outlines the protocol for a web-based multimodal self-care program, Digital Metabolic Rehabilitation, for managing MetS in patients with COPD. The Digital Metabolic Rehabilitation is a single-arm pilot trial that integrates the Canadian Health Advanced by Nutrition and Graded Exercise (CHANGE) Program and a web-based wellness platform. The web-based wellness platform employed in this study is My Viva Plan (MVP)®, which integrates a holistic, multicomponent approach to promote wellness. The intervention will primarily focus on lifestyle changes for patients with COPD. Over 6 months, participants will use the web-based wellness platform and engage in weekly online support group sessions. Fifty patients diagnosed with stage I-II COPD and MetS will participate. Blood tests, anthropometrics, body composition, physical function, muscle strength, physical activity, energy metabolism, quality of life and mental health will be assessed at baseline, 3, and 6 months. The Digital Metabolic Rehabilitation program aims to explore whether a multimodal integrative intervention delivered through a web-based wellness platform can be implemented by patients with COPD with MetS. By combining the expertise of the CHANGE Program with the digital delivery format, the intervention seeks to enhance self-monitoring and foster better self-management practices. The protocol outlines a novel and potentially impactful intervention for managing MetS in patients with COPD.

The sorting platform in the type III secretion pathway: From assembly to function.

The type III secretion system (T3SS) is a specialized nanomachine that enables bacteria to secrete proteins in a specific order and directly deliver a specific set of them, collectively known as effectors, into eukaryotic organisms. The core structure of the T3SS is a syringe-like apparatus composed of multiple building blocks, including both membrane-associated and soluble proteins. The cytosolic components organize together in a chamber-like structure known as the sorting platform (SP), responsible for recruiting, sorting, and initiating the substrates destined to engage this secretion pathway. In this article, we provide an overview of recent findings on the SP's structure and function, with a particular focus on its assembly pathway. Furthermore, we discuss the molecular mechanisms behind the recruitment and hierarchical sorting of substrates by this cytosolic complex. Overall, the T3SS is a highly specialized and complex system that requires precise coordination to function properly. A deeper understanding of how the SP orchestrates T3S could enhance our comprehension of this complex nanomachine, which is central to the host-pathogen interface, and could aid in the development of novel strategies to fight bacterial infections.

The Rise of Adaptive Platform Trials in Critical Care.

As durable learning research systems, adaptive platform trials represent a transformative new approach to accelerating clinical evaluation and discovery in critical care. This Perspective provides a brief introduction to the concept of adaptive platform trials, describes several established and emerging platforms in critical care, and surveys some opportunities and challenges for their implementation and impact.

A preclinical platform for assessing antitumor effects and systemic toxicities of cancer drug targets.

Anticancer drug development campaigns often fail due to an incomplete understanding of the therapeutic index differentiating the efficacy of the agent against the cancer and its on-target toxicities to the host. To address this issue, we established a versatile preclinical platform in which genetically defined cancers are produced using somatic tissue engineering in transgenic mice harboring a doxycycline-inducible short hairpin RNA against the target of interest. In this system, target inhibition is achieved by the addition of doxycycline, enabling simultaneous assessment of efficacy and toxicity in the same animal. As proof of concept, we focused on CDK9­a cancer target whose clinical development has been hampered by compounds with poorly understood target specificity and unacceptable toxicities. We systematically compared phenotypes produced by genetic Cdk9 inhibition to those achieved using a recently developed highly specific small molecule CDK9 inhibitor and found that both perturbations led to robust antitumor responses. Remarkably, nontoxic levels of CDK9 inhibition could achieve significant treatment efficacy, and dose-dependent toxicities produced by prolonged CDK9 suppression were largely reversible upon Cdk9 restoration or drug withdrawal. Overall, these results establish a versatile in vivo target validation platform that can be employed for rapid triaging of therapeutic targets and lend support to efforts aimed at advancing CDK9 inhibitors for cancer therapy.

Id genes are essential for early heart formation.

Deciphering the fundamental mechanisms controlling cardiac specification is critical for our understanding of how heart formation is initiated during embryonic development and for applying stem cell biology to regenerative medicine and disease modeling. Using systematic and unbiased functional screening approaches, we discovered that the Id family of helix-loop-helix proteins is both necessary and sufficient to direct cardiac mesoderm formation in frog embryos and human embryonic stem cells. Mechanistically, Id proteins specify cardiac cell fate by repressing two inhibitors of cardiogenic mesoderm formation-Tcf3 and Foxa2-and activating inducers Evx1, Grrp1, and Mesp1. Most importantly, CRISPR/Cas9-mediated ablation of the entire Id (Id1-4) family in mouse embryos leads to failure of anterior cardiac progenitor specification and the development of heartless embryos. Thus, Id proteins play a central and evolutionarily conserved role during heart formation and provide a novel means to efficiently produce cardiovascular progenitors for regenerative medicine and drug discovery applications.