Robust generation of human-chambered cardiac organoids from pluripotent stem cells for improved modelling of cardiovascular diseases.

BACKGROUND: Tissue organoids generated from human pluripotent stem cells are valuable tools for disease modelling and to understand developmental processes. While recent progress in human cardiac organoids revealed the ability of these stem cell-derived organoids to self-organize and intrinsically formed chamber-like structure containing a central cavity, it remained unclear the processes involved that enabled such chamber formation. METHODS: Chambered cardiac organoids (CCOs) differentiated from human embryonic stem cells (H7) were generated by modulation of Wnt/ß-catenin signalling under fully defined conditions, and several growth factors essential for cardiac progenitor expansion. Transcriptomic profiling of day 8, day 14 and day 21 CCOs was performed by quantitative PCR and single-cell RNA sequencing. Endothelin-1 (EDN1) known to induce oxidative stress in cardiomyocytes was used to induce cardiac hypertrophy in CCOs in vitro. Functional characterization of cardiomyocyte contractile machinery was performed by immunofluorescence staining and analysis of brightfield and fluorescent video recordings. Quantitative PCR values between groups were compared using two-tailed Student's t tests. Cardiac organoid parameters comparison between groups was performed using two-tailed Mann-Whitney U test when sample size is small; otherwise, Welch's t test was used. Comparison of calcium kinetics parameters derived from the fluorescent data was performed using two-tailed Student's t tests. RESULTS: Importantly, we demonstrated that a threshold number of cardiac progenitor was essential to line the circumference of the inner cavity to ensure proper formation of a chamber within the organoid. Single-cell RNA sequencing revealed improved maturation over a time course, as evidenced from increased mRNA expression of cardiomyocyte maturation genes, ion channel genes and a metabolic shift from glycolysis to fatty acid ß-oxidation. Functionally, CCOs recapitulated clinical cardiac hypertrophy by exhibiting thickened chamber walls, reduced fractional shortening, and increased myofibrillar disarray upon treatment with EDN1. Furthermore, electrophysiological assessment of calcium transients displayed tachyarrhythmic phenotype observed as a consequence of rapid depolarization occurring prior to a complete repolarization. CONCLUSIONS: Our findings shed novel insights into the role of progenitors in CCO formation and pave the way for the robust generation of cardiac organoids, as a platform for future applications in disease modelling and drug screening in vitro.

A Warhead Substitution Study on the Coronavirus Main Protease Inhibitor Nirmatrelvir.

The SARS-CoV-2 pandemic is currently causing an unprecedented global health emergency since its emergence in December 2019. In December 2021, the FDA granted emergency use authorization to nirmatrelvir, a SARS-CoV-2 main protease inhibitor, for treating infected patients. This peptidomimetic is designed with a nitrile warhead, which forms a covalent bond to the viral protease. Herein, we investigate nirmatrelvir analogs with different warheads and their inhibitory activities. In addition, antiviral activities against human alphacoronavirus 229E was also investigated along with a cell-based assay. We discovered that the hydroxymethylketone and ketobenzothiazole warheads were equipotent to the nitrile warhead, suggesting that these analogs can also be used for treating coronavirus infections.

A chemical genetic screen identifies Aurora kinases as a therapeutic target in EGFR T790M negative, gefitinib-resistant head and neck squamous cell carcinoma (HNSCC).

BACKGROUND: Overexpression of epidermal growth factor receptor (EGFR), and downstream pathway activation appears to be a common oncogenic driver in the majority of head and neck squamous cell cancers (HNSCCs); yet targeting EGFR for the treatment of HNSCC has met with limited success. Apart from the anti-EGFR antibody cetuximab, no small molecule EGFR/tyrosine kinase inhibitors (TKIs) have progressed to routine clinical use. The aim of this study was to determine factors contributing to the lack of response to TKIs and identify alternative therapeutic vulnerabilities. METHODS: Genomic and transcriptomic sequencing, high-throughput compound screens, overexpression and siRNA knockdown, western blot, in vivo xenograft studies. FINDINGS: We derived three pairs of isogenic gefitinib (TKI)-sensitive and resistant patient-derived HNSCC cell lines. Genomic sequencing of gefitinib-resistant cell lines identified a lack of activating and resistance-associated EGFR mutations. Instead, transcriptomic sequencing showed upregulated EMT gene signature in the gefitinib-resistant cells with a corresponding increase in their migratory phenotype. Additionally, the resistant cell displayed reduced growth rate. Surprisingly, while gefitinib-resistant cells were independent of EGFR for survival, they nonetheless displayed activation of downstream ERK and AKT signalling. High-throughput screening (HTS) of druggable, small molecule libraries revealed that the gefitinib-resistant cells were particularly sensitive to inhibitors of genes involved in cell cycle and mitosis, such as Aurora kinase inhibitors (AKIs), cyclin-dependent kinase (CDK) inhibitors, and microtubule inhibitors. Notably our results showed that in the EGFR inhibited state, Aurora kinases are essential for cell survival. INTERPRETATION: Our study demonstrates that in the absence of activating EGFR mutations, HNSCCs may gain resistance to gefitinib through decreased cell proliferation, which makes them exceptionally vulnerable to cell-cycle inhibitors. FUNDING: Agency for Science, Technology, and Research (A*STAR), National Medical Research Council (NMRC), and the National Institutes of Health (NIH)/National Cancer Institute (NCI).

Dynamic expression of tRNA-derived small RNAs define cellular states.

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) have recently emerged as important regulators of protein translation and shown to have diverse biological functions. However, the underlying cellular and molecular mechanisms of tsRNA function in the context of dynamic cell-state transitions remain unclear. Expression analysis of tsRNAs in distinct heterologous cell and tissue models of stem vs. differentiated states revealed a differentiation-dependent enrichment of 5'-tsRNAs. We report the identification of a set of 5'-tsRNAs that is upregulated in differentiating mouse embryonic stem cells (mESCs). Notably, interactome studies with differentially enriched 5'-tsRNAs revealed a switch in their association with "effector" RNPs and "target" mRNAs in different cell states. We demonstrate that specific 5'-tsRNAs can preferentially interact with the RNA-binding protein, Igf2bp1, in the RA-induced differentiated state. This association influences the transcript stability and thereby translation of the pluripotency-promoting factor, c-Myc, thus providing a mechanistic basis for how 5'-tsRNAs can modulate stem cell states in mESCs. Together our study highlights the role of 5'-tsRNAs in defining distinct cell states.

Phenotype-driven precision oncology as a guide for clinical decisions one patient at a time.

Genomics-driven cancer therapeutics has gained prominence in personalized cancer treatment. However, its utility in indications lacking biomarker-driven treatment strategies remains limited. Here we present a "phenotype-driven precision-oncology" approach, based on the notion that biological response to perturbations, chemical or genetic, in ex vivo patient-individualized models can serve as predictive biomarkers for therapeutic response in the clinic. We generated a library of "screenable" patient-derived primary cultures (PDCs) for head and neck squamous cell carcinomas that reproducibly predicted treatment response in matched patient-derived-xenograft models. Importantly, PDCs could guide clinical practice and predict tumour progression in two n = 1 co-clinical trials. Comprehensive "-omics" interrogation of PDCs derived from one of these models revealed YAP1 as a putative biomarker for treatment response and survival in ~24% of oral squamous cell carcinoma. We envision that scaling of the proposed PDC approach could uncover biomarkers for therapeutic stratification and guide real-time therapeutic decisions in the future.Treatment response in patient-derived models may serve as a biomarker for response in the clinic. Here, the authors use paired patient-derived mouse xenografts and patient-derived primary culture models from head and neck squamous cell carcinomas, including metastasis, as models for high-throughput screening of anti-cancer drugs.

Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis.

Proteomics has proved invaluable in generating large-scale quantitative data; however, the development of systems approaches for examining the proteome in vivo has lagged behind. To evaluate protein abundance and localization on a proteome scale, we exploited the yeast GFP-fusion collection in a pipeline combining automated genetics, high-throughput microscopy, and computational feature analysis. We developed an ensemble of binary classifiers to generate localization data from single-cell measurements and constructed maps of ∼3,000 proteins connected to 16 localization classes. To survey proteome dynamics in response to different chemical and genetic stimuli, we measure proteome-wide abundance and localization and identified changes over time. We analyzed >20 million cells to identify dynamic proteins that redistribute among multiple localizations in hydroxyurea, rapamycin, and in an rpd3Δ background. Because our localization and abundance data are quantitative, they provide the opportunity for many types of comparative studies, single cell analyses, modeling, and prediction. VIDEO ABSTRACT.

CYCLoPs: A Comprehensive Database Constructed from Automated Analysis of Protein Abundance and Subcellular Localization Patterns in Saccharomyces cerevisiae.

Changes in protein subcellular localization and abundance are central to biological regulation in eukaryotic cells. Quantitative measures of protein dynamics in vivo are therefore highly useful for elucidating specific regulatory pathways. Using a combinatorial approach of yeast synthetic genetic array technology, high-content screening, and machine learning classifiers, we developed an automated platform to characterize protein localization and abundance patterns from images of log phase cells from the open-reading frame-green fluorescent protein collection in the budding yeast, Saccharomyces cerevisiae. For each protein, we produced quantitative profiles of localization scores for 16 subcellular compartments at single-cell resolution to trace proteome-wide relocalization in conditions over time. We generated a collection of ∼300,000 micrographs, comprising more than 20 million cells and ∼9 billion quantitative measurements. The images depict the localization and abundance dynamics of more than 4000 proteins under two chemical treatments and in a selected mutant background. Here, we describe CYCLoPs (Collection of Yeast Cells Localization Patterns), a web database resource that provides a central platform for housing and analyzing our yeast proteome dynamics datasets at the single cell level. CYCLoPs version 1.0 is available at http://cyclops.ccbr.utoronto.ca. CYCLoPs will provide a valuable resource for the yeast and eukaryotic cell biology communities and will be updated as new experiments become available.

The budding yeast nuclear envelope adjacent to the nucleolus serves as a membrane sink during mitotic delay.

The mechanisms that dictate nuclear shape are largely unknown. Here we screened the budding yeast deletion collection for mutants with abnormal nuclear shape. A common phenotype was the appearance of a nuclear extension, particularly in mutants in DNA repair and chromosome segregation genes. Our data suggest that these mutations led to the abnormal nuclear morphology indirectly, by causing a checkpoint-induced cell-cycle delay. Indeed, delaying cells in mitosis by other means also led to the appearance of nuclear extensions, whereas inactivating the DNA damage checkpoint pathway in a DNA repair mutant reduced the fraction of cells with nuclear extensions. Formation of a nuclear extension was specific to a mitotic delay, because cells arrested in S or G2 had round nuclei. Moreover, the nuclear extension always coincided with the nucleolus, while the morphology of the DNA mass remained largely unchanged. Finally, we found that phospholipid synthesis continued unperturbed when cells delayed in mitosis, and inhibiting phospholipid synthesis abolished the formation of nuclear extensions. Our data suggest a mechanism that promotes nuclear envelope expansion during mitosis. When mitotic progression is delayed, cells sequester the added membrane to the nuclear envelope associated with the nucleolus, possibly to avoid disruption of intranuclear organization.

Essential gene profiles in breast, pancreatic, and ovarian cancer cells.

UNLABELLED: Genomic analyses are yielding a host of new information on the multiple genetic abnormalities associated with specific types of cancer. A comprehensive description of cancer-associated genetic abnormalities can improve our ability to classify tumors into clinically relevant subgroups and, on occasion, identify mutant genes that drive the cancer phenotype ("drivers"). More often, though, the functional significance of cancer-associated mutations is difficult to discern. Genome-wide pooled short hairpin RNA (shRNA) screens enable global identification of the genes essential for cancer cell survival and proliferation, providing a "functional genomic" map of human cancer to complement genomic studies. Using a lentiviral shRNA library targeting ~16,000 genes and a newly developed, dynamic scoring approach, we identified essential gene profiles in 72 breast, pancreatic, and ovarian cancer cell lines. Integrating our results with current and future genomic data should facilitate the systematic identification of drivers, unanticipated synthetic lethal relationships, and functional vulnerabilities of these tumor types. SIGNIFICANCE: This study presents a resource of genome-scale, pooled shRNA screens for 72 breast, pancreatic, and ovarian cancer cell lines that will serve as a functional complement to genomics data, facilitate construction of essential gene profiles, help uncover synthetic lethal relationships, and identify uncharacterized genetic vulnerabilities in these tumor types. SIGNIFICANCE: This study presents a resource of genome-scale, pooled shRNA screens for 72 breast, pancreatic, and ovarian cancer cell lines that will serve as a functional complement to genomics data, facilitate construction of essential gene profiles, help uncover synthetic lethal relationships, and identify uncharacterized genetic vulnerabilities in these tumor types.

COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines.

Genome-wide pooled shRNA screens enable global identification of the genes essential for cancer cell survival and proliferation and provide a 'functional genetic' map of human cancer to complement genomic studies. Using a lentiviral shRNA library targeting approximately 16,000 human genes and a newly developed scoring approach, we identified essential gene profiles in more than 70 breast, pancreatic and ovarian cancer cell lines. We developed a web-accessible database system for capturing information from each step in our standardized screening pipeline and a gene-centric search tool for exploring shRNA activities within a given cell line or across multiple cell lines. The database consists of a laboratory information and management system for tracking each step of a pooled shRNA screen as well as a web interface for querying and visualization of shRNA and gene-level performance across multiple cancer cell lines. COLT-Cancer Version 1.0 is currently accessible at http://colt.ccbr.utoronto.ca/cancer.

A comprehensive platform for highly multiplexed mammalian functional genetic screens.

BACKGROUND: Genome-wide screening in human and mouse cells using RNA interference and open reading frame over-expression libraries is rapidly becoming a viable experimental approach for many research labs. There are a variety of gene expression modulation libraries commercially available, however, detailed and validated protocols as well as the reagents necessary for deconvolving genome-scale gene screens using these libraries are lacking. As a solution, we designed a comprehensive platform for highly multiplexed functional genetic screens in human, mouse and yeast cells using popular, commercially available gene modulation libraries. The Gene Modulation Array Platform (GMAP) is a single microarray-based detection solution for deconvolution of loss and gain-of-function pooled screens. RESULTS: Experiments with specially constructed lentiviral-based plasmid pools containing ~78,000 shRNAs demonstrated that the GMAP is capable of deconvolving genome-wide shRNA "dropout" screens. Further experiments with a larger, ~90,000 shRNA pool demonstrate that equivalent results are obtained from plasmid pools and from genomic DNA derived from lentivirus infected cells. Parallel testing of large shRNA pools using GMAP and next-generation sequencing methods revealed that the two methods provide valid and complementary approaches to deconvolution of genome-wide shRNA screens. Additional experiments demonstrated that GMAP is equivalent to similar microarray-based products when used for deconvolution of open reading frame over-expression screens. CONCLUSION: Herein, we demonstrate four major applications for the GMAP resource, including deconvolution of pooled RNAi screens in cells with at least 90,000 distinct shRNAs. We also provide detailed methodologies for pooled shRNA screen readout using GMAP and compare next-generation sequencing to GMAP (i.e. microarray) based deconvolution methods.

Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.

Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.

The genetic landscape of a cell.

A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

DRYGIN: a database of quantitative genetic interaction networks in yeast.

Genetic interactions are highly informative for deciphering the underlying functional principles that govern how genes control cell processes. Recent developments in Synthetic Genetic Array (SGA) analysis enable the mapping of quantitative genetic interactions on a genome-wide scale. To facilitate access to this resource, which will ultimately represent a complete genetic interaction network for a eukaryotic cell, we developed DRYGIN (Data Repository of Yeast Genetic Interactions)-a web database system that aims at providing a central platform for yeast genetic network analysis and visualization. In addition to providing an interface for searching the SGA genetic interactions, DRYGIN also integrates other data sources, in order to associate the genetic interactions with pathway information, protein complexes, other binary genetic and physical interactions, and Gene Ontology functional annotation. DRYGIN version 1.0 currently holds more than 5.4 million measurements of genetic interacting pairs involving approximately 4500 genes, and is available at http://drygin.ccbr.utoronto.ca.

A molecular barcoded yeast ORF library enables mode-of-action analysis of bioactive compounds.

We present a yeast chemical-genomics approach designed to identify genes that when mutated confer drug resistance, thereby providing insight about the modes of action of compounds. We developed a molecular barcoded yeast open reading frame (MoBY-ORF) library in which each gene, controlled by its native promoter and terminator, is cloned into a centromere-based vector along with two unique oligonucleotide barcodes. The MoBY-ORF resource has numerous genetic and chemical-genetic applications, but here we focus on cloning wild-type versions of mutant drug-resistance genes using a complementation strategy and on simultaneously assaying the fitness of all transformants with barcode microarrays. The complementation cloning was validated by mutation detection using whole-genome yeast tiling microarrays, which identified unique polymorphisms associated with a drug-resistant mutant. We used the MoBY-ORF library to identify the genetic basis of several drug-resistant mutants and in this analysis discovered a new class of sterol-binding compounds.

Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes.

Synthetic lethal genetic interaction networks define genes that work together to control essential functions and have been studied extensively in Saccharomyces cerevisiae using the synthetic genetic array (SGA) analysis technique (ScSGA). The extent to which synthetic lethal or other genetic interaction networks are conserved between species remains uncertain. To address this question, we compared literature-curated and experimentally derived genetic interaction networks for two distantly related yeasts, Schizosaccharomyces pombe and S. cerevisiae. We find that 23% of interactions in a novel, high-quality S. pombe literature-curated network are conserved in the existing S. cerevisiae network. Next, we developed a method, called S. pombe SGA analysis (SpSGA), enabling rapid, high-throughput isolation of genetic interactions in this species. Direct comparison by SpSGA and ScSGA of approximately 220 genes involved in DNA replication, the DNA damage response, chromatin remodeling, intracellular transport, and other processes revealed that approximately 29% of genetic interactions are common to both species, with the remainder exhibiting unique, species-specific patterns of genetic connectivity. We define a conserved yeast network (CYN) composed of 106 genes and 144 interactions and suggest that this network may help understand the shared biology of diverse eukaryotic species.

AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins.

UNLABELLED: Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. AVAILABILITY: AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.

ALLERDB database and integrated bioinformatic tools for assessment of allergenicity and allergic cross-reactivity.

Databases and computational tools are increasingly important in the study of allergies, particularly in the assessment of allergenicity and allergic cross-reactivity. ALLERDB database contains sequences of allergens and information on reported cross-reactivity between allergens. It focuses on analysis of allergenicity and allergic cross-reactivity of clinically relevant protein allergens. The official IUIS allergen data were extracted from the IUIS Allergen Nomenclature Sub-Committee website, and their sequence information from the public databases, and reference publications. The analysis tools assist allergen data analysis and retrieval, and include keyword searching, BLAST, prediction of allergenicity, modification of BLAST that displays cross-reactive allergens, and graphics representation of cross-reactivity data. ALLERDB is new brand of allergen databases with a rich set of tools for sequence comparison, pattern identification, and visualization of results. It is accessible at http://research.i2r.a-star.edu.sg/Templar/DB/Allergen.

An update on the functional molecular immunology (FIMM) database.

Data on the major histocompatibility complex, T-cell epitopes, B-cell epitopes, antigens and diseases are heterogeneous and scattered among different databases and the literature. Since it has become increasingly difficult to obtain an integrated view of functional immune response components, we have developed and updated over several years the Functional molecular IMMunology (FIMM) database (http:// research.i2r.a-star.edu.sg/fimm/). FIMM contains integrated expert-curated data on protein antigens, and on human immunological receptors that recognise and bind them in healthy or disease states. Interfaces with multiple, intuitive query options and query reports provide immunologists with prioritised information that aids data interpretation, vaccine target discovery and immune disease research.