Single-cell RNA-sequencing reveals distinct patterns of cell state heterogeneity in mouse models of breast cancer.

Breast cancer stem cells (BCSCs) contribute to intra-tumoral heterogeneity and therapeutic resistance. However, the binary concept of universal BCSCs co-existing with bulk tumor cells is over-simplified. Through single-cell RNA-sequencing, we found that Neu, PyMT and BRCA1-null mammary tumors each corresponded to a spectrum of minimally overlapping cell differentiation states without a universal BCSC population. Instead, our analyses revealed that these tumors contained distinct lineage-specific tumor propagating cells (TPCs) and this is reflective of the self-sustaining capabilities of lineage-specific stem/progenitor cells in the mammary epithelial hierarchy. By understanding the respective tumor hierarchies, we were able to identify CD14 as a TPC marker in the Neu tumor. Additionally, single-cell breast cancer subtype stratification revealed the co-existence of multiple breast cancer subtypes within tumors. Collectively, our findings emphasize the need to account for lineage-specific TPCs and the hierarchical composition within breast tumors, as these heterogenous sub-populations can have differential therapeutic susceptibilities.

Up-regulation of micro-RNA765 in human failing hearts is associated with post-transcriptional regulation of protein phosphatase inhibitor-1 and depressed contractility.

AIMS: Impaired sarcoplasmic reticulum (SR) Ca(2+) cycling and depressed contractility, a hallmark of human and experimental heart failure, has been partially attributed to increased protein phosphatase 1 (PP-1) activity, associated with down-regulation of its endogenous inhibitor-1. The levels and activity of inhibitor-1 are reduced in failing hearts, contributing to dephosphorylation and inactivation of key calcium cycling proteins. Therefore, we investigated the mechanisms that mediate decreases in inhibitor-1 by post-transcriptional modification. METHODS AND RESULTS: Bioinformatics revealed that 17 human microRNAs may serve as modulators of inhibitor-1. However, real-time PCR analysis identified only one of these microRNAs, miR-765, as being increased in human failing hearts concomitant with decreased inhibitor-1 levels. Expression of miR-765 in HEK293 cells or mouse ventricular myocytes confirmed suppression of inhibitor-1 levels through binding of this miR-765 to the 3'-untranslated region of inhibitor-1 mRNA. To determine the functional significance of miR-765 in Ca(2+) cycling, pri-miR-765 as well as a non-translated nucleotide sequence (miR-Ctrl) were expressed in adult mouse ventricular myocytes. The inhibitor-1 expression levels were decreased, accompanied by enhanced PP-1 activity in the miR-765 cardiomyocytes, and these reflected depressed contractile mechanics and Ca(2+) transients, compared with the miR-Ctrl group. The depressive effects were associated with decreases in the phosphorylation of phospholamban and SR Ca(2+) load. These miR-765 negative inotropic effects were abrogated in inhibitor-1-deficient cardiomyocytes, suggesting its apparent specificity for inhibitor-1. CONCLUSIONS: miR-765 levels are increased in human failing hearts. Such increases may contribute to depressed cardiac function through reduced inhibitor-1 expression and enhanced PP-1 activity, associated with reduced SR Ca(2+) load.

A novel essential domain perspective for exploring gene essentiality.

MOTIVATION: Genes with indispensable functions are identified as essential; however, the traditional gene-level studies of essentiality have several limitations. In this study, we characterized gene essentiality from a new perspective of protein domains, the independent structural or functional units of a polypeptide chain. RESULTS: To identify such essential domains, we have developed an Expectation-Maximization (EM) algorithm-based Essential Domain Prediction (EDP) Model. With simulated datasets, the model provided convergent results given different initial values and offered accurate predictions even with noise. We then applied the EDP model to six microbial species and predicted 1879 domains to be essential in at least one species, ranging 10-23% in each species. The predicted essential domains were more conserved than either non-essential domains or essential genes. Comparing essential domains in prokaryotes and eukaryotes revealed an evolutionary distance consistent with that inferred from ribosomal RNA. When utilizing these essential domains to reproduce the annotation of essential genes, we received accurate results that suggest protein domains are more basic units for the essentiality of genes. Furthermore, we presented several examples to illustrate how the combination of essential and non-essential domains can lead to genes with divergent essentiality. In summary, we have described the first systematic analysis on gene essentiality on the level of domains. CONTACT: huilu.bioinfo@gmail.com or Long.Lu@cchmc.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Discovering essential domains in essential genes.

Genes with indispensable functions are identified as essential; however, the traditional gene-level perspective of essentiality has several limitations. We hypothesized that protein domains, the independent structural or functional units of a polypeptide chain, are responsible for gene essentiality. If the essentiality of domains is known, the essential genes could be identified. To find such essential domains, we have developed an EM algorithm-based Essential Domain Prediction (EDP) Model. With simulated datasets, the model provided convergent results given different initial values and offered accurate predictions even with noise. We then applied the EDP model to six microbes and predicted 3,450 domains to be essential in at least one species, ranging 8-24 % in each species.

Polysome profiling reveals broad translatome remodeling during endoplasmic reticulum (ER) stress in the pathogenic fungus Aspergillus fumigatus.

BACKGROUND: The unfolded protein response (UPR) is a network of intracellular signaling pathways that supports the ability of the secretory pathway to maintain a balance between the load of proteins entering the endoplasmic reticulum (ER) and the protein folding capacity of the ER lumen. Current evidence indicates that several pathogenic fungi rely heavily on this pathway for virulence, but there is limited understanding of the mechanisms involved. The best known functional output of the UPR is transcriptional upregulation of mRNAs involved in ER homeostasis. However, this does not take into account mechanisms of translational regulation that involve differential loading of ribosomes onto mRNAs. In this study, a global analysis of transcript-specific translational regulation was performed in the pathogenic mold Aspergillus fumigatus to determine the nature and scope of the translational response to ER stress. RESULTS: ER stress was induced by treating the fungus with dithiothreitol, tunicamycin, or a thermal up-shift. The mRNAs were then fractionated on the basis of ribosome occupancy into an under-translated pool (U) and a well-translated pool (W). The mRNAs were used to interrogate microarrays and the ratio of the hybridization signal (W/U) was used as an indicator of the relative translational efficiency of a mRNA under each condition. The largest category of translationally upregulated mRNAs during ER stress encoded proteins involved in translation. Components of the ergosterol and GPI anchor biosynthetic pathways also showed increased polysome association, suggesting an important role for translational regulation in membrane and cell wall homeostasis. ER stress induced limited remodeling of the secretory pathway translatome. However, a select group of transcription factors was translationally upregulated, providing a link to subsequent modification of the transcriptome. Finally, we provide evidence that one component of the ER stress translatome is a novel mRNA isoform from the yvc1 gene that is induced by ER stress in a UPR-dependent manner. CONCLUSIONS: Together, these findings define a core set of mRNAs subject to translational control during the adaptive response to acute ER stress in A. fumigatus and reveal a remarkable breadth of functions that are needed to resolve ER stress in this organism.

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus.

BACKGROUND: Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, "the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs". Expanding the target space by rapidly identifying new essential genes has thus been described as "the most important task of genomics-based target validation". RESULTS: In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus Neurospora crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach. CONCLUSIONS: The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to predict an inventory of essential genes in Eukaryotic fungal species, amongst which a preferred subset of suitable drug targets may be selected. By selecting the best new targets, we believe that resultant drugs would exhibit an unparalleled clinical impact against a naive pathogen population. Additional benefits that a compendium of essential genes can provide are important information on cell function and evolutionary biology. Furthermore, mapping essential genes to pathways may also reveal critical check points in the pathogen's metabolism. Finally, this approach is highly reproducible and portable, and can be easily applied to predict essential genes in many more pathogenic microbes, especially those unculturable.

A statistical framework for improving genomic annotations of prokaryotic essential genes.

Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model-based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene's essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model's broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.

HacA-independent functions of the ER stress sensor IreA synergize with the canonical UPR to influence virulence traits in Aspergillus fumigatus.

Endoplasmic reticulum (ER) stress is a condition in which the protein folding capacity of the ER becomes overwhelmed by an increased demand for secretion or by exposure to compounds that disrupt ER homeostasis. In yeast and other fungi, the accumulation of unfolded proteins is detected by the ER-transmembrane sensor IreA/Ire1, which responds by cleaving an intron from the downstream cytoplasmic mRNA HacA/Hac1, allowing for the translation of a transcription factor that coordinates a series of adaptive responses that are collectively known as the unfolded protein response (UPR). Here, we examined the contribution of IreA to growth and virulence in the human fungal pathogen Aspergillus fumigatus. Gene expression profiling revealed that A. fumigatus IreA signals predominantly through the canonical IreA-HacA pathway under conditions of severe ER stress. However, in the absence of ER stress IreA controls dual signaling circuits that are both HacA-dependent and HacA-independent. We found that a ΔireA mutant was avirulent in a mouse model of invasive aspergillosis, which contrasts the partial virulence of a ΔhacA mutant, suggesting that IreA contributes to pathogenesis independently of HacA. In support of this conclusion, we found that the ΔireA mutant had more severe defects in the expression of multiple virulence-related traits relative to ΔhacA, including reduced thermotolerance, decreased nutritional versatility, impaired growth under hypoxia, altered cell wall and membrane composition, and increased susceptibility to azole antifungals. In addition, full or partial virulence could be restored to the ΔireA mutant by complementation with either the induced form of the hacA mRNA, hacA(i), or an ireA deletion mutant that was incapable of processing the hacA mRNA, ireA(Δ10). Together, these findings demonstrate that IreA has both HacA-dependent and HacA-independent functions that contribute to the expression of traits that are essential for virulence in A. fumigatus.

A two-dimensional simulation model of the bicoid gradient in Drosophila.

BACKGROUND: Bicoid (Bcd) is a Drosophila morphogenetic protein responsible for patterning the anterior structures in embryos. Recent experimental studies have revealed important insights into the behavior of this morphogen gradient, making it necessary to develop a model that can recapitulate the biological features of the system, including its dynamic and scaling properties. METHODOLOGY/PRINCIPAL FINDINGS: We present a biologically realistic 2-D model of the dynamics of the Bcd gradient in Drosophila embryos. This model is based on equilibrium binding of Bcd molecules to non-specific, low affinity DNA sites throughout the Drosophila genome. It considers both the diffusion media within which the Bcd gradient is formed and the dynamic and other relevant properties of bcd mRNA from which Bcd protein is produced. Our model recapitulates key features of the Bcd protein gradient observed experimentally, including its scaling properties and the stability of its nuclear concentrations during development. Our simulation model also allows us to evaluate the effects of other biological activities on Bcd gradient formation, including the dynamic redistribution of bcd mRNA in early embryos. Our simulation results suggest that, in our model, Bcd protein diffusion is important for the formation of an exponential gradient in embryos. CONCLUSIONS/SIGNIFICANCE: The 2-D model described in this report is a simple and versatile simulation procedure, providing a quantitative evaluation of the Bcd gradient system. Our results suggest an important role of Bcd binding to non-specific, low-affinity DNA sites in proper formation of the Bcd gradient in our model. They demonstrate that highly complex biological systems can be effectively modeled with relatively few parameters.

Probing intrinsic properties of a robust morphogen gradient in Drosophila.

A remarkable feature of development is its reproducibility, the ability to correct embryo-to-embryo variations and instruct precise patterning. In Drosophila, embryonic patterning along the anterior-posterior axis is controlled by the morphogen gradient Bicoid (Bcd). In this article, we describe quantitative studies of the native Bcd gradient and its target Hunchback (Hb). We show that the native Bcd gradient is highly reproducible and is itself scaled with embryo length. While a precise Bcd gradient is necessary for precise Hb expression, it still has positional errors greater than Hb expression. We describe analyses further probing mechanisms for Bcd gradient scaling and correction of its residual positional errors. Our results suggest a simple model of a robust Bcd gradient sufficient to achieve scaled and precise activation of its targets. The robustness of this gradient is conferred by its intrinsic properties of "self-correcting" the inevitable input variations to achieve a precise and reproducible output.

Targeting the human cancer pathway protein interaction network by structural genomics.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.