Search

Poor graft function (PGF), manifested by multilineage cytopenias and complete donor chimerism post-allogeneic stem cell transplantation (alloSCT), and acquired aplastic anaemia (AA) are immune-mediated acquired bone marrow (BM) failure syndromes with a similar clinical presentation. In this study, we used spatial proteomics to compare the immunobiology of the BM microenvironment and identify common mechanisms of immune dysregulation under these conditions. Archival BM trephines from patients exhibited downregulation of the immunoregulatory protein VISTA and the M2 macrophage marker and suppressor of T-cell activation ARG1 with increased expression of the immune checkpoint B7-H3 compared to normal controls. Increased CD163 and CD14 expression suggested monocyte/macrophage skewing, which, combined with dysregulation of STING and VISTA, is indicative of an environment of reduced immunoregulation resulting in the profound suppression of hematopoiesis in these two conditions. There were no changes in the immune microenvironment between paired diagnostic AA and secondary MDS/AML samples suggesting that leukaemic clones develop in the impaired immune microenvironment of AA without the need for further alterations. Of the eight proteins with dysregulated expression shared by diagnostic AA and PGF, the diagnostic AA samples had a greater fold change in expression than PGF, suggesting that these diseases represent a spectrum of immune dysregulation. Unexpectedly, analysis of samples from patients with good graft function post-alloSCT demonstrated significant changes in the immune microenvironment compared to normal controls, with downregulation of CD44, STING, VISTA, and ARG1, suggesting that recovery of multilineage haematopoiesis post-alloSCT does not reflect recovery of immune function and may prime patients for the development of PGF upon further inflammatory insult. The demonstrable similarities in the immunopathology of AA and PGF will allow the design of clinical interventions that include both patient cohorts to accelerate therapeutic discovery and translation. [ABSTRACT FROM AUTHOR]

Background: Malignant pleural effusions (MPEs) are a common complication of advanced cancers, particularly those adjacent to the pleura, such as lung and breast cancer. The pathophysiology of MPE formation remains poorly understood, and although MPEs are routinely used for the diagnosis of breast cancer patients, their composition and biology are poorly understood. It is difficult to distinguish invading malignant cells from resident mesothelial cells and to identify the directionality of interactions between these populations in the pleura. There is a need to characterize the phenotypic diversity of breast cancer cell populations in the pleural microenvironment, and investigate how this varies across patients. Methods: Here, we used single‐cell RNA‐sequencing to study the heterogeneity of 10 MPEs from seven metastatic breast cancer patients, including three Miltenyi‐enriched samples using a negative selection approach. This dataset of almost 65 000 cells was analysed using integrative approaches to compare heterogeneous cell populations and phenotypes. Results: We identified substantial inter‐patient heterogeneity in the composition of cell types (including malignant, mesothelial and immune cell populations), in expression of subtype‐specific gene signatures and in copy number aberration patterns, that captured variability across breast cancer cell populations. Within individual MPEs, we distinguished mesothelial cell populations from malignant cells using key markers, the presence of breast cancer subtype expression patterns and copy number aberration patterns. We also identified pleural mesothelial cells expressing a cancer‐associated fibroblast‐like transcriptomic program that may support cancer growth. Conclusions: Our dataset presents the first unbiased assessment of breast cancer‐associated MPEs at a single cell resolution, providing the community with a valuable resource for the study of MPEs. Our work highlights the molecular and cellular diversity captured in MPEs and motivates the potential use of these clinically relevant biopsies in the development of targeted therapeutics for patients with advanced breast cancer. [ABSTRACT FROM AUTHOR]

Plant proteins that are secreted without a classical signal peptide leader sequence are termed leaderless secretory proteins (LSPs) and are implicated in both plant development and (a)biotic stress responses. In plant proteomics experimental workflows, identification of LSPs is hindered by the possibility of contamination from other subcellar compartments upon purification of the secretome. Applying machine learning algorithms to predict LSPs in plants is also challenging due to the rarity of experimentally validated examples for training purposes. This work attempts to address this issue by establishing criteria for identifying potential plant LSPs based on experimental observations and training random forest classifiers on the putative datasets. The resultant plant protein database LSPDB and bioinformatic prediction tools LSPpred and SPLpred are available at lsppred.lspdb.org. The LSPpred and SPLpred modules are internally validated on the training dataset, with false positives controlled at 5%, and are also able to classify the limited number of established plant LSPs (SPLpred (3/4, LSPpred 4/4). Until such time as a larger set of bona fide (independently experimentally validated) LSPs is established using imaging technologies (light/fluorescence/electron microscopy) to confirm sub-cellular location, these tools represent a bridging method for predicting and identifying plant putative LSPs for subsequent experimental validation. [ABSTRACT FROM AUTHOR]

Simple Summary: Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors. The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine. [ABSTRACT FROM AUTHOR]

The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques. [ABSTRACT FROM AUTHOR]

Protein phosphorylation is one of the best known post-translational mechanisms playing a key role in the regulation of cellular processes. Over 100,000 distinct phosphorylation sites have been discovered through constant improvement of mass spectrometry based phosphoproteomics in the last decade. However, data saturation is occurring and the bottleneck of assigning biologically relevant functionality to phosphosites needs to be addressed. There has been finite success in using data-driven approaches to reveal phosphosite functionality due to a range of limitations. The alternate, more suitable approach is making use of prior knowledge from literature-derived databases. Here, we analysed seven widely used databases to shed light on their suitability to provide functional insights into phosphoproteomics data. We first determined the global coverage of each database at both the protein and phosphosite level. We also determined how consistent each database was in its phosphorylation annotations compared to a global standard. Finally, we looked in detail at the coverage of each database over six experimental datasets. Our analysis highlights the relative strengths and weaknesses of each database, providing a guide in how each can be best used to identify biological mechanisms in phosphoproteomic data. [ABSTRACT FROM AUTHOR]

Abstract Background The use of ontologies to control vocabulary and structure annotation has added value to genome-scale data, and contributed to the capture and re-use of knowledge across research domains. Gene Ontology (GO) is widely used to capture detailed expert knowledge in genomic-scale datasets and as a consequence has grown to contain many terms, making it unwieldy for many applications. To increase its ease of manipulation and efficiency of use, subsets called GO slims are often created by collapsing terms upward into more general, high-level terms relevant to a particular context. Creation of a GO slim currently requires manipulation and editing of GO by an expert (or community) familiar with both the ontology and the biological context. Decisions about which terms to include are necessarily subjective, and the creation process itself and subsequent curation are time-consuming and largely manual. Results Here we present an objective framework for generating customised ontology slims for specific annotated datasets, exploiting information latent in the structure of the ontology graph and in the annotation data. This framework combines ontology engineering approaches, and a data-driven algorithm that draws on graph and information theory. We illustrate this method by application to GO, generating GO slims at different information thresholds, characterising their depth of semantics and demonstrating the resulting gains in statistical power. Conclusions Our GO slim creation pipeline is available for use in conjunction with any GO-annotated dataset, and creates dataset-specific, objectively defined slims. This method is fast and scalable for application to other biomedical ontologies.

Methylation is a common posttranslational modification of arginine and lysine in eukaryotic proteins. Methylproteomes are best characterized for higher eukaryotes, where they are functionally expanded and evolved complex regulation. However, this is not the case for protist species evolved from the earliest eukaryotic lineages. Here, we integrated bioinformatic, proteomic, and drug-screening data sets to comprehensively explore the methylproteome of Giardia duodenalis —a deeply branching parasitic protist. We demonstrate that Giardia and related diplomonads lack arginine-methyltransferases and have remodeled conserved RGG/RG motifs targeted by these enzymes. We also provide experimental evidence for methylarginine absence in proteomes of Giardia but readily detect methyllysine. We bioinformatically infer 11 lysine-methyltransferases in Giardia , including highly diverged Su(var)3-9, Enhancer-of-zeste and Trithorax proteins with reduced domain architectures, and novel annotations demonstrating conserved methyllysine regulation of eukaryotic elongation factor 1 alpha. Using mass spectrometry, we identify more than 200 methyllysine sites in Giardia , including in species-specific gene families involved in cytoskeletal regulation, enriched in coiled-coil features. Finally, we use known methylation inhibitors to show that methylation plays key roles in replication and cyst formation in this parasite. This study highlights reduced methylation enzymes, sites, and functions early in eukaryote evolution, including absent methylarginine networks in the Diplomonadida. These results challenge the view that arginine methylation is eukaryote conserved and demonstrate that functional compensation of methylarginine was possible preceding expansion and diversification of these key networks in higher eukaryotes. [ABSTRACT FROM AUTHOR]

Natural killer (NK) cell activation is controlled by a balance of activating and inhibitory signals and cytokines such as IL-15. We previously identified cytokine-inducible SH2-containing protein (CIS) as a negative regulator of IL-15 signaling in NK cells under inflammatory conditions. While the functional effect of Cish -deficiency in NK cells was obvious by their increased anti-tumor immunity and hyper-proliferative response to IL-15, it remained unclear how CIS regulates NK cell biology in steady-state. Here, we investigated the role of CIS in the homeostatic maintenance of NK cells and found CIS-ablation promoted terminal differentiation of NK cells and increased turnover, suggesting that under steady-state conditions, CIS plays a role in maintaining IL-15 driven regulation of NK cells in vivo. However, hyper-responsiveness to IL-15 did not manifest in NK cell accumulation, even when the essential NK cell apoptosis mediator, Bcl2l11 (BIM) was deleted in addition to Cish. Instead, loss of CIS conferred a lower activation threshold, evidenced by augmented functionality on a per cell basis both in vitro and in vivo without prior priming. We conclude that Cish regulates IL-15 signaling in NK cells in vivo , and through the rewiring of several activation pathways leads to a reduction in activation threshold, decreasing the requirement for priming and improving NK cell anti-tumor function. Furthermore, this study highlights the tight regulation of NK cell homeostasis by several pathways which prevent NK cell accumulation when IL-15 signaling and intrinsic apoptosis are dysregulated. [ABSTRACT FROM AUTHOR]

Enhancing NK cells: Natural killer (NK) cells are innate immune cells with a critical role in antitumor immunity. In the tumor microenvironment, the cytokine transforming growth factor–β (TGF-β) acts through its receptor to promote the differentiation of NK cells into a less suppressive cell type, thus inhibiting antitumor immunity. Rautela et al. showed that activin-A, another member of the TGF-β family, had similar effects on both mouse and human NK cells, although in a TGF-β receptor–independent manner. Inhibition of activin-A reduced orthotopic melanoma growth in mice, suggesting that targeting this pathway could therapeutically enhance NK cell function and antitumor immunity. Natural killer (NK) cells are innate lymphocytes that play a major role in immunosurveillance against tumor initiation and metastatic spread. The signals and checkpoints that regulate NK cell fitness and function in the tumor microenvironment are not well defined. Transforming growth factor–β (TGF-β) is a suppressor of NK cells that inhibits interleukin-15 (IL-15)–dependent signaling events and increases the abundance of receptors that promote tissue residency. Here, we showed that NK cells express the type I activin receptor ALK4, which, upon binding to its ligand activin-A, phosphorylated SMAD2/3 to suppress IL-15–mediated NK cell metabolism. Activin-A impaired human and mouse NK cell proliferation and reduced the production of granzyme B to impair tumor killing. Similar to TGF-β, activin-A also induced SMAD2/3 phosphorylation and stimulated NK cells to increase their cell surface expression of several markers of ILC1 cells. Activin-A also induced these changes in TGF-β receptor–deficient NK cells, suggesting that activin-A and TGF-β stimulate independent pathways that drive SMAD2/3-mediated NK cell suppression. Last, inhibition of activin-A by follistatin substantially slowed orthotopic melanoma growth in mice. These data highlight the relevance of examining TGF-β–independent SMAD2/3 signaling mechanisms as a therapeutic axis to relieve NK cell suppression and promote antitumor immunity. [ABSTRACT FROM AUTHOR]

Background: Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore (https://bioconductor.org/packages/singscore). Results: We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS < 25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. Conclusions: The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification. [ABSTRACT FROM AUTHOR]

Abstract: Natural Killer (NK) cells have long been considered an important part of the anti‐tumor immune response due to their potent cytolytic and cytokine‐secreting abilities. To date, a clear demonstration of the role NK cells play in human cancer is lacking, and there are still very few examples of therapies that efficiently exploit or enhance the spontaneous ability of NK cells to destroy the autologous cancer cells. Given the paradigm shift toward cancer immunotherapy over the past decade, there is a renewed push to understand how NK cell homeostasis and function are regulated in order to therapeutically harness these cells to treat cancer. This review will highlight recent advancements in our understanding of how growth factors impact on NK cell development, differentiation, survival and function with an emphasis on how these pathways may influence NK cell activity in the tumor microenvironment and control of cancer metastasis. [ABSTRACT FROM AUTHOR]

The mammary epithelium comprises two primary cellular lineages, but the degree of heterogeneity within these compartments and their lineage relationships during development remain an open question. Here we report single-cell RNA profiling of mouse mammary epithelial cells spanning four developmental stages in the post-natal gland. Notably, the epithelium undergoes a large-scale shift in gene expression from a relatively homogeneous basal-like program in pre-puberty to distinct lineage-restricted programs in puberty. Interrogation of single-cell transcriptomes reveals different levels of diversity within the luminal and basal compartments, and identifies an early progenitor subset marked by CD55. Moreover, we uncover a luminal transit population and a rare mixed-lineage cluster amongst basal cells in the adult mammary gland. Together these findings point to a developmental hierarchy in which a basal-like gene expression program prevails in the early post-natal gland prior to the specification of distinct lineage signatures, and the presence of cellular intermediates that may serve as transit or lineage-primed cells. [ABSTRACT FROM AUTHOR]

In proteomic analyses of the plant secretome, the presence of putative leaderless secretory proteins (LSPs) is difficult to confirm due to the possibility of contamination from other sub-cellular compartments. In the absence of a plant-specific tool for predicting LSPs, the mammalian-trained SecretomeP has been applied to plant proteins in multiple studies to identify the most likely LSPs. This study investigates the effectiveness of using SecretomeP on plant proteins, identifies its limitations and provides a benchmark for its use. In the absence of experimentally verified LSPs we exploit the common-feature hypothesis behind SecretomeP and use known classically secreted proteins (CSPs) of plants as a proxy to evaluate its accuracy. We show that, contrary to the common-feature hypothesis, plant CSPs are a poor proxy for evaluating LSP detection due to variation in the SecretomeP prediction scores when the signal peptide (SP) is modified. Removing the SP region from CSPs and comparing the predictive performance against non-secretory proteins indicates that commonly used threshold scores of 0.5 and 0.6 result in false-positive rates in excess of 0.3 when applied to plants proteins. Setting the false-positive rate to 0.05, consistent with the original mammalian performance of SecretomeP, yields only a marginally higher true positive rate compared to false positives. Therefore the use of SecretomeP on plant proteins is not recommended. This study investigates the trade-offs of using SecretomeP on plant proteins and provides insights into predictive features for future development of plant-specific common-feature tools. [ABSTRACT FROM AUTHOR]

Background: Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous group of tumours with a typical 5 year survival rate of <40 %. DNA methylation in tumour-suppressor genes often occurs at an early stage of tumorigenesis, hence DNA methylation can be used as an early tumour biomarker. Saliva is an ideal diagnostic medium to detect early HNSCC tumour activities due to its proximity to tumour site, non-invasiveness and ease of sampling. We test the hypothesis that the surveillance of DNA methylation in five tumour-suppressor genes (RASSF1α, p16INK4a, TIMP3, PCQAP/MED15) will allow us to diagnose HNSCC patients from a normal healthy control group as well as to discriminate between Human Papillomavirus (HPV)-positive and HPV-negative patients. Methods: Methylation-specific PCR (MSP) was used to determine the methylation levels of RASSF1α, p16INK4a, TIMP3 and PCQAP/MED15 in DNA isolated from saliva. Statistical analysis was carried out using non-parametric Mann- Whitney's U-test for individually methylated genes. A logistic regression analysis was carried out to determine the assay sensitivity when combing the five genes. Further, a five-fold cross-validation with a bootstrap procedure was carried out to determine how well the panel will perform in a real clinical scenario. Results: Salivary DNA methylation levels were not affected by age. Salivary DNA methylation levels for RASSF1α, p16INK4a, TIMP3 and PCQAP/MED15 were higher in HPV-negative HNSCC patients (n = 88) compared with a normal healthy control group (n = 122) (sensitivity of 71 % and specificity of 80 %). Conversely, DNA methylation levels for these genes were lower in HPV-positive HNSCC patients (n = 45) compared with a normal healthy control group (sensitivity of 80 % and specificity of 74 %), consistent with the proposed aetiology of HPV-positive HNSCCs. Conclusions: Salivary DNA tumour-suppressor methylation gene panel has the potential to detect early-stage tumours in HPV-negative HNSCC patients. HPV infection was found to deregulate the methylation levels in HPVpositive HNSCC patients. Large-scale double-blinded clinical trials are crucial before this panel can potentially be integrated into a clinical setting. [ABSTRACT FROM AUTHOR]

Background: The development of androgen resistance is a major limitation to androgen deprivation treatment in prostate cancer. We have developed an in vitro model of androgen-resistance to characterise molecular changes occurring as androgen resistance evolves over time. Our aim is to understand biological network profiles of transcriptomic changes occurring during the transition to androgen-resistance and to validate these changes between our in vitro model and clinical datasets (paired samples before and after androgen-deprivation therapy of patients with advanced prostate cancer).Methods: We established an androgen-independent subline from LNCaP cells by prolonged exposure to androgen-deprivation. We examined phenotypic profiles and performed RNA-sequencing. The reads generated were compared to human clinical samples and were analysed using differential expression, pathway analysis and protein-protein interaction networks.Results: After 24 weeks of androgen-deprivation, LNCaP cells had increased proliferative and invasive behaviour compared to parental LNCaP, and its growth was no longer responsive to androgen. We identified key genes and pathways that overlap between our cell line and clinical RNA sequencing datasets and analysed the overlapping protein-protein interaction network that shared the same pattern of behaviour in both datasets. Mechanisms bypassing androgen receptor signalling pathways are significantly enriched. Several steroid hormone receptors are differentially expressed in both datasets. In particular, the progesterone receptor is significantly differentially expressed and is part of the interaction network disrupted in both datasets. Other signalling pathways commonly altered in prostate cancer, MAPK and PI3K-Akt pathways, are significantly enriched in both datasets.Conclusions: The overlap between the human and cell-line differential expression profiles and protein networks was statistically significant showing that the cell-line model reproduces molecular patterns observed in clinical castrate resistant prostate cancer samples, making this cell line a useful tool in understanding castrate resistant prostate cancer. Pathway analysis revealed similar patterns of enriched pathways from differentially expressed genes of both human clinical and cell line datasets. Our analysis revealed several potential mechanisms and network interactions, including cooperative behaviours of other nuclear receptors, in particular the subfamily of steroid hormone receptors such as PGR and alteration to gene expression in both the MAPK and PI3K-Akt signalling pathways. [ABSTRACT FROM AUTHOR]

Introduction: The normal process of epithelial mesenchymal transition (EMT) is subverted by carcinoma cells tofacilitate metastatic spread. Cancer cells rarely undergo a full conversion to the mesenchymal phenotype, and instead adopt positions along the epithelial-mesenchymal axis, a propensity we refer to as epithelial mesenchymal plasticity (EMP). EMP is associated with increased risk of metastasis in breast cancer and consequent poor prognosis. Drivers towards the mesenchymal state in malignant cells include growth factor stimulation or exposure to hypoxic conditions. Methods: We have examined EMP in two cell line models of breast cancer: the PMC42 system (PMC42-ET and PMC42-LA sublines) and MDA-MB-468 cells. Transition to a mesenchymal phenotype was induced across all three cell lines using epidermal growth factor (EGF) stimulation, and in MDA-MB-468 cells by hypoxia. We used RNA sequencing to identify gene expression changes that occur as cells transition to a more-mesenchymal phenotype, and identified the cell signalling pathways regulated across these experimental systems. We then used inhibitors to modulate signalling through these pathways, verifying the conclusions of our transcriptomic analysis. Results: We found that EGF and hypoxia both drive MDA-MB-468 cells to phenotypically similar mesenchymal states. Comparing the transcriptional response to EGF and hypoxia, we have identified differences in the cellular signalling pathways that mediate, and are influenced by, EMT. Significant differences were observed for a number of important cellular signalling components previously implicated in EMT, such as HBEGF and VEGFA. We have shown that EGF- and hypoxia-induced transitions respond differently to treatment with chemical inhibitors (presented individually and in combinations) in these breast cancer cells. Unexpectedly, MDA-MB-468 cells grown under hypoxic growth conditions became even more mesenchymal following exposure to certain kinase inhibitors that prevent growth-factor induced EMT, including the mTOR inhibitor everolimus and the AKT1/2/3 inhibitor AZD5363. Conclusions: While resulting in a common phenotype, EGF and hypoxia induced subtly different signalling systems in breast cancer cells. Our findings have important implications for the use of kinase inhibitor-based therapeutic interventions in breast cancers, where these heterogeneous signalling landscapes will influence the therapeutic response. [ABSTRACT FROM AUTHOR]