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Abstract Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.

Abstract Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis and visualisation tool that organises information into semantic categories and provides various visualisation modules to characterise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE’s versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, empowering biologists during molecular discovery.

Summary: The utilization of single-cell resolved spatial transcriptomics to delineate immune responses during SARS-CoV-2 infection was able to identify M1 macrophages to have elevated expression of IFI27 in areas of infection.

Vitamin D3 is a steroid hormone that confers anti-tumorigenic properties in prostate cells. Serum vitamin D3 deficiency has been associated with advanced prostate cancer (PCa), particularly affecting African American (AA) men. Therefore, elucidating the pleiotropic effects of vitamin D on signaling pathways, essential to maintaining non-malignancy, may provide additional drug targets to mitigate disparate outcomes for men with PCa, especially AA men. We conducted RNA sequencing on an AA non-malignant prostate cell line, RC-77N/E, comparing untreated cells to those treated with 10 nM of vitamin D3 metabolite, 1α,25(OH)2D3, at 24 h. Differential gene expression analysis revealed 1601 significant genes affected by 1α,25(OH)2D3 treatment. Pathway enrichment analysis predicted 1α,25(OH)2D3- mediated repression of prostate cancer, cell proliferation, actin cytoskeletal, and actin-related signaling pathways (p < 0.05). Prioritizing genes with vitamin D response elements and associating expression levels with overall survival (OS) in The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA PRAD) cohort, we identified ANLN (Anillin) and ECT2 (Epithelial Cell Transforming 2) as potential prognostic PCa biomarkers. Both genes were strongly correlated and significantly downregulated by 1α,25(OH)2D3 treatment, where low expression was statistically associated with better overall survival outcomes in the TCGA PRAD public cohort. Increased ANLN and ECT2 mRNA gene expression was significantly associated with PCa, and Gleason scores using both the TCGA cohort (p < 0.05) and an AA non-malignant/tumor-matched cohort. Our findings suggest 1α,25(OH)2D3 regulation of these biomarkers may be significant for PCa prevention. In addition, 1α,25(OH)2D3 could be used as an adjuvant treatment targeting actin cytoskeleton signaling and actin cytoskeleton-related signaling pathways, particularly among AA men.

Abstract Background Medulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups based on gene expression and genomic features. Single-cell transcriptome studies have defined the cellular states underlying each MB subgroup; however, the spatial organisation of these diverse cell states and how this impacts response to therapy remains to be determined. Methods Here, we used spatially resolved transcriptomics to define the cellular diversity within a sonic hedgehog (SHH) patient-derived model of MB and show that cells specific to a transcriptional state or spatial location are pivotal for CDK4/6 inhibitor, Palbociclib, treatment response. We integrated spatial gene expression with histological annotation and single-cell gene expression data from MB, developing an analysis strategy to spatially map cell type responses within the hybrid system of human and mouse cells and their interface within an intact brain tumour section. Results We distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolve at a cellular resolution a distinct tumour interface where the tumour contacts neighbouring mouse brain tissue consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. Conclusions Our data highlight the power of using spatial transcriptomics to characterise the response of a tumour to a targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB.

Abstract 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.

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.

PurposeRobust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.MethodsWe conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients.ResultsWe show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients.ConclusionThese data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.

Abstract Motivation Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation. Method We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models—dubbed PPI-BioBERT-x10—to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions. Results and conclusion The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter $$\approx 5700$$ ≈ 5700 (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts.

Flexible perovskite solar cells introduce opportunities for high throughput, high specific weight, and short energy payback time photovoltaics. However, they require additional investigation into their mechanical resiliency. This work investigates the mechanical properties and behaviors of perovskite thin films and builds a robust model for future research. A two-pronged approach was utilized. Perovskite thin films were flexed in a three-point bend mode with in-situ SEM. Novel insights into the perovskite mechanical behaviors with varying substrate layers were gained. Modeling and validation, the second prong, was completed with finite element analysis. Model coupons of the imaged perovskite architectures were built, with sensitivity analysis completed to provide mechanical property estimates. The results demonstrate that mechanical degradation of perovskite thin films on polyethylene terephthalate (PET) primarily presents as a crack in the grain boundaries between crystals. Perovskite thin films on Indium Tin Oxide (ITO) and PET primarily crack in a periodic pattern regardless of the placement of perovskite crystals.

Abstract Background & Aims Cancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage of the disease. To this end, we aimed to utilize transcript expression profiling features to predict the site-specific metastases of primary tumors and second, to identify the determinants of tissue specific progression. Methods We used statistical machine learning for transcript feature selection to optimize classification and built tree-based classifiers to predict tissue specific sites of metastatic progression. Results We developed a novel machine learning architecture that analyzes 33 types of RNA transcriptome profiles from The Cancer Genome Atlas (TCGA) database. Our classifier identifies the tumor type, derives synthetic instances of primary tumors metastasizing to distant organs and classifies the site-specific metastases in 16 types of cancers metastasizing to 12 locations. Conclusions We have demonstrated that site specific metastatic progression is predictable using transcriptomic profiling data from primary tumors and that the overrepresented biological processes in tumors metastasizing to congruent distant loci are highly overlapping. These results indicate site-specific progression was organotropic and core features of biological signaling pathways are identifiable that may describe proliferative plasticity in distant soils.

Abstract Hairy cell leukaemia (HCL) is a rare CD20+ B cell malignancy characterised by rare “hairy” B cells and extensive bone marrow (BM) infiltration. Frontline treatment with the purine analogue cladribine (CDA) results in a highly variable response duration. We hypothesised that analysis of the BM tumour microenvironment would identify prognostic biomarkers of response to CDA. HCL BM immunology pre and post CDA treatment and healthy controls were analysed using Digital Spatial Profiling to assess the expression of 57 proteins using an immunology panel. A bioinformatics pipeline was developed to accommodate the more complex experimental design of a spatially resolved study. Treatment with CDA was associated with the reduction in expression of HCL tumour markers (CD20, CD11c) and increased expression of myeloid markers (CD14, CD68, CD66b, ARG1). Expression of HLA-DR, STING, CTLA4, VISTA, OX40L were dysregulated pre- and post-CDA. Duration of response to treatment was associated with greater reduction in tumour burden and infiltration by CD8 T cells into the BM post-CDA. This is the first study to provide a high multiplex analysis of HCL BM microenvironment demonstrating significant immune dysregulation and identify biomarkers of response to CDA. With validation in future studies, prospective application of these biomarkers could allow early identification and increased monitoring in patients at increased relapse risk post CDA.

Abstract The highly conserved Elongator complex is a translational regulator that plays a critical role in neurodevelopment, neurological diseases, and brain tumors. Numerous clinically relevant variants have been reported in the catalytic Elp123 subcomplex, while no missense mutations in the accessory subcomplex Elp456 have been described. Here, we identify ELP4 and ELP6 variants in patients with developmental delay, epilepsy, intellectual disability, and motor dysfunction. We determine the structures of human and murine Elp456 subcomplexes and locate the mutated residues. We show that patient‐derived mutations in Elp456 affect the tRNA modification activity of Elongator in vitro as well as in human and murine cells. Modeling the pathogenic variants in mice recapitulates the clinical features of the patients and reveals neuropathology that differs from the one caused by previously characterized Elp123 mutations. Our study demonstrates a direct correlation between Elp4 and Elp6 mutations, reduced Elongator activity, and neurological defects. Foremost, our data indicate previously unrecognized differences of the Elp123 and Elp456 subcomplexes for individual tRNA species, in different cell types and in different key steps during the neurodevelopment of higher organisms.

Women of African ancestry suffer higher rates of breast cancer mortality compared with all other groups in the United States. Though the precise reasons for these disparities remain unclear, many recent studies have implicated a role for differences in tumor biology. Using an epitope-validated antibody against the endoplasmic reticulum–associated E3 ligase, gp78, we show that elevated levels of gp78 in patient breast cancer cells predict poor survival. Moreover, high levels of gp78 are associated with poor outcomes in both ER+ and ER– tumors, and breast cancers expressing elevated amounts of gp78 protein are enriched in gene expression pathways that influence cell cycle, metabolism, receptor-mediated signaling, and cell stress response pathways. In multivariate analysis adjusted for subtype and grade, gp78 protein is an independent predictor of poor outcomes in women of African ancestry. Furthermore, gene expression signatures, derived from patients stratified by gp78 protein expression, are strong predictors of recurrence and pathological complete response in retrospective clinical trial data and share many common features with gene sets previously identified to be overrepresented in breast cancers based on race. These findings implicate a prominent role for gp78 in tumor progression and offer insights into our understanding of racial differences in breast cancer outcomes.

Abstract Background Medulloblastoma (MB) is the most common malignant paediatric brain tumour and a leading cause of cancer-related mortality and morbidity. Existing treatment protocols are aggressive in nature resulting in significant neurological, intellectual and physical disabilities for the children undergoing treatment. Thus, there is an urgent need for improved, targeted therapies that minimize these harmful side effects. Methods We identified candidate drugs for MB using a network-based systems-pharmacogenomics approach: based on results from a functional genomics screen, we identified a network of interactions implicated in human MB growth regulation. We then integrated drugs and their known mechanisms of action, along with gene expression data from a large collection of medulloblastoma patients to identify drugs with potential to treat MB. Results Our analyses identified drugs targeting CDK4, CDK6 and AURKA as strong candidates for MB; all of these genes are well validated as drug targets in other tumour types. We also identified non-WNT MB as a novel indication for drugs targeting TUBB, CAD, SNRPA, SLC1A5, PTPRS, P4HB and CHEK2. Based upon these analyses, we subsequently demonstrated that one of these drugs, the new microtubule stabilizing agent, ixabepilone, blocked tumour growth in vivo in mice bearing patient-derived xenograft tumours of the Sonic Hedgehog and Group 3 subtype, providing the first demonstration of its efficacy in MB. Conclusions Our findings confirm that this data-driven systems pharmacogenomics strategy is a powerful approach for the discovery and validation of novel therapeutic candidates relevant to MB treatment, and along with data validating ixabepilone in PDX models of the two most aggressive subtypes of medulloblastoma, we present the network analysis framework as a resource for the field.

Subunits of the Elongator complex have been implicated in several nervous system pathologies. Here, the authors identify ELP2 variants in six patients with neurodevelopmental anomalies and show in mouse models that these variants impact protein stability and the activity of the complex during brain development.

Abstract Background Aberrant changes in epigenetic mechanisms such as histone modifications play an important role in cancer progression. PRMT1 which triggers asymmetric dimethylation of histone H4 on arginine 3 (H4R3me2a) is upregulated in human colorectal cancer (CRC) and is essential for cell proliferation. However, how this dysregulated modification might contribute to malignant transitions of CRC remains poorly understood. Methods In this study, we integrated biochemical assays including protein interaction studies and chromatin immunoprecipitation (ChIP), cellular analysis including cell viability, proliferation, colony formation, and migration assays, clinical sample analysis, microarray experiments, and ChIP-Seq data to investigate the potential genomic recognition pattern of H4R3me2s in CRC cells and its effect on CRC progression. Results We show that PRMT1 and SMARCA4, an ATPase subunit of the SWI/SNF chromatin remodeling complex, act cooperatively to promote colorectal cancer (CRC) progression. We find that SMARCA4 is a novel effector molecule of PRMT1-mediated H4R3me2a. Mechanistically, we show that H4R3me2a directly recruited SMARCA4 to promote the proliferative, colony-formative, and migratory abilities of CRC cells by enhancing EGFR signaling. We found that EGFR and TNS4 were major direct downstream transcriptional targets of PRMT1 and SMARCA4 in colon cells, and acted in a PRMT1 methyltransferase activity-dependent manner to promote CRC cell proliferation. In vivo, knockdown or inhibition of PRMT1 profoundly attenuated the growth of CRC cells in the C57BL/6 J-ApcMin/+ CRC mice model. Importantly, elevated expression of PRMT1 or SMARCA4 in CRC patients were positively correlated with expression of EGFR and TNS4, and CRC patients had shorter overall survival. These findings reveal a critical interplay between epigenetic and transcriptional control during CRC progression, suggesting that SMARCA4 is a novel key epigenetic modulator of CRC. Our findings thus highlight PRMT1/SMARCA4 inhibition as a potential therapeutic intervention strategy for CRC. Conclusion PRMT1-mediated H4R3me2a recruits SMARCA4, which promotes colorectal cancer progression by enhancing EGFR signaling.

Abstract Large-scale efforts to identify breast cancer (BC) risk alleles have historically taken place among women of European ancestry. Recently, there are new efforts to verify if these alleles increase risk in African American (AA) women as well. We investigated the effect of previously reported AA breast cancer and triple-negative breast cancer (TNBC) risk alleles in our African-enriched International Center for the Study of Breast Cancer Subtypes (ICSBCS) cohort. Using case–control, case-series and race-nested approaches, we report that the Duffy-null allele (rs2814778) is associated with TNBC risk (OR = 3.814, p = 0.001), specifically among AA individuals, after adjusting for self-indicated race and west African ancestry (OR = 3.368, p = 0.007). We have also validated the protective effect of the minor allele of the ANKLE1 missense variant rs2363956 among AA for TNBC (OR = 0.420, p = 0.005). Our results suggest that an ancestry-specific Duffy-null allele and differential prevalence of a polymorphic gene variant of ANKLE1 may play a role in TNBC breast cancer outcomes. These findings present opportunities for therapeutic potential and future studies to address race-specific differences in TNBC risk and disease outcome.

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 Background Pan-cancer studies of somatic copy number alterations (SCNAs) have demonstrated common SCNA patterns across cancer types, but despite demonstrable differences in aggressiveness of some cancers by race, pan-cancer SCNA variation by race has not been explored. This study investigated a) racial differences in SCNAs in both breast and prostate cancer, b) the degree to which they are shared across cancers, and c) the impact of these shared, race-differentiated SCNAs on cancer survival. Methods Utilizing data from The Cancer Genome Atlas (TCGA), SCNAs were identified using GISTIC 2.0, and in each tumor type, differences in SCNA magnitude between African Americans (AA) and European Americans (EA) were tested using linear regression. Unsupervised hierarchical clustering of the copy number of genes residing in race-differentiated SCNAs shared between tumor types was used to identify SCNA-defined patient groups, and Cox proportional hazards regression was used to test for association between those groups and overall/progression-free survival (PFS). Results We identified SCNAs that differed by race in breast (n = 58 SCNAs; permutation p

B cell development is tightly regulated in a stepwise manner to ensure proper generation of repertoire diversity via somatic gene rearrangements. Here, the authors show that a transcription factor, Erg, functions at the earliest stage to critically control two downstream factors, Ebf1 and Pax5, for modulating this gene rearrangement process.

Although whole turkeys served at Thanksgiving are the ubiquitous kickoffs to the US winter holiday season, much remains unknown about shopping behaviors for holiday food items. Given the once-a-year purchase of the whole turkey for most households, collecting data about demand and preferences necessitated the collection of data during the week before Thanksgiving, while turkey shopping was at the forefront of consumers' minds. Despite a self-reported confidence in cooking turkeys, many respondents indicated they thawed frozen meat using improper methods. Ninety-five percent of respondents indicated that they consumed meat; 89% of respondents who consumed meat or had someone in the household who did, indicated they had purchased turkey products. Positive willingness to pay (WTP) was found for all attributes of whole turkeys studied: free range, fed a vegetarian diet, hormone use not permitted, and antibiotic use not permitted. Mean estimated WTP for free range ranged from $0.37/lb for industry verified free range to $0.74/lb for USDA verified free range; although those 2 estimates were not statistically different from each other, they were both statistically different from zero. The statistically significant estimated mean WTP for hormone use not permitted ranged from $0.85/lb for industry verification to $1.35 for USDA verification but were again not statistically different from each other. Mean WTP estimates, which were statistically significant but not different from one another for antibiotic use not permitted, ranged from $0.62/lb for industry certification to $0.72 for retailer certified. Turkeys certified to be fed a vegetarian diet had a mean WTP estimate of $0.39/lb for retailer verification to $0.60/lb for USDA verification; those mean WTP estimates were not statistically different from each other but were each statistically different from zero. Social desirability bias, which can be defined as the relative over-reporting of one's own goodness, was detected with respect to self-reported holiday eating and healthfulness statements. Relationships were found between social desirability bias, gender, and age for holiday eating statements using a seemingly unrelated regression.

Abstract Background Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions. Results In this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of “true” networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z-score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package. Conclusions Our analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.

Prostate cancer (PCa) is a leading cause of cancer-related deaths and is driven by aberrant androgen receptor (AR) signalling. For this reason, androgen deprivation therapies (ADTs) that suppress androgen-induced PCa progression either by preventing androgen biosynthesis or via AR signalling inhibition (ARSi) are common treatments. The N6-methyladenosine (m6A) RNA modification is involved in regulating mRNA expression, translation, and alternative splicing, and through these mechanisms has been implicated in cancer development and progression. RNA-m6A is dynamically regulated by the METTL3 RNA methyltransferase complex and the FTO and ALKBH5 demethylases. While there is evidence supporting a role for aberrant METTL3 in many cancer types, including localised PCa, the wider contribution of METTL3, and by inference m6A, in androgen signalling in PCa remains poorly understood. Therefore, the aim of this study was to investigate the expression of METTL3 in PCa patients and study the clinical and functional relevance of METTL3 in PCa. It was found that METTL3 is aberrantly expressed in PCa patient samples and that siRNA-mediated METTL3 knockdown or METTL3-pharmacological inhibition significantly alters the basal and androgen-regulated transcriptome in PCa, which supports targeting m6A as a novel approach to modulate androgen signalling in PCa.

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.

PURPOSE: Despite the availability of current standards of care treatments for triple negative breast cancer (TNBC), many patients still die from this disease. Quadruple negative tumors, which are TNBC tumors that lack androgen receptor (AR), represent a more aggressive subtype of TNBC; however, the molecular features are not well understood. METHODS: Immunohistochemistry of estrogen receptor (ER), progesterone receptor (PR), HER2, and AR was determined in 244 primary and 630 recurrent/metastatic site biopsies. Expression was correlated with a panel of 25 cancer-related genes and proteins by IHC and in situ hybridization (ISH). RESULTS: We observed that 80.2% (65 of 81) of primary TNBC tumors and 75.7% (159 of 210) of recurrent/metastatic TNBC tumors are QNBC. Bivariate fit analysis demonstrated that QNBC (n = 224) significantly (P

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 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 (N S

Summary: Hhex encodes a homeobox transcriptional regulator important for embryonic development and hematopoiesis. Hhex is highly expressed in NK cells, and its germline deletion results in significant defects in lymphoid development, including NK cells. To determine if Hhex is intrinsically required throughout NK cell development or for NK cell function, we generate mice that specifically lack Hhex in NK cells. NK cell frequency is dramatically reduced, while NK cell differentiation, IL-15 responsiveness, and function at the cellular level remain largely normal in the absence of Hhex. Increased IL-15 availability fails to fully reverse NK lymphopenia following conditional Hhex deletion, suggesting that Hhex regulates developmental pathways extrinsic to those dependent on IL-15. Gene expression and functional genetic approaches reveal that Hhex regulates NK cell survival by directly binding Bcl2l11 (Bim) and repressing expression of this key apoptotic mediator. These data implicate Hhex as a transcriptional regulator of NK cell homeostasis and immunity.

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

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.

The mammary epithelium comprises two cell lineages but the heterogeneity amongst these during development is unclear. Here, the authors report single-cell RNA sequencing of the mouse mammary epithelium at four developmental stages, revealing diversity in both compartments and a transcriptional shift with puberty onset.