LKB1 depletion-mediated epithelial-mesenchymal transition induces fibroblast activation in lung fibrosis

The factors that determine fibrosis progression or normal tissue repair are largely unknown. We previously demonstrated that autophagy inhibition-mediated epithelial-mesenchymal transition (EMT) in human alveolar epithelial type II (ATII) cells augments local myofibroblast differentiation in pulmonary fibrosis by paracrine signalling. Here, we report that liver kinase B1 (LKB1) inactivation in ATII cells inhibits autophagy and induces EMT as a consequence. In IPF lungs, this is caused by downregulation of CAB39L, a key subunit within the LKB1 complex. 3D co-cultures of ATII cells and MRC5 lung fibroblasts coupled with RNA sequencing (RNA-seq) confirmed that paracrine signalling between LKB1-depleted ATII cells and fibroblasts augmented myofibroblast differentiation. Together these data suggest that reduced autophagy caused by LKB1 inhibition can induce EMT in ATII cells and contribute to fibrosis via aberrant epithelial-fibroblast crosstalk.

Identification of MYCN non-amplified neuroblastoma subgroups points towards molecular signatures for precision prognosis and therapy stratification

BACKGROUND

Despite the extensive study of MYCN-amplified neuroblastomas, there is a significant unmet clinical need in MYCN non-amplified cases. In particular, the extent of heterogeneity within the MYCN non-amplified population is unknown.

METHODS

A total of 1566 samples from 16 datasets were identified in Gene Expression Omnibus (GEO) and ArrayExpress. Characterisation of the subtypes was analysed by ConsensusClusterPlus. Independent predictors for subgrouping were constructed from the single sample predictor based on the multiclassPairs package. Findings were verified using immunohistochemistry and CIBERSORTx analysis.

RESULTS

We demonstrate that MYCN non-amplified neuroblastomas are heterogeneous and can be classified into 3 subgroups based on their transcriptional signatures. Within these groups, subgroup_2 has the worst prognosis and this group shows a 'MYCN' signature that is potentially induced by the overexpression of Aurora Kinase A (AURKA); whilst subgroup_3 is characterised by an 'inflamed' gene signature. The clinical implications of this subtype classification are significant, as each subtype demonstrates a unique prognosis and vulnerability to investigational therapies. A total of 420 genes were identified as independent subgroup predictors with average balanced accuracy of 0.93 and 0.84 for train and test datasets, respectively.

CONCLUSION

We propose that transcriptional subtyping may enhance precision prognosis and therapy stratification for patients with MYCN non-amplified neuroblastomas.

Protein language models meet reduced amino acid alphabets

MOTIVATION

Protein language models (PLMs), which borrowed ideas for modelling and inference from natural language processing, have demonstrated the ability to extract meaningful representations in an unsupervised way. This led to significant performance improvement in several downstream tasks. Clustering amino acids based on their physical-chemical properties to achieve reduced alphabets has been of interest in past research, but their application to PLMs or folding models is unexplored.

RESULTS

Here, we investigate the efficacy of PLMs trained on reduced amino acid alphabets in capturing evolutionary information, and we explore how the loss of protein sequence information impacts learned representations and downstream task performance. Our empirical work shows that PLMs trained on the full alphabet and a large number of sequences capture fine details that are lost in alphabet reduction methods. We further show the ability of a structure prediction model(ESMFold) to fold CASP14 protein sequences translated using a reduced alphabet. For 10 proteins out of the 50 targets, reduced alphabets improve structural predictions with LDDT-Cα differences of up to 19%.

AVAILABILITY AND IMPLEMENTATION

Trained models and code are available at github.com/Ieremie/reduced-alph-PLM.

Integrated network pharmacology and cellular assay reveal the biological mechanisms of Limonium sinense (Girard) Kuntze against Breast cancer

BACKGROUND

Limonium Sinense (Girard) Kuntze (L. sinense) has been widely used for the treatment of anaemia, bleeding, cancer, and other disorders in Chinese folk medicine. The aim of this study is to predict the therapeutic effects of L. sinense and investigate the potential mechanisms using integrated network pharmacology methods and in vitro cellular experiments.

METHODS

The active ingredients of L. sinense were collected from published literature, and the potential targets related to L. sinense were obtained from public databases. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and DisGeNET enrichment analyses were performed to explore the underlying mechanisms. Molecular docking, cellular experiments, RNA-sequencing (RNA-seq) and Gene Expression Omnibus (GEO) datasets were employed to further evaluate the findings.

RESULTS

A total of 15 active ingredients of L. sinense and their corresponding 389 targets were obtained. KEGG enrichment analysis revealed that the biological effects of L. sinense were primarily associated with "Pathways in cancer". DisGeNET enrichment analysis highlighted the potential role of L. sinense in the treatment of breast cancer. Apigenin within L. sinense showed promising potential against cancer. Cellular experiments demonstrated that the L. sinense ethanol extract (LSE) exhibited a significant growth inhibitory effect on multiple breast cancer cell lines in both 2D and 3D cultures. RNA-seq analysis revealed a potential impact of LSE on breast cancer. Additionally, analysis of GEO datasets verified the significant enrichment of breast cancer and several cancer-related pathways upon treatment with Apigenin in human breast cancer cells.

CONCLUSION

This study predicts the biological activities of L. sinense and demonstrates the inhibitory effect of LSE on breast cancer cells, highlighting the potential application of L. sinense in cancer treatment.

Synthetic lethal approaches to target cancers with loss of PTEN function

Phosphatase and tensin homolog (PTEN) is a tumour suppressor gene and has a role in inhibiting the oncogenic AKT signalling pathway by dephosphorylating phosphatidylinositol 3,4,5-triphosphate (PIP3) into phosphatidylinositol 4,5-bisphosphate (PIP2). The function of PTEN is regulated by different mechanisms and inactive PTEN results in aggressive tumour phenotype and tumorigenesis. Identifying targeted therapies for inactive tumour suppressor genes such as PTEN has been challenging as it is difficult to restore the tumour suppressor functions. Therefore, focusing on the downstream signalling pathways to discover a targeted therapy for inactive tumour suppressor genes has highlighted the importance of synthetic lethality studies. This review focuses on the potential synthetic lethality genes discovered in PTEN-inactive cancer types. These discovered genes could be potential targeted therapies for PTEN-inactive cancer types and may improve the treatment response rates for aggressive types of cancer.

Impact of non-pharmaceutical interventions during COVID-19 on future influenza trends in Mainland China

BACKGROUND

Influenza is a common illness for its high rates of morbidity and transmission. The implementation of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic to manage its dissemination could affect the transmission of influenza.

METHODS

A retrospective analysis, between 2018 and 2023, was conducted to examine the incidence of influenza virus types A and B among patients in sentinel cities located in North or South China as well as in Wuhan City. For validations, data on the total count of influenza patients from 2018 to 2023 were collected at the Central Hospital of Wuhan, which is not included in the sentinel hospital network. Time series methods were utilized to examine seasonal patterns and to forecast future influenza trends.

RESULTS

Northern and southern cities in China had earlier outbreaks during the NPIs period by about 8 weeks compared to the 2018-2019. The implementation of NPIs significantly reduced the influenza-like illness (ILI) rate and infection durations. Influenza B Victoria and H3N2 were the first circulating strains detected after the relaxation of NPIs, followed by H1N1 across mainland China. The SARIMA model predicted synchronized H1N1 outbreak cycles in North and South China, with H3N2 expected to occur in the summer in southern cities and in the winter in northern cities over the next 3 years. The ILI burden is expected to rise in both North and South China over the next 3 years, with higher ILI% levels in southern cities throughout the year, especially in winter, and in northern cities mainly during winter. In Wuhan City and the Central Hospital of Wuhan, influenza levels are projected to peak in the winter of 2024, with 2 smaller peaks expected during the summer of 2023.

CONCLUSIONS

In this study, we report the impact of NPIs on future influenza trends in mainland China. We recommend that local governments encourage vaccination during the transition period between summer and winter to mitigate economic losses and mortality associated with influenza.

Integrated analysis reveals effects of bioactive ingredients from Limonium Sinense (Girard) Kuntze on hypoxia-inducible factor (HIF) activation

Limonium Sinense (Girard) Kuntze is a traditional Chinese medicinal herb, showing blood replenishment, anti-tumour, anti-hepatitis, and immunomodulation activities amongst others. However, the mechanism of its pharmacological activities remains largely unknown. Here, we investigated the effects of bioactive ingredients from Limonium Sinense using an integrated approach. Water extracts from Limonium Sinense (LSW) showed a strong growth inhibitory effect on multiple cells in both 2D and 3D cultures. Global transcriptomic profiling and further connectivity map (CMap) analysis identified several similarly acting therapeutic candidates, including Tubulin inhibitors and hypoxia-inducible factor (HIF) modulators. The effect of LSW on the cell cycle was verified with flow cytometry showing a G2/M phase arrest. Integrated analysis suggested a role for gallic acid in mediating HIF activation. Taken together, this study provides novel insights into the bioactive ingredients in Limonium Sinense, highlighting the rich natural resource and therapeutic values of herbal plants.

Pseudohypoxic HIF pathway activation dysregulates collagen structure-function in human lung fibrosis

Extracellular matrix (ECM) stiffening with downstream activation of mechanosensitive pathways is strongly implicated in fibrosis. We previously reported that altered collagen nanoarchitecture is a key determinant of pathogenetic ECM structure-function in human fibrosis (Jones et al., 2018). Here, through human tissue, bioinformatic and ex vivo studies we provide evidence that hypoxia-inducible factor (HIF) pathway activation is a critical pathway for this process regardless of the oxygen status (pseudohypoxia). Whilst TGFβ increased the rate of fibrillar collagen synthesis, HIF pathway activation was required to dysregulate post-translational modification of fibrillar collagen, promoting pyridinoline cross-linking, altering collagen nanostructure, and increasing tissue stiffness. In vitro, knockdown of Factor Inhibiting HIF (FIH), which modulates HIF activity, or oxidative stress caused pseudohypoxic HIF activation in the normal fibroblasts. By contrast, endogenous FIH activity was reduced in fibroblasts from patients with lung fibrosis in association with significantly increased normoxic HIF pathway activation. In human lung fibrosis tissue, HIF-mediated signalling was increased at sites of active fibrogenesis whilst subpopulations of human lung fibrosis mesenchymal cells had increases in both HIF and oxidative stress scores. Our data demonstrate that oxidative stress can drive pseudohypoxic HIF pathway activation which is a critical regulator of pathogenetic collagen structure-function in fibrosis.

TransformerGO: predicting protein-protein interactions by modelling the attention between sets of gene ontology terms

MOTIVATION

Protein-protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations, such as exaggerated false positives and negatives rates. The semantic similarity derived from the Gene Ontology (GO) annotation is regarded as one of the most powerful indicators for protein interactions. However, while computational approaches for prediction of PPIs have gained popularity in recent years, most methods fail to capture the specificity of GO terms.

RESULTS

We propose TransformerGO, a model that is capable of capturing the semantic similarity between GO sets dynamically using an attention mechanism. We generate dense graph embeddings for GO terms using an algorithmic framework for learning continuous representations of nodes in networks called node2vec. TransformerGO learns deep semantic relations between annotated terms and can distinguish between negative and positive interactions with high accuracy. TransformerGO outperforms classic semantic similarity measures on gold standard PPI datasets and state-of-the-art machine-learning-based approaches on large datasets from Saccharomyces cerevisiae and Homo sapiens. We show how the neural attention mechanism embedded in the transformer architecture detects relevant functional terms when predicting interactions.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Ieremie/TransformerGO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The USP7 protein interaction network and its roles in tumorigenesis

Ubiquitin-specific protease (USP7), also known as Herpesvirus-associated ubiquitin-specific protease (HAUSP), is a deubiquitinase. There has been significant recent attention on USP7 following the discovery that USP7 is a key regulator of the p53-MDM2 pathway. The USP7 protein is 130 kDa in size and has multiple domains which bind to a diverse set of proteins. These interactions mediate key developmental and homeostatic processes including the cell cycle, immune response, and modulation of transcription factor and epigenetic regulator activity and localization. USP7 also promotes carcinogenesis through aberrant activation of the Wnt signalling pathway and stabilization of HIF-1α. These findings have shown that USP7 may induce tumour progression and be a therapeutic target. Together with interest in developing USP7 as a target, several studies have defined new protein interactions and the regulatory networks within which USP7 functions. In this review, we focus on the protein interactions of USP7 that are most important for its cancer-associated roles.

Bidirectional epithelial-mesenchymal crosstalk provides self-sustaining profibrotic signals in pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is the prototypic progressive fibrotic lung disease with a median survival of 2 to 4 years. Injury to and/or dysfunction of the alveolar epithelium is strongly implicated in IPF disease initiation, but the factors that determine whether fibrosis progresses rather than normal tissue repair occurs remain poorly understood. We previously demonstrated that zinc finger E-box-binding homeobox 1-mediated epithelial-mesenchymal transition in human alveolar epithelial type II (ATII) cells augments transforming growth factor-β-induced profibrogenic responses in underlying lung fibroblasts via paracrine signaling. Here, we investigated bidirectional epithelial-mesenchymal crosstalk and its potential to drive fibrosis progression. RNA-Seq of lung fibroblasts exposed to conditioned media from ATII cells undergoing RAS-induced epithelial-mesenchymal transition identified many differentially expressed genes including those involved in cell migration and extracellular matrix regulation. We confirmed that paracrine signaling between RAS-activated ATII cells and fibroblasts augmented fibroblast recruitment and demonstrated that this involved a zinc finger E-box-binding homeobox 1-tissue plasminogen activator axis. In a reciprocal fashion, paracrine signaling from transforming growth factor-β-activated lung fibroblasts or IPF fibroblasts induced RAS activation in ATII cells, at least partially through the secreted protein acidic and rich in cysteine, which may signal via the epithelial growth factor receptor via epithelial growth factor-like repeats. Together, these data identify that aberrant bidirectional epithelial-mesenchymal crosstalk in IPF drives a chronic feedback loop that maintains a wound-healing phenotype and provides self-sustaining profibrotic signals.

GRK6 Depletion Induces HIF Activity in Lung Adenocarcinoma

G protein-coupled receptor kinase 6 (GRK6) is expressed in various tissues and is involved in the development of several diseases including lung cancer. We previously reported that GRK6 is down-regulated in lung adenocarcinoma patients, which induces cell invasion and metastasis. However, further understanding of the role of GRK6 in lung adenocarcinoma is required. Here we explored the functional consequence of GRK6 inhibition in lung epithelial cells. Analysis of TCGA data was coupled with RNA sequencing (RNA-seq) in alveolar epithelial type II (ATII) cells following depletion of GRK6 with RNA interference (RNAi). Findings were validated in ATII cells followed by tissue microarray analysis. Pathway analysis suggested that one of the Hallmark pathways enriched upon GRK6 inhibition is 'Hallmark_Hypoxia' (FDR = 0.014). We demonstrated that GRK6 depletion induces HIF1α (hypoxia-inducible factor 1 alpha) levels and activity in ATII cells. The findings were further confirmed in lung adenocarcinoma samples, in which GRK6 expression levels negatively and positively correlate with HIF1α expression (P = 0.015) and VHL expression (P < 0.0001), respectively. Mechanistically, we showed the impact of GRK6 on HIF activity could be achieved via regulation of VHL levels. Taken together, targeting the HIF pathway may provide new strategies for therapy in GRK6-depleted lung adenocarcinoma patients.

3-month, 6-month, 9-month, and 12-month respiratory outcomes in patients following COVID-19-related hospitalisation: a prospective study

Background

The consequences of COVID-19 in those who recover from acute infection requiring hospitalisation have yet to be clearly defined. We aimed to describe the temporal trends in respiratory outcomes over 12 months in patients hospitalised for severe COVID-19 and to investigate the associated risk factors.

Methods

In this prospective, longitudinal, cohort study, patients admitted to hospital for severe COVID-19 who did not require mechanical ventilation were prospectively followed up at 3 months, 6 months, 9 months, and 12 months after discharge from Renmin Hospital of Wuhan University, Wuhan, China. Patients with a history of hypertension; diabetes; cardiovascular disease; cancer; and chronic lung disease, including asthma or chronic obstructive pulmonary disease; or a history of smoking documented at time of hospital admission were excluded at time of electronic case-note review. Patients who required intubation and mechanical ventilation were excluded given the potential for the consequences of mechanical ventilation itself to influence the factors under investigation. During the follow-up visits, patients were interviewed and underwent physical examination, routine blood test, pulmonary function tests (ie, diffusing capacity of the lungs for carbon monoxide [DLCO]; forced expiratory flow between 25% and 75% of forced vital capacity [FVC]; functional residual capacity; FVC; FEV₁; residual volume; total lung capacity; and vital capacity), chest high-resolution CT (HRCT), and 6-min walk distance test, as well as assessment using a modified Medical Research Council dyspnoea scale (mMRC).

Findings

Between Feb 1, and March 31, 2020, of 135 eligible patients, 83 (61%) patients participated in this study. The median age of participants was 60 years (IQR 52–66). Temporal improvement in pulmonary physiology and exercise capacity was observed in most patients; however, persistent physiological and radiographic abnormalities remained in some patients with COVID-19 at 12 months after discharge. We found a significant reduction in DLCO over the study period, with a median of 77% of predicted (IQR 67–87) at 3 months, 76% of predicted (68–90) at 6 months, and 88% of predicted (78–101) at 12 months after discharge. At 12 months after discharge, radiological changes persisted in 20 (24%) patients. Multivariate logistic regression showed increasing odds of impaired DLCO associated with female sex (odds ratio 8·61 [95% CI 2·83–26·2; p=0·0002) and radiological abnormalities were associated with peak HRCT pneumonia scores during hospitalisation (1·36 [1·13–1·62]; p=0·0009).

Interpretation

In most patients who recovered from severe COVID-19, dyspnoea scores and exercise capacity improved over time; however, in a subgroup of patients at 12 months we found evidence of persistent physiological and radiographic change. A unified pathway for the respiratory follow-up of patients with COVID-19 is required.

Funding

National Natural Science Foundation of China, UK Medical Research Council, and National Institute for Health Research Southampton Biomedical Research Centre.

Translation

For the Chinese translation of the abstract see Supplementary Materials section.

Quantitative Proteomic Analysis in Alveolar Type II Cells Reveals the Different Capacities of RAS and TGF-β to Induce Epithelial-Mesenchymal Transition

Alveolar type II (ATII) epithelial cells function as stem cells, contributing to alveolar renewal, repair and cancer. Therefore, they are a highly relevant model for studying a number of lung diseases, including acute injury, fibrosis and cancer, in which signals transduced by RAS and transforming growth factor (TGF)-β play critical roles. To identify downstream molecular events following RAS and/or TGF-β activation, we performed proteomic analysis using a quantitative label-free approach (LC-HDMS^E) to provide in-depth proteome coverage and estimates of protein concentration in absolute amounts. Data are available via ProteomeXchange with identifier PXD023720. We chose ATII^ER:KRASV12 as an experimental cell line in which RAS is activated by adding 4-hydroxytamoxifen (4-OHT). Proteomic analysis of ATII cells treated with 4-OHT or TGF-β demonstrated that RAS activation induces an epithelial–mesenchymal transition (EMT) signature. In contrast, under the same conditions, activation of TGF-β signaling alone only induces a partial EMT. EMT is a dynamic and reversible biological process by which epithelial cells lose their cell polarity and down-regulate cadherin-mediated cell–cell adhesion to gain migratory properties, and is involved in embryonic development, wound healing, fibrosis and cancer metastasis. Thus, these results could help to focus research on the identification of processes that are potentially driving EMT-related human disease.

Correction: WDHD1 is essential for the survival of PTEN-inactive triple-negative breast cancer

A Correction to this paper has been published: https://doi.org/10.1038/s41419-021-03530-0

WDHD1 is essential for the survival of PTEN-inactive triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer that lacks the oestrogen receptor, progesterone receptor and human epidermal growth factor receptor 2, making it difficult to target therapeutically. Targeting synthetic lethality is an alternative approach for cancer treatment. TNBC shows frequent loss of phosphatase and tensin homologue (PTEN) expression, which is associated with poor prognosis and treatment response. To identify PTEN synthetic lethal interactions, TCGA analysis coupled with a whole-genome siRNA screen in isogenic PTEN-negative and -positive cells were performed. Among the candidate genes essential for the survival of PTEN-inactive TNBC cells, WDHD1 (WD repeat and high-mobility group box DNA-binding protein 1) expression was increased in the low vs. high PTEN TNBC samples. It was also the top hit in the siRNA screen and its knockdown significantly inhibited cell viability in PTEN-negative cells, which was further validated in 2D and 3D cultures. Mechanistically, WDHD1 is important to mediate a high demand of protein translation in PTEN-inactive TNBC. Finally, the importance of WDHD1 in TNBC was confirmed in patient samples obtained from the TCGA and tissue microarrays with clinic-pathological information. Taken together, as an essential gene for the survival of PTEN-inactive TNBC cells, WDHD1 could be a potential biomarker or a therapeutic target for TNBC.

Temporal radiographic changes in COVID-19 patients: relationship to disease severity and viral clearance

COVID-19 is "public enemy number one" and has placed an enormous burden on health authorities across the world. Given the wide clinical spectrum of COVID-19, understanding the factors that can predict disease severity will be essential since this will help frontline clinical staff to stratify patients with increased confidence. To investigate the diagnostic value of the temporal radiographic changes, and the relationship to disease severity and viral clearance in COVID-19 patients. In this retrospective cohort study, we included 99 patients admitted to the Renmin Hospital of Wuhan University, with laboratory confirmed moderate or severe COVID-19. Temporal radiographic changes and viral clearance were explored using appropriate statistical methods. Radiographic features from HRCT scans included ground-glass opacity, consolidation, air bronchogram, nodular opacities and pleural effusion. The HRCT scores (peak) during disease course in COVID-19 patients with severe pneumonia (median: 24.5) were higher compared to those with pneumonia (median: 10) (p = 3.56 × 10 -12), with more frequency of consolidation (p = 0.025) and air bronchogram (p = 7.50 × 10-6). The median values of days when the peak HRCT scores were reached in pneumonia or severe pneumonia patients were 12 vs. 14, respectively (p = 0.048). Log-rank test and Spearman's Rank-Order correlation suggested temporal radiographic changes as a valuable predictor for viral clearance. In addition, follow up CT scans from 11 pneumonia patients showed full recovery. Given the values of HRCT scores for both disease severity and viral clearance, a standardised HRCT score system for COVID-19 is highly demanded.

ASPP1 deficiency promotes epithelial-mesenchymal transition, invasion and metastasis in colorectal cancer

The apoptosis-stimulating protein of p53 (ASPP) family of proteins can regulate apoptosis by interacting with the p53 family and have been identified to play an important role in cancer progression. Previously, we have demonstrated that ASPP2 downregulation can promote invasion and migration by controlling β-catenin-dependent regulation of ZEB1, however, the role of ASPP1 in colorectal cancer (CRC) remains unclear. We analyzed data from The Cancer Genome Atlas (TCGA) and coupled this to in vitro experiments in CRC cell lines as well as to experimental pulmonary metastasis in vivo. Tissue microarrays of CRC patients with information of clinical-pathological parameters were also used to investigate the expression and function of ASPP1 in CRC. Here, we report that loss of ASPP1 is capable of enhancing migration and invasion in CRC, both in vivo and in vitro. We demonstrate that depletion of ASPP1 could activate expression of Snail2 via the NF-κB pathway and in turn, induce EMT; and this process is further exacerbated in RAS-mutated CRC. ASPP1 could be a prognostic factor in CRC, and the use of NF-κB inhibitors may provide new strategies for therapy against metastasis in ASPP1-depleted CRC patients.

Deconvolution of RNA-Seq Analysis of Hyperbaric Oxygen-Treated Mice Lungs Reveals Mesenchymal Cell Subtype Changes

Hyperbaric oxygen (HBO) is widely applied to treat several hypoxia-related diseases. Previous studies have focused on the immediate effect of HBO-exposure induced oxidative stress on the lungs, but knowledge regarding the chronic effects from repetitive HBO exposure is limited, especially at the gene expression level. We found that repetitive HBO exposure did not alter the morphology of murine lungs. However, by deconvolution of RNA-seq from those mice lungs using CIBERSORTx and the expression profile matrices of 8 mesenchymal cell subtypes obtained from bleomycin-treated mouse lungs, we identify several mesenchymal cell subtype changes. These include increases in Col13a1 matrix fibroblasts, mesenchymal progenitors and mesothelial cell populations and decreases in lipofibroblasts, endothelial and Pdgfrb high cell populations. Our data suggest that repetitive HBO exposure may affect biological processes in the lungs such as response to wounding, extracellular matrix, vasculature development and immune response.

An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction

Previous work has shown that proteins that have the potential to be vaccine candidates can be predicted from features derived from their amino acid sequences. In this work, we make an empirical comparison across various machine learning classifiers on this sequence-based inference problem. Using systematic cross validation on a dataset of 200 known vaccine candidates and 200 negative examples, with a set of 525 features derived from the AA sequences and feature selection applied through a greedy backward elimination approach, we show that simple classification algorithms often perform as well as more complex support vector kernel machines. The work also includes a novel cross validation applied across bacterial species, i.e. the validation proteins all come from a specific species of bacterium not represented in the training set. We termed this type of validation Leave One Bacteria Out Validation (LOBOV).

SGLT1 is required for the survival of triple-negative breast cancer cells via potentiation of EGFR activity

Sodium/glucose cotransporter 1 (SGLT1), an essential active glucose transport protein that helps maintain high intracellular glucose levels, was previously shown to interact with epidermal growth factor receptor (EGFR); the SGLT1-EGFR interaction maintains intracellular glucose levels to promote survival of cancer cells. Here, we explore the role of SGLT1 in triple-negative breast cancer (TNBC), which is the most aggressive type of breast cancer. We performed TCGA analysis coupled to in vitro experiments in TNBC cell lines as well as in vivo xenografts established in the mammary fat pad of female nude mice. Tissue microarrays of TNBC patients with information of clinical-pathological parameters were also used to investigate the expression and function of SGLT1 in TNBC. We show that high levels of SGLT1 are associated with greater tumour size in TNBC. Knockdown of SGLT1 compromises cell growth in vitro and in vivo. We further demonstrate that SGLT1 depletion results in decreased levels of phospho-EGFR, and as a result, the activity of downstream signalling pathways (such as AKT and ERK) is inhibited. Hence, targeting SGLT1 itself or the EGFR-SGLT1 interaction may provide novel therapeutics against TNBC.

Deep proteomic analysis of Dnmt1 mutant/hypomorphic colorectal cancer cells reveals dysregulation of epithelial-mesenchymal transition and subcellular re-localization of Beta-Catenin

DNA methyltransferase I plays the central role in maintenance of CpG DNA methylation patterns across the genome and alteration of CpG methylation patterns is a frequent and significant occurrence across many cancers. Cancer cells carrying hypomorphic alleles of Dnmt1 have become important tools for understanding Dnmt1 function and CpG methylation. In this study, we analyse colorectal cancer cells with a homozygous deletion of exons 3 to 5 of Dnmt1, resulting in reduced Dnmt1 activity. Although this cell model has been widely used to study the epigenome, the effects of the Dnmt1 hypomorph on cell signalling pathways and the wider proteome are largely unknown. In this study, we perform the first quantitative proteomic analysis of this important cell model and identify multiple signalling pathways and processes that are significantly dysregulated in the hypomorph cells. In Dnmt1 hypomorph cells, we observed a clear and unexpected signature of increased Epithelial-to-Mesenchymal transition (EMT) markers as well as reduced expression and sub-cellular re-localization of Beta-Catenin. Expression of wild-type Dnmt1 in hypomorph cells or knock-down of wild-type Dnmt1 did not recapitulate or rescue the observed protein profiles in Dnmt1 hypomorph cells suggesting that hypomorphic Dnmt1 causes changes not solely attributable to Dnmt1 protein levels. In summary, we present the first comprehensive proteomic analysis of the widely studied Dnmt1 hypomorph colorectal cancer cells and identify redistribution of Dnmt1 and its interaction partner Beta-Catenin.

iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data.

Paracrine signalling during ZEB1-mediated epithelial-mesenchymal transition augments local myofibroblast differentiation in lung fibrosis

The contribution of epithelial-mesenchymal transition (EMT) to human lung fibrogenesis is controversial. Here we provide evidence that ZEB1-mediated EMT in human alveolar epithelial type II (ATII) cells contributes to the development of lung fibrosis by paracrine signalling to underlying fibroblasts. Activation of EGFR-RAS-ERK signalling in ATII cells induced EMT via ZEB1. ATII cells had extremely low extracellular matrix gene expression even after induction of EMT, however conditioned media from ATII cells undergoing RAS-induced EMT augmented TGFβ-induced profibrogenic responses in lung fibroblasts. This epithelial-mesenchymal crosstalk was controlled by ZEB1 via the expression of tissue plasminogen activator (tPA). In human fibrotic lung tissue, nuclear ZEB1 expression was detected in alveolar epithelium adjacent to sites of extracellular matrix (ECM) deposition, suggesting that ZEB1-mediated paracrine signalling has the potential to contribute to early fibrotic changes in the lung interstitium. Targeting this novel ZEB1 regulatory axis may be a viable strategy for the treatment of pulmonary fibrosis.

Proteomic Analysis of Azacitidine-Induced Degradation Profiles Identifies Multiple Chromatin and Epigenetic Regulators Including Uhrf1 and Dnmt1 as Sensitive to Azacitidine

DNA methylation is a critical epigenetic modification that is established and maintained across the genome by DNA methyltransferase enzymes (Dnmts). Altered patterns of DNA methylation are a frequent occurrence in many tumor genomes, and inhibitors of Dnmts have become important epigenetic drugs. Azacitidine is a cytidine analog that is incorporated into DNA and induces the specific inhibition and proteasomal-mediated degradation of Dnmts. The downstream effects of azacitidine on CpG methylation and on gene transcription have been widely studied in many systems, but how azacitidine impacts the proteome is not well-understood. In addition, with its specific ability to induce the rapid degradation of Dnmts (in particular, the primary maintenance DNA methyltransferase, Dnmt1), it may be employed as a specific chemical knockdown for investigating the Dnmt1-associated functional or physical interactome. In this study, we use quantitative proteomics to analyze the degradation profile of proteins in the nuclear proteome of cells treated with azacitidine. We identify specific proteins as well as multiple pathways and processes that are impacted by azacitidine. The Dnmt1 interaction partner, Uhrf1, exhibits significant azacitidine-induced degradation, and this azacitidine-induced degradation is independent of the levels of Dnmt1 protein. We identify multiple other chromatin- and epigenetic-associated factors, including the bromodomain-containing transcriptional regulator, Brd2. We show that azacitidine induces highly specific perturbations of the Dnmt1-associated proteome, and while interaction partners such as Uhrf1 are sensitive to azacitidine, others such as the Dnmt1 interaction partner and stability regulator, Usp7, are not. In summary, we have conducted the first comprehensive proteomic analysis of the azacitidine-sensitive nuclear proteome, and we show how 5-azacitidine can be used as a specific probe to explore Dnmt- and chromatin-related protein networks.

Multiproteomic and Transcriptomic Analysis of Oncogenic β-Catenin Molecular Networks

The dysregulation of Wnt signaling is a frequent occurrence in many different cancers. Oncogenic mutations of CTNNB1/β-catenin, the key nuclear effector of canonical Wnt signaling, lead to the accumulation and stabilization of β-catenin protein with diverse effects in cancer cells. Although the transcriptional response to Wnt/β-catenin signaling activation has been widely studied, an integrated understanding of the effects of oncogenic β-catenin on molecular networks is lacking. We used affinity-purification mass spectrometry (AP-MS), label-free liquid chromatography-tandem mass spectrometry, and RNA-Seq to compare protein-protein interactions, protein expression, and gene expression in colorectal cancer cells expressing mutant (oncogenic) or wild-type β-catenin. We generate an integrated molecular network and use it to identify novel protein modules that are associated with mutant or wild-type β-catenin. We identify a DNA methyltransferase I associated subnetwork that is enriched in cells with mutant β-catenin and a subnetwork enriched in wild-type cells associated with the CDKN2A tumor suppressor, linking these processes to the transformation of colorectal cancer cells through oncogenic β-catenin signaling. In summary, multiomics analysis of a defined colorectal cancer cell model provides a significantly more comprehensive identification of functional molecular networks associated with oncogenic β-catenin signaling.

Proteomics and Network Analyses Reveal Inhibition of Akt-mTOR Signaling in CD4+ T Cells by Mycobacterium tuberculosis Mannose-Capped Lipoarabinomannan

Mycobacterium tuberculosis (Mtb) cell wall glycolipid mannose-capped lipoarabinomannan (ManLAM) inhibits CD4+ T-cell activation by inhibiting proximal T-cell receptor (TCR) signaling when activated by anti-CD3. To understand the impact of ManLAM on CD4+ T-cell function when both the TCR-CD3 complex and major costimulator CD28 are engaged, we performed label-free quantitative MS and network analysis. Mixed-effect model analysis of peptide intensity identified 149 unique peptides representing 131 proteins that were differentially regulated by ManLAM in anti-CD3- and anti-CD28-activated CD4+ T cells. Crosstalker, a novel network analysis tool identified dysregulated translation, TCA cycle, and RNA metabolism network modules. PCNA, Akt, mTOR, and UBC were found to be bridge node proteins connecting these modules of dysregulated proteins. Altered PCNA expression and cell cycle analysis showed arrest at the G2M phase. Western blot confirmed that ManLAM inhibited Akt and mTOR phosphorylation, and decreased expression of deubiquitinating enzymes Usp9x and Otub1. Decreased NF-κB phosphorylation suggested interference with CD28 signaling through inhibition of the Usp9x-Akt-mTOR pathway. Thus, ManLAM induced global changes in the CD4+ T-cell proteome by affecting Akt-mTOR signaling, resulting in broad functional impairment of CD4+ T-cell activation beyond inhibition of proximal TCR-CD3 signaling.

Proteomic and bioinformatics profile of paired human alveolar macrophages and peripheral blood monocytes

Little is known about proteomic differences between pluripotent human peripheral blood monocytes (MN) and their terminally-differentiated pulmonary counterparts, alveolar macrophages (AM). To better characterize these cell populations, we performed a label-free shotgun proteomics assessment of matched AM and MN preparations from eight healthy volunteers. With an FDR of less than 0.45%, we identified 1754 proteins within AM and 1445 from MN. Comparison of the two proteomes revealed that 1239 of the proteins found in AM were shared with MN, whereas 206 proteins were uniquely identified in MN and 515 were unique to AM. Molecular and cellular functions, protein classes, development associations, and membership in physiological systems and canonical pathways were identified among the detected proteins. Analysis of biologic processes represented by these proteomes indicated that MN were most prominently enriched for proteins involved in cellular movement and immune cell trafficking. In contrast, AM were enriched for proteins involved in protein trafficking, molecular transport, and cellular assembly and organization. These findings provide a baseline proteomic resource for further studies aimed at better understanding of the functional differences between MN and AM in both health and disease.

Alternate wiring of a KNOXI genetic network underlies differences in leaf development of A. thaliana and C. hirsuta

In this study, Rast-Somssich et al. investigated morphological differences between C. hirsuta, which has complex leaves with leaflets, and its relative, A. thaliana, which has simple leaves. By transferring single genes from one species into another under their endogenous regulatory elements, the authors show that leaf form can be modified in the recipient species, extending our knowledge of how paralogous genes are regulated in a complex eukaryote.

Two interrelated problems in biology are understanding the regulatory logic and predictability of morphological evolution. Here, we studied these problems by comparing Arabidopsis thaliana, which has simple leaves, and its relative, Cardamine hirsuta, which has dissected leaves comprising leaflets. By transferring genes between the two species, we provide evidence for an inverse relationship between the pleiotropy of SHOOTMERISTEMLESS (STM) and BREVIPEDICELLUS (BP) homeobox genes and their ability to modify leaf form. We further show that cis-regulatory divergence of BP results in two alternative configurations of the genetic networks controlling leaf development. In C. hirsuta, ChBP is repressed by the microRNA164A (MIR164A)/ChCUP-SHAPED COTYLEDON (ChCUC) module and ChASYMMETRIC LEAVES1 (ChAS1), thus creating cross-talk between MIR164A/CUC and AS1 that does not occur in A. thaliana. These different genetic architectures lead to divergent interactions of network components and growth regulation in each species. We suggest that certain regulatory genes with low pleiotropy are predisposed to readily integrate into or disengage from conserved genetic networks influencing organ geometry, thus rapidly altering their properties and contributing to morphological divergence.

How do oncoprotein mutations rewire protein-protein interaction networks?

The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein-protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein-protein interaction networks and drive the cancer phenotype.

A Protein Interaction between β-Catenin and Dnmt1 Regulates Wnt Signaling and DNA Methylation in Colorectal Cancer Cells

Aberrant activation of the Wnt signaling pathway is an important step in the initiation and progression of tumor development in diverse cancers. The central effector of canonical Wnt signaling, β-catenin (CTNNB1), is a multifunctional protein, and has been extensively studied with respect to its roles in cell-cell adhesion and in regulation of Wnt-driven transcription. Here, a novel mass spectrometry-based proteomics technique in colorectal cancer cells expressing stabilized β-catenin, was used to identify a protein-protein interaction between β-catenin and DNA methyltransferase I (Dnmt1) protein, the primary regulator of DNA methylation patterns in mammalian cells. Dnmt1 and β-catenin strongly colocalized in the nuclei of colorectal cancer cells, and the interaction is mediated by the central domain of the Dnmt1 protein. Dnmt1 protein abundance is dependent upon the levels of β-catenin, and is increased in cells expressing stabilized mutant β-catenin. Conversely, the Dnmt1 regulates the levels of nuclear β-catenin and β-catenin/TCF-driven transcription. In addition, lysine-specific demethylase 1 (LSD1/KDM1A), a regulator of DNMT1 stability, was identified as a component of the Dnmt1-β-catenin protein complex and perturbation of the Dnmt1-β-catenin interaction altered DNA methylation. In summary, a functional protein-protein interaction was identified between two critically important oncoproteins, in turn revealing a link between Wnt signaling and downstream nuclear functions mediated by Dnmt1.

IMPLICATIONS

Two critical oncoproteins, Dnmt1 and β-catenin, mutually regulate one each other's levels and activities in colorectal cancer cells.

Integrated analysis of the Wnt responsive proteome in human cells reveals diverse and cell-type specific networks

Wnt signalling is a fundamentally important signalling pathway that regulates many aspects of metazoan development and is frequently dysregulated in cancer. Although many of the core components of the Wnt signalling pathway, such as β-catenin, have been extensively studied, the broad systems level responses of the mammalian cell to Wnt signalling are less well understood. In addition, the cell- or tissue-specific protein networks that modulate Wnt signalling in the diverse tissues or developmental stages in which it functions remain to be defined. To address these questions, we undertook a broad survey of the Wnt response in different human cell lines using both interaction and expression proteomics approaches. Our data reveal both similar and divergent responses of pathways and processes in the three cell-lines analyzed as well as a marked attenuation of the response to exogenous Wnt treatment in cells harbouring a stabilizing (activating) mutation of β-catenin. We also identify cell-type specific components of the Wnt signalling network and find that by integrating expression and interaction proteomics data a more complete description of the Wnt interaction network can be achieved. Finally, our results attest to the power of LC-MS/MS to reveal novel cellular responses in even relatively well studied biological pathways such as Wnt signalling.

The CRAPome: a contaminant repository for affinity purification-mass spectrometry data

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/.

Gain of interaction with IRS1 by p110α-helical domain mutants is crucial for their oncogenic functions

PIK3CA, which encodes the p110α catalytic subunit of phosphatidylinositol 3-kinase α, is frequently mutated in human cancers. Most of these mutations occur at two hot-spots: E545K and H1047R located in the helical domain and the kinase domain, respectively. Here, we report that p110α E545K, but not p110α H1047R, gains the ability to associate with IRS1 independent of the p85 regulatory subunit, thereby rewiring this oncogenic signaling pathway. Disruption of the IRS1-p110α E545K interaction destabilizes the p110α protein, reduces AKT phosphorylation, and slows xenograft tumor growth of a cancer cell line expressing p110α E545K. Moreover, a hydrocarbon-stapled peptide that disrupts this interaction inhibits the growth of tumors expressing p110α E545K.

A semantic proteomics dashboard (SemPoD) for data management in translational research

BACKGROUND

One of the primary challenges in translational research data management is breaking down the barriers between the multiple data silos and the integration of 'omics data with clinical information to complete the cycle from the bench to the bedside. The role of contextual metadata, also called provenance information, is a key factor ineffective data integration, reproducibility of results, correct attribution of original source, and answering research queries involving "What", "Where", "When", "Which", "Who", "How", and "Why" (also known as the W7 model). But, at present there is limited or no effective approach to managing and leveraging provenance information for integrating data across studies or projects. Hence, there is an urgent need for a paradigm shift in creating a "provenance-aware" informatics platform to address this challenge. We introduce an ontology-driven, intuitive Semantic Proteomics Dashboard (SemPoD) that uses provenance together with domain information (semantic provenance) to enable researchers to query, compare, and correlate different types of data across multiple projects, and allow integration with legacy data to support their ongoing research.

RESULTS

The SemPoD platform, currently in use at the Case Center for Proteomics and Bioinformatics (CPB), consists of three components: (a) Ontology-driven Visual Query Composer, (b) Result Explorer, and (c) Query Manager. Currently, SemPoD allows provenance-aware querying of 1153 mass-spectrometry experiments from 20 different projects. SemPod uses the systems molecular biology provenance ontology (SysPro) to support a dynamic query composition interface, which automatically updates the components of the query interface based on previous user selections and efficiently prunes the result set usinga "smart filtering" approach. The SysPro ontology re-uses terms from the PROV-ontology (PROV-O) being developed by the World Wide Web Consortium (W3C) provenance working group, the minimum information required for reporting a molecular interaction experiment (MIMIx), and the minimum information about a proteomics experiment (MIAPE) guidelines. The SemPoD was evaluated both in terms of user feedback and as scalability of the system.

CONCLUSIONS

SemPoD is an intuitive and powerful provenance ontology-driven data access and query platform that uses the MIAPE and MIMIx metadata guideline to create an integrated view over large-scale systems molecular biology datasets. SemPoD leverages the SysPro ontology to create an intuitive dashboard for biologists to compose queries, explore the results, and use a query manager for storing queries for later use. SemPoD can be deployed over many existing database applications storing 'omics data, including, as illustrated here, the LabKey data-management system. The initial user feedback evaluating the usability and functionality of SemPoD has been very positive and it is being considered for wider deployment beyond the proteomics domain, and in other 'omics' centers.

Identifying novel protein complexes in cancer cells using epitope-tagging of endogenous human genes and affinity-purification mass spectrometry

Affinity-purification mass spectrometry (AP-MS) is the preeminent technique for identification of eukaryotic protein complexes in vivo. AP-MS workflows typically express epitope-tagged bait proteins, immunopurify, and then identify associated protein complexes using mass spectrometry. However, challenges of existing strategies include the construction of expression vectors for large open reading frames and the possibility that overexpression of bait proteins may result in expression of nonphysiological levels of the bait protein with concomitant perturbation of endogenous protein complexes. To address these issues, we use human cell lines with epitope-tagged endogenous genes as AP-MS substrates to develop a platform that we call "knock-in AP-MS", thereby avoiding the challenges of expression vector construction and ensuring that expression of tagged proteins is driven by endogenous regulatory mechanisms. Using three different bait genes (MRE11A, DNMT1 and APC), we show that cell lines expressing epitope-tagged endogenous genes make good substrates for sensitive and reproducible identification of protein interactions using AP-MS. In particular, we identify novel interactors of the important oncoprotein Adenomatous Polyposis Coli (APC), including an interaction with Flightless-1 homologue (FLII) that is enriched in nuclear fractions.

Computational framework for analysis of prey-prey associations in interaction proteomics identifies novel human protein-protein interactions and networks

Large-scale protein-protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific "bait" protein and its associated "prey" proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait-prey and cocomplexed preys (prey-prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein-protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait-prey and prey-prey interactions can be used to refine network topology and extend known protein networks.

DNA and chromatin modification networks distinguish stem cell pluripotent ground states

Pluripotent stem cells are capable of differentiating into all cell types of the body and therefore hold tremendous promise for regenerative medicine. Despite their widespread use in laboratories across the world, a detailed understanding of the molecular mechanisms that regulate the pluripotent state is currently lacking. Mouse embryonic (mESC) and epiblast (mEpiSC) stem cells are two closely related classes of pluripotent stem cells, derived from distinct embryonic tissues. Although both mESC and mEpiSC are pluripotent, these cell types show important differences in their properties suggesting distinct pluripotent ground states. To understand the molecular basis of pluripotency, we analyzed the nuclear proteomes of mESCs and mEpiSCs to identify protein networks that regulate their respective pluripotent states. Our study used label-free LC-MS/MS to identify and quantify 1597 proteins in embryonic and epiblast stem cell nuclei. Immunoblotting of a selected protein subset was used to confirm that key components of chromatin regulatory networks are differentially expressed in mESCs and mEpiSCs. Specifically, we identify differential expression of DNA methylation, ATP-dependent chromatin remodeling and nucleosome remodeling networks in mESC and mEpiSC nuclei. This study is the first comparative study of protein networks in cells representing the two distinct, pluripotent states, and points to the importance of DNA and chromatin modification processes in regulating pluripotency. In addition, by integrating our data with existing pluripotency networks, we provide detailed maps of protein networks that regulate pluripotency that will further both the fundamental understanding of pluripotency as well as efforts to reliably control the differentiation of these cells into functional cell fates.

ROCS: a reproducibility index and confidence score for interaction proteomics studies

Background

Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones.

Methods

To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Prey Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Prey Proteins account for measures of protein identifiability as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures.

Results

We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions, or in combination with other AP-MS scoring methods to significantly improve inferences.

Conclusions

Our method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called “ROCS”, freely available from the CRAN repository http://cran.r-project.org/.

Human biomarker discovery and predictive models for disease progression for idiopathic pneumonia syndrome following allogeneic stem cell transplantation

Allogeneic hematopoietic stem cell transplantation (SCT) is the only curative therapy for many malignant and nonmalignant conditions. Idiopathic pneumonia syndrome (IPS) is a frequently fatal complication that limits successful outcomes. Preclinical models suggest that IPS represents an immune mediated attack on the lung involving elements of both the adaptive and the innate immune system. However, the etiology of IPS in humans is less well understood. To explore the disease pathway and uncover potential biomarkers of disease, we performed two separate label-free, proteomics experiments defining the plasma protein profiles of allogeneic SCT patients with IPS. Samples obtained from SCT recipients without complications served as controls. The initial discovery study, intended to explore the disease pathway in humans, identified a set of 81 IPS-associated proteins. These data revealed similarities between the known IPS pathways in mice and the condition in humans, in particular in the acute phase response. In addition, pattern recognition pathways were judged to be significant as a function of development of IPS, and from this pathway we chose the lipopolysaccaharide-binding protein (LBP) protein as a candidate molecular diagnostic for IPS, and verified its increase as a function of disease using an ELISA assay. In a separately designed study, we identified protein-based classifiers that could predict, at day 0 of SCT, patients who: 1) progress to IPS and 2) respond to cytokine neutralization therapy. Using cross-validation strategies, we built highly predictive classifier models of both disease progression and therapeutic response. In sum, data generated in this report confirm previous clinical and experimental findings, provide new insights into the pathophysiology of IPS, identify potential molecular classifiers of the condition, and uncover a set of markers potentially of interest for patient stratification as a basis for individualized therapy.

DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

Background

High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.

Results

We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes.

Conclusions

These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/.

The bait compatibility index: computational bait selection for interaction proteomics experiments

Protein interaction network maps have been generated for multiple species, making use of large-scale methods such as yeast two-hybrid (Y2H) and affinity purification mass spectrometry (AP-MS). These methods take fundamentally different approaches toward characterizing protein networks, and the resulting data sets provide complementary views of the protein interactome. The specific determinants of the outcome of Y2H and AP-MS experiments, in terms of detection of interacting proteins are, however, poorly understood. Here we show that a statistical model built using sequence- and annotation-based features of bait proteins is able to identify bait features that are significant determinants of the outcome of interaction proteomics experiments. We show that bait features are able to explain in part the disparities observed between Y2H and AP-MS constructed networks and can be used to derive the "bait compatibility index", a numeric score that assesses the compatibility of bait proteins with each technology. Aside from understanding the bias and limitations of interaction proteomics, our approach provides a rational, data-driven method for prioritization of baits for interaction proteomics experiments, an essential requirement for future proteome-wide applications of these technologies.

Urinary protein profiles in a rat model for diabetic complications

Diabetes mellitus is estimated to affect approximately 24 million people in the United States and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which can be delayed by early diagnosis and treatment. Although assays for diabetes are well founded, tests for its complications lack sufficient specificity and sensitivity to adequately guide these treatment options. In our study, we employed a streptozotocin-induced rat model of diabetes to determine changes in urinary protein profiles that occur during the initial response to the attendant hyperglycemia (e.g. the first two months) with the goal of developing a reliable and reproducible method of analyzing multiple urine samples as well as providing clues to early markers of disease progression. After filtration and buffer exchange, urinary proteins were digested with a specific protease, and the relative amounts of several thousand peptides were compared across rat urine samples representing various times after administration of drug or sham control. Extensive data analysis, including imputation of missing values and normalization of all data was followed by ANOVA analysis to discover peptides that were significantly changing as a function of time, treatment and interaction of the two variables. The data demonstrated significant differences in protein abundance in urine before observable pathophysiological changes occur in this animal model and as function of the measured variables. These included decreases in relative abundance of major urinary protein precursor and increases in pro-alpha collagen, the expression of which is known to be regulated by circulating levels of insulin and/or glucose. Peptides from these proteins represent potential biomarkers, which can be used to stage urogenital complications from diabetes. The expression changes of a pro-alpha 1 collagen peptide was also confirmed via selected reaction monitoring.

Large-scale mapping of human protein-protein interactions by mass spectrometry

Mapping protein–protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein–protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.

Expression profile analysis of the low-oxygen response in Arabidopsis root cultures

We used DNA microarray technology to identify genes involved in the low-oxygen response of Arabidopsis root cultures. A microarray containing 3500 cDNA clones was screened with cDNA samples taken at various times (0.5, 2, 4, and 20 h) after transfer to low-oxygen conditions. A package of statistical tools identified 210 differentially expressed genes over the four time points. Principal component analysis showed the 0.5-h response to contain a substantially different set of genes from those regulated differentially at the other three time points. The differentially expressed genes included the known anaerobic proteins as well as transcription factors, signal transduction components, and genes that encode enzymes of pathways not known previously to be involved in low-oxygen metabolism. We found that the regulatory regions of genes with a similar expression profile contained similar sequence motifs, suggesting the coordinated transcriptional control of groups of genes by common sets of regulatory factors.