349 research outputs found

    Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism

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    Motivation: Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict cellular phenotype. During the process of gene expression, 95% of human genes can code for multiple proteins due to alternative splicing. While most splice variants of a gene carry the same function, variants within some key genes can have remarkably different roles. To bridge the gap between genotype and phenotype, condition- and tissue-specific models of metabolism have been constructed. However, current metabolic models only include information at the gene level. Consequently, as recently acknowledged by the scientific community, common situations where changes in splice-isoform expression levels alter the metabolic outcome cannot be modeled. Results: We here propose GEMsplice, the first method for the incorporation of splice-isoform expression data into genome-scale metabolic models. Using GEMsplice, we make full use of RNA-Seq quantitative expression profiles to predict, for the first time, the effects of splice isoform-level changes in the metabolism of 1455 patients with 31 different breast cancer types. We validate GEMsplice by generating cancer-versus-normal predictions on metabolic pathways, and by comparing with gene-level approaches and available literature on pathways affected by breast cancer. GEMsplice is freely available for academic use at https://github.com/GEMsplice/GEMsplice_code. Compared to state-of-the-art methods, we anticipate that GEMsplice will enable for the first time computational analyses at transcript level with splice-isoform resolution

    Multimodal regularised linear models with flux balance analysis for mechanistic integration of omics data

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    Motivation: High-throughput biological data, thanks to technological advances, have become cheaper to collect, leading to the availability of vast amounts of omic data of different types. In parallel, the in silico reconstruction and modeling of metabolic systems is now acknowledged as a key tool to complement experimental data on a large scale. The integration of these model- and data-driven information is therefore emerging as a new challenge in systems biology, with no clear guidance on how to better take advantage of the inherent multisource and multiomic nature of these data types while preserving mechanistic interpretation. Results: Here, we investigate different regularization techniques for high-dimensional data derived from the integration of gene expression profiles with metabolic flux data, extracted from strain-specific metabolic models, to improve cellular growth rate predictions. To this end, we propose ad-hoc extensions of previous regularization frameworks including group, view-specific and principal component regularization and experimentally compare them using data from 1143 Saccharomyces cerevisiae strains. We observe a divergence between methods in terms of regression accuracy and integration effectiveness based on the type of regularization employed. In multiomic regression tasks, when learning from experimental and model-generated omic data, our results demonstrate the competitiveness and ease of interpretation of multimodal regularized linear models compared to data-hungry methods based on neural networks. Availability and implementation: All data, models and code produced in this work are available on GitHub at https://github.com/Angione-Lab/HybridGroupIPFLasso_pc2Lasso. Supplementary information: Supplementary data are available at Bioinformatics online

    Mechanistic effects of influenza in bronchial cells through poly-omic genome-scale modelling

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    In this work we propose regularised bi-level constraint-based modelling to determine the fluxomic profiles for four different influenza viruses, H7N9, H7M7, H3N2 and H5N1. We report here the first step of the analysis of the flux data usingAutoSOME clustering, where we identify novel biomarkers of infection. This is a work in progress that can directly lead to novel therapeutic targets

    Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods

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    Background: Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries. Even though some studies have been conducted to understand the causes of its onset and discover potential biomarkers, the role of metabolic rewiring has not been investigated in depth so far.Methods: Here, we propose and implement an interpretable multi-omics pipeline that combines mechanis-tic knowledge from genome-scale metabolic models with machine learning algorithms, and we use it to characterise the underlying mechanisms controlling hepatoblastoma.Results and Conclusions: While the obtained machine learning models generally present a high diagnostic classification accuracy, our results show that the type of omics combinations used as input to the machine learning models strongly affects the detection of important genes, reactions and metabolic pathways linked to hepatoblastoma. Our method also suggests that, in the context of computer-aided diagnosis of cancer, optimal diagnostic accuracy can be achieved by adopting a combination of omics that depends on the patient's clinical characteristics

    A NOVEL COMPUTATIONAL FRAMEWORK FOR TRANSCRIPTOME ANALYSIS WITH RNA-SEQ DATA

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    The advance of high-throughput sequencing technologies and their application on mRNA transcriptome sequencing (RNA-seq) have enabled comprehensive and unbiased profiling of the landscape of transcription in a cell. In order to address the current limitation of analyzing accuracy and scalability in transcriptome analysis, a novel computational framework has been developed on large-scale RNA-seq datasets with no dependence on transcript annotations. Directly from raw reads, a probabilistic approach is first applied to infer the best transcript fragment alignments from paired-end reads. Empowered by the identification of alternative splicing modules, this framework then performs precise and efficient differential analysis at automatically detected alternative splicing variants, which circumvents the need of full transcript reconstruction and quantification. Beyond the scope of classical group-wise analysis, a clustering scheme is further described for mining prominent consistency among samples in transcription, breaking the restriction of presumed grouping. The performance of the framework has been demonstrated by a series of simulation studies and real datasets, including the Cancer Genome Atlas (TCGA) breast cancer analysis. The successful applications have suggested the unprecedented opportunity in using differential transcription analysis to reveal variations in the mRNA transcriptome in response to cellular differentiation or effects of diseases

    The effect of hypoxia on alternative splicing in prostate cancer cell lines

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    Hypoxia is defined as the state in which the availability or delivery of oxygen is insufficient to meet tissue demand. It occurs particularly in aggressive, fast-growing tumours in which the rate of new blood vessel formation (angiogenesis) cannot match the growth rate of tumour cells. Cellular stresses such as hypoxia can cause cells to undergo apoptosis; however some tumour cells adapt to hypoxic conditions and evade apoptosis. Tumour hypoxia has been linked to poor prognosis and to greater resistance to existing cancer therapies. This thesis provides evidence that alterations in alternative splicing patterns of key genes is one method tumour cells adapt to hypoxia.This study confirms a hypoxic-induced change in the alternative splicing of carbonic anhydrase IX (CA IX) following 1% oxygen treatment. CA IX is one of the best studied hypoxia markers, involved in maintaining an intracellular pH that favours tumour cell growth. Furthermore, evidence is provided here that in PC3 cells the regulation of CA IX splicing involves the SAFB1 and PRPF8 splice factors. Additionally, SAFB1 expression is shown to decrease in hypoxia. This study further demonstrates that alternative splicing patterns of previously documented cancer-associated genes are altered in hypoxia. PCR analysis showed that hypoxia significantly altered the alternative splicing of apoptotic-associated genes: caspase-9; Mcl-1; Bcl-x; survivin. The expression of the pro-apoptotic isoforms of the first two genes, and the anti-apoptotic isoforms of the latter two genes were favoured by hypoxia. Furthermore, high-throughput PCR analysis provided evidence of significant changes in the alternative splicing of several other cancer-associated genes in hypoxia: APAF1; BTN2A2; CDC42BPA; FGFR1OP; MBP; PTPN13; PUF60; RAP1GDS1; TTC23; UTRN. Most notably, the pro-oncogenic isoforms of APAF1, BTN2A2 and RAP1GDS1 were favoured in hypoxia. The majority of alternative splicing changes were found in the PC3 cell line. However changes in alternative splicing patterns that mirrored those in the PC3 cell line were also found in the VCaP (CDC42BPA, RAP1GDS1 and UTRN) and PNT2 (BTN2A2, CDC42BPA, FGFR1OP and TTC23) cell lines. The mRNA expression of splice factors (SRSF1, SRSF2, SRSF3, SAM68, HuR and hnRNP A1) and splice factor kinases (CLK1 and SRPK1) were shown to significantly increase in hypoxia. Subsequent experiments provided evidence that CLK1 and SRSF1 protein expression also increased in hypoxia. The phosphorylation of SRSF4 and SRSF5 were demonstrated to increase in hypoxia. However, the phosphorylation of SRSF6 was not. In addition, siRNAs and chemical inhibitors of CLK1 (TG003) and SRPK1 (SPHINX) were used to assess the effect of these splice factor kinases on the subsequent splicing of cancer-associated genes. There were no significant changes to splicing found with SRPK1 siRNA knockdown or SPHINX treatment. However CLK1 siRNA knockdown and TG003 treatment demonstrated a shift in FGFR1OP splicing that mirrored the effect of hypoxia on FGFR1OP splicing. This suggests that CLK1 activity is inhibited in hypoxia. Furthermore, in contrast to previous research CLK1 was found to be localised to the cytoplasm in both normoxia and hypoxia in the PC3 cell line. This work has uncovered factors and provided an insight into mechanisms that are involved in alternative splicing changes in hypoxia in mammalian cell lines. It is hoped that these novel research findings will aid in the understanding of how cells adapt to hypoxia especially in regards to alternative splicing, and may offer future therapeutic targets in hypoxic tumours

    Preferential accumulation of Foxp3E2+ regulatory T cells with highly immunosuppressive phenotype in breast cancer subjects

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    Regulatory T CD4+Foxp3+ (Treg) cells are a cellular subset involved in the maintenance of immune self-tolerance and homeostasis but, as a double-edged sword, they can also suppress anti-tumor immune response and favor tumor progression. Therefore, Foxp3+ Treg cells represent a primary target for cancer immunotherapy, which finally aims at restoring the ability of the immune system to detect and destroy cancer cells. The tumor microenvironment has been reported to contain a "rich milieu" of molecules able to increase the recruitment of Foxp3+ Treg cells to the tumor site. Compelling experimental evidence has shown an increased percentage of Foxp3+ Treg cells in the tumor microenvironment of subjects with different tumors, including breast cancer (BC). Moreover, their abundant presence in tumor infiltrates leads to reduced survival in cancer subjects and inversely correlates with clinical response of BC to therapy. The transcription factor Foxp3 plays a critical role in regulating the development and the immunosuppressive function of Treg cells and up to 8 different Foxp3 splicing variants have been described in human subjects, but their role and function still remain elusive. Recently, it has been found that among all the different Foxp3 splicing forms, those containing the exon2 (Foxp3E2) are necessary for the induction and establishment of the suppressive phenotype of Treg cells. The aim of this thesis was to evaluate the role of Foxp3E2+ Treg cells in the context of tumor growth, dissecting whether increased immunosuppression observed in BC subjects, could be secondary to the preferential accumulation of Foxp3E2+ Treg cells. In conclusion, the evaluation of the number of Foxp3E2+ Treg cells in BC tumors could represent a prognostic assay for the assessment of tumor progression, severity and prognosis. In addition, Foxp3E2+ Treg cells could be pharmacological targeted in order to inhibit their immunosuppressive activity in the tumor microenvironment, thus sustaining anti-tumor immune response and reducing tumor progression
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