Principles for the post-GWAS functional characterisation of risk loci

Several challenges lie ahead in assigning functionality to susceptibility SNPs. For example, most effect sizes are small relative to effects seen in monogenic diseases, with per allele odds ratios usually ranging from 1.15 to 1.3. It is unclear whether current molecular biology methods have enough resolution to differentiate such small effects. Our objective here is therefore to provide a set of recommendations to optimize the allocation of effort and resources in order maximize the chances of elucidating the functional contribution of specific loci to the disease phenotype. It has been estimated that 88% of currently identified disease-associated SNP are intronic or intergenic. Thus, in this paper we will focus our attention on the analysis of non-coding variants and outline a hierarchical approach for post-GWAS functional studies

Over-optimism in bioinformatics: an illustration

In statistical bioinformatics research, different optimization mechanisms potentially lead to "over-optimism" in published papers. The present empirical study illustrates these mechanisms through a concrete example from an active research field. The investigated sources of over-optimism include the optimization of the data sets, of the settings, of the competing methods and, most importantly, of the method’s characteristics. We consider a "promising" new classification algorithm that turns out to yield disappointing results in terms of error rate, namely linear discriminant analysis incorporating prior knowledge on gene functional groups through an appropriate shrinkage of the within-group covariance matrix. We quantitatively demonstrate that this disappointing method can artificially seem superior to existing approaches if we "fish for significance”. We conclude that, if the improvement of a quantitative criterion such as the error rate is the main contribution of a paper, the superiority of new algorithms should be validated using "fresh" validation data sets

Network-based methods for biological data integration in precision medicine

[eng] The vast and continuously increasing volume of available biomedical data produced during the last decades opens new opportunities for large-scale modeling of disease biology, facilitating a more comprehensive and integrative understanding of its processes. Nevertheless, this type of modelling requires highly efficient computational systems capable of dealing with such levels of data volumes. Computational approximations commonly used in machine learning and data analysis, namely dimensionality reduction and network-based approaches, have been developed with the goal of effectively integrating biomedical data. Among these methods, network-based machine learning stands out due to its major advantage in terms of biomedical interpretability. These methodologies provide a highly intuitive framework for the integration and modelling of biological processes. This PhD thesis aims to explore the potential of integration of complementary available biomedical knowledge with patient-specific data to provide novel computational approaches to solve biomedical scenarios characterized by data scarcity. The primary focus is on studying how high-order graph analysis (i.e., community detection in multiplex and multilayer networks) may help elucidate the interplay of different types of data in contexts where statistical power is heavily impacted by small sample sizes, such as rare diseases and precision oncology. The central focus of this thesis is to illustrate how network biology, among the several data integration approaches with the potential to achieve this task, can play a pivotal role in addressing this challenge provided its advantages in molecular interpretability. Through its insights and methodologies, it introduces how network biology, and in particular, models based on multilayer networks, facilitates bringing the vision of precision medicine to these complex scenarios, providing a natural approach for the discovery of new biomedical relationships that overcomes the difficulties for the study of cohorts presenting limited sample sizes (data-scarce scenarios). Delving into the potential of current artificial intelligence (AI) and network biology applications to address data granularity issues in the precision medicine field, this PhD thesis presents pivotal research works, based on multilayer networks, for the analysis of two rare disease scenarios with specific data granularities, effectively overcoming the classical constraints hindering rare disease and precision oncology research. The first research article presents a personalized medicine study of the molecular determinants of severity in congenital myasthenic syndromes (CMS), a group of rare disorders of the neuromuscular junction (NMJ). The analysis of severity in rare diseases, despite its importance, is typically neglected due to data availability. In this study, modelling of biomedical knowledge via multilayer networks allowed understanding the functional implications of individual mutations in the cohort under study, as well as their relationships with the causal mutations of the disease and the different levels of severity observed. Moreover, the study presents experimental evidence of the role of a previously unsuspected gene in NMJ activity, validating the hypothetical role predicted using the newly introduced methodologies. The second research article focuses on the applicability of multilayer networks for gene priorization. Enhancing concepts for the analysis of different data granularities firstly introduced in the previous article, the presented research provides a methodology based on the persistency of network community structures in a range of modularity resolution, effectively providing a new framework for gene priorization for patient stratification. In summary, this PhD thesis presents major advances on the use of multilayer network-based approaches for the application of precision medicine to data-scarce scenarios, exploring the potential of integrating extensive available biomedical knowledge with patient-specific data

HOXD8 exerts a tumor-suppressing role in colorectal cancer as an apoptotic inducer

Homeobox (HOX) genes are conserved transcription factors which determine the anterior-posterior body axis patterning. HOXD8 is a member of HOX genes deregulated in several tumors such as lung carcinoma, neuroblastoma, glioma and colorectal cancer (CRC) in a context-dependent manner. In CRC, HOXD8 is downregulated in cancer tissues and metastatic foci as compared to normal tissues. Whether HOXD8 acts as a tumor suppressor of malignant progression and metastasis is still unclear. Also, the underlying mechanism of its function including the downstream targets is totally unknown. Here, we clarified the lower expression of HOXD8 in clinical colorectal cancer vs. normal colon tissues. Also, we showed that stable expression of HOXD8 in colorectal cancer cells significantly reduced the cell proliferation, anchorage-independent growth and invasion. Further, using The Cancer Genome Atlas (TCGA), we identified the genes associated with HOXD8 in order to demonstrate its function as a suppressor or a promoter of colorectal carcinoma. Among inversely related genes, apoptotic inhibitors like STK38 kinase and MYC were shown to be negatively associated with HOXD8. We demonstrated the ability of HOXD8 to upregulate executioner caspases 6 & 7 and cleaved PARP, thus inducing the apoptotic events in colorectal cancer cells

Main findings and advances in bioinformatics and biomedical engineeringIWBBIO 2018

We want to thank the great work done by the reviewers of each of the papers, together with the great interest shown by the editorial of BMC Bioinformatics in IWBBIO Conference. Special thanks to D. Omar El Bakry for his interest and great help to make this Special Issue. Thank the Ministry of Spain for the economic resources within the project with reference RTI2018-101674-B-I00.In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.This research has been partially supported by the proyects with reference RTI2018-101674-B-I00 (Ministry of Spain) and B-TIC-414-UGR18 (FEDER, Junta Andalucia and UGR)

How to study basement membrane stiffness as a biophysical trigger in prostate cancer and other age-related pathologies or metabolic diseases

Here we describe a protocol that can be used to study the biophysical microenvironment related to increased thickness and stiffness of the basement membrane (BM) during age-related pathologies and metabolic disorders (e.g. cancer, diabetes, microvascular disease, retinopathy, nephropathy and neuropathy). The premise of the model is non-enzymatic crosslinking of reconstituted BM (rBM) matrix by treatment with glycolaldehyde (GLA) to promote advanced glycation endproduct (AGE) generation via the Maillard reaction. Examples of laboratory techniques that can be used to confirm AGE generation, non-enzymatic crosslinking and increased stiffness in GLA treated rBM are outlined. These include preparation of native rBM (treated with phosphate-buffered saline, PBS) and stiff rBM (treated with GLA) for determination of: its AGE content by photometric analysis and immunofluorescent microscopy, its non-enzymatic crosslinking by ((sodium dodecyl sulfate polyacrylamide gel electrophoresis)) (SDS PAGE) as well as confocal microscopy, and its increased stiffness using rheometry. The procedure described here can be used to increase the rigidity (elastic moduli, E) of rBM up to 3.2-fold, consistent with measurements made in healthy versus diseased human prostate tissue. To recreate the biophysical microenvironment associated with the aging and diseased prostate gland three prostate cell types were introduced on to native rBM and stiff rBM: RWPE-1, prostate epithelial cells (PECs) derived from a normal prostate gland; BPH-1, PECs derived from a prostate gland affected by benign prostatic hyperplasia (BPH); and PC3, metastatic cells derived from a secondary bone tumor originating from prostate cancer. Multiple parameters can be measured, including the size, shape and invasive characteristics of the 3D glandular acini formed by RWPE-1 and BPH-1 on native versus stiff rBM, and average cell length, migratory velocity and persistence of cell movement of 3D spheroids formed by PC3 cells under the same conditions. Cell signaling pathways and the subcellular localization of proteins can also be assessed

Bayesian network structure learning using characteristic properties of permutation representations with applications to prostate cancer treatment.

Over the last decades, Bayesian Networks (BNs) have become an increasingly popular technique to model data under presence of uncertainty. BNs are probabilistic models that represent relationships between variables by means of a node structure and a set of parameters. Learning efficiently the structure that models a particular dataset is a NP-hard task that requires substantial computational efforts to be successful. Although there exist many families of techniques for this purpose, this thesis focuses on the study and improvement of search and score methods such as Evolutionary Algorithms (EAs). In the domain of BN structure learning, previous work has investigated the use of permutations to represent variable orderings within EAs. In this thesis, the characteristic properties of permutation representations are analysed and used in order to enhance BN structure learning. The thesis assesses well-established algorithms to provide a detailed analysis of the difficulty of learning BN structures using permutation representations. Using selected benchmarks, rugged and plateaued fitness landscapes are identified that result in a loss of population diversity throughout the search. The thesis proposes two approaches to handle the loss of diversity. First, the benefits of introducing the Island Model (IM) paradigm are studied, showing that diversity loss can be significantly reduced. Second, a novel agent-based metaheuristic is presented in which evolution is based on the use of several mutation operators and the definition of a distance metric in permutation spaces. The latter approach shows that diversity can be maintained throughout the search while exploring efficiently the solution space. In addition, the use of IM is investigated in the context of distributed data, a common property of real-world problems. Experiments prove that privacy can be preserved while learning BNs of high quality. Finally, using UK-wide data related to prostate cancer patients, the thesis assesses the general suitability of BNs alongside the proposed learning approaches for medical data modeling. Following comparisons with tools currently used in clinical settings and with alternative classifiers, it is shown that BNs can improve the predictive power of prostate cancer staging tools, a major concern in the field of urology

Characterisation of Collagen Re-Modelling in Localised Prostate Cancer Using Second-Generation Harmonic Imaging and Transrectal Ultrasound Shear Wave Elastography

Prostate cancer has a poor prognosis and high mortality rate due to metastases. Extracellular matrix (ECM) re-modelling and stroma composition have been linked to cancer progression, including key components of cell migration, tumour metastasis, and tissue modulus. Moreover, collagens are one of the most significant components of the extracellular matrix and have been ascribed to many aspects of neoplastic transformation. This study characterises collagen re-modelling around localised prostate cancer using the second harmonic generation of collagen (SHG), genotyping and ultrasound shear wave elastography (USWE) measured modulus in men with clinically localised prostate cancer. Tempo-sequence assay for gene expression of COL1A1 and COL3A1 was used to confirm the expression of collagen. Second-harmonic generation imaging and genotyping of ECM around prostate cancer showed changes in content, orientation, and type of collagen according to Gleason grades (cancer aggressivity), and this correlated with the tissue modulus measured by USWE in kilopascals. Furthermore, there were clear differences between collagen orientation and type around normal and cancer tissues