Protein Structure Prediction for Disease-Related Insertions/Deletions in Presenilin 1 Gene.

More than 450 mutations, some of which have unknown toxicity, have been reported in the presenilin 1 gene, which is the most common cause of Alzheimer's disease (AD) with an early onset. PSEN1 mutations are thought to be responsible for approximately 80% of cases of monogenic AD, which are characterized by complete penetrance and an early age of onset. It is still unknown exactly how mutations in the presenilin 1 gene can cause dementia and neurodegeneration; however, both conditions have been linked to these changes. In this chapter, well-known computational analysis servers and accessible databases such as Uniprot, iTASSER, and PDBeFold are examined for their ability to predict the functional domains of mutant proteins and quantify the effect that these mutations have on the three-dimensional structure of the protein.

Computational and Functional Insights of Protein Misfolding in Neurodegeneration.

Protein folding is the process by which a polypeptide chain self-assembles into the correct three-dimensional structure, so that it ends up in the biologically active, native state. Under conditions of proteotoxic stress, mutations, or cellular aging, proteins can begin to aggregate into non-native structures such as ordered amyloid fibrils and plaques. Many neurodegenerative diseases involve the misfolding and aggregation of specific proteins into abnormal, toxic species. Experimental approaches including crystallography and AFM (atomic force microscopy)-based force spectroscopy are used to exploit the folding and structural characterization of protein molecules. At the same time, computational techniques through molecular dynamics, fold recognition, and structure prediction are widely applied in this direction. Benchmarking analysis for combining and comparing computational methodologies with functional studies can decisively unravel robust interactions between the side groups of the amino acid sequence and monitor alterations in intrinsic protein dynamics with high precision as well as adequately determine potent conformations of the folded patterns formed in the polypeptide structure.

Computational Methods for Protein Tertiary Structure Analysis.

Protein folding accuracy is fundamental to all cells. In spite of this, it is difficult to maintain the fidelity of protein synthesis and folding due to the fact that the implicit genetic and biochemical systems are inherently prone to error, which leads to the constant production of a certain amount of misfolded proteins. This problem is further compounded by genetic variation and the effects of environmental stress. To that end, the prediction of protein structures for tertiary protein structure analysis and prediction might be an ideal approach for the study of mutation effects in macromolecules and their complexes. With the development and accessibility to increasingly powerful computational systems, this type of study will enable a wide variety of opportunities for the creation of better-targeted peptide-based pharmacotherapy and prospects for precision medicine in future.

In Silico Structural Analysis Predicting the Pathogenicity of PLP1 Mutations in Multiple Sclerosis.

The X chromosome gene PLP1 encodes myelin proteolipid protein (PLP), the most prevalent protein in the myelin sheath surrounding the central nervous system. X-linked dysmyelinating disorders such as Pelizaeus-Merzbacher disease (PMD) or spastic paraplegia type 2 (SPG2) are typically caused by point mutations in PLP1. Nevertheless, numerous case reports have shown individuals with PLP1 missense point mutations which also presented clinical symptoms and indications that were consistent with the diagnostic criteria of multiple sclerosis (MS), a disabling disease of the brain and spinal cord with no current cure. Computational structural biology methods were used to assess the impact of these mutations on the stability and flexibility of PLP structure in order to determine the role of PLP1 mutations in MS pathogenicity. The analysis showed that most of the variants can alter the functionality of the protein structure such as R137W variants which results in loss of helix and H140Y which alters the ordered protein interface. In silico genomic methods were also performed to predict the significance of these mutations associated with impairments in protein functionality and could suggest a better definition for therapeutic strategies and clinical application in MS patients.

Handling the Cellular Complex Systems in Alzheimer's Disease Through a Graph Mining Approach.

In the last two decades, the medical sciences have changed their approach to pathogenesis as well as to the diagnosis and treatment of complex human diseases. The main reason for this change is the explosive development of biomedical technology and research, which produces a huge amount of information and data which are generated at an increasing rate. Toward this direction is the pathway analysis, a thriving research area of systems biology tools and methodologies which aim to unravel the inherent complexity of high-throughput biological data produced by the advent of omics technologies. Through this graph mining approach, we can deal with the complexity of the cellular systems of various diseases such as Alzheimer's disease. In this work, we developed a subpathway analysis method for single-cell RNA-seq experiments which isolates differentially expressed subpathways indicating potentially perturbed biological processes. The differential expression status of each gene is negotiated among well-established RNA-seq differential expression analysis tools in order to minimize false discoveries. Also, we demonstrate the efficacy of our method on a single-cell RNA-seq dataset for temporal tracking of microglia activation in neurodegeneration. Results suggest that our approach succeeds in isolating several perturbed biological processes known to be associated with neurodegeneration.

Emerging Machine Learning Techniques for Modelling Cellular Complex Systems in Alzheimer's Disease.

We live in the big data era in the biomedical field, where machine learning has a very important contribution to the interpretation of complex biological processes and diseases, since it has the potential to create predictive models from multidimensional data sets. Part of the application of machine learning in biomedical science is to study and model complex cellular systems such as biological networks. In this context, the study of complex diseases, such as Alzheimer's diseases (AD), benefits from established methodologies of network science and machine learning as they offer algorithmic tools and techniques that can address the limitations and challenges of modeling and studying cellular AD-related networks. In this paper we analyze the opportunities and challenges at the intersection of machine learning and network biology and whether this can affect the biological interpretation and clarification of diseases. Specifically, we focus on GRN techniques which through omics data and the use of machine learning techniques can construct a network that captures all the information at the molecular level for the disease under study. We record the emerging machine learning techniques that are focus on ensemble tree-based techniques in the area of classification and regression. Their potential for unraveling the complexity of model cellular systems in complex diseases, such as AD, offers the opportunity for novel machine learning methodologies to decipher the mechanisms of the various AD processes.

Buccal Mucosa Biomarkers in Alzheimer's Disease.

Alzheimer's disease (AD) precipitation in the elderly population increases the need for sensitive biomarkers that can be applied to large population screening. Buccal cells can be obtained easily, noninvasively, and contain many proteins related to cerebral processes. Hence, they offer an ideal candidate for AD biomarker discovery. The purpose of this study is to provide an overview of the current research landscape covering both clinical and methodological issues. A brief summary is given on related laboratory techniques to ascertain protein concentration changes due to AD. At the end, we describe a protocol designed in our laboratory for disease early diagnosis.

Alzheimer's Disease: The Role of Mutations in Protein Folding.

Misfolded proteins result when a protein follows the wrong folding pathway. Accumulation of misfolded proteins can cause disorders, known as amyloid diseases. Unfortunately, some of them are very common. The most prevalent one is Alzheimer's disease. Alzheimer's disease is a neurodegenerative disorder and the commonest form of dementia. The current study aims to assess the impact of somatic mutations in PSEN1 gene. The said mutations are the most common cause of familial Alzheimer's disease. As protein functionality can be affected by mutations, the study of possible alterations in the tertiary structure of proteins may reveal new insights related to the relationship between mutations and protein functions. To examine the effect of mutations, the primary structures and their related mutations were retrieved from public databases. Each structure (mutated and unmutated) was predicted based on effective structure prediction methodologies. A benchmarking of the structural predictive tools was accomplished. Comparative analyses of mutated and unmutated proteins were performed based on classic bioinformatics methods (TM-Score, RMSD, etc.) as well as on established shape-based descriptors retrieved from object recognition methodologies. Unsupervised methodologies were applied to the structures, in order to identify groups of mutation with similar mutational impact. Our results provide an essential knowledge toward protein's functionality in structure-based drug design.

Validation of Modeling and Simulation Methods in Computational Biology.

In recent years, a highly sophisticated array of modeling and simulation tools in all areas of biological and biomedical research has been developed. These tools have the potential to provide new insights into biological mechanisms integrating subcellular, cellular, tissue, organ, and potentially whole organism levels. Current research is focused on how to use these methods for translational medical research, such as for disease diagnosis and understanding, as well as drug discovery. In addition, these approaches enhance the ability to use human-derived data and to contribute to the refinement of high-cost experimental-based research. Additionally, the conflicting conceptual frameworks and conceptions of modeling and simulation methods from the broad public of users could have a significant impact on the successful implementation of aforementioned applications. This in turn could result in successful collaborations across academic, clinical, and industrial sectors. To that end, this study provides an overview of the frameworks and disciplines used for validation of computational methodologies in biomedical sciences.

The Use of Translational Research Platforms in Clinical and Biomedical Data Exploration.

The rise of precision medicine combined with the variety of biomedical data sources and their heterogeneous nature make the integration and exploration of information that they retain more complicated. In light of these issues, translational research platforms were developed as a promising solution. Research centers have used translational tools for the study of integrated data for hypothesis development and validation, cohort discovery and data-exploration. For this article, we reviewed the literature in order to determine the use of translational research platforms in precision medicine. These tools are used to support scientists in various domains regarding precision medicine research. We identified eight platforms: BRISK, iCOD, iDASH, tranSMART, the recently developed OncDRS, as well as caTRIP, cBio Cancer Portal and G-DOC. The last four platforms explore multidimensional data specifically for cancer research. We focused on tranSMART, for it is the most broadly used platform, since its development in 2012.

Integrating Omic Technologies in Alzheimer's Disease.

Scientific advances in biomedical disciplines have allowed us to identify the underlying causes of many diseases with increased comprehension-leading the way towards precision medicine. In this context, unique disease and medical traits pave the way for the development of adapted disease management, drugs and therapies tailored to each patient. Bearing in mind that reductionism, an approach that has dominated biomedical research for many years and has resulted in the identification of definite cellular phenotypes and human diseases which are linked with specific integral molecules, we strongly believe that Alzheimer's Disease, one of the most common neurodegenerative diseases, could not be applied to the model of one disease-one assay-one drug. Regarding the discrete complexities in the molecular pathogenesis combined with the limited knowledge of inherited and sporadic forms of Alzheimer's disease, the great heterogeneity in the clinical development, as well as the plethora of validated biomarkers that have been proposed for early diagnosis or prognosis of the disease, we presume that a radically different way of thinking is in demand for comprehensive explanations of the molecular pathogenesis of the disease. In this article we highlight the most recent advances made in the omics field of systems biology towards a more complete understanding of Alzheimer's disease mechanisms, emphasizing to the paramount emergence of the development of various high-throughput strategies applied to the omics sciences.

Scheduling and Modeling a Cognitive Assessment Guide for Screening AD by Primary Care Physicians.

New research data on Alzheimer's Disease define it as a clinicobiological entity, which has a long preclinical and presymptomatic phase. Emphasis has been given in early detection and diagnosis, which will allow professionals, caregivers and patients themselves to plan and adjust better to the response of the disease. Primary care physicians, who most often are the first to witness and perceive cognitive impairment, may play a central role in the diagnostic procedure, but frequently they are reluctant to be engaged to the screening procedure. The aim of this study is to model a practical guideline for mental assessment and screening, which will be part of a whole step-to-step medical instruction policy for primary care physicians. After a careful review of the literature, we propose a two-visits approach. This approach combines the measures to be administered in each visit, with a detailed list of close-ended questions on the factors concerning Alzheimer's screening. The tests are automatically available to the physician through hyperlink connection and the scores are immediately calculated. Clinical trials will follow to test the validity of the proposed guidance.

Methylation status of RASSF1A in patients with chronic myeloid leukemia.

RASSF1A, a key cell cycle related gene, is expressed in all hematopoietic cells, it is implicated in ras signaling pathway and its promoter hypermethylation is observed in a wide variety of solid tumors. Till now, RASSF1A methylation status has not been investigated in patients with chronic myeloid leukemia (CML). In this study, we analyzed 41 patients carrying the BCR-ABL rearrangement, in different stages of the disease. No patient displayed RASSF1A promoter methylation, although the K562 erythroleukemia cell line, bearing the BCR-ABL rearrangement, was found methylated. Thus, our findings indicate that RASSF1A methylation does not appear to represent a critical step in the pathogenesis and/or the progression of CML.