Hepatitis C virus infection and Parkinson's disease: insights from a joint sex-stratified BioOptimatics meta-analysis.

Hepatitis C virus (HCV) infection poses a significant public health challenge and often leads to long-term health complications and even death. Parkinson's disease (PD) is a progressive neurodegenerative disorder with a proposed viral etiology. HCV infection and PD have been previously suggested to be related. This work aimed to identify potential biomarkers and pathways that may play a role in the joint development of PD and HCV infection. Using BioOptimatics-bioinformatics driven by mathematical global optimization-, 22 publicly available microarray and RNAseq datasets for both diseases were analyzed, focusing on sex-specific differences. Our results revealed that 19 genes, including MT1H, MYOM2, and RPL18, exhibited significant changes in expression in both diseases. Pathway and network analyses stratified by sex indicated that these gene expression changes were enriched in processes related to immune response regulation in females and immune cell activation in males. These findings suggest a potential link between HCV infection and PD, highlighting the importance of further investigation into the underlying mechanisms and potential therapeutic targets involved.

A Cost Model for Neurological Diseases in Puerto Rico: Parkinson's Disease, Alzheimer's Disease and Huntington's Disease.

OBJECTIVE: This article proposes an engineering-economics model to determine the total cost of a neurological disease along its temporal progression. The objective was to develop a planning tool faithful to the reality of this type of ailment as well as to that of Puerto Rico (PR). METHODS: The proposed model organizes a given neurological disease into 3 progressive phases of deterioration; in each, the model collects the typical associated costs and adjusts them based on their value over time. This way, the total cost of the ailment is calculated and its present day dollar value expressed. Model verification was carried out using data from Puerto Rico related to Parkinson's, Alzheimer's, and Huntington's diseases. RESULTS: The method demonstrated here considered Parkinson's disease in PR. Our model calculated a total annual cost of $64,915 for a patient at the medium stage. This figure is larger than estimates from other authors, which fall between $41,689 and $51,600 for the USA. This difference is partially due to the proposed model considering the individual's opportunity cost of the loss of productive years, an original contribution of our work. CONCLUSION: A neurological disease is one in which an individual goes through progressive phases of deterioration that will require significant economic resources. The model proposed here is designed across the commonalities between Alzheimer's, Parkinson's, and Huntington's diseases and illustrated using costs from PR. As an additional contribution, it allows the consideration of the opportunity cost of lost productivity, a characteristic that makes it more realistic.

Individual emergency response and recovery: What people worried about during and after Hurricane Maria.

Hurricane Maria was a disastrous weather event that devastated Puerto Rico (PR) in September 2017. Yet, little is known about people's perceptions of this event. In this investigation, we offer insight about Hurricane Maria's impact on PR's inhabitants. More specifically, we study a sample (n = 542 responses) of individuals' worry levels through four time points during the aftermath of Hurricane Maria: their variation through time, their relationship to decision making, and if and how certain demographic variables may influence them. For these purposes, we designed and implemented the Individual Emergency Response and Recovery Questionnaire, a web-based survey that measured several aspects of the objective and subjective experiences of individuals who underwent Hurricane Maria in PR. Results of a statistical analysis using nonparametric tests show that some of the demographic variables selected as factors of interest influence the worry levels reported by respondents. Most significant results coincide with conclusions drawn by literature: that time, age group, and the level of information influence worry levels. Another key finding is that the worry level may influence individual decision-making frequency. Understanding principal factors in people's behavior and perceptions during hurricanes is crucial to help us learn how to better prepare for and respond to natural disasters in the future.

Potential colon cancer biomarker search using more than two performance measures in a multiple criteria optimization approach.

OBJECTIVE: A new method using Multiple Criteria Optimization (MCO) proposed by our research group has shown evidence of being able to identify gene-based biomarkers for the detection of cancer using microarray data. Herein, we explore this method, considering more than two conflicting criteria for the MCO problem. Using this method would result in stronger outcomes when using different results from microarray analyses. It would also demonstrate that the method is suitable for carrying out meta-analysis. METHODS: Statistical comparisons between normal and cancer tissues were performed using a colon cancer microarray database. The different comparisons were carried out with a Mann-Whitney non-parametric test using partial permutations of the data. An MCO problem was built using the different p-values obtained. The associated solution was the set of genes reaching the best compromises between the p-values under consideration that were located in the so-called efficient frontier. Data Envelopment Analysis (DEA) was used to find the efficient frontier of the MCO problem. The capacity of DEA was explored using different numbers of p-values (criteria) in the model. RESULTS: The set of identified genes was consistent across the instances using different numbers of p-values in the DEA model, thereby providing evidence of the outcome stability of the proposed strategy. It was also observed that convergence to a larger number of potential biomarkers is faster with additional criteria, i.e., more p-values. CONCLUSION: The MCO problem proposed for the cancer biomarker search using microarray data can be solved efficiently with DEA using more than two conflicting criteria. This approach can result in robust results when using different analyses of microarray data and, indeed, in a faster convergence to highly potential biomarkers.

Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio.

Identifying genes with the largest expression changes (gene selection) to characterize a given condition is a popular first step to drive exploration into molecular mechanisms and is, therefore, paramount for therapeutic development. Reproducibility in the sciences makes it necessary to emphasize objectivity and systematic repeatability in biological and informatics analyses, including gene selection. With these two characteristics in mind, in previous works our research team has proposed using multiple criteria optimization (MCO) in gene selection to analyze microarray datasets. The result of this effort is the MCO algorithm, which selects genes with the largest expression changes without user manipulation of neither informatics nor statistical parameters. Furthermore, the user is not required to choose either a preference structure among multiple measures or a predetermined quantity of genes to be deemed significant a priori. This implies that using the same datasets and performance measures (PMs), the method will converge to the same set of selected differentially expressed genes (repeatability) despite who carries out the analysis (objectivity). The present work describes the development of an open-source tool in RStudio to enable both: (1) individual analysis of single datasets with two or three PMs and (2) meta-analysis with up to five microarray datasets, using one PM from each dataset. The capabilities afforded by the code include license-free portability and the possibility to carry out analyses via modest computer hardware, such as personal laptops. The code provides affordable, repeatable, and objective detection of differentially expressed genes from microarrays. It can be used to analyze other experiments with similar experimental comparative layouts, such as microRNA arrays and protein arrays, among others. As a demonstration of the capabilities of the code, the analysis of four publicly-available microarray datasets related to Parkinson´s Disease (PD) is presented here, treating each dataset individually or as a four-way meta-analysis. These MCO-supported analyses made it possible to identify MMP9 and TUBB2A as potential PD genetic biomarkers based on their persistent appearance across each of the case studies. A literature search confirmed the importance of these genes in PD and indeed as PD biomarkers, which evidences the code´s potential.

Two novel nonparametric methods for cancer diagnosis through microarray analysis.

Diagnosing cancer using microarray analysis to study differential gene expression has been a recent focus of intense research Although several very sophisticated analysis tools have been developed with this aim in mind, it still remains a challenge to keep these methods free of parametric adjustments as well as maintain their transparency for the final user. Nonparametric methods in general have been associated with these last two characteristics, thus becoming attractive tools for microarray analysis in cancer research. In particular, diagnosing cancer via microarray analysis is an exercise whereby tissue is characterized according to its differential gene expression levels. In this manuscript, two novel nonparametric methods for cancer diagnosis using microarray data are described and their performance assessed against a baseline approach that utilizes the Mann-Whitney test for median differences. Both methods show promising results in terms of their potential use in making diagnoses.

Insight into the substrate length restriction of M32 carboxypeptidases: characterization of two distinct subfamilies.

M32 carboxypeptidases are a distinct family of HEXXH metalloproteases whose structures exhibit a narrow substrate groove that is blocked at one end. Structural alignments with other HEXXH metalloprotease-peptide complexes suggested an orientation in which the substrate is directed towards the back of the groove. This led us to hypothesize, and subsequently confirm that the maximum substrate length for M32 carboxypeptidases is restricted. Structural and sequence analyses implicate a highly conserved Arg at the back of the groove as being critical for this length restriction. However, the Thermus thermophilus and Bacillus subtilis M32 members lack this conserved Arg. Herein, we present the biochemical and structural characterization of these two proteins. Our findings support the important role of the conserved Arg in maintaining the length restriction, and reveal a proline-rich loop as an alternate blocking strategy. Based on our results, we propose that M32 carboxypeptidases from Bacilli belong to a separate subfamily.

A proposed role for Leishmania major carboxypeptidase in peptide catabolism.

Leishmaniasis is a tropical disease caused by Leishmania, eukaryotic parasites transmitted to humans by sand flies. Towards the development of new chemotherapeutic targets for this disease, biochemical and in vivo expression studies were performed on one of two M32 carboxypeptidases present within the Leishmania major (LmaCP1) genome. Enzymatic studies reveal that like previously studied M32 carboxypeptidases, LmaCP1 cleaves substrates with a variety of C-terminal amino acids--the primary exception being those having C-terminal acidic residues. Cleavage assays with a series of FRET-based peptides suggest that LmaCP1 exhibits a substrate length restriction, preferring peptides shorter than 9-12 amino acids. The in vivo expression of LmaCP1 was analyzed for each major stage of the L. major life cycle. These studies reveal that LmaCP1 expression occurs only in procyclic promastigotes--the stage of life where the organism resides in the abdominal midgut of the insect. The implications of these results are discussed.

A Selection of Important Genes and Their Correlated Behavior in Alzheimer's Disease.

In 2017, approximately 5 million Americans were living with Alzheimer's disease (AD), and it is estimated that by 2050 this number could increase to 16 million. In this study, we apply mathematical optimization to approach microarray analysis to detect differentially expressed genes and determine the most correlated structure among their expression changes. The analysis of GSE4757 microarray dataset, which compares expression between AD neurons without neurofibrillary tangles (controls) and with neurofibrillary tangles (cases), was casted as a multiple criteria optimization (MCO) problem. Through the analysis it was possible to determine a series of Pareto efficient frontiers to find the most differentially expressed genes, which are here proposed as potential AD biomarkers. The Traveling Sales Problem (TSP) model was used to find the cyclical path of maximal correlation between the expression changes among the genes deemed important from the previous stage. This leads to a structure capable of guiding biological exploration with enhanced precision and repeatability. Ten genes were selected (FTL, GFAP, HNRNPA3, COX1, ND2, ND3, ND4, NUCKS1, RPL41, and RPS10) and their most correlated cyclic structure was found in our analyses. The biological functions of their products were found to be linked to inflammation and neurodegenerative diseases and some of them had not been reported for AD before. The TSP path connects genes coding for mitochondrial electron transfer proteins. Some of these proteins are closely related to other electron transport proteins already reported as important for AD.

Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge.

Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel multiple criteria optimization-based strategy aiming to detect highly differentially expressed genes. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.