Biomarker Discovery for Arsenic Exposure Using Functional Data. Analysis and Feature Learning of Mass Spectrometry Proteomic Data

The effect of vaccine on COVID-19 spread by function-on-scalar regression model: a case study of Africa

Aim

This paper aimed to study the effect of the vaccine on the reproduction rate of coronavirus in Africa from January 2021 to November 2021.

Subject and methods

Functional data analysis (FDA), a relatively new area in statistics, can describe, analyze, and predict data collected over time, space, or other continuum measures in many countries every day and is increasingly common across scientific domains. For our data, the first step of functional data is smoothing. We used the B-spline method to smooth our data. Then, we apply the function-on-scalar and Bayes function-on-scalar models to fit our data.

Results

Our results indicate a statistically significant relationship between the vaccine and the rate of virus reproduction and spread. When the vaccination rate falls, the reproduction rate also decreases. Furthermore, we found that the effect of latitude and the region on the reproduction rate depends on the region. We discovered that in Middle Africa, from the beginning of the year until the end of the summer, the impact is negative, implying that the virus spread due to a decrease in the vaccination rates.

Conclusion

The study found that vaccination rates significantly impact the virus's reproduction rate.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Elucidating age and sex-dependent association between frontal EEG asymmetry and depression: An application of multiple imputation in functional regression

Frontal power asymmetry (FA), a measure of brain function derived from electroencephalography, is a potential biomarker for major depressive disorder (MDD). Though FA is functional in nature, it is typically reduced to a scalar value prior to analysis, possibly obscuring its relationship with MDD and leading to a number of studies that have provided contradictory results. To overcome this issue, we sought to fit a functional regression model to characterize the association between FA and MDD status, adjusting for age, sex, cognitive ability, and handedness using data from a large clinical study that included both MDD and healthy control (HC) subjects. Since nearly 40% of the observations are missing data on either FA or cognitive ability, we propose an extension of multiple imputation (MI) by chained equations that allows for the imputation of both scalar and functional data. We also propose an extension of Rubin's Rules for conducting valid inference in this setting. The proposed methods are evaluated in a simulation and applied to our FA data. For our FA data, a pooled analysis from the imputed data sets yielded similar results to those of the complete case analysis. We found that, among young females, HCs tended to have higher FA over the θ, α, and β frequency bands, but that the difference between HC and MDD subjects diminishes and ultimately reverses with age. For males, HCs tended to have higher FA in the β frequency band, regardless of age. Young male HCs had higher FA in the θ and α bands, but this difference diminishes with increasing age in the α band and ultimately reverses with increasing age in the θ band.

Comparison of functional and discrete data analysis regimes for Raman spectra

Raman spectral data are best described by mathematical functions; however, due to the spectroscopic measurement setup, only discrete points of these functions are measured. Therefore, we investigated the Raman spectral data for the first time in the functional framework. First, we approximated the Raman spectra by using B-spline basis functions. Afterwards, we applied the functional principal component analysis followed by the linear discriminant analysis (FPCA-LDA) and compared the results with those of the classical principal component analysis followed by the linear discriminant analysis (PCA-LDA). In this context, simulation and experimental Raman spectra were used. In the simulated Raman spectra, normal and abnormal spectra were used for a classification model, where the abnormal spectra were built by shifting one peak position. We showed that the mean sensitivities of the FPCA-LDA method were higher than the mean sensitivities of the PCA-LDA method, especially when the signal-to-noise ratio is low and the shift of the peak position is small. However, for a higher signal-to-noise ratio, both methods performed equally. Additionally, a slight improvement of the mean sensitivity could be shown if the FPCA-LDA method was applied to experimental Raman data.

Arsenic Exposure and Cancer-Related Proteins in Urine of Indigenous Bolivian Women

Indigenous people living in the Bolivian Andes are exposed through their drinking water to inorganic arsenic, a potent carcinogen. However, the health consequences of arsenic exposure in this region are unknown. The aim of this study was to evaluate associations between arsenic exposure and changes in cancer-related proteins in indigenous women (n = 176) from communities around the Andean Lake Poopó, Bolivia. Arsenic exposure was assessed in whole blood (B-As) and urine (as the sum of arsenic metabolites, U-As) by inductively coupled plasma-mass spectrometry (ICP-MS). Cancer-related proteins (N = 92) were measured in urine using the proximity extension assay. The median B-As concentration was 2.1 (range 0.60-9.1) ng/g, and U-As concentration was 67 (12-399) μg/L. Using linear regression models adjusted for age, urinary osmolality, and urinary leukocytes, we identified associations between B-As and four putative cancer-related proteins: FASLG, SEZ6L, LYPD3, and TFPI2. Increasing B-As concentrations were associated with lower protein expression of SEZ6L, LYPD3, and TFPI2, and with higher expression of FASLG in urine (no association was statistically significant after correcting for multiple comparisons). The associations were similar across groups with different arsenic metabolism efficiency, a susceptibility factor for arsenic toxicity. In conclusion, arsenic exposure in this region was associated with changes in the expression of some cancer-related proteins in urine. Future research is warranted to understand if these proteins could serve as valid biomarkers for arsenic-related toxicity.

Proteomic profiling reveals candidate markers for arsenic-induced skin keratosis

Proteomics technology is an attractive biomarker candidate discovery tool that can be applied to study large sets of biological molecules. To identify novel biomarkers and molecular targets in arsenic-induced skin lesions, we have determined the protein profile of arsenic-affected human epidermal stratum corneum by shotgun proteomics. Samples of palm and foot sole from healthy subjects were analyzed, demonstrating similar protein patterns in palm and sole. Samples were collected from the palms of subjects with arsenic keratosis (lesional and adjacent non-lesional samples) and arsenic-exposed subjects without lesions (normal). Samples from non-exposed healthy individuals served as controls. We found that three proteins in arsenic-exposed lesional epidermis were consistently distinguishably expressed from the unaffected epidermis. One of these proteins, the cadherin-like transmembrane glycoprotein, desmoglein 1 (DSG1) was suppressed. Down-regulation of DSG1 may lead to reduced cell-cell adhesion, resulting in abnormal epidermal differentiation. The expression of keratin 6c (KRT6C) and fatty acid binding protein 5 (FABP5) were significantly increased. FABP5 is an intracellular lipid chaperone that plays an essential role in fatty acid metabolism in human skin. This raises a possibility that overexpression of FABP5 may affect the proliferation or differentiation of keratinocytes by altering lipid metabolism. KRT6C is a constituent of the cytoskeleton that maintains epidermal integrity and cohesion. Abnormal expression of KRT6C may affect its structural role in the epidermis. Our findings suggest an important approach for future studies of arsenic-mediated toxicity and skin cancer, where certain proteins may represent useful biomarkers of early diagnoses in high-risk populations and hopefully new treatment targets. Further studies are required to understand the biological role of these markers in skin pathogenesis from arsenic exposure.

Metabonomic analysis of serum of workers occupationally exposed to arsenic, cadmium and lead for biomarker research: a preliminary study

Environmental metabonomics is the application of metabonomics to characterize the interactions of organisms with their environment. Metabolic profiling is an exciting addition to the armory of the epidemiologist for the discovery of new disease risk biomarkers and diagnostics. This work is a continuation of research searching for preclinical serum markers in a group of 389 healthy smelter workers exposed to lead, cadmium and arsenic. Changes in the metabolic profiles were studied using Proton Nuclear Magnetic Resonance Spectroscopy on pooled serum samples from both the metal exposed and control groups. These multivariate metabonomic datasets were analyzed with Principal Component Analysis and Partial Least Squares Discriminant Analysis. Analysis of metabolic profiles of people exposed to heavy metals suggests energy metabolism disturbance induced by heavy metals. Changes in lipid fraction (very-low-density lipoprotein - VLDL, low-density lipoprotein - LDL), unsaturated lipids and in the level of amino acids suggest perturbation of the metabolism of lipids and amino acids. This study illustrated the high reliability of NMR-based metabonomic profiling on the study of the biochemical effects induced by the mixture of heavy metals. This approach is capable of identifying intermediate biomarkers of response to toxicants at environmental/occupational concentrations, paving the way to its use in a monitoring of smelter workers exposed to low doses of lead, cadmium and arsenic.

Alzheimer's and Parkinson's diseases: an environmental proteomic point of view

Alzheimer's and Parkinson's diseases are severe neurodegenerative conditions triggered by complex biochemical routes. Many groups are currently pursuing the search for valuable biomarkers to either perform early diagnostic or to follow the disease's progress. Several studies have reported relevant findings regarding environmental issues and the progression of such diseases. Here the etiology and mechanisms of these diseases are briefly reviewed. Approaches that might reveal candidate biomarkers and environmental stressors associated to the diseases were analyzed under a proteomic perspective. This article is part of a Special Issue entitled: Environmental and structural proteomics.

Use of OMIC technologies to study arsenic exposure in human populations

Exposure to arsenic (As) in drinking water is a major health concern. More than 100 million individuals are exposed to levels over the current World Health Organization standard of 10 µg/L worldwide. Arsenic is one of the few agents established as a human carcinogen prior to understanding its mechanism of carcinogenicity. OMIC technologies have enabled researchers to utilize agnostic approaches to explore new, unknown mechanisms through which As causes disease in exposed human populations. In this article, we present recent studies in which OMIC technologies have been used to explore differences in human biological samples to identify markers of exposure, disease susceptibility, and effect in As-exposed and/or diseased tissues.

Applications of functional data analysis: A systematic review

BACKGROUND

Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods.

METHODS

A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995-2010. Papers reporting methodological considerations only were excluded, as were non-English articles.

RESULTS

In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data.

CONCLUSIONS

Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions.

An integrated proteomics analysis of bone tissues in response to mechanical stimulation

Bone cells can sense physical forces and convert mechanical stimulation conditions into biochemical signals that lead to expression of mechanically sensitive genes and proteins. However, it is still poorly understood how genes and proteins in bone cells are orchestrated to respond to mechanical stimulations. In this research, we applied integrated proteomics, statistical, and network biology techniques to study proteome-level changes to bone tissue cells in response to two different conditions, normal loading and fatigue loading. We harvested ulna midshafts and isolated proteins from the control, loaded, and fatigue loaded Rats. Using a label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) experimental proteomics technique, we derived a comprehensive list of 1,058 proteins that are differentially expressed among normal loading, fatigue loading, and controls. By carefully developing protein selection filters and statistical models, we were able to identify 42 proteins representing 21 Rat genes that were significantly associated with bone cells' response to quantitative changes between normal loading and fatigue loading conditions. We further applied network biology techniques by building a fatigue loading activated protein-protein interaction subnetwork involving 9 of the human-homolog counterpart of the 21 rat genes in a large connected network component. Our study shows that the combination of decreased anti-apoptotic factor, Raf1, and increased pro-apoptotic factor, PDCD8, results in significant increase in the number of apoptotic osteocytes following fatigue loading. We believe controlling osteoblast differentiation/proliferation and osteocyte apoptosis could be promising directions for developing future therapeutic solutions for related bone diseases.

MULTIPLE TESTING OF LOCAL MAXIMA FOR DETECTION OF PEAKS IN 1D

A topological multiple testing scheme for one-dimensional domains is proposed where, rather than testing every spatial or temporal location for the presence of a signal, tests are performed only at the local maxima of the smoothed observed sequence. Assuming unimodal true peaks with finite support and Gaussian stationary ergodic noise, it is shown that the algorithm with Bonferroni or Benjamini-Hochberg correction provides asymptotic strong control of the family wise error rate and false discovery rate, and is power consistent, as the search space and the signal strength get large, where the search space may grow exponentially faster than the signal strength. Simulations show that error levels are maintained for nonasymptotic conditions, and that power is maximized when the smoothing kernel is close in shape and bandwidth to the signal peaks, akin to the matched filter theorem in signal processing. The methods are illustrated in an analysis of electrical recordings of neuronal cell activity.

Toxicogenomic profiling of chemically exposed humans in risk assessment

Gene-environment interactions contribute to complex disease development. The environmental contribution, in particular low-level and prevalent environmental exposures, may constitute much of the risk and contribute substantially to disease. Systematic risk evaluation of the majority of human chemical exposures, has not been conducted and is a goal of regulatory agencies in the U.S. and worldwide. With the recent recognition that toxicological approaches more predictive of effects in humans are required for risk assessment, in vitro human cell line data as well as animal data are being used to identify toxicity mechanisms that can be translated into biomarkers relevant to human exposure studies. In this review, we discuss how data from toxicogenomic studies of exposed human populations can inform risk assessment, by generating biomarkers of exposure, early effect, and/or susceptibility, elucidating mechanisms of action underlying exposure-related disease, and detecting response at low doses. Good experimental design incorporating precise, individual exposure measurements, phenotypic anchors (pre-disease or traditional toxicological markers), and a range of relevant exposure levels, is necessary. Further, toxicogenomic studies need to be designed with sufficient power to detect true effects of the exposure. As more studies are performed and incorporated into databases such as the Comparative Toxicogenomics Database (CTD) and Chemical Effects in Biological Systems (CEBS), data can be mined for classification of newly tested chemicals (hazard identification), and, for investigating the dose-response, and inter-relationship among genes, environment and disease in a systems biology approach (risk characterization).

Proteomic analysis of serum of workers occupationally exposed to arsenic, cadmium, and lead for biomarker research: a preliminary study

The main factor of environmental contamination is the presence of the heavy metals lead, cadmium, and arsenic. The aim of serum protein profile analysis of people chronically exposed to heavy metals is to find protein markers of early pathological changes. The study was conducted in a group of 389 healthy men working in copper foundry and 45 age-matched non-exposed healthy men. Toxicological test samples included whole blood, serum, and urine. Thirty-seven clinical parameters were measured. Based on the parameters values of the healthy volunteers, the centroid in 37-dimensional space was calculated. The individuals in the metal-exposed and control groups were ordered based on the Euclidean distance from the centroid defined by the first component according to Principal Component Analysis (PCA). Serum samples of two individuals, one from the control and one from the metal-exposed group, were chosen for proteomic analysis. In optimized conditions of two-dimensional gel electrophoresis (2-DE), two protein maps were obtained representing both groups. Twenty-eight corresponding protein spots from both protein maps were chosen and identified based on PDQuest analysis and the SWISS-2DPAGE database. From a panel of six proteins with differences in expression greater than a factor of two, three potential markers with the highest differences were selected: hemoglobin-spot 26 (pI 7.05, Mw 10.53), unidentified protein-spot 27 (pI 6.73, Mw 10.17), and unidentified protein-spot 25 (pI 5.75, Mw 12.07). Further studies are required to prove so far obtained results. Identified proteins could serve as potential markers of preclinical changes and could be in the future included in biomonitoring of people exposed to heavy metals.

Biomonitoring for environmental exposures to arsenic

Arsenic (As) is a widely occurring environmental contaminant. To assess human exposures to As, public health officials and researchers often conduct biomonitoring. Samples of urine, hair, nails, or blood are collected from potentially exposed people and are analyzed for As compounds and their metabolites. When analyzing for As exposure, it is useful to distinguish between As species, since they differ in their origin and toxicity. Urine is the most frequently used biological medium for biomonitoring. Measuring the urinary concentration of As is useful in assessing recent exposure to As, and high-quality reference ranges are available for urinary As concentrations in the U.S. population. Biomonitoring for As in hair and nails has been used in many studies and is particularly useful in evaluating chronic exposures to As. Interpreting the health implications of As concentrations in biological samples is limited by the small number of studies that provide information on the correlation and dose-response relationship between biomonitoring test results and adverse health effects. This study discusses the advantages and limitations of biomonitoring for As in biological samples and provides illustrative case studies.

Analysis of environmental stress response on the proteome level

Thousands of man-made chemicals are annually released into the environment by agriculture, transport, industries, and other human activities. In general, chemical analysis of environmental samples used to assess the pollution status of a specific ecosystem is complicated by the complexity of the mixture, and in some cases by the very low toxicity thresholds of chemicals present. In that sense, a proteomics approach, capable of detecting subtle changes in the level and structure of individual proteins within the whole proteome in response to the altered surroundings, has obvious applications in the field of ecotoxicology. In addition to identifying new protein biomarkers, it can also help to provide an insight into underlying mechanisms of toxicity. Despite being a comparatively new field with a number of caveats, proteomics applications have spread from microorganisms and plants to invertebrates and vertebrates, gradually becoming an established technology used in environmental research. This review article highlights recent advances in the field of environmental proteomics, mainly focusing on experimental approaches with a potential to understand toxic modes of action and to identify novel ecotoxicological biomarkers.