Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women’s reproductive health. Pregnancy thus became a highly demanding phase in a woman’s life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.publishedVersio

Probiotics: current landscape and future horizons

In recent years there has been a rapid rise in interest for the application of probiotic supplements to act as mediators in health and disease. This appeal is predominantly due to ever-increasing evidence of the interaction of the microbiota and pathophysiological processes of disease within the human host. This narrative review considers the current landscape of the probiotic industry and its research, and discusses current pitfalls in the lack of translation from laboratory science to clinical application. Future considerations into how industry and academia must adapt probiotic research to maximize success are suggested, including more targeted application of probiotic strains dependent on individual capabilities as well as application of multiple advanced analytical technologies to further understand and accelerate microbiome science. Lay abstract: The global market for probiotic supplements is continually expanding. Despite the public perception of benefits provided by probiotics, the evidence to conclusively link probiotic strains to improved characteristics of health or disease is lacking. This is owing, in part, to the lack of large-scale research trials, but also to the insufficient understanding of the interactions occurring within the human system following supplementation. More in-depth research into individual probiotic strains, combined with the application of multiple advanced measurement techniques will provide a future direction for probiotic research and, in turn, aim to provide useful data to translate into routine healthcare practice

Untargeted Gut Metabolomics to Delve the Interplay between Selenium Supplementation and Gut Microbiota

El selenio (Se) es un oligoelemento con funciones importantes para la salud debido a las propiedades antioxidantes de las selenoproteínas. Para analizar la interacción entre Se y la microbiota intestinal, se determinaron los perfiles metabolómicos intestinales en ratones convencionales (C) y con microbiota empobrecida (Abx) después de la suplementación con Se (Abx-Se) mediante metabolómica no dirigida, utilizando una multiplataforma analítica basada en GC-MS y UHPLC-QTOF-MS (MassIVE ID MSV000087829). El perfil de la microbiota intestinal se realizó mediante la secuenciación del amplicón del gen 16S rRNA. Se encontraron diferencias significativas en los niveles de aproximadamente el 70 % de los metabolitos intestinales determinados, incluidos los acilos grasos, los glicerolípidos, los glicerofosfolípidos y los esteroides, en Abx-Se en comparación con Abx, y solo el 30 % fue diferente entre Abx-Se y C, lo que sugiere un efecto importante de la suplementación con Se en el metabolismo de los ratones Abx. A nivel de género, el análisis de correlación mostró fuertes asociaciones entre los metabolitos y los perfiles bacterianos intestinales. Asimismo, una mayor abundancia de Lactobacillus spp., un género potencialmente beneficioso enriquecido tras la suplementación con Se, se asoció con niveles más altos de lípidos de prenol, fosfatidilgliceroles (C-Se), esteroides y diterpenoides (Abx-Se), y también con niveles más bajos de ácidos grasos (Abx-Se). Por lo tanto, observamos una interacción crucial entre la ingesta de Se-microbiota-metabolitos, aunque se necesitan más estudios para aclarar los mecanismos específicos. Este es el primer estudio sobre la metabolómica intestinal no dirigida después del agotamiento de la microbiota y la suplementación con Se.Selenium (Se) is an essential trace element with important health roles due to the antioxidant properties of selenoproteins. To analyze the interplay between Se and gut microbiota, gut metabolomic profiles were determined in conventional (C) and microbiota depleted mice (Abx) after Se-supplementation (Abx-Se) by untargeted metabolomics, using an analytical multiplatform based on GC-MS and UHPLC-QTOF-MS (MassIVE ID MSV000087829). Gut microbiota profiling was performed by 16S rRNA gene amplicon sequencing. Significant differences in the levels of about 70% of the gut metabolites determined, including fatty acyls, glycerolipids, glycerophospholipids, and steroids, were found in Abx-Se compared to Abx, and only 30% were different between Abx-Se and C, suggesting an important effect of Se-supplementation on Abx mice metabolism. At genus level, the correlation analysis showed strong associations between metabolites and gut bacterial profiles. Likewise, higher abundance of Lactobacillus spp., a potentially beneficial genus enriched after Se-supplementation, was associated with higher levels of prenol lipids, phosphatidylglycerols (C-Se), steroids and diterpenoids (Abx-Se), and also with lower levels of fatty acids (Abx-Se). Thus, we observed a crucial interaction between Se intake–microbiota–metabolites, although further studies to clarify the specific mechanisms are needed. This is the first study about untargeted gut metabolomics after microbiota depletion and Se-supplementation.This work was supported by the projects PG2018-096608-B-C21 f from the Spanish Ministry of Science and innovation (MCIN). Generación del Conocimiento. MCIN/ AEI /10.13039/501100011033/ FEDER “Una manera de hacer Europa” and UHU-1256905 from the FEDER Andalusian Operative Program 2014–2020 (Ministry of Economy, Knowledge, Business and Universities, Regional Government of Andalusia, Spain). Authors would like to acknowledge the support from The Ramón Areces Foundation (ref CIVP19A5918). Authors are grateful to FEDER (European Community) for financial support, Grant UNHU13-1X10-1611. Funding for open access charge: Universidad de Huelva / CBU

Evolution of International Psychiatry

International psychiatry is currently facing serious challenges triggered by the global economic crisis and the COVID-19 pandemic. These global events lead to the need to broaden our nosographic and therapeutic horizons, and to make use of the newest psychological approaches and the latest neuroscience acquisitions. The focus should be on the psychological consequences of the pandemic, not only on people suffering from mental disorders, but also on the general population, for which the risk of developing psychic symptoms appears to be increased. A population that needs special attention is that of health workers involved in the management of the pandemic. In facing these problems, psychiatry today can use numerous new clinical applications and technologies in the fields of precision medicine. These include genomics, neuroimaging, and microbiomics, which can also be integrated with each other through machine learning systems. They can provide new contributions both in treatment personalization and in the evolution of nosographic systems. Besides this, the contribution of psychotherapies and dynamic and clinical psychology appears to be indispensable for a complete understanding of the clinical and personological aspects of patients. This journal aims to include innovative studies deriving from original, clinical, and basic research in the fields of mental health, precision psychiatry, genomics, neuroimaging, neuropsychopharmacology, and dynamic and clinical psychology

Connecting environmental exposure and neurodegeneration using cheminformatics and high resolution mass spectrometry: potential and challenges

Connecting chemical exposures over a lifetime to complex chronic diseases with multifactorial causes such as neurodegenerative diseases is an immense challenge requiring a long-term, interdisciplinary approach. Rapid developments in analytical and data technologies, such as non-target high resolution mass spectrometry (NT-HR-MS), have opened up new possibilities to accomplish this, inconceivable 20 years ago. While NT-HR-MS is being applied to increasingly complex research questions, there are still many unidentified chemicals and uncertainties in linking exposures to human health outcomes and environmental impacts. In this perspective, we explore the possibilities and challenges involved in using cheminformatics and NT-HR-MS to answer complex questions that cross many scientific disciplines, taking the identification of potential (small molecule) neurotoxicants in environmental or biological matrices as a case study. We explore capturing literature knowledge and patient exposure information in a form amenable to high-throughput data mining, and the related cheminformatic challenges. We then briefly cover which sample matrices are available, which method(s) could potentially be used to detect these chemicals in various matrices and what remains beyond the reach of NT-HR-MS. We touch on the potential for biological validation systems to contribute to mechanistic understanding of observations and explore which sampling and data archiving strategies may be required to form an accurate, sustained picture of small molecule signatures on extensive cohorts of patients with chronic neurodegenerative disorders. Finally, we reflect on how NT-HR-MS can support unravelling the contribution of the environment to complex diseases

Mass Spectrometric Based Approaches in Urine Metabolomics and Biomarker Discovery

Urine metabolomics has recently emerged as a prominent field for the discovery of non-invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non-biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid-liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4 °C) and repetitive freeze-thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its related technologies (i.e. ultra high-performance liquid chromatography [UPLC] and hydrophilic interaction liquid chromatography [HILIC]), liquid chromatography (LC)-MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC-MS, other MS-based technologies have been utilized in urine metabolomics. These include direct injection (infusion)-MS, capillary electrophoresis-MS and gas chromatography-MS. In this article, the current progresses of different MS-based techniques in exploring the urine metabolome as well as the recent findings in providing potentially diagnostic urinary biomarkers are discussed

Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment

Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach

The Human Serum Metabolome

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca

Influence of missing values substitutes on multivariate analysis of metabolomics data

Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%–20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the most well known substitute for missing values is a mean imputation. In fact, some researchers consider this aspect of data analysis in their metabolomics pipeline as so routine that they do not even mention using this replacement approach. However, this may have a significant influence on the data analysis output(s) and might be highly sensitive to the distribution of samples between different classes. Therefore, in this study we have analysed different substitutes of missing values namely: zero, mean, median, k-nearest neighbours (kNN) and random forest (RF) imputation, in terms of their influence on unsupervised and supervised learning and, thus, their impact on the final output(s) in terms of biological interpretation. These comparisons have been demonstrated both visually and computationally (classification rate) to support our findings. The results show that the selection of the replacement methods to impute missing values may have a considerable effect on the classification accuracy, if performed incorrectly this may negatively influence the biomarkers selected for an early disease diagnosis or identification of cancer related metabolites. In the case of GC-MS metabolomics data studied here our findings recommend that RF should be favored as an imputation of missing value over the other tested methods. This approach displayed excellent results in terms of classification rate for both supervised methods namely: principal components-linear discriminant analysis (PC-LDA) (98.02%) and partial least squares-discriminant analysis (PLS-DA) (97.96%) outperforming other imputation methods