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Background: Successful embryo implantation is a complex process that requires the coordination of a series of events, involving both the embryo and the maternal endometrium. Key to this process is the intricate cascade of molecular mechanisms regulated by endocrine, paracrine and autocrine modulators of embryonic and maternal origin. Despite significant progress in ART, implantation failure still affects numerous infertile couples worldwide and fewer than 10% of embryos successfully implant. Improved selection of both the viable embryos and the optimal endometrial phenotype for transfer remains crucial to enhancing implantation chances. However, both classical morphological embryo selection and new strategies incorporated into clinical practice, such as embryonic genetic analysis, morphokinetics or ultrasound endometrial dating, remain insufficient to predict successful implantation. Additionally, no techniques are widely applied to analyse molecular signals involved in the embryo-uterine interaction. More reliable biological markers to predict embryo and uterine reproductive competence are needed to improve pregnancy outcomes. Recent years have seen a trend towards 'omics' methods, which enable the assessment of complete endometrial and embryonic molecular profiles during implantation. Omics have advanced our knowledge of the implantation process, identifying potential but rarely implemented biomarkers of successful implantation.Objective and Rationale: Differences between the findings of published omics studies, and perhaps because embryonic and endometrial molecular signatures were often not investigated jointly, have prevented firm conclusions being reached. A timely review summarizing omics studies on the molecular determinants of human implantation in both the embryo and the endometrium will help facilitate integrative and reliable omics approaches to enhance ART outcomes.Search Methods: In order to provide a comprehensive review of the literature published up to September 2019, Medline databases were searched using keywords pertaining to omics, including 'transcriptome', 'proteome', 'secretome', 'metabolome' and 'expression profiles', combined with terms related to implantation, such as 'endometrial receptivity', 'embryo viability' and 'embryo implantation'. No language restrictions were imposed. References from articles were also used for additional literature.Outcomes: Here we provide a complete summary of the major achievements in human implantation research supplied by omics approaches, highlighting their potential to improve reproductive outcomes while fully elucidating the implantation mechanism. The review highlights the existence of discrepancies among the postulated biomarkers from studies on embryo viability or endometrial receptivity, even using the same omic analysis.Wider Implications: Despite the huge amount of biomarker information provided by omics, we still do not have enough evidence to link data from all omics with an implantation outcome. However, in the foreseeable future, application of minimally or non-invasive omics tools, together with a more integrative interpretation of uniformly collected data, will help to overcome the difficulties for clinical implementation of omics tools. Omics assays of the embryo and endometrium are being proposed or already being used as diagnostic tools for personalised single-embryo transfer in the most favourable endometrial environment, avoiding the risk of multiple pregnancies and ensuring better pregnancy rates. [ABSTRACT FROM AUTHOR]

The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: Complete resection constitutes the only curative approach in pancreatic cancer but is possible only in a minority of patients due to advanced stages upon diagnosis. Consequently, early detection is crucial for curative treatment. Clinical routine still lacks efficient, non-invasive screening assays, and 80-90% of pancreatic carcinomas are detected at unresectable stages. A wide range of serum proteins have been in the focus of intensive search for biomarkers specific for pancreatic cancer. This article will give an overview on serum biomarkers with screening potential for pancreatic malignancy. Design and methods: PUBMED database was searched for articles, and 43 manuscripts were selected that provided data regarding biomarkers used, type of assay, study population, sample cohort quality and diagnostic performance. Results: Superior values for diagnostic performance were shown for MIC-1, PAM4, OPN, HSP27, TPS, TSGF, and CAM17.1 as individual markers. Panels of biomarkers comprised CA 19-9, MCSF, CEA, SAA, Haptoglobin, TSGF, CA 242, and HSP27. Individually or in concerted form, sensitivity and specificity ranged from 77 to 100% and 84-100%, respectively. Conclusions: While the above named markers show high screening potential for pancreatic cancer, standardized validation studies using multiplex assays are required to pave the way for clinical routine application. [ABSTRACT FROM AUTHOR]

Background: Mass spectrometry is increasingly being used to discover proteins or protein profiles associated with disease. Experimental design of mass-spectrometry studies has come under close scrutiny and the importance of strict protocols for sample collection is now understood. However, the question of how best to process the large quantities of data generated is still unanswered. Main challenges for the analysis are the choice of proper pre-processing and classification methods. While these two issues have been investigated in isolation, we propose to use the classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. Results: Two in-house generated clinical SELDI-TOF MS datasets are used in this study as an example of high throughput mass-spectrometry data. We perform a systematic comparison of two commonly used pre-processing methods as implemented in Ciphergen ProteinChip Software and in the Cromwell package. With respect to reproducibility, Ciphergen and Cromwell pre-processing are largely comparable. We find that the overlap between peaks detected by either Ciphergen ProteinChip Software or Cromwell is large. This is especially the case for the more stringent peak detection settings. Moreover, similarity of the estimated intensities between matched peaks is high. We evaluate the pre-processing methods using five different classification methods. Classification is done in a double cross-validation protocol using repeated random sampling to obtain an unbiased estimate of classification accuracy. No pre-processing method significantly outperforms the other for all peak detection settings evaluated. Conclusion: We use classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. Both pre-processing methods lead to similar classification results on an ovarian cancer and a Gaucher disease dataset. However, the settings for pre-processing parameters lead to large differences in classification accuracy and are therefore of crucial importance. We advocate the evaluation over a range of parameter settings when comparing pre-processing methods. Our analysis also demonstrates that reliable classification results can be obtained with a combination of strict sample handling and a well-defined classification protocol on clinical samples. [ABSTRACT FROM AUTHOR]

Schizophrenia remains an elusive multifaceted disorder with all evidence of its onset and aetiology pointing to a complex interplay of genetic, nutritional, environmental and developmental factors. Although several molecular and structural abnormalities have been reported for schizophrenia, no diagnostic test or other application of clinical use has yet emerged from this research. The heterogeneity of schizophrenia symptoms and its similarity to other psychiatric disorders, the accessibility of appropriate samples and the complexity of molecular alterations have greatly slowed down research. Biomarker discovery approaches should ultimately facilitate objective diagnosis, allow the identification of at-risk individuals, predict treatment success and revolutionize drug-discovery approaches. For psychiatric disorders, large sample numbers are necessary if disease-intrinsic alterations are to be detected in an environment of high biological variability. Only recent technological advances facilitate the profiling of proteins and metabolites of large sample numbers. These approaches promise to provide interesting insights into disease mechanisms, as they enable capturing the dynamic nature of disease-related alterations. By means of parallel profiling using a multi-omics approach, we aim to disentangle the complex nature of schizophrenia's aetiology. Here, we will outline how this system-based analysis approach can contribute to the discovery of disease mechanisms in schizophrenia and in turn other psychiatric disorders.British Journal of Pharmacology (2008) 153, S133–S136; doi:10.1038/sj.bjp.0707658; published online 14 January 2008 [ABSTRACT FROM AUTHOR]

The availability of ‘omics’ technologies is transforming scientific approaches to physiological problems from a reductionist viewpoint to that of a holistic viewpoint. This is of profound importance in nutrition, since the integration of multiple systems at the level of gene expression on the synthetic side through to metabolic enzyme activity on the degradative side combine to govern nutrient availability to tissues. Protein activity is central to the process of nutrition from the initial absorption of nutrients via uptake carriers in the gut, through to distribution and transport in the blood, metabolism by degradative enzymes in tissues and excretion through renal tubule exchange proteins. Therefore, the global profiling of the proteome, defined as the entire protein complement of the genome expressed in a particular cell or organ, or in plasma or serum at a particular time, offers the potential for identification of important biomarkers of nutritional state that respond to alterations in diet. The present review considers the published evidence of nutritional modulation of the proteome in vivo which has expanded exponentially over the last 3 years. It highlights some of the challenges faced by researchers using proteomic approaches to understand the interactions of diet with genomic and metabolic–phenotypic variables in normal populations. [ABSTRACT FROM AUTHOR]

Background: Proteomics may help to detect subtle pollution-related changes, such as responses to mixture pollution at low concentrations, where clear signs of toxicity are absent. The challenges associated with the analysis of large-scale multivariate proteomic datasets have been widely discussed in medical research and biomarker discovery. This concept has been introduced to ecotoxicology only recently, so data processing and classification analysis need to be refined before they can be readily applied in biomarker discovery and monitoring studies. Results: Data sets obtained from a case study of oil pollution in the Blue mussel were investigated for differential protein expression by retentate chromatography-mass spectrometry and decision tree classification. Different tissues and different settings were used to evaluate classifiers towards their discriminatory power. It was found that, due the intrinsic variability of the data sets, reliable classification of unknown samples could only be achieved on a broad statistical basis (n > 60) with the observed expression changes comprising high statistical significance and sufficient amplitude. The application of stringent criteria to guard against overfitting of the models eventually allowed satisfactory classification for only one of the investigated data sets and settings. Conclusion: Machine learning techniques provide a promising approach to process and extract informative expression signatures from high-dimensional mass-spectrometry data. Even though characterisation of the proteins forming the expression signatures would be ideal, knowledge of the specific proteins is not mandatory for effective class discrimination. This may constitute a new biomarker approach in ecotoxicology, where working with organisms, which do not have sequenced genomes render protein identification by database searching problematic. However, data processing has to be critically evaluated and statistical constraints have to be considered before supervised classification algorithms are employed. [ABSTRACT FROM AUTHOR]

The sequencing of the complete genome for many organisms, including man, has opened the door to the systematic understanding of how complex structures such as the brain integrate and function, not only in health but also in disease. This blueprint, however, means that the piecemeal analysis regimes of the past are being rapidly superseded by new methods that analyse not just tens of genes or proteins at any one time, but thousands, if not the entire repertoire of a cell population or tissue under investigation. Using the most appropriate method of analysis to maximise the available data therefore becomes vital if a complete picture is to be obtained of how a system or individual cell is affected by a treatment or disease. This review examines what methods are currently available for the large scale analysis of gene and protein expression, and what are their limitations. [ABSTRACT FROM AUTHOR]

Nutrition research has slowly started to adopt the proteomics techniques to measure changes in the protein complement of a biological system. This enables modelling of biological processes in response to dietary interventions, as well as the elucidation of novel biomarkers for health or disease that are sensitive to such interventions. There are limited studies on the effect of micronutrients on the proteome, so this review concentrates rather more on dietary intervention studies that have used proteomics (mainly classical 2D gel electrophoresis combined with mass spectrometry) to elucidate changes in pathways that relate to glucose and fatty acid metabolism, oxidative stress, anti-oxidant defence mechanisms and redox status. The ability to measure regulation of more low abundant proteins, such as those involved in inflammatory pathways, as well as the evaluation and validation of newly discovered candidate biomarkers in human biofluids, may depend on the introduction of more quantitative and sensitive methods like multiple reaction monitoring (MRM) and multiplexed immunoassays in nutrition research. [ABSTRACT FROM PUBLISHER]