scholarly journals Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

2021 ◽  
Author(s):  
Florence Anne Castelli ◽  
Giulio Rosati ◽  
Christian Moguet ◽  
Celia Fuentes ◽  
Jose Marrugo-Ramírez ◽  
...  
Keyword(s):  
Analytical Chemistry ◽  
Personalized Medicine ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Point Of Care ◽  
Metabolite Identification ◽  
Individual Characteristics ◽  
Molecular Signatures ◽  
Laboratory Equipment ◽  
Metabolomics Data

AbstractMetabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine. Graphical abstract

Bioinformatics

2020 ◽  
pp. 67-70
Author(s):  
Afzal Chaudhry
Keyword(s):  
Clinical Data ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Biomarker Discovery ◽  
Point Of Care ◽  
Applied Mathematics ◽  
Data Sets ◽  
Research Knowledge ◽  
Clinical Bioinformatics ◽  
Support Mechanisms

Bioinformatics may be defined as ‘conceptualizing biology in terms of molecules and applying “informatics techniques” (e.g. applied mathematics, computer science and statistics) to understand and organize the information associated with these molecules, on a large scale’. Clinical bioinformatics may be defined as ‘the clinical application of bioinformatics-associated sciences and technologies to understand molecular mechanisms and potential therapies for human diseases’. If clinical bioinformatics is to deliver the integration of molecular and clinical data and thereby translate research knowledge into effective ‘personalized’ medicine, then two broad constituencies need to be supported. Clinicians at the point of care need to understand and integrate, perhaps via decision support mechanisms, entities such as genotype/phenotype correlations, biomarker discovery, and pharmacogenomics; while researchers require accurate, structured, and (ideally) coded clinical data, as well as biological reference data sets.

Role of Bioinformatics in Nanotechnology

2020 ◽  
pp. 1875-1894
Author(s):  
Harishchander Anandaram
Keyword(s):  
Personalized Medicine ◽  
Biomarker Discovery ◽  
Individual Therapy ◽  
Molecular Profile ◽  
Molecular Signatures ◽  
Nano Technology ◽  
Recent Developments ◽  
Novel Biomarkers ◽  
Molecular Information

Recent advancements in bio-computing and nano-technology accelerated the discovery of novel biomarkers in the emerging field of personalized medicine. Personalized medicine deals with disease detection and therapy from the molecular profile of each individual. Personalized medicine is also called as predictive medicine that uses genetic/molecular information to predict disease development, progression, and clinical outcome. In this chapter, we discuss the advantages of using nanotechnology to understand biological systems with an example of the biomarker discovery of cancer. Recent developments in bio computing served as the base for the identification of multiplexed probes in a nano particle. Together we have correlated the bio molecular signatures with clinical outcomes and we have also addressed an emerging field called bio-nano-informatics to suggest an individual therapy for cancer and other diseases.

scholarly journals From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 308 ◽  
Author(s):  
Julijana Ivanisevic ◽  
Elizabeth J. Want
Keyword(s):  
Statistical Power ◽  
Biomarker Discovery ◽  
Disease Onset ◽  
Metabolite Identification ◽  
Holistic Approach ◽  
Data Interpretation ◽  
Relevant Information ◽  
Metabolomics Data ◽  
Biologically Relevant ◽  
Key Steps

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Role of Bioinformatics in Nanotechnology

2017 ◽  
pp. 293-317
Author(s):  
Harishchander Anandaram
Keyword(s):  
Personalized Medicine ◽  
Biomarker Discovery ◽  
Individual Therapy ◽  
Molecular Profile ◽  
Molecular Signatures ◽  
Nano Technology ◽  
Recent Developments ◽  
Novel Biomarkers ◽  
Molecular Information

Recent advancements in bio-computing and nano-technology accelerated the discovery of novel biomarkers in the emerging field of personalized medicine. Personalized medicine deals with disease detection and therapy from the molecular profile of each individual. Personalized medicine is also called as predictive medicine that uses genetic/molecular information to predict disease development, progression, and clinical outcome. In this chapter, we discuss the advantages of using nanotechnology to understand biological systems with an example of the biomarker discovery of cancer. Recent developments in bio computing served as the base for the identification of multiplexed probes in a nano particle. Together we have correlated the bio molecular signatures with clinical outcomes and we have also addressed an emerging field called bio-nano-informatics to suggest an individual therapy for cancer and other diseases.

scholarly journals MetPC: Metabolite Pipeline Consisting of Metabolite Identification and Biomarker Discovery Under the Control of Two-Dimensional FDR

Metabolites ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 103
Author(s):  
Jaehwi Kim ◽  
Jaesik Jeong
Keyword(s):  
Expectation Maximization ◽  
Latent Variables ◽  
Biomarker Discovery ◽  
Metabolite Identification ◽  
Two Dimensional ◽  
Bioinformatics Tool ◽  
Metabolomics Data ◽  
False Discovery ◽  
Hierarchical Statistical Model ◽  
Complex Features

Due to the complex features of metabolomics data, the development of a unified platform, which covers preprocessing steps to data analysis, has been in high demand over the last few decades. Thus, we developed a new bioinformatics tool that includes a few of preprocessing steps and biomarker discovery procedure. For metabolite identification, we considered a hierarchical statistical model coupled with an Expectation–Maximization (EM) algorithm to take care of latent variables. For biomarker metabolite discovery, our procedure controls two-dimensional false discovery rate (fdr2d) when testing for multiple hypotheses simultaneously.

Deep learning meets metabolomics: a methodological perspective

2020 ◽  
Author(s):  
Partho Sen ◽  
Santosh Lamichhane ◽  
Vivek B Mathema ◽  
Aidan McGlinchey ◽  
Alex M Dickens ◽  
...  
Keyword(s):  
Deep Learning ◽  
Biomarker Discovery ◽  
Metabolite Identification ◽  
Metabolomics Data ◽  
Metabolic Phenotyping ◽  
Metabolic Modelling ◽  
Actionable Knowledge ◽  
The Past ◽  
Genome Scale ◽  
Biological Outcomes

Abstract Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of ‘big data’, including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

scholarly journals The combined detection of Amphiregulin, Cyclin A1 and DDX20/Gemin3 expression predicts aggressive forms of oral squamous cell carcinoma

2021 ◽  
Author(s):  
Ekaterina Bourova-Flin ◽  
Samira Derakhshan ◽  
Afsaneh Goudarzi ◽  
Tao Wang ◽  
Anne-Laure Vitte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Gene Expression Signature ◽  
Predictive Biomarkers ◽  
Specific Gene ◽  
Specific Expression ◽  
Intrinsic Nature ◽  
Independent Cohort

Abstract Background Large-scale genetic and epigenetic deregulations enable cancer cells to ectopically activate tissue-specific expression programmes. A specifically designed strategy was applied to oral squamous cell carcinomas (OSCC) in order to detect ectopic gene activations and develop a prognostic stratification test. Methods A dedicated original prognosis biomarker discovery approach was implemented using genome-wide transcriptomic data of OSCC, including training and validation cohorts. Abnormal expressions of silent genes were systematically detected, correlated with survival probabilities and evaluated as predictive biomarkers. The resulting stratification test was confirmed in an independent cohort using immunohistochemistry. Results A specific gene expression signature, including a combination of three genes, AREG, CCNA1 and DDX20, was found associated with high-risk OSCC in univariate and multivariate analyses. It was translated into an immunohistochemistry-based test, which successfully stratified patients of our own independent cohort. Discussion The exploration of the whole gene expression profile characterising aggressive OSCC tumours highlights their enhanced proliferative and poorly differentiated intrinsic nature. Experimental targeting of CCNA1 in OSCC cells is associated with a shift of transcriptomic signature towards the less aggressive form of OSCC, suggesting that CCNA1 could be a good target for therapeutic approaches.

scholarly journals Identification of Novel Mutations in Colorectal Cancer Patients Using AmpliSeq Comprehensive Cancer Panel

2021 ◽  
Vol 11 (6) ◽  
pp. 535
Author(s):  
Bader Almuzzaini ◽  
Jahad Alghamdi ◽  
Alhanouf Alomani ◽  
Saleh AlGhamdi ◽  
Abdullah A. Alsharm ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Personalized Medicine ◽  
Cancer Patients ◽  
Biomarker Discovery ◽  
Local Population ◽  
Network Analyses ◽  
Functional Perspective ◽  
Novel Variants ◽  
Colorectal Cancer Patients ◽  
Cancer Panel

Biomarker discovery would be an important tool in advancing and utilizing the concept of precision and personalized medicine in the clinic. Discovery of novel variants in local population provides confident targets for developing biomarkers for personalized medicine. We identified the need to generate high-quality sequencing data from local colorectal cancer patients and understand the pattern of occurrence of variants. In this report, we used archived samples from Saudi Arabia and used the AmpliSeq comprehensive cancer panel to identify novel somatic variants. We report a comprehensive analysis of next-generation sequencing results with a coverage of >300X. We identified 466 novel variants which were previously unreported in COSMIC and ICGC databases. We analyzed the genes associated with these variants in terms of their frequency of occurrence, probable pathogenicity, and clinicopathological features. Among pathogenic somatic variants, 174 were identified for the first time in the large intestine. APC, RET, and EGFR genes were most frequently mutated. A higher number of variants were identified in the left colon. Occurrence of variants in ERBB2 was significantly correlated with those of EGFR and ATR genes. Network analyses of the identified genes provide functional perspective of the identified genes and suggest affected pathways and probable biomarker candidates. This report lays the ground work for biomarker discovery and identification of driver gene mutations in local population.

scholarly journals Integrating syphilis screening in a large-scale HIV prevention program for key populations: the Avahan experience from India

2013 ◽  
Vol 7 (06) ◽  
pp. 484-488 ◽  
Author(s):  
Mugundu Ramien Parthasarathy ◽  
Prakash Narayanan ◽  
Anjana Das ◽  
Anup Gurung ◽  
Parimi Prabhakar ◽  
...  
Keyword(s):  
Hiv Prevention ◽  
Prevention Program ◽  
Large Scale ◽  
Point Of Care ◽  
Key Populations ◽  
Immunochromatographic Strip ◽  
Transmitted Infection ◽  
Sexually Transmitted ◽  
Syphilis Screening ◽  
Strip Test

Introduction: Documented experiences from India on the implementation of syphilis screening in large-scale HIV prevention programs for “key populations at higher risk’ (KPs) are limited. Avahan is a large-scale HIV prevention program providing services to more than 300,000 KPs in six high HIV prevalence states of India since 2004. Avahan clinics provide a sexually transmitted infection service package which includes bi-annual syphilis screening. The trends in the coverage of syphilis screening among Avahan clinic attendees were studied retrospectively. Methodology: Screening was performed using either the Rapid Plasma Reagin (RPR) test or point-of-care immunochromatographic strip test (ICST). Clinic records from 2005 to 2009 were collated in an individual tracking database and analyzed with STATA-10. Results: Initially the coverage of syphilis screening (2.6% in 2005) was constrained by the availability and operational complexity of the RPR test. After its introduction in 2007, the use of ICST for screening increased from 7.4% to 77.0% and the proportion of clinic attendees screened increased from 9.0% to 21.6% during 2007-2009. The RPR reactivity rates declined from 6.6% (2006) to 4.4% (2009). Conclusion: The data showed improved rates of screening of clinic attendees and declining trends in sero-reactivity over time. The introduction of point-of-care syphilis tests may have contributed to the improved coverage of syphilis screening. The ICST may be considered for initial syphilis screening at other resource-constrained primary care sites in India such as ante-natal clinics and other KP interventions.

scholarly journals Current State of “Omics” Biomarkers in Pancreatic Cancer

2021 ◽  
Vol 11 (2) ◽  
pp. 127 ◽  
Author(s):  
Beste Turanli ◽  
Esra Yildirim ◽  
Gizem Gulfidan ◽  
Kazim Yalcin Arga ◽  
Raghu Sinha
Keyword(s):  
Pancreatic Cancer ◽  
Personalized Medicine ◽  
Biomarker Discovery ◽  
Survival Rates ◽  
Postoperative Recurrence ◽  
Molecular Biomarkers ◽  
Background Information ◽  
Current State ◽  
Pancreatic Cancer Screening ◽  
The Future

Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different “omics” levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.

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