Omic methodologies have become key analytical tools in a wide number of research topics such as systems biology, environmental analysis, biomedicine or food analysis. They are especially useful when they are combined providing a new perspective and a holistic view of the analytical problem. Methodologies for microbiota analysis have been mostly focused on genome sequencing. However, information provided by these metagenomic studies is limited to the identification of the presence of genes, taxa and their inferred functionality. To achieve a deeper knowledge of microbial functionality in health and disease, especially in dysbiosis conditions related to metal and metalloid exposure, the introduction of additional meta-omic approaches including metabolomics, metallomics, metatranscriptomics and metaproteomics results essential. The possible impact of metals and metalloids on the gut microbiota and their effects on gut-brain axis (GBA) only begin to be figured out. To this end new analytical workflows combining powerful tools are claimed such as high resolution mass spectrometry and heteroatom-tagged proteomics for the absolute quantification of metal-containing biomolecules using the metal as a "tag" in a sensitive and selective detector (e.g. ICP-MS). This review focus on current analytical methodologies related with the analytical techniques and procedures available for metallomics and microbiota analysis with a special attention on their advantages and drawbacks.
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http://dx.doi.org/10.1016/j.aca.2021.338620 | DOI Listing |
Endocr Relat Cancer
January 2025
S Dehm, Masonic Cancer Center, University of Minnesota, Minneapolis, United States.
Treatment for castration-resistant prostate cancer (CRPC) primarily involves the suppression of androgen receptor (AR) activity using androgen receptor signaling inhibitors (ARSIs). While ARSIs have extended patient survival, resistance inevitably develops. Mechanisms of resistance include genomic aberrations at the AR locus that reactivate AR signaling, or lineage plasticity that drives emergence of AR-independent phenotypes.
View Article and Find Full Text PDFBackground: There is currently an unmet need for novel accessible biomarkers that capture the complex and heterogenous pathophysiology of Alzheimer's disease (AD). Over the past decade, the systems-based multi-omic approaches employed by the Accelerating Medicines Partnership in AD (AMP-AD) have resulted in the identification of promising peripheral markers of disease heterogeneity. This scientific review will highlight these advances with a particular focus on the consortium's successes in peripheral protein biomarker discovery in cerebrospinal fluid (CSF) and plasma.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: The FunGen-xQTL project has significantly advanced genetics by developing and exploring novel quantitative trait loci (QTL) types in human brains, enriching our understanding of complex neurological disease etiology. We broadened the scope of epigenomic QTL analysis, integrating histone acetylation QTLs (haQTLs) and methylation QTLs (mQTLs) that affect multiple histone acetylation peaks or methylation CpG sites spatially. Additionally, we investigated a new category of splicing QTLs (sQTLs) implicated in nonsense-mediated decay (NMD).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Stanford University, Palo Alto, CA, USA.
Background: Genome-wide association studies (GWAS) have identified thousands of genomic regions associated with complex diseases but understanding the underlying causal mechanisms remains a significant challenge. The FunGen-xQTL project has addressed this by generating and harmonizing molecular quantitative trait loci (xQTL) across multiple layers of molecular traits in human brains, cerebrospinal fluid, and blood-derived cells relevant to neurodegenerative disorders. Existing approaches for integrating xQTL data with GWAS have typically focused on individual molecular traits in individual QTL layers.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Surgery, Trinity St. James's Cancer Institute, Trinity Translational Medicine Institute, Trinity College Dublin, St. James's Hospital, Dublin 8, Ireland.
Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n = 32) and serum (n = 68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk.
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