Nanoparticle isolation from biological media for protein corona analysis : the impact of incubation and recovery protocols on nanoparticle properties

Nanoparticles are increasingly implemented in biomedical applications, including the diagnosis and treatment of disease. When exposed to complex biological media, nanoparticles spontaneously interact with their surrounding environment, leading to the surface-adsorption of small and bio- macromolecules- termed the "corona". Corona composition is governed by nanoparticle properties and incubation parameters. While the focus of most studies is on the protein signature of the nanoparticle corona, the impact of experimental protocols on nanoparticle size in the presence of complex biological media, and the impact of nanoparticle recovery from biological media has not yet been reported. Here using a non-degradable robust model, we show how centrifugation-resuspension protocols used for the isolation of nanoparticles from incubation media, incubation duration and shear flow conditions alter nanoparticle parameters including particle size, zeta potential and total protein content. Our results show significant changes in nanoparticle size following exposure to media containing protein under different flow conditions, which also altered the composition of surface-adsorbed proteins profiled by SDS-PAGE. Our in situ analysis of nanoparticle size in media containing protein using particle tracking analysis highlights that centrifugation-resuspension is disruptive to agglomerates that are spontaneously formed in protein containing media, highlighting the need for in situ analytical methods that do not alter the intermediates formed following nanoparticle exposure to biological media. Nanomedicines are mostly intended for parenteral administration, and our findings show that parameters such as shear flow can significantly alter nanoparticle physicochemical parameters. Overall, we show that the centrifugation-resuspension isolation of nanoparticles from media significantly alters particle parameters in addition to the overall protein composition of surface-adsorbed proteins. We recommend that nanoparticle characterization pipelines studying bio-nano interactions during early nanomedicine development consider biologically-relevant shear flow conditions and media composition that can significantly alter particle physical parameters and subsequent conclusions from these studies

Quantification of drug metabolising enzymes and transporter proteins in the paediatric duodenum via LC-MS/MS proteomics using a QconCAT technique

Characterising the small intestine absorptive membrane is essential to enable prediction of the systemic exposure of oral formulations. In particular, the ontogeny of key intestinal Drug Metabolising Enzymes and Transporter (DMET) proteins involved in drug disposition needs to be elucidated to allow for accurate prediction of the PK profile of drugs in the paediatric cohort. Using pinch biopsies from the paediatric duodenum (n = 36; aged 11 months to 15 years), the abundance of 21 DMET proteins and two enterocyte markers were quantified via LC-MS/MS. An established LCMS nanoflow method was translated to enable analysis on a microflow LC system, and a new stable-isotope-labelled QconCAT standard developed to enable quantification of these proteins. Villin-1 was used to standardise abundancy values. The observed abundancies and ontogeny profiles, agreed with adult LC-MS/MS-based data, and historic paediatric data obtained via western blotting. A linear trend with age was observed for duodenal CYP3A4 and CES2 only. As this work quantified peptides on a pinch biopsy coupled with a microflow method, future studies using a wider population range are very feasible. Furthermore, this DMET ontogeny data can be used to inform paediatric PBPK modelling and to enhance the understanding of oral drug absorption and gut bioavailability in paediatric populations

Urinary paraben concentrations and associations with the periconceptional urinary metabolome : untargeted and targeted metabolomics analyses of participants from the early pregnancy study

BACKGROUND: Parabens, found in everyday items from personal care products to foods, are chemicals with endocrine-disrupting activity, which has been shown to influence reproductive function. OBJECTIVES: This study investigated whether urinary concentrations of methylparaben, propylparaben, or butylparaben were associated with the urinary metabolome during the periconceptional period, a critical window for female reproductive function. Changes to the periconceptional urinary metabolome could provide insights into the mechanisms by which parabens could impact fertility. METHODS: Urinary paraben concentrations were measured in paired pre- and postconception urine samples from 42 participants in the Early Pregnancy Study, a prospective cohort of 221 women attempting to conceive. We performed untargeted and targeted metabolomics analyses using ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry. We used principal component analysis, orthogonal partial least-squares discriminant analysis, and permutation testing, coupled with univariate statistical analyses, to find metabolites associated with paraben concentration at the two time points. Potential confounders were identified with a directed acyclic graph and used to adjust results with multivariable linear regression. Metabolites were identified using fragmentation data. RESULTS: Seven metabolites were associated with paraben concentration (variable importance to projection score formula presented , false discovery rate-corrected formula presented ). We identified four diet-related metabolites to the Metabolomics Standards Initiative (MSI) certainty of identification level 2, including metabolites from smoke flavoring, grapes, and olive oil. One metabolite was identified to the class level only (MSI level 3). Two metabolites were unidentified (MSI level 4). After adjustment, three metabolites remained associated with methylparaben and propylparaben, two of which were diet-related. No metabolomic markers of endocrine disruption were associated with paraben concentrations. DISCUSSION: This study identified novel relationships between urinary paraben concentrations and diet-related metabolites but not with metabolites on endocrine-disrupting pathways, as hypothesized. It demonstrates the feasibility of integrating untargeted metabolomics data with environmental exposure information and epidemiological adjustment for confounders. The findings underscore a potentially important connection between diet and paraben exposure, with applications to nutritional epidemiology and dietary exposure assessment

Palbociclib and fulvestrant act in synergy to modulate central carbon metabolism in breast cancer cells

The aims of this study were to determine whether combination chemotherapeutics exhibit a synergistic effect on breast cancer cell metabolism. Palbociclib, is a selective inhibitor of cyclin-dependent kinases 4 and 6, and when patients are treated in combination with fulvestrant, an estrogen receptor antagonist, they have improved progression-free survival. The mechanisms for this survival advantage are not known. Therefore, we analyzed metabolic and transcriptomic changes in MCF-7 cells following single and combination chemotherapy to determine whether selective metabolic pathways are targeted during these different modes of treatment. Individually, the drugs caused metabolic disruption to the same metabolic pathways, however fulvestrant additionally attenuated the pentose phosphate pathway and the production of important coenzymes. A comprehensive effect was observed when the drugs were applied together, confirming the combinatory therapy’s synergism in the cell model. This study also highlights the power of merging high-dimensional datasets to unravel mechanisms involved in cancer metabolism and therapy

OMICmAge : an integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process

A gap analysis of UK biobank publications reveals SNPs associated with intrinsic subtypes of breast cancer

Breast cancer is a multifaceted disease and a leading cause of cancer morbidity and mortality in females across the globe. In 2020 alone, 2.3 million women were diagnosed and 685,000 died of breast cancer worldwide. With the number of diagnoses projected to increase to 3 million per year by 2040 it is essential that new methods of detection and disease stratification are sought to decrease this global cancer burden. Although significant improvements have been made in breast cancer diagnosis and treatment, the prognosis of breast cancer remains poor in some patient groups (i.e. triple negative breast cancer), necessitating research into better patient stratification, diagnosis and drug discovery. The UK Biobank, a comprehensive biomedical and epidemiological database with a wide variety of multiomics data (genomics, proteomics, metabolomics) offers huge potential to uncover groundbreaking discoveries in breast cancer research leading to improved patient stratification. Combining genomic, proteomic, and metabolic profiles of breast cancer in combination with histological classification, can aid treatment decisions through accurate diagnosis and prognosis prediction of tumor behaviour. Here, we systematically reviewed PubMed publications reporting the analysis of UK Biobank data in breast cancer research. Our analysis of UK Biobank studies in the past five years identified 125 publications, of which 76 focussed on genomic data analysis. Interestingly, only two studies reported the analysis of metabolomics and proteomics data, with none performing multiomics analysis of breast cancer. A meta-analysis of the 76 publications identified 2,870 genetic variants associated with breast cancer across 445 genes. Subtype analysis revealed differential genetic alteration in 13 of the 445 genes and the identification of 59 well-established breast cancer genes. in differential pathways. Pathway interaction analyses illuminated their involvement in general cancer biomolecular pathways (e.g. DNA damage repair, Gene expression). While our meta-analysis only measured genetic differences in breast cancer due to current usage of UK Biobank data, minimal multi-omics analyses have been performed and the potential for harnessing multi-omics strategies within the UK Biobank cohort holds promise for unravelling the biological signatures of distinct breast cancer subtypes further in the future

High-throughput metabolic screening of microalgae genetic variation in response to nutrient limitation

Microalgae produce metabolites that could be useful for applications in food, biofuel or fine chemical production. The identification and development of suitable strains require analytical methods that are accurate and allow rapid screening of strains or cultivation conditions. We demonstrate the use of Fourier transform infrared (FT-IR) spectroscopy to screen mutant strains of Chlamydomonas reinhardtii. These mutants have knockdowns for one or more nutrient starvation response genes, namely PSR1, SNRK2.1 and SNRK2.2. Limitation of nutrients including nitrogen and phosphorus can induce metabolic changes in microalgae, including the accumulation of glycerolipids and starch. By performing multivariate statistical analysis of FT-IR spectra, metabolic variation between different nutrient limitation and non-stressed conditions could be differentiated. A number of mutant strains with similar genetic backgrounds could be distinguished from wild type when grown under specific nutrient limited and replete conditions, demonstrating the sensitivity of FT-IR spectroscopy to detect specific genetic traits. Changes in lipid and carbohydrate between strains and specific nutrient stress treatments were validated by other analytical methods, including liquid chromatography–mass spectrometry for lipidomics. These results demonstrate that the PSR1 gene is an important determinant of lipid and starch accumulation in response to phosphorus starvation but not nitrogen starvation. However, the SNRK2.1 and SNRK2.2 genes are not as important for determining the metabolic response to either nutrient stress. We conclude that FT-IR spectroscopy and chemometric approaches provide a robust method for microalgae screening

ENT2 facilitates brain endothelial cell penetration and blood-brain barrier transport by a tumor-targeting anti-DNA autoantibody

The blood-brain barrier (BBB) prevents antibodies from penetrating the CNS and limits conventional antibody-based approaches to brain tumors. We now show that ENT2, a transporter that regulates nucleoside flux at the BBB, may offer an unexpected path to circumventing this barrier to allow targeting of brain tumors with an anti-DNA autoantibody. Deoxymab-1 (DX1) is a DNA-damaging autoantibody that localizes to tumors and is synthetically lethal to cancer cells with defects in the DNA damage response. We found that DX1 penetrated brain endothelial cells and crossed the BBB, and mechanistic studies identify ENT2 as the key transporter. In efficacy studies, DX1 crosses the BBB to suppress orthotopic glioblastoma and breast cancer brain metastases. ENT2-linked transport of autoantibodies across the BBB has potential to be exploited in brain tumor immunotherapy, and its discovery raises hypotheses on actionable mechanisms of CNS penetration by neurotoxic autoantibodies in CNS lupus

Mitochondrial hyperfusion via metabolic sensing of regulatory amino acids

The relationship between nutrient starvation and mitochondrial dynamics is poorly understood. We find that cells facing amino acid starvation display clear mitochondrial fusion as a means to evade mitophagy. Surprisingly, further supplementation of glutamine (Q), leucine (L), and arginine (R) did not reverse, but produced stronger mitochondrial hyperfusion. Interestingly, the hyperfusion response to Q + L + R was dependent upon mitochondrial fusion proteins Mfn1 and Opa1 but was independent of MTORC1. Metabolite profiling indicates that Q + L + R addback replenishes amino acid and nucleotide pools. Inhibition of fumarate hydratase, glutaminolysis, or inosine monophosphate dehydrogenase all block Q + L + R-dependent mitochondrial hyperfusion, which suggests critical roles for the tricarboxylic acid (TCA) cycle and purine biosynthesis in this response. Metabolic tracer analyses further support the idea that supplemented Q promotes purine biosynthesis by serving as a donor of amine groups. We thus describe a metabolic mechanism for direct sensing of cellular amino acids to control mitochondrial fusion and cell fate