Biology subjects

Pietzner, M.

Publications and source records attributed to Pietzner, M..

9 recordsLinked to original sources

bioRxiv2026

Massively parallel characterization and predictive modelling of neuronal regulatory variation

Disease-associated variants reside frequently in noncoding cis-regulatory elements (CREs), yet their functional consequences remain poorly understood. We performed a large-scale lentiMPRA in human excitatory neurons, quantifying the impact of >46,000 naturally occurring variants across >27,000 candidate CREs near 524 disease-associated genes. These data improved regulatory variant effect predictions beyond state-of-the-art models. Significant allelic effects occurred at comparable rates across common, rare, and singleton variants, demonstrating that, within MPRA-measurable effects, population frequency carries limited information about per-variant regulatory impact. Variant effect detectability and magnitude were governed primarily by baseline activity of the enclosing regulatory element and local sequence context. Regulatory effects were distributed across numerous transcription factors rather than concentrated in master regulators, consistent with a combinatorial enhancer architecture. We establish a large-scale functional variant catalog and provide a complementary benchmark and resource for developing and evaluating models of noncoding regulatory variation.

genetics↗

bioRxiv2024

Damaging mutations in LXRα uncouple lipogenesis from hepatotoxicity and implicate hepatic cholesterol sensing in human liver health

The nuclear receptor Liver X Receptor- (LXR) activates lipogenic gene expression in hepatocytes. Its inhibition has therefore been proposed as a strategy to treat metabolic-dysfunction-associated steatotic liver disease (MASLD). In order to understand the impact of reducing LXR activity on human health we first examined the association between the carriage of rare loss of function mutations in NR1H3 (encoding LXR) and metabolic and hepatic phenotypes. We identified 63 rare predicted damaging variants in the ligand binding domain of LXR in 454,787 participants in UK Biobank. On functional characterisation, 42 of these were found to be severely impaired. Consistent with loss of the lipogenic actions of LXR, carriers of damaging mutations in LXR had reduced serum triglycerides ({beta}=-0.13 s.d. {+/-}0.03, P=2.7x10-5, N(carriers)=971). Surprisingly, these carriers also had elevated concentrations of serum liver enzymes (e.g. ALT: {beta}=0.17s.d. {+/-}0.03, P=1.1x10-8, N(carriers)=972) with a 35% increased risk of clinically significant elevations in ALT (OR=1.32, 95%CI:1.15-1.53, P=1.2x10-4, N(carriers)=972), suggestive of hepatotoxicity. We generated a knock-in mouse carrying one of the most severely damaging mutations (Nr1h3 p.W441R) which we demonstrated to have dominant negative properties. Homozygous knock-in mice rapidly developed severe hepatitis and fibrotic liver injury following exposure to western diet despite markedly reduced steatosis, liver triglycerides and lipogenic gene expression. This phenotype was completely rescued by viral over-expression of wildtype LXR specifically in hepatocytes, indicating a cell-autonomous effect of the mutant on hepatocyte health. While homozygous LXR knockout mice showed some evidence of hepatocyte injury under similar dietary conditions, the phenotype of the LXRW441R/W441R mouse was much more severe, suggesting that dominant negative mutations that actively co-repress target genes can result in pathological impacts significantly more severe than those seen with simple absence of the receptor. In summary, our results show that loss of function mutations in LXR occur in at least 1/450 people and are associated with evidence of liver dysfunction. These findings implicate LXR in the maintenance of human liver health, identify a new murine model of rapidly progressive fibrotic liver disease and caution against LXR antagonism as a therapeutic strategy for MASLD.

genetics↗

bioRxiv2023

Optimizing linear ion trap data independent acquisition towards single cell proteomics

A linear ion trap (LIT) is an affordable, robust mass spectrometer that proves fast scanning speed and high sensitivity, where its primary disadvantage is inferior mass accuracy compared to more commonly used time-of-flight (TOF) or orbitrap (OT) mass analyzers. Previous efforts to utilize the LIT for low-input proteomics analysis still rely on either built-in OTs for collecting precursor data or OT-based library generation. Here, we demonstrate the potential versatility of the LIT for low-input proteomics as a stand-alone mass analyzer for all mass spectrometry measurements, including library generation. To test this approach, we first optimized LIT data acquisition methods and performed library-free searches with and without entrapment peptides to evaluate both the detection and quantification accuracy. We then generated matrix-matched calibration curves to estimate the lower limit of quantification using only 10 ng of starting material. While LIT-MS1 measurements provided poor quantitative accuracy, LIT-MS2 measurements were quantitatively accurate down to 0.5 ng on column. Finally, we optimized a suitable strategy for spectral library generation from low-input material, which we used to analyze single-cell samples by LIT-DIA using LIT-based libraries generated from as few as 40 cells.

biochemistry↗

bioRxiv2022

An atlas of genetic scores to predict multi-omic traits

Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; N=50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, N=3,175; Olink, N=4,822), plasma metabolomics (Metabolon HD4, N=8,153), serum metabolomics (Nightingale, N=37,359), and whole blood Illumina RNA sequencing (N=4,136). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal (OmicsPred.org) to facilitate public access to all genetic scores and validation results as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

genomics↗

bioRxiv2021

Cross-platform proteomics to advance genetic prioritisation strategies

Discovery of protein quantitative trait loci (pQTLs) has been enabled by affinity-based proteomic techniques and is increasingly used to guide genetically informed drug target evaluation. Large-scale proteomic data are now being created, but systematic, bidirectional assessment of platform differences is lacking, restricting clinical translation. We compared genetic, technical, and phenotypic determinants of 871 protein targets measured using both aptamer-(SomaScan(R) Platform v4) and antibody-based (Olink) assays in up to 10,708 individuals. Correlations coefficients for overlapping protein targets varied widely (median 0.38, IQR: 0.08-0.64). We found that 64% of pQTLs were shared across both platforms among all identified 608 cis- and 1,315 trans-pQTLs with sufficient power for replication, but with correlations of effect estimates being lower than previously reported (cis: 0.41, trans: 0.34). We identified technical, protein, and variant characteristics that contributed significantly to platform differences and found contradicting phenotypic associations attributable to those. We demonstrate how integrating phenomic and gene expression data improves genetic prioritisation strategies, including platform-specific pQTLs.

genetics↗

bioRxiv2020

Genetic architecture of host proteins interacting with SARS-CoV-2

Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid in silico assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver (https://omicscience.org/apps/covidpgwas/).

genomics↗

bioRxiv2020

Extreme phenotypes define epigenetic and metabolic signatures in cardiometabolic syndrome.

Improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis are ongoing healthcare challenges. Using plasma biomarkers analysis coupled with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (obese and lipodystrophy) and comparing these to lean individuals and blood donors, the present study identifies the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells and shows that extreme phenotype groups could be distinguished from lean individuals, and from each other, across all data layers. The characterisation of the same obese group, six months after bariatric surgery shows the loss of the patterns of abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurs via the establishment of novel gene expression landscapes. Netosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention. Taken together, by integrating across data layers, the observed molecular and metabolic differences form a disease signature that is able to discriminate, amongst the blood donors, those individuals with a higher likelihood of having cardiometabolic syndrome, even when not presenting with the classic features.

genomics↗

bioRxiv2020

Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes

Circulating levels of small molecules or metabolites are highly heritable, but the impact of genetic differences in metabolism on human health is not well understood. In this cross-platform, genome-wide meta-analysis of 174 metabolite levels across six cohorts including up to 86,507 participants (70% unpublished data), we identify 499 (362 novel) genome-wide significant associations (p<4.9×10-10) at 144 (94 novel) genomic regions. We show that inheritance of blood metabolite levels in the general population is characterized by pleiotropy, allelic heterogeneity, rare and common variants with large effects, non-linear associations, and enrichment for nonsynonymous variation in transporter and enzyme encoding genes. The majority of identified genes are known to be involved in biochemical processes regulating metabolite levels and to cause monogenic inborn errors of metabolism linked to specific metabolites, such as ASNS (rs17345286, MAF=0.27) and asparagine levels. We illustrate the influence of metabolite-associated variants on human health including a shared signal at GLP2R (p.Asp470Asn) associated with higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes risk, and demonstrate beta-arrestin signalling as the underlying mechanism in cellular models. We link genetically-higher serine levels to a 95% reduction in the likelihood of developing macular telangiectasia type 2 [odds ratio (95% confidence interval) per standard deviation higher levels 0.05 (0.03-0.08; p=9.5×10-30)]. We further demonstrate the predictive value of genetic variants identified for serine or glycine levels for this rare and difficult to diagnose degenerative retinal disease [area under the receiver operating characteristic curve: 0.73 (95% confidence interval: 0.70-0.75)], for which low serine availability, through generation of deoxysphingolipids, has recently been shown to be causally relevant. These results show that integration of human genomic variation with circulating small molecule data obtained across different measurement platforms enables efficient discovery of genetic regulators of human metabolism and translation into clinical insights.Competing Interest StatementA.S.B. has received grants from AstraZeneca, Biogen, Bioverativ, Merck, Novartis, and Sanofi. J. D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), the Steering Committee of UK Biobank (since 2011), the MRC International Advisory Group (ING) member, London (since 2013), the MRC High Throughput Science Omics Panel Member, London (since 2013), the Scientific Advisory Committee for Sanofi (since 2013), the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis and the Astra Zeneca Genomics Advisory Board (2018). E.B.F. is an employee and stock holder of Pfizer.View Full Text

genetics↗

bioRxiv2019

Genome-wide scan identifies novel genetic loci regulating salivary metabolite levels

Saliva, as a biofluid, is inexpensive and non-invasive to obtain, and provides a vital tool to investigate oral health and its interaction with systemic health conditions. There is growing interest in salivary biomarkers for systemic diseases, notably cardiovascular disease. Whereas hundreds of genetic loci have been shown to be involved in the regulation of blood metabolites leading to unprecedented insights into the pathogenesis of complex human diseases, little is known about the impact of host genetics on salivary metabolites. Here we report the first genome-wide association study exploring 476 salivary metabolites in 1,419 subjects of European ancestry from the TwinsUK cohort (discovery phase). A total of 14 salivary metabolites were significantly associated (p<10-10) with genetic variants that mapped to 11 distinct loci, most of which replicated in the Study of Health in Pomerania (SHIP-2) cohort. Interestingly, while only a limited number of the loci that are known to regulate blood metabolites were also associated with salivary metabolites in our study, we identified several novel saliva-specific locus-metabolite associations, including associations for the AGMAT (with the metabolites 4-guanidinobutanoate and beta-guanidinopropanoate), ATP13A5 (with the metabolite creatinine) and DPYS (with the metabolites 3-ureidopropionate and 3-ureidoisobutyrate) loci. Our study suggests that there are biological pathways which are specific to the regulation of the salivary metabolome. In addition, some of our findings may have clinical relevance, such as the utility of the pyrimidine (uracil) degradation metabolites in predicting 5-fluorouracil toxicity and the role of the agmatine pathway metabolites as biomarkers of oral health.

genetics↗