Biology subjects

Sadler, M. C.

Publications and source records attributed to Sadler, M. C..

4 recordsLinked to original sources

bioRxiv2025

Integration of genetic evidence to identify approved drug targets

Drugs targeting genes supported by human genetic evidence are more likely to succeed in clinical trials. While previous approaches have benchmarked individual methods such as genome-wide association studies (GWAS), rare variant burden testing, and quantitative trait locus (QTL)-informed Mendelian randomization, it remains unclear how best to integrate these signals for drug target discovery. Here, we compared gene-prioritization strategies across 30 complex traits, evaluating their ability to recover approved drug targets compiled into lenient and moderate gold-standard sets from six curated databases. Gene-level association scores from GWAS, expression QTL, protein QTL, and exome-based analyses were integrated using five unsupervised approaches. Predictive performance was assessed with area under the receiver operating characteristic curve (AUROC) and enrichment-based statistics. Across traits, GWAS alone ranked known drug targets on average [~]652 ranks (3.42%) above random expectation, and the minimum-rank-based integration strategy provided an improvement of further [~]558 positions (2.93%), achieving the best AUROC in 23 of 30 traits. When comparing genetic correlation and drug target overlap across trait pairs, we observed a significant positive association (r = 0.193; p = 5.46e-5), and cross-trait analyses further revealed that prioritization scores derived from related diseases could at times equal or even surpass a traits own performance. For instance, coronary artery disease data improved the prediction of stroke targets (p = 0.004), while inflammatory bowel disease data enhanced the prioritization of chronic kidney disease targets (p = 0.014). Taken together, these results demonstrate that integrating complementary genetic signals through a minimum-rank-based framework, combined with information from genetically related traits, systematically strengthens drug target identification across complex diseases.

genetics↗

bioRxiv2025

Denitrification genes in SAR11 and other ubiquitous lineages of marine bacteria isolated from the northern Benguela Upwelling System

Denitrification is a microbial process that leads to nitrogen loss from marine oxygen minimum zones (OMZs). The complete metabolic pathway for denitrification reduces nitrate to dinitrogen gas in four sequential steps. Many anaerobic and facultatively anaerobic bacteria are capable of the initial step of nitrate reduction to nitrite. Far fewer reduce nitrite to nitric oxide, nitrous oxide, or dinitrogen gas. In this study, we cultured and sequenced the complete genomes of 24 bacteria isolated from low dissolved oxygen waters (DO = 24 {micro}M) in the northern Benguela Upwelling System (nBUS) OMZ to identify facultatively anaerobic bacteria with the genetic potential to contribute to denitrification. Most of the isolates obtained from the nBUS have denitrification genes (79%). They include several new species in the order Pelagibacterales (SAR11), as well as representatives from a previously uncultured family of Arenicellales (UBA868), a previously uncultured genus of Paracoccaceae, and a previously undescribed genus of Porticoccaceae. All ten nBUS SAR11 have a previously unidentified genomic region that codes for a copper-containing nitrite reductase (nirK), suggesting that they have the potential to contribute to nitrogen loss by respiring nitrite to nitric oxide. Significance StatementVast genomic diversity has confounded sequencing efforts to identity the potential for marine bacteria to contribute to denitrification in marine oxygen minimum zones (OMZs). To identify facultatively anaerobic microbes with the genetic potential to contribute to marine nitrogen loss, we cultured and sequenced the complete genomes of bacteria from low-oxygen waters of the northern Benguela Upwelling System. Complete genomes allowed for a comprehensive analysis of denitrification. Most bacterial isolates, including all ten SAR11, have the genetic potential to contribute to denitrification. This suggests that some of the most abundant lineages of marine bacteria in the oceans are adapted to anoxic conditions in OMZs and may have a more direct role in marine nitrogen loss than previously suspected.

microbiology↗

bioRxiv2022

Exploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations

Despite the success of genome-wide association studies (GWASs) in identifying genetic variants associated with complex traits, understanding the mechanisms behind these statistical associations remains challenging. Several methods that integrate methylation, gene expression, and protein quantitative trait loci (QTLs) with GWAS data to determine their causal role in the path from genotype to phenotype have been proposed. Here, we developed and applied a multi-omics Mendelian randomization (MR) framework to study how metabolites mediate the effect of gene expression on complex traits. We identified 206 transcript-metabolite-trait causal triplets for 28 medically relevant phenotypes. Sixty-seven of these associations were missed by classical transcriptome-wide MR, which only uses gene expression and GWAS data. Among these, we identify biologically relevant pathways, such as between ANKH and calcium levels mediated by citrate and SLC6A12 and serum creatinine through modulation of the levels of the renal osmolyte betaine. We show that the signals missed by transcriptome-wide MR are found thanks to the gain in power allowed by integrating multiple omics-layer. Simulation analyses show that with larger molecular QTL studies and in case of mediated effects, our multi-omics MR framework outperforms classical MR approaches designed to detect causal relationships between single molecular traits and complex phenotypes.

genetics↗

bioRxiv2021

Quantifying mediation between omics layers and complex traits

BackgroundHigh-dimensional omics datasets provide valuable resources to determine the causal role of molecular traits in mediating the path from genotype to phenotype. Making use of quantitative trait loci (QTL) and genome-wide association studies (GWASs) summary statistics, we developed a multivariable Mendelian randomization (MVMR) framework to quantify the connectivity between three omics layers (DNA methylome (DNAm), transcriptome and proteome) and their cascading causal impact on complex traits and diseases. ResultsEvaluating 50 complex traits, we found that on average 37.8% (95% CI: [36.0%-39.5%]) of DNAm-to-trait effects were mediated through transcripts in the cis-region, while only 15.8% (95% CI: [11.9%-19.6%]) are mediated through proteins in cis. DNAm sites typically regulate multiple transcripts, and while found to predominantly decrease gene expression, this was only the case for 53.4% across {approx} 47,000 significant DNAm-transcript pairs. The average mediation proportion for transcript-to-trait effects through proteins (encoded for by the assessed transcript or located in trans) was estimated to be 5.27% (95%CI: [4.11%-6.43%]). Notable differences in the transcript and protein QTL architectures were detected with only 22% of protein levels being causally driven by their corresponding transcript levels. Several regulatory mechanisms were hypothesized including an example where cg10385390 (chr1:8022505) increases the risk of irritable bowel disease by reducing PARK7 transcript and protein expression. ConclusionsThe proposed integrative framework identified putative causal chains through omics layers providing a powerful tool to map GWAS signals. Quantification of causal effects between successive layers indicated that molecular mechanisms can be more complex than what the central dogma of biology would suggest.

genomics↗