Biology subjects

Adams, A. M.

Publications and source records attributed to Adams, A. M..

5 recordsLinked to original sources

bioRxiv2026

Rationally designed split Lettuce aptamer based on large scale mutational analysis

Split aptamer biosensors offer exceptionally low background by assembling only in the presence of a target analyte; however, their performance is frequently limited by the lack of robust design rules for selecting effective split sites. Existing approaches largely rely on heuristic, structure-based assumptions that are poorly validated and often yield suboptimal signal. Herein, we introduce a systematic, data-driven strategy for identifying high-performance split sites within fluorogenic DNA aptamers. Using our massively-parallel aptamer performance analyzer (MAPA) platform, we performed comprehensive single- and double-mutant analysis of the DFAME-binding region of the fluorogenic DNA aptamer Lettuce, informed by its three-dimensional structure. Dimensionality reduction and clustering of the resulting sequence-function landscape revealed mutation-tolerant elements within the binding domain that are suitable for splitting while preserving fluorophore activation. Sensors constructed using these non-intuitive split sites, which are unconventional by standard design principles, exhibited a nearly four-fold improvement in fluorescence signal-to-background ratio for SARS-CoV-2 RNA detection compared to a prior split-Lettuce design. The same split architecture also enabled robust detection of high-pathogenicity H5Nx avian influenza RNA. These results demonstrate that large-scale, data-driven interrogation of aptamer sequence-function relationships can identify non-intuitive split sites and provide a proof-of-concept framework for developing measurement-based design principles for split-aptamer biosensors.

bioengineering↗

bioRxiv2026

Gene-drug interactions identify genomic loci that enhance statin effectiveness in lowering LDL cholesterol.

Hyperlipidemia, and high low-density lipoprotein cholesterol (LDL-c) in particular, is a risk factor for cardiovascular disease, including atherosclerosis, myocardial infarction, and stroke. Nearly 200 million people worldwide take HMG-CoA reductase inhibitors, commonly known as statins, to lower their LDL-c. If statins interfere with the genetic pathways that endogenously increase the risk for hyperlipidemia, gene-statin interactions may identify genomic variants, and thereby individuals with those genotypes, that are particularly sensitive to these medications. We performed a series of genome-wide gene-statin interaction analyses in the UK Biobank for LDL-c and two related lipids: high-density lipoprotein cholesterol (HDL-c) and triglycerides (TG). We identified five genome-wide significant gene-statin interactions for LDL-c, two interactions for HDL-c, and four interactions for TG. Importantly, only the SNP-based heritability of LDL-c was reduced by statin use. Using data from All of Us, we replicated all five significant gene-statin interaction loci for LDL-c in the European-like ancestry sample, two loci in the Americas-like ancestry sample, and one locus in the African-like ancestry sample. We also identified fifteen loci that remained associated with LDL-c despite statin treatment, highlighting potential additional genetic targets for drug development, enhancement, and repurposing. These loci include gene-targets for the recently developed hyperlipidemia drug class (PCSK9 inhibitors) validating our approach to finding new treatments. These results are an important step towards personalized medicine for patients with hyperlipidemia. Author SummaryHigh cholesterol raises the risk of heart attacks and strokes and nearly 200 million people worldwide take statins to lower it. While statins work for nearly everyone, they work better for some people than others. We examined how genetic differences enhance the effectiveness of statin medication as a step toward enhancing personalized medicine for those with high cholesterol. By analyzing genetic and health data from about 390,000 people, we found that statins primarily disrupt the link between genes and LDL or "bad" cholesterol levels. Across the genome, statins reduce the impact of genes on LDL-c levels, but not other blood lipids like HDL-c or triglycerides. For LDL-c specifically, we found five regions where genetic differences increase the effectiveness of statins. People with the protective genetic variants are expected to see greater cholesterol reduction with statin use. We confirmed four of the five findings in European-like ancestry samples, with partial replication in Americas-like and African-like ancestry samples (two variants and one variant, respectively). We also found 15 genomic regions where cholesterol stays high despite statin treatment. These genomic regions could be targets for new or enhanced cholesterol medications. In fact, a newer drug class targets one of these regions supporting our approach to finding new treatments.

genetics↗

bioRxiv2025

Targeting CREB remodels the immune microenvironment to enhance immunotherapy responses in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) remains a challenging disease in need of improved treatments. Cyclic adenosine monophosphate response element binding protein 1 (CREB) is an emerging therapeutic target whose oncogenic effects in PDAC have been largely attributed to a key molecular interplay between oncogenic KrasG12D/+ (Kras*) and chronic inflammation driving irreversible acinar to ductal reprogramming. Here, we demonstrate that CREB activation fosters tumor associated macrophage (TAM) mediated immunosuppression and promotes PDAC growth in an aggressive LSL-KrasG12D/+;Trp53R172H/+;Pdx1Cre/+(KPC) genetically engineered mouse model. Selective deletion of CREB (Crebfl/fl) in KPC(KPCC-/-) mice attenuates primary disease burden. Unbiased transcriptomic analysis and validation using diverse molecular, genetic and pharmacological approaches in vitro and in vivo identify CREB-mediated transcriptional regulation of leukemia inhibitory factor (Lif) as one of the potential mediators of tumor cell-macrophage crosstalk promoting a pro-tumor polarization of TAMs, thereby attenuating the infiltration of effector T cells. Mechanistically, cancer cell derived LIF facilitates an immunosuppressive, pro-tumorigenic state. Importantly, pharmacological targeting of the CREB-LIF signaling axis between cancer cells and macrophages, using a CREB-specific inhibitor (CREBi), significantly suppresses tumor growth and sensitizes PDAC to immunotherapy, highlighting the therapeutic potential of this treatment combination to improve outcomes in this aggressive disease.

cancer biology↗

bioRxiv2023

Theoretical framework and experimental validation of multiplexed analyte quantification using cross-reactive affinity reagents

Gold standard immunoassays depend on specific affinity reagents for accurate molecular quantification. Any cross-reactivity of affinity reagents, wherein the reagent non-specifically binds to unintended molecules, can create false positive binding signals and result in inaccurate quantification of analytes. Mitigating cross-reactivity represents one of the greatest challenges in molecular diagnostics, and remains an unsolved problem. To instead overcome the effects of cross-reactivity, we present a mathematical framework that uses generalized binding equations and noise estimation to enable the use of multiple cross-reactive reagents for multiplexed molecular quantification. As a proof-of-concept, we experimentally demonstrate accurate quantification of a small molecule for which no specific affinity reagents are available, even at high concentrations of a cross-reactive molecule. Furthermore, this robust schema yields well-defined bounds of quantification that make it easier to assess the quality of assay results and predicts under which conditions assay performance is likely to break down. This work turns cross-reactive affinity reagents, which were previously a liability, into an asset for achieving accurate quantification of analytes.

bioengineering↗

bioRxiv2022

The sulfur cycle connects microbiomes and biogeochemistry in deep-sea hydrothermal plumes

In globally distributed deep-sea hydrothermal vent plumes, microbiomes are shaped by the redox energy landscapes created by reduced hydrothermal vent fluids mixing with oxidized seawater. Plumes can disperse over thousands of kilometers and are complex. Their characteristics are determined by geochemical sources from hydrothermal vents, e.g., hydrothermal inputs, nutrients, and trace metals. However, the impacts of plume biogeochemistry on the oceans are poorly constrained due to a lack of integrated understanding of microbiomes, population genetics, and geochemistry. Here, we use microbial genomes to understand links between biogeography, evolution, and metabolic connectivity, and elucidate their impacts on biogeochemical cycling in the deep sea. Using data from 37 diverse plumes from 8 ocean basins, we show that sulfur metabolism defines the core microbiome of plumes and drives metabolic connectivity. Amongst all microbial metabolisms, sulfur transformations had the highest MW-score, a measure of metabolic connectivity in microbial communities. Our findings provide the ecological and evolutionary basis of change in sulfur-driven microbial communities and their population genetics in adaptation to changing geochemical gradients in the oceans.

microbiology↗