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Medicinal chemistry of benzoisoxazole-4,7-dione analogues identifies the ribosomal small subunit as a target for specific mycobacterial translational fidelity

A central dogma of molecular biology is the "speed-accuracy trade-off," where ribosomes must slow down to ensure accurate protein synthesis. In mycobacteria, a high basal level of mistranslation at glutamine and asparagine codons, caused by an indirect tRNA aminoacylation pathway, promotes tolerance to the antibiotic rifampicin. While pharmacologically increasing translational fidelity is a promising strategy to combat antibiotic tolerance, the underlying mechanisms remain poorly understood. Here, we screened 9,000 synthetic compounds and identified benzo[d]isoxazole-4,7-diones as a novel chemical class that reduces mycobacterial mistranslation. Medicinal chemistry optimization yielded a lead compound, 9787, with superior potency in decreasing mistranslation and reversing rifampicin tolerance. Using competitive chemical proteomics, we identified the 30S ribosomal protein S5 (RpS5) as the specific cellular target. Remarkably, compound 9787 enhances translational fidelity at concentrations that do not measurably impact the overall rate of protein synthesis. Our findings challenge the universality of the speed-accuracy trade-off, demonstrating that fidelity can be improved independently of translation speed. This work reveals that the ribosomal small subunit is a druggable target for modulating translational quality control and introduces a new strategy for combating antibiotic-tolerant bacteria without the associated fitness cost of slowed translation. ImportanceA fundamental principle in molecular biology holds that ribosomes face a trade-off between translation speed and accuracy: going faster means making more errors, while maintaining high fidelity requires slowing down. This study challenges that paradigm by identifying a small molecule that increases translational accuracy in mycobacteria without affecting the rate of protein synthesis. The compound targets ribosomal protein S5 and specifically reduce errors arising from physiologically mischarged tRNAs - a quality control problem distinct from the well-studied codon*anticodon mismatches. This form of mistranslation contributes to antibiotic tolerance in tuberculosis, making it a potential therapeutic target. Our findings reveal unexpected flexibility in how ribosomes maintain translation quality and suggest that pharmacologically increasing fidelity without the fitness cost of slowed protein synthesis may be an attractive strategy for combating antibiotic-tolerant bacteria.

microbiology↗

Decoding Prognostic Markers Bioinformatics Insights into PIH1D1 and p53 in Children's Brain Cancer

Genetic alterations in normal brain cells lead to the development of brain tumors (BT). The incidence of newly diagnosed cases is on the rise over time. Understanding the molecular biology of pediatric brain tumors is crucial for advancing novel therapeutic approaches to prevent or effectively manage this disease. The R2TP complex, a conserved co-chaperone from yeast to mammals, including RUVBL1, RUVBL2, PIH1D1, and RPAP3 in humans, plays a crucial role in the assembly and maturation of various multi-subunit complexes. This study evaluates the expression of PIH1D1 and p53 in pediatric brain cancers using The Cancer Genome Atlas (TCGA) data through the UALCAN platform--a novel, user-friendly resource for cancer OMICS data analysis. Our analysis revealed elevated expression levels of PIH1D1 in pediatric brain tumors across all age groups compared to normal tissues, suggesting its potential as an early detection marker and a prognostic indicator. Additionally, P53 emerged as a promising target for brain tumor treatment, warranting exploration for age-specific applications. The Venn Diagram analysis further delineated key genes within the PIH1D1 and P53 networks, providing insights into the genetic landscape of childhood brain cancer. Despite these findings, the study has limitations, primarily relying on bioinformatics databases. Future research will incorporate clinical tissues and cell lines in in vivo and in vitro experiments to unravel the mechanistic roles of PIH1D1 and p53 in brain tumors. This comprehensive approach bridges bioinformatics and clinical exploration, contributing to a holistic understanding of pediatric brain cancer molecular biology and paving the way for targeted therapeutic interventions.

bioinformatics↗

GOFlowLLM - Curating miRNA literature with Large Language Models and flowcharts

The exponential growth of non-coding RNA research--with over 230,000 papers published since 2000--has created an urgent knowledge management crisis in molecular biology. Despite their crucial regulatory roles, microRNAs (miRNAs) face a significant curation bottleneck, with only 1,400 articles manually curated to the Gene Ontology (GO) knowledgebase over a decade. We present GOFlowLLM, an automated curation pipeline powered by reasoning-enabled Large Language Models (LLMs) that follows established GO curation flowcharts to extract and structure miRNA-mediated gene silencing data at scale. When evaluated on existing curation, GOFlowLLM selects the correct GO term in 90% of cases. Curators also agree with 95% of the systems reasoning steps and 90% of the evidence selected. Applied to 6,996 previously uncurated articles, our system identified 2,538 new candidate GO annotations on 1,785 articles in just 58 hours--potentially doubling the available miRNA GO curation. Manual review of a subset of these annotations shows that curators agreed with the selected term in 87% of cases, the models reasoning in 92% of cases, and the extracted evidence in 93%. GOFlowLLM demonstrates how LLMs can significantly accelerate biocuration while maintaining high-quality standards by following expert-designed reasoning frameworks. The integration of reasoning traces in our system provides transparent justification for annotations that can be reviewed by human curators, addressing one of the key challenges in adopting AI for scientific curation, potentially transforming how we manage the growing corpus of scientific knowledge in molecular biology. GoFlowLLM is available on github: https://github.com/RNAcentral/GO_Flow_LLM.

bioinformatics↗

Hot Pursuit: Bioinformatic and Biochemical Characterization of a Hyperthermophilic Family B DNA Polymerase from Pyrolobus fumarii A1

Thermostable DNA polymerases are indispensable tools in molecular biology, yet enzymes from the most extreme hyperthermophiles remain largely uncharacterized. Here, we report the biochemical and structural characterization of a family B DNA polymerase from Pyrolobus fumarii A1 (Pyrfu pol), one of the most thermoresistant archaea described to date. The enzyme was efficiently overproduced in E. coli Rosetta 2(DE3)[pLysS] and purified to homogeneity using a two-step protocol that combined heat treatment with immobilized metal affinity chromatography (IMAC). Bioinformatic analysis confirmed the canonical family B architecture, while AlphaFold-based structural modeling and comparative analysis with mesophilic RB69 DNA polymerase revealed a well-conserved structural core alongside thermoadaptive features. Radiolabel incorporation assays demonstrated enzymatic activity over a broad ionic strength range and an absolute requirement for Mg2{square}. PCR-based optimization confirmed these findings and revealed broad pH tolerance (6.5-11.0). Notably, Tris inhibited radiolabel-based assays (pH 7.0) yet proved essential for efficient PCR amplification (pH 8.5), suggesting a context-dependent role of buffer composition in polymerase activity. Processivity assays confirmed amplification of DNA fragments up to approximately 8,000 bp. Replication fidelity, assessed by the lacZ-based assay, showed a 2.9-fold improvement over Taq polymerase. Urea-nanoDSF yielded an exceptional melting temperature of 105.9 {+/-} 0.08 {degrees}C. Pyrfu pol also demonstrated tolerance to common PCR inhibitors, highlighting its potential utility in molecular biology applications. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/734501v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1a94cb0org.highwire.dtl.DTLVardef@1e9e1b9org.highwire.dtl.DTLVardef@1e240d8org.highwire.dtl.DTLVardef@10f47b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

O antigen biogenesis sensitises Escherichia coli K-12 to bile salts, a likely cause for how it lost its O antigen

Escherichia coli K-12 is a model organism for bacteriology and has served as a workhorse for molecular biology and biochemistry for over a century since its first isolation in 1922. However, Escherichia coli K-12 strains are phenotypically devoid of an O antigen (OAg) since early reports in the scientific literature. Recent studies reported the presence of independent mutations that abolish OAg biogenesis in E. coli K-12 strains from the same original source, suggesting unknown evolutionary forces have selected for loss of OAg during the early propagation of K-12. Here, we show for the first time that restoration of OAg in E. coli K-12 strain MG1655 synergistically sensitises bacteria to vancomycin with bile salts (VBS). Suppressor mutants surviving lethal doses of VBS mostly contained disruptions in OAg biogenesis. We present data supporting a model where the transient presence and accumulation of lipid-carried OAg intermediates in the bacterial periplasm interfere with peptidoglycan synthesis, causing growth defects that are synergistically enhanced by bile salts. Lastly, we demonstrate that continuous bile salt exposure of OAg-producing MG1655 in the laboratory, can recreate a scenario where OAg disruption is selected for. Hence our work provides a likely explanation for the long-held mystery of how E. coli K-12 lost its OAg production and opens new avenues for exploring long-standing questions on the intricate network coordinating the synthesis of different cell envelope components in Gram-negative bacteria. Significance statementEscherichia coli K-12 is the most studied microorganism, widely used in laboratories for studying bacteriology and as a tool for molecular biology. The reason why it is devoid of O antigen remains a long-standing question. Our work has uncovered a previously unknown selection pressure of bile salts on bacterial O antigen biogenesis, which provides a plausible scenario for how the early propagation of E. coli K-12 strains in bile salt containing media could have led to loss of O antigen in K-12. Our results also suggest that the accumulation of O antigen intermediates in the bacterial periplasm may interfere with bacterial cell wall synthesis, which paves a new research direction into the interplay of different cell envelope component synthesis pathways.

microbiology↗

A computational framework for a Lyapunov-enabled analysis of biochemical reaction networks

Complex molecular biological processes such as transcription and translation, signal transduction, post-translational modification cascades, and metabolic pathways can be described in principle by biochemical reactions that explicitly take into account the sophisticated network of chemical interactions regulating cell life. The ability to deduce the possible qualitative behaviors of such networks from a set of reactions is a central objective and an ongoing challenge in the field of systems biology. Unfortunately, the construction of complete mathematical models is often hindered by a pervasive problem: despite the wealth of qualitative graphical knowledge about network interactions, the form of the governing nonlinearities and/or the values of kinetic constants are hard to uncover experimentally. The kinetics can also change with environmental variations. This work addresses the following question: given a set of reactions and without assuming a particular form for the kinetics, what can we say about the asymptotic behavior of the network? Specifically, it introduces a class of networks that are "structurally (mono) attractive" meaning that they are incapable of exhibiting multiple steady states, oscillation, or chaos by virtue of their reaction graphs. These networks are characterized by the existence of a universal energy-like function called a Robust Lyapunov function (RLF). To find such functions, a finite set of rank-one linear systems is introduced, which form the extremals of a linear convex cone. The problem is then reduced to that of finding a common Lyapunov function for this set of extremals. Based on this characterization, a computational package, Lyapunov-Enabled Analysis of Reaction Networks (LEARN), is provided that constructs such functions or rules out their existence. An extensive study of biochemical networks demonstrates that LEARN offers a new unified framework. Basic motifs, three-body binding, and genetic networks are studied first. The work then focuses on cellular signalling networks including various post-translational modification cascades, phosphotransfer and phosphorelay networks, T-cell kinetic proofreading, and ERK signalling. The Ribosome Flow Model is also studied. Author summaryA theoretical and computational framework is developed for the identification of biochemical networks that are "structurally attractive". This means that they only allow global point attractors and they cannot exhibit any other asymptotic behavior such as multi-stability, oscillations, or chaos for any choice of the kinetics. They are characterized by the existence of energy-like functions. A computational package is made available for usage by a wider community. Many relevant networks in molecular biology satisfy the assumptions, and some are analyzed for the first time.

systems biology↗

INSIHGT: Accessible multimodal systems biology with quantitative molecular phenotyping in 3D

Biological systems are complex, encompassing intertwined spatial, molecular and functional features. However, methodological constraints always limit the completeness of information that can be extracted. Here, we report the development of INSIHGT, a minimally perturbative, accessible and cost-efficient three-dimensional (3D) spatial biology method utilizing superchaotropes and host-guest chemistry. This allows highly multiplexed and multi-modal readout of tissue biomolecules in biological systems up to centimeter scales, permitting radio-histological correlation of phosphorylated alpha-synuclein pathologies in human hemi-brainstem. The homogeneous penetration permits reliable semi-quantitative signals in 3D compared to reference signals. Diverse antigens, mRNA transcripts, neurotransmitters, and post-translational and epigenetic modifications, are well-preserved and visualized. INSIHGT also allows multi-round molecular probing for high-dimensional spatial biology and compatibility with downstream traditional histology. With INSIHGT, we mapped previously undescribed podocyte-to-parietal epithelial cell microfilaments and demonstrated their geodesic clustering in mouse glomeruli, and catalogued sparsely located neurofilament-intensive inclusion bodies in the human cerebellum, and identified NPY-proximal cell types defined by spatial morpho-proteomics in mouse hypothalamus. We anticipate INSIHGT can form the foundations for 3D spatial multi-omics technology development and holistic systems biology studies.

bioengineering↗

PrimerServer: a high-throughput primer design and specificity-checking platform

SummaryDesigning specific primers for multiple sites across the whole genome is still challenging, especially in species with complex genomes. Here we present PrimerServer, a high-throughput primer design and specificity-checking platform with both web and command-line interfaces. This platform efficiently integrates site selection, primer design, specificity checking and data presentation. In our case study, PrimerServer achieved high accuracy and a fast running speed for a large number of sites, suggesting its potential for molecular biology applications such as molecular breeding or medical testing.\n\nAvailability and ImplementationSource code for PrimerServer is available at https://github.com/billzt/PrimerServer. A demo server is freely accessible at https://primerserver.org, with all major browsers supported.\n\nContactzhangrui@caas.cn or guosandui@caas.cn

bioinformatics↗

Integration of Multi-omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers

Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast-growing technological advances in high throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, we integrate multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA), while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. We were able to find a set of highly correlated features distinguishing glioblastoma from lower-grade gliomas (LGG) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. On the other hand, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, we could identify several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A and HEPN1. Overall, this classification method allowed to discriminate the different TCGA glioma patients with very high performance, while seeking for common information across multiple data types, ultimately enabling the understanding of essential mechanisms driving glioma heterogeneity and unveiling potential therapeutic targets.

bioinformatics↗

Molecular and biological characterization of a distinct species of Lolavirus infecting different accessions of seashore paspalum, a turfgrass, widely grown in the United States

Seashore paspalum (Paspalum sp.), is an economically significant grass used in golf courses, sports fields, and landscaping in the United States. A novel Lolavirus, tentatively named paspalum latent virus (PaLV), was identified for the first time in seashore paspalum plants from the USDA National Plant Germplasm System (NPGS) using high-throughput sequencing. Three complete genome sequences of PaLV from different Paspalum accessions, with a length of 6,995 nucleotides (nt), not including the poly(A) tail, were obtained by Rapid Amplification of cDNA Ends and Sanger sequencing. Phylogenetic analysis based on the replicase protein sequences from the Alphaflexiviridae family revealed that PaLV grouped with the Lolavirus genus, with the closest relative being Lolium latent virus (LoLV). PaLV shares less than 72% nt identity to the replicase and coat protein genes of LoLV, which demarks PaLV as a new species and the second member of the genus. Furthermore, the coat protein region showed intense negative selection pressure and low spatially structured diversity. Host range analysis of PaLV showed that wheat, corn, sorghum, and Lolium are systemic hosts of PaLV. A one-step RT-PCR technique was developed to reliably detect PaLV infection.

pathology↗

POMBOX: a fission yeast toolkit for molecular and synthetic biology

Schizosaccharomyces pombe is a popular model organism in molecular biology and cell physiology. With its ease of genetic manipulation and growth, supported by in-depth functional annotation in the PomBase database and genome-wide metabolic models, S. pombe is an attractive option for synthetic biology applications. However, S. pombe currently lacks modular tools for generating genetic circuits with more than one transcriptional unit. We have developed a toolkit to address this issue. Adapted from the MoClo- YTK plasmid kit for Saccharomyces cerevisiae and using the same Golden Gate grammar, our POMBOX toolkit is designed to facilitate the fast, efficient and modular construction of genetic circuits in S. pombe. It allows for interoperability when working with DNA sequences that are functional in both S. cerevisiae and S. pombe (e.g. protein tag, antibiotic resistance cassette, coding sequences). Moreover, POMBOX enables the modular assembly of multi-gene pathways and increases possible pathway length from 6 to 12 transcriptional units. We also adapted the stable integration vector homology arms to Golden Gate assembly and tested the genomic integration success rate depending on different sequence sizes, from four to twenty-four kilobases. We included fourteen S. pombe promoters that we characterized for two fluorescent proteins, in both minimal defined media (EMM2) and complex media (YES). Then we tested six S. cerevisiae and six synthetic terminators in S. pombe. Finally, we used the POMBOX kit for a synthetic biology application in metabolic engineering and expressed plant enzymes in S. pombe to produce specialized metabolite precursors, namely methylxanthine, amorpha-4,11-diene and cinnamic acid from the purine, mevalonate and amino acid pathways.

synthetic biology↗

Quantification of all 12 canonical ribonucleotides by real-time fluorogenic in vitro transcription

Enzymatic methods to quantify deoxyribonucleoside triphosphates have existed for decades. In contrast, no general enzymatic method to quantify ribonucleoside triphosphates (rNTPs), which drive almost all cellular processes and serve as precursors of RNA, exists to date. ATP can be measured with an enzymatic luminometric method employing the firefly luciferase, but the quantification of the other ribonucleoside mono-, di-, and triphosphates is still a challenge for a non-specialized laboratory and practically impossible without chromatography equipment. To allow feasible quantification of ribonucleoside phosphates in any laboratory with typical molecular biology and biochemistry tools, we developed a robust microplate assay based on real-time detection of the Broccoli aptamer during in vitro transcription. The assay employs the bacteriophage T7 and SP6 RNA polymerases, two oligonucleotide templates encoding the 49-nucleotide Broccoli aptamer, and a high-affinity fluorogenic aptamer-binding dye to quantify each of the four canonical rNTPs. The inclusion of nucleoside mono- and diphosphate kinases in the assay reactions enabled the quantification of the mono- and diphosphate counterparts. The assay is inherently specific and tolerates concentrated tissue and cell extracts. In summary, we describe the first chromatography-free method to quantify ATP, ADP, AMP, GTP, GDP, GMP, UTP, UDP, UMP, CTP, CDP, and CMP in biological samples.

molecular biology↗

An EWS::FLI1 monoclonal antibody with multimodal utility and specificity for FLI1

Ewing sarcoma (ES) is a rare tumor that affects children, adolescents, and young adults. This tumor has a high morbidity in all patients and for those who present with metastatic disease a high mortality. A canonical chromosomal translocation, either t(11;22)(q24;p12) or t(21;22)(q22;q12) leads to the fusion oncoproteins EWS::FLI1 or EWS::ERG in 95% of ES patients. We recognized a critical need for a stably sourced high affinity antibody that recognizes EWS::FLI1 with maximal specificity. We created and validated a hybridoma that produces a mouse derived monoclonal antibody that recognizes EWS::FLI1 in multiple molecular biology applications.

molecular biology↗

Selection and validation of reference genes for quantitative Real-Time PCR in Arabis alpina

Arabis alpina is a perennial arctic-alpine plant and an upcoming model organism for genetics and molecular biology for the Brassicaceae family. One essential method for most molecular approaches is the analysis of gene expression by quantitative Real-Time PCR (qPCR). For the normalisation of expression data in qPCR experiments, it is essential to use reliable reference genes that are not affected under a wide range of conditions. In this study we establish a set of 15 A. alpina reference genes that were tested under different conditions including cold, drought, heat, salt and gibberellic acid treatments. Data analyses with geNORM, BestKeeper and NormFinder revealed the most stable reference genes for the tested conditions: RAN3, HCF and PSB33 are most suitable for cold treatments; UBQ10 and TUA5 for drought; RAN3, PSB33 and EIF4a for heat; CAC, TUA5, ACTIN 2 and PSB33 for salt and PSB33 and TUA5 for gibberellic acid treatments. CAC and ACTIN 2 showed the least variation over all tested samples. In addition, we show that two reference genes are sufficient to normalize qPCR data under our treatment conditions. In future studies, these reference genes can be used for an adequate normalisation and thus help to generate high quality qPCR data in A. alpina.

molecular biology↗

Intercellular signaling network underlies biological time across multiple temporal scales

MotivationCellular, physiological and molecular processes must be organized and regulated across multiple time domains throughout the lifespan of an organism. The technological revolution in molecular biology has led to the identification of numerous genes implicated in the regulation of diverse temporal biological processes. However, it is natural to question whether there is an underlying regulatory network governing multiple timescales simultaneously.\n\nResultsUsing queries of relevant databases and literature searches, a single dense multiscale temporal regulatory network was identified involving core sets of genes that regulate circadian, cell cycle, and aging processes. The network was highly enriched for genes involved in signal transduction (P = 1.82e-82), with p53 and its regulators such as p300 and CREB binding protein forming key hubs, but also for genes involved in metabolism (P = 6.07e-127) and cellular response to stress (P = 1.56e-93). These results suggest an intertwined molecular signaling network that affects biological time across multiple temporal scales in response to environmental stimuli and available resources.\n\nContactjoshua.millstein@usc.edu\n\nSupplementary informationSupplementary data are available online.

systems biology↗

RiboXYZ: A comprehensive database for visualizing and analyzing ribosomestructures

Recent advances in Cryo-EM led to a surge of ribosome structures deposited over the past years, including structures from different species, conformational states, or bound with different ligands. Yet, multiple conflicts of nomenclature make the identification and comparison of structures and ortholog components challenging. We present RiboXYZ (available at https://ribosome.xyz), a database that provides organized access to ribosome structures, with several tools for visualisation and study. The database is up-to-date with the Protein Data Bank (PDB) but provides a standardized nomenclature that allows for searching and comparing ribosomal components (proteins, RNA, ligands) across all the available structures. In addition to structured and simplified access to the data, the application has several specialized visualization tools, including the identification and prediction of ligand binding sites, and 3D superimposition of ribosomal components. Overall, RiboXYZ provides a useful toolkit that complements the PDB database, by implementing the current conventions and providing a set of auxiliary tools that have been developed explicitly for analyzing ribosome structures. This toolkit can be easily accessible by both experts and non-experts in structural biology so that they can search, visualize and compare structures, with various potential applications in molecular biology, evolution, and biochemistry.

molecular biology↗

The Role of Extended Range of Interactions in the Dynamics of Interacting Molecular Motors

Motor proteins, also known as biological molecular motors, play important roles in various intracellular processes. Experimental investigations suggest that molecular motors interact with each other during the cellular transport, but the nature of such interactions remains not well understood. Stimulated by these observations, we present a theoretical study aimed to understand the effect of the range of interactions on dynamics of interacting molecular motors. For this purpose, we develop a new version of the totally asymmetric simple exclusion processes in which nearest-neighbor as well as the next nearest-neighbor interactions are taken into account in a thermodynamically consistent way. A theoretical framework based on a cluster mean-field approximation, which partially takes correlations into account, is developed to evaluate the stationary properties of the system. It is found that fundamental current-density relations in the system strongly depend on the strength and the sign of interactions, as well as on the range of interactions. For repulsive interactions stronger than some critical value, increasing the range of interactions leads to a change from unimodal to trimodal dependence in the flux-density fundamental diagram. Theoretical calculations are tested with extensive Monte Carlo computer simulations. Although in most ranges of parameters excellent agreement between theoretical predictions and computer simulations is observed, there are situations when the cluster mean-field approach fails to describe properly the dynamics in the system. Theoretical arguments to explain these observations are presented. Our theoretical analysis clarifies the microscopic picture of how the range of interactions influences the dynamics of interacting molecular motors.

biophysics↗

Coal-Miner: A Coalescent-Based Method For GWA Studies Of Quantitative Traits With Complex Evolutionary Origins

Association mapping (AM) methods are used in genome-wide association (GWA) studies to test for statistically significant associations between genotypic and phenotypic data. The genotypic and phenotypic data share common evolutionary origins - namely, the evolutionary history of sampled organisms - introducing covariance which must be distinguished from the covariance due to biological function that is of primary interest in GWA studies. A variety of methods have been introduced to perform AM while accounting for sample relatedness. However, the state of the art predominantly utilizes the simplifying assumption that sample relatedness is effectively fixed across the genome. In contrast, population genetic theory and empirical studies have shown that sample relatedness can vary greatly across different loci within a genome; this phenomena - referred to as local genealogical variation - is commonly encountered in many genomic datasets. New AM methods are needed to better account for local variation in sample relatedness within genomes.\n\nWe address this gap by introducing Coal-Miner, a new statistical AM method. The Coal-Miner algorithm takes the form of a methodological pipeline. The initial stages of Coal-Miner seek to detect candidate loci, or loci which contain putatively causal markers. Subsequent stages of Coal-Miner perform test for association using a linear mixed model with multiple effects which account for sample relatedness locally within candidate loci and globally across the entire genome.\n\nUsing synthetic and empirical datasets, we compare the statistical power and type I error control of Coal-Miner against state-of-theart AM methods. The simulation conditions reflect a variety of genomic architectures for complex traits and incorporate a range of evolutionary scenarios, each with different evolutionary processes that can generate local genealogical variation. The empirical benchmarks include a large-scale dataset that appeared in a recent high-profile publication. Across the datasets in our study, we find that Coal-Miner consistently offers comparable or typically better statistical power and type I error control compared to the state-of-art methods.\n\nCCS CONCEPTSApplied computing [->] Computational genomics; Computational biology; Molecular sequence analysis; Molecular evolution; Computational genomics; Systems biology; Bioinformatics; Population genetics;\n\nACM Reference formatHussein A. Hejase, Natalie Vande Pol, Gregory M. Bonito, Patrick P. Edger, and Kevin J. Liu. 2017. Coal-Miner: a coalescent-based method for GWA studies of quantitative traits with complex evolutionary origins. In Proceedings of ACM BCB, Boston, MA, 2017 (BCB), 10 pages. DOI: 10.475/123 4

bioinformatics↗