Biology subjects

King, E.

Publications and source records attributed to King, E..

32 records · Page 2Linked to original sources

bioRxiv2023

Ancient diversity in host-parasite interaction genes in a model parasitic nematode

Host-parasite interactions exert strong selection pressures on the genomes of both host and parasite. These interactions can lead to increased fitness of rare alleles, resulting in negative frequency-dependent selection, a form of balancing selection that is hypothesised to explain the high levels of polymorphism seen in many host immune and parasite antigen loci. Despite their economic and ecological importance, there is limited evidence for balancing selection in parasitism-relevant genes in parasitic nematodes. Here, we sequenced the genomes of several individuals of Heligmosomoides bakeri, a parasite of house mice that is a well-established model parasitic nematode, and Heligmosomoides polygyrus, a closely related parasite of wood mice. We combined our single nematode genome assemblies with chromatin conformation capture (Hi-C) libraries derived from pools of individuals to generate chromosome-level reference genomes for both species. Although H. bakeri is commonly referred to as H. polygyrus in the literature, their genomes show levels of divergence that are consistent with millions of years of independent evolution. We found that the H. bakeri genome, which we expected to be highly homozygous through inbreeding, contained hundreds of hyper-divergent haplotypes, similar to those recently reported in free-living nematodes. Within these haplotypes, we found an enrichment of protein families that interact with the host immune response, including protease inhibitors and transthyretin-related proteins. We also found that many of these haplotypes originated prior to the divergence between H. bakeri and H. polygyrus, suggesting that they have been maintained since the last common ancestor of the two species by long-term balancing selection. Together, our results suggest that selection pressures exerted on parasites by their hosts have led to unexpected modes and levels of genetic diversity in the genomes of these economically and ecologically important species.

evolutionary biology↗

bioRxiv2023

Discovery and characterization of cross-reactive intrahepatic antibodies in severe alcoholic hepatitis

The pathogenesis of antibodies in severe alcoholic hepatitis (SAH) remains unknown. We sought to determine if there was antibody deposition in SAH livers and whether antibodies extracted from SAH livers were cross-reactive against both bacterial antigens and human proteins. We analyzed immunoglobulins (Ig) in explanted livers from SAH patients (n=45) undergoing liver transplantation and tissue from corresponding healthy donors (HD, n=10) and found massive deposition of IgG and IgA isotype antibodies associated with complement fragment C3d and C4d staining in ballooned hepatocytes in SAH livers. Ig extracted from SAH livers, but not patient serum exhibited hepatocyte killing efficacy in an antibody-dependent cell-mediated cytotoxicity (ADCC) assay. Employing human proteome arrays, we profiled the antibodies extracted from explanted SAH, alcoholic cirrhosis (AC), nonalcoholic steatohepatitis (NASH), primary biliary cholangitis (PBC), autoimmune hepatitis (AIH), hepatitis B virus (HBV), hepatitis C virus (HCV) and HD livers and found that antibodies of IgG and IgA isotypes were highly accumulated in SAH and recognized a unique set of human proteins as autoantigens. The use of an E. coli K12 proteome array revealed the presence of unique anti-E. coli antibodies in SAH, AC or PBC livers. Further, both Ig and E. coli captured Ig from SAH livers recognized common autoantigens enriched in several cellular components including cytosol and cytoplasm (IgG and IgA), nucleus, mitochondrion and focal adhesion (IgG). Except IgM from PBC livers, no common autoantigen was recognized by Ig and E. coli captured Ig from AC, HBV, HCV, NASH or AIH suggesting no cross-reacting anti-E. coli autoantibodies. The presence of cross-reacting anti-bacterial IgG and IgA autoantibodies in the liver may participate in the pathogenesis of SAH.

pathology↗

bioRxiv2023

QC-GN2oMS2: a Graph Neural Net for High Resolution Mass Spectra Prediction.

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. Quantum chemical modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectral prediction.1 To explore this technique further, we investigate the effects of including quantum chemically derived features as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from Extended Tight-Binding (xTB) quantum chemistry, and acyclic bond dissociation energies. We evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectral prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

biochemistry↗

bioRxiv2022

Machine Learning Reveals Genetic Modifiers of the Immune Microenvironment of Cancer

Heritability in the immune tumor microenvironment (iTME) has been widely observed, yet remains largely uncharacterized and systematic approaches to discover germline genetic modifiers of the iTME still being established. Here, we developed the first machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWAS) for breast cancer (BrCa) incidence and outcome. A random forest model was trained on a positive set of immune-oncology (I-O) targets using BrCa and immune phenotypes from genetic perturbation studies, comparative genomics, Mendelian genetics, and colocalization with autoimmunity and inflammatory disease risk loci. Compared with random negative sets, an I-O target probability score was assigned to the 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Pathway analysis of the most probable I-O targets revealed significant enrichment in drivers of BrCa and immune biology, including the LSP1 locus associated with BrCa incidence and outcome. Quantitative cell type-specific immunofluorescent imaging of 1,109 BrCa patient biopsies revealed that LSP1 expression is restricted to tumor infiltrating leukocytes and correlated with BrCa patient outcome (HR = 1.73, p < 0.001). The human BrCa patient-based genomic and proteomic evidence, combined with phenotypic evidence that LSP1 is a negative regulator of leukocyte trafficking, prioritized LSP1 as a novel I-O target. Finally, a novel comparative mapping strategy using mouse genetic linkage revealed TLR1 as a plausible therapeutic candidate with strong genomic and phenotypic evidence. Collectively, these data demonstrate a robust and flexible analytical framework for functionally fine-mapping GWAS risk loci to identify the most translatable therapeutic targets for the associated disease.

cancer biology↗

bioRxiv2022

Differential genetic strategies of Burkholderia vietnamiensis and Paraburkholderia kururiensis for root colonization of Oryza sativa ssp. japonica and ssp. indica, as revealed by Tn-seq

Burkholderia vietnamiensis LMG10929 (Bv) and Paraburkholderia kururiensis M130 (Pk) are bacterial rice growth-promoting models. Besides this common ecological niche, species of the Burkholderia genus are also found as opportunistic human pathogens while Paraburkholderia are mostly environmental and plant-associated species. Here, we compared the genetic strategies used by Bv and Pk to colonize two subspecies of their common host, Oryza sativa ssp. japonica (cv. Nipponbare) and ssp. indica (cv. IR64). We used high-throughput screening of transposon insertional mutant libraries (Tn-seq) to infer which genetic elements have the highest fitness contribution during root surface colonization at 7 days post inoculation. Overall, we detected twice more genes in Bv involved in rice roots colonization compared to Pk, including genes contributing to the tolerance of plant defenses, which suggests a stronger adverse reaction of rice towards Bv compared to Pk. For both strains, the bacterial fitness depends on a higher number of genes when colonizing indica rice compared to japonica. These divergences in host pressure on bacterial adaptation could be partly linked to the cultivars differences in nitrogen assimilation. We detected several functions commonly enhancing root colonization in both bacterial strains e.g., Entner-Doudoroff (ED) glycolysis. Less frequently and more strain-specifically, we detected functions limiting root colonization such as biofilm production in Bv and quorum sensing in Pk. The involvement of genes identified through the Tn-seq procedure as contributing to root colonization i.e., ED pathway, c-di-GMP cycling and cobalamin synthesis, was validated by directed mutagenesis and competition with WT strains in rice root colonization assays. ImportanceBurkholderiaceae are frequent and abundant colonizers of the rice rhizosphere and interesting candidates to investigate for growth promotion. Species of Paraburkholderia have repeatedly been described to stimulate plant growth. However, the closely related Burkholderia genus hosts both beneficial and phytopathogenic species, as well as species able to colonize animal hosts and cause disease in humans. We need to understand to what extent the bacterial strategies used for the different biotic interactions differ depending on the host and if strains with agricultural potential could also pose a threat towards other plant hosts or humans. To start answering these questions, we used here transposon sequencing to identify genetic traits in Burkholderia vietnamiensis and Paraburkholderia kururiensis that contribute to the colonization of two different rice varieties. Our results revealed large differences in the fitness gene sets between the two strains and between the host plants, suggesting a strong specificity in each bacterium-plant interaction.

microbiology↗

bioRxiv2022

Antimicrobial resistance in dairy slurry tanks: a critical point for measurement and control

Waste from dairy production is one of the worlds largest sources of contamination from antimicrobial resistant bacteria (ARB) and genes (ARGs). However, studies to date do not provide necessary evidence to inform antimicrobial resistance (AMR) countermeasures. We undertook a detailed, interdisciplinary, longitudinal analysis of dairy slurry waste. The slurry contained a population of ARB and ARGs, with resistances to current, historical and never-used on-farm antibiotics; resistances were associated with Gram-negative and Gram-positive bacteria and mobile elements (ISEcp1, Tn916, Tn21-family transposons). Modelling and experimental work suggested that these populations are in dynamic equilibrium, with microbial death balanced by fresh input. Consequently, storing slurry without further waste input for at least 60 days was predicted to reduce ARB spread onto land, with >99% reduction in cephalosporin resistant Escherichia coli. The model also indicated that for farms with low antibiotic use, further reductions are unlikely to reduce AMR further. We conclude that the slurry tank is a critical point for prevalence and control of AMR, and that measures to limit the spread of AMR from dairy waste should combine responsible antibiotic use, including low total quantity, avoidance of human critical antibiotics, and choosing antibiotics with shorter half-lives, coupled with appropriate slurry storage.

microbiology↗

bioRxiv2021

The genetic basis of Drosophila melanogaster defense against Beauveria bassiana explored through evolve and resequence and quantitative trait locus mapping

Many of the molecular mechanisms for antifungal immunity in Drosophila melanogaster have been defined, but relatively little is known about the genetic basis for variation in antifungal immunity in natural populations. Using two population genetic approaches, Quantitative Trait Locus (QTL) Mapping and Evolve and Resequence (E&R), we explored the genetics underlying D. melanogaster immune defense against infection with the fungus Beauveria bassiana. Immune defense was highly variable both in the recombinant inbred lines from the Drosophila Synthetic Population Resource used for our QTL Mapping and in the synthetic outbred populations used in our E&R study. Survivorship of infection improved dramatically over just 10 generations in the E&R study, and continued to increase for an additional 9 generations, revealing a trade-off with uninfected longevity. Populations selected for increased defense against B. bassiana evolved cross resistance to a second, distinct B. bassiana strain but not to bacterial pathogens. The QTL mapping study revealed that sexual dimorphism in defense depends on host genotype, and the E&R study indicated that dimorphism also depends on the specific pathogen to which the host is exposed. Both the QTL Mapping and E&R experiments generated lists of potentially causal candidate genes, although these lists were non-overlapping.

genomics↗

bioRxiv2020

Validation and Performance Comparison of Three SARS-CoV-2 Antibody Assays

Serology testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is increasingly being used during the current pandemic of Coronavirus Disease 2019 (COVID-19). The clinical and epidemiologic utilities of antibody-based SARS-CoV-2 testing are under debate. Characterizing these assays helps to understand the disease and provides scientific basis for deciding how to best use these assays. The study assessed one chemiluminescent assay (Abbott COVID-2 IgG) and two lateral flow assays (STANDARD Q [SQ] IgM/IgG Duo and Wondfo Total Antibody Test). Validation included 113 blood samples from 71 PCR-confirmed COVID-19 patients and 1182 samples from negative controls with potential interferences/cross-reactions, including 1063 pre-pandemic samples. IgM antibodies against SARS-CoV-2 were detected as early as post-symptom onset days 3-4. IgG antibodies were first detected post-onset days 5-6 by SQ assays. The detection rates increased gradually, and SQ IgG, Abbott IgG and Wondfo Total detected antibodies from all the PCR-confirmed patients 14 days after symptom onset. Overall agreements between SQ IgM/IgG and Wondfo Total was 88.5% and between SQ IgG and Abbott IgG was 94.6% (Kappa = 0.75, 0.89). No cross-reaction with other endemic coronavirus infections were identified. Viral hepatitis and autoimmune samples were the main cross-reactions observed. However, the interferences/cross-reactions were low. The specificities were 100% for SQ IgG and Wondfo Total and 99.62% for Abbott IgG and 98.87% for SQ IgM. These findings demonstrate high sensitivity and specificity of appropriately validated antibody-based SARS-CoV-2 assays with implications for clinical use and epidemiological seroprevalence studies.View Full Text

pathology↗

bioRxiv2020

Pairing two growth-based, high-throughput selections to fine tune conformational dynamics in oxygenase engineering

Cyclohexanone monooxygenases (CHMO) consume molecular oxygen and NADPH to catalyze the valuable oxidation of cyclic ketones. However, CHMO usage is restricted by poor thermostability and stringent specificity for NADPH. Efforts to engineer CHMO have been limited by the sensitivity of the enzyme to perturbations in conformational dynamics and long-range interactions that cannot be predicted. We demonstrate a pair of aerobic, high-throughput growth selection platforms in Escherichia coli for oxygenase evolution, based on NADPH or NADH redox balance. We utilize the NADPH-dependent selection in the directed evolution of thermostable CHMO and discover the variant CHMO GV (A245G-A288V) with a 2.7-fold improvement in residual activity compared to the wild type after 40 {degrees}C incubation. Addition of a previously reported mutation resulted in A245G-A288V-T415C which has further improved thermostability at 45 {degrees}C. We apply the NADH-dependent selection to alter the cofactor specificity of CHMO to accept NADH, a less expensive cofactor than NADPH. We identified the variant CHMO DTNP (S208D-K326T-K349N-L143P) with a 21-fold cofactor specificity switch from NADPH to NADH compared to the wild type. Molecular modeling indicates that CHMO GV experiences more favorable residue packing and backbone torsions, and CHMO DTNP activity is driven by cooperative fine-tuning of cofactor contacts. Our introduced tools for oxygenase evolution enable the rapid engineering of properties critical to industrial scalability.

bioengineering↗

bioRxiv2020

A growth-based, high-throughput selection platform enables remodeling of 4-hydroxybenzoate hydroxylase active site

We report an aerobic, growth-based selection platform founded on NADP(H) redox balance restoration in Escherichia coli, and demonstrate its application in high-throughput evolution of oxygenase. A single round of selection enabled Pseudomonas aeruginoasa 4-hydroxybenzoate hydroxylase (PobA) to accept 3,4-dihydroxybenzoic acid efficiently, an essential step toward gallic acid biosynthesis. The best variant DA015 exhibited more than 5-fold higher catalytic efficiency compared to previously engineered enzymes. Structural modeling suggests precise re-organization of active site hydrogen bond network, which is difficult to obtain without deep navigation of combinatorial sequence space. We envision universal application of this selection platform in engineering NADPH-dependent oxidoreductases.

bioengineering↗

bioRxiv2020

Metabolic engineering of Escherichia coli for optimized biosynthesis of nicotinamide mononucleotide, a noncanonical redox cofactor

Background Noncanonical redox cofactors are emerging as important tools in cell-free biosynthesis to increase the economic viability, to enable exquisite control, and to expand the range of chemistries accessible. However, these noncanonical redox cofactors need to be biologically synthesized to achieve full integration with renewable biomanufacturing processes.Results In this work, we engineered Escherichia coli cells to biosynthesize the noncanonical cofactor nicotinamide mononucleotide (NMN+), which has been efficiently used in cell-free biosynthesis. First, we developed a growth-based screening platform to identify effective NMN+ biosynthetic pathways in E. coli. Second, we explored various pathway combinations and host gene disruption to achieve an intracellular level of ~1.5 mM NMN+, a 130-fold increase over the cell’s basal level, in the best strain, which features a previously uncharacterized nicotinamide phosphoribosyltransferase (NadV) from Ralstonia solanacearum. Last, we revealed mechanisms through which NMN+ accumulation impacts E. coli cell fitness, which sheds light on future work aiming to improve the production of this noncanonical redox cofactor.Conclusion These results further the understanding of effective production and integration of NMN+ into E. coli. This may enable the implementation of NMN+-directed biocatalysis without the need for exogenous cofactor supply.Competing Interest StatementThe authors have declared no competing interest.AbbreviationsNAD+nicotinamide adenine dinucleotideNADP+nicotinamide adenine dinucleotide phosphateP2NA+3-carbomoyl-1-phenethylpyridin-1-ium chlorideNMN+nicotinamide mononucleotideNadVnicotinamide phosphoribosyltransferasesNadE*nicotinamide mononucleotide synthasePncCnicotinamide mononucleotideNaMN+nicotinic acid mononucleotideNRnicotinamide ribosidePnuCnicotinamide riboside transporterPnuC*mutant nicotinamide riboside transporterNrk1nicotinamide riboside kinase from Saccharomyces cerevisiaeNadRnicotinamide riboside kinase (Salmonella enterica)NAnicotinamideLC-MSliquid chromatography-mass spectrometryNaADnicotinic acid adenine dinucleotidePCRpolymerase chain reactionIPTGisopropyl-β-D-thiogalactopyranosider.p.m.rotations per minuteView Full Text

synthetic biology↗

bioRxiv2019

Diverse biological processes coordinate the transcriptional response to nutritional changes in a Drosophila melanogaster multiparent population

BackgroundEnvironmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear.\n\nResultsTo describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2,471 genes) were significantly differentially expressed for the effect of diet, 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, Padj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (false discovery rate, FDR Padj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing a large number of cellular and nuclear processes (Fisher exact test, Padj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5, Rheb, and Sirt2 showed significant elevation in expression, known key genes such as InR, chico, insulin peptide genes, and the nutrient-sensing pathways were not observed.\n\nConclusionsOur results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.

genomics↗

bioRxiv2019

Multiple genetic loci affect place learning and memory performance in Drosophila melanogaster

Learning and memory are critical functions for all animals, giving individuals the ability to respond to changes in their environment. Within populations, individuals vary, however the mechanisms underlying this variation in performance are largely unknown. Thus, it remains to be determined what genetic factors cause an individual to have high learning ability, and what factors determine how well an individual will remember what they have learned. To genetically dissect learning and memory performance, we used the DSPR, a multiparent mapping resource in the model system Drosophila melanogaster, consisting of a large set of recombinant inbred lines (RILs) that naturally vary in these and other traits. Fruit flies can be trained in a \"heat box\" to learn to remain on one side of a chamber (place learning), and can remember this (place memory) over short timescales. Using this paradigm, we measured place learning and memory for [~]49,000 individual flies from over 700 DSPR RILs. We identified 16 different loci across the genome that significantly affect place learning and/or memory performance, with 5 of these loci affecting both traits. To identify transcriptomic differences associated with performance, we performed RNA-Seq on pooled samples of 7 high performing and 7 low performing RILs for both learning and memory and identified hundreds of genes with differences in expression in the two sets. Integrating our transcriptomic results with the mapping results allowed us to identify nine promising candidate genes, advancing our understanding of the genetic basis underlying natural variation in learning and memory performance.

genetics↗

bioRxiv2018

Analyzing Human Movements - Introducing A Framework To Extract And Evaluate Biomechanical Data

This study discusses possible sources of discrepancy between findings of previous human motion studies and presents a framework that seeks to address these issues. Motion analysis systems are widely employed to identify movement deficiencies - e.g. patterns that potentially increase the risk of injury or inhibit performance. However, findings across studies are often conflicting in respect to what a movement deficiency is or the magnitude of the relationship to a specific injury. To test the information content of movement data, a framework was build to differentiate between movements performed by a control (NORM) and abnormal (IMP-L and IMP-C) cohort using solely movement data. Movement data was recorded during jumping, hopping and change of direction exercises and was mathematically decomposed into subject scores. Subjects scores were then used to identify the most appropriate machine learning technique, which was subsequently utilized to create a prediction model that classified if a movement was performed by: IMP-L, IMP-C or NORM. The Monte Carlo method was used to obtain a measure of expected accuracy for each step within the analysis. Findings demonstrate that even the worst classification model outperformed the best guess observed and that not all members of the NORM group represent a NORM pattern as they were repeatedly classified as IMP-L or IMP-C. This highlights that some NORM limbs share movement characteristics with the abnormal group and consequently should not be considered when describing NORM.

scientific communication and education↗