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Smith, K. P.

Publications and source records attributed to Smith, K. P..

At least 19 recordsLinked to original sources

Sodium azide mutagenesis induces a unique pattern of mutations

The nature and effect of mutations are of fundamental importance to the evolutionary process. The generation of mutations with mutagens has also played important roles in genetics. Applications of mutagens include dissecting the genetic basis of trait variation, inducing desirable traits in crops, and understanding the nature of genetic load. Previous studies of sodium azide-induced mutations have reported single nucleotide variants (SNVs) found in individual genes. To characterize the nature of mutations induced by sodium azide, we analyze whole-genome sequencing (WGS) of 11 barley lines derived from sodium azide mutagenesis, where all lines were selected for diminution of plant fitness owing to induced mutations. We contrast observed mutagen-induced variants with those found in standing variation in WGS of 13 barley landraces. Here, we report indels that are two orders of magnitude more abundant than expected based on nominal mutation rates. We found induced SNVs are very specific, with C[->]T changes occurring in a context followed by another C on the same strand (or the reverse complement). The codons most affected by the mutagen include the sodium azide-specific CC motif (or the reverse complement), resulting in a handful of amino acid changes and relatively few stop codons. The specific nature of induced mutations suggests that mutagens could be chosen based on experimental goals. Sodium azide would not be ideal for gene knockouts but will create many missense mutations with more subtle effects on protein function.

genomics↗

SAXS/MC studies of the mixed-folded protein Cdt1 reveal monomeric, folded over conformations

Cdt1 is a protein critical for DNA replication licensing and is well-established to be a binding partner of the minichromosome maintenance (MCM) complex. Cdt1 has also been demonstrated to have an emerging, "moonlighting" role at the kinetochore via direct binding to microtubules and to the Ndc80 complex. However, it is not known how the structure and conformations of Cdt1 could allow for these multiple, completely unique sets of protein complexes. And while there exist multiple robust methods to study entirely folded or entirely unfolded proteins, structure-function studies of combined, mixed folded/disordered proteins remain challenging. It this work, we employ multiple orthogonal biophysical and computational techniques to provide a detailed structural characterization of human Cdt1 92-546. DSF and DSCD show both folded winged helix (WH) domains of Cdt1 are relatively unstable. CD and NMR show the N-terminal and the linker regions are intrinsically disordered. Using DLS and SEC-MALS, we show that Cdt1 is polydisperse, monomeric at high concentrations, and without any apparent inter-molecular self-association. SEC-SAXS of the monomer in solution enabled computational modeling of the protein in silico. Using the program SASSIE, we performed rigid body Monte Carlo simulations to generate a conformational ensemble. Using experimental SAXS data, we filtered for conformations which did and did not fit our data. We observe that neither fully extended nor extremely compact Cdt1 conformations are consistent with our SAXS data. The best fit models have the N-terminal and linker regions extended into solution and the two folded domains close to each other in apparent "folded over" conformations. The best fit Cdt1 conformations are consistent with a function as a scaffold protein which may be sterically blocked without the presence of binding partners. Our studies also provide a template for combining experimental and computational biophysical techniques to study mixed-folded proteins.

biophysics↗

An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat

Fusarium head blight (FHB) in wheat is an economically important disease, which can cause yield losses exceeding 50% and the causal pathogen that infects spikes produces harmful mycotoxins. Breeding for host resistance remains the most effective disease control method; but time, labor, and human subjectivity during disease scoring limits selection advancements. In this study we describe an innovative, high-throughput phenotyping rover for capturing in-field RGB images and a deep neural network pipeline for wheat spike detection and FHB disease quantification. The image analysis pipeline successfully detects wheat spikes from images under variable field conditions, segments spikes and diseased tissue in the spikes, and quantifies disease severity as the region of intersection between spike and disease masks. Model inferences on an individual spike and plot basis were compared to human visual disease scoring in the field and on imagery for model evaluation. The precision and throughput of the model surpassed traditional field rating methods. The accuracy of FHB severity assessments of the model was equivalent to human disease annotations of images, however individual spike disease assessment was influenced by field location. The model was able to quantify FHB in images taken with different camera orientations in an unseen year, which demonstrates strong generalizability. This innovative pipeline represents a breakthrough in FHB phenotyping, offering precise and efficient assessment of FHB on both individual spikes and plot aggregates. The model is robust to different conditions and the potential to standardize disease evaluation methods across the community make it a valuable tool for studying and managing this economically significant fungal disease.

plant biology↗

RenSeq of Silphium integrifolium Reveals a Biotic Gradient for Disease Resistance

Intracellular plant defense against pathogens is mediated by a class of disease resistance genes known as NB-LRRs or NLRs (R genes). Many of the diseases these genes protect against are more prevalent in regions of higher rainfall, which provide better growth conditions for the pathogens. As such, we expect a higher selective pressure for the maintenance and proliferation of R genes in plants adapted to wetter conditions. In this study, we enriched libraries for R genes using RenSeq from baits primarily developed from the common sunflower (Helianthus annuus) reference genome. We sequenced the R gene libraries of Silphium integrifolium Michx, a perennial relative of sunflower, from 12 prairie remnants across a rainfall gradient in the Central Plains of the United States, with both Illumina short-read (n=99) and PacBio long-read (n=10) approaches. We found a positive relationship between the mean effective annual precipitation of a plants source prairie remnant and the number of R genes in its genome, consistent with intensity of plant pathogen coevolution increasing with precipitation. We show that RenSeq can be applied to the study of ecological hypotheses in non-model relatives of model organisms.

evolutionary biology↗

Interferon receptor gene dosage determines multiple hallmarks of Down syndrome

Trisomy 21 causes Down syndrome, a condition characterized by cognitive impairments, immune dysregulation, and atypical morphogenesis. Using whole blood transcriptome analysis, we demonstrate that specific overexpression of four interferon receptors encoded on chromosome 21 associates with chronic interferon hyperactivity and systemic inflammation in Down syndrome. To define the contribution of interferon receptor overexpression to Down syndrome phenotypes, we used genome editing to correct interferon receptor gene dosage in mice carrying triplication of a large genomic region orthologous to human chromosome 21. Normalization of interferon receptor copy number attenuated lethal antiviral responses, prevented heart malformations, decreased developmental delays, improved cognition and normalized craniofacial anomalies. Therefore, interferon receptor gene dosage determines major hallmarks of Down syndrome, indicating that trisomy 21 elicits an interferonopathy amenable to therapeutic intervention. One-Sentence SummaryCorrection of interferon receptor gene dosage rescues multiple key phenotypes in a mouse model of trisomy 21.

genetics↗

Generalizable approaches for genomic prediction of metabolites in plants

Plant metabolites are important for plant breeders to improve nutrition and agronomic performance, yet integrating selection for metabolomic traits is limited by phenotyping expense and limited genetic characterization, especially of uncommon metabolites. As such, developing biologically-based and generalizable genomic selection methods for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for more than 600 metabolites measured by GC-MS and LC-MS in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite GWAS (mGWAS) and selected loci to use in multi-kernel models that encompassed metabolome-wide mGWAS results, or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel, consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We tested if similar metabolites had consistent model ranks and found that, while different metrics of similarity had different results, using annotation-free methods to group metabolites led to consistent within-group model rankings. Overall, testing biological rationales for developing kernels for genomic prediction across populations, contributes to developing frameworks for plant breeding for metabolite traits.

genomics↗

Selection for seed size has indirectly shaped specialized metabolite abundance in oat (Avena sativa L.)

Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties and their agronomic role in mitigating disease severity, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic and phenotypic analyses for three types of seed specialized metabolites - avenanthramides, avenacins, and avenacosides - we found reduced genetic variation in modern germplasm compared to diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes, but avenacoside abundance decreases with seed size and plant breeding intensity. Overall, we show that increased seed size associated with plant breeding has uneven effects on the seed metabolome, and broadly contributes to understanding how selection shapes plant specialized metabolism.

plant biology↗

Profiling the in vitro and in vivo activity of streptothricin-F against carbapenem-resistant Enterobacterales: a historic scaffold with a novel mechanism of action

Streptothricins are components of the natural product, nourseothricin; each containing identical streptolidine and gulosamine aminosugar moieties attached to varying numbers of linked {beta}-lysines. Nourseothricin was discovered by Waksman and colleagues in the early 1940s, generating intense interest because of excellent Gram-negative activity. However, the natural product mixture was associated with toxicity, and subsequent exploration was limited. Here, we establish the activity spectrum of nourseothricin and its main components, streptothricin-F (S-F, one lysine) and streptothricin D (S-D, three lysines), purified to homogeneity, against highly drug-resistant, carbapenem-resistant Enterobacterales (CRE). The MIC50 and MIC90 for S-F and S-D were 2 and 4 {micro}M, and 0.25 and 0.5 {micro}M, respectively. S-F and nourseothricin showed rapid, bactericidal activity. S-F and S-D both showed approximately 40-fold greater selectivity for prokaryotic than eukaryotic ribosomes in in vitro translation assays. There was >10-fold in vitro selectivity of S-F compared with S-D on LLC-PK1 and J774 cell lines. In vivo, delayed renal toxicity occurred at >10-fold higher doses of S-F compared with S-D. Substantial treatment effect of S-F in the murine thigh model was observed against the otherwise pandrug-resistant, NDM-1-expressing Klebsiella pneumoniae Nevada strain at dosing levels without observable or minimal toxicity. Resistance mutations obtained in single ribosomal operon E. coli identify novel interactions with 16S rRNA helix 34, i.e., C1504A and A1196G/C conferred high level resistance to nourseothricin. Based on promising, unique activity, we suggest that the streptothricin scaffold deserves further pre-clinical exploration as a potential therapeutic for the treatment of CRE and potentially other multidrug-resistant, gram-negative pathogens. IMPORTANCEStreptothricins are a historic class of antibiotics discovered by Waksman and colleagues in the 1940s. Toxicities associated with the streptothricin natural product mixture, also known as nourseothricin, discouraged further development. However, we found that a component of nourseothricin, streptothricin-F, retained potent activity against contemporary carbapenem-resistant Enterobacterales with significant selectivity in in vitro and in vivo assays. This included demonstration of rapid bactericidal activity in vitro and substantial therapeutic effect in the murine thigh model against a pandrug-resistant Klebsiella pneumoniae isolate at non-toxic concentrations. Through resistance mutation analysis, we identified helix 34 of 16S rRNA in the prokaryotic ribosome, and specifically bases C1054 and A1196, as critical for streptothricins activity. The mechanism of action is distinct from other known translation inhibitors. Based on promising and unique activity, we believe the streptothricin scaffold deserves further pre-clinical exploration as a potential therapeutic for the treatment of CRE and potentially other multidrug-resistant, Gram-negative pathogens.

microbiology↗

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly-related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G+T, G+M and G+T+M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17 and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly-related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

genetics↗

Homotopic contralesional excitation suppresses spontaneous circuit repair and global network reconnections following ischemic stroke

Understanding circuit-level changes that affect the brains capacity for plasticity will inform the design of targeted interventions for treating stroke recovery. We combine optogenetic photostimulation with optical neuroimaging to examine how contralesional excitatory activity affects cortical remodeling after stroke in mice. Following photothrombosis of left primary somatosensory forepaw (S1FP) cortex, mice received chronic excitation of right S1FP, a maneuver mimicking the use of the unaffected limb during recovery. Contralesional excitation suppressed perilesional S1FP remapping and was associated with abnormal patterns of evoked activity in the unaffected limb. Contralesional stimulation prevented the restoration of resting-state functional connectivity (RSFC) within the S1FP network, RSFC in several networks functionally-distinct from somatomotor regions, and resulted in persistent limb-use asymmetry. In stimulated mice, perilesional tissue exhibited suppressed transcriptional changes in several genes important for recovery. These results suggest that contralesional excitation impedes local and global circuit reconnection through suppression of several neuroplasticity-related genes after stroke.

neuroscience↗

Development of first linkage map for Silphium integrifolium (Asteraceae) enables identification of sporophytic self-incompatibility locus

O_LISilphium integrifolium (Asteraceae) has been identified as a candidate for domestication as a perennial oilseed crop and has a sporophytic self-incompatibility system--the genetic basis of which is not well understood in the Asteraceae. To address this gap, we sought to map the genomic location of the self-recognition locus (S-locus) in this species. C_LIO_LIWe used a biparental population and genotyping-by-sequencing to create the first genetic linkage map for this species. Then we developed a novel crossing scheme and set of analysis methods in order to infer S-locus genotypes for a subset of these individuals, allowing us to map the trait. Finally, we identified potential gene candidates using synteny analysis with the annual sunflower (Helianthus annuus) genome. C_LIO_LIOur linkage map contains 198 SNP markers and resolved into the correct number of linkage groups. We were able to successfully map the S-locus and identify several potential gene candidates in the sunflower syntenic region. C_LIO_LIOur method is effective and efficient, allowed us to map the S. integrifolium S-locus using fewer resources than previous studies, and could be readily be applied to other species. Our best gene candidate appears to be worthy of future work in S. integrifolium and other Asteraceae species. C_LI

plant biology↗

SARS-CoV2 Testing: The Limit of Detection Matters

Resolving the COVID-19 pandemic requires diagnostic testing to determine which individuals are infected and which are not. The current gold standard is to perform RT-PCR on nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of ~100 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved assays vary over 10,000-fold. Assays with higher LoDs will miss more infected patients, resulting in more false negatives. However, the false-negative rate for a given LoD remains unknown. Here we address this question using over 27,500 test results for patients from across our healthcare network tested using the Abbott RealTime SARS-CoV-2 EUA. These results suggest that each 10-fold increase in LoD is expected to increase the false negative rate by 13%, missing an additional one in eight infected patients. The highest LoDs on the market will miss a majority of infected patients, with false negative rates as high as 70%. These results suggest that choice of assay has meaningful clinical and epidemiological consequences. The limit of detection matters.

microbiology↗

Loss of Photosynthetic Rhythm Thermal Plasticity Under Domestication and RepurposingDrivers of Circadian Clock (DOC) Loci for Adaptive Breeding in Barley

Circadian clock rhythms are critical to control physiological and development traits, allowing, plants to adapt to changing environments. Here we show that the circadian rhythms of cultivated barley (Hordeum vulgare) have slowed and amplitude increased under domestication by comparing with its wild ancestor (H. spontaneum). Moreover, we show a significant loss of thermal plasticity during barley evolution for the period and more extensively for amplitude. Our genetic analysis indicates that wild allele at epistatic loci, which mutually condition clock variation and its thermal plasticity in interspecific crosses, are absent in a contemporary barley breeding panel. These epistatic interactions include conditioned effects of Drivers of Circadian (DOC) clock loci on chromosome 3 and 5, which mediate amplitude decrease and period lengthening, respectively, under domestication. Notably, two significant loci, DOC3.1 and DOC5.1, which are not associated with clock diversity in cultivated breeding material, do show pleiotropic effects on flowering time and grain yield at multiple experimental sites across the U.S. in a temperature-dependent manner. We suggest that transition from winter growth of wild barley (H. spontaneum) to spring growth of modern cultivars included the loss and repurposing of circadian clock regulators to yield adaptation by mechanisms yet to be clarified. Significance statementCircadian clock rhythms are crucial factors affecting crop adaptation to changing environments. If faced with increased temperature plants could respond with temperature compensation adaptation and maintain clock rhythms, or they can change period and/or amplitude to adapt. We used a combination of approaches: high-throughput clock analysis under optimal and elevated heat conditions, genome-wide association study (GWAS) with cultivated and wild diversity panels to identify changes under domestication and quantitative trait loci (QTL) that control the clock and its responses, and QTL-environment association for testing environmentally-conditioned effects of these QTLongrain yield and flowering timingacross US. Our findings provide insights into changes of circadian rhythms under domestication and genetic tools for plant breeders to develop better-adapted cultivars to changing environments.

genetics↗

Natural genetic variation underlying tiller development in barley (Hordeum vulgare L)

In barley (Hordeum vulgare L.), lateral branches called tillers contribute to grain yield and define shoot architecture, but genetic control of tiller number and developmental rate are not well characterized. The primary objectives of this work were to examine relationships between tiller number and other agronomic and morphological traits and identify natural genetic variation associated with tiller number and rate, and related traits. We grew 768 lines from the USDA National Small Grain Core Collection in the field and collected data over two years for tiller number and rate, and agronomic and morphological traits. Our results confirmed that spike row-type and days to heading are correlated with tiller number, and as much as 28% of tiller number variance is attributed to these traits. In addition, negative correlations between tiller number and leaf width and stem diameter were observed, indicating trade-offs between tiller development and other vegetative growth. Thirty-three quantitative trait loci (QTL) were associated with tiller number or rate. Of these, 40% overlapped QTL associated with days to heading and 22% overlapped QTL associated with spike row-type, further supporting that tiller development is influenced by these traits. Despite this, some QTL associated with tiller number or rate, including the major QTL on chromosome 3H, were not associated with any other traits, suggesting that tiller number can be modified independently of other important agronomic traits. These results enhance our knowledge of the genetic control of tiller development in barley, which is important for optimizing tiller number and rate for yield improvement.

genetics↗

Structural assembly of the human Miro1/2 GTPases based on the crystal structure of the N-terminal GTPase domain

Dysfunction in mitochondrial dynamics is believed to contribute to a host of neurological disorders and has recently been implicated in cancer metastasis. The outer mitochondrial membrane adapter protein Miro functions in the regulation of mitochondrial mobility and degradation, however, the structural basis for its roles in mitochondrial regulation remain unknown. Here, we report a 1.7[A] crystal structure of N-terminal GTPase domain (nGTPase) of human Miro1 bound unexpectedly to GTP, thereby revealing a non-catalytic configuration of the putative GTPase active site. We identify two conserved surfaces of the nGTPase, the \"SELFYY\" and \"ITIP\" motifs, that are potentially positioned to mediate dimerization or interaction with binding partners. Additionally, we report small angle X-ray scattering (SAXS) data obtained from the intact soluble HsMiro1 and its paralog HsMiro2. Taken together, the data allow modeling of a crescent-shaped assembly of the full-length soluble domain of HsMiro1/2.\n\nPDB referenceCrystal structure of the human Miro1 N-terminal GTPase bound to GTP, 6D71

cell biology↗

Multi-Trait Improvement by Predicting Genetic Correlations in Breeding Crosses

The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (rG). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e. pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11-27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.

genetics↗

The fate of deleterious variants in a barley genomic prediction population

Targeted identification and purging of deleterious genetic variants has been proposed as a novel approach to animal and plant breeding. This strategy is motivated, in part, by the observation that demographic events and strong selection associated with cultivated species pose a \"cost of domestication.\" This includes an increase in the proportion of genetic variants where a mutation is likely to reduce fitness. Recent advances in DNA resequencing and sequence constraint-based approaches to predict the functional impact of a mutation permit the identification of putatively deleterious SNPs (dSNPs) on a genome-wide scale. Using exome capture resequencing of 21 barley 6-row spring breeding lines, we identify 3,855 dSNPs among 497,754 total SNPs. In order to polarize SNPs as ancestral versus derived, we generated whole genome resequencing data of Hordeum murinum ssp. glaucum as a phylogenetic outgroup. The dSNPs occur at higher density in portions of the genome with a higher recombination rate than in pericentromeric regions with lower recombination rate and gene density. Using 5,215 progeny from a genomic prediction experiment, we examine the fate of dSNPs over three breeding cycles. Average derived allele frequency is lower for dSNPs than any other class of variants. Adjusting for initial frequency, derived alleles at dSNPs reduce in frequency or are lost more often than other classes of SNPs. The highest yielding lines in the experiment, as chosen by standard genomic prediction approaches, carry fewer homozygous dSNPs than randomly sampled lines from the same progeny cycle. In the final cycle of the experiment, progeny selected by genomic prediction have a mean of 5.6% fewer homozygous dSNPs relative to randomly chosen progeny from the same cycle.\n\nAuthor SummaryThe nature of genetic variants underlying complex trait variation has been the source of debate in evolutionary biology. Here, we provide evidence that agronomically important phenotypes are influenced by rare, putatively deleterious variants. We use exome capture resequencing and a hypothesis-based test for codon conservation to predict deleterious SNPs (dSNPS) in the parents of a multi-parent barley breeding population. We also generated whole-genome resequencing data of Hordeum murinum, a phylogenetic outgroup to barley, to polarize dSNPs by ancestral versus derived state. dSNPs occur disproportionately in the gene-rich chromosome arms, rather than in the recombination-poor pericentromeric regions. They also decrease in frequency more often than other variants at the same initial frequency during recurrent selection for grain yield and disease resistance. Finally, we identify a region on chromosome 4H that strongly associated with agronomic phenotypes in which dSNPs appear to be hitchhiking with favorable variants. Our results show that targeted identification and removal of dSNPs from breeding programs is a viable strategy for crop improvement, and that standard genomic prediction approaches may already contain some information about unobserved segregating dSNPs.

genomics↗

Trisomy 21 drives production of neurotoxic tryptophan catabolites via the interferon-inducible kynurenine pathway

Trisomy 21 (T21) causes Down syndrome (DS), affecting immune and neurological function by unknown mechanisms. We report here the results of a large metabolomics study showing that people with DS produce elevated levels of kynurenine and quinolinic acid, two tryptophan catabolites with potent immunosuppressive and neurotoxic properties, respectively. We found that immune cells of people with DS overexpress IDO1, the rate-limiting enzyme in the kynurenine pathway (KP) and a known interferon (IFN)-stimulated gene. Furthermore, we found a positive correlation between levels of specific inflammatory cytokines and KP dysregulation. Using metabolic flux assays, we found that IFN stimulation causes IDO1 overexpression and kynurenine overproduction in cells with T21, dependent on overexpression of IFN receptors encoded on chromosome 21. Finally, KP dysregulation is conserved in a mouse model of DS carrying triplication of the IFN receptors. Altogether, these results reveal a mechanism by which T21 could drive immunosuppression and neurotoxicity in DS.

physiology↗