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222 results for “genetic interactions”
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
<p>Files generated from the study described in <a href="https://doi.org/10.1101/2024.02.08.579534">Fernandes et. al (2024)</a> .</p> <p>The file "cvs_h2s.csv" comprises the coefficient of variation and the Cullis heritability for each environment.</p> <p>The file "all_predictions.csv" contains the predictions from all the models evaluated, in different cross-validation (CV) scenarios.</p> <p>The file "coincidence_index.csv" has the Coincidence Index (CI) for each CV and models evaluated in our study.</p> <p>Our study used the multi-environment maize yield trials data from the Genomes to Fields 2022 initiative (<a href="https://doi.org/10.1186/s13104-023-06421-z">Lima et. al 2024</a>).</p>
Whole genome sequencing of Turkish genomes reveals functional private alleles and impact of genetic interactions with Europe, Asia and Africa.
<p>BACKGROUND:</p> <p>Turkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32×-48×).</p> <p>RESULTS:</p> <p>We show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency.</p> <p>CONCLUSION:</p> <p>This study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey.</p>
Processed data for the study on "Chromatin 3D interactions mediate genetic effects on gene expression"
<p>This repository contains the processed data that was generated as part of the following study:</p> <p>Delaneau et al. (2019) <strong>Chromatin 3D interactions mediate genetic effects on gene expression.</strong></p> <p><em>Abstract:</em> Studying the genetic basis of gene expression and chromatin organization is key to characterize the effect of genetic variability on the function and structure of the human genome. Here, we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and inter-chromosomal levels within 12,583 Cis Regulatory Domains and 30 Trans Regulatory Hubs that highly reflect the local (i.e. Topologically Associating Domains) and global (i.e. open/close chromatin compartments) nuclear chromatin organization. These structures delimit cell type specific regulatory networks that control gene expression/co-expression and mediate the genetic effects of <em>cis</em>- and <em>trans</em>-acting regulatory variants on genes.</p> <p> </p> <p>This repository contains:</p> <ol> <li>Chromatin QTLs for H3K27ac, H3K4me1 and H3K4me3 discovered in 317 Lymphoblastoids Cell Lines (LCLs) and 78 Fibroblasts.</li> <li>Molecular QTLs affecting the activity and structure of Cis Regulatory Domains (CRDs) in LCLs.</li> <li>Basic information about the full set of genetic variants being analyzed in the study.</li> <li>The peak coordinates, their hierarchy based on inter-individual correlation and the CRD calls for both LCLs and Fibroblasts.</li> <li>The functional links discovered in LCLs between CRDs and genes.</li> <li>eQTLs for LCLs.</li> <li>A README file containing the description of the file format for each file.</li> </ol>
Alliance of Genome Resources Genetic Interactions
<p>These files provide a set of annotations of genetic interactions for genes for human, rat, mouse, zebrafish, fruit fly, nematode, African clawed frog,and yeast). The files are in the <a href="https://github.com/HUPO-PSI/miTab/blob/master/PSI-MITAB27Format.md">PSI-MI TAB 2.7 format</a>, a tab-delimited format established by the <a href="http://www.psidev.info/">HUPO Proteomics Standards Initiative</a> Molecular Interactions (PSI-MI) working group. The interaction data are sourced from Alliance members WormBase and FlyBase, as well as the <a href="https://thebiogrid.org/">BioGRID database</a>. Identities or types of genetic perturbations for each interactor (if available) are provided in columns 26 and 27 and relevant phenotypes or traits (if available) are provided in column 28.</p> <ul> <li>Homo sapiens (human; NCBI:txid 9606)</li> <li>Caenorhabditis elegans (nematode; NCBI:txid 6239)</li> <li>Danio rerio (zebrafish;NCBI:txid 7955)</li> <li>Drosophila melanogaster (fruit fly; NCBI:txid 7227)</li> <li>Mus musculus (mouse; NCBI:txid10090)</li> <li>Rattus norvegicus (rat; NCBI:txid 10116)</li> <li>Saccharomyces cerevisiae (yeast; NCBI:txid 559292)</li> <li>Xenopus laevis (African clawed frog; NCBI:txid 8355)</li> </ul>
IMPACT OF US BROWN SWISS GENETICS ON MILK QUALITY FROM LOW-INPUT HERDS IN SWITZERLAND: INTERACTIONS WITH SEASON
<p>This study aimed to investigate the effect of, and interactions between, US Brown Swiss genetics and season on milk yield, basic composition and fatty acid profiles, from cows on low-input farms in Switzerland. Milk samples (n=1,976) were collected from 1,220 crossbreed cows with differing proportions of BS, Braunvieh and Original Braunvieh genetics on 40 farms during winter-indoor and summer-grazing seasons. Cows with more Brown Swiss genetics produced more milk in winter but not in summer, possibly because of underfeeding high-yielding cows on low-input pasture-based diets. Cows with more Original Braunvieh genetics produced milk with higher concentrations of (i) nutritionally desirable <em>trans</em>-9 palmitoleic, eicosapentaenoic and docosapentaenoic acids, throughout the year, and (ii) vaccenic and α-linolenic acids, total omega-3 fatty acids concentrations and a higher omega-3/omega-6 ratio during the summer-grazing period only. This suggests that overall milk quality could be improved by re-focusing breeding strategies on the cows’ ability to respond to local dietary environments and seasonal changes in feeding regimes.</p>
Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients
<p><em>Background:</em></p> <p>Understanding the mechanisms by which genes control and regulate complex quantitative traits during periods of fluctuating resources remains a challenging and uncertain task in photosynthesis studies. Most studies have focused on the structure of photosynthesis, the photosynthetic response under stress, or the genetic mechanisms involved in photosynthetic effects and neglected the interactive genetic mechanism that governs various traits through significant quantitative trait loci (QTLs). Results In this study, we have developed a differential dynamic system that enables the identification of QTLs based on the photosynthetic phenotypic and genotypic data under varying levels of light intensity gradients. The framework not only allows for the assessment of the direct effects of QTLs on phenotypes but also captures how they influence interactions among phenotypes as light intensities change. We have analyzed the genetic effects and genetic variance, visualized the genetic network associated with photosynthesis interactions, and validated the effectiveness and stability of the DDS framework. Pivotal QTLs were identified individually to uncover the process and pattern of interaction. Through functional annotation, we made an intriguing discovery that seemingly unimportant QTLs can still have significant genetic effects on phenotypic changes through their regulation with other QTLs. Conclusions This finding emphasizes the significance of considering the interactive genetic architecture when seeking to understand the genetic interaction mechanism of photosynthesis in natural populations of woody plants. Moreover, our research provides a novel framework that can be extended to explore the interactive genetic architecture among organisms, contributing to a deeper understanding of stress resistance mechanisms in woody plants.</p>
Elution profiles and protein interaction data accompanying "Ancient eukaryotic protein interactions illuminate modern genetic disorders"
<div> <p> </p> <table> <tbody> <tr> <td> <h2><strong>DESCRIPTION</strong></h2> </td> <td> <h2><strong>FILENAME</strong></h2> </td> <td> <h2><strong>LOCATION</strong></h2> </td> </tr> <tr> <td> <p>LECA 10K OG set</p> </td> <td> <p>leca_ogs_annotated.xlsx</p> </td> <td> <p>Paper, Table S1</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Summary of biological resources</p> </td> <td> <p>resource_summary.xlsx</p> </td> <td> <p>Paper, Table S2</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>LECA interactome (complexes)</p> </td> <td> <p>leca_ppis_fdr10_clustered_annotated.xlsx</p> </td> <td> <p>Paper, Table S3</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - ref proteomes</p> </td> <td> <p>cfms_ref_proteomes.xlsx</p> </td> <td> <p>Paper, Table S4</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>ML - top algorithms</p> </td> <td> <p>tpot_top_algorithms.xlsx</p> </td> <td> <p>Paper, Table S5</p> <p>Zenodo</p> </td> </tr> <tr> <td> <p>LECA interactome (pairwise)</p> </td> <td> <p>leca_ppis_fdr10_pairwise.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>UniProt Subcellular Localization IDs</p> </td> <td> <p>uniprot_localization_codes.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Dollo parsimony - ref proteomes</p> </td> <td> <p>dollo_parsimony_ref_proteomes.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Dollo parsimony - input trait matrix</p> </td> <td> <p>dollo_parsimony_count_matrix.tsv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - raw elution profiles</p> </td> <td> <p>amorphea_raw_elution_vectors.csv</p> <p>excavata_raw_elution_vectors.csv</p> <p>tsar_raw_elution_vectors.csv</p> <p>archaeplastida_raw_elution_vectors.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS - normalized elution profiles</p> </td> <td> <p>amorphea_norm_elution_vectors.csv</p> <p>excavata_norm_elution_vectors.csv</p> <p>tsar_norm_elution_vectors.csv</p> <p>archaeplastida_norm_elution_vectors.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>CFMS/APMS - complete feature matrix</p> </td> <td> <p>feature_matrix.csv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>ML - top features</p> </td> <td> <p>linearsvc_top_100_features.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>OMIM disease propagation, statistics</p> </td> <td> <p>omim_disease_propagation_stats.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>OMIM disease propagation, top 20 hits per disease</p> </td> <td> <p>omim_disease_propagation_top20hits_per_disease.xlsx</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td> <p>Curated OMIM gene-disease relationships for LECA OGs</p> </td> <td> <p>omim_disease_network.tsv</p> </td> <td> <p>Zenodo</p> </td> </tr> <tr> <td>Curated OMIM gene-disease relationships for human UniProt IDs</td> <td>omim_disease_groups.csv</td> <td>Zenodo</td> </tr> </tbody> </table> </div> <p> </p>
Datasets for "Intraspecific interactions in the annual legume Medicago minima are shaped by both genetic variation for competitive ability and reduced competition among kin"
<p>Datasets for “Intraspecific interactions in the annual legume <em>Medicago minima</em> are shaped by both genetic variation for competitive ability and reduced competition among kin”</p> <p>Two datasets are provided.</p> <p>root_behavior_experiment_for_ms.csv: provides data relative to a root behaviour experiment where <em>Medicago minima</em> genotypes grew either with a kin or a non kin. Direction of root growth, root length and biomass were measured.</p> <p>Medicago_minima_biomass_dataMerge.csv: provides data relative to a minicommunity experiment where <em>Medicago minima </em>genotypes were grown surrounded by three kin genotypes, or three non-kin genotypes (i.e. stranger to the central plant but identical to each other) or three stranger genotypes (stranger to the central plant and to each other). For this second experiment above-ground growth and biomass were monitored.</p> <p>Detailed information on the dataset variables are provided in the metadata file.</p> <p>Code for data wrangling and analyses is included in the manuscript as an appendix.</p>
Data from: Additive genetic and environmental variation interact to shape the dynamics of seasonal migration in a wild bird population
<p><span>Dissecting joint micro-evolutionary and plastic responses to environmental perturbations requires quantifying interacting components of genetic and environmental variation underlying expression of key traits. This ambition is particularly challenging for phenotypically discrete traits where multiscale decompositions are required to reveal non-linear transformations of underlying genetic and environmental variation into phenotypic variation, and when effects must be estimated from incomplete field observations. We devised a joint multistate capture-recapture and quantitative genetic animal model and fitted this model to full-annual-cycle resighting data from partially-migratory European shags (<em>Gulosus</em> <em>aristotelis</em>) to estimate key components of genetic, environmental and phenotypic variance in the ecologically critical discrete trait of seasonal migration versus residence. We demonstrate non-negligible additive genetic variance in latent liability for migration, resulting in detectable micro-evolutionary responses following two episodes of strong survival selection. Further, liability-scale additive genetic effects interacted with substantial permanent individual and temporary environmental effects to generate complex non-additive effects on expressed phenotypes, causing substantial intrinsic gene-by-environment interaction variance on the phenotypic scale. Our analyses therefore reveal how temporal dynamics of partial seasonal migration arise from combinations of instantaneous micro-evolution and within-individual phenotypic consistency, and highlight how intrinsic phenotypic plasticity could expose genetic variation underlying discrete traits to complex forms of selection.</span></p>
Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients
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Data from: Additive genetic and environmental variation interact to shape the dynamics of seasonal migration in a wild bird population
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Data and code for: Multiple genetic impacts of immigration interact to shape local population persistence versus extinction
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Interaction of Genetic Variations in NFE2L2 and SELENOS Modulates the Risk of Hashimoto's Thyroiditis
<p>This is a dataset for the following paper: doi: 10.1089/thy.2018.0480. PMID: 31426718</p> <p>Each book in the excel file refers to the paper's figure panel specified.</p> <p>For more info do not hesitate to contact me directly at gerasimos.sykiotis@chuv.ch</p>
Data from: Genetics-based interactions of foundation species affect community diversity, stability, and network structure
We examined the hypothesis that genetics-based interactions between strongly interacting foundation species, the tree Populus angustifolia and the aphid Pemphigus betae, affect arthropod community diversity, stability and species interaction networks of which little is known. In a 2-year experimental manipulation of the tree and its aphid herbivore four major findings emerged: (i) the interactions of these two species determined the composition of an arthropod community of 139 species; (ii) both tree genotype and aphid presence significantly predicted community diversity; (iii) the presence of aphids on genetically susceptible trees increased the stability of arthropod communities across years; and (iv) the experimental removal of aphids affected community network structure (network degree, modularity and tree genotype contribution to modularity). These findings demonstrate that the interactions of foundation species are genetically based, which in turn significantly contributes to community diversity, stability and species interaction networks. These experiments provide an important step in understanding the evolution of Darwin's 'entangled bank', a metaphor that characterizes the complexity and interconnectedness of communities in the wild.
Data from: Genetic variation in mutualistic and antagonistic interactions in an invasive legume
Mutualists may play an important role in invasion success. The ability to take advantage of novel mutualists or survive and reproduce despite a lack of mutualists may facilitate invasion by those individuals with such traits. Here, we used two greenhouse studies to examine how soil microbial communities in general and mutualistic rhizobia in particular affect the performance of a legume species (Medicago polymorpha) that has invaded five continents. We performed two plant growth experiments with Medicago polymorpha, inoculating them with soil slurries in one experiment or rhizobial cultures in another experiment. For both experiments, we compared the growth of Medicago in competition with conspecific or heterospecific plants and examined variation among plant genotypes collected from the native and introduced ranges. We found that all genotypes experienced similar increases in biomass and formed more nodules that house rhizobia bacteria when inoculated with soil from a previously invaded site, compared to uninoculated plants or plants inoculated with soil from uninvaded and low invasion sites. In a second experiment, plants inoculated with rhizobia generally produced more biomass, had greater tolerance to interspecific competition, and had greater effects on competitor biomass than uninoculated plants. However, plant genotypes collected from the native range benefited more from rhizobia and were less tolerant of competition relative to genotypes collected from the introduced range. In the introduced range, compatible mutualists may not be readily available but competition is intense, causing Medicago to evolve to benefit less from interactions with rhizobia mutualists, while simultaneously becoming more tolerant of competition.
The potential for genotype-by-environment interactions to maintain genetic variation in a model legume–rhizobia mutualism
<p>The maintenance of genetic variation in mutualism-related traits is key for understanding mutualism evolution, yet the mechanisms maintaining variation remain unclear. We asked whether genotype-by-environment (G×E) interaction is a potential mechanism maintaining variation in the model legume–rhizobia system, <em>Medicago truncatula–Ensifer meliloti</em>. We planted 50 legume genotypes in a greenhouse under ambient light and shade to reflect reduced carbon availability for plants. We found an expected reduction under shaded conditions for plant performance traits, such as leaf number, aboveground and belowground biomass, and a mutualism-related trait, nodule number. We also found G×E for nodule number, with ∼83% of this interaction due to shifts in genotype fitness rank order across light environments, coupled with strong positive directional selection on nodule number regardless of light environment. Our results suggest that G×E can maintain genetic variation in a mutualism-related trait that is under consistent positive directional selection across light environments.</p>
Data from: Genetic responsiveness of African buffalo to environmental stressors: a role for epigenetics in balancing autosomal and sex chromosome interactions?
In the African buffalo (Syncerus caffer) population of the Kruger National Park (South Africa) a primary sex-ratio distorter and a primary sex-ratio suppressor have been shown to occur on the Y chromosome. A subsequent autosomal microsatellite study indicated that two types of deleterious alleles with a negative effect on male body condition, but a positive effect on relative fitness when averaged across sexes and generations, occur genome-wide and at high frequencies in the same population. One type negatively affects body condition of both sexes, while the other acts antagonistically: it negatively affects male but positively affects female body condition. Here we show that high frequencies of male-deleterious alleles are attributable to Y-chromosomal distorter-suppressor pair activity and that these alleles are suppressed in individuals born after three dry pre-birth years, likely through epigenetic modification. Epigenetic suppression was indicated by statistical interactions between pre-birth rainfall, a proxy for parental body condition, and the phenotypic effect of homozygosity/heterozygosity status of microsatellites linked to male-deleterious alleles, while a role for the Y-chromosomal distorter-suppressor pair was indicated by between-sex genetic differences among pre-dispersal calves. We argue that suppression of male-deleterious alleles results in negative frequency-dependent selection of the Y distorter and suppressor; a prerequisite for a stable polymorphism of the Y distorter-suppressor pair. The Y distorter seems to be responsible for positive selection of male-deleterious alleles during resource-rich periods and the Y suppressor for positive selection of these alleles during resource-poor periods. Male-deleterious alleles were also associated with susceptibility to bovine tuberculosis, indicating that Kruger buffalo are sensitive to stressors such as diseases and droughts. We anticipate that future genetic studies on African buffalo will provide important new insights into gene fitness and epigenetic modification in the context of sex-ratio distortion and infectious disease dynamics.
Data from: The impact of plant genetic variation, drought, and leaf nitrogen on plant-herbivore interactions
<p>Plant genotype, drought stress, and their interaction are among the factors contributing to the susceptibility of plants to herbivory. The plant's nitrogen concentration, a critical and often limiting nutrient, differs with plant genotype and drought. Still, few studies have investigated the impact of the interaction of genotype and drought on herbivory and plant nitrogen. We established a common garden in Duluth, MN, of tall goldenrod, <em>Solidago altissima,</em> collected from a local Minnesota site to analyze the effects of goldenrod genotype and drought stress on leaf nitrogen and the preference and performance of the chrysanthemum lace bug, <em>Corythucha marmorata</em>. Lace bugs had oviposition, nymph, and adult preferences among host plant genotypes, water treatments, and among genotype and water treatment combinations. Nymph and adult survival and adult mass varied significantly due to plant genotype, water treatment, the interaction of plant and water treatment, and the interaction of treatment with lace bug density. Oviposition preference and offspring performance were significantly positively related. Leaf nitrogen increased with the increasing severity of the water limitation in the absence of lace bugs. However, in the presence of lace bugs, there was no difference in nitrogen among water treatments.</p>
Minimal dataset for the manuscript "Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank".
<p>Dataset "lin2024-sex_combined_interaction-association_signifincant_in_one_or_more_tests-summary.txt" is a minimal dataset to reproduce the figures and tables in the manuscript "Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank". </p> <p><br>To generate this dataset, see "https://github.com/BoxiLin/t2meta" Steps 0, 1.</p> <p>This dataset is the input for Steps 2, 3, 4, 5 to generate Figures 1-3 and Table 2-3.</p> <p> </p> <p>##### Column information ########################</p> <p>The following columns are annotations on each variant in the GWAS, calculated across the analysis subset of 361,194 samples by the Neale lab:</p> <p>code: Phenotype identifier in the form of "[UKB Data field]_raw"<br>variant: Unique variant identifier in the form "chr:pos:ref:alt", where "ref" is aligned to the forward strand.<br>chr: Chromosome of the variant.<br>pos: Position of the variant in GRCh37 coordinates.<br>rsid: rs ID<br>ref: Reference allele on the forward strand.<br>alt: Alternate allele (not necessarily minor allele).<br>p_hwe: Hardy-Weinberg p-value.<br>info: Imputation INFO score as provided by UK Biobank.</p> <p> </p> <p>The following columns are sex-stratified test statistics calculated by the Neale lab:</p> <p>minor_allele.x: Minor allele (AF < 0.5) in the female GWAS <br>minor_AF.x: Minor allele frequency in the female GWAS <br>beta.x: Estimated effect size of alt allele in the female GWAS <br>se.x: Estimated standard error of beta in the female GWAS<br>tstat.x: t-statistic of beta estimate (= beta/se) in the female GWAS <br>pval.x: p-value of beta significance test in the female GWAS </p> <p>minor_allele.y: Minor allele (AF < 0.5) in the male GWAS <br>minor_AF.y: Minor allele frequency in the male GWAS <br>beta.y: Estimated effect size of alt allele in the male GWAS <br>se.y: Estimated standard error of beta in the male GWAS <br>tstat.y: t-statistic of beta estimate (= beta/se) in the male GWAS <br>pval.y: p-value of beta significance test in the male GWAS </p> <p> </p> <p><br>The following columns are sex-combined test statistics calculated in our analysis:</p> <p>T.I: test statsitic for interaction effect-only <br>p.T.I: p-value of the interaction effect-only test <br>TSG.L: test statsitic for inverse variance weighted meta-analysis<br>p.TSG.L: p-value of the inverse variance weighted meta-analysis<br>TSG.Q: test statsitic for the omnibus meta-analysis<br>p.TSG.Q: p-value for the omnibus meta-analysis</p>
Microscopy data from: Identification of genetic interactions with priB links the PriA/PriB DNA replication restart pathway to double-strand DNA break repair in Escherichia coli
<p>Collisions between DNA replication complexes (replisomes) and impediments such as damaged DNA or proteins tightly bound to the chromosome lead to premature dissociation of replisomes at least once per cell cycle in <em>Escherichia coli</em>. Left unrepaired, these events produce incompletely replicated chromosomes that cannot be properly partitioned into daughter cells. DNA replication restart, the process that reloads replisomes at prematurely terminated sites, is therefore essential in <em>E. coli</em> and other bacteria. Three replication restart pathways have been identified in <em>E. coli</em>: PriA/PriB, PriA/PriC, and PriC/Rep. A limited number of genetic interactions between replication restart and other genome maintenance pathways have been defined, but a systematic study placing replication restart reactions in a broader cellular context has not been performed. We have utilized transposon insertion sequencing to identify new genetic interactions between DNA replication restart pathways and other cellular systems. Known genetic interactors with the <em>priB</em> replication restart gene (uniquely involved in the PriA/PriB pathway) were confirmed and several novel <em>priB </em>interactions were discovered. Far fewer connections were found with the PriA/PriC or PriC/Rep pathways, suggesting a primacy role for the PriA/PriB pathway in <em>E. coli</em>. Targeted genetic and imaging-based experiments with <em>priB</em> and its genetic partners revealed significant double-strand DNA break (DSB) accumulation in strains with mutations in <em>dam</em>, <em>rep</em>, <em>rdgC</em>, <em>lexA</em>, or <em>polA</em>. Modulating the activity of the RecA recombinase partially suppressed the detrimental effects of <em>rdgC</em> or <em>lexA</em> mutations in Δ<em>priB</em> cells. Taken together, our results highlight roles for several genes in DSB homeostasis and define a genetic network that facilitates DNA repair/processing upstream of PriA/PriB-mediated DNA replication restart in <em>E. coli</em>.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.