bluuebunny/crossref_metadata_2025_split huggingface dataset:
Colonization of a novel host plant reduces phenotypic variation
Authors: Nilsson Kalle J , Tsuboi Masahito , Opedal Øystein , Runemark Anna 
Published: January 2023

AbstractUnderstanding the evolutionary potential of populations –evolvability– is key to predicting their ability to cope with novel environments. Despite growing evidence that evolvability determines the tempo and mode of adaptation, it remains unclear how adaptations to novel environments influence evolvability in turn. Here we address the interplay between adaptation and evolvability in the peacock flyTephritis conura, which recently underwent an adaptive change in the length of female ovipositor following a host shift. By comparing evolvability in various morphological traits including female ovipositor length between ancestral and derived host races, we found that evolvability is decreased in females of the derived host race compared to the ancestral host race. We found a correlation between evolvability and divergence between populations in both sexes, indicating that the overall pattern of evolvability has not been disrupted by the host shift despite the reduction in females of the derived host race. Exploration of the pattern of phenotypic integration further revealed that the ovipositor length constitutes a module that is separated from other measured traits. These results suggest that adaptation to novel environments can affect evolvability, and that modularity helps minimizing detrimental effects that adaptations may cause to other correlated traits.
nf-root: a best-practice pipeline for deep learning-based analysis of apoplastic pH in microscopy images of developmental zones in plant root tissue
Authors: Wanner Julian , Cuellar Luis Kuhn , Rausch Luiselotte , Berendzen Kenneth W. , Wanke Friederike , Gabernet Gisela , Harter Klaus , Nahnsen Sven 
Published: January 2023

AbstractHere we reportnextflow-root(nf-root), a novel best-practice pipeline for deep learning-based analysis of fluorescence microscopy images of plant root tissue, aimed at studying hormonal mechanisms associated with cell elongation, given the vital role that plant hormones play in the development and growth of plants. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images, and analysis of apoplastic pH measurements of tissue zones, which is useful for modeling plant hormone signaling and cell physiological responses. Mathematical models of physiological responses of plant hormones, such as brassinolide, have been successfully established for certain root tissue types, by evaluating apoplastic pH via fluorescence imaging. However, the generation of data for this modeling is time-consuming, as it requires the manual segmentation of tissue zones and evaluation of large amounts of microscopy data. We introduce a high-throughput, highly reproducibleNextflowpipeline based onnf-corestandards that automates tissue zone segmentation by implementing a deep-learning module, which deploys deterministically trained (i.e. bit-exact reproducible) convolutional neural network models, and augments the segmentation predictions with measures of predictionuncertaintyand modelinterpretability, aiming to facilitate result interpretation and verification by experienced plant biologists. To train our segmentation prediction models, we created a publicly available dataset composed of confocal microscopy images ofA. thalianaroot tissue using the pH-sensitive fluorescence indicator, and manually annotated segmentation masks that identify relevant tissue zones. We applied this pipeline to analyze exemplary data, and observed a high statistical similarity between the manually generated results and the output ofnf-root. Our results indicate that this approach achieves near human-level performance, and significantly reduces the time required to analyze large volumes of data, from several days to hours.
A field theoretic approach to non-equilibrium population genetics in the strong selection regime
Authors: Balick Daniel J. 
Published: January 2023

AbstractNatural populations are virtually never observed in equilibrium, yet equilibrium approximations comprise the majority of our understanding of population genetics. Using standard tools from statistical physics, a formalism is presented that re-expresses the stochastic equations describing allelic evolution as a partition functional over all possible allelic trajectories (‘paths’) governed by selection, mutation, and drift. A perturbative field theory is developed for strong additive selection, relevant to disease variation, that facilitates the straightforward computation of closed-form approximations for time-dependent moments of the allele frequency distribution across a wide range of non-equilibrium scenarios; examples are presented for constant population size, exponential growth, bottlenecks, and oscillatory size, all of which align well to simulations and break down just above the drift barrier. Equilibration times are computed and, even for static population size, generically extend beyond the order 1/stimescale associated with exponential frequency decay. Though the mutation load is largely robust to variable population size, perturbative drift-based corrections to the deterministic trajectory are readily computed. Under strong selection, the variance of a new mutation’s frequency (related to homozygosity) is dominated by drift-driven dynamics and a transient increase in variance often occurs prior to equilibrating. The excess kurtosis over skew squared is roughly constant (i.e., independent of selection, provided 2Ns≳ 5) for static population size, and thus potentially sensitive to deviation from equilibrium. These insights highlight the value of such closed-form approximations, naturally generated from Feynman diagrams in a perturbative field theory, which can simply and accurately capture the parameter dependences describing a variety of non-equilibrium population genetic phenomena of interest.
Breast Milk Oligosaccharides Contain Immunomodulatory Glucuronic Acid and LacdiNAc
Authors: Jin Chunsheng , Lundstrøm Jon , Korhonen Emma , Luis Ana S. , Bojar Daniel 
Published: January 2023

AbstractBreast milk is abundant with functionalized milk oligosaccharides (MOs), to nourish and protect the neonate. Yet we lack a comprehensive understanding of the repertoire and evolution of MOs across Mammalia. We report ∼400 MO-species associations (>100 novel structures) from milk glycomics of nine mostly understudied species: alpaca, beluga whale, black rhinoceros, bottlenose dolphin, impala, L’Hoest’s monkey, pygmy hippopotamus, domestic sheep, and striped dolphin. This revealed the hitherto unknown existence of the LacdiNAc motif (GalNAcβ1-4GlcNAc) in MOs of all species except alpaca, sheep, and striped dolphin, indicating widespread occurrence of this potentially antimicrobial motif in MOs. We also characterize glucuronic acid-containing MOs in the milk of impala, dolphins, sheep, and rhinoceros, previously only reported in cows. We demonstrate that these GlcA-MOs exhibit potent immunomodulatory effects. Our study extends the number of known MOs by >15%. Combined with >1,900 curated MO-species associations, we characterize MO motif distributions, presenting an exhaustive overview of MO biodiversity.
IDENTIFYING ASSOCIATIONS BETWEEN GENETIC CONDITIONS IN OFFSPRING AND PREGNANCY HEALTH COMPLICATIONS
Authors: Gibson Rebecca A. , Abar Bijan , Elman Lily , Walsh Kyle M. , Hurst Jillian H. , Cohen Jennifer L. 
Published: January 2023

ABSTRACTBackgroundPrior research has identified associations between pregnancy complications and specific fetal genetic diagnoses in the offspring.ObjectiveThe purpose of this study is to provide a large-scale investigation of associations between infant genetic diagnoses and pregnancy complications.MethodsRetrospective chart review of maternal-infant dyads born at Duke University (2013-2021) identified 236 mothers who had infants with genetic diseases (exposed group) and 472 matched dyads without concern for newborn genetic disease (unexposed group). Logistic regression examined associations between gestational factors and infant genetic diseases.ResultsThe risk of gestational diabetes was elevated among mothers of infants with aneuploidy (RR=1.65; 95% CI 1.05,2.61). The risk of preeclampsia was elevated among mothers of infants with imprinting conditions (RR=5.08; 95% CI 2.41,10.73). The presence of any infant genetic disease was associated with an increase in placental disorders (RR=1.98; 95% CI 1.61,2.43), including a 2.25-fold increase in placental infarction (RR=2.25; 95% CI 1.72,2.93).ConclusionsThese findings provide insight into relationships between fetal genetic diseases and gestational complications and may guide identification of pregnancies that would benefit from expanded prenatal and antenatal genetic screening for earlier identification and treatment of genetic conditions.SYNPOSISStudy questionAre there associations between genetic conditions in offspring and pregnancy health complications?What’s already knownPrior research has identified associations between pregnancy complications and specific fetal genetic diseases, including pregnancy-related hypertension/preeclampsia in association withEP300-related Rubinstein-Taybi syndrome.What this study addsThis retrospective cohort study provides the first comprehensive investigation into potential links between offspring genetic diagnoses and pregnancy complications, identifying associations between aneuploidy and gestational diabetes, imprinting disorders and preeclampsia, and fetal genetic conditions and specific placental abnormalities. These findings can inform future medical management of pregnancies affected by a fetal genetic condition and help guide the future development of algorithms to prospectively identify parent/infant dyads who may benefit from expanded prenatal and neonatal genetic testing.
Phenotypic convergence: a novel phenomenon in the diagnostic process of Mendelian genetic disorders
Authors: Tinker Rory J , Peterson Josh , Bastarache Lisa 
Published: January 2023

AbstractIntroductionThe study of Mendelian disease has yielded a large body of knowledge about the phenotypic presentation of disease. Less is known about the way the diseases are reflected in the electronic health record (EHR).AimTo develop an EHR-based model of the diagnostic trajectory and investigate data availability and the longitudinal distribution of signs and symptoms of a Mendelian disorder within EHRs.MethodsWe created a conceptual model to specify key time points of the diagnostic trajectory and applied it to individuals with genetically confirmed hereditary connective tissue diseases (HCTD). Using the model, we assessed EHR data availability within each time interval. We tested the performance of phenotype risk scores (PheRS), an algorithm that detects Mendelian disease patterns and assessed the phenotypic expression of HCTD over the diagnostic trajectory.ResultsWe identified 251 individuals with HCTD; 79 (35%) of these patients had a fully ascertained diagnostic trajectory. There were few documented signs and symptoms prior to clinical suspicion that evoked an HCTD disorder (median PheRS 0.14); once suspicion was documented, median PheRS increased to 1.87 (SD). The majority (72%) of phenotypic features were identified post clinical suspicion.DiscussionUsing a novel conceptual model for the diagnostic trajectory of Mendelian disease, we demonstrated that phenotype ascertainment is, in part, driven by the diagnostic process and that many findings are only documented following clinical suspicion and diagnosis, a process we term phenotypic convergence. Therefore, algorithms that aim to detect undiagnosed Mendelian disease should censor EHR data to avoid data leakage.
ANTsX neuroimaging-derived structural phenotypes of UK Biobank
Authors: Tustison Nicholas J. , Yassa Michael A. , Rizvi Batool , Cook Philip A. , Holbrook Andrew J. , Sathishkumar Mithra T. , Tustison Mia G. , Gee James C. , Stone James R. , Avants Brian B. 
Published: January 2023

AbstractUK Biobank is a large-scale epidemiological resource for investigating prospective correlations between various lifestyle, environmental, and genetic factors with health and disease progression. In addition to individual subject information obtained through surveys and physical examinations, a comprehensive neuroimaging battery consisting of multiple modalities provides imaging-derived phenotypes (IDPs) that can serve as biomarkers in neuroscience research. In this study, we augment the existing set of UK Biobank neuroimaging structural IDPs, obtained from well-established software libraries such as FSL and FreeSurfer, with related measurements acquired through the Advanced Normalization Tools Ecosystem. This includes previously established cortical and subcortical measurements defined, in part, based on the Desikan-Killiany-Tourville atlas. Also included are morphological measurements from two recent developments: medial temporal lobe parcellation of hippocampal and extra-hippocampal regions in addition to cerebellum parcellation and thickness based on the Shanneman anatomical labeling. Through predictive modeling, we assess the clinical utility of these IDP measurements, individually and in combination, using commonly studied phenotypic correlates including age, fluid intelligence, numeric memory, and several other sociodemographic variables. The predictive accuracy of these IDP-based models, in terms of root-mean-squared-error or area-under-the-curve for continuous and categorical variables, respectively, provides comparative insights between software libraries as well as potential clinical interpretability. Results demonstrate varied performance between package-based IDP sets and their combination, emphasizing the need for careful consideration in their selection and utilization.
Identification and evaluation of small-molecule inhibitors against the dNTPase SAMHD1viaa comprehensive screening funnel
Authors: Zhang Si Min , Paulin Cynthia B.J. , Michel Maurice , Marttila Petra , Yagüe-Capilla Miriam , Bwanika Henri Colyn , Shu Huazhang , Vekatram Rajagopal Papagudi , Wiita Elisée , Jemth Ann-Sofie , Almlöf Ingrid , Loseva Olga , Ortis Florian , Dirks Christopher , Koolmeister Tobias , Linde Erika , Lee Sun , Llona-Minguez Sabin , Haraldsson Martin , Strömberg Kia , Homan Evert J. , Scobie Martin , Lundbäck Thomas , Helleday Thomas , Rudd Sean G. 
Published: January 2023

AbstractSterile alpha motif and histidine-aspartic acid domain containing protein-1 (SAMHD1) is a deoxynucleoside triphosphate (dNTP) triphosphohydrolase central to cellular nucleotide pool homeostasis. Recent literature has also demonstrated how SAMHD1 can detoxify chemotherapy metabolites thereby controlling their clinical responses. To further understand SAMHD1 biology and to investigate the potential of targeting this enzyme as a neoadjuvant to existing chemotherapies we set out to discover selective small molecule-based inhibitors of SAMHD1. Here we report a discovery pipeline encompassing a biochemical screening campaign and a set of complementary biochemical, biophysical, and cell-based readouts for further characterisation of the screen output. The identified hit compound TH6342 and its analogues, accompanied by their inactive negative control analogue TH7126, demonstrated specific, low μM potency in inhibiting the hydrolysis of both natural substrates and nucleotide analogue therapeutics, shown using complementary enzyme-coupled and direct enzymatic activity assays. Their mode of inhibition was subsequently detailed by coupling kinetic studies with thermal shift assays, where TH6342 and analogues were shown to engage with pre-tetrameric SAMHD1 and deter the oligomerisation and allosteric activation of SAMHD1 without occupying nucleotide binding pockets. We further outline the development and application of multiple cellular assays for assessing cellular target engagement and associated functional effects, including CETSA and an in-cell dNTP hydrolase activity assay, which highlighted future optimisation strategies of this chemotype. In summary, with a novel mode of inhibition, TH6342 and analogues broaden the set of tool compounds available in deciphering SAMHD1 enzymology and functions, and furthermore, the discovery pipeline reported herein represents a thorough framework for future SAMHD1 inhibitor development.Abstract Figure
Natural variation inPrdm9affecting hybrid sterility phenotypes
Authors: AbuAlia Khawla FN , Damm Elena , Ullrich Kristian K , Mukaj Amisa , Parvanov Emil , Forejt Jiri , Odenthal-Hesse Linda 
Published: January 2023

AbstractPRDM9-mediated reproductive isolation was first described in offspring ofMus musculus musculusstrain PWD/Ph andMus musculus domesticusstrain C57BL/6J. Male F1-hybrids do not complete chromosome synapsis and arrest meiosis at Prophase I. Currently, all data supports an oligogenic control of hybrid sterility based on incompatibilities between PRDM9 and hybrid-sterility locusHstx2inMus musculushybrids. Erosion of PRDM9 binding sites was proposed to result in asymmetric binding on diverged homologs of intersubspecific F1hybrids. Numerous alleles ofPrdm9have been characterized for different subspecies ofMus musculus, but only a few were analyzed for their impact on hybrid sterility. We analyzedPrdm9diversity in natural wild mouse populations from Europe, Asia, and the Middle East and identified several novelPrdm9alleles. We established that a singlePrdm9allele is associated witht-haplotype Chromosome 17 in all three subspecies ofMus musculusand characterized the phylogenetic relationships of novelPrdm9alleles with established sterility alleles. Novel wildPrdm9alleles produced F1-hybrid male offspring that were either fertile or showedPrdm9-dependent reduction of fertility and high levels of asynapsis. Fertility or sterility phenotypes segregated purely with thePrdm9genotype, although theMus musculus musculusbackground varied. Our data substantiate that hybrid sterility is under oligogenic control withPrdm9as the leading player but is consistent with a nonbinary regulation of hybrid sterility and gradual fertility decline when homologs diverge.
Metagenomic analysis of ecological niche overlap and community collapse in microbiome dynamics
Authors: Fujita Hiroaki , Ushio Masayuki , Suzuki Kenta , Abe Masato S. , Yamamichi Masato , Okazaki Yusuke , Canarini Alberto , Hayashi Ibuki , Fukushima Keitaro , Fukuda Shinji , Kiers E. Toby , Toju Hirokazu 
Published: January 2023

AbstractSpecies utilizing the same resources often fail to coexist for extended periods of time. Such competitive exclusion mechanisms potentially underly microbiome dynamics, causing breakdowns of communities composed of species with similar genetic backgrounds of resource utilization. Although genes responsible for competitive exclusion among a small number of species have been investigated in pioneering studies, it remains a major challenge to integrate genomics and ecology for understanding stable coexistence in species-rich communities. Here, we show that community-scale analyses of functional gene redundancy can provide a useful platform for interpreting and predicting collapse of bacterial communities. Through 110-day time-series of experimental microbiome dynamics, we analyzed the metagenome-assembled genomes of co-occurring bacterial species. We then inferred ecological niche space based on the multivariate analysis of the genome compositions. The analysis allowed us to evaluate potential shifts in the level of niche overlap between species through time. We hypothesized that community-scale pressure of competitive exclusion could be evaluated by quantifying overlap of genetically determined resource-use profiles (metabolic pathway profiles) among coexisting species. We found that the degree of community compositional changes observed in the experimental microbiome was explained by the magnitude of gene-repertoire overlaps among bacterial species. The metagenome-based analysis of genetic potential for competitive exclusion will help us forecast major events in microbiome dynamics such as sudden community collapse (i.e., dysbiosis).
Comparative analysis of chemical cross-linking mass spectrometry data indicates that protein STY residues rarely react with N-hydroxysuccinimide ester cross-linkers
Authors: Cao Yong , Liu Xin-Tong , Mao Peng-Zhi , Tarn Ching , Dong Meng-Qiu 
Published: January 2023

AbstractChemical cross-linking of proteins coupled with mass spectrometry (CXMS) has enjoyed growing popularity in biomedical research. Most CXMS experiments utilize cross-linkers based on N-hydroxysuccinimide (NHS) ester, which react selectively with the amine groups found on the free N-termini of proteins and on the side chain of lysine (K) residues. It is also reported that under certain conditions they can react with the hydroxyl groups of serine (S), threonine (T), and tyrosine (Y). Some of the popular cross-link search engines including MeroX and xiSearch set STY, in addition to K, as cross-linkable sites by default. However, to what extent NHS ester cross-linkers react with STY under the typical CXMS experimental conditions remains unclear, nor has the reliability of STY-cross-link identifications. Here, by setting amino acids with chemically inert side chains such as glycine (G), valine (V), and leucine (L) as cross-linkable sites, which serves as a negative control, we show that software-identified STY-cross-links are only as reliable as GVL-cross-links. This is true across different NHS ester cross-linkers including DSS, DSSO, and DSBU, and across different search engines including MeroX, xiSearch, and pLink. Using a published dataset originated from synthetic peptides, we demonstrate that STY-cross-links indeed have a high false discovery rate. Further analysis revealed that depending on the data and the CXMS search engine used to analyze the data, up to 65% of the STY-cross-links identified are actually K-K cross-links of the same peptide pairs, up to 61% are actually K-mono-links, and the rest tend to contain short peptides at high risk of false identification.