bluuebunny/crossref_metadata_2025_split huggingface dataset:
Population genomics ofXenopus laevisin southern Africa
Authors: Premachandra Tharindu , Cauret Caroline M. S. , Conradie Werner , Measey John , Evans Ben J. 
Published: July 2022

AbstractAllotetraploid genomes have two distinct genomic compartments called subgenomes that are derived from separate diploid ancestral species. Many genomic characteristics such as gene function, expression, recombination, and transposable element mobility may differ significantly between subgenomes. To explore the possibility that subgenome population structure and gene flow may differ as well, we examined genetic variation in an allotetraploid frog – the African clawed frog (Xenopus laevis) – over the dynamic and varied habitat of its native range in southern Africa. Using reduced representation genome sequences from 91 samples from 12 localities, we found no strong evidence that population structure and gene flow differed substantially by subgenome. We then compared patterns of population structure in the nuclear genome to the mitochondrial genome using Sanger sequences from 455 samples from 183 localities. Our results provide further resolution to the geographic distribution of mitochondrial and nuclear diversity in this species and illustrate that population structure in both genomes corresponds roughly with variation in seasonal rainfall and with the topography of the southern Africa.
Durability of the Neutralizing Antibody Response to mRNA Booster Vaccination Against SARS-CoV-2 BA.2.12.1 and BA.4/5 Variants
Authors: Qu Panke , Faraone Julia N. , Evans John P. , Zheng Yi-Min , Carlin Claire , Lozanski Gerard , Saif Linda J. , Oltz Eugene M. , Gumina Richard J. , Liu Shan-Lu 
Published: July 2022

SummaryThe recent emergence of the SARS-CoV-2 BA.4/5 and BA.2.12.1 variants has led to rising COVID-19 case numbers and concerns over the continued efficacy of mRNA booster vaccination. Here we examine the durability of neutralizing antibody (nAb) responses against these SARS-CoV-2 Omicron subvariants in a cohort of health care workers 1-40 weeks after mRNA booster dose administration. Neutralizing antibody titers fell by ∼1.5-fold 4-6 months and by ∼2.5-fold 7-9 months after booster dose, with average nAb titers falling by 11-15% every 30 days, far more stable than two dose induced immunity. Notably, nAb titers from booster recipients against SARS-CoV-2 BA.1, BA.2.12.1, and BA.4/5 variants were ∼4.7-, 7.6-, and 13.4-fold lower than against the ancestral D614G spike. However, the rate of waning of booster dose immunity was comparable across variants. Importantly, individuals reporting prior infection with SARS-CoV-2 exhibited significantly higher nAb titers compared to those without breakthrough infection. Collectively, these results highlight the broad and stable neutralizing antibody response induced by mRNA booster dose administration, implicating a significant role of virus evolution to evade nAb specificity, versus waning humoral immunity, in increasing rates of breakthrough infection.
Human Brain Development: a cross-sectional and longitudinal study integrating multiple neuromorphological features
Authors: Kalantar-Hormozi Hadis , Patel Raihaan , Dai Alyssa , Ziolkowski Justine , Dong Hao-Ming , Holmes Avram , Raznahan Armin , Devenyi Gabriel A. , Chakravarty M. Mallar 
Published: July 2022

AbstractBrain maturation studies typically examine relationships linking a single morphometric feature with aspects of cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the long-running National Institute of Mental Health developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. We sought to use this technique longitudinally to examine covariance in rates of change, which highlighted a preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV; 96 % covariance explained) that recapitulated patterns of reduced CT, GI, and SA that are generally related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.SignificanceThe complex anatomy of the cortical sheet is best characterized using multiple morphometric characteristics. We expanded on recent developments in matrix factorization to identify spatial patterns of covariance across the cortical sheet. Using a large, well-characterized dataset, we examined the differential contributions of neuroanatomical features to cortical covariation in a single analytical framework using both cross-sectional and longitudinal data. We identified dominant modes of covariance between cortical morphometric features and their coordinated pattern of change, demonstrating sexually differentiated patterns and a strong association with variability in age, socioeconomic status, and cognitive ability. This novel characterization of cortical morphometry provides an important understanding of the interdependencies between neuroanatomical measures in the brain and behavioral development context.
IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry
Authors: Lu Peng , Oetjen Karolyn A. , Bender Diane E. , Ruzinova Marianna B. , Fisher Daniel A.C. , Shim Kevin G. , Pachynski Russell K. , Brennen W. Nathaniel , Oh Stephen T. , Link Daniel C. , Thorek Daniel L.J. 
Published: July 2022

ABSTRACTImaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments that has the ability to detect the spatial distribution of at least 40 cell markers. However, this new modality has unique image data processing requirements, particularly when applying this technology to patient tissue specimens. In these cases, signal-to-noise ratio for particular markers can be low despite optimization of staining conditions, and the presence of pixel intensity artifacts can deteriorate image quality and the subsequent performance of downstream analysis. Here we demonstrate an automated content-aware pipeline, IMC-Denoise, to restore IMC images. Specifically, we deploy a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for filtering shot noise (DeepSNF). IMC-Denoise outperforms existing methods for adaptive hot pixel removal, and delivers significant image quality improvement and background noise removal to a diverse set of IMC channels and datasets. This includes a unique, technically challenging, human bone marrow IMC dataset; in which we achieve noise level reduction of 87% for a 5.6-fold higher contrast-to-noise ratio, and more accurate background noise removal with approximately two-fold improved F1 score. Our approach remarkably enhances both manual gating and automated phenotyping with cell-scale down-stream analysis on these complex data. We anticipate that IMC-Denoise will provide similar benefits in mass cytometry imaging domains to more deeply characterize the complex and diverse tissue microenvironment.
Encoding of hunger by the neuronal epigenome slows aging in Drosophila
Authors: Weaver KJ , Holt RA , Henry E , Pletcher SD 
Published: July 2022

AbstractHunger is, by necessity, an ancient motivational drive, yet the molecular nature of homeostatic pressures of this sort and how they modulate health and physiology are largely unknown. Here we show that the molecular encoding of hunger slows aging in Drosophila. We identify the branched-chain amino acids (BCAAs) as dietary hunger signals that extend lifespan despite increasing food intake when reduced, and in parallel show that optogenetic activation of a subset of hunger-promoting neurons is sufficient to recapitulate these effects. We find that remodeling of the neuronal histone acetylome is associated with dietary BCAA reduction, and that this requires BCAA metabolism in specific subsets of neurons. Preventing the histone acetylome from being molded by dietary BCAAs abrogates both increased feeding and extended lifespan. However, the mechanisms that promote feeding and modulate aging downstream of alterations in histone acetylation occur through spatially and temporally distinct responses; differential usage of the histone variant H3.3A in the brain is an acute response to hunger that promotes increased feeding without modulating lifespan, while a prolonged experience of hunger may slow aging by promoting a beneficial decrease of a set-point around which hunger levels are regulated. Identification of a molecular basis for the encoding of hunger and demonstration of its sufficiency in extending lifespan reveals that motivational states alone are deterministic drivers of aging and behavior.
TLR4 regulates proinflammatory intestinal immune responses mediated by an atopic gut microbiota
Authors: Campbell Evelyn , Maccio-Maretto Lisa , Hesser Lauren A. , Kemter Andrea M. , Canani Roberto Berni , Nocerino Rita , Paparo Lorella , Patry Robert T. , Nagler Cathryn R. 
Published: July 2022

AbstractThe increasing prevalence of food allergies has been causally associated with the depletion of allergy protective intestinal bacteria. However, few studies have investigated the role of the gut microbiota in promoting allergic responses. In a cohort of infants affected by cow’s milk allergy (CMA), we have identified a patient with a proinflammatory and atopic microbiota. In comparison to a healthy microbiota, this CMA-associated gut microbiota has increased abundance of Bacteroidetes, a Gram-negative phylum of bacteria that has been associated with increased incidence of allergy. Using this microbiota, we investigated the host-microbe interactions that mediate these intestinal inflammatory responses. To examine these interactions, we used mice with global and conditional abrogation in TLR4 signaling, since Gram- negative bacteria signal through this receptor via membrane-derived lipopolysaccharide (LPS). We show that this donor’s microbiota induces expression of serum amyloid A1 (Saa1) and other Th17-, B cell-, and Th2-associated genes in the ileal epithelium. Accordingly, this microbiota also induces Th17 cells, as well as regulatory T cell populations and fecal IgA. Importantly, we used both antibiotic treated SPF and rederived germ-free mice with a conditional mutation of TLR4 in the CD11c+compartment to demonstrate that the induction of proinflammatory genes, fecal IgA, and Th17 cells is dependent on TLR4 signaling. Furthermore, metagenomic sequencing revealed that the CMA-associated gut microbiota also has increased abundance of LPS biosynthesis genes. Lastly, upon sensitization with β-lactoglobulin, this CMA microbiota induces a TLR4-dependent mixed type 2/type 3 response in innate lymphoid cells (ILCs) during the early phases of allergic sensitization. Taken together, our results show that a Bacteroidetes-enriched microbiota with increased abundance of LPS genes promotes proinflammatory gene expression and a mixed type 2/type 3 response in a subset of infants with cow’s milk allergy.Paper HighlightsA cow’s milk allergy (CMA)-associated gut microbiota has an enrichment of Bacteroidetes, which is associated with atopyThe CMA-associated gut microbiota promotes intestinal inflammation, which includes inflammatory gene expression, induction of Th17 cells, and production of IgAProinflammatory responses induced by the CMA-associated gut microbiota are dependent on TLR4 signaling in various cellular compartmentsUpon sensitization, the CMA-associated gut microbiota induces an innate mixed type 2/type 3 inflammatory response
Modeling the metabolic evolution of mixotrophic phytoplankton in response to rising ocean surface temperatures
Authors: Gonzalez Logan M. , Proulx Stephen R. , Moeller Holly V. 
Published: July 2022

ABSTRACTBackgroundClimate change is expected to lead to warming in ocean surface temperatures which will have unequal effects on the rates of photosynthesis and heterotrophy. As a result of this changing metabolic landscape, directional phenotypic evolution will occur, with implications that cascade up to the ecosystem level. While mixotrophic phytoplankton, organisms that combine photosynthesis and heterotrophy to meet their energetic and nutritional needs, are expected to become more heterotrophic with warmer temperatures due to heterotrophy increasing at a faster rate than photosynthesis, it is unclear how evolution will influence how these organisms respond to warmer temperatures. In this study, we used adaptive dynamics to model the consequences of temperature-mediated increases in metabolic rates for the evolution of mixotrophic phytoplankton, focusing specifically on phagotrophic mixotrophs.ResultsWe find that mixotrophs tend to evolve to become more reliant on phagotrophy as temperatures rise, leading to reduced prey abundance through higher grazing rates. However, if prey abundance becomes too low, evolution favors greater reliance on photosynthesis. These responses depend upon the trade-off that mixotrophs experience between investing in photosynthesis and phagotrophy. Mixotrophs with a convex trade-off maintain mixotrophy over the greatest range of temperatures; evolution in these “generalist” mixotrophs was found to exacerbate carbon cycle impacts, with evolving mixotrophs exhibiting increased sensitivity to rising temperature.ConclusionsOur results show that mixotrophs may respond more strongly to climate change than predicted by phenotypic plasticity alone due to evolutionary shifts in metabolic investment. However, the type of metabolic trade-off experienced by mixotrophs as well as ecological feedback on prey abundance may ultimately limit the extent of evolutionary change along the heterotrophy-phototrophy spectrum.
Selection of an Ideal Machine Learning Framework for Predicting Perturbation Effects on Network Topology of Bacterial KEGG Pathways
Authors: Robben Michael , Nasr Mohammad Sadegh , Das Avishek , Huber Manfred , Jaworski Justyn , Weidanz Jon , Luber Jacob 
Published: July 2022

ABSTRACTBiological networks for bacterial species are used to assign functional information to newly sequenced organisms but network quality can be largely affected by poor gene annotations. Current methods of gene annotation use homologous alignment to determine orthology, and have been shown to degrade network accuracy in non-model bacterial species. To address these issues in the KEGG pathway database, we investigated the ability for machine learning (ML) algorithms to re-annotate bacterial genes based on motif or homology information. The majority of the ensemble, clustering, and deep learning algorithms that we explored showed higher prediction accuracy than CD-hit in predicting EC ID, Map ID, and partial Map ID. Motif-based, machine-learning methods of annotation in new species were more accurate, faster, and had higher precisionrecall than methods of homologous alignment or orthologous gene clustering. Gradient boosted ensemble methods and neural networks also predicted higher connectivity of networks, finding twice as many new pathway interactions than blast alignment. The use of motif-based, machine-learning algorithms in annotation software will allow researchers to develop powerful network tools to interact with bacterial microbiomes in ways previously unachievable through homologous sequence alignment.CCS CONCEPTS• Applied computing → Computational biology; Life and medical sciences; Bioinformatics; • Computing methodologies → Machine learning algorithms; Machine learning approaches.ACM Reference FormatMichael Robben, Mohammad Sadegh Nasr, Avishek Das, Manfred Huber, Justyn Jaworski, Jon Weidanz, and Jacob Luber. 2022. Selection of an Ideal Machine Learning Framework for Predicting Perturbation Effects on Network Topology of Bacterial KEGG Pathways. In The 13th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, August 07–10, 2022, Chicago, IL. ACM, New York, NY, USA, 11 pages. https://doi.org/XXXXXXX.XXXXXXX
Temporal dynamics of metabolic acquisition in grafted engineered human liver tissue
Authors: Fortin Chelsea L. , McCray Tara N. , Saxton Sarah H. , Johansson Fredrik , Andino Christian B. , Mene Jonathan , Wang Yuliang , Stevens Kelly R. 
Published: July 2022

AbstractLiver disease affects millions globally and end-stage liver failure is only cured by organ transplant. Unfortunately, there is a growing shortage of donor organs and disparities in equitable access to transplants among different populations. Less than 10% of global transplantation needs are currently met, highlighting the demand for alternative therapies. Engineered liver tissue grafts that supplement organ function could address these demands. While engineered liver tissues built from human hepatocytes, endothelial cells, and fibroblasts encased in hydrogel have been successfully engrafted in rodent models previously, the extent to which these tissues express human liver metabolic genes and proteins remains unknown. Here, we built engineered human liver tissues and characterized their engraftment, expansion, and metabolic phenotype at sequential stages post-implantation by RNA sequencing, histology, and host serology. Expression of metabolic genes was observed at weeks 1-2, followed by cellular organization into hepatic cords by weeks 4-9.5. Furthermore, grafted engineered tissues exhibited progressive spatially restricted expression of critical functional proteins known to be zonated in the native human liver. To our knowledge, this is the first report of engineered human liver tissue zonation after implantation in vivo, which could have important translational implications for this field.
Single-cell profiling identifies ACE+granuloma macrophages as a non-permissive niche for intracellular bacteria during persistentSalmonellainfection
Authors: Pham Trung H. M. , Xue Yuan , Brewer Susan M. , Bernstein KE , Quake Stephen R. , Monack Denise M. 
Published: July 2022

AbstractMacrophages mediate key antimicrobial responses against intracellular bacterial pathogens, such asSalmonella enterica. Yet, they can also act as a permissive niche for these pathogens to persist in infected tissues within granulomas, which are immunological structures comprised of macrophages and other immune cells. We apply single-cell transcriptomics to investigate macrophage functional diversity during persistentSalmonella entericaserovar Typhimurium (STm) infection in mice. We identify determinants of macrophage heterogeneity in infected spleens and describe populations of distinct phenotypes, functional programming, and spatial localization. Using aSTm mutant with impaired ability to polarize macrophage phenotypes, we find that angiotensin converting enzyme (ACE) defines a granuloma macrophage population that is non-permissive for intracellular bacteria and their abundance anticorrelates with tissue bacterial burden. Disruption of pathogen control by neutralizing TNF preferentially depletes ACE+macrophages in infected tissues. Thus ACE+macrophages have differential capacity to serve as cellular niche for intracellular bacteria to establish persistent infection.TeaserThis study shows that ACE+granuloma macrophages have restricted capacity to act as a cellular niche that enables intracellular bacterial persistence.
Allelic diversity of the pharmacogene CYP2D6 in New Zealand Māori and Pacific peoples
Authors: Hitchman Leonie M. , Faatoese Allamanda , Merriman Tony R. , Miller Allison L. , Liau Yusmiati , Graham Oscar E.E. , Kee Ping Siu , Pearson John F. , Fakahau Tony , Cameron Vicky A. , Kennedy Martin A. , Maggo Simran D.S. 
Published: July 2022

AbstractThe enzyme cytochrome P450 2D6 (CYP2D6) metabolises approximately 25% of commonly prescribed drugs, including analgesics, anti-hypertensives, and anti-depressants, among many others. Genetic variation in drug metabolising genes can alter how an individual responds to prescribed drugs, including predisposing to adverse drug reactions. The majority of research on the CYP2D6 gene has been carried out in European and East Asian populations, with Indigenous and minority populations greatly underrepresented. However, genetic variation is often population specific and analysis of diverse ethnic groups can reveal differences in alleles that may be of clinical significance. For this reason, we set out to examine the range and frequency of CYP2D6 variants in a sample of 202 Māori and Pacific people living in Aotearoa (New Zealand). We carried out a long PCR to isolate the CYP2D6 region before performing nanopore sequencing to identify all variants and alleles in these samples. We identified eleven novel variants, three of which were exonic missense variations. Six of these occurred in single samples and one was found in 19 samples (9.4% of the cohort). The remaining four novel variants were identified in two samples each. In addition, five new suballeles of CYP2D6 were identified. One striking finding was that CYP2D6*71, an allele of unknown functional status which has been rarely observed in previous studies, occurs at a relatively high frequency (9.2%) within this cohort. These data will help to ensure that CYP2D6 genetic analysis for pharmacogenetic purposes can be carried out accurately and effectively in this population group.
AIFS: A novel perspective, Artificial Intelligence infused wrapper based Feature Selection Algorithm on High Dimensional data analysis
Authors: Jain Rahi , Xu Wei 
Published: July 2022

AbstractBackgroundFeature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper approaches primarily focus on shortening the path to find an optimal feature set. However, these approaches underutilize the capability of feature subset models, which impacts feature selection and its predictive performance.Method and ResultsThis study proposes a novel Artificial Intelligence infused wrapper based Feature Selection (AIFS), a new feature selection method that integrates artificial intelligence with wrapper based feature selection. The approach creates a Performance Prediction Model (PPM) using artificial intelligence (AI) which predicts the performance of any feature set and allows wrapper based methods to predict and evaluate the feature subset model performance without building actual model. The algorithm can make wrapper based method more relevant for high-dimensional data and is flexible to be applicable in any wrapper based method. We evaluate the performance of this algorithm using simulated studies and real research studies. AIFS shows better or at par feature selection and model prediction performance than standard penalized feature selection algorithms like LASSO and sparse partial least squares.ConclusionAIFS approach provides an alternative method to the existing approaches for feature selection. The current study focuses on AIFS application in continuous cross-sectional data. However, it could be applied to other datasets like longitudinal, categorical and time-to-event biological data.