bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phenotypic characterization and analysis of complete genomes of two distinct strains of the proposed species “L. swaminathanii”
Authors: Hudson Lauren K. , Chaggar Harleen K. , Schamp Claire N. , Claxton Michelle L. , Bryan Daniel W. , Peters Tracey L. , Song Yaxiong , Carlin Catharine R. , den Bakker Henk C. , Denes Thomas G. 
Published: May 2022

ABSTRACTRecently, a newListeriaspecies, “Listeria swaminathanii”, was proposed. Here, we phenotypically and genotypically characterize two additional strains that were previously obtained from soil samples and compare the results to the type strain. Complete genomes for both strains were assembled from hybrid Illumina and Nanopore sequencing reads and annotated. Further genomic analysis including average nucleotide identity (ANI) and detection of mobile genetic elements and genes of interest (e.g., virulence-associated) were conducted. The strains showed 98.7-98.8% ANI with the type strain. The UTK C1-0015 genome contained a partial monocin locus and a plasmid while the UTK C1-0024 genome contained a full monocin locus and a prophage. Phenotypic characterization consistent with those performed on the proposed type strain was conducted to assess consistency of phenotypes across a greater diversity of the proposed species (n=3 instead of n=1). Only a few findings were notably different from those of the type strain, such as catalase activity, glycerol metabolism, starch metabolism, and growth at 41°C. This study further expands our understanding of this newly proposedsensu stricto Listeriaspecies.
Active mechanics of sea star oocytes
Authors: Foster Peter J. , Fürthauer Sebastian , Fakhri Nikta 
Published: May 2022

AbstractActomyosin is a canonical example of an active material, driven out of equilibrium in part through the injection of energy by myosin motors. This influx of energy allows actomyosin networks to generate cellular-scale contractility, which underlies cellular processes ranging from division to migration. While the molecular players underlying actomyosin contractility have been well characterized, how cellular-scale deformation in disordered actomyosin networks emerges from filament-scale interactions is not well understood. Here, we address this question in vivo using the meiotic surface contraction wave of Patiria miniata oocytes. Using pharmacological treatments targeting actin polymerization, we find that the cellular deformation rate is a nonmonotonic function of cortical actin density peaked near the wild type density. To understand this, we develop an active fluid model coarse-grained from filament-scale interactions and find quantitative agreement with the measured data. This model further predicts the dependence of the deformation rate on the concentration of passive actin crosslinkers and motor proteins, including the surprising prediction that deformation rate decreases with increasing motor concentration. We test these predictions through protein overexpression and find quantitative agreement. Taken together, this work is an important step for bridging the molecular and cellular length scales for cytoskeletal networks in vivo.
Statistical analysis of spatially resolved transcriptomic data by incorporating multi-omics auxiliary information
Authors: Li Yan , Zhou Xiang , Cao Hongyuan 
Published: May 2022

AbstractEffective control of false discovery rate is key for multiplicity problems. Here, we consider incorporating informative covariates from external datasets in the multiple testing procedure to boost statistical power while maintaining false discovery rate control. In particular, we focus on the statistical analysis of innovative high-dimensional spatial transcriptomic data while incorporating external multi-omics data that provide distinct but complementary information to the detection of spatial expression patterns. We extend OrderShapeEM, an efficient covariate-assisted multiple testing procedure that incorporates one auxiliary study, to make it permissible to incorporate multiple external omics studies, to boost statistical power of spatial expression pattern detection. Specifically, we first use a recently proposed computationally efficient statistical analysis method, spatial pattern recognition via kernels, to produce the primary test statistics for spatial transcriptomic data. Afterwards, we construct the auxiliary covariate by combining information from multiple external omics studies, such as bulk or single-cell RNA-seq data and genome wise association study data, using the Cauchy combination rule. Finally, we extend and implement the integrative analysis method OrderShapeEM on the primary p-values along with auxiliary data incorporating multi-omics information for efficient covariate-assisted spatial expression analysis. We conduct a series of realistic simulations to evaluate the performance of our method with known ground truth. Four case studies in mouse olfactory bulb, mouse cerebellum, human breast cancer and human heart tissues further demonstrate the substantial power gain of our method in detecting genes with spatial expression patterns compared to existing classic approaches that do not utilize any external information.
A highly sensitive strand-specific multiplex RT-qPCR assay for quantitation of Zika virus replication
Authors: Barnard Trisha R. , Wang Alex B. , Sagan Selena M. 
Published: May 2022

AbstractReverse-transcription quantitative polymerase chain reaction (RT-qPCR) is widely used to quantify viral RNA genomes for diagnostics and research, yet conventional RT-qPCR protocols are unable to accurately distinguish between the different viral RNA species that exist during infection. Here we show that false-priming and self-priming occur during reverse transcription with several published Zika virus (ZIKV) primer sets. We developed a RT-qPCR assay using tagged primers and thermostable reverse transcriptase, which greatly reduced the occurrence of nonspecific cDNA products. Furthermore, we optimized the assay for use in multiplex qPCR which allows for simultaneous quantitative detection of positive-strand and negative-strand ZIKV RNA along with an internal control from both human and mosquito cells. Importantly, this assay is sensitive enough to study early stages of virus infection in vitro. Strikingly, using this assay, we detected ZIKV negative-strand RNA as early as 3 h post-infection in mammalian cell culture, at a time point prior to the onset of positive-strand RNA synthesis. Overall, the strand-specific RT-qPCR assay developed herein is a valuable tool to quantify ZIKV RNA and to study viral replication dynamics during infection. The application of these findings has the potential to increase accuracy of RNA detection methods for a variety of viral pathogens.HighlightsSelf-primed cDNA is amplified by widely-used ZIKV qPCR primer setsUse of tagged primers and thermostable RT increases strand-specificity for RT-qPCRMultiplexed qPCR allows for simultaneous quantitation of (+) and (-) strand viral RNAs, and an internal controlStrand-specific RT-qPCR can detect fewer than one copy of viral RNA per cell in human and mosquito cells
cMYC protein interactions and chromatin association in NUT carcinoma
Authors: Zee Barry M. , Alekseyenko Artyom A. , Smolko Anne E. , Kang Hyuckjoon , Kuroda Mitzi I. 
Published: May 2022

AbstractThe oncogene cMYC (HGNC:7553) is a critical genomic target of the BRD4-NUT (B4N) protein that defines many of the NUTM1-rearrangement cancer subtypes in NUT carcinoma (NC). Previously, we reported that B4N interacts with the EP300 lysine acetyltransferase (KAT3B) and creates hyperacetylated “megadomains” that activate downstream genes such as cMYC. Here we ask how misregulated cMYC in turn interacts with protein partners and target genes in patient-derived NC797 cells, and whether these interactions change in response to B4N inactivation. We used CRISPR-Cas9 mediated knock-in of a BioTAP affinity tag to analyze cMYC protein expressed from its normal chromosomal context. This allowed us to implement a crosslinking purification method termed BioTAP-XL to preserve cMYC integrity and chromatin association for genomic and mass spectrometry-based proteomic analyses. We found that in the NC797 cell line, cMYC interacts primarily with the NuA4 KAT5 lysine acetyltransferase complex, with interactions that are maintained despite a decrease in cMYC levels after JQ1 treatment. We propose that a cascade of aberrant acetyltransferase activities in NC797 cells, first via EP300 recruitment by B4N to mis-regulate cMYC, and then by KAT5 interaction with the resulting overexpressed cMYC protein, drive NC cell proliferation and blockade to differentiation.Graphical AbstractSimple SummaryA longstanding goal in biology is to understand how protein interactions influence and reflect cellular disease states. MYC is a critical regulator of cellular proliferation that is mis-regulated in many cancers, including those with NUTM1-rearrangements featured in this issue. NUT carcinoma cells are dependent on MYC expression, which blocks differentiation. Using a crosslinking approach to identify MYC protein interactions in a NUTM1-rearranged patient cell line, we found that MYC interacts primarily with subunits of the NuA4 lysine acetyltransferase (KAT5), one of two KAT complexes previously discovered as MYC interactors in non-NUTM1 cell lines.
Detection and discrimination of single nucleotide polymorphisms by quantification of CRISPR-Cas catalytic efficiency
Authors: Blanluet Charles , Huyke Diego A. , Ramachandran Ashwin , Avaro Alexandre S. , Santiago Juan G. 
Published: May 2022

ABSTRACTThe specificity of CRISPR-Cas12 assays is attractive for the detection of single nucleotide polymorphisms (SNPs) implicated in, e.g., SARS-CoV-2 variants. Such assays often employ endpoint measurements of SNP or wild type (WT) activated Cas12 trans-cleavage activity; however, the fundamental kinetic effects of SNP versus WT activation remain unknown. We here show that endpoint-based assays are limited by arbitrary experimental choices (like used reporter concentration and assay duration) and work best for known target concentrations. More importantly, we show that SNP (versus WT) activation results in measurable shifts in the Cas12 trans-cleavage substrate affinity (KM) and apparent catalytic efficiency . To address endpoint-based assay limitations, we then develop an assay based on the quantification of Michalis-Menten parameters and apply this assay to a 20-base pair WT target of the SARS-CoV-2 E gene. We find that the measured for WT is 130-fold greater than the lowest among all 60 measured SNPs (compared to a 4.8-fold for endpoint fluorescence of the same SNP). KM also offers strong ability to distinguish SNPs, varies 27-fold over all the cases, and is insensitive to target concentration. Lastly, we point out trends among kinetic rates and SNP base and location within the CRISPR-Cas12 targeted region.
Utilization of Landscape of Kinases and Phosphosites To Predict Kinase-Substrate Association
Authors: Ayati Marzieh , Yılmaz Serhan , Blasco Tavares Pereira Lopes Filipa , Chance Mark R. , Koyutürk Mehmet 
Published: May 2022

AbstractMotivationProtein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, the kinase(s) that phosphorylate most (> 90%) of the identified phosphorylation sites are unknown. Knowledge of kinase-substrate associations is also mostly limited to a small number of well-studied kinases, with 20% of known kinases accounting for the phosphorylation of 87% of currently annotated sites. The scarcity of available annotations calls for the development of computational algorithms for more comprehensive and reliable prediction of kinase-substrate associations.ResultsTo broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations, we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms state-of-the-art algorithms and resources, including KinomeXplorer and LinkPhinder, in reliably predicting KSAs. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases. Finally, we observe that the performance of NetKSA is robust to the choice of network embedding algorithms, while each type of network contributes valuable information that is complementary to the information provided by other networks.ConclusionRepresentation of available functional information on kinases and phosphorylation sites, along with integrative machine learning algorithms, has the potential to significantly enhance our knowledge on kinase-substrate associations.AvailabilityThe code and data are available atcompbio.case.edu/NetKSA.
Genetic elements orchestrating Lactobacillus crispatus glycogen metabolism in the vagina
Authors: Hertzberger Rosanne , May Ali , Kramer Gertjan , van Vondelen Isabelle , Molenaar Douwe , Kort Remco 
Published: May 2022

AbstractGlycogen in the female lower reproductive tract is a major carbon source for vaginal colonization and acidification by common vaginal Lactobacillus species, such as Lactobacillus crispatus. Previously we identified the pullulanase gene pulA in Lactobacillus crispatus, correlating with its ability to autonomously utilize glycogen for growth. Here we further characterize genetic variation and differential regulation of pulA affecting the presence of its gene product on the outer surface layer. We show that alpha-glucan degrading activity dissipates when Lactobacilllus crispatus is grown on glucose, maltose and maltotriose, in agreement with carbon catabolite repression elements flanking the pulA gene. Proteome analysis of the S-layer confirmed that the pullulanase protein is highly abundant in an S-layer enriched fraction, but not in a strain with a defective pullulanase variant or in a pullulanase-sufficient strain grown on glucose. In addition, we provide evidence that Lactobacillus crispatus pulA mutants are relevant in vivo, as they are commonly observed in metagenome datasets of human vaginal microbial communities. Analysis of the largest publicly available human vaginal metagenome dataset indicates that 15 out of 272 samples, containing a Lactobacillus crispatus pulA gene, contain a defective variant of this gene. Another 23 out of 272 samples show large deletions or transposon insertions. Taken together, these results demonstrate that both environmental as well as genetic factors explain variation of Lactobacillus crispatus alpha-glucosidases in the vaginal environment.
Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference
Authors: Reagor Caleb C. , Velez-Angel Nicolas , Hudspeth A. J. 
Published: May 2022

AbstractIdentifying the causal interactions in gene-regulatory networks requires an accurate understanding of the time-lagged relationships between transcription factors and their target genes. Here we describe DELAY, a convolutional neural network for the inference of gene-regulatory relationships across pseudotime-ordered single-cell trajectories. We show that combining supervised deep learning with joint-probability matrices of pseudotime-lagged trajectories allows the network to overcome important limitations of ordinary Granger causality-based methods, such as the inability to infer cyclic relationships such as feedback loops. Our network outperforms several common methods for inferring gene regulation and predicts novel regulatory networks from scRNA-seq and scATAC-seq datasets given partial ground-truth labels. To validate this approach, we used DELAY to identify important genes and modules in the regulatory network of auditory hair cells, as well as likely DNA-binding partners for two hair cell cofactors (Hist1h1c and Ccnd1) and a novel binding sequence for the hair cell-specific transcription factor Fiz1. We provide an open-source implementation of DELAY at https://github.com/calebclayreagor/DELAY.
Deconstructing body axis morphogenesis in zebrafish embryos using robot-assisted tissue micromanipulation
Authors: Özelçi Ece , Mailand Erik , Rüegg Matthias , Oates Andrew C. , Sakar Mahmut Selman 
Published: May 2022

AbstractMicrosurgical techniques, such as those used in the early 1900s by Mangold and Spemann, were not only foundational for experimental embryology, but are still in use today. Here, we build on these classic techniques by introducing a user-friendly robotic microsurgery platform that allows precise mechanical manipulation of soft tissues in zebrafish embryos. As a proof of principle, we investigated the contribution of different embryonic tissues to vertebrate anteroposterior (AP) axis elongation and segmentation. Using our platform, we reproducibly targeted precise regions of tail explants, and quantified the response in real-time by following notochord and presomitic mesoderm (PSM) morphogenesis and segmentation clock dynamics. We find an extension force generated through the posterior notochord, independent of notochord cell vacuolation, that is strong enough to buckle the structure. Our data suggest that this force generates a unidirectional notochord extension towards the tailbud because PSM tissue around the posterior notochord does not let it slide anteriorly. These results complement existing biomechanical models of axis elongation, revealing a critical coupling between the posterior notochord, the tailbud, and the PSM, and show that somite patterning is robust against structural perturbations.
The shifting importance of abiotic and biotic factors across the life cycles of wild pollinators
Authors: Ogilvie Jane E. , CaraDonna Paul J. 
Published: May 2022

AbstractOrganisms living in seasonal environments are exposed to different environmental conditions as they transition from one life stage to the next across their life cycle. How different life stages respond to these varying conditions is a fundamental aspect of biology and is critical for understanding how organisms will respond to environmental change.Despite the importance of animal pollinators, we lack a basic understanding of the influence of different environmental factors across their life cycles. We investigated the relative importance of climate conditions, food availability, and previous life stage abundance in a community of wild bumble bee species, asking: how do these three factors influence bee abundance at each life stage?We used a 7-year dataset to examine the importance of environmental conditions on the abundance of life stages in seven wild bumble bee species. We monitored climate conditions, the abundance of floral resources, and abundances of bees in each life stage across the active colony life cycle in a highly seasonal subalpine ecosystem in the Colorado Rocky Mountains, USA.Bee abundance at different life stages responded to environmental conditions in a consistent manner across the seven species. The survival and recruitment stage of the life cycle (assessed as abundance of overwintered queens) responded negatively to longer winters; the growth stage (assessed as abundance of workers) responded positively to floral resource availability; and the reproductive stage (assessed as abundance of males) was positively related to the abundance of the previous life stage (workers).Our long-term examination of annual bumble bees reveals a consistent set of responses in the abundance of each life stage to climate conditions, floral resource availability, and previous life stage abundance. Across species, these three factors each influenced a distinct life stage, illustrating how their relative importance can shift throughout the life cycle. The life-cycle approach we have taken with wild bumble bees highlights that important details about pollinator demography are overlooked without considering life stage-specific responses. Ultimately, it is these life-stage specific responses that shape population outcomes, not only for animal pollinators, but for many organisms living in seasonal environments.
ILC3s select for RORγt+ Tregs and establish tolerance to intestinal microbiota
Authors: Lyu Mengze , Suzuki Hiroaki , Kang Lan , Gaspal Fabrina , Zhou Wenqing , Goc Jeremy , Zhou Lei , Zhou Jordan , Zhang Wen , Live Cell Bank JRI , Shen Zeli , Fox James G. , Sockolow Robbyn E. , Laufer Terri M. , Fan Yong , Eberl Gerard , Withers David R. , Sonnenberg Gregory F. 
Published: May 2022

AbstractMicrobial colonization of the mammalian intestine elicits inflammatory or tolerogenic T cell responses, but the mechanisms controlling these distinct outcomes remain poorly understood and accumulating evidence indicates that aberrant immunity to intestinal microbiota is causally associated with infectious, inflammatory, and malignant diseases1–8. Here, we define a critical pathway controlling the fate of inflammatory versus tolerogenic T cells that are specific for the microbiota and express the transcription factor RORγt. We profiled all RORγt+ immune cells at single cell resolution from the intestine-draining lymph nodes of mice and reveal a dominant presence of Tregs and lymphoid tissue-induced (LTi)-like group 3 innate lymphoid cells (ILC3s), which co-localize at interfollicular regions. These ILC3s have interconverting potential with RORγt+ extrathymic Aire-expressing cells, abundantly express major histocompatibility complex class II, and are necessary and sufficient to promote microbiota-specific RORγt+ Tregs and prevent their expansion as inflammatory T helper (Th)17 cells. This occurs through ILC3-mediated antigen-presentation, interleukin-2 gradients, and αv integrin. Finally, single-cell analyses demonstrate that ILC3 and RORγt+ Treg interactions are impaired in inflammatory bowel disease. Our results define a novel paradigm whereby ILC3s positively select for antigen specific RORγt+ Tregs, and against Th17 cells, to establish immune tolerance to the microbiota and intestinal health.