bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large-scale identification of RBP-RNA interactions by RAPseq refines essentials of post-transcriptional gene regulation
Authors: Atanasoai Ionut , Papavasileiou Sofia , Preiß Natalie , Kutter Claudia 
Published: November 2021

SUMMARYOver the past decade, thousands of putative human RNA binding proteins (RBPs) have been identified and increased the demand for specifying RNA binding capacities. Here, we developed RNA affinity purification followed by sequencing (RAPseq) that enablesin vitrolarge-scale profiling of RBP binding to native RNAs. First, by employing RAPseq, we found that vertebrate HURs recognize a conserved RNA binding motif and bind predominantly to introns in zebrafish compared to 3’UTRs in human RNAs. Second, our dual RBP assays (co-RAPseq) uncovered cooperative RNA binding of HUR and PTBP1 within an optimal distance of 27 nucleotides. Third, we developed T7-RAPseq to discern m6A-dependent and - independent RNA binding sites of YTHDF1. Fourth, RAPseq of 26 novel non-canonical RBPs revealed specialized moonlighting interactions. Last, five pathological IGF2BP family variants exhibited different RNA binding patterns. Overall, our simple, scalable and versatile method enables to fast-forward RBP-related questions.Graphical AbstractHIGHLIGHTSRAPseq revealsin vitro-derived RBP-RNA interactomesthe vertebrate-conserved HUR binding motif adapted to species-unique RNA featuresco-RAPseq and T7-RAPseq uncover binding cooperativity and modification dependenciesnon-canonical RBPs have specialized RNA interactomes
Closely Related Tree Species with Overlapping Ranges Exhibit Divergent Adaptation to Climate
Authors: Whale John W. , Ahrens Collin W. , Tissue David T. , Rymer Paul D. 
Published: November 2021

ABSTRACTWith global climate change shifting and altering temperature and precipitation regimes, the ability of natural forest stands to persist in their local environments are being challenged. For many taxa, particularly among long lived tree species, the potential to respond is underpinned by genetic and trait diversity and may be limited. We sampled 326 and 366 individuals of two widely distributed and closely-related red gum Eucalyptus species (E. blakelyi and E. tereticornis) from across their entire Australian range. We identified putatively adaptive variants associated within genes of key biological processes for both species. We mapped the change of allele frequencies of two hierarchical gene ontology groups shared by both species across geography and climate and predict genomically vulnerable regions under a projected 2070 climate scenario. Regions of potential vulnerability to decline under future climate differed between species and may be applied to guide conservation and restoration strategies. Our study indicated that some populations may contain the adaptive genomic variation necessary for these species to persist through climate change, while others may benefit from the adaptive variation of those populations to enhance resilience.
Standardised empirical dispersal kernels emphasise the pervasiveness of long-distance dispersal in European birds
Authors: Fandos Guillermo , Talluto Matthew , Fiedler Wolfgang , Robinson Robert A. , Thorup Kasper , Zurell Damaris 
Published: November 2021

AbstractDispersal is a key life-history trait for most species and essential to ensure connectivity and gene flow between populations and facilitate population viability in variable environments. Despite the increasing importance of range shifts due to global change, dispersal has proved difficult to quantify, limiting empirical understanding of this phenotypic trait and wider synthesis.Here we aim to estimate and compare empirical dispersal kernels for European breeding birds considering average dispersal, natal (before first breeding) and breeding dispersal (between subsequent breeding attempts), and test whether different dispersal properties are phylogenetically conserved.We standardised and analysed data from an extensive volunteer-based bird ring-recoveries database in Europe (EURING) by accounting for biases related to different censoring thresholds in reporting between countries and to migratory movements. Then, we fitted four widely used probability density functions in a Bayesian framework to compare and provide the best statistical descriptions of the average, the natal and the breeding dispersal kernels for each bird species.The dispersal movements of the 234 European bird species analysed were statistically best explained by heavy-tailed kernels, meaning that while most individuals disperse over short distances, long-distance dispersal is a feature in almost all bird species. The overall phylogenetic signal in both median and long dispersal distances was low (Pagel’s λ < 0.40), implying a high degree of taxonomic generality in our findings. As expected in birds, natal dispersal was 5 Km greater as an average than breeding dispersal for most species (88% species analysed).Our comprehensive analysis of empirical kernels indicates that long-distance dispersal is common among European breeding bird species and across life stages. The dispersal estimates offer a first guide to selecting appropriate dispersal kernels in range expansion studies and provide new avenues to improve our understanding of the mechanisms and rules underlying dispersal events.
XomicsToModel: Omics data integration and generation of thermodynamically consistent metabolic models
Authors: Preciat German , Wegrzyn Agnieszka B. , Thiele Ines , Hankemeier Thomas , Fleming Ronan M.T. 
Published: November 2021

AbstractConstraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, including modelling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in a given context. However, existing model extraction algorithms are unable to ensure that the context-specific model is thermodynamically feasible. This protocol introducesXomicsToModel, a semi-automated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. TheXomicsToModelpipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model, but it enables omics data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. With all input data fully prepared, algorithmic completion of the pipeline takes ~10 min, however manual review of intermediate results may also be required, e.g., when inconsistent input data lead to an infeasible model.
Low abundance members of the gut microbiome are potent drivers of immune cell education
Authors: Han Geongoo , Luong Hien , Vaishnava Shipra 
Published: November 2021

AbstractOne of the main goals of microbiome research is to identify bacterial members that significantly affect host phenotypes and understand their contributions to disease pathogenesis. Studies identifying bacterial members that dictate host phenotype have focused mainly on the dominant members, and the role of low abundance microbes in determining host phenotypes and pathogenesis of diseases remains unexplored. In this study, we compared the gut bacterial community of mice with wide-ranging microbial exposure to determine if low abundance bacteria vary based on microbial exposure or remain consistent. We noted that similar to the high abundance bacterial community, a core community of low abundance bacteria made up a significant portion of the gut microbiome irrespective of microbial exposure. To determine the effect of low abundance bacteria on community structure and host gene expression, we devised a microbiome dilution strategy to “delete” out low abundance bacteria and engrafted the diluted microbiomes into germ-free mice. Our approach successfully excluded low abundance bacteria from small and large intestinal bacterial communities and induced global changes in microbial community structure and composition in the large intestine. Gene expression analysis of intestinal tissue revealed that loss of low abundance bacteria resulted in a drastic reduction in expression of multiple genes involved MHC class II antigen presentation pathway and T-cell cytokine production in the small intestine. The effect of low abundance bacteria on MHC class II expression was found specific to the intestinal epithelium at an early timepoint post-colonization and correlated with bacteria belonging to the family Erysipelotrichaceae. We conclude that low abundance bacteria have a significantly higher immuno-stimulatory effect compared to dominant bacteria and are thus potent drivers of early immune education in the gut. Therefore, characterizing immune interaction of low abundance bacteria with the host will offer greater insight into the intestinal immune landscape and disease pathogenesis.
A biologically inspired architecture with switching units can learn to generalize across backgrounds
Authors: Voina Doris , Shea-Brown Eric , Mihalas Stefan 
Published: November 2021

AbstractHumans and other animals navigate different landscapes and environments with ease, a feat that requires the brain’s ability to rapidly and accurately adapt to different visual domains, generalizing across contexts/backgrounds. Despite recent progress in deep learning applied to classification and detection in the presence of multiple confounds including contextual ones [25, 30], there remain important challenges to address regarding how networks can perform context-dependent computations and how contextually-invariant visual concepts are formed. For instance, recent studies have shown artificial networks that repeatedly misclassified familiar objects set on new backgrounds, e.g. incorrectly labelling known animals when they appeared in a different setting [3]. Here, we show how a bio-inspired network motif can explicitly address this issue. We do this using a novel dataset which can be used as a benchmark for future studies probing invariance to backgrounds. The dataset consists of MNIST digits of varying transparency, set on one of two backgrounds with different statistics: a Gaussian noise or a more naturalistic background from the CIFAR-10 dataset. We use this dataset to learn digit classification when contexts are shown sequentially, and find that both shallow and deep networks have sharply decreased performance when returning to the first background after experience learning the second – thecatastrophic forgettingphenomenon in continual learning. To overcome this, we propose an architecture with additional “ switching” units that are activated in the presence of a new background. We find that the switching network can learn the new context even with very few switching units, while maintaining the performance in the previous context – but that they must berecurrentlyconnected to network layers. When the task is difficult due to high transparency, the switching network trained on both contexts outperforms networks without switching trained on only one context. The switching mechanism leads to sparser activation patterns, and we provide intuition for why this helps to solve the task. We compare our architecture with other prominent learning methods, and find that elastic weight consolidation is not successful in our setting, while progressive nets are more complex but less effective. Our study therefore shows how a bio-inspired architectural motif can contribute to task generalization across context.
Spatial relationships of intra-lesion heterogeneity in Mycobacterium tuberculosis microenvironment, replication status, and drug efficacy
Authors: Lavin Richard C. , Tan Shumin 
Published: November 2021

ABSTRACTA hallmark of Mycobacterium tuberculosis (Mtb) infection with critical impact on disease development and outcome is the marked heterogeneity that exists, spanning differences in lesion types to changes in microenvironment as the infection progresses1–7. A mechanistic understanding of how this heterogeneity affects Mtb growth and treatment efficacy necessitates single bacterium-level studies in the context of intact host tissue architecture; however, such an evaluation has been technically challenging. Here, we exploit fluorescent reporter Mtb strains and the C3HeB/FeJ murine model in an integrated imaging approach to study microenvironment heterogeneity within a single lesion in situ, and analyze how these differences relate to non-uniformity in Mtb replication state, activity, and drug efficacy. We show that the pH and chloride environments differ spatially in a caseous necrotic lesion, with increased acidity and chloride levels in the lesion cuff versus the necrotic core. Conversely, a higher percentage of Mtb in the necrotic core versus the lesion cuff were in an actively replicating state, and correspondingly active in transcription and translation. Finally, examination of three first-line anti-tubercular drugs showed that efficacy of isoniazid was strikingly poor against bacteria in the lesion cuff. Our study reveals spatial relationships of intra-lesion heterogeneity, sheds light on important considerations in the development of anti-tubercular treatment strategies, and establishes a foundational framework for Mtb infection heterogeneity analysis at the single cell level in situ.
Interpreting ruminant specific conserved non-coding elements by developmental gene regulatory network
Authors: Pan Xiangyu , Ma Zhaoxia , Sun Xinqi , Li Hui , Zhang Tingting , Zhao Chen , Wang Nini , Heller Rasmus , Wong Wing Hung , Wang Wen , Jiang Yu , Wang Yong 
Published: November 2021

AbstractBackgroundBiologists long recognized that the genetic information encoded in DNA leads to trait innovation via gene regulatory network (GRN) in development.ResultsHere, we generated paired expression and chromatin accessibility data during rumen and esophagus development in sheep and revealed 1,601 active ruminant-specific conserved non-coding elements (active-RSCNEs). To interpret the function of these active-RSCNEs, we developed a Conserved Non-coding Element interpretation method by gene Regulatory network (CNEReg) to define toolkit transcription factors (TTF) and model its regulation on rumen specific gene via batteries of active-RSCNEs during development. Our developmental GRN reveals 18 TTFs and 313 active-RSCNEs regulating the functional modules of the rumen and identifies OTX1, SOX21, HOXC8, SOX2, TP63, PPARG and 16 active-RSCNEs that functionally distinguish the rumen from the esophagus.ConclusionsWe argue that CNEReg is an attractive systematic approach to integrate evo-devo concepts with omics data to understand how gene regulation evolves and shapes complex traits.
Learning Feedback Molecular Network Models Using Integer Linear Programming
Authors: Ozen Mustafa , Abdi Ali , Emamian Effat S. 
Published: November 2021

AbstractAnalysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding the effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network models constructed using literature and pathway databases may not accurately predict and reproduce experimental network data. This can be due to the incompleteness of literature on molecular pathways, the resources used to construct the networks, or some conflicting information in the resources. In this paper, we propose a network learning approach via an integer linear programming formulation that can efficiently incorporate biological dynamics and regulatory mechanisms of molecular networks in the learning process. Moreover, we present a method to properly take into account the feedback paths, while learning the network from data. Examples are also provided to show how one can apply the proposed learning approach to a network of interest. Overall, the proposed methods are useful for reducing the gap between the curated networks and experimental network data, and result in calibrated networks that are more reliable for making biologically meaningful predictions.
NorA, HmpX, and NorB cooperate to reduce NO toxicity during denitrification and plant pathogenesis in Ralstonia solanacearum
Authors: Truchon Alicia N. , Hendrich Connor G. , Bigott Adam F. , Dalsing Beth L. , Allen Caitilyn 
Published: November 2021

AbstractRalstonia solanacearum, which causes bacterial wilt disease of many crops, needs denitrifying respiration to succeed inside its plant host. In the hypoxic environment of plant xylem vessels this pathogen confronts toxic oxidative radicals like nitric oxide (NO), which is generated by both bacterial denitrification and host defenses. R. solanacearum has multiple distinct mechanisms that could mitigate this stress, including Repair of Iron Cluster (RIC) homolog NorA, nitric oxide reductase NorB, and flavohaemoglobin HmpX. During denitrification and tomato pathogenesis and in response to exogenous NO, R. solanacearum upregulated norA, norB, and hmpX. Single mutants lacking ΔnorB, ΔnorA, or ΔhmpX increased expression of many iron and sulfur metabolism genes, suggesting that losing even one NO detoxification system demands metabolic compensation. Single mutants suffered only moderate fitness reductions in host plants, possibly because they upregulated their remaining detoxification genes. However, ΔnorA/norB, ΔnorB/hmpX, and ΔnorA/hmpX double mutants grew poorly in denitrifying culture and in planta. Loss of norA, norB, and hmpX may be lethal, since the methods used to construct the double mutants did not generate a triple mutant. Aconitase activity assays showed that NorA, HmpX and especially NorB are important for maintaining iron-sulfur cluster proteins. Additionally, plant defense genes were upregulated in tomatoes infected with the NO-overproducing ΔnorB mutant, suggesting that bacterial detoxification of NO reduces pathogen visibility. Thus, R. solanacearum’s three NO detoxification systems each contribute to and are collectively essential for overcoming metabolic oxidative stress during denitrification, for virulence and growth in tomato, and for evading host plant defenses.ImportanceThe soilborne plant pathogen Ralstonia solanacearum (Rs) causes bacterial wilt, a serious and widespread threat to global food security. Rs is metabolically adapted to low oxygen conditions, using denitrifying respiration to survive in the host and cause disease. However, bacterial denitrification and host defenses generate nitric oxide (NO), which is toxic and also alters signaling pathways in both plants and the pathogen. Rs mitigates NO with a trio of mechanistically distinct proteins: NO-reductase NorB, Repair of Iron Centers NorA, and oxidoreductase HmpX. This redundancy, together with analysis of mutants and in-planta dual transcriptomes, indicates that maintaining low NO levels is integral to Rs fitness in tomatoes (because NO damages iron-cluster proteins) and to evading host recognition (because bacterially produced NO can trigger plant defenses).
Characterization of the humoral immune response to BNT162b2 in elderly residents of long-term care facilities five to seven months after vaccination
Authors: Delbrück Marla , Hoehl Sebastian , Toptan Tuna , Schenk Barbara , Grikscheit Katharina , Metzler Melinda , Herrmann Eva , Ciesek Sandra 
Published: November 2021

AbstractThe elderly residing in long-term care facilities (LTCFs) are a group at high risk for COVID-19. Hence, monitoring of the vaccine-based immunity has a pivotal role in identifying strategies to provide optimal protection in this population. We examined the immune response to the mRNA vaccine BNT162b2 against COVID-19 five to seven months after completing a two-dose regimen.We determined significantly lower anti-SARS-CoV-2 antibody titers in 298 SARS-CoV-2 naïve residents who were at least 75 years of age (mean 51.60 BAU/ml) (median age 87 years, range 75 to 101 years) when compared to health care workers (HCWs) aged 18 to 70 years (mean 156.99 BAU/ml, p < 0.001). Of the SARS-CoV-2 naïve residents, 29 had detectable neutralizing antibodies against the Delta variant (9.5%), and 14 of those (48.3%) only had a borderline titer of 1:10. Of 114 HCWs, 36 (31.6%) had detectable neutralizing antibodies. In a group of 14 elderly residents who had had a PCR-confirmed breakthrough infection, the mean antibody titer was significantly higher than in the other two groups (3199.65 BAU/mL) (p < 0.001), and 12 (85.7%) had detectable neutralizing antibodies against the Delta variant.Our data demonstrate that 90.5% of elderly residents of LTCFs had no detectable neutralization-competent antibodies against the dominant Delta variant five to seven months after vaccination, and that neutralizing antibody titers were restored following a break-through infection. Our results suggest that both residents and health care workers in LTCFs would benefit from a booster vaccine six months after completing the two-dose schedule or earlier.
Cellular and Humoral Immunity to Ebola Zaire Glycoprotein and Viral Vector Proteins Following Immunization with Recombinant Vesicular Stomatitis Virus-Based Ebola Vaccine (rVSVΔG-EBOV-GP)
Authors: Raabe Vanessa , Lai Lilin , Morales Juliet , Xu Yongxian , Rouphael Nadine , Davey Richard T. , Mulligan Mark J. 
Published: November 2021

AbstractWhile effective at preventing Zaire ebolavirus (EBOV) disease, cellular immunity to EBOV and vector-directed immunity elicited by the recombinant vesicular stomatitis virus expressing Ebola glycoprotein (rVSVΔG-EBOV-GP) vaccine remains poorly understood. Sera and peripheral blood mononuclear cells were collected from 32 participants enrolled in a prospective multicenter study [ClinicalTrials.govNCT02788227] before vaccination and up to six months post-vaccination. IgM and IgG antibodies, IgG-producing memory B cells, and T cell reactivity to EBOV glycoprotein, vesicular stomatitis virus-Indiana strain (VSV-I) matrix protein, and VSV-I nucleoprotein were measured using ELISA, ELISpot, and intracellular cytokine staining, respectively. Eleven participants previously received a different investigational Ebola vaccine. All participants met positivity criteria for IgG antibodies to, and circulating IgG-producing memory B cells to, EBOV glycoprotein following rVSVΔG-EBOV-GP vaccination. Transient IgM and IgG antibody responses to VSV-I matrix protein (n=1/32 and n=0/32, respectively) and nucleoprotein (n=2/32 and n=1/32, respectively) were infrequently detected, as were IgG-producing memory B cells recognizing VSV-I matrix protein (n=3/31) and nucleoprotein (n=2/31). CD4+ and CD8+ T cell responses to EBOV glycoprotein were present in 15/32 and 19/32 participants at baseline and in 32/32 and 23/32 participants one month post-vaccination, respectively. CD4+ and CD8+ T cell responses to VSV-I matrix protein (n=17/32 and n=16/32, respectively) and VSV-I nucleoprotein (n=23/32 for both CD4+ and CD8+ responses) were common post-vaccination. T cell responses were predominantly mono-cytokine, except CD8+ responses to EBOV glycoprotein among heterologous Ebola vaccine-experienced participants and CD8+ responses to VSV-I nucleoprotein. Overall, rVSVΔG-EBOV-GP elicits robust humoral and memory B cell responses to EBOV glycoprotein in both Ebola vaccine-naïve and heterologous Ebola vaccine-experienced individuals and can generate vector-directed T cell immunity. Further research is needed to understand the significance of pre-existing vector-directed immunity on responses to booster doses of rVSVΔG-EBOV-GP and other rVSV-vectored vaccines.