bluuebunny/crossref_metadata_2025 huggingface dataset:
A single-cell clusters similarity measure for different batches, datasets, and samples
Authors: González-Velasco Óscar , Sanchez-Luis Elena , De La Rosa Enrique , Sánchez-Santos José Manuel , De Las Rivas Javier
Published: March 2022

Since the inception of single-cell level measuring techniques, identification of distinct cell stages, phenotypes and populations has been a challenge. Cell clustering and dimensionality reduction methods are the most popular approaches to identify heterogeneity of single-cell data. But, as public repositories continue to grow in number, integrative analyses and merging of large pools of samples from different and heterogeneous datasets becomes a difficult challenge, which showcases the impossibility of scalability of some of the existing methods. Here we present ClusterFoldSimilarity, an R package that calculates a measure of similarity between clusters from different datasets/batches, without the need of correcting for batch effect or normalizing and merging the data, thus avoiding artifacts and the loss of information derived from these kinds of techniques. The similarity metric is based on the average vector module and sign of the product of logarithmic fold-changes. ClusterFoldSimilarity compares every single pair of clusters from any number of different samples/datasets, including different number of clusters for each sample. Additionally, the algorithm is able to select the top genes which contribute the most to the similarity of two specific clusters, serving also as a feature selection tool.

Availability and implementation

The algorithm is freely available as an R package at: https://github.com/OscarGVelasco/ClusterFoldSimilarity

Contact

oscargvelasco@gmail.com
Integrated Diffusion Image Operator (iDIO): A tool for automated configuration and processing of diffusion MRI data
Authors: Hsu Chih-Chin Heather , Chong Shin Tai , Kung Yi-Chia , Kuo Kuan-Tsen , Huang Chu-Chung , Lin Ching-Po
Published: March 2022

The preprocessing of diffusion magnetic resonance imaging (dMRI) data involves numerous steps, including the corrections for head motion, susceptibility distortion, low signal-to-noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for non-expert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi-automated pipeline tool for dMRI named integrated Diffusion Image Operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one-click solution for dMRI data analysis, via automatic configuration for a set of optimal processing steps based on the image header of the input data. Additionally, the pipeline provides options for post-processing, such as estimation of diffusion tensor metrics and whole-brain tractography-based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy-to-interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO’s workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or non-specialist users to adopt the state-of-art dMRI processing steps.
Decomposing predictability to identify dominant causal drivers in complex ecosystems
Authors: Suzuki Kenta , Matsuzaki Shin-ichiro S. , Masuya Hiroshi
Published: March 2022

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time-series based causal inferences. Here we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded new but interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.

Significance Statement

Effective use of ecosystem monitoring data to resolve global environmental issues is a major challenge of the 21st century ecology. A promising solution to address this challenge is a time-series-based causal analysis which can provide insight on the mechanical links between ecosystem components. In this work, a model-free framework named EcohNet is proposed. EcohNet utilizes ensemble predictions of echo state networks, which are known to be fast, accurate, and highly relevant for a variety of dynamical systems, and can robustly predict causal networks of ecosystem components. It also can provide an optimized forecasting of overall ecosystem components, and could be used to analyze complex and hybrid multivariate time series in many scientific areas, not limited to ecosystems.
Geometrical factors determining dendritic domain intersection between neurons: a modeling study
Authors: Gatica Rafael Ignacio , Montero Trinidad , Farassat Navid , Henny Pablo
Published: March 2022

Overlap between dendritic trees of neighboring neurons is a feature of nervous systems. Overlap allows neurons to share common afferences and defines the topography of the circuit they belong to. Proximity is also a requirement for dendritic communication, including dendro-dendritic synaptic contacts. We simulated overlap dynamics between pairs of ventral tegmental area dopamine neurons, a population characterized by diverse and extensive dendritic domains. Using each neuron’s 3D convex hull (CH) as a proxy for dendritic domain size and shape, we examined intersection versus cell-to-cell distance curves for 210 pairs of neurons, and found that decay dynamics were diverse and complex, indicating that intersection between real dendritic domains does not comply with spherical or isotropic shape assumptions. We re-examined intersection dynamics for the same 210 pairs, but this time with either: a) a normalized volume corresponding to the average volume of all CHs, b) normalized shapes, using an average CH shape, or CHs from neurons exhibiting average dendritic distribution, isotropy or morphology, or c) a normalized cell body position, which we artificially placed in each CHs’ centroid. All three interventions significantly increased pair intersection and simplified decay dynamics, yet shape normalization had the strongest influence. Critically, shape uniformity was also the most relevant factor for increased dendritic domain pair intersection using the neurons’ original brain position and distances. We applied this experimental approach to other populations using reconstructions fromneuromorpho.orgdatabase and found that shape and intersection dynamics were cell-type dependent. We conclude that intersection between neurons is not only maximized by proximity, but that individual-specific dendritic domain geometries have a profound impact too. The results predict that one biological solution for circuits requiring selective connectivity is to exhibit greater heterogeneity in size, cell body location, and especially shape, among their constituent neuronal elements.
A Drosophila in vivo chemical screen reveals that combination drug treatment targeting MEK and DGKα mitigates Ras-driven polarity-impaired tumourigenesis
Authors: La Marca John E. , Ely Robert W. , Diepstraten Sarah T. , Burke Peter , Kelly Gemma L. , Humbert Patrick O. , Richardson Helena E.
Published: March 2022

The RAS oncogene and upregulation of the RAS signalling pathway is highly prevalent in human cancer, and therefore, therapeutically targeting the RAS pathway is a common treatment in cancer. However, RAS pathway upregulation is not sufficient to drive malignant cancer, since senescence mechanisms prevent cancer progression. Thus, additional mutations, such as mutations that prevent senescence or alter the tissue architecture (cell polarity), are required for RAS-driven tumour progression. Moreover, targeting RAS-driven cancers with RAS pathway inhibitors can often lead to undesirable side-effects and to drug resistance. Thus, identifying compounds that synergise with RAS-pathway inhibitors would enable lower doses of the RAS pathway inhibitors to be used and also decrease the acquisition of drug resistance. Here, in a boutique chemical screen using a Drosophila model of Ras-driven cell polarity-impaired cancer, we have identified compounds that reduce tumour burden by synergising with subtherapeutic doses of the RAS pathway inhibitor, Trametinib, which inhibits mitogen-activated kinase kinase (MEK). Analysis of one of the hits from the screen, Ritanserin, which targets serotonin receptors and diacy glycerol kinase alpha (DGKα), revealed that DGKα was the critical target in its synergism with Trametinib. We show that human mammary epithelial cells harbouring the H-RAS oncogene and knockdown of the cell polarity gene, SCRIB, are also sensitive to treatment with low doses of Trametinib and DGKα inhibition. Mechanistically, DGKα inhibition synergises with Trametinib by inhibiting MEK and mTOR activity. Altogether, our results provide evidence that targeting RAS-driven human cancers with RAS pathway and DGKα inhibitors will be an effective combination therapy.
Knowledge-guided data mining on the standardized architecture of NRPS: subtypes, novel motifs, and sequence entanglements
Authors: He Ruolin , Zhang Jinyu , Shao Yuanzhe , Gu Shaohua , Song Chen , Qian Long , Yin Wen-Bing , Li Zhiyuan
Published: March 2022

Non-ribosomal peptide synthetase (NRPS) is a diverse family of biosynthetic enzymes for the assembly of bioactive peptides. Despite advances in microbial sequencing, the lack of a consistent standard for annotating NRPS domains and modules has made data-driven discoveries challenging. To address this, we introduced a standardized architecture for NRPS, by using known conserved motifs to partition typical domains. This motif-and-intermotif standardization allowed for systematic evaluations of sequence properties from a large number of NRPS pathways, resulting in the most comprehensive cross-kingdom C domain subtype classifications to date, as well as the discovery and experimental validation of novel conserved motifs with functional significance. Furthermore, our coevolution analysis revealed important barriers associated with reengineering NRPSs and uncovered the entanglement between phylogeny and substrate specificity in NRPS sequences. Our findings provide a comprehensive and statistically insightful analysis of NRPS sequences, opening avenues for future data-driven discoveries.

Author Summary

NRPS, a gigantic enzyme that produces diverse microbial secondary metabolites, provides a rich source for important medical products including antibiotics. Despite the extensive knowledge gained about its structure and the large amount of sequencing data available, the frequent failure of reengineering NRPS in synthetic biology highlights the fact that much is still unknown. In this work, we applied existing knowledge to data mining of NRPS sequences, using well-known conserved motifs to partition NRPS sequences into motif-intermotif architectures. This standardization allows for integrating large amounts of sequences from different sources, providing a comprehensive overview of NRPSs across different kingdoms. Our findings included new C domain subtypes, novel conserved motifs with implication in structural flexibility, and insights into why NRPSs are so difficult to reengineer. To facilitate researchers in related fields, we constructed an online platform “NRPS Motif Finder” for parsing the motif-and-intermotif architecture and C domain subtype classification (http://www.bdainformatics.org/page?type=NRPSMotifFinder). We believe that this knowledge-guided approach not only advances our understanding of NRPSs but also provides a useful methodology for data mining in large-scale biological sequences.
MR-DoC2: Bidirectional Causal Modeling with Instrumental Variables and Data from Relatives
Authors: Castro-de-Araujo Luis FS , Singh Madhurbain , Zhou Yi (Daniel) , Vinh Philip , Verhulst Brad , Dolan Conor V , Neale Michael C
Published: March 2022

Establishing causality is an essential step towards developing interventions for psychiatric disorders, substance use and many other conditions. While randomized controlled trials (RCTs) are considered the gold standard for causal inference, they are unethical in many scenarios. Mendelian randomization (MR) can be used in such cases, but importantly both RCTs and MR assume unidirectional causality. In this paper, we developed a new model, MRDoC2, that can be used to identify bidirectional causation in the presence of confounding due to both familial and non- familial sources. Our model extends the MRDoC model (Minică et al 2018), by simultaneously including risk scores for each trait. Furthermore, the power to detect causal effects in MRDoC2 does not require the phenotypes to have different additive genetic or shared environmental sources of variance, as is the case in the direction of causation twin model (Heath et al., 1993).
Learning deep representations of enzyme thermal adaptation
Authors: Li Gang , Buric Filip , Zrimec Jan , Viknander Sandra , Nielsen Jens , Zelezniak Aleksej , Engqvist Martin KM
Published: March 2022

Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a dataset of over 3 million enzymes labeled with optimal growth temperatures (OGT) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other recent deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.
Sexually antagonistic co-evolution can explain female display signals and male sensory adaptations
Authors: Wiberg R. Axel W. , Murray Rosalind L. , Herridge Elizabeth , Pärssinen Varpu , Gwynne Darryl T. , Bussière Luc F.
Published: March 2022

The prevalence and diversity of female ornaments poses a challenge to evolutionary theory because males should prefer mates that spend resources on offspring rather than ornaments. Among dance flies, there is extraordinary variation in sexual dimorphism. Females of many species have conspicuous ornaments (leg scales and inflatable abdominal sacs). Meanwhile males of some species have exaggerated regions of their eyes with larger ommatidial facets that allow for regionally elevated photosensitivity and/or acuity. Here, we conduct a comparative study of these traits using both species descriptions available from the literature, as well as quantitative measures of eyes and ornaments from wild-caught flies. We show a conspicuous covariance across species between exaggerated male dorsal eye regions and the extent of female ornaments: species with highly ornamented females have males with more exaggerated eyes. We discuss this pattern in the context of competing hypotheses for the evolution of these traits and propose a plausible role for sexually antagonistic coevolution.
Pharmacokinetics of Sustained-release Buprenorphine and Extended-release Buprenorphine in Mice with Surgical Catheterization
Authors: Saenz Marissa , Bloom-Saldana Elizabeth , Synold Tim , Ermel Richard W , Fueger Patrick T , Finlay James B
Published: March 2022

The Guide for the Care and Use of Laboratory Animals strongly encourages the use of pharmaceutical grade chemicals and analgesics. The extra-label use of sustained-release buprenorphine (SRB) is commonly administered to rodents to mitigate moderate-to-severe pain. An FDA-indexed buprenorphine formulation, known as extended-release buprenorphine (XRB), has recently become available and is currently the only pharmaceutical grade slow-release buprenorphine approved for use in mice and rats. However, no studies have directly compared the pharmacokinetic (PK) parameters and therapeutic efficacy of SRB and XRB in surgically catheterized mice. Thus, we compared the plasma buprenorphine concentrations and PK parameters of SRB and XRB in mice after surgical catheterization. We hypothesized that mice treated with SRB or XRB would have circulating buprenorphine concentrations exceeding the therapeutic threshold for up to 72-hours post-operatively. Male and female C57Bl/6J mice were anesthetized, treated with either SRB (1 mg/kg, SC, once) or XRB (3.25 mg/kg, SC, once) and underwent surgical catheterization. At 6, 24, 48, and 72 h after SRB or XRB administration, arterial blood samples were collected. Post-operative weight loss was similar between groups with a decline of 11.7 ± 1.6 and 12.3 ± 0.7% in males and 7.6 ± 2.2 and 8.1 ± 1.1% (mean ± SEM) in females treated with SRB and XRB, respectively. Both SRB and XRB maintained circulating buprenorphine concentrations above the therapeutic level of 1.0 ng/mL for 72 h after administration. XRB buprenorphine concentrations were significantly greater (3-4-fold) than SRB concentrations at 6, 24, and 48 h, commensurate with the increase dose concentration of XRB to SRB. These results support the use of either SRB or XRB for the alleviation of postoperative pain in mice. The new availability of FDA-indexed XRB increases the options for safe and effective pharmaceutical grade analgesia in rodents.
Angiotensin blockade enhances motivational reward learning via enhancing striatal prediction error signaling and frontostriatal communication
Authors: Xu Ting , Zhou Xinqi , Kanen Jonathan W. , Wang Lan , Li Jialin , Chen Zhiyi , Zhang Ran , Jiao Guojuan , Zhou Feng , Zhao Weihua , Yao Shuxia , Becker Benjamin
Published: March 2022

Adaptive human learning utilizes reward prediction errors (RPEs) that scale the differences between expected and actual outcomes to optimize future choices. Depression has been linked with biased RPE signaling and an exaggerated impact of negative outcomes on learning which may promote amotivation and anhedonia. The present proof-of-concept study combined computational modelling and multivariate decoding with neuroimaging to determine the influence of the selective competitive angiotensin II type 1 receptor antagonist losartan on learning from positive or negative outcomes and the underlying neural mechanisms in healthy humans. In a double-blind, between-subjects, placebo-controlled pharmaco-fMRI experiment, 61 healthy male participants (losartan, n=30; placebo, n=31) underwent a probabilistic selection reinforcement learning task incorporating a learning and transfer phase. Losartan improved choice accuracy for the hardest stimulus pair via increasing expected value sensitivity towards the rewarding stimulus relative to the placebo group during learning. Computational modelling revealed that losartan reduced the learning rate for negative outcomes and increased exploitatory choice behaviors while preserving learning for positive outcomes. These behavioral patterns were paralleled on the neural level by increased RPE signaling in orbitofrontal-striatal regions and enhanced positive outcome representations in the ventral striatum (VS) following losartan. In the transfer phase, losartan accelerated response times and enhanced VS functional connectivity with left dorsolateral prefrontal cortex when approaching maximum rewards. These findings elucidate the potential of losartan to reduce the impact of negative outcomes during learning and subsequently facilitate motivational approach towards maximum rewards in the transfer of learning. This may indicate a promising therapeutic mechanism to normalize distorted reward learning and fronto-striatal functioning in depression.
Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features and populations
Authors: Dhamala Elvisha , Rong Ooi Leon Qi , Chen Jianzhong , Kong Ru , Anderson Kevin M. , Chin Rowena , Yeo B.T. Thomas , Holmes Avram J.
Published: March 2022

Individual differences in brain anatomy can be used to predict variability in cognitive function. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n=1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n=1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranium volumes from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.
SLC15A4 favors inflammasome function via mTORC1 signaling and autophagy restraint in dendritic cells
Authors: López-Haber Cynthia , Hutchins Zachary , Ma Xianghui , Hamilton Kathryn E. , Mantegazza Adriana R.
Published: March 2022

Phagocytosis is the first step in the assessment of foreign microbes or particles and enables activation of innate immune pathways such as the inflammasome. However, missing links between phagosomes and inflammasomes remain to be discovered. We show that in murine dendritic cells (DCs) the lysosomal histidine/peptide solute carrier transporter SLC15A4, associated with human inflammatory disorders, is recruited to phagosomes and is required for optimal inflammasome activity after infectious or sterile stimuli. Dextran sodium sulphate-treated SLC15A4-deficient mice exhibit decreased colon inflammation, reduced IL-1β production by intestinal DCs and increased autophagy. Similarly, SLC15A4-deficient DCs infected withSalmonellatyphimurium show reduced caspase-1 cleavage and IL-1β production. This correlates with peripheral NLRC4 inflammasome assembly and increased autophagy. Overexpression of constitutively active mTORC1 rescues decreased IL-1β levels and caspase-1 cleavage, and restores perinuclear inflammasome positioning. Our findings suggest that SLC15A4 is a novel link that couples phagocytosis with inflammasome perinuclear assembly and inhibition of autophagy through phagosomal content sensing. Our data also reveal the previously unappreciated importance of mTORC1 signaling pathways to promote and sustain inflammasome activity.
Optogenetic stimulation of medial septal glutamatergic neurons modulates theta-gamma coupling in the hippocampus
Authors: Dmitrieva Elena , Malkov Anton
Published: March 2022

The oscillatory activity of the hippocampus and parahippocampal structures encodes information during cognition. Multiple researchers emphasize hippocampal cross-frequency theta-gamma coupling (TGC) as a basic mechanism for information processing, retrieval, and consolidation of long-term and working memory. While the role of entorhinal afferents in the modulation of hippocampal TGC is widely accepted, the influence of other main input to the hippocampus, from the medial septal area (MSA, the pacemaker of the hippocampal theta rhythm) is poorly understood. This area innervates multiple targets in the hippocampus, so information about the participation of MSA in cross-frequency interactions might be critical for an understanding of neural coding.

Optogenetics allows us to explore how different neuronal populations of septohippocampal circuits control neuronal oscillations in vivo. It was shown that optogenetic activation of glutamatergic projections to the hippocampus with theta-frequency stimuli paces the hippocampal theta rhythm. Here we investigated the influence of phasic activation of MSA glutamatergic neurons expressing channelrhodopsin II on theta-gamma coupling in the hippocampus. Transgenic mice B6.Cg-Tg (Thy1-COP4/EYFP)18Gfng/J were used in this experiment. Expression of ChRII in MSA was verified immunohistochemically and the optic fibers just above MSA and the recording electrodes in the CA1 hippocampal field were implanted. During the experiment, local field potentials of MSA and hippocampus of freely behaving mice were modulated by 470nm light flashes with theta frequency (2-10) Hz.

It was shown that both the power and the strength of modulation of gamma rhythm nested on hippocampal theta waves depend on the frequency of stimulation. The modulation of the amplitude of slow gamma rhythm (30-50 Hz) prevailed over modulation of fast gamma (55-100 Hz) during flash trains and the observed effects were specific for theta stimulation of MSA. We discuss the possibility that phasic depolarization of septal glutamatergic neurons controls theta-gamma coupling in the hippocampus and plays a role in memory retrieval and consolidation.
Targeted deletion of uterine glandular Foxa2 induces embryonic diapause in mice
Authors: Matsuo Mitsunori , Yuan Jia , Kim Yeon Sun , Dewar Amanda , Fujita Hidetoshi , Dey Sudhansu K. , Sun Xiaofei
Published: March 2022

Embryonic diapause is a reproductive strategy in which embryo development and growth is temporarily arrested within the uterus to ensure the survival of neonates and mothers during unfavorable conditions. Pregnancy is reinitiated when conditions become favorable for neonatal survival. The mechanism of how the uterus enters diapause remains unclear. Mice with uterine deletion of Foxa2, a transcription factor, are infertile. In this study, we show that dormant blastocysts are recovered from these mice on day 8 of pregnancy with persistent uterine Msx1 expression, a gene critical to maintaining the uterine quiescent state, suggesting that these mice enter embryonic diapause. Leukemia inhibitory factor (LIF) can resume implantation in these mice. Although estrogen is critical for implantation in progesterone (P4)-primed uterus, our current model reveals that FOXA2-independent estrogenic effects are detrimental to sustaining uterine quiescence. Interestingly, P4 and anti-estrogen can prolong uterine quiescence in the absence of FOXA2. While we find that Msx1 expression persists in the uterus deficient in Foxa2, the complex relationship of FOXA2 with Msx genes and estrogen receptors remains to be explored.
Developmental phenomics suggests that H3K4 monomethylation catalyzed by Trr functions as a phenotypic capacitor
Authors: Gandara Lautaro , Tsai Albert , Ekelöf Mans , Galupa Rafael , Noon Ella Preger-Ben , Alexandrov Theodore , Crocker Justin
Published: March 2022

How epigenetic modulators of gene regulation affect the development and evolution of animals has been difficult to ascertain. Despite the widespread presence of histone 3 lysine 4 monomethylation (H3K4me1) on enhancers, hypomethylation appears to have minor effects on animal development and viability. In this study, we performed quantitative, unbiased and multi-dimensional explorations of key phenotypes on Drosophila melanogaster with genetically induced hypomethylation. Hypomethylation reduced transcription factor enrichment in nuclear microenvironments, leading to reduced gene expression, and phenotypes outside of standard laboratory conditions. Our developmental phenomics survey further showed that H3K4me1 hypomethylation led to context-dependent changes in morphology, metabolism, and behavior. Therefore, H3K4me1 may contribute to phenotypic evolution as a phenotypic capacitor by buffering the effects of chance, genotypes and environmental conditions on transcriptional enhancers.

Quote

“Developmental biologists are often not so much opposed to a role for ecology as they simply ignore it” –Doug Erwin1
A conserved immune trajectory of recovery in hospitalized COVID-19 patients
Authors: Burnett Cassandra E. , Hauge Okholm Trine Line , Tenvooren Iliana , Marquez Diana M. , Tamaki Stanley , Sandoval Priscila Munoz ,   The UCSF COMET Consortium, Calfee Carolyn S. , Hendrickson Carolyn M. , Kangelaris Kirsten N. , Langelier Charles R. , Krummel Matthew F. , Woodruff Prescott G. , Erle David J. , Ansel K. Mark , Spitzer Matthew H.
Published: March 2022

Many studies have provided insights into the immune response to COVID-19; however, little is known about the immunological changes and immune signaling occurring during COVID-19 resolution. Individual heterogeneity and variable disease resolution timelines obscure unifying immune characteristics. Here, we collected and profiled >200 longitudinal peripheral blood samples from patients hospitalized with COVID-19, with other respiratory infections, and healthy individuals, using mass cytometry to measure immune cells and signaling states at single cell resolution. COVID-19 patients showed a unique immune composition and an early, coordinated and elevated immune cell signaling profile, which correlated with early hospital discharge. Intra-patient time course analysis tied to clinically relevant events of recovery revealed a conserved set of immunological processes that accompany, and are unique to, disease resolution and discharge. This immunological process, together with additional changes in CD4 regulatory T cells and basophils, accompanies recovery from respiratory failure and is associated with better clinical outcomes at the time of admission. Our work elucidates the biological timeline of immune recovery from COVID-19 and provides insights into the fundamental processes of COVID-19 resolution in hospitalized patients.
Increases in vein length compensate for leaf area lost to lobing in grapevine
Authors: Migicovsky Zoë , Swift Joel F. , Helget Zachary , Klein Laura L. , Ly Anh , Maimaitiyiming Matthew , Woodhouse Karoline , Fennell Anne , Kwasniewski Misha , Miller Allison J. , Cousins Peter , Chitwood Daniel H.
Published: March 2022

Premise

There is considerable variation in leaf lobing and leaf size, including among grapevines, some of the most well-studied leaves. We examined the relationship between leaf lobing and leaf size across grapevine populations which varied in extent of leaf lobing.

Methods

We used homologous landmarking techniques to measure 2,632 leaves across two years in 476 unique, genetically distinct grapevines from 5 biparental crosses which vary primarily in the extent of lobing. We determined to what extent leaf area could explain variation in lobing, vein length, and vein to blade ratio.

Results

Although lobing was the primary source of variation in shape across the leaves we measured, leaf area varied only slightly as a function of lobing. Rather, leaf area increases as a function of total major vein length, total branching vein length, and decreases as a function of vein to blade ratio. These relationships are stronger for more highly lobed leaves, with the residuals for each model differing as a function of distal lobing.

Conclusions

For a given leaf area, more highly lobed leaves have longer veins and higher vein to blade ratios, allowing them to maintain similar leaf areas despite increased lobing. These findings show how more highly lobed leaves may compensate for what would otherwise result in a reduced leaf area, allowing for increased photosynthetic capacity through similar leaf size.
MiXcan: a Framework for Cell-Type-Specific Transcriptome-Wide Association Studies with an Application to Breast Cancer
Authors: Song Xiaoyu , Ji Jiayi , Rothstein Joseph H. , Alexeeff Stacey E. , Sakoda Lori C. , Sistig Adriana , Achacoso Ninah , Jorgenson Eric , Whittemore Alice S. , Klein Robert J. , Habel Laurel A. , Wang Pei , Sieh Weiva
Published: March 2022

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study (TWAS) approaches predict gene expression at the tissue level from genotype data, without considering cell-type heterogeneity, and test associations of the predicted tissue-level gene expression with disease. Here we develop MiXcan, a new TWAS approach that predicts cell-type-specific gene expression levels, identifies disease-associated genes via combination of cell-type-specific association signals for multiple cell types, and provides insight into the disease-critical cell type. We conducted the first cell-type-specific TWAS of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-specific TWAS can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.
Is virulence phenotype evolution driven exclusively by Lr gene deployment in French Puccinia triticina populations?
Authors: Fontyn Cécilia , Zippert Anne-Catherine , Delestre Ghislain , Marcel Thierry C , Suffert Frédéric , Goyeau Henriette
Published: March 2022

Puccinia triticina is a highly damaging wheat pathogen. The efficacy of leaf rust control by genetic resistance is mitigated by the adaptive capacity of the pathogen, expressed as changes in its virulence combinations (pathotypes). An extensive P. triticina population survey has been carried out in France over the last 30 years, describing the evolution dynamics of this pathogen in response to cultivar deployment. We analyzed the dataset for the 2006-2016 period to determine the relationship between the Lr genes in the cultivars and virulence in the pathotypes. Rust populations were dominated by a small number of pathotypes, with variations in most of the virulence frequencies related to the corresponding Lr gene frequencies in the cultivated landscape. Furthermore, the emergence and spread of a new virulence matched the introduction and use of the corresponding Lr gene (Lr28), confirming that the deployment of qualitative resistance genes is an essential driver of evolution in P. triticina populations. However, PCA revealed that certain pathotype-cultivar associations cannot be explained solely by the distribution of Lr genes in the landscape. This conclusion is supported by the predominance of a few pathotypes on some cultivars, with the persistence of several other compatible pathotypes at low frequencies. Specific interactions are not, therefore, sufficient to explain the distribution of virulence in rust populations. Our findings suggest that aggressiveness is a driver of changes in pathotype frequencies. Accordingly, the hypothesis of “dual selection”, based on both qualitative and quantitative interactions between P. triticina populations and bread wheat cultivars, is favored.
Genetic interaction analysis reveals that Cryptococcus neoformans utilizes multiple acetyl-CoA-generating pathways during infection
Authors: Alden Katy M. , Jezewski Andrew J. , Beattie Sarah R. , Fox David , Krysan Damian J.
Published: March 2022

Cryptococcus neoformans is an important human fungal pathogen for which the external environment is its primary niche. To cause infection and disease, C. neoformans must adapt to a plethora of conditions and stresses inherent to the host environment. Of these stresses, the role of central carbon metabolism has been of interest not only at the level of fundamental pathobiology but also as a potential target for new antifungal drug therapy. Previous work has shown that two non-essential acetyl-CoA metabolism enzymes, ATP-citrate lyase (ACL1) and acetyl-CoA synthetase (ACS1), play important roles in C. neoformans infection. Here, we took a genetic interaction approach to studying the interplay between these two enzymes along with an enzyme initially called ACS2 but which we have found is an acetoacetyl-CoA synthetase; we have renamed the gene 2-ketobutyryl CoA synthetase 1 (KBC1) based on its biochemical activity and the systematic name of its substrate. ACL1 and ACS1 represent a synthetic lethal pair of genes based on our genetic interaction studies. Double mutants of KBC1 with either ACS1 or ACL1 do not have significant synthetic phenotypes in vitro, although we find that deletion of any one of these enzymes reduces fitness within macrophages. Importantly, the acs1Δ kbc1Δ double mutant has significantly reduced fitness in the CNS relative to either single mutant as well as WT (~2 log10 CFU reduction in fungal burden), indicating the important role these enzymes play during infection. The expression of both ACS1 and KBC1 is increased in vivo relative to in vitro conditions. The acs1Δ mutant is hypersusceptible to fluconazole in vivo despite its minimal in vitro phenotypes. These data not only provide insights into the in vivo mechanism of action for a new class of antifungal Acs inhibitors but also into metabolic adaptations of C. neoformans to the host environment.

Author Summary

The adaptation of environmental fungal pathogens to the mammalian host is critical to pathogenesis. Of these adaptations, the remodeling of carbon metabolism is particularly important. Here, we generated a focused set of double mutants of non-essential genes (ACL1, ACS1, KBC1) involved in acetyl-CoA metabolism and examined their fitness in vitro and during CNS infection. From these studies, we found that all three enzymes play important roles during infection but that the role of ACS1/KBC1 was minimal in vitro. Consistent with these observations, the expression of ACS1 and KBC1 was increased in vivo relative to standard in vitro conditions. Furthermore, strains lacking both ACL1 and ACS1 were not viable. These data clearly show that C. neoformans employs multiple carbon metabolism pathways to adapt to the host environment. They also provide insights into the potential mechanism of action for anti-cryptococcal Acs inhibitors.
PeroxiHUB: a modular cell-free biosensing platform using H2O2 as signal integrator
Authors: Soudier Paul , Duigou Thomas , Voyvodic Peter L. , Zúñiga Ana , Bazi-Kabbaj Kenza , Kushwaha Manish , Bonnet Jerome , Faulon Jean-Loup
Published: March 2022

Cell-free systems have great potential for delivering robust, cheap, and field-deployable biosensors. Many cell-free biosensors rely on transcription factors responding to small molecules, but their discovery and implementation still remain challenging. Here we report the engineering of PeroxiHUB, an optimized H2O2-centered sensing platform supporting cell-free detection of different metabolites. H2O2 is a central metabolite and a by-product of numerous enzymatic reactions. PeroxiHUB uses enzymatic transducers to convert metabolites of interest into H2O2, enabling rapid reprogramming of sensor specificity using alternative transducers. We first screen several transcription factors and optimize OxyR for the transcriptional response to H2O2 in cell-free, highlighting the need for pre-incubation steps to obtain suitable signal-to-noise ratios. We then demonstrate modular detection of metabolites of clinical interest – lactate, sarcosine, and choline – using different transducers mined via a custom retro-synthesis workflow publicly available on the SynBioCAD Galaxy portal. We find that expressing the transducer during the pre-incubation step is crucial for optimal sensor operation. Finally, we show that different reporters can be connected to PeroxiHUB, providing high adaptability for various applications. Given the wide range of enzymatic reactions producing H2O2, the PeroxiHUB platform will support cell-free detection of a large number of metabolites in a modular and scalable fashion.
Robots at your service: value facilitation and value co-creation in restaurants
Authors: Zhang Xiya , Balaji M.S. , Jiang Yangyang
Published: March 2022

Purpose

This paper aims to understand the process of guest-robot value co-creation in the restaurant context. It empirically examines the guest perception of value facilitation by service robots and its impact on guest value co-creation and advocacy intentions. It also investigates the moderating role of interaction comfort in the relationship between service robot value facilitation and guest value co-creation.

Design/methodology/approach

A mixed-methods approach was adopted. Ten customers who had dined at a service robot restaurant in China were interviewed in the qualitative study, followed by a quantitative study with 252 restaurant patrons to test the relationships between service robot value facilitation, guest value co-creation, interaction comfort and advocacy intentions.

Findings

Guest perceptions of six robot attributes, including role significance, competence, social presence, warmth, autonomy and adaptability, determine service robot value facilitation. Interaction comfort moderates the influence of service robot value facilitation on guest value co-creation. Additionally, guest value co-creation mediates the effect of service robot value facilitation on advocacy intentions.

Research limitations/implications

This study offers an understanding of six robot attributes that can improve service robot value facilitation. Nevertheless, the authors collected data from guests who had experience at service robot restaurants. The authors encourage future research to use random sampling methods to ensure study representativeness.

Practical implications

This study offers strategic guidance for managers to deploy service robots in frontline roles in restaurants and provides important implications for service robot design to improve their facilitating role in the guest value co-creation process.

Originality/value

This study responds to a recent call for research on the role of service robots in the guest value co-creation experience. Unlike prior studies that focused on the adoption or acceptance of service robots, it examines the role of service robots in the value co-creation process (post-adoption stage). Furthermore, it is one of the early studies to identify and empirically examine the service robot attributes that enable value facilitation and foster value co-creation in guest-robot service encounters.
Applications of blockchain for vaccine passport and challenges
Authors: Mishra Vinaytosh
Published: March 2022

Purpose

The purpose of this study is to investigate the applications of blockchain in vaccine passport solution. The world is facing an unprecedented situation because of the COVID-19 pandemic. Many countries have witnessed sporadic lockdown and travel restrictions and it has marred trade and tourism. As the mass vaccination has started the life is slowly and steadily returning to true normal. Various countries are issuing vaccination passports to manage the immunization information and validate it. To realize vaccine-passport’s true potential, security and privacy concerns should be being taken care of. There is a need for studies to evaluate the emerging technology for the vaccine passport.

Design/methodology/approach

This study uses a mix of qualitative and quantitative methods to achieve its objective. This study uses a systematic literature review to analyze the potential of blockchain for vaccine passports. The case study of three different types of organizations implementing blockchain for vaccine passports was analyzed and results were presented. Last but not least, focus group discussion and search of secondary literature was to done to identify scientific, ethical and legal challenges associated with the use of vaccine passports. The method used for calculating the importance score of these challenges was analytical hierarchy process.

Findings

This study concludes that blockchain-based solutions are very suitable for vaccine passports and addresses the concern related to interoperability, privacy and security. The case study approach was used to elaborate the use of blockchain in three different options available for the vaccine. Last but not least, this study identifies the challenges faced by vaccine passport programs and suggests measures to overcome them. This study concludes that the ethical challenges associated with vaccine passports are more important and should be preferentially treated.

Research limitations/implications

This study is timely and will be he lpful for policymakers in designing the vaccine passport programs. It gives valuable insight to decision-makers evaluating technologies for the development of vaccine passport programs. This study identifies nine challenges to be tackled to making a vaccine passport program successful.

Originality/value

To the best of the author’s knowledge, this study is not able to find out a review on the use of blockchain technology for vaccine passports, and this study attempts to fill this gap. This study further discusses the cases of organizations that have implemented blockchain technology for vaccine passport programs.