bluuebunny/crossref_metadata_2025 huggingface dataset:
Population bottleneck has only marginal effect on fitness evolution and its repeatability in dioecious C. elegans
Authors: Bisschop Karen , Blankers Thomas , Mariën Janine , Wortel Meike T. , Egas Martijn , Groot Astrid T. , Visser Marcel E. , Ellers Jacintha
Published: October 2021

The predictability of evolution is expected to depend on the relative contribution of deterministic and stochastic processes. This ratio is modulated by effective population size. Smaller effective populations harbor less genetic diversity and stochastic processes are generally expected to play a larger role, leading to less repeatable evolutionary trajectories. Empirical insight into the relationship between effective population size and repeatability is limited and focused mostly on asexual organisms. Here, we tested whether fitness evolution was less repeatable after a population bottleneck in obligately outcrossing populations of Caenorhabditis elegans. Replicated populations founded by 500, 50, or 5 individuals (no/moderate/strong bottleneck) were exposed to a novel environment with a different bacterial prey. As a proxy for fitness, population size was measured after one week of growth before and after 15 weeks of evolution. Surprisingly, we found no significant differences among treatments in their fitness evolution. Even though the strong bottleneck reduced the relative contribution of selection to fitness variation, this did not translate to a significant reduction in the repeatability of fitness evolution. Thus, although a bottleneck reduced the contribution of deterministic processes, we conclude that the predictability of evolution may not universally depend on effective population size, especially in sexual organisms.
Pseudo-dynamic analysis of heart tube formation in the mouse reveals strong regional variability and early left-right asymmetry
Authors: Esteban Isaac , Schmidt Patrick , Temiño Susana , Kobbelt Leif , Torres Miguel
Published: October 2021

Understanding organ morphogenesis requires a precise geometrical description of the tissues involved in the process. In highly regulative embryos, like those of mammals, morphological variability hinders the quantitative analysis of morphogenesis. In particular, the study of early heart development in mammals remains a challenging problem, due to imaging limitations and innate complexity. Around embryonic day 7.5 (E7.5), the cardiac crescent folds in an intricate and coordinated manner to produce a pumping linear heart tube at E8.25, followed by heart looping at E8.5. In this work we provide a complete morphological description of this process based on detailed imaging of a temporally dense collection of embryonic heart morphologies. We apply new approaches for morphometric staging and quantification of local morphological variations between specimens at the same stage. We identify hot spots of regionalized variability and identify left-right asymmetry in the inflow region starting at the late cardiac crescent stage, which represents the earliest signs of organ left-right asymmetry in the mammalian embryo. Finally, we generate a 3D+t digital model that provides a framework suitable for co-representation of data from different sources and for the computer modelling of the process.

SUMMARY STATEMENT

We provide the first complete atlas for morphometric analysis and visualization of heart tube morphogenesis, reporting morphological variability and early emergence of left-right asymmetry patterns.
Glutamine metabolism enables NKT cell homeostasis and function through the AMPK-mTORC1 signaling axis
Authors: Kumar Ajay , Yarosz Emily L. , Andren Anthony , Zhang Li , Lyssiotis Costas A. , Chang Cheong-Hee
Published: October 2021

Cellular metabolism is essential in dictating conventional T cell development and function, but its role in natural killer T (NKT) cells has not been well studied. We have previously shown that NKT cells operate distinctly different metabolic programming from CD4 T cells, including a strict requirement for glutamine metabolism to regulate NKT cell homeostasis. However, the mechanisms by which NKT cells regulate glutamine metabolism for their homeostasis and effector functions remain unknown. In this study, we report that steady state NKT cells have higher glutamine levels than CD4 T cells and NKT cells increase glutaminolysis upon activation. Among its many metabolic fates, NKT cells use glutamine to fuel the tricarboxylic acid cycle and glutathione synthesis, and glutamine-derived nitrogen enables protein glycosylation via the hexosamine biosynthesis pathway (HBP). Each of these functions of glutamine metabolism was found to be critical for NKT cell survival and proliferation. Furthermore, we demonstrate that glutaminolysis and the HBP differentially regulate IL-4 and IFNγ production. Finally, glutamine metabolism appears to be controlled by AMP-activated protein kinase (AMPK)-mTORC1 signaling. These findings highlight a unique metabolic requirement of NKT cells which can be potentially serve as an effective immunotherapeutic agent against certain nutrient restricted tumors.

Significance

NKT cells get activated very early during an immune response and produce cytokines and chemokines, which further activate other immune cell types. Although metabolism regulates these functions in other T cell subsets, little is understood about how metabolic pathways are controlled in NKT cells. The present study shows that NKT cells metabolize the amino acid glutamine through two different branches of metabolism, which control NKT cell homeostasis and expansion in a similar manner but control cytokine production differently. This glutamine dependency seems to be regulated by AMP-activated protein kinase (AMPK), which is a central regulator of energy homeostasis. Together, our study demonstrates a unique metabolic profile of glutamine metabolism in NKT cells which could be harnessed for NKT cell-based immunotherapy.
Morphodynamical cell state description via live-cell imaging trajectory embedding
Authors: Copperman Jeremy , Gross Sean M. , Chang Young Hwan , Heiser Laura M. , Zuckerman Daniel M.
Published: October 2021

Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of “trajectory embedding” to analyze cellular behavior using morphological feature trajectory histories—that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.
Drought stress delays photosynthetic induction and accelerates photoinhibition of photosystem I under fluctuating light
Authors: Sun Hu , Shi Qi , Liu Ning-Yu , Zhang Shi-Bao , Huang Wei
Published: October 2021

Fluctuating light (FL) and drought stress usually occur concomitantly. However, whether drought stress affects photosynthetic performance under FL remains unknown. Here, we measured gas exchange, chlorophyll fluorescence, and P700 redox state under FL in drought-stressed tomato (Solanum lycopersicum) seedlings. Drought stress significantly affected stomatal opening and mesophyll conductance after transition from low to high light and thus delayed photosynthetic induction under FL. Therefore, drought stress exacerbated the loss of carbon gain under FL. Furthermore, restriction of CO2 fixation under drought stress aggravated the over-reduction of photosystem I (PSI) upon transition from low to high light. The resulting stronger FL-induced PSI photoinhibition significantly supressed linear electron flow and PSI photoprotection. These results indicated that drought stress not only affected gas exchange under FL but also accelerated FL-induced photoinhibition of PSI. Furthermore, drought stress enhanced relative cyclic electron flow in FL, which partially compensated for restricted CO2 fixation and thus favored PSI photoprotection under FL. Therefore, drought stress has large effects on photosynthetic dark and light reactions under FL.
The effector FEP3/IRON MAN1 modulates interaction between BRUTUS-LIKE1 and bHLH subgroup IVb and IVc proteins
Authors: Lichtblau Daniela M. , Schwarz Birte , Baby Dibin , Endres Christopher , Sieberg Christin , Bauer Petra
Published: October 2021

Plants use the micronutrient iron (Fe) efficiently to balance the requirements for Fe during growth with its potential cytotoxic effects. A cascade of basic helix-loop-helix (bHLH) transcription factors is initiated by bHLH proteins of the subgroups IVb and IVc. This induces more than 50 genes in higher plants that can be grouped in co-expression clusters. Gene co-expression networks contain information on functional protein interactomes. We conducted a targeted yeast two-hybrid screen with pairwise combinations of 23 proteins stemming from previously characterized Fe-deficiency-induced gene co-expression clusters and regulators. We identified novel and described interactions, as well as interaction hubs with multiple interactions within the network. We found that BRUTUS-LIKE E3 ligases (BTSL1, BTSL2) interacted with basic helix-loop-helix (bHLH) transcription factors of the subgroups IVb and IVc including PYE, bHLH104 and ILR3, and with small FE UPTAKE-INDUCING PEPTIDE3/IRON MAN1 (FEP3/IMA1). Through deletion studies and with support of molecular docking, we mapped the interaction sites to three-amino-acid regions in BTSL1 and FEP3/IMA1. The FEP3/IMA1 active residues are present in interacting sites of the bHLH IVc factors. FEP3/IMA1 attenuated interaction of BTSL1 with bHLH proteins in a quantitative yeast three-hybrid assay suggesting that it is an inhibitor. Co-expression of BTSL1 and bHLH IVb and IVc factors uncovered unexpected patterns of subcellular localization. Combining deletion mapping, protein interaction and physiological analysis, we discuss the model that FEP3/IMA1 is a small effector protein inhibiting BTSL1/BTSL2-mediated degradation of bHLH subgroup IVb and IVc proteins.

Highlights

A targeted yeast two-hybrid screen of Fe deficiency-regulated proteins reveals a regulatory protein interactome consisting of E3 ligases BTS/BTSL, bHLH transcription factors of subgroups IVb and IVc and small protein FEP3/IMA1.

Interaction sites between BTSL1, FEP3/IMA1, and bHLH IVc transcription factors were fine-mapped.

FEP3/IMA1 is as a small effector protein that selectively attenuates the bHLH interaction with BTSL1 to regulate Fe deficiency responses.

One sentence summary

A targeted protein interaction screen uncovered a interactions of E3 ligase BTSL1, bHLH proteins of subgroup IVb and IVc and effector protein FEP3/IMA1 to regulate Fe deficiency responses.
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
Authors: Schimel Marine , Kao Ta-Chu , Jensen Kristopher T. , Hennequin Guillaume
Published: October 2021

Abstract

Understanding how neural dynamics give rise to behaviour is one of the most fundamental questions in systems neuroscience. To achieve this, a common approach is to record neural populations in behaving animals, and model these data as emanating from a latent dynamical system whose state trajectories can then be related back to behavioural observations via some form of decoding. As recordings are typically performed in localized circuits that form only a part of the wider implicated network, it is important to simultaneously learn the local dynamics and infer any unobserved external input that might drive them. Here, we introduce iLQR-VAE, a control-based approach to variational inference in nonlinear dynamical systems, capable of learning both latent dynamics, initial conditions, and ongoing external inputs. As in recent deep learning approaches, our method is based on an input-driven sequential variational autoencoder (VAE). The main novelty lies in the use of the powerful iterative linear quadratic regulator algorithm (iLQR) in the recognition model. Optimization of the standard evidence lower-bound requires differentiating through iLQR solutions, which is made possible by recent advances in differentiable control. Importantly, the recognition model is naturally tied to the generative model, greatly reducing the number of free parameters and ensuring high-quality inference throughout the course of learning. Moreover, iLQR can be used to perform inference flexibly on heterogeneous trials of varying lengths. This allows for instance to evaluate the model on a single long trial after training on smaller chunks. We demonstrate the effectiveness of iLQR-VAE on a range of synthetic systems, with autonomous as well as input-driven dynamics. We further apply it to neural and behavioural recordings in non-human primates performing two different reaching tasks, and show that iLQR-VAE yields high-quality kinematic reconstructions from the neural data.
Learning from the physical consequences of our actions improves motor memory
Authors: Bakkum Amanda , Marigold Daniel S.
Published: October 2021

Actions have consequences. Motor learning involves correcting actions that lead to movement errors and remembering these actions for future behavior. In most laboratory situations, movement errors have no physical consequences and simply indicate the progress of learning. Here we asked how experiencing a physical consequence when making a movement error affects motor learning. Two groups of participants adapted to a new, prism-induced mapping between visual input and motor output while performing a precision walking task. Importantly, one group experienced an unexpected slip perturbation when making foot-placement errors during adaptation. Because of our innate drive for safety, and the fact that balance is fundamental to movement, we hypothesized that this experience would enhance motor memory. Learning generalized to different walking tasks to a greater extent in the group who experienced the adverse physical consequence. This group also showed faster relearning one week later despite exposure to a competing mapping during initial learning—evidence of greater memory consolidation. The group differences in generalization and consolidation occurred even though they both experienced similar magnitude foot-placement errors and adapted at similar rates. Our results suggest the brain considers the potential physical consequences of movement error when learning and that balance-threatening consequences serve to enhance this process.
Modelling the effects of land use on mangroves in a RAMSAR site of Panamá
Authors: López-Angarita Juliana , Diaz Juan M , Tilley Alexander
Published: October 2021

The role of mangroves as pivotal providers of ecosystem services has been widely acknowledged. In Latin America, mangroves play an important role in traditional coastal livelihoods, but the growing economy of these nations demands the expansion of land for development, putting pressure in ecosystems such as mangroves. Here we examine the impact of land use activities on mangroves in the Gulf of Montijo, a RAMSAR site located in the Pacific coast of Panamá. Spatial information of land use was analysed, ground-truthed and classified into agriculture, aquaculture and coastal development, and subsequently ranked according to estimated level of impact on mangroves based on 27 interviews with local informants. We developed a spatially-referenced cumulative impact model of human activities on mangroves. Results showed that despite the protection status of the Gulf of Montijo, its mangrove forests are affected by localised human activities, dominated by agriculture. Given the importance of fishing for local livelihoods, evaluating the effects of agriculture, rice in particular, on mangroves and their associated fauna will be essential for the sustainable management of this RAMSAR site.
BDNF signaling in correlation-dependent structural plasticity in the developing visual system
Authors: Kutsarova Elena , Schohl Anne , Munz Martin , Wang Alex , Zhang Yuan Yuan , Bilash Olesia M , Ruthazer Edward S
Published: October 2021

During development, patterned neural activity instructs topographic map refinement. Axons with similar patterns of neural activity, converge onto target neurons and stabilize their synapses with these postsynaptic partners, restricting exploratory branch elaboration (Hebbian structural plasticity). On the other hand, non-correlated firing in inputs leads to synapse weakening and increased exploratory growth of axons (Stentian structural plasticity). We used visual stimulation to control the correlation structure of neural activity in a few ipsilaterally projecting (ipsi) retinal ganglion cell (RGC) axons with respect to the majority contralateral eye inputs in the optic tectum of albinoXenopus laevistadpoles. Multiphoton live imaging of ipsi axons, combined with specific targeted disruptions of brain-derived neurotrophic factor (BDNF) signaling, revealed that both presynaptic p75NTRand TrkB are required for Stentian axonal branch addition, whereas presumptive postsynaptic BDNF signaling is necessary for Hebbian axon stabilization. Additionally, we found that BDNF signaling mediates local suppression of branch elimination in response to correlated firing of inputs. DailyIn vivoimaging of contralateral RGC axons demonstrated that p75NTRknockdown reduces axon branch elongation and arbor spanning field volume.
Effects of culture medium and temperature on fungal growth of Mycena and marasmioid isolates and in vitro symbiotic culture in mycoheterotrophic orchid, Gastrodia pubilabiata
Authors: Shimazaki Akane , Higaki Kana , Rammitsu Kento , Ogura-Tsujita Yuki
Published: October 2021

In vitro symbiotic culture of Gastrodia pubilabiata seeds were conducted with the fungal isolates from G. pubilabiata roots. We obtained five fungal isolates, which belonged to Mycena, Marasmiaceae and Omphalotaceae. Firstly, optimal temperature and culture medium for subculture of these fungal isolates were examined. All five isolates grew the fastest on malt extract agar medium. Mycelial growth rate was highest at 25 °C between 10 °C and 40 °C. Secondly, we evaluated suitable culture vessels and organic materials for symbiotic culture. Seeds germinated well in petri dishes with Quercus leaf disc on water agar medium, and the seed germination process was well observed without dense mycelium. The most developed seedlings were found in glass bottles filled with Japanese cedar leaves, but densely grew mycelium prevent accurate seedling counts. Leaves of Quercus, Japanese cedar and bamboo were used as organic materials for symbiotic culture. All three leaves induced seed germination with Mycena and Marasmiaceae fungi, but material types affected subsequent seedling growth. Our method will contribute to understanding the mycorrhizal association of Gastrodia species and also other mycoheterotrophic plants.
Mining hidden knowledge: Embedding models of cause-effect relationships curated from the biomedical literature
Authors: Krämer Andreas , Green Jeff , Billaud Jean-Noël , Pasare Nicoleta Andreea , Jones Martin , Tugendreich Stuart
Published: October 2021

We explore the use of literature-curated signed causal gene expression and gene-function relationships to construct un-supervised embeddings of genes, biological functions, and diseases. Our goal is to prioritize and predict activating and inhibiting functional associations of genes, and to discover hidden relationships between functions. As an application, we are particularly interested in the automatic construction of networks that capture relevant biology in a given disease context.

We evaluated several unsupervised gene embedding models leveraging literature-curated signed causal gene expression findings. Using linear regression, it is shown that, based on these gene embeddings, gene-function relationships can be predicted with about 95% precision for the highest scoring genes. Function embedding vectors, derived from parameters of the linear regression model, allow to infer relationships between different functions or diseases. We show for several diseases that gene and function embeddings can be used to recover key drivers of pathogenesis, as well as underlying cellular and physiological processes. These results are presented as disease-centric networks of genes and functions. To illustrate the applicability of the computed gene and function embeddings to other machine learning tasks we expanded the embedding approach to drug molecules, and used a simple neural network to predict drug-disease associations.
COVID-19 Vaccine Effectiveness by Product and Timing in New York State
Authors: Rosenberg Eli S. , Dorabawila Vajeera , Easton Delia , Bauer Ursula E. , Kumar Jessica , Hoen Rebecca , Hoefer Dina , Wu Meng , Lutterloh Emily , Conroy Mary Beth , Greene Danielle , Zucker Howard A.
Published: October 2021

Background

US population-based data on COVID-19 vaccine effectiveness (VE) for the 3 currently FDA-authorized products is limited. Whether declines in VE are due to waning immunity, the Delta variant, or other causes, is debated.

Methods

We conducted a prospective study of 8,834,604 New York adults, comparing vaccine cohorts defined by product, age, and month of full-vaccination to age-specific unvaccinated cohorts, by linking statewide testing, hospital, and vaccine registry databases. VE was estimated from May 1, 2021 for incident laboratory-confirmed COVID-19 cases (weekly life-table hazard rates through September 3) and hospitalizations (monthly incidence rates through August 31).

Results

155,092 COVID-19 cases and 14,862 hospitalizations occurred. Estimated VE for cases declined contemporaneously across age, products, and time-cohorts, from high levels beginning May 1 (1.8% Delta variant prevalence), to a nadir around July 10 (85.3% Delta), with limited changes thereafter (>95% Delta). Decreases were greatest for Pfizer-BioNTech (−24.6%, −19.1%, −14.1% for 18-49, 50-64 years, and ≥65 years, respectively), and similar for Moderna (−18.0%, −11.6%, −9.0%, respectively) and Janssen (−19.2%, −10.8, −10.9%, respectively). VE for hospitalization for adults 18-64 years was >86% across cohorts, without time trend. Among persons ≥65 years, VE declined from May to August for Pfizer-BioNTech (95.0% to 89.2%) and Moderna (97.2% to 94.1%). VE was lower for Janssen, without trend, ranging 85.5%-82.8%.

Conclusions

Declines in VE for cases may have been primarily driven by factors other than waning. VE for hospitalizations remained high, with modest declines limited to Pfizer-BioNTech and Moderna recipients ≥65 years, supporting targeted booster dosing recommendations.
The seasonality of three childhood infections in a pre-industrial society without schools
Authors: Briga Michael , Ukonaho Susanna , Pettay Jenni E , Taylor Robert J , Ketola Tarmo , Lummaa Virpi
Published: October 2021

Background

The burden of many infectious diseases varies seasonally and a better understanding of the drivers of infectious disease seasonality would help to improve public health interventions. For directly transmitted highly-immunizing childhood infections, the leading hypothesis is that seasonality is strongly driven by social gatherings imposed by schools, with maxima and minima during school terms and holidays respectively. However, we currently have a poor understanding of the seasonality of childhood infections in societies without schools and whether these are driven by human social gatherings. Here, we used unique nationwide data consisting of >40 epidemics over 100 years in 18th and 19th century Finland, an agricultural pre-health care society without schools, to (i) quantify the seasonality of three easily identifiable childhood infections, smallpox, pertussis and measles and (ii) test the extent to which seasonality of these diseases is driven by seasonal social gatherings.

Methods

We quantified the seasonality of transmission using time series Susceptible-Infected-Recovery models, wavelet analyses and general additive mixed models.

Results

We found that all three infections were seasonal and the seasonality patterns differed from those in industrialized societies with schools. Smallpox and measles showed high transmission in the first half of the year, but we could not associate this with seasonal human gatherings events. For pertussis, however, transmission was higher during social gathering events such as New Year and Easter.

Conclusions

Our results show that the seasonality of childhood infections is more variable than previously described in other populations and indicate a pathogen-specific role of human social aggregation in driving the infectious disease dynamics.

Funding

Academy of Finland (278751, 292368), Nordforsk (104910), the Ehrnrooth Foundation, the Finnish Cultural Foundation, the University of Turku Foundation and the Doctoral Programme in Biology, Geography and Geology, University of Turku.

Clinical trial number

NA
COVIDNearTerm: A Simple Method to Forecast COVID-19 Hospitalizations
Authors: Olshen Adam B. , Garcia Ariadna , Kapphahn Kristopher I. , Weng Yingjie , Wesson Paul D. , Rutherford George W. , Gonen Mithat , Desai Manisha
Published: October 2021

1

COVID-19 has caused tremendous death and suffering since it first emerged in 2019. In response, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy. Here we present a method called COVIDNearTerm to “forecast” hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT). We found that that COVIDNearTerm pre-dictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16% to 36%. For those same comparisons the CalCAT ensemble errors were between 30% and 59%. COVIDNearT-erm is also easier to use than some other methods. It requires only previous hospitalization data and there is an open source R package that implements the algorithm.
Automated skull stripping in mouse fMRI analysis using 3D U-Net
Authors: Ruan Guohui , Liu Jiaming , An Ziqi , Wu Kaiibin , Tong Chuanjun , Liu Qiang , Liang Ping , Liang Zhifeng , Chen Wufan , Zhang Xinyuan , Feng Yanqiu
Published: October 2021

Skull stripping is an initial and critical step in the pipeline of mouse fMRI analysis. Manual labeling of the brain usually suffers from intra- and inter-rater variability and is highly time-consuming. Hence, an automatic and efficient skull-stripping method is in high demand for mouse fMRI studies. In this study, we investigated a 3D U-Net based method for automatic brain extraction in mouse fMRI studies. Two U-Net models were separately trained on T2-weighted anatomical images and T2*-weighted functional images. The trained models were tested on both interior and exterior datasets. The 3D U-Net models yielded a higher accuracy in brain extraction from both T2-weighted images (Dice > 0.984, Jaccard index > 0.968 and Hausdorff distance < 7.7) and T2*-weighted images (Dice > 0.964, Jaccard index > 0.931 and Hausdorff distance < 3.3), compared with the two widely used mouse skull-stripping methods (RATS and SHERM). The resting-state fMRI results using automatic segmentation with the 3D U-Net models are identical to those obtained by manual segmentation for both the seed-based and group independent component analysis. These results demonstrate that the 3D U-Net based method can replace manual brain extraction in mouse fMRI analysis.
Global Plant Extinction Risk Assessment Inform Novel Biodiversity Hotspots
Authors: Haevermans Thomas , Tressou Jessica , Kwon Joon , Pellens Roseli , Dubéarnès Anne , Veron Simon , Bel Liliane , Dervaux Stéphane , Dibie-Barthelemy Juliette , Gaudeul Myriam , Govaerts Rafaël , Le Bras Gwenaël , Muller Serge , Rouhan Germinal , Sarthou Corinne , Soler Lydie
Published: October 2021

Curbing biodiversity loss and its impact on ecosystem services, resilience and Nature’s Contributions to People is one of the main challenges of our generation (IPBES, 2019b, 2019a; Secretariat of the United Nations Convention on Biological Diversity, 2020). A global baseline assessment of the threat status of all of biodiversity is crucial to monitor the progress of conservation policies worldwide (Mace & al., 2000; Secretariat of the United Nations Convention on Biological Diversity, 2021) and target priority areas for conservation (Walker & al., 2021). However, the magnitude of the task seems insurmountable, as even listing the organisms already known to science is a challenge (Nic Lughadha & al., 2016; Borsch & al., 2020; Govaerts & al., 2021). A new approach is needed to overcome this stumbling block and scale-up the assessment of extinction risk. Here we show that analyses of natural history mega-datasets using artificial intelligence allows us to predict a baseline conservation status for all vascular plants and identify target areas for conservation corresponding to hotspots optimally capturing different aspects of biodiversity. We illustrate the strong potential of AI-based methods to reliably predict extinction risk on a global scale. Our approach not only retrieved recognized biodiversity hotspots but identified new areas that may guide future global conservation action (Myers & al., 2000; Brooks & al., 2006). To further work in this area and guide the targets of the post-2020 biodiversity framework (Díaz & al., 2020a; Secretariat of the United Nations Convention on Biological Diversity, 2020; Mair & al., 2021), it will be necessary to accelerate the acquisition of fundamental data and allow inclusion of social and economic factors (Possingham & Wilson, 2005).
Detecting expressed cancer somatic mutations from single-cell RNA sequencing data
Authors: Zhang Tianyun , Shen Ning
Published: October 2021

Identifying expressed somatic mutations directly from single-cell RNA sequencing (scRNA-seq) data is challenging but highly valuable. Computational methods have been attempted but no reliable methods have been reported to identify somatic mutations with high fidelity. We present RESA -- Recurrently Expressed SNV Analysis, a computational framework that identifies expressed somatic mutations from scRNA-seq data with high precision. We test RESA in multiple cancer cell line datasets, where RESA demonstrates average area under the curve (AUC) of 0.9 on independently held out test sets, and achieves average precision of 0.71 when evaluated by bulk whole exome, which is substantially higher than previous approaches. In addition, RESA detects a median of 201 mutations per cell, 50 times more than what was reported in experimental technologies with simultaneous expression and mutation profiling. Furthermore, applying RESA to scRNA-seq from a melanoma patient, we demonstrate that RESA recovers the known BRAF driver mutation of the sample and melanoma dominating mutational signatures, identifies mutation associated expression signatures, reveals nondriver perturbed and stage specific cancer hallmarks, and unveils the complex relationship between genomic and transcriptomic intratumor heterogeneity. Therefore, RESA could provide novel views in the study of intratumor heterogeneity and relate genetic alterations to transcriptional changes at single cell level.
Cloacimonadota metabolisms include adaptations for engineered environments that are reflected in the evolutionary history of the phylum
Authors: Johnson Lisa A. , Hug Laura A.
Published: October 2021

Phylum Cloacimonadota (previously Cloacimonetes, WWE1) is an understudied bacterial lineage frequently associated with engineered and wastewater systems. Cloacimonadota members were abundant and diverse in metagenomic datasets from a municipal landfill, prompting an examination of phylogenetic relationships, metabolic diversity, and pangenomic dynamics across the phylum, based on 22 publicly available genomes and 24 from landfill samples. Cloacimonadota formed two discrete clades, with one clade’s genomes principally deriving from engineered systems. A few more-divergent genomes were placed basal in the tree, and not associated with either clade. Metabolic reconstructions for metagenome-assembled genomes predict an anaerobic, acetogenic, and fermentative lifestyle for the majority of Cloacimonadota surveyed. Genomes from engineered ecosystems (first clade) encode a unique suite of genes not typically found in genomes from natural environments (second clade). These encoded functions include acetate kinase, the enzyme responsible for the formation of acetate from acetyl phosphate, and carbon utilization enzymes, suggesting different substrate degradation and energy generation strategies in these ecologically and phylogenetically distinct lineages.

Originality/Significance Statement

Cloacimonadota is a bacterial phylum that is under-described compared to its members’ prevalence in genome repositories. Cloacimonadota are frequently associated with engineered systems, including being identified as abundant and diverse in the municipal landfill site surveyed in this study. We reconstructed twenty-four landfill-associated Cloacimonadota metagenome assembled genomes (MAGs), more than doubling the number of publicly available Cloacimonadota genomes. We combined these MAGs with available reference genomes to predict major metabolic pathways and to describe the conserved features in the lifestyle of phylum Cloacimonadota. We found that Cloacimonadota have distinct evolutionary histories associated with engineered versus natural environments. Prior studies have evaluated metabolism from individual Cloacimonadota genomes – this work is the first to examine trait distribution across a more-complete representation of the phylum, including identification of genomic features and metabolic strategies that correlate to habitat of origin.
Speech sound categorization: The contribution of non-auditory and auditory cortical regions
Authors: Preisig Basil , Riecke Lars , Hervais-Adelman Alexis
Published: October 2021

Which processes in the human brain lead to the categorical perception of speech sounds? Investigation of this question is hampered by the fact that categorical speech perception is normally confounded by acoustic differences in the stimulus. By using ambiguous sounds, however, it is possible to dissociate acoustic from perceptual stimulus representations. Twenty-seven normally hearing individuals took part in an fMRI study in which they were presented with an ambiguous syllable (intermediate between /da/ and /ga/) in one ear and with disambiguating acoustic feature (third formant, F3) in the other ear. Multi-voxel pattern searchlight analysis was used to identify brain areas that consistently differentiated between response patterns associated with different syllable reports. By comparing responses to different stimuli with identical syllable reports and identical stimuli with different syllable reports, we disambiguated whether these regions primarily differentiated the acoustics of the stimuli or the syllable report. We found that BOLD activity patterns in left perisylvian regions (STG, SMG), left inferior frontal regions (vMC, IFG, AI), left supplementary motor cortex (SMA/pre-SMA), and right motor and somatosensory regions (M1/S1) represent listeners’ syllable report irrespective of stimulus acoustics. Most of these regions are outside of what is traditionally regarded as auditory or phonological processing areas. Our results indicate that the process of speech sound categorization implicates decision-making mechanisms and auditory-motor transformations.

Highlights

Ambiguous dichotic syllables elicit distinct percepts of identical stimuli

Multivariate searchlight analysis reveals syllabic-category sensitive brain areas

Categorical responses arise in non-auditory cortical areas including motor areas

SMA is a possible locus for transforming sensory signals into perceptual decisions
Heterogeneity and developmental dynamics of LYVE-1 perivascular macrophages distribution in the mouse brain
Authors: Karam Marie , Malkinson Guy , Brunet Isabelle
Published: October 2021

Brain perivascular macrophages (PVMs) belong to border-associated macrophages. PVMs are situated along blood vessels in the Virchow-Robin space and are thus found at a unique anatomical position between the endothelium and the parenchyma. Owing to their location and phagocytic capabilities, PVMs are regarded as important components that regulate various aspects of brain physiology in health and pathophysiological states. Here, used LYVE-1 to identify PVMs in the mouse brain. We used brain-tissue sections and cleared whole-brains to learn how they are distributed within the brain and across different developmental postnatal stages. We find that LYVE-1+ PVMs associate with the vasculature in a brain-region-dependent manner, where the hippocampus shows the highest density of LYVE-1+ PVMs. We show that their postnatal distribution is developmentally dynamic and peaks at P10-P20 depending on the brain region. We further demonstrate that their density is reduced in the APP/PS1 mouse model of Alzheimer’s Disease. In conclusion, our results show an unexpected heterogeneity and dynamics of LYVE-1+ PVMs, and support an important role for this population of PVMs during development and in regulating brain functions in steady-state and disease conditions.
Age-related change in task-evoked amygdala—prefrontal circuitry: a multiverse approach with an accelerated longitudinal cohort aged 4-22 years
Authors: Bloom Paul Alexander , VanTieghem Michelle , Gabard-Durnam Laurel , Gee Dylan G. , Flannery Jessica , Caldera Christina , Goff Bonnie , Telzer Eva H. , Humphreys Kathryn L. , Fareri Dominic S. , Shapiro Mor , Algharazi Sameah , Bolger Niall , Aly Mariam , Tottenham Nim
Published: October 2021

The amygdala and its connections with medial prefrontal cortex (mPFC) play central roles in the development of emotional processes. While several studies have suggested that this circuitry exhibits functional changes across the first two decades of life, findings have been mixed – perhaps resulting from differences in analytic choices across studies. Here we used multiverse analyses to examine the robustness of task-based amygdala–mPFC function findings to analytic choices within the context of an accelerated longitudinal design (4-22 years- old; N=98; 183 scans; 1-3 scans/participant). Participants, recruited from the greater Los Angeles area, completed an event-related emotional face (fear, neutral) task. Parallel analyses varying in preprocessing and modeling choices found that age-related change estimates for amygdala reactivity were more robust than task-evoked amygdala–mPFC functional connectivity to varied analytical choices. Specification curves indicated evidence for age-related decreases in amygdala reactivity to faces, though within-participant changes in amygdala reactivity could not be differentiated from between-participant differences. In contrast, amygdala—mPFC functional connectivity results varied across methods much more, and evidence for age-related change in amygdala–mPFC connectivity was not consistent. Generalized psychophysiological interaction (gPPI) measurements of connectivity were especially sensitive to whether a deconvolution step was applied. Our findings demonstrate the importance of assessing the robustness of findings to analysis choices, although the age-related changes in our current work cannot be overinterpreted given low test-retest reliability. Together, these findings highlight both the challenges in estimating developmental change in longitudinal cohorts and the value of multiverse approaches in developmental neuroimaging for assessing robustness of results. (Preprint: https://www.biorxiv.org/content/10.1101/2021.10.08.463601v1).

Key Points

Multiverse analyses applied to fMRI data are valuable for determining the robustness of findings to varied analytical choices

In the current study, age-related change estimates for amygdala reactivity were relatively robust to analytical decisions, though gPPI functional connectivity analyses were much more sensitive, leading some estimates to flip sign

Both test-retest reliability and robustness to analytical choices are important considerations for developmental research
Prediction and Expression Analysis of Deleterious Nonsynonymous SNPs of Arabidopsis ACD11 Gene by Combining Computational Algorithms and Molecular Docking Approach
Authors: Rifat Mahmudul Hasan , Ahmed Jamil , Ahmed Milad , Ahmed Foeaz , Gulsan Airin , Hasan Mahmudul
Published: October 2021

Accelerated cell death 11 (ACD11) is an autoimmune gene that suppresses pathogen infection in plants by preventing plant cells from becoming infected by any pathogen. This gene is widely known for growth inhibition, premature leaf chlorosis, and defense-related programmed cell death (PCD) in seedlings before flowering in Arabidopsis plant. Specific amino acid changes in the ACD11 protein’s highly conserved domains are linked to autoimmune symptoms including constitutive defensive responses and necrosis without pathogen awareness. The molecular aspect of the aberrant activity of the ACD11 protein is difficult to ascertain. The purpose of our study was to find the most deleterious mutation position in the ACD11 protein and correlate them with their abnormal expression pattern. Using several computational methods, we discovered PCD vulnerable single nucleotide polymorphisms (SNPs) in ACD11. We analysed the RNA-Seq data, identified the detrimental nonsynonymous SNPs (nsSNP), built genetically mutated protein structures and used molecular docking to assess the impact of mutation. Our results demonstrated that the A15T and A39D variations in the GLTP domain were likely to be extremely detrimental mutations that inhibit the expression of the ACD11 protein domain by destabilizing its composition, as well as disrupt its catalytic effectiveness. When compared to the A15T mutant, the A39D mutant was more likely to destabilize the protein structure. In conclusion, these mutants can aid in the better understanding of the vast pool of PCD susceptibilities connected to ACD11 gene GLTP domain activation.
Development of transcriptomics-based eukaryotes growth rate indices
Authors: Kong Wye-Lup , Machida Ryuji J.
Published: October 2021

Growth rate estimation is important to understand the flow of energy and nutrient elements in an ecosystem, but it has remained challenging, especially on microscopic organisms.

In this study, we propose four growth rate indices that use mRNA abundance ratios between nuclear and mitochondrial genes: (1) total nuclear and mitochondrial mRNA ratio (Nuc:Mito-TmRNA), (2) nuclear and mitochondrial ribosomal protein mRNA ratio (Nuc:Mito-RPmRNA), (3) gene ontology (GO) terms and total mitochondrial mRNA ratios and (4) nuclear and mitochondrial specific gene mRNA ratio. We examine these proposed ratios using RNA-Seq datasets of Daphnia magna and Saccharomyces cerevisiae.

The results showed that both Nuc:Mito-TmRNA and Nuc:Mito-RPmRNA ratio indices showed significant correlations with the growth rate for both species. A large number of GO terms mRNA ratios showed significant correlations with the growth rate of S. cerevisiae. Lastly, we identified mRNA ratios of several specific nuclear and mitochondrial gene pairs that showed significant correlations.

We foresee future implications for the proposed mRNA ratios used in metatranscriptome analyses to estimate the growth rate of communities and species.
Social information use for spatial decision inZootoca vivipara
Authors: Brevet M. , Jacob S. , Rutschmann A. , Richard M. , Cote J. , Clobert J.
Published: October 2021

Movements of individuals are conditioned by information acquisition coming from either personal or social sources. Yet, little is known about the processes used by individuals to make movement decisions when facing multiple sources of social information simultaneously. This study aimed to test experimentally how social information from multiple sources is used to make movement decisions, and whether a contrast in this information allows individuals to orientate in space. We used common lizards (Zootoca vivipara) in a replicated experimental setting: one focal individual received information from two other individuals coming from peripheral environments, before being given the opportunity to relocate in one or another of the peripheral environments.

Our analyses revealed that the behavior of informants, their mother’s morphology, as well as the quality of informants’ environment, affected movement decisions: the probability to relocate from the focal area increased when informants displayed traits associated with low resources (no food intake, poor maternal condition) or high competition (high activity). The physical condition of individuals also mediated the use of social information about food intake, with a match between resource availability in informants and personal condition. Conversely, spatial orientation was not affected by the contrast of phenotype between informants nor by spatial variability in resource availability.

This study highlights that multiple social information sources can be used for movement decisions, likely because these information sources reflect the quality of the surrounding environment (e.g., competition level or resources availability). It also emphasizes that social information use for movement is conditioned by individual phenotype.