bluuebunny/crossref_metadata_2025 huggingface dataset:
Habitat Protection Indexes - new monitoring measures for the conservation of coastal and marine habitats
Authors: Kumagai Joy A. , Favoretto Fabio , Pruckner Sara , Rogers Alex D. , Weatherdon Lauren V. , Aburto-Oropeza Octavio , Niamir Aidin
Published: June 2021

A worldwide call to implement habitat protection aims to halt biodiversity loss. To monitor the extent of coastal and marine habitats within protected areas (PAs) in a standardized, open source, and reproducible way, we constructed the Local and the Global Habitat Protection Indexes (LHPI and GHPI, respectively). The LHPI pinpoints the jurisdictions with the greatest opportunity to expand their own PAs, while the GHPI showcases which jurisdictions contribute the most in area to the protection of these habitats globally. Jurisdictions were evaluated to understand which have the highest opportunity to contribute globally to the protection of habitats by meeting a target of 30% coverage of PAs with Areas Beyond National Jurisdiction (ABNJ) having the greatest opportunity to do so. While we focus on marine and coastal habitats, our workflow can be extended to terrestrial and freshwater habitats. These indexes are useful to monitor aspects of Sustainable Development Goal 14 and the emerging post-2020 Global Biodiversity Framework, to understand the current status of international cooperation on coastal and marine habitats conservation.
Molecular insights into differentiated ligand recognition of the human parathyroid hormone receptor 2
Authors: Wang Xi , Cheng Xi , Zhao Lihua , Wang Yuzhe , Ye Chenyu , Zou Xinyu , Dai Antao , Cong Zhaotong , Chen Jian , Zhou Qingtong , Xia Tian , Jiang Hualiang , Xu Eric H. , Yang Dehua , Wang Ming-Wei
Published: June 2021

The parathyroid hormone receptor 2 (PTH2R) is a class B1 G protein-coupled receptor (GPCR) involved in regulation of calcium transport, nociception mediation, and wound healing. Naturally occurring mutations in PTH2R were reported to cause hereditary diseases, including syndromic short stature. Here we report the cryo-electron microscopy structure of PTH2R bound to its endogenous ligand, tuberoinfundibular peptide (TIP39), and a heterotrimeric Gs protein at a global resolution of 2.8 Å. The structure reveals that TIP39 adopts a unique loop conformation at N terminus and deeply inserts into the orthosteric ligand-binding pocket in the transmembrane (TM) domain. Molecular dynamics (MD) simulation and site-directed mutagenesis studies uncover the basis of ligand specificity relative to three PTH2R agonists, TIP39, PTH, and PTH-related peptide (PTHrP). We also compare the action of TIP39 with an antagonist lacking six residues from the peptide N terminus, TIP(7–39), which underscores the indispensable role of the N terminus of TIP39 in PTH2R activation. Additionally, we unveil that a disease-associated mutation G258D significantly diminished cAMP accumulation induced by TIP39. Together, these results not only provide structural insights into ligand specificity and receptor activation of class B1 GPCRs, but also offer a foundation to systematically rationalize the available pharmacological data to develop novel therapies for various disorders associated with PTH2R.
High-quality complete genome resource of tomato rhizosphere strain Pseudomonas donghuensis P482, a representative of a species with biocontrol activity against plant pathogens
Authors: Krzyżanowska Dorota M. , Iwanicki Adam , Czajkowski Robert , Jafra Sylwia
Published: June 2021

Strain P482 was isolated from a tomato rhizosphere and classified as Pseudomonas donghuensis. The P. donghuensis species was first established in 2015 and currently comprises only four strains: P482, HYST, SVBP6, and 22G5. P. donghuensis strains antagonize plant pathogens, including bacteria, fungi and oomycetes, and therefore are of high interest regarding their biological control potential to combat plant diseases. The antimicrobial activity of P. donghuensis P482 is based on the production of iron scavenging compound 7-hydroxytropolone, antifungal volatile organic compounds, and yet unidentified secondary metabolite(s). Here, we report a complete genome resource for P. donghuensis strain P482. The genome consists of a single chromosome (5 656 185 bp) with 5258 ORFs (5158 protein-coding genes, 74 tRNAs, 22 rRNAs, 3 ncRNAs and 1 tmRNA) and no plasmid. We believe that the information of the first high-quality, complete genome of P. donghuensis will provide resources for analyses targeting the biological control potential of this species and understanding the traits essential for plant-microbe interaction.
The extracellular matrix supports cancer cell growth under amino acid starvation by promoting tyrosine catabolism
Authors: Nazemi Mona , Yanes Bian , Martinez Montserrat Llanses , Walker Heather , Bard Frederic , Rainero Elena
Published: June 2021

Breast and pancreatic tumours are embedded in a collagen I-rich extracellular matrix (ECM) network, where nutrients are scarce due to limited blood flow and elevated tumour growth. Metabolic adaptation is required for cancer cells to endure these conditions. Here, we demonstrated that the presence of ECM supported the growth of invasive breast cancer cells, but not non-transformed mammary epithelial cells, and pancreatic cancer cells under amino acid starvation, through a mechanism that required macropinocytosis-dependent ECM uptake. Importantly, we showed that this behaviour was acquired during carcinoma progression. ECM internalisation, followed by lysosomal degradation, contributed to the upregulation of the intracellular levels of several amino acids, most notably tyrosine and phenylalanine. This resulted in elevated tyrosine catabolism on ECM under starvation, leading to increased fumarate levels, potentially feeding into the tricarboxylic acid cycle. Interestingly, this pathway was required for ECM-dependent cell growth under amino acid starvation, as the knockdown of p-hydroxyphenylpyruvate hydroxylase-like protein (HPDL), the third enzyme of the pathway, opposed cell growth on ECM in both 2D and 3D systems, without affecting cell proliferation on plastic. Finally, high HPDL expression correlated with poor prognosis in breast and pancreatic cancer patients. Collectively, our results highlight that the ECM in the tumour microenvironment represents an alternative source of nutrients to support cancer cell growth, by regulating phenylalanine and tyrosine metabolism.
Second-Generation Antibodies Neutralize Emerging SARS-CoV-2 Variants of Concern
Authors: Kovacech Branislav , Fialova Lubica , Filipcik Peter , Skrabana Rostislav , Zilkova Monika , Paulenka-Ivanovova Natalia , Kovac Andrej , Palova Denisa , Paulíkova Rolkova Gabriela , Tomkova Katarina , Csokova Natalia Turic , Markova Karina , Skrabanova Michaela , Sinska Kristina , Basheer Neha , Majerova Petra , Hanes Jozef , Parrak Vojtech , Prcina Michal , Cehlar Ondrej , Cente Martin , Piestansky Juraj , Fresser Michal , Novak Michal , Slavikova Monika , Borsova Kristina , Cabanova Viktoria , Brejova Bronislava , Vinař Tomas , Nosek Jozef , Klempa Boris , Zilka Norbert , Kontsekova Eva
Published: June 2021

Recently emerged SARS-CoV-2 variants show resistance to some antibodies that were authorized for emergency use. We employed hybridoma technology combined with authentic virus assays to develop second-generation antibodies, which were specifically selected for their ability to neutralize new variants of SARS-CoV-2. AX290 and AX677, two monoclonal antibodies with non-overlapping epitopes, exhibit subnanomolar or nanomolar affinities to the receptor binding domain of the viral Spike protein carrying amino acid substitutions N501Y, N439K, E484K, K417N, and a combination N501Y/E484K/K417N found in the circulating virus variants. The antibodies showed excellent neutralization of an authentic SARS-CoV-2 virus representing strains circulating in Europe in spring 2020 and also the variants of concern B.1.1.7 and B.1.351. Finally, the combination of the two antibodies prevented the appearance of escape mutations of the authentic SARS-CoV-2 virus. The neutralizing properties were fully reproduced in chimeric mouse-human versions, which may represent a promising tool for COVID-19 therapy.
Cas12a is a dynamic and precise RNA-guided nuclease without off-target activity on λ-DNA
Authors: Paul Bijoya , Chaubet Loïc , Verver Emma , Montoya Guillermo
Published: June 2021

Cas12a is an RNA-guided endonuclease that is emerging as a powerful genome-editing tool. Here we combined optical tweezers with fluorescence to monitor Cas12a binding onto λ-DNA, providing insight into its DNA binding and cleavage mechanisms. At low forces Cas12a binds DNA specifically with two off-target sites, while at higher forces numerous binding events appear driven by the mechanical distortion of the DNA and partial matches to the crRNA. Despite the multiple binding events, cleavage is only observed on the target site at low forces, when the DNA is flexible. Activity assays show that the preferential off-target sites are not cleaved, and the λ-DNA is severed at the target site. This precision is also observed in Cas12a variants where the specific dsDNA and the unspecific ssDNA cleavage are dissociated or nick the target DNA. We propose that Cas12a and its variants are precise endonucleases that efficiently scan the DNA for its target but only cleave the selected site in the λ-DNA.
Discovering fragment length signatures of circulating tumor DNA using Non-negative Matrix Factorization
Authors: Renaud Gabriel , Nørgaard Maibritt , Lindberg Johan , Grönberg Henrik , De Laere Bram , Jensen Jørgen Bjerggaard , Borre Michael , Andersen Claus Lindbjerg , Sørensen Karina Dalsgaard , Maretty Lasse , Besenbacher Søren
Published: June 2021

Sequencing of cell-free DNA (cfDNA) is currently being used to detect cancer by searching both for mutational and non-mutational alterations. Recent work has shown that the length distribution of cfDNA fragments from a cancer patient can inform tumor load and type. Here, we propose non-negative matrix factorization (NMF) of fragment length distributions as a novel method to study fragment lengths in cfDNA samples.

Using shallow whole-genome sequencing (sWGS) of cfDNA from a cohort of patients with metastatic castration-resistant prostate cancer (mCRPC), we demonstrate how NMF accurately infers the true tumor fragment length distribution as an NMF component - and that the sample weights of this component correlate with ctDNA levels (r=0.75). We further demonstrate how using several NMF components enables accurate cancer detection on data from various early stage cancers (AUC=0.96). Finally, we show that NMF, when applied across genomic regions, can be used to discover fragment length signatures associated with open chromatin.
In vivo profiling of site-specific human cancer cell states in zebrafish
Authors: Segal Dagan , Mazloom-Farsibaf Hanieh , Chang Bo-Jui , Roudot Philippe , Rajendran Divya , Fiolka Reto , Warren Mikako , Amatruda James F. , Danuser Gaudenz
Published: June 2021

Tissue microenvironments affect the functional states of cancer cells, but determining these influences in vivo has remained a significant challenge. We present a quantitative high-resolution imaging assay of cancer cell morphology in zebrafish xenografts to probe functional adaptation to variable cell extrinsic cues and molecular interventions. We focus on Ewing Sarcoma, a pediatric cancer driven by a single oncogenic fusion protein EWSR1-FLI1, and with little to no additional somatic mutations, making it a prototypical form of cancer whose adaptation to microenvironments is likely driven by acute, non-genomic mechanisms. Using computer vision analysis of 3D cell shapes, we find systematic shifts in the distribution of cell morphotypes between distinct sites in the fish embryo. We also find site-specific morphological responses to differential expression of EWSR1-FLI1. Combining these data we propose a model where Ewing Sarcoma cancer cell plasticity is sensitive both to expression fluctuation of EWSR1-FLI1 and signals from the surrounding tissue microenvironment, with either or both factors possibly contributing to the oncogenic potential of these cells.
Geometry of inter-areal interactions in mouse visual cortex
Authors: Iyer Ramakrishnan , Siegle Joshua H. , Mahalingam Gayathri , Olsen Shawn , Mihalas Stefan
Published: June 2021

The response of a set of neurons in an area is the result of the sensory input, the interaction of the neurons within the area as well as the long range interactions between areas. We aimed to study the relation between interactions among multiple areas, and if they are fixed or dynamic. The structural connectivity provides a substrate for these interactions, but anatomical connectivity is not known in sufficient detail and it only gives us a static picture. Using the Allen Brain Observatory Visual Coding Neuropixels dataset, which includes simultaneous recordings of spiking activity from up to 6 hierarchically organized mouse cortical visual areas, we estimate the functional connectivity between neurons using a linear model of responses to flashed static grating stimuli. We characterize functional connectivity between populations via interaction subspaces. We find that distinct subspaces of a source area mediate interactions with distinct target areas, supporting the notion that cortical areas use distinct channels to communicate. Most importantly, using a piecewise linear model for activity within each trial, we find that these interactions evolve dynamically over tens of milliseconds following a stimulus presentation. Inter-areal subspaces become more aligned with the intra-areal subspaces during epochs in which a feedforward wave of activity propagates through visual cortical areas. When the short-term dynamics are averaged over, we find that the interaction subspaces are stable over multiple stimulus blocks. These findings have important implications for understanding how information flows through biological neural networks composed of interconnected modules, each of which may have a distinct functional specialization.
Interpersonal synchronization of movement intermittency
Authors: Tomassini Alice , Laroche Julien , Emanuele Marco , Nazzaro Giovanni , Petrone Nicola , Fadiga Luciano , D’Ausilio Alessandro
Published: June 2021

Most animal species group together and coordinate their behavior in quite sophisticated manners for mating, hunting or defense purposes. In humans, coordination at a macroscopic level (the pacing of movements) is evident both in daily life (e.g., walking) and skilled (e.g., music and dance) behaviors. By examining the fine structure of movement, we here show that interpersonal coordination is established also at a microscopic – sub-movement – level. Natural movements appear as marked by recurrent (2-3 Hz) speed breaks, i.e., submovements, that are traditionally considered the result of intermittency in (visuo)motor control. In a series of interpersonal motor coordination tasks, we demonstrate that submovements are not independent between interacting partners but produced in a tight temporal relation that reflects the directionality in the partners’ informational coupling. These findings unveil a potential core mechanism for behavioral coordination that is based on between-persons synchronization of the intrinsic dynamics of action-perception cycles.
Somatic HCN channels augment and speed up GABAergic basket cell input-output function in human neocortex
Authors: Szegedi Viktor , Bakos Emőke , Furdan Szabina , Barzo Pal , Tamas Gabor , Lamsa Karri
Published: June 2021

Neurons in the mammalian brain exhibit evolution-driven species-specific differences in their functional properties. Therefore, understanding the human brain requires unraveling the human neuron “uniqueness” and how it contributes to the operation of specific neuronal circuits. We show here that a highly abundant type of inhibitory neurons in the neocortex, GABAergic parvalbumin-expressing basket cell (pv+BC), exhibits in the human brain a specific somatic leak current mechanism, which is absent in their rodent neuronal counterparts. Human pv+BC soma shows electric leak conductance mediated by hyperpolarization-activated cyclic nucleotide-gated channels. This leak conductance has depolarizing effects on the resting membrane potential and it accelerates the rise of synaptic potentials in the cell soma. The leak facilitates the human pv+BC input-to-output fidelity and shortens the action potential generation to excitatory inputs. This mechanism constitutes an adaptation that enhances signal transmission fidelity and speed in the common inhibitory circuit in the human but not in the rodent neocortex.
Adolescent to young adult longitudinal development of subcortical volumes in two European sites with four waves
Authors: Backhausen Lea L. , Fröhner Juliane H. , Lemaître Hervé , Artiges Eric , Palillère Martinot Marie-Laure , Herting Megan M. , Sticca Fabio , Banaschewski Tobias , Barker Gareth J. , Bokde Arun L.W. , Desrivières Sylvane , Flor Herta , Grigis Antoine , Garavan Hugh , Gowland Penny , Heinz Andreas , Brühl Rüdiger , Nees Frauke , Papadopoulos-Orfanos Dimitri , Poustka Luise , Hohmann Sarah , Robinson Lauren , Walter Henrik , Winterer Jeanne , Whelan Robert , Schumann Gunter , Martinot Jean-Luc , Smolka Michael N. , Vetter Nora C. ,   the IMAGEN Consortium
Published: June 2021

Adolescent subcortical structural brain development might underlie psychopathological symptoms, which often emerge in adolescence. At the same time, sex differences exist in psychopathology, which might be mirrored in underlying sex differences in structural development. However, previous studies showed inconsistencies in subcortical trajectories and potential sex differences. Therefore, we aimed to investigate the subcortical structural trajectories and their sex differences across adolescence using for the first time a single cohort design, the same quality control procedure, software and a general additive mixed modeling approach. We investigated two large European sites from ages 14 to 24 with 503 participants and 1408 total scans from France and Germany as part of the IMAGEN project including four waves of data acquisition. We found significantly larger volumes in males versus females in both sites and across all seven subcortical regions. Sex differences in age-related trajectories were observed across all regions in both sites. Our findings provide further evidence of sex differences in longitudinal adolescent brain development of subcortical regions and thus might eventually support the relationship of underlying brain development and different adolescent psychopathology in boys and girls.
Bee foraging preferences, microbiota and pathogens revealed by direct shotgun metagenomics of honey
Authors: Galanis Anastasios , Vardakas Philippos , Reczko Martin , Harokopos Vaggelis , Hatzis Pantelis , Skoulakis Efthimios M. C. , Pavlopoulos Georgos A. , Patalano Solenn
Published: June 2021

Honeybees (Apis mellifera) continue to succumb to human and environmental pressures despite their crucial role in providing essential ecosystem services. Owing to their foraging and honey production activities, honeybees form complex relationships with species across all domains, such as plants, viruses, bacteria (symbiotic and pathogenic), and other hive pests, making honey a valuable biomonitoring tool for assessing their ecological niche. Thus, the application of honey shotgun metagenomics (SM) has paved the way for a detailed description of the species honeybees interact with, in order to better assess the multiple factors governing their health. Here, we describe the implementation of optimized honey DNA extraction methodology coupled to direct shotgun metagenomics (Direct-SM) analysis, and to a computationally optimised and validated pipeline for taxonomic classification of species detected in honey. By comparing honey collected across 3 harvesting seasons in a stable apiary, we show that Direct-SM can describe the variability of sampled plant species, revealing honeybee behavioural adaptation. In addition, we reveal that Direct-SM can non-invasively capture the diversity of species comprising the core and non-core bacterial communities of the gut microbiome. Finally, we show that this methodology is applicable for the monitoring of pathogens and particularly for the biomonitoring varroa infestation. These results suggest that Direct-SM can accurately and comprehensively describe honeybee ecological niches and can be deployed to assess bee health in the field.
A Comprehensive Model of Glucose-Insulin Regulation Including Acute and Prolonged Effects of Physical Activity in Type 1 Diabetes
Authors: Deichmann Julia , Bachmann Sara , Pfister Marc , Szinnai Gabor , Kaltenbach Hans-Michael
Published: June 2021

Objective

For type 1 diabetic patients, accurate adjustment of insulin treatment to physical activity (PA) is a challenging open problem. Glucose uptake by the exercising muscles increases acutely, causing increased hepatic glucose production to maintain glucose homeostasis. Meanwhile, insulin sensitivity is elevated for a prolonged period to drive glycogen repletion during recovery. These processes strongly depend on PA duration and intensity, making their combined effects difficult to predict accurately. In this work, we develop a model of glucose-insulin regulation that captures PA from low to high intensity including acute and prolonged effects on glucose metabolism.

Methods

We extended an existing minimal model of glucose-insulin regulation to capture PA-driven changes in glucose metabolism. We incorporated the insulin-independent increase in glucose uptake and production, including the effects of glycogen depletion and of high-intensity PA on production. The model also captures the prolonged increase in insulin sensitivity.

Results

The model accurately predicts glucose dynamics of published data during short and prolonged PA of moderate to high intensity and during subsequent recovery. In-silico full-day studies elucidate the effects of timing, duration and intensity of PA and of insulin bolus reduction on glucose levels during and after the activity.

Conclusion

The proposed model captures the blood glucose dynamics during all main PA regimes.

Significance

Mathematical models of glucose-insulin regulation are critical components of closed-loop insulin delivery and clinical decision support systems for achieving good glycemic control. The presented model shows potential for the development and assessment of algorithms targeting treatment adjustment to PA.
Frontal cortical functional connectivity is impacted by anaesthesia in macaques
Authors: Giacometti C , Dureux A , Autran-Clavagnier D , Wilson C. R. E. , Sallet J , Dirheimer M. , Procyk E , Hadj-Bouziane F , Amiez C
Published: June 2021

Abstract

A critical aspect of neuroscience is to establish whether and how brain networks evolved across primates. To date, most comparative studies have used resting-state functional Magnetic Resonance Imaging (rs-fMRI) in anaesthetized non-human primates and in awake humans. However, anaesthesia strongly affects rs-fMRI signals. The present study investigated the impact of the awareness state (anaesthesia vs. awake) within the same group of macaque monkeys on the rs-fMRI functional connectivity (FC) organization of a well characterized network in the human brain, the cingulo-frontal lateral network. Results in awake macaques revealed a similar FC pattern to that previously uncovered in the human brain. Rostral seeds in the cingulate sulcus exhibited stronger correlation strength with rostral compared to caudal lateral frontal cortical areas while caudal seeds in the cingulate sulcus displayed stronger correlation strength with caudal compared to anterior lateral frontal cortical areas. Critically, this inverse rostro-caudal functional gradient was abolished under anaesthesia. This study demonstrates that the FC pattern of cingulo-frontal cortical networks is preserved from macaque to human but some of its properties can only be observed in the awake state, warranting significant caution when comparing FC patterns across species under different states.
Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor revealed by binding site image processing
Authors: Eguida Merveille , Rognan Didier
Published: June 2021

Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets.

AUTHOR SUMMARY

Computational comparison of binding sites in proteins can provide insights on potential unrelated proteins that may bind to similar ligands. However, accurate prediction of binding site similarity requires powerful methods, ideally able to detect even local similarities. We herewith applied a recently developed computer vision method to identify an unexpected binding site similarity between two totally unrelated proteins that was confirmed experimentally by in vitro biophysical binding assays. Considering more precisely local similarities can therefore efficiently guide drug discovery, notably to repurpose existing drug candidates.
Formation, collective motion, and merging of macroscopic bacterial aggregates
Authors: Courcoubetis George , Gangan Manasi , Lim Sean , Guo Xiaokan , Haas Stephan , Boedicker James Q.
Published: June 2021

Chemotactic bacteria form emergent spatial patterns of variable cell density within cultures that are initially spatially uniform. These patterns are the result of chemical gradients that are created from the directed movement and metabolic activity of billions of cells. A recent study on pattern formation in wild bacterial isolates has revealed unique collective behaviors of the bacteria Enterobacter cloacae. As in other bacteria species, Enterobacter cloacae form macroscopic aggregates. Once formed, these bacterial clusters can migrate several millimeters, sometimes resulting in the merging of two or more clusters. To better understand these phenomena, we examine the formation and dynamics of thousands of bacterial clusters that form within a 22 cm square culture dish filled with soft agar over two days. At the macroscale, the aggregates display spatial order at short length scales, and the migration of cell clusters is superdiffusive, with a merging acceleration that is correlated with aggregate size. At the microscale, aggregates are composed of immotile cells surrounded by low density regions of motile cells. The collective movement of the aggregates is the result of an asymmetric flux of bacteria at the boundary. An agent based model is developed to examine how these phenomena are the result of both chemotactic movement and a change in motility at high cell density. These results identify and characterize a new mechanism for collective bacterial motility driven by a transient, density-dependent change in motility.

Author summary

Bacteria growing and swimming in soft agar often aggregate to form elaborate spatial patterns. Here we examine the patterns formed by the bacteria Enterobacter cloacae. An unusual behavior of this bacteria is the movement of cell clusters, millions of bacteria forming a tiny spot and moving together in the same direction. These spots sometimes run into each other and combine. By looking at the cells within these spots under a microscope, we find that cells within each spot stop swimming. The process of switching back and forth between swimming and not swimming causes the movement and fusion of the spots. A numerical simulation shows that the migration and merging of these spots can be expected if the cells swim towards regions of space with high concentrations of attractant molecules and stop swimming in locations crowded with many cells. This work identifies a novel process through which populations of bacteria cooperate and control the movement of large groups of cells.
High-throughput screening and selection of PCB-bioelectrocholeaching, electrogenic microbial communities using single chamber microbial fuel cells based on 96-well plate array
Authors: Szydlowski L. , Ehlich J. , Shibata N. , Goryanin I.
Published: June 2021

We demonstrate a single chamber, 96-well plate based Microbial Fuel Cell (MFC). This invention is aimed at robust selection of electrogenic microbial community under specific conditions, (pH, external resistance, inoculum) that can be altered within the 96 well plate array. Using this device, we selected and multiplicated electrogenic microbial communities fed with acetate and lactate that can operate under different pH and produce current densities up to 19.4 A/m3 (0.6 A/m2) within 5 days past inoculation. Moreover, studies shown that Cu mobilization through PCB bioleaching occurred, thus each community was able to withstand presence of Cu2+ ions up to 600 mg/L. Metagenome analysis reveals high abundance of Dietzia spp., previously characterized in MFCs, but not reported to grow at pH 4, as well as novel species, closely related to Actinotalea ferrariae, not yet associated with electrogenicity. Microscopic observations (combined SEM and EDS) reveal that some of the species present in the anodic biofilm were adsorbing copper on their surface, probably due to the presence of metalloprotein complexes on their outer membranes. Taxonomy analysis indicated that similar consortia populate anodes, cathodes and OCP controls, although total abundances of aforementioned species are different among those groups. Annotated metagenomes showed high presence of multicopper oxidases and Cu-resistance genes, as well as genes encoding aliphatic and aromatic hydrocarbon-degrading enzymes. Comparison between annotated and binned metagenomes from pH 4 and 7 anodes, as well as their OCP controls revealed unique genes present in all of them, with majority of unique genes present in pH 7 anode, where novel Actinotalea spp. was present.
ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes
Authors: Iida Keita , Kondo Jumpei , Wibisana Johannes Nicolaus , Inoue Masahiro , Okada Mariko
Published: June 2021

Motivation

Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret the biological implications of computational results. Hence, a theory for efficiently annotating individual cells is necessary.

Results

We present ASURAT, a computational pipeline for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process, and signaling pathway activity for single-cell transcriptomic data, using correlation graph-based decomposition of genes based on database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for small cell lung cancer and pancreatic ductal adenocarcinoma, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data.

Availability

A GPLv3-licensed implementation of ASURAT is on GitHub (https://github.com/keita-iida/ASURAT).
Desmosomal connectomics of all somatic muscles in an annelid larva
Authors: Jasek Sanja , Verasztó Csaba , Brodrick Emelie , Shahidi Réza , Kazimiers Tom , Kerbl Alexandra , Jékely Gáspár
Published: June 2021

Cells form networks in animal tissues through synaptic, chemical and adhesive links. Invertebrate muscle cells often connect to other cells through desmosomes, adhesive junctions anchored by intermediate filaments. To study desmosomal networks, we skeletonised 853 muscle cells and their desmosomal partners in volume electron microscopy data covering an entire larva of the annelidPlatynereis. Muscle cells adhere to each other, to epithelial, glial, ciliated, and bristle-producing cells and to the basal lamina, forming a desmosomal connectome of over 2,000 cells. The aciculae – chitin rods that form an endoskeleton in the segmental appendages – are highly connected hubs in this network. This agrees with the many degrees of freedom of their movement, as revealed by video microscopy. Mapping motoneuron synapses to the desmosomal connectome allowed us to infer the extent of tissue influenced by motoneurons. Our work shows how cellular-level maps of synaptic and adherent force networks can elucidate body mechanics.
Characterisation of the symbionts in the Mediterranean fruitfly gut
Authors: Darrington M. , Leftwich P.T. , Holmes N.A. , Friend L.A. , Clarke N.V.E. , Worsley S.F. , Margaritopolous J.T. , Hogenhout S.A. , Hutchings M.I. , Chapman T
Published: June 2021

Symbioses between bacteria and their insect hosts can range from very loose associations through to obligate interdependence. While fundamental evolutionary insights have been gained from the in-depth study of obligate mutualisms, there is increasing interest in the evolutionary potential of flexible symbiotic associations between hosts and their gut microbiomes. Understanding relationships between microbes and hosts also offers the potential for exploitation for insect control. Here, we investigate the gut microbiome of a global agricultural pest, the Mediterranean fruitfly (Ceratitis capitata). We used 16S rRNA profiling to compare the gut microbiomes of laboratory and wild strains raised on different diets and from flies collected from various natural plant hosts. The results showed that medfly guts harbour a fairly simple microbiome, primarily determined by the larval diet in both wild and laboratory flies. However, regardless of the laboratory diet or natural plant host on which flies were raised,Klebsiellaspp dominated the medfly microbiomes and resisted removal by antibiotic treatment. We sequenced the genome of the dominant putativeKlebsiellaspp (designated ‘Medkleb’) isolated from the gut of the Toliman wild type fruitfly strain. Genome-wide ANI analysis placed Medkleb within theK. oxytoca / michiganensisgroup. Molecular, sequence and phenotypic analyses supported its identity asK. oxytoca. Medkleb has a genome size (5 825 435 bp) which is 1.6 standard deviations smaller than the mean genome size of free-livingKlebsiellaspp, and lacks some genes involved in environmental sensing. Moreover, the Medkleb genome contains at least two recently acquired unique genomic islands as well as genes that encode pectinolytic enzymes capable of degrading plant cell walls. This may be advantageous given that the medfly diet includes unripe fruits containing high proportions of pectin. These results suggest that the medfly harbours a commensal gut bacterium that may have developed a mutualistic association with its host and provide nutritional benefits.
Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets
Authors: Kar Anuradha , Petit Manuel , Refahi Yassin , Cerutt Guillaume , Godin Christophe , Traas Jan
Published: June 2021

Segmenting three dimensional microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state-of-the-art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3 dimensional (3D) cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted which isolates segmentation errors due to under/over segmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end to end 3D and were originally designed for cell boundary detection, show high performance, and offer clear advantages in terms of adaptability to new data.

Author summary

In recent years a number of deep learning (DL) algorithms based on computational neural networks have been developed which claim to achieve high accuracy and automatic segmentation of 3D microscopy images. Although these algorithms have received considerable attention in the literature, it is difficult to evaluate their relative performances, while it remains unclear whether they really perform better than other, more classical segmentation methods.

To clarify these issues, we performed a detailed, quantitative analysis of a number of representative DL pipelines for cell instance segmentation from 3D confocal microscopy image datasets. We developed a protocol for benchmarking the performances of such DL based segmentation pipelines using common training and test datasets, evaluation metrics and visualizations. Using this protocol, we evaluated and compared four different DL pipelines to identify their strengths and limitations. A high performance non-DL method was also included in the evaluation. We show that DL pipelines may show significant differences in their performances depending on their model architecture and pipeline components but overall show excellent adaptability to unseen data. We also show that our benchmarking protocol could be extended to a variety of segmentation pipelines and datasets.
A late-stage assembly checkpoint of the human mitochondrial ribosome large subunit
Authors: Rebelo-Guiomar Pedro , Pellegrino Simone , Dent Kyle C. , Sas-Chen Aldema , Miller-Fleming Leonor , Garone Caterina , Haute Lindsey Van , Rogan Jack F. , Dinan Adam , Firth Andrew E. , Andrews Byron , Whitworth Alexander J. , Schwartz Schraga , Warren Alan J. , Minczuk Michal
Published: June 2021

The epitranscriptome plays a key regulatory role in cellular processes in health and disease, including ribosome biogenesis. Here, analysis of the human mitochondrial transcriptome shows that 2’-O-methylation is limited to residues of the mitoribosomal large subunit (mtLSU) 16S mt-rRNA, modified by MRM1, MRM2, and MRM3. Ablation of MRM2 leads to a severe impairment of the oxidative phosphorylation system, caused by defective mitochondrial translation and accumulation of mtLSU assembly intermediates. Structures of these particles (2.58 Å) present disordered RNA domains, partial occupancy of bL36m and bound MALSU1:L0R8F8:mtACP anti-association module. Additionally, we present five mtLSU assembly states with different intersubunit interface configurations. Complementation studies demonstrate that the methyltransferase activity of MRM2 is dispensable for mitoribosome biogenesis. The Drosophila melanogaster orthologue, DmMRM2, is an essential gene, with its knock-down leading to developmental arrest. This work identifies a key late-stage quality control step during mtLSU assembly, ultimately contributing to the maintenance of mitochondrial homeostasis.
The Brain Time Toolbox, a software library to retune electrophysiology data to brain dynamics
Authors: van Bree Sander , Melcón María , Kolibius Luca D. , Kerrén Casper , Wimber Maria , Hanslmayr Simon
Published: June 2021

Human thought is highly flexible, achieved by evolving patterns of brain activity across groups of cells. Neuroscience aims to understand cognition in the brain by analysing these intricate patterns. We argue this goal is impeded by the time format of our data – clock time. The brain is a system with its own dynamics and regime of time, with no intrinsic concern for the human-invented second. Here, we present the Brain Time Toolbox, a software library that retunes electrophysiology data in line with oscillations that orchestrate neural patterns of cognition. These oscillations continually slow down, speed up, and undergo abrupt changes, introducing a disharmony between the brain’s internal regime and clock time. The toolbox overcomes this disharmony by warping the data to the dynamics of coordinating oscillations, setting oscillatory cycles as the data’s new time axis. This enables the study of neural patterns as they unfold in the brain, aiding neuroscientific inquiry into dynamic cognition. In support of this, we demonstrate that the toolbox can reveal results that are absent in a default clock time format.
Molecular characterization of AMPA receptor trafficking vesicles
Authors: Peters John Jacob , Leitz Jeremy , Oses-Prieto Juan A , Burlingame Alma L , Brunger Axel T.
Published: June 2021

Regulated delivery of AMPA receptors (AMPARs) to the postsynaptic membrane is an essential step in synaptic strength modification, and in particular, long-term potentiation (LTP). While LTP has been extensively studied using electrophysiology and light microscopy, several questions regarding the molecular mechanisms of AMPAR delivery via trafficking vesicles remain outstanding, including the gross molecular make up of AMPAR trafficking organelles and identification and location of calcium sensors required for SNARE complex-dependent membrane fusion of such trafficking vesicles with the plasma membrane. Here, we isolated AMPAR trafficking vesicles (ATVs) from whole mouse brains via immunoprecipitation and characterized them using immunoelectron microscopy, immunoblotting, and liquid chromatography tandem mass spectrometry (LC-MS/MS). We identified several proteins on ATVs that were previously found to play a role in AMPAR trafficking, including SNARES (including synaptobrevin 2), Rabs, the SM protein Munc18-1, a calcium-sensor (synaptotagmin-1), as well as several new markers, including synaptophysin and synaptogyrin on ATV membranes. Additionally, we identified two populations of ATVs based on size and molecular composition: small-diameter, synaptobrevin-2- and GluA1-containing ATVs and larger transferrin-receptor-, GluA1-, GluA2-, GluA3-containing ATVs. The smaller population of ATVs likely represents a trafficking vesicle whose fusion is essential for LTP. These findings reveal the important role of AMPAR sorting into fusion-competent trafficking vesicles that are implicated in synaptic strength modification and reveal candidates of putative effectors and regulators of AMPAR trafficking.
A robust field-based method to screen heat tolerance in wheat
Authors: Ullah Najeeb , Christopher Jack , Frederiks Troy , Ma Shangyu , Tan Daniel KY , Chenu Karine
Published: June 2021

Wheat is highly sensitive to elevated temperatures, particularly during pollen meiosis and early-to-mid grain filling. The impact of heat stress greatly depends on the plant developmental stage. Thus, germplasm ranking for heat tolerance in field trials may be confounded by variations in developmental phase between genotypes at the time of heat events. A photoperiod-extension method (PEM) was developed allowing screening of 35 diverse genotypes at matched developmental phase despite phenological variations. Paired trials were conducted to compare the new PEM against conventional field screening in plots. In the PEM, plants were sown in single rows or small plots. Artificial lighting was installed at one end of each row or plot to extend day length, inducing a gradient of flowering times with distance from the lights. Individual stems or plot quadrats of each genotype were tagged at flowering. Late-sown plants received more heat shocks during early to mid grain filling than earlier sowings, suffering reductions in both individual grain weight (IGW) and yield. IGW was reduced by 1.5 mg for each additional post-flowering day with temperature > 30°C. Significant genotypic differences in heat tolerance ranking were observed between PEM versus conventional plot screening. Strong correlations between trials experiencing similar degree of heat were found both for IGW and for total grain weight with the PEM either with individual-stem tagging (e.g. average r of 0.59 and 0.54, respectively for environments with moderate postflowering heat) or quadrat tagging (r of 0.53 and 0.47). However, correlations for IGW and yield in these environments were either poor or negative for conventional trials (e.g. average r of 0.11 and 0.12, respectively for environments with moderate postflowering heat). Accordingly, a PCA grouped genotypes consistently for heir performance across environments with similar heat stress in PEM trials but not in conventional trials. In this study, most consistent genotype ranking for heat tolerance was achieved with the PEM with tagging and harvesting individual spikes at matched developmental phase. The PEM with quadrat sampling provided slightly less consistent rankings but appears overall more suitable for high-throughput phenotyping. The method promises to improve the efficiency of heat tolerance field screening, particularly when comparing genotypes of different maturity types.
EPIC: inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing
Authors: Wang Rujin , Lin Dan-Yu , Jiang Yuchao
Published: June 2021

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.