bluuebunny/crossref_metadata_2025 huggingface dataset:
A genetic probe for visualizing glutamatergic synapses and vesicles by 3D electron microscopy
Authors: Steinkellner Thomas , Madany Matthew , Haberl Matthias G. , Zell Vivien , Li Carolina , Hu Junru , Mackey Mason , Ramachandra Ranjan , Adams Stephen , Ellisman Mark H. , Hnasko Thomas , Boassa Daniela
Published: August 2020

Communication between neurons relies on the release of diverse neurotransmitters, which represent a key-defining feature of a neuron’s chemical and functional identity. Neurotransmitters are packaged into vesicles by specific vesicular transporters. However, tools for labeling and imaging synapses and synaptic vesicles based on their neurochemical identity remain limited. We developed a genetically encoded probe to identify glutamatergic synaptic vesicles at the levels of both light and electron microscopy (EM) by fusing the mini singlet oxygen generator (miniSOG) probe to an intra-lumenal loop of the vesicular glutamate transporter-2. We then used a 3D imaging method, serial block face scanning EM, combined with a deep learning approach for automatic segmentation of labeled synaptic vesicles to assess the subcellular distribution of transporter-defined vesicles at nanometer scale. These tools represent a new resource for accessing the subcellular structure and molecular machinery of neurotransmission and for transmitter-defined tracing of neuronal connectivity.
Variation in VKORC1 is associated with vascular dementia
Authors: Mur Jure , McCartney Daniel L. , Chasman Daniel I. , Visscher Peter M. , Muniz-Terrera Graciela , Cox Simon R. , Russ Tom C. , Marioni Riccardo E.
Published: August 2020

Genetic variation inVKORC1is associated with differences in the coagulation of blood and consequentially with sensitivity to the drug warfarin. Variation inVKORC1has also been linked to parental dementia. However, it is unclear whether the relationship persists for the diagnosis in patients themselves, whether the association holds only for certain forms of dementia, and if those taking warfarin are at greater risk. Here, we use data from 211,423 participants from UK Biobank to examine the relationship betweenVKORC1, risk of dementia, and the interplay with warfarin use. We find that the T-allele in rs9923231 confers a greater risk for vascular dementia (OR=1.28, p=0.0069), but not for general dementia (OR=1.04, p=0.21) or Alzheimer dementia (OR=1.05, p=0.35), and that the risk of vascular dementia is not affected by warfarin use in carriers of the T-allele. Our study reports for the first time an association between rs9923231 and vascular dementia, but further research is warranted to explore potential mechanisms and specify the relationship between rs9923231 and features of vascular dementia.
A structured observation distribution for generative biological sequence prediction and forecasting
Authors: Weinstein Eli N. , Marks Debora S.
Published: August 2020

Generative probabilistic modeling of biological sequences has widespread existing and potential application across biology and biomedicine, from evolutionary biology to epidemiology to protein design. Many standard sequence analysis methods preprocess data using a multiple sequence alignment (MSA) algorithm, one of the most widely used computational methods in all of science. However, as we show in this article, training generative probabilistic models with MSA preprocessing leads to statistical pathologies in the context of sequence prediction and forecasting. To address these problems, we propose a principled drop-in alternative to MSA preprocessing in the form of a structured observation distribution (the ``MuE" distribution). The MuE is a latent alignment model in which not only the alignment variable but also the regressor sequence can be latent. We prove theoretically that the MuE distribution comprehensively generalizes popular methods for inferring biological sequence alignments, and provide a precise characterization of how such biological models have differed from natural language latent alignment models. We show empirically that models that use the MuE as an observation distribution outperform comparable methods across a variety of datasets, and apply MuE models to a novel problem for generative probabilistic sequence models: forecasting pathogen evolution.
Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy
Authors: Shastry Vivaswat , Adams Paula E. , Lindtke Dorothea , Mandeville Elizabeth G. , Parchman Thomas L. , Gompert Zachariah , Buerkle C. Alex
Published: August 2020

Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can use low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low depth sequencing across a larger fraction of the genome than with high depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploidArabidopsis arenosa.
Sex differences in nociceptor translatomes contribute to divergent prostaglandin signaling in male and female mice
Authors: Tavares-Ferreira Diana , Ray Pradipta R. , Sankaranarayanan Ishwarya , Mejia Galo L. , Wangzhou Andi , Shiers Stephanie , Uttarkar Ruta , Megat Salim , Barragan-Iglesias Paulino , Dussor Gregory , Akopian Armen N. , Price Theodore J.
Published: August 2020

Background

There are clinically relevant sex differences in acute and chronic pain mechanisms, but we are only beginning to understand their mechanistic basis. Transcriptome analyses of rodent whole dorsal root ganglion (DRG) have revealed sex differences, mostly in immune cells. We examined the transcriptome and translatome of the mouse DRG with the goal of identifying sex differences.

Methods

We used Translating Ribosome Affinity Purification (TRAP) sequencing and behavioral pharmacology to test the hypothesis that nociceptor (Nav1.8 expressing neurons) translatomes would differ by sex.

Results

We found 66 genes whose mRNA were sex-differentially bound to nociceptor ribosomes. Many of these genes have known neuronal functions but have not been explored in sex differences in pain. We focused onPtgds, which was increased in female mice. The mRNA encodes the prostaglandin D2(PGD2) synthesizing enzyme. We observed increased Ptgds protein and PGD2in female mouse DRG. The Ptgds inhibitor AT-56 caused intense pain behaviors in male mice but was only effective at high doses in females. Conversely, female mice responded more robustly to another major prostaglandin, PGE2, than did male mice. Ptgds protein expression was also higher in female cortical neurons, suggesting DRG findings may be generalizable to other nervous system structures.

Conclusions

Nociceptor TRAP sequencing (TRAP-seq) reveals unexpected sex differences in one of the oldest known nociceptive signaling molecule families, the prostaglandins. Our results demonstrate that translatome analysis reveals physiologically relevant sex differences important for fundamental protective behaviors driven by nociceptors.
Analytical and clinical performances of five immunoassays for the detection of SARS-CoV-2 antibodies in comparison with neutralization activity
Authors: Padoan Andrea , Bonfante Francesco , Pagliari Matteo , Bortolami Alessio , Negrini Davide , Zuin Silvia , Bozzato Dania , Cosma Chiara , Sciacovelli Laura , Plebani Mario
Published: August 2020

Background

Reliable high-throughput serological assays for SARS-CoV-2 antibodies (Abs) are urgently needed for the effective containment of the COVID-19 pandemic, as it is of crucial importance to understand the strength and duration of immunity after infection, and to make informed decisions concerning the activation or discontinuation of physical distancing restrictions.

Methods

In 184 serum samples from 130 COVID-19 patients and 54 SARS-CoV-2 negative subjects, the analytical and clinical performances of four commercially available chemiluminescent assays (Abbott SARS-Cov-2 IgG, Roche Elecsys anti-SARS-CoV-2, Ortho SARS-CoV-2 total and IgG) and one enzyme-linked immunosorbent assay (Diesse ENZY-WELL SARS-CoV-2 IgG) were evaluated and compared with the neutralization activity achieved using the plaque reduction neutralization test (PRNT).

Findings

Precision results ranged from 0.9% to 11.8% for all assays. Elecsys anti-SARS-CoV-2 demonstrated linearity of results at concentrations within the cut-off value. Overall, sensitivity ranged from 78.5 to 87.8%, and specificity, from 97.6 to 100%. On limiting the analysis to samples collected 12 days after onset of symptoms, the sensitivity of all assays increased, the highest value (95.2%) being obtained with VITRO Anti-SARS-CoV-2 Total and Architect SARS-CoV-2 IgG. The strongest PRNT50 correlation with antibody levels was obtained with ENZY-Well SARS-CoV-2 IgG (rho = 0.541, p < 0.001).

Interpretation

The results confirmed that all immunoassays had an excellent specificity, whereas sensitivity varied across immunoassays, depending strongly on the time interval between symptoms onset and sample collection. Further studies should be conducted to achieve a stronger correlation between antibody measurement and PRNT50 in order to obtain useful information for providing effective passive antibody therapy, and developing a vaccine against the SARS-CoV-2 virus.
Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks
Authors: Levy Joshua , Haudenschild Christian , Barwick Clark , Christensen Brock , Vaickus Louis
Published: August 2020

Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework,WSI-GTFEthat integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer.
Monitoring migratory birds in stopover habitat: assessing the value of extended duration audio recording
Authors: Roark Ellie , Gaul Willson
Published: August 2020

Because birds are frequently detected by sound, autonomous audio recorders (called automated recording units or ARUs) are now an established tool in addition to in-person observations for monitoring the status and trends of bird populations. ARUs have been evaluated and applied during breeding seasons, and to monitor the nocturnal flight calls of migrating birds. However, birds behave differently during migration stopover than during the breeding season. Here we present a method for using ARUs to monitor land birds in migration stopover habitat.

We conducted in-person point counts next to continuously recording ARUs, and compared estimates of the number of species detected and focal species relative abundance from point counts and ARUs. We used a desk-based audio bird survey method for processing audio recordings, which does not require automated species identification algorithms. We tested two methods of using extended duration ARU recording: surveying consecutive minutes, and surveying randomly selected minutes.

Desk-based surveys using randomly selected minutes from extended duration ARU recordings performed similarly to point counts, and better than desk-based surveys using consecutive minutes from ARU recordings. Surveying randomly selected minutes from ARUs provided estimates of relative abundance that were strongly correlated with estimates from point counts, and successfully showed the increase in abundance associated with migration timing. Randomly selected minutes also provided estimates of the number of species present that were comparable to estimates from point counts.

ARUs are an effective way to track migration timing and intensity in remote or seasonally inaccessible migration stopover habitats. We recommend that desk-based surveys use randomly sampled minutes from extended duration ARU recordings, rather than using consecutive minutes from recordings. Our methods can be immediately applied by researchers with the skills to conduct point counts, with no additional expertise necessary in automated species identification algorithms.
Evaluating the Immunological cross-reactivity of Indian polyvalent antivenoms towards the venom ofHypnale hypnale(hump-nosed pit viper) from the Western Ghats
Authors: Vanuopadath Muralidharan , Raveendran Dileepkumar , Gopalakrishnan Nair Bipin , Sadasivan Nair Sudarslal
Published: August 2020

Hypnale hypnale(hump-nosed pit viper) is a venomous pit viper species found in the Western Ghats of India and Sri Lanka. Due to the severe life-threatening envenomation effects induced by its venom components,Hypnale hypnalehas been classified under ‘category 1’ of medically important snake species by the World Health Organization. Since there are no specific antivenoms available to combat its envenomation in India, the only option available is to administer Indian polyvalent antivenoms. However, the cross-neutralization potential of the commercially available polyvalent antivenoms on IndianHypnale hypnalevenom has not been explored so far. In the current study, in vitro immunological cross-reactivity ofHypnale hypnalevenom towards various Indian polyvalent antivenoms were assessed using end point titration ELISA and Western blotting. A three to four-fold increase in EC50values were obtained forHypnale hypnalevenom towards all the antivenoms tested. Observation of minimal binding specificities towards low and high molecular mass venom proteins are suggestive of the fact that commercially available polyvalent antivenoms failed to detect and bind to the antigenic epitopes of considerable number of proteins present inHypnale hypnalevenom. This highlights the importance of includingHypnale hypnalevenom in the immunization mixture while raising antivenoms.
Predictive visual motion extrapolation emerges spontaneously and without supervision at each layer of a hierarchical neural network with spike-timing-dependent plasticity
Authors: Burkitt Anthony N. , Hogendoorn Hinze
Published: August 2020

The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localisation of a moving object. One way this problem might be solved is extrapolation: using an object’s past trajectory to predict its location in the present moment. Here, we investigate how a simulatedin silicolayered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing dependent plasticity. We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Due to the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalisation that arises when human observers are required to localise a moving object relative to a flashed static object (the flash-lag effect).

Significance Statement

Our ability to track and respond to rapidly changing visual stimuli, such as a fast moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object in order to predict its current position, despite the delays that result from neural transmission. Here we show how the neural circuits underlying this ability can be learned through spike-timing dependent synaptic plasticity, and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.
Schizophrenia-associated differential DNA methylation in the superior temporal gyrus is distributed to many sites across the genome and annotated by the risk gene MAD1L1
Authors: McKinney Brandon C. , Hensler Christopher M. , Wei Yue , Lewis David A. , Wang Jiebiao , Ding Ying , Sweet Robert A.
Published: August 2020

Background

Many genetic variants and multiple environmental factors increase risk for schizophrenia (SZ). SZ-associated genetic variants and environmental risk factors have been associated with altered DNA methylation (DNAm), the addition of a methyl group to a cytosine in DNA. DNAm changes, acting through effects on gene expression, represent one potential mechanism by which genetic and environmental factors confer risk for SZ and alter neurobiology.

Methods

We investigated the hypothesis that DNAm in superior temporal gyrus (STG) is altered in SZ. We measured genome-wide DNAm in postmortem STG from 44 SZ subjects and 44 non-psychiatric comparison (NPC) subjects using Illumina Infinium MethylationEPIC BeadChip microarrays. We applied tensor composition analysis to extract cell type-specific DNAm signals.

Results

We found that DNAm levels differed between SZ and NPC subjects at 242 sites, and 44 regions comprised of two or more sites, with a false discovery rate cutoff of q=0.1. We determined differential methylation at nine of the individual sites were driven by neuron-specific DNAm alterations. Glia-specific DNAm alterations drove the differences at two sites. Notably, we identied SZ-associated differential methylation within within mitotic arrest deficient 1-like 1 (MAD1L1), a gene strongly associated with SZ through genome-wide association studies.

Conclusions

This study adds to a growing number of studies that implicate DNAm, and epigenetic pathways more generally, in SZ. Our findings suggest differential methylation may contribute to STG dysfunction in SZ. Future studies to identify the mechanisms by which altered DNAm, especially within MAD1L1, contributes to SZ neurobiology are warranted.
Neutralizing antibody against SARS-CoV-2 spike in COVID-19 patients, health care workers and convalescent plasma donors: a cohort study using a rapid and sensitive high-throughput neutralization assay
Authors: Zeng Cong , Evans John P. , Pearson Rebecca , Qu Panke , Zheng Yi-Min , Robinson Richard T. , Hall-Stoodley Luanne , Yount Jacob , Pannu Sonal , Mallampalli Rama K. , Saif Linda , Oltz Eugene , Lozanski Gerard , Liu Shan-Lu
Published: August 2020

Rapid and specific antibody testing is crucial for improved understanding, control, and treatment of COVID-19 pathogenesis. Herein, we describe and apply a rapid, sensitive, and accurate virus neutralization assay for SARS-CoV-2 antibodies. The new assay is based on an HIV-1 lentiviral vector that contains a secreted intron Gaussia luciferase or secreted Nano-luciferase reporter cassette, pseudotyped with the SARS-CoV-2 spike (S) glycoprotein, and is validated with a plaque reduction assay using an authentic, infectious SARS-CoV-2 strain. The new assay was used to evaluate SARS-CoV-2 antibodies in serum from individuals with a broad range of COVID-19 symptoms, including intensive care unit (ICU) patients, health care workers (HCWs), and convalescent plasma donors. The highest neutralizing antibody titers were observed among ICU patients, followed by general hospitalized patients, HCWs and convalescent plasma donors. Our study highlights a wide phenotypic variation in human antibody responses against SARS-CoV-2, and demonstrates the efficacy of a novel lentivirus pseudotype assay for high-throughput serological surveys of neutralizing antibody titers in large cohorts.
Natural Killer cell activation, reduced ACE2, TMPRSS2, cytokines G-CSF, M-CSF and SARS-CoV-2-S pseudovirus infectivity by MEK inhibitor treatment of human cells
Authors: Zhou Lanlan , Huntington Kelsey , Zhang Shengliang , Carlsen Lindsey , So Eui-Young , Parker Cassandra , Sahin Ilyas , Safran Howard , Kamle Suchitra , Lee Chang-Min , Lee Chun Geun , Elias Jack A. , Campbell Kerry S. , Naik Mandar T. , Atwood Walter J. , Youssef Emile , Pachter Jonathan A. , Navaraj Arunasalam , Seyhan Attila A. , Liang Olin , El-Deiry Wafik S.
Published: August 2020

COVID-19 affects vulnerable populations including elderly individuals and patients with cancer. Natural Killer (NK) cells and innate-immune TRAIL suppress transformed and virally-infected cells. ACE2, and TMPRSS2 protease promote SARS-CoV-2 infectivity, while inflammatory cytokines IL-6, or G-CSF worsen COVID-19 severity. We show MEK inhibitors (MEKi) VS-6766, trametinib and selumetinib reduce ACE2 expression in human cells. In some human cells, remdesivir increases ACE2-promoter luciferase-reporter expression, ACE2 mRNA and protein, and ACE2 expression is attenuated by MEKi. In serum-deprived and stimulated cells treated with remdesivir and MEKi we observed correlations between pRB, pERK, and ACE2 expression further supporting role of proliferative state and MAPK pathway in ACE2 regulation. We show elevated cytokines in COVID-19-(+) patient plasma (N=9) versus control (N=11). TMPRSS2, inflammatory cytokines G-CSF, M-CSF, IL-1α, IL-6 and MCP-1 are suppressed by MEKi alone or with remdesivir. We observed MEKi stimulation of NK-cell killing of target-cells, without suppressing TRAIL-mediated cytotoxicity. Pseudotyped SARS-CoV-2 virus with a lentiviral core and SARS-CoV-2 D614 or G614 SPIKE (S) protein on its envelope infected human bronchial epithelial cells, small airway epithelial cells, or lung cancer cells and MEKi suppressed infectivity of the pseudovirus. We show a drug class-effect with MEKi to stimulate NK cells, inhibit inflammatory cytokines and block host-factors for SARS-CoV-2 infection leading also to suppression of SARS-CoV-2-S pseudovirus infection of human cells. MEKi may attenuate SARS-CoV-2 infection to allow immune responses and antiviral agents to control disease progression.
A semi-automated design for high-throughput Lepidoptera larvae feeding bioassays
Authors: Sanane Inoussa , Legrand Judith , Dillmann Christine , Marion-Poll Frederic
Published: August 2020

Lepidopteran pests cause considerable damage to all crops over the world. As larvae are directly responsible for these damages, many research efforts are devoted to find plant cultivars which are resistant against them. However, such studies take time, efforts and are costly, especially when one wants to not only find resistance traits but also evaluate their heritability. We present here a high throughput approach to screen plants for resistance or chemicals for their deterrence, using a leaf-disk consumption assay, which is both suitable for large scale tests and economically affordable. To monitor larvae feeding on leaf disks placed over a layer of agar, we designed 3D models of 50 cages arrays. One webcam can sample simultaneously 3 of such arrays at a rate of 1 image/min, and follow individual feeding activities in each cage as the movements of 150 larvae. The resulting image stacks are first processed with a custom program running under an open-source image analysis package (Icy) to measure the surface of each leaf disk over time. We further developed statistical procedures running under the R package, to analyze the time course of the feeding activities of the larvae and to compare them between treatments. As a test case, we compared how European corn borer larvae respond to quinine, considered as a bitter alkaloid for many organisms, and to Neemazal containing azadirachtin, which is a common antifeedant against pest insects. We found that increasing doses of azadirachtin reduce and delay feeding. However, contrary to our expectation, quinine was found poorly effective at the range of concentrations tested. The 3D printed model of the cage, of the camera holder, the plugins running under Icy, and the R procedures are freely available, and can be modified according to the particular needs of the users.
Permutation tests for hypothesis testing with animal social network data: problems and potential solutions
Authors: Farine Damien R. , Carter Gerald G.
Published: August 2020

Permutation tests are widely used to test null hypotheses with animal social network data, but suffer from high rates of type I and II error when the permutations do not properly simulate the intended null hypothesis.

Two common types of permutations each have limitations. Pre-network (or datastream) permutations can be used to control “nuisance effects” like spatial, temporal, or sampling biases, but only when the null hypothesis assumes random social structure. Node (or node-label) permutation tests can test null hypotheses that include nonrandom social structure, but only when nuisance effects do not shape the observed network.

We demonstrate one possible solution addressing these limitations: using pre-network permutations to adjust the values for each node or edge before conducting a node permutation test. We conduct a range of simulations to estimate error rates caused by confounding effects of social or non-social structure in the raw data.

Regressions on simulated datasets suggest that this “double permutation” approach is less likely to produce elevated error rates relative to using only node permutations, pre-network permutations, or node permutations with simple covariates, which all exhibit elevated type I errors under at least one set of simulated conditions. For example, in scenarios where type I error rates from pre-network permutation tests exceed 30%, the error rates from double permutation remain at 5%.

The double permutation procedure provides one potential solution to issues arising from elevated type I and type II error rates when testing null hypotheses with social network data. We also discuss alternative approaches that can provide robust inference, including fitting mixed effects models, restricted node permutations, testing multiple null hypotheses, and splitting large datasets to generate replicated networks. Finally, we highlight ways that uncertainty can be explicitly considered and carried through the analysis.
Src activates retrograde membrane traffic through phosphorylation of GBF1
Authors: Chia Joanne , Wang Shyi-Chyi , Wee Sheena , Gill David James , Tay Felicia , Kannan Srinivasaraghavan , Verma Chandra S , Gunaratne Jayantha , Bard Frederic A.
Published: August 2020

The Src tyrosine kinase controls cancer-critical protein glycosylation through Golgi to ER relocation of GALNTs enzymes. How Src induces this trafficking event is unknown. Golgi to ER transport depends on the GTP Exchange factor (GEF) GBF1 and small GTPase Arf1. Here we show that Src induces the formation of tubular transport carriers containing GALNTs through promotion of a GBF1-Arf1 complex. The complex is initiated by phosphorylation on GBF1 on 10 tyrosine residues; two of them, Y876 and Y898 are located near the C-terminus of the Sec7 GEF domain. Their phosphorylation promotes partial melting of the Sec7 domain, favoring binding to the GTPase. Perturbation of these rearrangements prevent GALNTs relocation. In sum, Src promotes GALNTs relocation by favoring binding of GBF1 to Arf1. Regulation of a GEF-Arf axis by tyrosine phosphorylation appears to be a highly conserved and wide-spread mechanism.
BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts
Authors: Poole William , Pandey Ayush , Shur Andrey , Tuza Zoltan A. , Murray Richard M.
Published: August 2020

Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to be represented succinctly with design choices propagated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts andin vivomilieus. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions.

Author summary

This paper describes a new software package BioCRNpyler (pronounced “Biocompiler”) designed to support rapid development and exploration of mathematical models of biochemical networks and circuits by computational biologists, systems biologists, and synthetic biologists. BioCRNpyler allows its users to generate large complex models using very few lines of code in a way that is modular. To do this, BioCRNpyler uses a powerful new representation of biochemical circuits which defines their parts, underlying biochemical mechanisms, and chemical context independently. BioCRNpyler was developed as a Python scripting language designed to be accessible to beginning users as well as easily extendable and customizable for advanced users. Ultimately, we see Biocrnpyler being used to accelerate computer automated design of biochemical circuits and model driven hypothesis generation in biology.
Cross-neutralization of a SARS-CoV-2 antibody to a functionally conserved site is mediated by avidity
Authors: Liu Hejun , Wu Nicholas C. , Yuan Meng , Bangaru Sandhya , Torres Jonathan L. , Caniels Tom G. , Schooten Jelle van , Zhu Xueyong , Lee Chang-Chun D. , Brouwer Philip J.M. , Gils Marit J. van , Sanders Rogier W. , Ward Andrew B. , Wilson Ian A.
Published: August 2020

Most antibodies isolated from COVID-19 patients are specific to SARS-CoV-2. COVA1-16 is a relatively rare antibody that also cross-neutralizes SARS-CoV. Here we determined a crystal structure of COVA1-16 Fab with the SARS-CoV-2 RBD, and a negative-stain EM reconstruction with the spike glycoprotein trimer, to elucidate the structural basis of its cross-reactivity. COVA1-16 binds a highly conserved epitope on the SARS-CoV-2 RBD, mainly through a long CDR H3, and competes with ACE2 binding due to steric hindrance rather than epitope overlap. COVA1-16 binds to a flexible up conformation of the RBD on the spike and relies on antibody avidity for neutralization. These findings, along with structural and functional rationale for the epitope conservation, provide a blueprint for development of more universal SARS-like coronavirus vaccines and therapies.
Proteomic analysis of SUMO1-SUMOylome changes during defense elicitation inArabidopsis
Authors: Ingole Kishor D. , Dahale Shraddha K. , Bhattacharjee Saikat
Published: August 2020

Rapid adaptation of plants to developmental or physiological cues is facilitated by specific receptors that transduce the signals mostly via post-translational modification (PTM) cascades of downstream partners. Reversible covalent attachment of SMALL UBIQUITIN-LIKE MODIFIER (SUMO), a process termed as SUMOylation, influence growth, development and adaptation of plants to various stresses. Strong regulatory mechanisms maintain the steady-state SUMOylome and mutants with SUMOylation disturbances display mis-primed immunity often with growth consequences. Identity of the SUMO-substrates undergoing SUMOylation changes during defences however remain largely unknown. Here we exploit either the auto-immune property of an Arabidopsis mutant or defense responses induced in wild-type plants againstPseudomonas syringaepvtomato(PstDC3000) to enrich and identify SUMO1-substrates. Our results demonstrate massive enhancement of SUMO1-conjugates due to increased SUMOylation efficiencies during defense responses. Of the 261 proteins we identify, 29 have been previously implicated in immune-associated processes. Role of others expand to diverse cellular roles indicating massive readjustments the SUMOylome alterations may cause during induction of immunity. Overall, our study highlights the complexities of a plant immune network and identifies multiple SUMO-substrates that may orchestrate the signalling.
TractoFlow-ABS (Atlas-Based Segmentation)
Authors: Theaud Guillaume , Houde Jean-Christophe , Boré Arnaud , Rheault François , Morency Felix , Descoteaux Maxime
Published: August 2020

In Diffusion MRI (dMRI), pathological brains are a challenge for tractography processing, where most pipelines are not are not robust to white matter lesions. Intensity of white matter lesions on T1 images can have similar contrasts to gray matter tissue, which leads to misclassifications or “holes” in the white matter mask. These holes produce premature stop for tracking algorithms. To handle these issues, we developedTractoFlow-ABS(Atlas-BasedSegmentation).TractoFlow-ABSuses theFreesurferatlas to compute tissue masks instead ofFSL fastin standardTractoFlow.TractoFlow-ABSis therefore a derived version ofTractoFlowthat is robust to white matter anomalies such as hyperintensities and lesions.
Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdowns
Authors: Gola Abhinav , Arya Ravi Kumar , Animesh  , Dugh Ravi
Published: August 2020

Background

COVID-19 is widely spreading across the globe right now. While some countries have flattened the curve, others are struggling to control the spread of the infection. Precise risk prediction modeling is key to accurate prevention and containment of COVID-19 infection, as well as for the preparation of resources needed to deal with the pandemic in different regions.

Methods

Given the vast differences in approaches and scenarios used by these models to predict future infection rates, in this study, we compared the accuracy among different models such as regression models, ARIMA model, multilayer perceptron, vector autoregression, susceptible exposed infected recovered (SEIR), susceptible infected recovered (SIR), recurrent neural networks (RNNs), long short term memory networks (LSTM) and exponential growth model in prediction of the total COVID-19 confirmed cases. We did so by comparing the predicted rates of these models with actual rates of COVID-19 in India during the nationwide lockdowns.

Results

Few of these models accurately predicted COVID-19 incidence and mortality rates in six weeks, though some provided close results. While advanced warning can help mitigate and prepare for an impending or ongoing epidemic, using poorly fitting models for prediction could lead to substantial adverse outcomes.

Implications

As the COVID-19 pandemic continues, accurate risk prediction is key to effective public health interventions. Caution should be taken when choosing different risk prediction models based on specific scenarios and needs. To improve risk prediction of infectious disease such as COVID-19 for policy guidance and recommendations on best practices, both internal (e.g., specific virus characteristics in transmission and mutation) and external factors (e.g., large-scale human behaviors such as school opening, parties, and breaks) should be considered and appropriately weighed.
Leverage Real-world Longitudinal Data in Large Clinical Research Networks for Alzheimer’s Disease and Related Dementia (ADRD)
Authors: Duan Rui , Chen Zhaoyi , Tong Jiayi , Luo Chongliang , Lyu Tianchen , Tao Cui , Maraganore Demetrius , Bian Jiang , Chen Yong
Published: August 2020

With vast amounts of patients’ medical information, electronic health records (EHRs) are becoming one of the most important data sources in biomedical and health care research. Effectively integrating data from multiple clinical sites can help provide more generalized real-world evidence that is clinically meaningful. To analyze the clinical data from multiple sites, distributed algorithms are developed to protect patient privacy without sharing individual-level medical information. In this paper, we applied the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to the longitudinal data from the OneFlorida Clinical Research Consortium to demonstrate the feasibility of implementing the distributed algorithms in large research networks. We studied the associations between the clinical risk factors and Alzheimer’s disease and related dementia (ADRD) onsets to advance clinical research on our understanding of the complex risk factors of ADRD and ultimately improve the care of ADRD patients.
COVID-19 pandemic in Djibouti: epidemiology and the response strategy followed to contain the virus during the first two months, 17 March to 16 May 2020
Authors: Elhakim Mohamed , Tourab Saleh Banoita , Zouiten Ahmed
Published: August 2020

Background

First cases of COVID-19 were reported from Wuhan, China, in December 2019, and it progressed rapidly. On 30 January, WHO declared the new disease as a PHEIC, then as a Pandemic on 11 March. By mid-March, the virus spread widely; Djibouti was not spared and was hit by the pandemic with the first case detected on 17 March. Djibouti worked with WHO and other partners to develop a preparedness and response plan, and implemented a series of intervention measures. MoH together with its civilian and military partners, closely followed WHO recommended strategy based on four pillars: testing, isolating, early case management, and contact tracing. From 17 March to 16 May, Djibouti performed the highest per capita tests in Africa and isolated, treated and traced the contacts of each positive case, which allowed for a rapid control of the epidemic.

Methods

COVID-19 data included in this study was collected through MoH Djibouti during the period from 17 March to 16 May 2020.

Results

A total of 1,401 confirmed cases of COVID-19 were included in the study with 4 related deaths (CFR: 0.3%) and an attack rate of 0.15%. Males represented (68.4%) of the cases, with the age group 31-45 years old (34.2%) as the most affected. Djibouti conducted 17,532 tests, and was considered as a champion for COVID-19 testing in Africa with 18.2 tests per 1000 habitant. All positive cases were isolated, treated and had their contacts traced, which led to early and proactive diagnosis of cases and in turn yielded up to 95-98% asymptomatic cases. Recoveries reached 69% of the infected cases with R0 (0.91). The virus was detected in 4 regions in the country, with the highest percentage in the capital (83%).

Conclusion

Djibouti responded to COVID-19 pandemic following an efficient and effective strategy, using a strong collaboration between civilian and military health assets that increased the response capacities of the country. Partnership, coordination, solidarity, proactivity and commitment were the pillars to confront COVID-19 pandemic.
SalivaDirect: A simplified and flexible platform to enhance SARS-CoV-2 testing capacity
Authors: Vogels Chantal B.F. , Watkins Anne E. , Harden Christina A. , Brackney Doug E. , Shafer Jared , Wang Jianhui , Caraballo César , Kalinich Chaney C. , Ott Isabel M. , Fauver Joseph R. , Kudo Eriko , Lu Peiwen , Venkataraman Arvind , Tokuyama Maria , Moore Adam J. , Muenker M. Catherine , Casanovas-Massana Arnau , Fournier John , Bermejo Santos , Campbell Melissa , Datta Rupak , Nelson Allison ,   Yale IMPACT Research Team, Dela Cruz Charles S. , Ko Albert I. , Iwasaki Akiko , Krumholz Harlan M. , Matheus JD , Hui Pei , Liu Chen , Farhadian Shelli F. , Sikka Robby , Wyllie Anne L. , Grubaugh Nathan D.
Published: August 2020

Current bottlenecks for improving accessibility and scalability of SARS-CoV-2 testing include diagnostic assay costs, complexity, and supply chain shortages. To resolve these issues, we developed SalivaDirect, which received Emergency Use Authorization (EUA) from the U.S. Food and Drug Administration on August 15th, 2020. The critical component of our approach is to use saliva instead of respiratory swabs, which enables non-invasive frequent sampling and reduces the need for trained healthcare professionals during collection. Furthermore, we simplified our diagnostic test by(1)not requiring nucleic acid preservatives at sample collection,(2)replacing nucleic acid extraction with a simple proteinase K and heat treatment step, and(3)testing specimens with a dualplex quantitative reverse transcription PCR (RT-qPCR) assay. We validated SalivaDirect with reagents and instruments from multiple vendors to minimize the risk for supply chain issues. Regardless of our tested combination of reagents and instruments from different vendors, we found that SalivaDirect is highly sensitive with a limit of detection of 6-12 SARS-CoV-2 copies/μL. When comparing SalivaDirect to paired nasopharyngeal swabs using the authorized ThermoFisher Scientific TaqPath COVID-19 combo kit, we found high agreement in testing outcomes (>94%). In partnership with the National Basketball Association (NBA) and Players Association, we conducted a large-scale(n= 3,779) SalivaDirect usability study and comparison to standard nasal/oral tests for asymptomatic and presymptomatic SARS-CoV-2 detection. From this cohort of healthy NBA players, staff, and contractors, we found that 99.7% of samples were valid using our saliva collection techniques and a 89.5% positive and >99.9% negative test agreement to swabs, demonstrating that saliva is a valid and noninvasive alternative to swabs for large-scale SARS-CoV-2 testing. SalivaDirect is a flexible and inexpensive ($1.21-$4.39/sample in reagent costs) option to help improve SARS-CoV-2 testing capacity. Register to become a designated laboratory to use SalivaDirect under our FDA EUA on our website: publichealth.yale.edu/salivadirect/.
A nonsense mutation of bone morphogenetic protein-15 (BMP15) causes both infertility and increased litter size in pigs
Authors: Flossmann Gabriele , Wurmser Christine , Pausch Hubert , Tenghe Amabel , Dodenhoff Jörg , Dahinten Günther , Götz Kay-Uwe , Russ Ingolf , Fries Ruedi
Published: August 2020

Background

Atypical external genitalia are often a sign of reproductive organ pathologies and infertility with both environmental or genetic causes, including karyotypic abnormalities. Genome-wide association studies (GWAS) provide a means for identifying chromosomal regions harboring deleterious DNA-variants causing such phenotypes. We performed a GWAS to unravel the causes of incidental cases of atypically small vulvae in German Landrace gilts.

Results

A case-control GWAS involving Illumina porcine SNP60 BeadChip-called genotypes of 17 gilts with atypically small vulvae and 1,818 control animals (fertile German Landrace sows) identified a significantly associated region on the X-chromosome (P = 8.81 × 10-43). Inspection of whole-genome sequencing data in the critical area allowed us to pinpoint a likely causal variant in the form of a nonsense mutation of bone morphogenetic protein-15 (Sscrofa11.1_X:g.44618787C>T, BMP15:p.R212X). The mutant allele occurs at a frequency of 6.2% in the German Landrace breeding population. Homozygous gilts exhibit underdeveloped, most likely not functional ovaries and are not fertile. Male carriers do not seem to manifest defects. Heterozygous sows produce 0.41±0.02 (P=4.5 × 10-83) piglets more than wildtype animals. However, the mutant allele’s positive effect on litter size accompanies a negative impact on lean meat growth.

Conclusion

Our results provide an example for the power of GWAS in identifying the genetic causes of a fuzzy phenotype and add to the list of natural deleterious BMP15 mutations that affect fertility in a dosage-dependent manner, the first time in a poly-ovulatory species. We advise eradicating the mutant allele from the German Landrace breeding population since the adverse effects on the lean meat growth outweigh the larger litter size in heterozygous sows.
Twisting of the heart tube during cardiac looping is atbx5-dependent and tissue-intrinsic process
Authors: Tessadori Federico , Kruse Fabian , van den Brink Susanne C. , van den Boogaard Malou , Christoffels Vincent M. , Bakkers Jeroen
Published: August 2020

Organ laterality refers to the Left-Right (LR) asymmetry in disposition and conformation of internal organs, established in the developing embryo. The heart is the first organ to display visible LR asymmetries as it is positioned to the left side of the midline and undergoes rightward looping morphogenesis. Cardiac looping morphogenesis is tightly controlled by a combination of heart-intrinsic and -extrinsic mechanisms. As the mechanisms that drive cardiac looping are not well understood, we performed a forward genetic screen for zebrafish mutants with defective heart looping. We describe a new loss-of-function allele fortbx5a, which displays normal leftward positioning but defective rightward looping morphogenesis. By using live two-photon confocal imaging to map cardiomyocyte behavior during cardiac looping at a single-cell level we establish that during looping, ventricular and atrial cardiomyocytes rearrange in opposite directions towards the outer curvatures of the chambers. As a consequence, the cardiac chambers twist around the atrioventricular canal resulting in torsion of the heart tube, which is compromised intbx5amutants. Manipulations of cardiac looping by chemical treatment andex vivoculture establishes that the twisting of the heart tube depends on intrinsic mechanisms and is independent from tissue growth by cell addition. Furthermore, the cardiac looping defect intbx5amutants is rescued intbx5a/tbx2bdouble mutants, indicating that it requires proper tissue patterning. Together, our results establish that cardiac looping in zebrafish involves twisting of the chambers around the AV canal, which requires correct tissue patterning by Tbx5a.