bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences
Authors: Nag Abhishek , Middleton Lawrence , Dhindsa Ryan S. , Vitsios Dimitrios , Wigmore Eleanor , Allman Erik L. , Reznichenko Anna , Carss Keren , Smith Katherine R. , Wang Quanli , Challis Benjamin , Paul Dirk S. , Harper Andrew R. , Petrovski Slavé
Published: December 2021

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis – that evaluated a range of genetic architectures – identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1×10−8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants inGIGYF1and glucose and lipid biomarkers,SYT7and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.
Early Computational Detection of Potential High Risk SARS-CoV-2 Variants
Authors: Beguir Karim , Skwark Marcin J. , Fu Yunguan , Pierrot Thomas , Carranza Nicolas Lopez , Laterre Alexandre , Kadri Ibtissem , Korched Abir , Lowegard Anna U. , Lui Bonny Gaby , Sänger Bianca , Liu Yunpeng , Poran Asaf , Muik Alexander , Sahin Ugur
Published: December 2021

The ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants on a daily basis. While most variants do not impact the course of the pandemic, some variants pose a significantly increased risk when the acquired mutations allow better evasion of antibody neutralisation in previously infected or vaccinated subjects or increased transmissibility. Early detection of such high risk variants (HRVs) is paramount for the proper management of the pandemic. However, experimental assays to determine immune evasion and transmissibility characteristics of new variants are resource-intensive and time-consuming, potentially leading to delays in appropriate responses by decision makers. Here we present a novelin silicoapproach combining spike (S) protein structure modelling and large protein transformer language models on S protein sequences to accurately rank SARS-CoV-2 variants for immune escape and fitness potential. These metrics can be combined into an automated Early Warning System (EWS) capable of evaluating new variants in minutes and risk-monitoring variant lineages in near real-time. The system accurately pinpoints the putatively dangerous variants by selecting on average less than 0.3% of the novel variants each week. With only the S protein nucleotide sequence as input, the EWS detects HRVs earlier and with better precision than baseline metrics such as the growth metric (which requires real-world observations) or random sampling. Notably, Omicron BA.1 was flagged by the EWS on the day its sequence was made available. Additionally, our immune escape and fitness metrics were experimentally validated usingin vitropseudovirus-based virus neutralisation test (pVNT) assays and binding assays. The EWS flagged as potentially dangerous all 16 variants (Alpha-Omicron BA.1/2/4/5) designated by the World Health Organisation (WHO) with an average lead time of more than one and a half months ahead of them being designated as such.

One-Sentence Summary

A COVID-19 Early Warning System combining structural modelling with machine learning to detect and monitor high risk SARS-CoV-2 variants, identifying all 16 WHO designated variants on average more than one and a half months in advance by selecting on average less than 0.3% of the weekly novel variants.
PyLiger: Scalable single-cell multi-omic data integration in Python
Authors: Lu Lu , Welch Joshua D.
Published: December 2021

Motivation

LIGER is a widely-used R package for single-cell multi-omic data integration. However, many users prefer to analyze their single-cell datasets in Python, which offers an attractive syntax and highly-optimized scientific computing libraries for increased efficiency.

Results

We developed PyLiger, a Python package for integrating single-cell multi-omic datasets. PyLiger offers faster performance than the previous R implementation (2-5× speedup), interoperability with AnnData format, flexible on-disk or in-memory analysis capability, and new functionality for gene ontology enrichment analysis. The on-disk capability enables analysis of arbitrarily large single-cell datasets using fixed memory.

Availability

PyLiger is available on Github at https://github.com/welch-lab/pyliger and on the Python Package Index.

Contact

welchjd@umich.edu

Supplementary information

Supplementary data are available at Bioinformatics online.
Clinical grade ACE2 effectively inhibits SARS-CoV-2 Omicron infections
Authors: Monteil Vanessa , Stephanie Devignot , Klingström Jonas , Thålin Charlotte , Kellner Max J. , Christ Wanda , Havervall Sebastian , Mereiter Stefan , Knapp Sylvia , Montserrat Nuria , Braunsfeld Benedict , Kozieradzki Ivona , Ali Omar Hasan , Hagelkruys Astrid , Stadlmann Johannes , Oostenbrink Chris , Wirnsberger Gerald , Penninger Josef M. , Mirazimi Ali
Published: December 2021

The recent emergence of the SARS-CoV-2 variant Omicron has caused considerable concern due to reduced vaccine efficacy and escape from neutralizing antibody therapeutics. Omicron is spreading rapidly around the globe and is suspected to account for most new COVID-19 cases in several countries, though the severity of Omicron-mediated disease is still under debate. It is therefore paramount to identify therapeutic strategies that inhibit the Omicron SARS-CoV-2 variant. Here we report using 3D structural modelling that Spike of Omicron can still associate with human ACE2. Sera collected after the second mRNA-vaccination did not exhibit a protective effect against Omicron while strongly neutralizing infection of VeroE6 cells with the reference Wuhan strain, confirming recent data by other groups on limited vaccine and convalescent sera neutralization efficacy against Omicron. Importantly, clinical grade recombinant human soluble ACE2, a drug candidate currently in clinical development, potently neutralized Omicron infection of VeroE6 cells with markedly enhanced potency when compared to reference SARS-CoV-2 isolates. These data show that SARS-CoV-2 variant Omicron can be readily inhibited by soluble ACE2, providing proof of principle of a viable and effective therapeutic approach against Omicron infections.
Expanded ACE2 dependencies of diverse SARS-like coronavirus receptor binding domains
Authors: Roelle Sarah M. , Shukla Nidhi , Pham Anh T. , Bruchez Anna M. , Matreyek Kenneth A.
Published: December 2021

Viral spillover from animal reservoirs can trigger public health crises and cripple the world economy. Knowing which viruses are primed for zoonotic transmission can focus surveillance efforts and mitigation strategies for future pandemics. Successful engagement of receptor protein orthologs is necessary during cross-species transmission. The clade 1 sarbecoviruses including SARS-CoV and SARS-CoV-2 enter cells via engagement of ACE2, while the receptor for clade 2 and clade 3 remains largely uncharacterized. We developed a mixed cell pseudotyped virus infection assay to determine whether various clade 2 and 3 sarbecovirus spike proteins can enter HEK 293T cells expressing human orRhinolophushorseshoe bat ACE2 proteins. The receptor binding domains from BtKY72 and Khosta-2 used human ACE2 for entry, while BtKY72 and Khosta-1 exhibited widespread use of diverse rhinolophid ACE2s. A lysine at ACE2 position 31 appeared to be a major determinant of the inability of these RBDs to use a certain ACE2 sequence. The ACE2 protein fromR. alcyoneengaged all known clade 3 and clade 1 receptor binding domains. We observed little use ofRhinolophusACE2 orthologs by the clade 2 viruses, supporting the likely use of a separate, unknown receptor. Our results suggest that clade 3 sarbecoviruses from Africa and Europe useRhinolophusACE2 for entry, and their spike proteins appear primed to contribute to zoonosis under the right conditions.
A combination of potently neutralizing monoclonal antibodies isolated from an Indian convalescent donor protects against the SARS-CoV-2 delta variant
Authors: Hingankar Nitin , Deshpande Suprit , Das Payel , Rizvi Zaigham Abbas , Burns Alison , Barman Shawn , Zhao Fangzhu , Ansari Mohammed Yousuf , Mukherjee Sohini , Torres Jonathan L. , Chattopadhyay Souvick , Mehdi Farha , Sutar Jyoti , Rathore Deepak Kumar , Pargai Kamal , Singh Janmejay , Sonar Sudipta , Jakhar Kamini , Bhattacharyya Sankar , Mani Shailendra , Singh Savita , Dandotiya Jyotsna , Kshetrapal Pallavi , Thiruvengadam Ramachandran , Batra Gaurav , Medigeshi Guruprasad R , Ward Andrew , Bhatnagar Shinjini , Awasthi Amit , Sok Devin , Bhattacharya Jayanta
Published: December 2021

Although efficacious vaccines have significantly reduced the morbidity and mortality due to COVID-19, there remains an unmet medical need for treatment options, which monoclonal antibodies (mAbs) can potentially fill. This unmet need is exacerbated by the emergence and spread of SARS-CoV-2 variants of concern (VOCs) that have shown some resistance to vaccine responses. Here we report the isolation of two highly potently neutralizing mAbs (THSC20.HVTR04 and THSC20.HVTR26) from an Indian convalescent donor, that neutralize SARS-CoV-2 VOCs at picomolar concentrations including the delta variant (B.1.617.2). These two mAbs target non-overlapping epitopes on the receptor-binding domain (RBD) of the spike protein thereby preventing the virus attachment to its host receptor, human angiotensin converting enzyme-2 (hACE2). Furthermore, the mAb cocktail demonstrated protection against the Delta variant at low antibody doses when passively administered in the K18 hACE2 transgenic mice model, highlighting their potential as cocktail for prophylactic and therapeutic applications. Developing the capacity to rapidly discover and develop mAbs effective against highly transmissible pathogens like coronaviruses at a local level, especially in a low- and middle-income country (LMIC) such as India, will enable prompt responses to future pandemics as an important component of global pandemic preparedness.
Pandemic-related decline in injuries related to women wearing high-heeled shoes: Analysis of U.S. data for 2016-2020
Authors: Cohen Philip N.
Published: December 2021

Background

Wearing high-heeled shoes is associated with injury risk. During the COVID-19 pandemic, changes in work and social behavior may have reduced women’s use of such footwear.

Methods

This study assessed the trend in high-heel related injuries among U.S. women, using 2016-2020 data from the U.S. Consumer Product Safety Commission’s National Electronic Injury Surveillance System (NEISS).

Results

In 2020 there were an estimated 6,290 high-heel related emergency department visits involving women ages 15-69, down from 16,000 per year in 2016-2019. The 2020 decline began after the start of the COVID-19 shutdowns on March 15. There was no significant change in the percentage of fractures or hospital admissions.

Conclusions

The COVID-19 pandemic was associated with a decline in reported injuries related to high-heeled shoes among US women. If this resulted from fewer women wearing such shoes, and such habits influence future behavior, the result may be fewer injuries in the future.
Evaluation of maternal-infant dyad inflammatory cytokines in pregnancies affected by maternal SARS-CoV-2 infection in early and late gestation
Authors: Taglauer Elizabeth S. , Dhole Yashoda , Boateng Jeffery , Snyder-Cappione Jennifer , Parker Samantha E. , Clarke Katherine , Juttukonda Lillian , Devera Jean , Hunnewell Jessica , Barnett Elizabeth , Jia Hongpeng , Yarrington Christina , Sabharwal Vishakha , Wachman Elisha M.
Published: December 2021

Objective

SARS-CoV-2 infection induces significant inflammatory cytokine production in adults, but infant cytokine signatures in pregnancies affected by maternal SARS-CoV-2 are less well characterized. We aimed to evaluate cytokine profiles of mothers and their infants following COVID-19 in pregnancy.

Study Design

Serum samples at delivery from 31 mother-infant dyads with maternal SARS-CoV-2 infection in pregnancy (COVID) were examined in comparison to 29 control dyads (Control). Samples were evaluated using a 13-plex cytokine assay.

Results

In comparison with controls, interleukin (IL)-6 and interferon gamma-induced protein 10 (IP-10) were higher in COVID maternal and infant samples (p<0.05) and IL-8 uniquely elevated in COVID infant samples (p<0.05). Significant elevations in IL-6, IP-10 and IL-8 were found among both early (1st/2nd Trimester) and late (3rd Trimester) maternal SARS-CoV-2 infections.

Conclusions

Maternal SARS-CoV-2 infections throughout gestation are associated with increased maternal and infant inflammatory cytokines at birth with potential to impact long-term infant health.
Structural basis for broad anti-phage immunity by DISARM
Authors: Bravo Jack P. K. , Aparicio-Maldonado Cristian , Nobrega Franklin L. , Brouns Stan J. J. , Taylor David W.
Published: December 2021

In the evolutionary arms race against phage, bacteria have assembled a diverse arsenal of antiviral immune strategies. While the recently discovered DISARM (Defense Island System Associated with Restriction-Modification) systems can provide protection against a wide range of phage, the molecular mechanisms that underpin broad antiviral targeting but avoiding autoimmunity remain enigmatic. Here, we report cryo-EM structures of the core DISARM complex, DrmAB, both alone and in complex with an unmethylated phage DNA mimetic. These structures reveal that DrmAB core complex is autoinhibited by a trigger loop (TL) within DrmA and binding to DNA substrates containing a 5’ overhang dislodges the TL, initiating a long-range structural rearrangement for DrmAB activation. Together with structure-guidedin vivostudies, our work provides insights into the mechanism of phage DNA recognition and specific activation of this widespread antiviral defense system.
Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation
Authors: Chereda Hryhorii , Leha Andreas , Beißbarth Tim
Published: December 2021

High-throughput technologies are increasingly important in discovering prognostic molecular signatures and identifying novel drug targets. Molecular signatures can be obtained as a subset of features that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. However, feature selection is inherently unstable in this case. Several studies have identified gene sets that provide predictive success for patient prognosis, but these sets usually have only a few genes in common. The stability of feature selection (and reproducibility of identified gene sets) can be improved by including information on molecular networks in ML methods. Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior knowledge molecular network. Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) are techniques to explain individual decisions of deep learning models. We used both GCNN+LRP and GCNN+SHAP techniques to explain GCNNs and to construct feature sets that are relevant to models by aggregating their individual explanations. We also applied more classical ML-based feature selection approaches and analyzed the stability, impact on the classification performance, and interpretability of selected feature sets.

Availability

https://gitlab.gwdg.de/UKEBpublic/graph-lrp

Contact

tim.beissbarth@bioinf.med.uni-goettingen.de
Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory Genomics
Authors: Prakash Eva , Shrikumar Avanti , Kundaje Anshul
Published: December 2021

Deep neural networks and support vector machines have been shown to accurately predict genome-wide signals of regulatory activity from raw DNA sequences. These models are appealing in part because they can learn predictive DNA sequence features without prior assumptions. Several methods such as in-silico mutagenesis, GradCAM, DeepLIFT, Integrated Gradients and Gkm-Explain have been developed to reveal these learned features. However, the behavior of these methods on regulatory genomic data remains an area of active research. Although prior work has benchmarked these methods on simulated datasets with known ground-truth motifs, these simulations employed highly simplified regulatory logic that is not representative of the genome. In this work, we propose a novel pipeline for designing simulated data that comes closer to modeling the complexity of regulatory genomic DNA. We apply the pipeline to build simulated datasets based on publicly-available chromatin accessibility experiments and use these datasets to bench-mark different interpretation methods based on their ability to identify ground-truth motifs. We find that a GradCAM-based method, which was reported to perform well on a more simplified dataset, does not do well on this dataset (particularly when using an architecture with shorter convolutional kernels in the first layer), and we theoretically show that this is expected based on the nature of regulatory genomic data. We also show that Integrated Gradients sometimes performs worse than gradient-times-input, likely owing to its linear interpolation path. We additionally explore the impact of user-defined settings on the interpretation methods, such as the choice of “reference”/”baseline”, and identify recommended settings for genomics. Our analysis suggests several promising directions for future research on these model interpretation methods. Code and links to data are available athttps://github.com/kundajelab/interpret-benchmark.
Plankton-infecting relatives of herpesviruses clarify the evolutionary trajectory of giant viruses
Authors: Gaïa Morgan , Meng Lingjie , Pelletier Eric , Forterre Patrick , Vanni Chiara , Fernandez-Guerra Antonio , Jaillon Olivier , Wincker Patrick , Ogata Hiroyuki , Krupovic Mart , Delmont Tom O.
Published: December 2021

DNA viruses have a major influence on the ecology and evolution of cellular organisms, but their overall diversity and evolutionary trajectories remain elusive. Here, we performed a phylogeny-guided genome-resolved metagenomic survey of the sunlit oceans and discovered plankton-infecting relatives of herpesviruses that form a putative new phylum dubbed ‘Mirusviricota’. The virion morphogenesis module of this large monophyletic clade is typical of viruses from the realmDuplodnaviria, with the major capsid protein fold being a likely structural intermediate between the capsid proteins ofCaudoviricetes(tailed phages) andHerpesvirales(animal-infecting viruses). Yet, a substantial fraction of ‘Mirusviricota’genes, including hallmark transcription machinery genes missing in herpesviruses, are closely related homologs of large and giant eukaryotic DNA viruses from another viral realm. The remarkable chimeric attributes of ‘Mirusviricota’ provide missing links in the evolution of both herpesviruses and giant viruses. Furthermore, mirusviruses are widespread and transcriptionally active from pole to pole, encoding complex functional traits used during the infection of microbial eukaryotes. The ‘Mirusviricota’ prevalence, functional activity, diversification, and atypical evolutionary traits point to a lasting role of mirusviruses in the ecology of marine ecosystems that might have not only predated but also contributed to the emergence of herpesviruses and giant viruses.
Comparative analysis of two NGS platforms and different databases for analysis of AMR genes
Authors: Soni Twinkle , Pandit Ramesh , Blake Damer , Joshi Chaitanya , Joshi Madhvi
Published: December 2021

The use of antibiotics in human medicine and livestock production has contributed to the widespread occurrence of antimicrobial resistance (AMR). Recognizing the relevance of AMR to human and livestock health, it is important to assess the occurrence of genetic determinants of resistance in medical, veterinary, and public health settings in order to understand risks of transmission and treatment failure. Advances in Next Generation Sequencing (NGS) technologies have had a significant impact on research in microbial genetics and microbiome analyses. Now, strategies for high throughput sequencing from panels of PCR amplicons representing known AMR genes offer opportunities for targeted characterization of complex microbial populations. Aim of the present study was to compare the Illumina MiSeq and Ion Torrent S5 Plus sequencing platforms for use with the Ion AmpliSeq™ AMR Research Panel in a veterinary/public health setting. All samples were processed in parallel for the two sequencing technologies, subsequently following a common bioinformatics workflow to define the occurrence and abundance of AMR gene sequences. Regardless of sequencing platform, the results were closely comparable with minor differences. The Comprehensive Antibiotic Resistance Database (CARD), QIAGEN Microbial Insight - Antimicrobial Resistance (QMI-AR), Antimicrobial resistance database (AR), and CARD-CLC databases were compared for analysis, with the most genes identified using CARD. Drawing on these results we describe an end-to-end workflow for AMR gene analysis using NGS.
Network-guided supervised learning on gene expression using a graph convolutional neural network
Authors: Yingtaweesittikul Hatairat , Suphavilai Chayaporn
Published: December 2021

Background

Transcriptomic profiles have become crucial information in understanding diseases and improving treatments. While dysregulated gene sets are identified via pathway analysis, various machine learning models have been proposed for predicting phenotypes such as disease type and drug response based on gene expression patterns. However, these models still lack interpretability, as well as the ability to integrate prior knowledge from a protein-protein interaction network.

Results

We proposeGrandline, a graph convolutional neural network that can integrate gene expression data and structure of the protein interaction network to predict a specific phenotype. Transforming the interaction network into a spectral domain enables convolution of neighbouring genes and pinpointing high-impact subnetworks, which allow better interpretability of deep learning models. Grandline achieves high phenotype prediction accuracy (67-85% in 8 use cases), comparable to state-of-the-art machine learning models while requiring a smaller number of parameters, allowing it to learn complex but interpretable gene expression patterns from biological datasets.

Conclusion

To improve the interpretability of phenotype prediction based on gene expression patterns, we developed Grandline using graph convolutional neural network technique to integrate protein interaction information. We focus on improving the ability to learn nonlinear relationships between gene expression patterns and a given phenotype and incorporation of prior knowledge, which are the main challenges of machine learning models for biological datasets. The graph convolution allows us to aggregate information from relevant genes and reduces the number of trainable parameters, facilitating model training for a small-sized biological dataset.
Improving the computation efficiency of polygenic risk score modeling: Faster in Julia
Authors: Faucon Annika , Samaroo Julian , Ge Tian , Davis Lea K. , Tao Ran , Cox Nancy J. , Shuey Megan M.
Published: December 2021

To enable large-scale application of polygenic risk scores in a computationally efficient manner we translate a widely used polygenic risk score construction method, Polygenic Risk Score – Continuous Shrinkage (PRS-CS), to the Julia programing language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average run time by 5.5x. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes utilization of polygenic risk scores by lowering the computational burden of the PRS-CS method.
Hybrid Gaussian Process Models for continuous time series in bolus fed-batch cultures
Authors: Cruz-Bournazou M. Nicolás , Narayanan Harini , Fagnani Alessandro , Butté Alessandro
Published: December 2021

Hybrid modeling, meaning the integration of data-driven and knowledge-based methods, is quickly gaining popularity among many research fields, including bioprocess engineering and development. Recently, the data-driven part of hybrid methods have been largely extended with machine learning algorithms (e.g., artificial neural network, support vector regression), while the mechanistic part is typically using differential equations to describe the dynamics of the process based on its current state. In this work we present an alternative hybrid model formulation that merges the advantages of Gaussian Process State Space Models and the numerical approximation of differential equation systems through full discretization.

The use of Gaussian Process Models to describe complex bioprocesses in batch, fed-batch, has been reported in several applications. Nevertheless, handling the dynamics of the states of the system, known to have a continuous time-dependent evolution governed by implicit dynamics, has proven to be a major challenge. Discretization of the process on the sampling steps is a source of several complications, as are: 1) not being able to handle multi-rate date sets, 2) the step-size of the derivative approximation is defined by the sampling frequency, and 3) a high sensitivity to sampling and addition errors. We present a coupling of polynomial regression with Gaussian Process Models as representation of the right-hand side of the ordinary differential equation system and demonstrate the advantages in a typical fed-batch cultivation for monoclonal antibody production.
Construction and characterization of a genome-scale ordered mutant collection of Bacteroides thetaiotaomicron
Authors: Arjes Heidi A. , Sun Jiawei , Liu Hualan , Nguyen Taylor H. , Culver Rebecca N. , Celis Arianna I. , Walton Sophie Jean , Vasquez Kimberly S. , Yu Feiqiao Brian , Xue Katherine S. , Newton Daniel , Zermeno Ricardo , Weglarz Meredith , Deutschbauer Adam , Huang Kerwyn Casey , Shiver Anthony L.
Published: December 2021

Genomic analyses have revealed how the gut microbiota impacts human health. However, knowledge about the physiology of most gut commensals is largely lacking. Here, we sorted cells from a pooled library to construct an ordered collection of transposon-insertion mutants in the model commensal Bacteroides thetaiotaomicron. We applied a pooling strategy with barcode sequencing to locate mutants and created a condensed collection with single insertions in 2,565 genes. This effort enabled the development of an accurate model for progenitor-collection assembly, which identified strain-abundance biases and multi-insertion strains as important factors that limit coverage. To demonstrate the potential for phenotypic screening, we analyzed growth dynamics and morphology of the condensed collection and identified growth defects and altered cell shape in the sphingolipid-synthesis gene BT0870 and the thiamine scavenging gene BT2397. Analyses of this collection and utilization of the platform described herein to construct future ordered libraries will increase understanding of gut commensal physiology and colonization strategies.
Encoding of odors by mammalian olfactory receptors
Authors: Vihani Aashutosh , Nagai Maira H. , Juan Conan , de March Claire A. , Hu Xiaoyang S. , Pearson John , Matsunami Hiroaki
Published: December 2021

Identified ligands for > 500 mouse ORs

ORs are specifically tuned towards individual odorants and their molecular properties

Odor molecular properties are informative of odor responses

Predictive modeling and convergent evolution analyses suggest specific residues within a canonical location for odorant binding

Olfactory receptors (ORs) constitute the largest multi-gene family in the mammalian genome, with hundreds to thousands of loci in humans and mice respectively1. The rapid expansion of this massive family of genes has been generated by numerous duplication and diversification events throughout evolutionary history. This size, similarity, and diversity has made it challenging to define the principles by which ORs encode olfactory stimuli. Here, we performed a broad surveying of OR responses, using an in vivo strategy, against a diverse panel of odorants. We then used the resulting interaction profiles to uncover relationships between OR responses, odorants, odor molecular properties, and OR sequences. Our data and analyses revealed that ORs generally exhibited sparse tuning towards odorants and their molecular properties. Odor molecular property similarity between pairs of odorants was informative of odor response similarity. Finally, ORs sharing response to an odorant possessed amino acids at poorly conserved sites that exhibited both, predictive power towards odorant selectivity and convergent evolution. The localization of these residues occurred primarily at the interface of the upper halves of the transmembrane domains, implying that canonical positions govern odor selectivity across ORs. Altogether, our results provide a basis for translating odorants into receptor neuron responses for the unraveling of mammalian odor coding.
Chloroplast-localized translation for protein targeting in Chlamydomonas reinhardtii
Authors: Sun Yi , Bakhtiari Shiva , Valente-Paterno Melissa , Wu Yanxia , Law Christopher , Dai Daniel , Dhaliwal James , Bui Khanh Huy , Zerges William
Published: December 2021

Translation is localized within cells to target proteins to their proper locations. We asked whether translation occurs on the chloroplast surface in Chlamydomonas and, if so, whether it is involved in co-translational protein targeting, aligned spatially with localized translation by the bacterial-type ribosomes within this organelle, or both. Our results reveal a domain of the chloroplast envelope which is bound by translating ribosomes. Purified chloroplasts retained ribosomes and mRNAs encoding two chloroplast proteins specifically on this “translation domain”, but not a mRNA encoding a cytoplasmic protein. Ribosomes clusters were seen on this domain by electron tomography. Activity of the chloroplast-bound ribosomes is supported by results of the ribopuromycylation and puromycin-release assays. Co-translational chloroplast protein import is supported by nascent polypeptide dependency of the ribosome-chloroplast associations. This cytoplasmic translation domain aligns localized translation by organellar bacterial-type ribosomes in the chloroplast. This juxtaposition the dual translation systems facilitates the targeting and assembly of the polypeptide products.

One-Sentence Summary

Translation is localized to a domain of the chloroplast envelope for co-translational protein targeting in Chlamydomonas.
Single-cell transcriptome atlas of Drosophila gastrula 2.0
Authors: Sakaguchi Shunta , Okochi Yasushi , Tanegashima Chiharu , Nishimura Osamu , Uemura Tadashi , Kadota Mitsutaka , Naoki Honda , Kondo Takefumi
Published: December 2021

During development, positional information directs cells to specific fates, leading them to differentiate with their own transcriptomes and express specific behaviors and functions. However, the mechanisms underlying these processes in a genome-wide view remain ambiguous, partly because the single-cell transcriptomic data of early developing embryos containing both accurate spatial and lineage information is still lacking. Here, we report a new single-cell transcriptome atlas of Drosophila gastrulae, divided into 65 transcriptomically distinct clusters. We found that the expression profiles of plasma-membrane-related genes, but not those of transcription factor genes, represented each germ layer, supporting the nonequivalent contribution of each transcription factor mRNA level to effector gene expression profiles at the transcriptome level. We also reconstructed the spatial expression patterns of all genes at the single-cell stripe level as the smallest unit. This atlas is an important resource for the genome-wide understanding of the mechanisms by which genes cooperatively orchestrate Drosophila gastrulation.
Effects of homoeologous exchange on gene expression and alternative splicing in a newly formed allotetraploid wheat
Authors: Zhang Zhibin , Xun Hongwei , Lv Ruili , Gou Xiaowan , Ma Xintong , Li Juzuo , Zhao Jing , Li Ning , Gong Lei , Liu Bao
Published: December 2021

Homoeologous exchange (HE) is a major mechanism generating post-polyploidization genetic variation with important evolutionary consequences. However, the direct impacts of HE without entangling with additional evolutionary forces on gene expression remains to be fully understood. Here, we analyzed high-throughput RNA-seq data of young leaves from individuals of a synthetic allotetraploid wheat (AADD), which contain variable numbers of HEs. We aimed to investigate if and to which extent HE directly impacts gene expression and alternative splicing (AS). We found that HE impacts expression of genes located within HE regions primarily via cis-acting dosage effect, which led to significant changes in the total expression of homoeolog pairs, especially for homoeologs whose original expression was biased. In parallel, HE influences expression of a large amount of genes residing in non-HE regions by trans-regulation leading to convergent expression of homoeolog pairs. Intriguingly, when taking into account of the original relative homoeolog expression states, homoeolog pairs under trans-effect are more prone to showing convergent response to HE whereas those under cis-effect trended to show subgenome-specific expression. Moreover, HE induced quantitative, largely individual-specific, changes of alternative splicing (AS) events. Like homoeologs expression, homoeo-AS events which related to trans effect were more responsive to HE. HE therefore exerts multifaceted immediate effects on gene expression and, to a less extent, also transcript diversity in nascent allopolyploidy.
Oncomodulin derived from regeneration-associated macrophages in dorsal root ganglia promotes axon regeneration in the spinal cord
Authors: Kwon Min Jung , Seo Yeojin , Cho Hana , Choi Jihye , Kim Hyung Soon , Oh Youngjoo , Kim Minjae , Park Hee Hwan , Joe Eun-Hye , Kwon Myung-Hee , Kang Han Chang , Kim Byung Gon
Published: December 2021

Preconditioning nerve injury drives pro-regenerative perineuronal macrophage activation in dorsal root ganglia (DRG). The present study reports that oncomodulin (ONCM) is produced from the regeneration-associated macrophages (RAMs) and strongly influences regeneration of DRG sensory axons. Preconditioning injury upregulated ONCM in DRG macrophages in a CCR2 dependent manner. ONCM in macrophages was necessary to produce RAMs in the in vitro model of neuron-macrophage interaction and played an essential role in for preconditioning or CCL2-induced neurite outgrowth. ONCM potently increased neurite outgrowth in cultured DRG neurons by activating a distinct gene set, particularly neuropeptide-related genes. Increasing extracellularly secreted ONCM in DRGs sufficiently enhanced capacity of neurite outgrowth. To achieve sustained ONCM activity in vivo, recombinant ONCM was encapsulated by a reducible epsilon-poly(L-lysine)-nanogel (REPL-NG) system based on electrostatic interaction. Localized injection of REPL-NG/ONCM complex into DRGs achieved a remarkable long-range axonal regeneration beyond spinal cord lesion, surpassing the extent of the preconditioning effects.
31P Transversal Relaxation Times and Metabolite Concentrations in the Human Brain at 9.4T
Authors: Dorst Johanna , Borbath Tamas , Ruhm Loreen , Henning Anke
Published: December 2021

A method to estimate phosphorus (31P) transversal relaxation times (T2) of coupled spin systems is demonstrated. Additionally, intracellular and extracellular pH (pHext, pHint) and relaxation corrected metabolite concentrations are reported. Echo time (TE) series of 31P metabolite spectra were acquired using STEAM localization. Spectra were fitted using LCModel with accurately modeled Vespa basis sets accounting for J-evolution of the coupled spin systems. T2s were estimated by fitting a single exponential two-parameter model across the TE series. Fitted inorganic phosphate frequencies were used to calculate pH, and relaxation times were used to determine the brain metabolite concentrations. The method was demonstrated in the healthy human brain at a field strength of 9.4T. T2 relaxation times of ATP and NAD are the shortest between 8 ms and 20 ms, followed by T2s of inorganic phosphate between 25 ms and 50 ms, and PCr with a T2 of 100 ms. Phosphomonoesters and –diesters have the longest T2s of about 130 ms. Measured T2s are comparable to literature values and fit in a decreasing trend with increasing field strengths. Calculated pHs and metabolite concentrations are also comparable to literature values.
Improved binding affinity of the Omicron’s spike protein with hACE2 receptor is the key factor behind its increased virulence
Authors: Kumar Rajender , Murugan N. Arul , Srivastava Vaibhav
Published: December 2021

The new variant of SARS-CoV-2, Omicron, has been quickly spreading in many countries worldwide. Compared to the original virus, Omicron is characterized by several mutations in its genomic region, including spike protein’s receptor-binding domain (RBD). We have computationally investigated the interaction between RBD of both wild-type and omicron variants with hACE2 receptor using molecular dynamics and MM-GBSA based binding free energy calculations. The mode of the interaction between Omicron’s RBD to the human ACE2 (hACE2) receptor is similar to the original SARS-CoV-2 RBD except for a few key differences. The binding free energy difference shows that the spike protein of Omicron has increased binding affinity for the hACE-2 receptor. The mutated residues in the RBD showed strong interactions with a few amino acid residues of the hACE2. More specifically, strong electrostatic interactions (salt bridges) and hydrogen bonding were observed between R493 and R498 residues of the Omicron RBD with D30/E35 and D38 residues of the hACE2, respectively. Other mutated amino acids in the Omicron RBD, e.g. S496 and H505, also exhibited hydrogen bonding with the hACE2 receptor. The pi-stacking interaction was also observed between tyrosine residues (RBD-Tyr501: hACE2-Tyr41) in the complex, which contributes majorly to binding free energies suggesting this as one of the key interactions stabilizing the complex formation. The structural insights of RBD:hACE2 complex, their binding mode information and residue wise contributions to binding free energy provide insight on the increased transmissibility of Omicron and pave the way to design and optimize novel antiviral agents.
GBP2 engages Galectin-9 for immunity againstToxoplasma gondii
Authors: Kravets Elisabeth , Poschmann Gereon , Hänsch Sebastian , Raba Veronica , Weidtkamp-Peters Stefanie , Degrandi Daniel , Stühler Kai , Pfeffer Klaus
Published: December 2021

Guanylate binding proteins (GBPs) are large interferon-inducible GTPases, executing essential host defense activities againstToxoplasma gondii,an invasive intracellular apicomplexan protozoan parasite of global importance.T. gondiiestablishes a parasitophorous vacuole (PV) which shields the parasite from the host’s intracellular defense mechanisms. Murine GBPs (mGBPs) recognizeT. gondiiPVs and assemble into supramolecular mGBP homo- and heterocomplexes that are required for the disruption of the membrane of PVs eventually resulting in the cell-autonomous immune control of vacuole-resident pathogens. We have previously shown that mGBP2 plays an important role inT. gondiiimmune control. Here, to unravel mGBP2 functions, we report Galectin-9 (Gal9) as a critical mGBP2 interaction partner engaged for immunity toT. gondii. Interestingly, Gal9 also accumulates and colocalizes with mGBP2 at theT. gondiiPV. Furthermore, we could prove the requirement of Gal9 for growth control ofT. gondiiby CRISPR/Cas9 mediated gene editing. These discoveries clearly indicate that Gal9 is a crucial factor for the mGBP2 coordinated cell autonomous host defense mechanism againstT. gondii.
Classification of Preeclamptic Placental Extracellular Vesicles Using Femtosecond Laser-fabricated Nanoplasmonic Sensors and Machine Learning
Authors: Kazemzadeh Mohammadrahim , Martinez-Calderon Miguel , Paek Song Y. , Lowe MoiMoi , Aguergaray Claude , Xu Weiliang , Chamley Lawrence W. , Broderick Neil G.R. , Hisey Colin L.
Published: December 2021

Placental extracellular vesicles (EVs) play an essential role in pregnancy by protecting and transporting diverse biomolecules that aid in fetomaternal communication. However, in preeclampsia, they have also been implicated in contributing to disease progression. Despite their potential clinical value, most current technologies cannot provide a rapid and effective means of differentiating between healthy and diseased placental EVs. To address this, we developed a fabrication process called laser-induced nanostructuring of SERS-active thin films (LINST), which produces nanoplasmonic substrates that provide exceptional Raman signal enhancement and allow the biochemical fingerprinting of EVs. After validating LINST performance with chemical standards, we used placental EVs from tissue explant cultures and demonstrated that preeclamptic and normotensive placental EVs have classifiably distinct Raman spectra following the application of both conventional and advanced machine learning algorithms. Given the abundance of placental EVs in maternal circulation, these findings will encourage immediate exploration of surface-enhanced Raman spectroscopy (SERS) as a promising method for preeclampsia liquid biopsies, while our novel fabrication process can provide a versatile and scalable substrate for many other SERS applications.
Structure of telomerase-bound CST with Polymerase α-Primase
Authors: He Yao , Song He , Chan Henry , Wang Yaqiang , Liu Baocheng , Susac Lukas , Zhou Z. Hong , Feigon Juli
Published: December 2021

Telomeres are the physical ends of linear chromosomes, composed of short repeating sequences (e.g. TTGGGG in Tetrahymena for the G-strand) of double-stranded DNA with a single-strand 3’-overhang of the G-strand and a group of proteins called shelterin1,2. Among these, TPP1 and POT1 associate with the 3’-overhang, with POT1 binding the G-strand3 and TPP1 recruiting telomerase via interaction with telomerase reverse transcriptase (TERT)4. The ends of the telomeric DNA are replicated and maintained by telomerase5, for the G-strand, and subsequently DNA Polymerase α-Primase6,7 (PolαPrim), for the C-strand8. PolαPrim is stimulated by CTC1–STN1–TEN1 (CST)9–12, but the structural basis of both PolαPrim and CST recruitment to telomere ends remains unknown. Here we report cryo-EM structures of Tetrahymena CST in the context of telomerase holoenzyme, both in the absence and presence of PolαPrim, as well as of PolαPrim alone. Ctc1 binds telomerase subunit p50, a TPP1 ortholog, on a flexible Ctc1 binding motif unveiled jointly by cryo-EM and NMR spectroscopy. PolαPrim subunits are arranged in a catalytically competent conformation, in contrast to previously reported autoinhibited conformation. Polymerase POLA1 binds Ctc1 and Stn1, and its interface with Ctc1 forms an entry port for G-strand DNA to the POLA1 active site. Together, we obtained a snapshot of four key players required for telomeric DNA synthesis in a single complex—telomerase core RNP, p50/TPP1, CST and PolαPrim—that provides unprecedented insights into CST and PolαPrim recruitment and handoff between G-strand and C-strand synthesis.
Catch the star! Spatial information activates the manual motor system
Authors: Miklashevsky A.
Published: December 2021

Previous research demonstrated a close bidirectional relationship between spatial attention and the manual motor system. However, it is unclear whether an explicit hand movement is necessary for this relationship to appear. A novel method with high temporal resolution – bimanual grip force registration – sheds light on this issue. Participants held two grip force sensors while being presented with lateralized stimuli (exogenous attentional shifts, Experiment 1), left- or right-pointing central arrows (endogenous attentional shifts, Experiment 2), or the words “left” or “right” (endogenous attentional shifts, Experiment 3). There was an early interaction between the presentation side or arrow direction and grip force: lateralized objects and central arrows led to an increase of the ipsilateral force and a decrease of the contralateral force. Surprisingly, words led to the opposite pattern: increased force in the contralateral hand and decreased force in the ipsilateral hand. The effect was stronger and appeared earlier for lateralized objects (60 ms after stimulus presentation) than for arrows (100 ms) or words (250 ms). Thus, processing visuospatial information automatically activates the manual motor system, but the timing and direction of this effect vary depending on the type of stimulus.
Antibody-free profiling of transcription factor occupancy during early embryogenesis by FitCUT&RUN
Authors: Wang Xiangxiu , Wang Wen , Wang Yiman , Chen Jia , Liu Guifen , Zhang Yong
Published: December 2021

Key transcription factors (TFs) play critical roles in zygotic genome activation (ZGA) during early embryogenesis, whereas genome-wide occupancies of only a few factors have been profiled during ZGA due to the limitation of cell numbers or the lack of high-quality antibodies. Here, we present FitCUT&RUN, a modified CUT&RUN method, in which an Fc fragment of immunoglobulin G is used for tagging, to profile TF occupancy in an antibody-free manner and demonstrate its reliability and robustness using as few as 5000 K562 cells. We applied FitCUT&RUN to zebrafish undergoing embryogenesis to generate reliable occupancy profiles of three known activators of zebrafish ZGA: Nanog, Pou5f3, and Sox19b. By profiling the time-series occupancy of Nanog during zebrafish ZGA, we observed a clear trend toward a gradual increase in Nanog occupancy and found that Nanog occupancy prior to the major phase of ZGA is important for the activation of some early transcribed genes.
Convergence theorem of Pettis integrable multivalued pramart
Authors: El-Louh M'Hamed , El Allali Mohammed , Ezzaki Fatima
Published: December 2021

Purpose

In this work, the authors are interested in the notion of vector valued and set valued Pettis integrable pramarts. The notion of pramart is more general than that of martingale. Every martingale is a pramart, but the converse is not generally true.

Design/methodology/approach

In this work, the authors present several properties and convergence theorems for Pettis integrable pramarts with convex weakly compact values in a separable Banach space.

Findings

The existence of the conditional expectation of Pettis integrable mutifunctions indexed by bounded stopping times is provided. The authors prove the almost sure convergence in Mosco and linear topologies of Pettis integrable pramarts with values in (cwk(E)) the family of convex weakly compact subsets of a separable Banach space.

Originality/value

The purpose of the present paper is to present new properties and various new convergence results for convex weakly compact valued Pettis integrable pramarts in Banach space.