bluuebunny/crossref_metadata_2025 huggingface dataset:
KBoost: a new method to infer gene regulatory networks from gene expression data
Authors: Iglesias-Martinez Luis F. , De Kegel Barbara , Kolch Walter
Published: April 2021

Reconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24000 genes in less than 2 hours on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at:https://github.com/Luisiglm/KBoost.
Revisiting the recombinant history of HIV-1 group M with dynamic network community detection
Authors: Olabode Abayomi S , Ng Garway T , Wade Kaitlyn E , Salnikov Mikhail , Dick David W , Poon Art FY
Published: April 2021

A new abundance of full-length HIV-1 genome sequences provides an opportunity to revisit the standard model of HIV-1/M diversity that clusters genomes into largely non-recombinant subtypes, which is not consistent with recent evidence of deep recombinant histories for SIV and other HIV-1 groups. Here we develop an unsupervised non-parametric clustering approach, which does not rely on predefined non-recombinant genomes, by adapting a community detection method developed for dynamic social network analysis. We show that this method (DSBM) attains a significantly lower mean error rate in detecting recombinant breakpoints in simulated data (quasibinomial GLM,P< 8 × 10−8), compared to other reference-free recombination detection programs (GARD, RDP4 and RDP5). Applied to a representative sample ofn= 525 actual HIV-1 genomes, we determinedk= 25 as the optimal number of DSBM clusters, and used change point detection to estimate that at least 95% of these genomes are recombinant. Further, we identified both known and novel recombination hotspots in the HIV-1 genome, and evidence of inter-subtype recombination in HIV-1 subtype reference genomes. We propose that clusters generated by DSBM can provide an informative new framework for HIV-1 classification.
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes
Authors: Kawaguchi Risa Karakida , Tang Ziqi , Fischer Stephan , Rajesh Chandana , Tripathy Rohit , Koo Peter K. , Gillis Jesse
Published: April 2021

Motivation

Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate.

Results

In this study, we perform a systematic comparison of 7 scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available athttps://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner.

Contact

jesse.gillis@utoronto.ca

Supplementary information

Supplementary data are available atxxxxxxonline.

Key points

7 scATAC-seq datasets of mouse brain are systematically compared to benchmark the efficacy of neuronal cell-type annotation from gene sets.

Redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation beyond the heterogeneity of scATAC-seq data.

We reannotated all scATAC-seq data for detailed cell types using robust marker genes and their meta scATAC-seq profiles are publicly available at a new Meta scATAC-seq server.

Predicted profiles from only DNA sequence using a deep neural network are visualized together to explore sequence-dependent and -independent epigenetic regulation.
A Comprehensive Phylogenetic and Bioinformatics Survey of Lectins in the Fungal kingdom
Authors: Lebreton Annie , Bonnardel François , Dai Yu-Cheng , Imberty Anne , Martin Francis M. , Lisacek Frédérique
Published: April 2021

Fungal lectins are a large family of glycan-binding proteins, with no enzymatic activity. They play fundamental biological roles in the interactions of fungi with their environment and are found in many different species throughout the fungal kingdom. In particular, their contribution to defence against feeders has been emphasized and extracellular lectins may be involved in the recognition of bacteria, fungal competitors and specific host plants. Their carbohydrate specificities and quaternary structures vary widely, but evidence for an evolutionary relationship within the different classes of lectins is provided by the high degree of amino acid sequence identity shared by the different fungal lectins. The UniLectin3D database contains 194 3D structures of fungal lectins, of which 129 are characterized with their carbohydrate ligand. UniLectin3D lectin classes from all origins were used to construct 107 lectin motifs in 26 folding configurations and to screen 1,223 species deposited in the genomic portal MycoCosm of the Joint Genome Institute. The resulting 33 485 protein sequences of putative lectins are organized in MycoLec, a publicly available and searchable database. The characterization of the lectin candidates in fungal genomes is based on systematic statistics regarding potential carbohydrate ligands, protein lengths, signal peptides, relative motif positions and amino acid compositions of fungal lectins. These results shed light on the evolution of the lectin gene families.
Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry Based Workflow
Authors: Charkow Joshua , Röst Hannes L.
Published: April 2021

In bottom-up mass spectrometry based proteomics, deep proteome coverage is limited by high cofragmentation rates. This occurs when more than one analyte is isolated by the quadrupole and the subsequent fragmentation event produces fragment ions of heterogeneous origin. One strategy to reduce cofragmentation rates is through effective peptide separation techniques such as chromatographic separation and, the more recently popularized, ion mobility (IM) spectrometry which separates peptides by their collisional cross section. Here we investigate the capability of the Trapped Ion Mobility Spectrometry (TIMS) device to effectively separate peptide ions and quantify the separation power of the TIMS device in the context of a Parallel Accumulation-Serial Fragmentation (PASEF) workflow. We found that TIMS IM separation increases the number of interference-free MS1 features 9.2-fold, while decreasing the average peptide density in precursor spectra 6.5 fold. In a Data Dependent Acquisition (DDA) PASEF workflow, IM separation increased the number of spectra without cofragmentation by a factor of 4.1 and the number of high quality spectra 17-fold. This observed decrease in spectral complexity results in a substantial increase in peptide identification rates when using our data-driven model. In the context of a Data Independent Acquisition (DIA), the reduction in spectral complexity resulting from IM separation is estimated to be equivalent to a 4-fold decrease in isolation window width (from 25Da to 6.5Da). Our study shows that TIMS IM separation dramatically reduces cofragmentation rates leading to an increase in peptide identification rates.
Thermodynamics of droplets undergoing liquid-liquid phase separation
Authors: Biswas Subhadip , Mukherjee Biswaroop , Chakrabarti Buddhapriya
Published: April 2021

We study the thermodynamics of binary mixtures wherein the volume fraction of the minority component is less than the amount required to form a flat interface. Based on an explicit microscopic mean field theory, we show that the surface tension dominated equilibrium phase of a polymer mixture forms a single macroscopic droplet. A combination of elastic interactions that renormalize the surface tension, and arrests phase separation for a gel-polymer mixture, stabilize a micro-droplet phase. We compute the droplet size as a function of the interfacial tension, Flory parameter, and elastic moduli of the gel. Our results illustrate the importance of the rheological properties of the solvent in dictating the thermodynamic phase behavior of biopolymers undergoing liquid-liquid phase separation.
A general framework to learn tertiary structure for protein sequence characterization
Authors: Gao Mu , Skolnick Jeffrey
Published: April 2021

During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.
Susceptibility of rosaceous fruits and apple cultivars to postharvest rot by Paecilomyces niveus
Authors: Wang Tristan W. , Hodge Kathie T.
Published: April 2021

Paecilomyces rot of apples is a postharvest disease caused byPaecilomyces niveus, a problematic spoiling agent of fruit juices and derivatives. The fungus produces ascospores that can survive food processing and germinate in finished fruit products. Processing apple fruits infected with Paecilomyces rot can lead toP. niveuscontaminated juices. Because the fungus produces the mycotoxin patulin, juice spoilage byP. niveusis an important health hazard. Little is known about the disease biology and control mechanisms of this recently described postharvest disease. Following Koch’s postulates, we determined that a range of previously untested rosaceous fruits and popular apple cultivars are susceptible to Paecilomyces rot infection. We also observed that two closely related food spoiling fungi,Paecilomyces fulvusandPaecilomyces variotti, were unable to infect, cause symptoms in, or reproduce in wounded fruits. Our results highlight the unique abilities ofPaecilomyces niveusto infect a variety of fruits, produce patulin, and form highly-resistant spores capable of spoiling normally shelf-stable products.
The MLR COMPASS-like complex is required to regulate triglyceride depletion and stress response in the Drosophila fat body
Authors: Ford David J , Zraly Claudia B , Ng Jeffrey , Dingwall Andrew K
Published: April 2021

MLR COMPASS-like complexes are highly conserved histone modifiers and enhancer regulators recruited by multiple transcription factors during differentiation and development, including lineage-determining factors, nuclear receptors, and developmental signaling pathway effectors. While the essential functions of MLR complexes during differentiation-associated transcriptional reprogramming has been well-explored, roles that these complexes play during reprogramming events in terminally differentiated cells remain understudied. We determined that the Drosophila MLR complex is required in fat body adipocytes for proper regulation of the triglyceride depletion rate during non-feeding periods, including metamorphosis and adult starvation. Transcriptome analysis revealed that the complex plays a critical role in controlling stress-related reprogramming in these cells, suggesting that the metabolic phenotypes are indirect effects of dysregulated stress transcription. Furthermore, our evidence suggests that the complex interacts with Foxo and Relish (Nf-κb) pathways and is required for proper expression of their targets. This investigation further elucidates the necessary functions of MLR complexes in regulating transcriptional reprogramming in terminally differentiated cells as well as suggests novel binding partners. Apparent roles for these complexes in the proper regulation of stress-induced transcription implies new mechanisms involved in cancer and other human diseases associated with MLR subunit mutation.
Quantitative prey species detection in predator guts across multiple trophic levels by DNA shotgun sequencing
Authors: Paula Débora P. , Timbó Renata V. , Togawa Roberto C. , Vogler Alfried P. , Andow David A.
Published: April 2021

Metabarcoding has revolutionized the study of ecological communities, but PCR bias hampers quantitative analyses, as required in studies of trophic interactions. Direct DNA shotgun sequencing can avoid the amplification step when unassembled reads are mapped to a reference database, enabling identification and quantification of prey items.

Two feeding bioassays tested the precision and accuracy of quantitative assessments with the coccinellids Harmonia axyridis and Hippodamia convergens, the chrysopid Chrysoperla externa, and the aphid Myzus persicae. Guts were dissected and the total DNA extracted was directly sent to sequence by Illumina HiSeq2500 (insert 350 bp, PE 250). Predator gut content reads were blasted against an arthropod mitochondrial DNA reference database for taxonomic assignment and the matches curated for false positive prey identification using a series of bioinformatics pipelines, mostly in R. Taxonomic assignment through KrakenUnique, which is based on the counts of unique k-mers, was compared.

In a Prey Quantity bioassay, the number of prey reads was correlated to the amount of prey consumed and the elapsed time since consumption. In a Direct and Indirect Predation bioassay, prey was detectable in the predator 6 h after feeding (direct predation) and the prey’s prior prey was detectable 3 h after feeding (indirect predation). Detection of indirect predation was related to the species-specific decay rates of the two predators, but not to the order of predation.

We demonstrate that degraded prey DNA was quantifiable across trophic levels with high accuracy (98.4% positive predictive value) and taxonomic resolution.
A literature-derived knowledge graph augments the interpretation of single cell RNA-seq datasets
Authors: Doddahonnaiah Deeksha , Lenehan Patrick , Hughes Travis , Zemmour David , Garcia-Rivera Enrique , Venkatakrishnan AJ , Chilaka Ramakrisha , Khare Apoorv , Anand Akash , Barve Rakesh , Thiagarajan Viswanathan , Soundararajan Venky
Published: April 2021

Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have rapidly advanced in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p < 6.15×10−76, r = 0.24; cohen’s D = 2.6). Building on this, we developed an augmented annotation algorithm that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 185 clusters in 13 datasets from human blood, pancreas, lung, liver, kidney, retina, and placenta. With the optimized settings, the true cellular identity matched the top prediction in 66% of tested clusters and was present among the top five predictions for 94% of clusters. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of established cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.
Individual variability in the human connectome maintains selective cross-modal consistency and shares microstructural signatures
Authors: Karahan Esin , Tait Luke , Si Ruoguang , Özkan Ayşegül , Szul Maciek , Zhang Jiaxiang
Published: April 2021

Individuals are different in their behavioural responses and cognitive abilities. Neural underpinnings of individual differences are largely unknown. Here, by using multimodal imaging data including diffusion MRI, functional MRI and MEG, we show the consistency of interindividual variation of connectivity across modalities. We demonstrated that regional differences in individual variability of structural and functional connectomes is characterized by higher variability in association cortices and lower variability in sensory and visual cortices. This pattern is consistent across all modalities at varying degrees as shown by significant alignment between functional and structural connectome variabilities at several clusters of brain regions. Variability in connectivity is associated with cortical myelin content and microstructural properties of connections. Our findings contribute to understanding of individual differences in functional and structural organization of brain and facilitate fingerprinting applications.
The SPARC DRC: Building a resource for the autonomic nervous system community
Authors: Osanlouy Mahyar , Bandrowski Anita , de Bono Bernard , Brooks David , Cassarà Antonio M. , Christie Richard , Ebrahimi Nazanin , Gillespie Tom , Grethe Jeffrey S. , Guercio Leonardo A. , Heal Maci , Lin Mabelle , Kuster Niels , Martone Maryann E. , Neufeld Esra , Nickerson David P. , Soltani Elias G. , Tappan Susan , Wagenaar Joost B. , Zhuang Katie , Hunter Peter J.
Published: April 2021

The Data and Resource Center (DRC) of the NIH-funded SPARC program is developing databases, connectivity maps and simulation tools for the mammalian autonomic nervous system. The experimental data and mathematical models supplied to the DRC by the SPARC consortium are curated, annotated and semantically linked via a single knowledgebase. A data portal has been developed that allows discovery of data and models both via semantic search and via an interface that includes Google Map-like 2D flatmaps for displaying connectivity, and 3D anatomical organ scaffolds that provide a common coordinate framework for cross-species comparisons. We discuss examples that illustrate the data pipeline, which includes data upload, curation, segmentation (for image data), registration against the flatmaps and scaffolds, and finally display via the web portal, including the link to freely available online computational facilities that will enable neuromodulation hypotheses to be investigated by the autonomic neuroscience community and device manufacturers.
Soil-free pathogenicity assay ofFusarium oxysporumf.sp.vasinfectumrace 4 on Pima cotton (Gossypium barbadense) seedlings
Authors: Murty Lia D. , Shim Won Bo
Published: April 2021

One of the devastating early season diseases of cotton is Fusarium wilt caused byFusarium oxysporumf. sp.vasinfectum(Fov). Recent emergence of highly virulent Fov race 4 (Fov4) and its aggressiveness towardGossypium barbadense(pima) cultivars are raising significant concerns for the US cotton industry. One of the key challenges in studying Fov4 virulence and cotton Fusarium wilt pathogenesis is establishing a disease assay strategy that can help researcher overcome several technical challenges, including efficient infection and highly reproducible and consistent symptom development. Here, we have developed a small-scale, soil-free Fusarium wilt disease assay that can complement conventional assays with faster symptom development and high reproducibility in infected pima cotton seedlings. Our data showed statistically significant differences (p<0.0001) between Fov4-infected and non-infected pima cotton at 4 and 6 days post inoculation (dpi) when compared to control experiments. At 6 dpi, longitudinal observations under magnification showed Fov4 colonization in primary xylem of infected plants, which is a common symptom observed in Fov4 triggered Fusarium wilt in pima cotton. While this is an artificial assay system, this soil-free disease testing strategy can offer another strategy to supplement current assays when studying pathogen-host interaction in soil-borne diseases.
The Actin Bundling Protein Fascin Promotes Lung Cancer Growth and Metastasis by Enhancing Glycolysis and PFKFB3 Expression
Authors: Lin Shengchen , Li Yunzhan , Wang Dezhen , Huang Chongbiao , Marino David , Bollt Oana , Wu Chaodong , Taylor Matthew , Li Wei , DeNicola Gina M. , Hao Jihui , Singh Pankaj K. , Yang Shengyu
Published: April 2021

Fascin is a pro-metastasis actin bundling protein upregulated in essentially all the metastatic carcinoma. It is believed that fascin promotes cancer cell migration and invasion by facilitating membrane protrusions such as filopodia and invadopodia. Aerobic glycolysis is a key feature of cancer metabolism that provides critical intermediate metabolites for tumor growth and cell proliferation. Here we report that fascin increase glycolysis in lung cancer to promote tumor growth and metastasis. Fascin promotes glycolytic flux by increasing the expression and activities of phosphofructose kinase 1 and 2 (PFK1 and 2). The glycolytic function of fascin depends on activation of YAP1 through its canonical actin bundling activity. Fascin promotes the binding of YAP1 to a TEAD1/4 binding motif located 30 bp upstream of the PFKFB3 transcription start site to activate its transcription. Our interrogation of the TCGA database suggest that the fascin-YAP1-PFKFB3 circuit is likely conserved across different types of cancer. We further showed that the glycolytic function of fascin is essential for promotion of lung cancer growth and metastasis. Importantly, pharmacological inhibitors of fascin could be used to suppress the YAP1-PFKFB3 signaling and inhibit glycolysis in cancer cell lines, organoid cultures and xenograft metastasis models. Taken together, our data reveal an important glycolytic role of fascin in lung cancer metabolism, and suggest that pharmacological inhibitors of fascin could be used to reprogram cancer metabolism in lung cancer and potentially other cancer with fascin upregulation.
Autologous humanized mouse models of iPSC-derived tumors allow for the evaluation and modulation of cancer-immune cell interactions
Authors: Moquin-Beaudry Gaël , Benabdallah Basma , Maggiorani Damien , Le Oanh , Li Yuanyi , Colas Chloé , Raggi Claudia , Ellezam Benjamin , M’Callum Marie-Agnès , Dal Soglio Dorothée , Guimond Jean V. , Paganelli Massimiliano , Haddad Elie , Beauséjour Christian
Published: April 2021

Modeling the tumor-immune cell interactions in humanized mice is complex and limits drug development. Here, we generated easily accessible tumor models by transforming either primary skin fibroblasts or iPSC-derived cell lines injected in immune-deficient mice reconstituted with human autologous immune cells. Our results showed that either fibroblastic, hepatic or neural tumors were all efficiently infiltrated and partially or totally rejected by autologous immune cells in humanized mice. Characterization of tumor immune infiltrates revealed high expression levels of the dysfunction markers Tim3 and PD-1 in T cells and an enrichment in regulatory T cell suggesting rapid establishment of an immunosuppressive microenvironment. Inhibition of PD-1 by Nivolumab in humanized mice resulted in an increased immune cell infiltration and a slight decrease in tumor growth. We expect these versatile and accessible cancer models will facilitate preclinical studies and the evaluation of autologous cancer immunotherapies across a range of different tumor cell types.

SIGNIFICANCE STATEMENT

Preclinical models capable of providing an environment where human tumors are confronted with autologous immune cells are not easily accessible and limit drug development. As an alternative we generated genetically-defined tumor cell lines from primary and iPSC-derived cells for the evaluation of cancer-immune cell interactions in autologous humanized mice.
Serum CSF1 levels are elevated in patients following partial hepatectomy and acetaminophen induced acute liver failure: Reanalysis of previous data
Authors: Stutchfield BM , Starkey Lewis PJ , Wigmore SJ , Simpson K , Hume DA , Forbes SJ
Published: April 2021

A previous study analysed serum CSF1 concentration in patients who had undergone partial hepatectomy and patients with acute liver failure (ALF) induced by acetaminophen. A correlation between low CSF1 and poor prognosis was reported in ALF. In May 2018, an allegation of research misconduct was made against the second author of the published study, D.J.Antoine, who carried out the CSF1 analysis. The current study describes the independent reanalysis of circulating CSF1 concentration in sera from the same patient cohorts. Patient serum CSF1 levels increased following partial hepatectomy. By contrast to the published report, serum CSF1 concentration was greatest in ALF patients who died compared to survivors. The results confirm the relationship between liver injury and circulating serum CSF1.
Egocentric Biases are Determined by the Precision of Self-related Predictions
Authors: Sevi Leora , Stantic Mirta , Murphy Jennifer , Coll Michel-Pierre , Catmur Caroline , Bird Geoffrey
Published: April 2021

According to predictive processing theories, emotional inference involves simultaneously minimising discrepancies between predictions and sensory data relating to both one’s own and others’ states, achievable by altering either one’s own state (empathy) or perception of another’s state (egocentric bias) so they are more congruent. We tested a key hypothesis of these accounts, that predictions are weighted in inference according to their precision (inverse variance). If correct, more precise self-related predictions should bias perception of another’s emotional expression to a greater extent than less precise predictions. We manipulated predictions about upcoming own-pain (low or high magnitude) using cues that afforded either precise (a narrow range of possible magnitudes) or imprecise (a wide range) predictions. Participants judged pained facial expressions presented concurrently with own-pain to be more intense when own-pain was greater, and precise cues increased this biasing effect. Implications of conceptualising interpersonal influence in terms of predictive processing are discussed.
Oncogenic RAS activity predicts response to chemotherapy and outcome in lung adenocarcinoma
Authors: East Philip , Kelly Gavin P. , Biswas Dhruva , Marani Michaela , Hancock David C. , Creasy Todd , Sachsenmeier Kris , Swanton Charles , de Carné Trécesson Sophie , Downward Julian
Published: April 2021

Activating mutations in the driver oncogeneKRASoccur in 32% of lung adenocarcinomas, leading to more aggressive disease and resistance to therapy in preclinical studies. However, the association betweenKRASmutational status and patient outcome or response to treatment remains unclear, likely due to additional events modulating RAS pathways. To obtain a broader measure of RAS pathway activation beyondKRASmutation only, we developed RAS84, a transcriptional signature optimised to capture RAS oncogenic activity in lung adenocarcinoma. Using RAS84 to classify lung cell lines, we show that RAS transcriptional activity outperformsKRASmutation to predict resistance to chemotherapy drugsin vitro. We report that 84% of lung adenocarcinomas show clear transcriptional evidence of RAS oncogenic activation, falling into four groups characterised by coincident mutation ofSTK11/LKB1,TP53orCDKN2A. Given that 65% of these RAS pathway active tumours do not haveKRASmutations, we find that the classifications developed when considering onlyKRASmutant tumours have significance in a much broader cohort of patients. Critically, patients in the highest RAS activity groups show adverse clinical outcome and reduced response to chemotherapy. The stratification of patients using gene expression patterns linked to oncogenic RAS signalling activity instead of genetic alterations in cancer genes could ultimately help clinical decision making.
Integrated spatial multi-omics reveals fibroblast fate during tissue repair
Authors: Foster Deshka S , Januszyk Michael , Chinta Malini S , Yost Kathryn E , Gulati Gunsagar S , Nguyen Alan T , Burcham Austin R , Salhotra Ankit , Chase Ransom R , Henn Dominic , Chen Kellen , Mascharak Shamik , Tolentino Karen , Titan Ashley L , Ellen Jones R , da Silva Oscar , Tripp Leavitt W , Marshall Clement D , desJardins-Park Heather E , Hu Michael S , Wan Derrick C , Wernig Gerlinde , Wagh Dhananjay , Coller John , Norton Jeffrey A , Gurtner Geoffrey C , Newman Aaron M , Chang Howard Y , Longaker Michael T
Published: April 2021

In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to define the mechanisms controlling cell fate during migration, proliferation, and differentiation following tissue injury and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.
Case based learning as a dynamic approach towards learning oral pathology
Authors: Shrestha Ashish , Marla Vinay , Rimal Jyotsna , Shrestha Sushmita , Keshwar Shashi , Zhimin Jia
Published: April 2021

Introduction

Oral pathology comprises of extensive teaching-learning of histopathology. Teaching histopathology is always challenging more when numerous histopathology slides are involved. There is lack of interest among the students and also face challenges to correlate with the clinical presentation. For effective learning, need for a method which is interactive and helps create clinico pathological correlation was felt. This study was designed to assess the impact and feasibility of case-based learning in routine teaching among dental students.

Methods

A cross sectional study was conducted among 58 undergraduate dental students, wherein a case-based learning method was introduced in the practical classes. Thirty students were randomly selected as case based learning and remaining as conventional group. Multiple paper-based cases on oral cancer was designed and used for the case based learning group of students. A self-designed pre and posttest interventional tools along with post intervention assessment using modified essay question was designed and applied.

Results

Significant difference in the scores between pre and post intervention questionnaire was observed among the case-based learning group (p<0.0001), however non-significant among the conventional. Similarly, significant difference in the scores of modified essay question was observed between the study groups. Majority of the students among the case-based learning group agreed that it benefitted their personal, professional and communicative skills. The students expressed their enthusiasm in learning with this method and suggested to apply it more often.

Discussion

Case-based learning is an effective method and incorporation of it on a regular basis could help favor an effective learning environment.
Reference transcriptomes of porcine peripheral immune cells created through bulk and single-cell RNA sequencing
Authors: Herrera-Uribe Juber , Wiarda Jayne E. , Sivasankaran Sathesh K. , Daharsh Lance , Liu Haibo , Byrne Kristen A. , Smith Timothy P.L. , Lunney Joan K. , Loving Crystal L. , Tuggle Christopher K.
Published: April 2021

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B cell, conventional CD4 and CD8 αβ T cells, NK cells, and γδ T cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.
Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons
Authors: Laschowski Brokoslaw , McNally William , Wong Alexander , McPhee John
Published: April 2021

Robotic exoskeletons require human control and decision making to switch between different locomotion modes, which can be inconvenient and cognitively demanding. To support the development of automated locomotion mode recognition systems (i.e., high-level controllers), we designed an environment recognition system using computer vision and deep learning. We collected over 5.6 million images of indoor and outdoor real-world walking environments using a wearable camera system, of which ~923,000 images were annotated using a 12-class hierarchical labelling architecture (called the ExoNet database). We then trained and tested the EfficientNetB0 convolutional neural network, designed for efficiency using neural architecture search, to predict the different walking environments. Our environment recognition system achieved ~73% image classification accuracy. While these preliminary results benchmark Efficient-NetB0 on the ExoNet database, further research is needed to compare different image classification algorithms to develop an accurate and real-time environment-adaptive locomotion mode recognition system for robotic exoskeleton control.
ARIC: Accurate and robust inference of cell type proportions from bulk gene expression or DNA methylation data
Authors: Zhang Wei , Xu Hanwen , Qiao Rong , Zhong Bixi , Zhang Xianglin , Gu Jin , Zhang Xuegong , Wei Lei , Wang Xiaowo
Published: April 2021

Quantifying the cell proportions, especially for rare cell types in some scenarios, is of great value to track signals related to certain phenotypes or diseases. Although some methods have been pro-posed to infer cell proportions from multi-component bulk data, they are substantially less effective for estimating rare cell type proportions since they are highly sensitive against feature outliers and collinearity. Here we proposed a new deconvolution algorithm named ARIC to estimate cell type proportions from bulk gene expression or DNA methylation data. ARIC utilizes a novel two-step marker selection strategy, including component-wise condition number-based feature collinearity elimination and adaptive outlier markers removal. This strategy can systematically obtain effective markers that ensure a robust and precise weighted υ-support vector regression-based proportion prediction. We showed that ARIC can estimate fractions accurately in both DNA methylation and gene expression data from different experiments. Taken together, ARIC is a promising tool to solve the deconvolution problem of bulk data where rare components are of vital importance.
Common ALS/FTD risk variants inUNC13Aexacerbate its cryptic splicing and loss upon TDP-43 mislocalization
Authors: Brown Anna-Leigh , Wilkins Oscar G. , Keuss Matthew J. , Hill Sarah E. , Zanovello Matteo , Lee Weaverly Colleen , Lee Flora C.Y. , Masino Laura , Qi Yue A. , Bryce-Smith Sam , Bampton Alexander , Gatt Ariana , Phatnani Hemali ,   NYGC ALS Consortium, Schiavo Giampietro , Fisher Elizabeth M.C. , Raj Towfique , Secrier Maria , Lashley Tammaryn , Ule Jernej , Buratti Emanuele , Humphrey Jack , Ward Michael E. , Fratta Pietro
Published: April 2021

Variants within theUNC13Agene have long been known to increase risk of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), two related neurodegenerative diseases defined by mislocalization of the RNA-binding protein TDP-43. Here, we show that TDP-43 depletion induces robust inclusion of a cryptic exon (CE) withinUNC13A, a critical synaptic gene, resulting in nonsense-mediated decay and protein loss. Strikingly, two common polymorphisms strongly associated with ALS/FTD risk directly alter TDP-43 binding within the CE or downstream intron, increasing CE inclusion in cultured cells and in patient brains. Our findings, which are the first to demonstrate a genetic link specifically between loss of TDP-43 nuclear function and disease, reveal both the mechanism by whichUNC13Avariants exacerbate the effects of decreased nuclear TDP-43 function, and provide a promising therapeutic target for TDP-43 proteinopathies.

One-Sentence Summary

Shared ALS/FTD risk variants increase the sensitivity of a cryptic exon in the synaptic geneUNC13Ato TDP-43 depletion.
The Alzheimer susceptibility geneBIN1induces isoform-dependent neurotoxicity through early endosome defects
Authors: Lambert Erwan , Saha Orthis , Landeira Bruna Soares , de Farias Ana Raquel Melo , Hermant Xavier , Carrier Arnaud , Pelletier Alexandre , Davoine Lindsay , Dupont Cloé , Amouyel Philippe , Bonnefond Amélie , Lafont Frank , Adelfettah Farida , Verstreken Patrik , Chapuis Julien , Barois Nicolas , Delahaye Fabien , Dermaut Bart , Lambert Jean-Charles , Costa Marcos R. , Dourlen Pierre
Published: April 2021

TheBridging Integrator 1(BIN1) gene is a major susceptibility gene for Alzheimer’s disease (AD). Deciphering its pathophysiological role is challenging due to its numerous isoforms. Here we observed in Drosophila that human BIN1 isoform1 (BIN1iso1) overexpression, contrary to BIN1iso8 and BIN1iso9, induced an accumulation of endosomal vesicles and neurodegeneration. Systematic search for endosome regulators able to prevent BIN1iso1-induced neurodegeneration indicated that a defect at the early endosome level is responsible for the neurodegeneration. In human induced neurons (hiNs) and cerebral organoids,BIN1knock-out resulted in the narrowing of early endosomes. This phenotype was rescued by BIN1iso1 but not BIN1iso9 expression. Finally, BIN1iso1 overexpression also led to an increase in the size of early endosomes and neurodegeneration in hiNs. Altogether, our data demonstrate that the AD susceptibility geneBIN1, and especially BIN1iso1, contributes to early-endosome size deregulation, which is an early pathophysiological hallmark of AD pathology.
Rapid Evolution of Knockdown Resistance Haplotypes in Response to Pyrethroid Selection inAedes aegypti
Authors: Baltzegar Jennifer , Vella Michael , Gunning Christian , Vasquez Gissella , Astete Helvio , Stell Fred , Fisher Michael , Scott Thomas W. , Lenhart Audrey , Lloyd Alun L. , Morrison Amy , Gould Fred
Published: April 2021

This study describes the evolution ofknockdown resistance (kdr)haplotypes inAedes aegyptiin response to pyrethroid insecticide use over the course of 18 years in Iquitos, Peru. Based on the duration and intensiveness of sampling (∼10,000 samples), this is the most thorough study of kdr population genetics inAe. aegyptito date within a city. We provide evidence for the direct connection between programmatic citywide pyrethroid spraying and the increase in frequency of specifickdrhaplotypes by identifying two evolutionary events in the population. The relatively high selection coefficients, even under infrequent insecticide pressure, emphasizes how quickly populations can evolve. The observed rapid increase in frequency of resistance alleles might have been aided by the incomplete dominance of resistance-conferring alleles over corresponding susceptibility alleles. In addition to dramatic temporal shifts, spatial suppression experiments reveal that genetic heterogeneity existed not only at the citywide scale, but also on a very fine scale within the city.
TDP-43 represses cryptic exon inclusion in FTD/ALS geneUNC13A
Authors: Ma X. Rosa , Prudencio Mercedes , Koike Yuka , Vatsavayai Sarat C. , Kim Garam , Harbinski Fred , Rodriguez Caitlin M. , Schmidt H. Broder , Cummings Beryl B. , Wyatt David W. , Kurylo Katherine , Miller Georgiana , Mekhoubad Shila , Sallee Nathan , Jansen-West Karen , Cook Casey N. , Pickles Sarah , Oskarsson Björn , Graff-Radford Neill R. , Boeve Bradley F. , Knopman David S. , Petersen Ronald C. , Dickson Dennis W. , Green Eric M. , Seeley William W. , Petrucelli Leonard , Gitler Aaron D.
Published: April 2021

A hallmark pathological feature of neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) is the depletion of RNA-binding protein TDP-43 from the nucleus of neurons in the brain and spinal cord. A major function of TDP-43 is as a repressor of cryptic exon inclusion during RNA splicing. Single nucleotide polymorphisms (SNPs) inUNC13Aare among the strongest genome-wide association study (GWAS) hits associated with FTD/ALS in humans, but how those variants increase risk for disease is unknown. Here we show that TDP-43 represses a cryptic exon splicing event inUNC13A. Loss of TDP-43 from the nucleus in human brain, neuronal cell lines, and iPSC-derived motor neurons resulted in the inclusion of a cryptic exon inUNC13AmRNA and reduced UNC13A protein expression. Remarkably, the top variants associated with FTD/ALS risk in humans are located in the cryptic exon harboring intron itself and we show that they increaseUNC13Acryptic exon splicing in the face of TDP-43 dysfunction. Together, our data provide a direct functional link between one of the strongest genetic risk factors for FTD/ALS (UNC13Agenetic variants) and loss of TDP-43 function.
The evolution of germ-soma specialization under different genetic and environmental effects
Authors: Tverskoi Denis , Gavrilets Sergey
Published: April 2021

Division of labor exists at different levels of biological organization - from cell colonies to human societies. One of the simplest examples of the division of labor in multicellular organisms is germ-soma specialization, which plays a key role in the evolution of organismal complexity. Here we formulate and study a general mathematical model exploring the emergence of germ-soma specialization in colonies of cells. We consider a finite population of colonies competing for resources. Colonies are of the same size and are composed by asexually reproducing haploid cells. Each cell can contribute to activity and fecundity of the colony, these contributions are traded-off. We assume that all cells within a colony are genetically identical but gene expression is affected by variation in the microenvironment experienced by individual cells. Through analytical theory and evolutionary agent-based modeling we show that the shape of the trade-off relation between somatic and reproductive functions, the type and extent of variation in within-colony microenvironment, and, in some cases, the number of genes involved, are important predictors of the extent of germ-soma specialization. Specifically, increasing convexity of the trade-off relation, the number of different environmental gradients acting within a colony, and the number of genes (in the case of random microenvironmental effects) promote the emergence of germ-soma specialization. Overall our results contribute towards a better understanding of the role of genetic, environmental, and microenvironmental factors in the evolution of germ-soma specialization.