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Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity. Nat Commun 2022; 13:2929. [PMID: 35614043 PMCID: PMC9132991 DOI: 10.1038/s41467-022-30511-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 05/04/2022] [Indexed: 01/02/2023] Open
Abstract
In mammals, chemoperception relies on a diverse set of neuronal sensors able to detect chemicals present in the environment, and to adapt to various levels of stimulation. The contribution of endogenous and external factors to these neuronal identities remains to be determined. Taking advantage of the parallel coding lines present in the olfactory system, we explored the potential variations of neuronal identities before and after olfactory experience. We found that at rest, the transcriptomic profiles of mouse olfactory sensory neuron populations are already divergent, specific to the olfactory receptor they express, and are associated with the sequence of these latter. These divergent profiles further evolve in response to the environment, as odorant exposure leads to reprogramming via the modulation of transcription. These findings highlight a broad range of sensory neuron identities that are present at rest and that adapt to the experience of the individual, thus adding to the complexity and flexibility of sensory coding.
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52
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Chen K, Henn D, Januszyk M, Barrera JA, Noishiki C, Bonham CA, Griffin M, Tevlin R, Carlomagno T, Shannon T, Fehlmann T, Trotsyuk AA, Padmanabhan J, Sivaraj D, Perrault DP, Zamaleeva AI, Mays CJ, Greco AH, Kwon SH, Leeolou MC, Huskins SL, Steele SR, Fischer KS, Kussie HC, Mittal S, Mermin-Bunnell AM, Diaz Deleon NM, Lavin C, Keller A, Longaker MT, Gurtner GC. Disrupting mechanotransduction decreases fibrosis and contracture in split-thickness skin grafting. Sci Transl Med 2022; 14:eabj9152. [PMID: 35584231 DOI: 10.1126/scitranslmed.abj9152] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Burns and other traumatic injuries represent a substantial biomedical burden. The current standard of care for deep injuries is autologous split-thickness skin grafting (STSG), which frequently results in contractures, abnormal pigmentation, and loss of biomechanical function. Currently, there are no effective therapies that can prevent fibrosis and contracture after STSG. Here, we have developed a clinically relevant porcine model of STSG and comprehensively characterized porcine cell populations involved in healing with single-cell resolution. We identified an up-regulation of proinflammatory and mechanotransduction signaling pathways in standard STSGs. Blocking mechanotransduction with a small-molecule focal adhesion kinase (FAK) inhibitor promoted healing, reduced contracture, mitigated scar formation, restored collagen architecture, and ultimately improved graft biomechanical properties. Acute mechanotransduction blockade up-regulated myeloid CXCL10-mediated anti-inflammation with decreased CXCL14-mediated myeloid and fibroblast recruitment. At later time points, mechanical signaling shifted fibroblasts toward profibrotic differentiation fates, and disruption of mechanotransduction modulated mesenchymal fibroblast differentiation states to block those responses, instead driving fibroblasts toward proregenerative, adipogenic states similar to unwounded skin. We then confirmed these two diverging fibroblast transcriptional trajectories in human skin, human scar, and a three-dimensional organotypic model of human skin. Together, pharmacological blockade of mechanotransduction markedly improved large animal healing after STSG by promoting both early, anti-inflammatory and late, regenerative transcriptional programs, resulting in healed tissue similar to unwounded skin. FAK inhibition could therefore supplement the current standard of care for traumatic and burn injuries.
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Affiliation(s)
- Kellen Chen
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.,Department of Surgery, University of Arizona College of Medicine, Tucson, AZ 85724, USA
| | - Dominic Henn
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael Januszyk
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Janos A Barrera
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Chikage Noishiki
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Clark A Bonham
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michelle Griffin
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruth Tevlin
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Theresa Carlomagno
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tara Shannon
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tobias Fehlmann
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Artem A Trotsyuk
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jagannath Padmanabhan
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dharshan Sivaraj
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - David P Perrault
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alsu I Zamaleeva
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Chyna J Mays
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Autumn H Greco
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sun Hyung Kwon
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Melissa C Leeolou
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Savana L Huskins
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sydney R Steele
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Katharina S Fischer
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hudson C Kussie
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Smiti Mittal
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alana M Mermin-Bunnell
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Nestor M Diaz Deleon
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Christopher Lavin
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
| | - Michael T Longaker
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Geoffrey C Gurtner
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.,Department of Surgery, University of Arizona College of Medicine, Tucson, AZ 85724, USA
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53
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Genome-wide analyses of introgression between two sympatric Asian oak species. Nat Ecol Evol 2022; 6:924-935. [PMID: 35513577 DOI: 10.1038/s41559-022-01754-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/29/2022] [Indexed: 12/13/2022]
Abstract
Introgression can be an important source of new alleles for adaption under rapidly changing environments, perhaps even more important than standing variation. Though introgression has been extensively studied in many plants and animals, key questions on the underlying mechanisms of introgression still remain unanswered. In particular, we are yet to determine the genomic distribution of introgressed regions along the genome; whether the extent and patterns of introgression are influenced by ecological factors; and when and how introgression contributes to adaptation. Here, we generated high-quality genomic resources for two sympatric widespread Asian oak species, Quercus acutissima and Q. variabilis, sampled in multiple forests to study introgression between them. We show that introgressed regions are broadly distributed across the genome. Introgression was affected by genetic divergence between pairs of populations and by the similarity of the environments in which they live-populations occupying similar ecological sites tended to share the same introgressed regions. Introgressed genomic footprints of adaptation were preferentially located in regions with suppressed recombination rate. Introgression probably confers adaptation in these oak populations by introducing allelic variation in cis-regulatory elements, in particular through transposable element insertions, thereby altering the regulation of genes related to stress. Our results provide new avenues of research for uncovering mechanisms of adaptation due to hybridization in sympatric species.
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54
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Offei-Danso A, Hassanali A, Rodriguez A. High-Dimensional Fluctuations in Liquid Water: Combining Chemical Intuition with Unsupervised Learning. J Chem Theory Comput 2022; 18:3136-3150. [PMID: 35472272 DOI: 10.1021/acs.jctc.1c01292] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The microscopic description of the local structure of water remains an open challenge. Here, we adopt an agnostic approach to understanding water's hydrogen bond network using data harvested from molecular dynamics simulations of an empirical water model. A battery of state-of-the-art unsupervised data-science techniques are used to characterize the free-energy landscape of water starting from encoding the water environment using local atomic descriptors, through dimensionality reduction and finally the use of advanced clustering techniques. Analysis of the free energy under ambient conditions was found to be consistent with a rough single basin and independent of the choice of the water model. We find that the fluctuations of the water network occur in a high-dimensional space, which we characterize using a combination of both atomic descriptors and chemical-intuition-based coordinates. We demonstrate that a combination of both types of variables is needed in order to adequately capture the complexity of the fluctuations in the hydrogen bond network at different length scales both at room temperature and also close to the critical point of water. Our results provide a general framework for examining fluctuations in water under different conditions.
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Affiliation(s)
- Adu Offei-Danso
- The Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34151 Trieste, Italy.,SISSA─International School for Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy
| | - Ali Hassanali
- The Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34151 Trieste, Italy
| | - Alex Rodriguez
- The Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34151 Trieste, Italy
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55
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Chiu AM, Molloy EK, Tan Z, Talwalkar A, Sankararaman S. Inferring population structure in biobank-scale genomic data. Am J Hum Genet 2022; 109:727-737. [PMID: 35298920 PMCID: PMC9069078 DOI: 10.1016/j.ajhg.2022.02.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/21/2022] [Indexed: 01/07/2023] Open
Abstract
Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. Although a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.
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Affiliation(s)
- Alec M Chiu
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Erin K Molloy
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA; Institute for Advanced Computer Studies, University of Maryland, College Park, College Park, MD 20742, USA
| | - Zilong Tan
- Facebook, Inc., Menlo Park, CA 94025, USA
| | - Ameet Talwalkar
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Sriram Sankararaman
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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56
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Milošević D, Medeiros AS, Stojković Piperac M, Cvijanović D, Soininen J, Milosavljević A, Predić B. The application of Uniform Manifold Approximation and Projection (UMAP) for unconstrained ordination and classification of biological indicators in aquatic ecology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 815:152365. [PMID: 34963591 DOI: 10.1016/j.scitotenv.2021.152365] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 06/14/2023]
Abstract
The analysis of community structure in studies of freshwater ecology often requires the application of dimensionality reduction to process multivariate data. A high number of dimensions (number of taxa/environmental parameters × number of samples), nonlinear relationships, outliers, and high variability usually hinder the visualization and interpretation of multivariate datasets. Here, we proposed a new statistical design using Uniform Manifold Approximation and Projection (UMAP), and community partitioning using Louvain algorithms, to ordinate and classify the structure of aquatic biota in two-dimensional space. We present this approach with a demonstration of five previously published datasets for diatoms, macrophytes, chironomids (larval and subfossil), and fish. Principal Component Analysis (PCA) and Ward's clustering were also used to assess the comparability of the UMAP approach compared to traditional approaches for ordination and classification. The ordination of sampling sites in 2-dimensional space showed a much denser, and easier to interpret, grouping using the UMAP approach in comparison to PCA. The classification of community structure using the Louvain algorithm in UMAP ordinal space showed a high classification strength for data with a high number of dimensions than the cluster patterns obtained with the use of a Ward's algorithm in PCA. Environmental gradients, presented via heat maps, were overlayed with the ordination patterns of aquatic communities, confirming that the ordinations obtained by UMAP were ecologically meaningful. This is the first study that has applied a UMAP approach with classification using Louvain algorithms on ecological datasets. We show that the performance of local and global structures, as well as the number of clusters determined by the algorithm, make this approach more powerful than traditional approaches.
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Affiliation(s)
- Djuradj Milošević
- University of Niš, Faculty of Sciences and Mathematics, Department of Biology and Ecology, Višegradska 33, 18000 Niš, Serbia.
| | - Andrew S Medeiros
- School for Resource and Environmental Studies, Dalhousie University, Halifax, Canada
| | - Milica Stojković Piperac
- University of Niš, Faculty of Sciences and Mathematics, Department of Biology and Ecology, Višegradska 33, 18000 Niš, Serbia
| | - Dušanka Cvijanović
- University of Novi Sad, Faculty of Sciences, Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia
| | - Janne Soininen
- Department of Geosciences and Geography, University of Helsinki, Finland
| | - Aleksandar Milosavljević
- University of Niš, Faculty of Electronic Engineering, Aleksandra Medvedeva 14, 18000 Niš, Serbia
| | - Bratislav Predić
- University of Niš, Faculty of Electronic Engineering, Aleksandra Medvedeva 14, 18000 Niš, Serbia
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57
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Le HM, Kumar S, May N, Martinez-Baez E, Sundararaman R, Krishnamoorthy B, Clark AE. Behavior of Linear and Nonlinear Dimensionality Reduction for Collective Variable Identification of Small Molecule Solution-Phase Reactions. J Chem Theory Comput 2022; 18:1286-1296. [PMID: 35225611 DOI: 10.1021/acs.jctc.1c00983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Identifying collective variables (CVs) for chemical reactions is essential to reduce the 3N-dimensional energy landscape into lower dimensional basins and barriers of interest. However, in condensed phase processes, the nonmeaningful motions of bulk solvent often overpower the ability of dimensionality reduction methods to identify correlated motions that underpin collective variables. Yet solvent can play important indirect or direct roles in reactivity, and much can be lost through treatments that remove or dampen solvent motion. This has been amply demonstrated within principal component analysis (PCA), although less is known about the behavior of nonlinear dimensionality reduction methods, e.g., uniform manifold approximation and projection (UMAP), that have become recently utilized. The latter presents an interesting alternative to linear methods though often at the expense of interpretability. This work presents distance-attenuated projection methods of atomic coordinates that facilitate the application of both PCA and UMAP to identify collective variables in the presence of explicit solvent and further the specific identity of solvent molecules that participate in chemical reactions. The performance of both methods is examined in detail for two reactions where the explicit solvent plays very different roles within the collective variables. When applied to raw molecular dynamics data in solution, both PCA and UMAP representations are dominated by bulk solvent motions. On the other hand, when applied to data preprocessed by our attenuated projection methods, both PCA and UMAP identify the appropriate collective variables (though varying sensitivity is observed due to the presence of explicit solvent that results from the projection method). Importantly, this approach allows identification of specific solvent molecules that are relevant to the CVs and their importance.
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Affiliation(s)
- Hung M Le
- Department of Chemistry, Washington State University, Pullman, Washington 99164, United States
| | - Sushant Kumar
- Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Nathan May
- Department of Mathematics and Statistics, Washington State University, Vancouver, Washington 98686, United States
| | - Ernesto Martinez-Baez
- Department of Chemistry, Washington State University, Pullman, Washington 99164, United States
| | - Ravishankar Sundararaman
- Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Bala Krishnamoorthy
- Department of Mathematics and Statistics, Washington State University, Vancouver, Washington 98686, United States
| | - Aurora E Clark
- Department of Chemistry, Washington State University, Pullman, Washington 99164, United States
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Constantinescu AE, Mitchell RE, Zheng J, Bull CJ, Timpson NJ, Amulic B, Vincent EE, Hughes DA. A framework for research into continental ancestry groups of the UK Biobank. Hum Genomics 2022; 16:3. [PMID: 35093177 PMCID: PMC8800339 DOI: 10.1186/s40246-022-00380-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/18/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The UK Biobank is a large prospective cohort, based in the UK, that has deep phenotypic and genomic data on roughly a half a million individuals. Included in this resource are data on approximately 78,000 individuals with "non-white British ancestry." While most epidemiology studies have focused predominantly on populations of European ancestry, there is an opportunity to contribute to the study of health and disease for a broader segment of the population by making use of the UK Biobank's "non-white British ancestry" samples. Here, we present an empirical description of the continental ancestry and population structure among the individuals in this UK Biobank subset. RESULTS Reference populations from the 1000 Genomes Project for Africa, Europe, East Asia, and South Asia were used to estimate ancestry for each individual. Those with at least 80% ancestry in one of these four continental ancestry groups were taken forward (N = 62,484). Principal component and K-means clustering analyses were used to identify and characterize population structure within each ancestry group. Of the approximately 78,000 individuals in the UK Biobank that are of "non-white British" ancestry, 50,685, 6653, 2782, and 2364 individuals were associated to the European, African, South Asian, and East Asian continental ancestry groups, respectively. Each continental ancestry group exhibits prominent population structure that is consistent with self-reported country of birth data and geography. CONCLUSIONS Methods outlined here provide an avenue to leverage UK Biobank's deeply phenotyped data allowing researchers to maximize its potential in the study of health and disease in individuals of non-white British ancestry.
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Affiliation(s)
- Andrei-Emil Constantinescu
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
| | - Ruth E Mitchell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Borko Amulic
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.
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59
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Nowling RJ, Fallas-Moya F, Sadovnik A, Emrich S, Aleck M, Leskiewicz D, Peters JG. Fast, low-memory detection and localization of large, polymorphic inversions from SNPs. PeerJ 2022; 10:e12831. [PMID: 35116204 PMCID: PMC8784018 DOI: 10.7717/peerj.12831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 01/04/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Large (>1 Mb), polymorphic inversions have substantial impacts on population structure and maintenance of genotypes. These large inversions can be detected from single nucleotide polymorphism (SNP) data using unsupervised learning techniques like PCA. Construction and analysis of a feature matrix from millions of SNPs requires large amount of memory and limits the sizes of data sets that can be analyzed. METHODS We propose using feature hashing construct a feature matrix from a VCF file of SNPs for reducing memory usage. The matrix is constructed in a streaming fashion such that the entire VCF file is never loaded into memory at one time. RESULTS When evaluated on Anopheles mosquito and Drosophila fly data sets, our approach reduced memory usage by 97% with minimal reductions in accuracy for inversion detection and localization tasks. CONCLUSION With these changes, inversions in larger data sets can be analyzed easily and efficiently on common laptop and desktop computers. Our method is publicly available through our open-source inversion analysis software, Asaph.
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Affiliation(s)
- Ronald J. Nowling
- Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, Wisconsin, United States of America
| | - Fabian Fallas-Moya
- Electrical Engineering and Computer Science, University of Tennessee-Knoxville, Knoxville, Tennessee, United States
| | - Amir Sadovnik
- Electrical Engineering and Computer Science, University of Tennessee-Knoxville, Knoxville, Tennessee, United States
| | - Scott Emrich
- Electrical Engineering and Computer Science, University of Tennessee-Knoxville, Knoxville, Tennessee, United States
| | - Matthew Aleck
- Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, Wisconsin, United States of America
| | - Daniel Leskiewicz
- Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, Wisconsin, United States of America
| | - John G. Peters
- Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, Wisconsin, United States of America
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60
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Bezzan VP, Rocco CD. Using bi-dimensional representations to understand patterns in COVID-19 blood exam data. INFORMATICS IN MEDICINE UNLOCKED 2021; 28:100828. [PMID: 34981033 PMCID: PMC8716149 DOI: 10.1016/j.imu.2021.100828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 12/15/2021] [Accepted: 12/19/2021] [Indexed: 11/30/2022] Open
Abstract
Blood tests play an essential role in everyday medicine and are used by doctors in several diagnostic procedures. Moreover, this data is multivariate - and often some diseases, such as COVID-19, could have different symptom manifestations and outcomes. This study proposes a method of extracting useful information from blood tests using UMAP technique - Uniform Manifold Approximation and Projection for Dimension Reduction combined with DBSCAN clustering and statistical approaches. The analysis performed here indicates several clusters of infection prevalence varying between 2%-37%, showing that our procedure is indeed capable of finding different patterns. A possible explanation is that COVID-19 is not just a respiratory infection but a systemic disease with critical hematological implications, primarily on white-cell fractions, as indicated by relevant statistical test p -values in the range of 0.03-0.1. The novel analysis procedure proposed could be adopted in other data-sets of different illnesses to help researchers to discover new patterns of data that could be used in various diseases and contexts.
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Affiliation(s)
- Vitor P Bezzan
- Instituto de Matemática, Estatistica e Computação Científica, Universidade Estadual de Campinas, Brazil
| | - Cleber D Rocco
- Faculdade de Ciências Aplicadas, Universidade Estadual de Campinas, Brazil
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61
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Friedrich P, Patil KR, Mochalski LN, Li X, Camilleri JA, Kröll JP, Wiersch L, Eickhoff SB, Weis S. Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality. Brain Struct Funct 2021; 227:425-440. [PMID: 34882263 PMCID: PMC8844166 DOI: 10.1007/s00429-021-02418-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 10/18/2021] [Indexed: 11/09/2022]
Abstract
Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.
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Affiliation(s)
- Patrick Friedrich
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa N Mochalski
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Xuan Li
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Julia A Camilleri
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Jean-Philippe Kröll
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa Wiersch
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
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62
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Henn D, Chen K, Fehlmann T, Trotsyuk AA, Sivaraj D, Maan ZN, Bonham CA, Barrera JA, Mays CJ, Greco AH, Moortgat Illouz SE, Lin JQ, Steele SR, Foster DS, Padmanabhan J, Momeni A, Nguyen D, Wan DC, Kneser U, Januszyk M, Keller A, Longaker MT, Gurtner GC. Xenogeneic skin transplantation promotes angiogenesis and tissue regeneration through activated Trem2 + macrophages. SCIENCE ADVANCES 2021; 7:eabi4528. [PMID: 34851663 PMCID: PMC8635426 DOI: 10.1126/sciadv.abi4528] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 10/09/2021] [Indexed: 05/07/2023]
Abstract
Skin allo- and xenotransplantation are the standard treatment for major burns when donor sites for autografts are not available. The relationship between the immune response to foreign grafts and their impact on wound healing has not been fully elucidated. Here, we investigated changes in collagen architecture after xenogeneic implantation of human biologic scaffolds. We show that collagen deposition in response to the implantation of human split-thickness skin grafts (hSTSGs) containing live cells recapitulates normal skin architecture, whereas human acellular dermal matrix (ADM) grafts led to a fibrotic collagen deposition. We show that macrophage differentiation in response to hSTSG implantation is driven toward regenerative Trem2+ subpopulations and found that hydrogel delivery of these cells significantly accelerated wound closure. Our study identifies the preclinical therapeutic potential of Trem2+ macrophages to mitigate fibrosis and promote wound healing, providing a novel effective strategy to develop advanced cell therapies for complex wounds.
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Affiliation(s)
- Dominic Henn
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
- Department of Hand, Plastic, and Reconstructive Surgery, BG Trauma Center Ludwigshafen, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany
| | - Kellen Chen
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Tobias Fehlmann
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Artem A. Trotsyuk
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Dharshan Sivaraj
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Zeshaan N. Maan
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Clark A. Bonham
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Janos A. Barrera
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Chyna J. Mays
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Autumn H. Greco
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Sylvia E. Moortgat Illouz
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - John Qian Lin
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Sydney R. Steele
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Deshka S. Foster
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Jagannath Padmanabhan
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Arash Momeni
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Dung Nguyen
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Derrick C. Wan
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Ulrich Kneser
- Department of Hand, Plastic, and Reconstructive Surgery, BG Trauma Center Ludwigshafen, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany
| | - Michael Januszyk
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Michael T. Longaker
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Geoffrey C. Gurtner
- Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
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63
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Versluys TMM, Flintham EO, Mas-Sandoval A, Savolainen V. Why do we pick similar mates, or do we? Biol Lett 2021; 17:20210463. [PMID: 34813721 DOI: 10.1098/rsbl.2021.0463] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Humans often mate with those resembling themselves, a phenomenon described as positive assortative mating (PAM). The causes of this attract broad interest, but there is little agreement on the topic. This may be because empirical studies and reviews sometimes focus on just a few explanations, often based on disciplinary conventions. This review presents an interdisciplinary conceptual framework on the causes of PAM in humans, drawing on human and non-human biology, the social sciences, and the humanities. Viewing causality holistically, we first discuss the proximate causes (i.e. the 'how') of PAM, considering three mechanisms: stratification, convergence and mate choice. We also outline methods to control for confounders when studying mate choice. We then discuss ultimate explanations (i.e. 'the why') for PAM, including adaptive and non-adaptive processes. We conclude by suggesting a focus on interdisciplinarity in future research.
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Affiliation(s)
- Thomas M M Versluys
- Georgina Mace Centre for the Living Planet, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, United Kingdom
| | - Ewan O Flintham
- Georgina Mace Centre for the Living Planet, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, United Kingdom
| | - Alex Mas-Sandoval
- Georgina Mace Centre for the Living Planet, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, United Kingdom
| | - Vincent Savolainen
- Georgina Mace Centre for the Living Planet, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, United Kingdom
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64
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O'Connell J, Yun T, Moreno M, Li H, Litterman N, Kolesnikov A, Noblin E, Chang PC, Shastri A, Dorfman EH, Shringarpure S, Auton A, Carroll A, McLean CY. A population-specific reference panel for improved genotype imputation in African Americans. Commun Biol 2021; 4:1269. [PMID: 34741098 PMCID: PMC8571350 DOI: 10.1038/s42003-021-02777-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 10/12/2021] [Indexed: 12/17/2022] Open
Abstract
There is currently a dearth of accessible whole genome sequencing (WGS) data for individuals residing in the Americas with Sub-Saharan African ancestry. We generated whole genome sequencing data at intermediate (15×) coverage for 2,294 individuals with large amounts of Sub-Saharan African ancestry, predominantly Atlantic African admixed with varying amounts of European and American ancestry. We performed extensive comparisons of variant callers, phasing algorithms, and variant filtration on these data to construct a high quality imputation panel containing data from 2,269 unrelated individuals. With the exception of the TOPMed imputation server (which notably cannot be downloaded), our panel substantially outperformed other available panels when imputing African American individuals. The raw sequencing data, variant calls and imputation panel for this cohort are all freely available via dbGaP and should prove an invaluable resource for further study of admixed African genetics.
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Affiliation(s)
| | | | | | - Helen Li
- Google Health, Cambridge, MA, USA
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65
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Chmiel FP, Burns DK, Azor M, Borca F, Boniface MJ, Zlatev ZD, White NM, Daniels TWV, Kiuber M. Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments. Sci Rep 2021; 11:21513. [PMID: 34728706 PMCID: PMC8563762 DOI: 10.1038/s41598-021-00937-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 10/20/2021] [Indexed: 01/04/2023] Open
Abstract
Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of emergency departments by enabling targeted post-discharge intervention. In this manuscript, we present a retrospective, single-centre study where we created and evaluated an extreme gradient boosting decision tree model trained to identify patients at risk of reattendance within 72 h of discharge from an emergency department (University Hospitals Southampton Foundation Trust, UK). Our model was trained using 35,447 attendances by 28,945 patients and evaluated on a hold-out test set featuring 8847 attendances by 7237 patients. The set of attendances from a given patient appeared exclusively in either the training or the test set. Our model was trained using both visit level variables (e.g., vital signs, arrival mode, and chief complaint) and a set of variables available in a patients electronic patient record, such as age and any recorded medical conditions. On the hold-out test set, our highest performing model obtained an AUROC of 0.747 (95% CI 0.722–0.773) and an average precision of 0.233 (95% CI 0.194–0.277). These results demonstrate that machine-learning models can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. We explained our models predictions using SHAP values, a concept developed from coalitional game theory, capable of explaining predictions at an attendance level. We demonstrated how clustering techniques (the UMAP algorithm) can be used to investigate the different sub-groups of explanations present in our patient cohort.
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Affiliation(s)
- F P Chmiel
- School of Electronics and Computer Science, University of Southampton, Southampton, UK.
| | - D K Burns
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - M Azor
- University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - F Borca
- University Hospitals Southampton NHS Foundation Trust, Southampton, UK.,Clinical Informatics Research Unit Faculty of Medicine, University of Southampton, Southampton, UK
| | - M J Boniface
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Z D Zlatev
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - N M White
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - T W V Daniels
- Department of Respiratory Medicine, University Hospital NHS Foundation Trust, Southampton, UK.,NIHR Southampton Biomedical Research Centre, Southampton Centre for Biomedical Research, Southampton General Hospital, Southampton, UK
| | - M Kiuber
- Emergency Department, University Hospital Southampton NHS Foundation Trust, Southampton, UK
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66
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Effects of Sample Size on Plant Single-Cell RNA Profiling. Curr Issues Mol Biol 2021; 43:1685-1697. [PMID: 34698115 PMCID: PMC8929096 DOI: 10.3390/cimb43030119] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/17/2021] [Accepted: 10/15/2021] [Indexed: 01/01/2023] Open
Abstract
Single-cell RNA (scRNA) profiling or scRNA-sequencing (scRNA-seq) makes it possible to parallelly investigate diverse molecular features of multiple types of cells in a given plant tissue and discover cell developmental processes. In this study, we evaluated the effects of sample size (i.e., cell number) on the outcome of single-cell transcriptome analysis by sampling different numbers of cells from a pool of ~57,000 Arabidopsis thaliana root cells integrated from five published studies. Our results indicated that the most significant principal components could be achieved when 20,000–30,000 cells were sampled, a relatively high reliability of cell clustering could be achieved by using ~20,000 cells with little further improvement by using more cells, 96% of the differentially expressed genes could be successfully identified with no more than 20,000 cells, and a relatively stable pseudotime could be estimated in the subsample with 5000 cells. Finally, our results provide a general guide for optimizing sample size to be used in plant scRNA-seq studies.
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67
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Volatile Oil Profile of Prickly Ash ( Zanthoxylum) Pericarps from Different Locations in China. Foods 2021; 10:foods10102386. [PMID: 34681436 PMCID: PMC8535335 DOI: 10.3390/foods10102386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 09/29/2021] [Accepted: 10/02/2021] [Indexed: 11/18/2022] Open
Abstract
Volatile oils of prickly ash (Zanthoxylum) pericarps have various potential biological functions with considerable relevance to food, pharmacological, and industrial applications. The volatile profile of oils extracted from prickly ash pericarps obtained from 72 plantations in China was determined by gas chromatography and mass spectrometry. Several chemometric analyses were used to better understand the volatile oil profile differences among different pericarps and to determine the key factors that affected geographical variations in the main volatile constituents of oils. A total of 47 constituents were detected with D-limonene, alfa-myrcene, and linalool as the most abundant. The volatile profile of pericarp oils was significantly affected by prickly ash species and some environmental factors, and the key factors that affected volatile profile variations for different prickly ash species were diverse. Chemometric analyses based on the volatile oil profile could properly distinguish Z. armatum pericarps from other pericarps. This study provides comprehensive information on the volatile oil profile of pericarps from different prickly ash species and different plantations, and it can be beneficial to a system for evaluating of pericarp quality. Moreover, this study speculates on the key environmental factors that cause volatile oil variations for each species, and can help to obtain better prickly ash pericarp volatile oils by improving the cultivated environments.
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68
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Skuladottir AT, Bjornsdottir G, Nawaz MS, Petersen H, Rognvaldsson S, Moore KHS, Olafsson PI, Magnusson SH, Bjornsdottir A, Sveinsson OA, Sigurdardottir GR, Saevarsdottir S, Ivarsdottir EV, Stefansdottir L, Gunnarsson B, Muhlestein JB, Knowlton KU, Jones DA, Nadauld LD, Hartmann AM, Rujescu D, Strupp M, Walters GB, Thorgeirsson TE, Jonsdottir I, Holm H, Thorleifsson G, Gudbjartsson DF, Sulem P, Stefansson H, Stefansson K. A genome-wide meta-analysis uncovers six sequence variants conferring risk of vertigo. Commun Biol 2021; 4:1148. [PMID: 34620984 PMCID: PMC8497462 DOI: 10.1038/s42003-021-02673-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 09/09/2021] [Indexed: 11/17/2022] Open
Abstract
Vertigo is the leading symptom of vestibular disorders and a major risk factor for falls. In a genome-wide association study of vertigo (Ncases = 48,072, Ncontrols = 894,541), we uncovered an association with six common sequence variants in individuals of European ancestry, including missense variants in ZNF91, OTOG, OTOGL, and TECTA, and a cis-eQTL for ARMC9. The association of variants in ZNF91, OTOGL, and OTOP1 was driven by an association with benign paroxysmal positional vertigo. Using previous reports of sequence variants associating with age-related hearing impairment and motion sickness, we found eight additional variants that associate with vertigo. Although disorders of the auditory and the vestibular system may co-occur, none of the six genome-wide significant vertigo variants were associated with hearing loss and only one was associated with age-related hearing impairment. Our results uncovered sequence variants associating with vertigo in a genome-wide association study and implicated genes with known roles in inner ear development, maintenance, and disease.
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Affiliation(s)
| | | | - Muhammad Sulaman Nawaz
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Hannes Petersen
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Akureyri Hospital, Akureyri, Iceland
| | | | | | | | | | | | - Olafur A Sveinsson
- Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Saedis Saevarsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | | | | | | | - Joseph B Muhlestein
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - David A Jones
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
| | - Lincoln D Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
- Stanford University, School of Medicine, Stanford, CA, USA
| | - Annette M Hartmann
- Department of Psychiatry, Psychotherapy and Psychosomatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Dan Rujescu
- Department of Psychiatry, Psychotherapy and Psychosomatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Michael Strupp
- Department of Neurology and German Center for Vertigo and Balance Disorders, Ludwig Maximilians University, Munich, Germany
| | - G Bragi Walters
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Ingileif Jonsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | | | | | | | | | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
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69
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Shi G, Kuang Q. Ancestral Spectrum Analysis With Population-Specific Variants. Front Genet 2021; 12:724638. [PMID: 34646302 PMCID: PMC8503515 DOI: 10.3389/fgene.2021.724638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/30/2021] [Indexed: 11/13/2022] Open
Abstract
With the advance of sequencing technology, an increasing number of populations have been sequenced to study the histories of worldwide populations, including their divergence, admixtures, migration, and effective sizes. The variants detected in sequencing studies are largely rare and mostly population specific. Population-specific variants are often recent mutations and are informative for revealing substructures and admixtures in populations; however, computational methods and tools to analyze them are still lacking. In this work, we propose using reference populations and single nucleotide polymorphisms (SNPs) specific to the reference populations. Ancestral information, the best linear unbiased estimator (BLUE) of the ancestral proportion, is proposed, which can be used to infer ancestral proportions in recently admixed target populations and measure the extent to which reference populations serve as good proxies for the admixing sources. Based on the same panel of SNPs, the ancestral information is comparable across samples from different studies and is not affected by genetic outliers, related samples, or the sample sizes of the admixed target populations. In addition, ancestral spectrum is useful for detecting genetic outliers or exploring co-ancestry between study samples and the reference populations. The methods are implemented in a program, Ancestral Spectrum Analyzer (ASA), and are applied in analyzing high-coverage sequencing data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP). In the analyses of American populations from the 1000 Genomes Project, we demonstrate that recent admixtures can be dissected from ancient admixtures by comparing ancestral spectra with and without indigenous Americans being included in the reference populations.
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Affiliation(s)
- Gang Shi
- State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China
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70
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Disrupting biological sensors of force promotes tissue regeneration in large organisms. Nat Commun 2021; 12:5256. [PMID: 34489407 PMCID: PMC8421385 DOI: 10.1038/s41467-021-25410-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 08/06/2021] [Indexed: 12/31/2022] Open
Abstract
Tissue repair and healing remain among the most complicated processes that occur during postnatal life. Humans and other large organisms heal by forming fibrotic scar tissue with diminished function, while smaller organisms respond with scarless tissue regeneration and functional restoration. Well-established scaling principles reveal that organism size exponentially correlates with peak tissue forces during movement, and evolutionary responses have compensated by strengthening organ-level mechanical properties. How these adaptations may affect tissue injury has not been previously examined in large animals and humans. Here, we show that blocking mechanotransduction signaling through the focal adhesion kinase pathway in large animals significantly accelerates wound healing and enhances regeneration of skin with secondary structures such as hair follicles. In human cells, we demonstrate that mechanical forces shift fibroblasts toward pro-fibrotic phenotypes driven by ERK-YAP activation, leading to myofibroblast differentiation and excessive collagen production. Disruption of mechanical signaling specifically abrogates these responses and instead promotes regenerative fibroblast clusters characterized by AKT-EGR1. Humans and other large mammals heal wounds by forming fibrotic scar tissue with diminished function. Here, the authors show that disrupting mechanotransduction through the focal adhesion kinase pathway in large animals accelerates healing, prevents fibrosis, and enhances skin regeneration.
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Ioannidis AG, Blanco-Portillo J, Sandoval K, Hagelberg E, Barberena-Jonas C, Hill AVS, Rodríguez-Rodríguez JE, Fox K, Robson K, Haoa-Cardinali S, Quinto-Cortés CD, Miquel-Poblete JF, Auckland K, Parks T, Sofro ASM, Ávila-Arcos MC, Sockell A, Homburger JR, Eng C, Huntsman S, Burchard EG, Gignoux CR, Verdugo RA, Moraga M, Bustamante CD, Mentzer AJ, Moreno-Estrada A. Paths and timings of the peopling of Polynesia inferred from genomic networks. Nature 2021; 597:522-526. [PMID: 34552258 PMCID: PMC9710236 DOI: 10.1038/s41586-021-03902-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 08/12/2021] [Indexed: 02/08/2023]
Abstract
Polynesia was settled in a series of extraordinary voyages across an ocean spanning one third of the Earth1, but the sequences of islands settled remain unknown and their timings disputed. Currently, several centuries separate the dates suggested by different archaeological surveys2-4. Here, using genome-wide data from merely 430 modern individuals from 21 key Pacific island populations and novel ancestry-specific computational analyses, we unravel the detailed genetic history of this vast, dispersed island network. Our reconstruction of the branching Polynesian migration sequence reveals a serial founder expansion, characterized by directional loss of variants, that originated in Samoa and spread first through the Cook Islands (Rarotonga), then to the Society (Tōtaiete mā) Islands (11th century), the western Austral (Tuha'a Pae) Islands and Tuāmotu Archipelago (12th century), and finally to the widely separated, but genetically connected, megalithic statue-building cultures of the Marquesas (Te Henua 'Enana) Islands in the north, Raivavae in the south, and Easter Island (Rapa Nui), the easternmost of the Polynesian islands, settled in approximately AD 1200 via Mangareva.
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Affiliation(s)
- Alexander G Ioannidis
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
- National Laboratory of Genomics for Biodiversity (LANGEBIO)-Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato, Mexico.
| | - Javier Blanco-Portillo
- National Laboratory of Genomics for Biodiversity (LANGEBIO)-Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato, Mexico
| | - Karla Sandoval
- National Laboratory of Genomics for Biodiversity (LANGEBIO)-Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato, Mexico
| | | | - Carmina Barberena-Jonas
- National Laboratory of Genomics for Biodiversity (LANGEBIO)-Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato, Mexico
| | - Adrian V S Hill
- Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, UK
- The Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Juan Esteban Rodríguez-Rodríguez
- National Laboratory of Genomics for Biodiversity (LANGEBIO)-Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato, Mexico
| | - Keolu Fox
- Department of Anthropology, University of California San Diego, La Jolla, CA, USA
| | - Kathryn Robson
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | | | - Consuelo D Quinto-Cortés
- National Laboratory of Genomics for Biodiversity (LANGEBIO)-Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato, Mexico
| | | | - Kathryn Auckland
- Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, UK
| | - Tom Parks
- Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, UK
| | - Abdul Salam M Sofro
- Department of Biochemistry, Faculty of Medicine, Yayasan Rumah Sakit Islam (YARSI) University, Cempaka Putih, Jakarta, Indonesia
| | - María C Ávila-Arcos
- International Laboratory for Human Genome Research (LIIGH), UNAM Juriquilla, Queretaro, Mexico
| | - Alexandra Sockell
- Center for Computational, Evolutionary and Human Genomics (CEHG), Stanford University, Stanford, CA, USA
| | - Julian R Homburger
- Center for Computational, Evolutionary and Human Genomics (CEHG), Stanford University, Stanford, CA, USA
| | - Celeste Eng
- Program in Pharmaceutical Sciences and Pharmacogenomics, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Program in Pharmaceutical Sciences and Pharmacogenomics, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Esteban G Burchard
- Program in Pharmaceutical Sciences and Pharmacogenomics, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Christopher R Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA
| | - Ricardo A Verdugo
- Human Genetics Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
- Translational Oncology Department, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Mauricio Moraga
- Human Genetics Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
- Department of Anthropology, Faculty of Social Sciences, University of Chile, Santiago, Chile
| | - Carlos D Bustamante
- Center for Computational, Evolutionary and Human Genomics (CEHG), Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Alexander J Mentzer
- Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Andrés Moreno-Estrada
- National Laboratory of Genomics for Biodiversity (LANGEBIO)-Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato, Mexico.
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72
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Hsu AI, Yttri EA. B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors. Nat Commun 2021; 12:5188. [PMID: 34465784 PMCID: PMC8408193 DOI: 10.1038/s41467-021-25420-x] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders.
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Affiliation(s)
- Alexander I Hsu
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Eric A Yttri
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
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73
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Lee EK, Balasubramanian H, Tsolias A, Anakwe SU, Medalla M, Shenoy KV, Chandrasekaran C. Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex. eLife 2021; 10:e67490. [PMID: 34355695 PMCID: PMC8452311 DOI: 10.7554/elife.67490] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 08/04/2021] [Indexed: 11/13/2022] Open
Abstract
Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.
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Affiliation(s)
- Eric Kenji Lee
- Psychological and Brain Sciences, Boston UniversityBostonUnited States
| | - Hymavathy Balasubramanian
- Bernstein Center for Computational Neuroscience, Bernstein Center for Computational NeuroscienceBerlinGermany
| | - Alexandra Tsolias
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
| | | | - Maria Medalla
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford UniversityStanfordUnited States
- Department of Bioengineering, Stanford UniversityStanfordUnited States
- Department of Neurobiology, Stanford UniversityStanfordUnited States
- Wu Tsai Neurosciences Institute, Stanford UniversityStanfordUnited States
- Bio-X Institute, Stanford UniversityStanfordUnited States
- Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Chandramouli Chandrasekaran
- Psychological and Brain Sciences, Boston UniversityBostonUnited States
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
- Center for Systems Neuroscience, Boston UniversityBostonUnited States
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
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74
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Kim M, Min YK, Jang J, Park H, Lee S, Lee CH. Single-cell RNA sequencing reveals distinct cellular factors for response to immunotherapy targeting CD73 and PD-1 in colorectal cancer. J Immunother Cancer 2021; 9:jitc-2021-002503. [PMID: 34253638 PMCID: PMC8276303 DOI: 10.1136/jitc-2021-002503] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Although cancer immunotherapy is one of the most effective advanced-stage cancer therapies, no clinically approved cancer immunotherapies currently exist for colorectal cancer (CRC). Recently, programmed cell death protein 1 (PD-1) blockade has exhibited clinical benefits according to ongoing clinical trials. However, ongoing clinical trials for cancer immunotherapies are focused on PD-1 signaling inhibitors such as pembrolizumab, nivolumab, and atezolizumab. In this study, we focused on revealing the distinct response mechanism for the potent CD73 ectoenzyme selective inhibitor AB680 as a promising drug candidate that functions by blocking tumorigenic ATP/adenosine signaling in comparison to current therapeutics that block PD-1 to assess the value of this drug as a novel immunotherapy for CRC. METHODS To understand the distinct mechanism of AB680 in comparison to that of a neutralizing antibody against murine PD-1 used as a PD-1 blocker, we performed single-cell RNA sequencing of CD45+ tumor-infiltrating lymphocytes from untreated controls (n=3) and from AB680-treated (n=3) and PD-1-blockade-treated murine CRC in vivo models. We also used flow cytometry, Azoxymethane (AOM)/Dextran Sulfate Sodium (DSS) models, and in vitro functional assays to validate our new findings. RESULTS We initially observed that the expressions of Nt5e (a gene for CD73) and Entpd1 (a gene for CD39) affect T cell receptor (TCR) diversity and transcriptional profiles of T cells, thus suggesting their critical roles in T cell exhaustion within tumor. Importantly, PD-1 blockade significantly increased the TCR diversity of Entpd1-negative T cells and Pdcd1-positive T cells. Additionally, we determined that AB680 improved the anticancer functions of immunosuppressed cells such as Treg and exhausted T cells, while the PD-1 blocker quantitatively reduced Malat1high Treg and M2 macrophages. We also verified that PD-1 blockade induced Treg depletion in AOM/DSS CRC in vivo models, and we confirmed that AB680 treatment caused increased activation of CD8+ T cells using an in vitro T cell assay. CONCLUSIONS The intratumoral immunomodulation of CD73 inhibition is distinct from PD-1 inhibition and exhibits potential as a novel anticancer immunotherapy for CRC, possibly through a synergistic effect when combined with PD-1 blocker treatments. This study may contribute to the ongoing development of anticancer immunotherapies targeting refractory CRC.
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Affiliation(s)
- Miok Kim
- Therapeutics & Biotechnology Division, Drug Discovery Platform Research Center, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
| | - Yong Ki Min
- Therapeutics & Biotechnology Division, Drug Discovery Platform Research Center, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
| | - Jinho Jang
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.,Korean Genomics Center, UNIST, Ulsan, Republic of Korea
| | - Hyejin Park
- Therapeutics & Biotechnology Division, Drug Discovery Platform Research Center, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
| | - Semin Lee
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea .,Korean Genomics Center, UNIST, Ulsan, Republic of Korea
| | - Chang Hoon Lee
- Therapeutics & Biotechnology Division, Drug Discovery Platform Research Center, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
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75
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Tirado-Gonzalez I, Descot A, Soetopo D, Nevmerzhitskaya A, Schaffer A, Kur IM, Czlonka E, Wachtel C, Tsoukala I, Muller L, Schafer AL, Weitmann M, Dinse P, Alberto E, Buck MC, Landry JJM, Baying B, Slotta-Huspenina J, Roesler J, Harter PN, Kubasch AS, Meinel J, Elwakeel E, Strack E, Tran Quang C, Abdel-Wahab O, Schmitz M, Weigert A, Schmid T, Platzbecker U, Benes V, Ghysdael J, Bonig H, Gotze KS, Rothlin CV, Ghosh S, Medyouf H. AXL inhibition in macrophages stimulates host-versus-leukemia immunity and eradicates naive and treatment resistant leukemia. Cancer Discov 2021; 11:2924-2943. [PMID: 34103328 DOI: 10.1158/2159-8290.cd-20-1378] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 05/04/2021] [Accepted: 06/04/2021] [Indexed: 11/16/2022]
Abstract
Acute leukemias are systemic malignancies associated with a dire outcome. Due to low immunogenicity, leukemias display a remarkable ability to evade immune control and are often resistant to checkpoint blockade. Here, we discover that leukemia cells actively establish a suppressive environment to prevent immune attacks by co-opting a signaling axis that skews macrophages towards a tumor promoting tissue repair phenotype, namely the GAS6/AXL axis. Using aggressive leukemia models, we demonstrate that ablation of the AXL receptor specifically in macrophages, or its ligand GAS6 in the environment, stimulates anti-leukemic immunity and elicits effective and lasting NK- and T-cell dependent immune response against naive and treatment resistant leukemia. Remarkably, AXL deficiency in macrophages also enables PD1 checkpoint blockade in PD1-refractory leukemias. Lastly, we provide proof-of-concept that a clinical grade AXL inhibitor can be used in combination with standard of care therapy to cure established leukemia, regardless on AXL expression in malignant cells.
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Affiliation(s)
| | - Arnaud Descot
- Cell Biology and Tumor Biology Program, German Cancer Research Center
| | | | | | | | | | | | | | | | - Luise Muller
- Institute of Immunology, Medical Faculty, TU Dresden
| | | | | | | | | | - Michele C Buck
- Department of Medicine III, Klinikum rechts der Isar, Technische Universität München (TUM)
| | | | | | | | - Jenny Roesler
- Institute of Neurology (Edinger Institute), Goethe-University
| | | | - Anne-Sophie Kubasch
- Department of Hematology, Cellular Therapy and Hemostaseology, University Hospital Leipzig
| | - Jörn Meinel
- Department of Pathology, University of Cologne
| | | | | | | | | | - Marc Schmitz
- Institute of Immunology, Medical Faculty, TU Dresden
| | | | - Tobias Schmid
- Institute of Biochemistry I, Goethe University Frankfurt
| | - Uwe Platzbecker
- Med. Klinik und Poliklinik I, Universitätsklinikum Carl Gustav Carus
| | - Vladimir Benes
- GeneCore, European Molecular Biology Laboratory, Heidelberg, Germany, Genomics Core Facility
| | | | - Halvard Bonig
- Medicine/Transfusion Medicine and Immunohematology, Goethe University
| | - Katharina S Gotze
- Department of Medicine III, Klinikum rechts der Isar, Technische Universität München (TUM)
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76
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Sub-genic intolerance, ClinVar, and the epilepsies: A whole-exome sequencing study of 29,165 individuals. Am J Hum Genet 2021; 108:965-982. [PMID: 33932343 DOI: 10.1016/j.ajhg.2021.04.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/08/2021] [Indexed: 12/23/2022] Open
Abstract
Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.
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77
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Assessing single-cell transcriptomic variability through density-preserving data visualization. Nat Biotechnol 2021; 39:765-774. [PMID: 33462509 PMCID: PMC8195812 DOI: 10.1038/s41587-020-00801-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 12/14/2020] [Indexed: 01/29/2023]
Abstract
Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.
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78
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Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits. NPJ Syst Biol Appl 2021; 7:24. [PMID: 34045472 PMCID: PMC8160250 DOI: 10.1038/s41540-021-00186-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 04/28/2021] [Indexed: 01/03/2023] Open
Abstract
Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10−16) and stemness (p < 2.2 × 10−16). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease.
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79
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Trozzi F, Wang X, Tao P. UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study. J Phys Chem B 2021; 125:5022-5034. [PMID: 33973773 PMCID: PMC8356557 DOI: 10.1021/acs.jpcb.1c02081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Proteins are the molecular machines of life. The multitude of possible conformations that proteins can adopt determines their free-energy landscapes. However, the inherently high dimensionality of a protein free-energy landscape poses a challenge to deciphering how proteins perform their functions. For this reason, dimensionality reduction is an active field of research for molecular biologists. The uniform manifold approximation and projection (UMAP) is a dimensionality reduction method based on a fuzzy topological analysis of data. In the present study, the performance of UMAP is compared with that of other popular dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), and time-structure independent components analysis (tICA) in the context of analyzing molecular dynamics simulations of the circadian clock protein VIVID. A good dimensionality reduction method should accurately represent the data structure on the projected components. The comparison of the raw high-dimensional data with the projections obtained using different dimensionality reduction methods based on various metrics showed that UMAP has superior performance when compared with linear reduction methods (PCA and tICA) and has competitive performance and scalable computational cost.
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Affiliation(s)
- Francesco Trozzi
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, 75275, United States of America
| | - Xinlei Wang
- Department of Statistical Science, Southern Methodist University, Dallas, Texas, 75275, United States of America
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, 75275, United States of America
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80
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Vermeulen M, Smith K, Eremin K, Rayner G, Walton M. Application of Uniform Manifold Approximation and Projection (UMAP) in spectral imaging of artworks. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 252:119547. [PMID: 33588368 DOI: 10.1016/j.saa.2021.119547] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 01/22/2021] [Accepted: 01/24/2021] [Indexed: 05/20/2023]
Abstract
This study assesses the potential of Uniform Manifold Approximation and Projection (UMAP) as an alternative tool to t-distributed Stochastic Neighbor Embedding (t-SNE) for the reduction and visualization of visible spectral images of works of art. We investigate the influence of UMAP parameters-such as, correlation distance, minimum embedding distance, as well as number of embedding neighbors- on the reduction and visualization of spectral images collected from Poèmes Barbares (1896), a major work by the French artist Paul Gauguin in the collection of the Harvard Art Museums. The use of a cosine distance metric and number of neighbors equal to 10 preserves both the local and global structure of the Gauguin dataset in a reduced two-dimensional embedding space thus yielding simple and clear groupings of the pigments used by the artist. The centroids of these groups were identified by locating the densest regions within the UMAP embedding through a 2D histogram peak finding algorithm. These centroids were subsequently fit to the dataset by non-negative least square thus forming maps of pigments distributed across the work of art studied. All findings were correlated to macro XRF imaging analyses carried out on the same painting. The described procedure for reduction and visualization of spectral images of a work of art is quick, easy to implement, and the software is opensource thus promising an improved strategy for interrogating reflectance images from complex works of art.
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Affiliation(s)
- Marc Vermeulen
- Northwestern University / Art Institute of Chicago Center for Scientific Studies in the Arts (NU-ACCESS), 2145 Sheridan Road, Evanston, IL, United States
| | - Kate Smith
- Harvard Art Museums, Straus Center for Conservation and Technical Studies, 32 Quincy St, Cambridge, MA, United States
| | - Katherine Eremin
- Harvard Art Museums, Straus Center for Conservation and Technical Studies, 32 Quincy St, Cambridge, MA, United States
| | - Georgina Rayner
- Harvard Art Museums, Straus Center for Conservation and Technical Studies, 32 Quincy St, Cambridge, MA, United States
| | - Marc Walton
- Northwestern University / Art Institute of Chicago Center for Scientific Studies in the Arts (NU-ACCESS), 2145 Sheridan Road, Evanston, IL, United States.
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81
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Sengupta D, Choudhury A, Fortes-Lima C, Aron S, Whitelaw G, Bostoen K, Gunnink H, Chousou-Polydouri N, Delius P, Tollman S, Gómez-Olivé FX, Norris S, Mashinya F, Alberts M, Hazelhurst S, Schlebusch CM, Ramsay M. Genetic substructure and complex demographic history of South African Bantu speakers. Nat Commun 2021; 12:2080. [PMID: 33828095 PMCID: PMC8027885 DOI: 10.1038/s41467-021-22207-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 02/10/2021] [Indexed: 02/01/2023] Open
Abstract
South Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny (separation of Nguni, Sotho-Tswana and Tsonga speakers). Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ~400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa.
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Affiliation(s)
- Dhriti Sengupta
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Cesar Fortes-Lima
- Human Evolution, Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Gavin Whitelaw
- KwaZulu-Natal Museum, Pietermaritzburg, South Africa
- School of Geography, Archaeology & Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa
| | - Koen Bostoen
- UGent Centre for Bantu Studies, Department of Languages and Cultures, Ghent University, Ghent, Belgium
| | - Hilde Gunnink
- UGent Centre for Bantu Studies, Department of Languages and Cultures, Ghent University, Ghent, Belgium
| | - Natalia Chousou-Polydouri
- Department of Comparative Linguistic Science and Center for the Interdisciplinary Study of Language Evolution, University of Zürich, Zürich, Switzerland
| | - Peter Delius
- Department of History, University of the Witwatersrand, Johannesburg, South Africa
| | - Stephen Tollman
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - F Xavier Gómez-Olivé
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Shane Norris
- MRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Felistas Mashinya
- Department of Pathology and Medical Sciences; School of Health Care Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Marianne Alberts
- Department of Pathology and Medical Sciences; School of Health Care Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Carina M Schlebusch
- Human Evolution, Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
- SciLifeLab, Uppsala, Sweden
- Palaeo-Research Institute, University of Johannesburg, Johannesburg, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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82
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Gain C, François O. LEA 3: Factor models in population genetics and ecological genomics with R. Mol Ecol Resour 2021; 21:2738-2748. [PMID: 33638893 DOI: 10.1111/1755-0998.13366] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 01/21/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Abstract
A major objective of evolutionary biology is to understand the processes by which organisms have adapted to various environments, and to predict the response of organisms to new or future conditions. The availability of large genomic and environmental data sets provides an opportunity to address those questions, and the R package LEA has been introduced to facilitate population and ecological genomic analyses in this context. By using latent factor models, the program computes ancestry coefficients from population genetic data and performs genotype-environment association analyses with correction for unobserved confounding variables. In this study, we present new functionalities of LEA, which include imputation of missing genotypes, fast algorithms for latent factor mixed models using multivariate predictors for genotype-environment association studies, population differentiation tests for admixed or continuous populations, and estimation of genetic offset based on climate models. The new functionalities are implemented in version 3.1 and higher releases of the package. Using simulated and real data sets, our study provides evaluations and examples of applications, outlining important practical considerations when analysing ecological genomic data in R.
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Affiliation(s)
- Clément Gain
- Centre National de la Recherche Scientifique, Grenoble INP, TIMC-IMAG CNRS UMR 5525, Université Grenoble-Alpes, Grenoble, France
| | - Olivier François
- Centre National de la Recherche Scientifique, Grenoble INP, TIMC-IMAG CNRS UMR 5525, Université Grenoble-Alpes, Grenoble, France
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83
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Butterfield RJ, Imburgia C, Mayne K, Newcomb T, Dunn DM, Duval B, Feldkamp ML, Johnson NE, Weiss RB. High throughput screening for expanded CTG repeats in myotonic dystrophy type 1 using melt curve analysis. Mol Genet Genomic Med 2021; 9:e1619. [PMID: 33624941 PMCID: PMC8123750 DOI: 10.1002/mgg3.1619] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 12/23/2020] [Accepted: 01/29/2021] [Indexed: 12/24/2022] Open
Abstract
Background Myotonic dystrophy type 1 (DM1) is caused by CTG repeat expansions in the DMPK gene and is the most common form of muscular dystrophy. Patients can have long delays from onset to diagnosis, since clinical signs and symptoms are often nonspecific and overlapping with other disorders. Clinical genetic testing by Southern blot or triplet‐primed PCR (TP‐PCR) is technically challenging and cost prohibitive for population surveys. Methods Here, we present a high throughput, low‐cost screening tool for CTG repeat expansions using TP‐PCR followed by high resolution melt curve analysis with saturating concentrations of SYBR GreenER dye. Results We determined that multimodal melt profiles from the TP‐PCR assay are a proxy for amplicon length stoichiometry. In a screen of 10,097 newborn blood spots, melt profile analysis accurately reflected the tri‐modal distribution of common alleles from 5 to 35 CTG repeats, and identified the premutation and full expansion alleles. Conclusion We demonstrate that robust detection of expanded CTG repeats in a single tube can be achieved from samples derived from specimens with minimal template DNA such as dried blood spots (DBS). This technique is readily adaptable to large‐scale testing programs such as population studies and newborn screening programs.
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Affiliation(s)
- Russell J Butterfield
- Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA.,Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Carina Imburgia
- Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Katie Mayne
- Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Tara Newcomb
- Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Diane M Dunn
- Department of Human Genetics, University of Utah, Salt Lake City, Utah, USA
| | - Brett Duval
- Department of Human Genetics, University of Utah, Salt Lake City, Utah, USA
| | - Marcia L Feldkamp
- Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Nicholas E Johnson
- Department of Neurology, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Robert B Weiss
- Department of Human Genetics, University of Utah, Salt Lake City, Utah, USA
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84
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A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance. SENSORS 2021; 21:s21041504. [PMID: 33671525 PMCID: PMC7926772 DOI: 10.3390/s21041504] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/10/2021] [Accepted: 02/15/2021] [Indexed: 12/12/2022]
Abstract
ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement.
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85
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Anusewicz D, Orzechowska M, Bednarek AK. Notch Signaling Pathway in Cancer-Review with Bioinformatic Analysis. Cancers (Basel) 2021; 13:cancers13040768. [PMID: 33673145 PMCID: PMC7918426 DOI: 10.3390/cancers13040768] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/26/2021] [Accepted: 02/08/2021] [Indexed: 01/19/2023] Open
Abstract
Simple Summary The Notch signaling pathway, which controls multiple cell differentiation processes during the embryonic stage and adult life, is associated with carcinogenesis and disease progression. The aim of the present study was to highlight cancer heterogeneity with respect to the Notch pathway. Our analysis concerns the effects of the Notch signaling at different levels, including core components and downstream target genes. We also demonstrate overall and disease-free survival results, pointing out the characteristics of particular Notch components. Depending on tissue context, Notch members can be either oncogenic or suppressive. We observed different expression profile core components and target genes that could be associated with distinct survival of patients. Advances in our understanding of the Notch signaling in cancer are very promising for the development of new treatment strategies for the benefit of patients. Abstract Notch signaling is an evolutionarily conserved pathway regulating normal embryonic development and homeostasis in a wide variety of tissues. It is also critically involved in carcinogenesis, as well as cancer progression. Activation of the Notch pathway members can be either oncogenic or suppressive, depending on tissue context. The present study is a comprehensive overview, extended with a bioinformatics analysis of TCGA cohorts, including breast, bladder, cervical, colon, kidney, lung, ovary, prostate and rectum carcinomas. We performed global expression profiling of the Notch pathway core components and downstream targets. For this purpose, we implemented the Uniform Manifold Approximation and Projection algorithm to reduce the dimensions. Furthermore, we determined the optimal cutpoint using Evaluate Cutpoint software to established disease-free and overall survival with respect to particular Notch members. Our results demonstrated separation between tumors and their corresponding normal tissue, as well as between tumors in general. The differentiation of the Notch pathway, at its various stages, in terms of expression and survival resulted in distinct profiles of biological processes such as proliferation, adhesion, apoptosis and epithelial to mesenchymal transition. In conclusion, whether oncogenic or suppressive, Notch signaling is proven to be associated with various types of malignancies, and thus may be of interest as a potential therapeutic target.
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86
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Koss CK, Wohnhaas CT, Baker JR, Tilp C, Przibilla M, Lerner C, Frey S, Keck M, Williams CMM, Peter D, Ramanujam M, Fine J, Gantner F, Thomas M, Barnes PJ, Donnelly LE, El Kasmi KC. IL36 is a critical upstream amplifier of neutrophilic lung inflammation in mice. Commun Biol 2021; 4:172. [PMID: 33558616 PMCID: PMC7870940 DOI: 10.1038/s42003-021-01703-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 01/13/2021] [Indexed: 01/30/2023] Open
Abstract
IL-36, which belongs to the IL-1 superfamily, is increasingly linked to neutrophilic inflammation. Here, we combined in vivo and in vitro approaches using primary mouse and human cells, as well as, acute and chronic mouse models of lung inflammation to provide mechanistic insight into the intercellular signaling pathways and mechanisms through which IL-36 promotes lung inflammation. IL-36 receptor deficient mice exposed to cigarette smoke or cigarette smoke and H1N1 influenza virus had attenuated lung inflammation compared with wild-type controls. We identified neutrophils as a source of IL-36 and show that IL-36 is a key upstream amplifier of lung inflammation by promoting activation of neutrophils, macrophages and fibroblasts through cooperation with GM-CSF and the viral mimic poly(I:C). Our data implicate IL-36, independent of other IL-1 family members, as a key upstream amplifier of neutrophilic lung inflammation, providing a rationale for targeting IL-36 to improve treatment of a variety of neutrophilic lung diseases.
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MESH Headings
- Animals
- Cells, Cultured
- Cigarette Smoking
- Disease Models, Animal
- Female
- Fibroblasts/immunology
- Fibroblasts/metabolism
- Humans
- Influenza A Virus, H1N1 Subtype/pathogenicity
- Interleukin-1/genetics
- Interleukin-1/metabolism
- Lung/immunology
- Lung/metabolism
- Lung/virology
- Macrophage Activation
- Macrophages, Alveolar/immunology
- Macrophages, Alveolar/metabolism
- Male
- Mice, Inbred BALB C
- Mice, Inbred C57BL
- Mice, Knockout
- Neutrophil Activation
- Neutrophils/immunology
- Neutrophils/metabolism
- Neutrophils/virology
- Orthomyxoviridae Infections/genetics
- Orthomyxoviridae Infections/immunology
- Orthomyxoviridae Infections/metabolism
- Orthomyxoviridae Infections/virology
- Pneumonia, Viral/genetics
- Pneumonia, Viral/immunology
- Pneumonia, Viral/metabolism
- Pneumonia, Viral/virology
- Receptors, Interleukin-1/genetics
- Receptors, Interleukin-1/metabolism
- Signal Transduction
- Mice
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Affiliation(s)
- Carolin K Koss
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Christian T Wohnhaas
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Jonathan R Baker
- Airway Disease, National Heart and Lung Institute, Imperial College London, London, UK
| | - Cornelia Tilp
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
| | | | - Carmen Lerner
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
| | - Silvia Frey
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
| | - Martina Keck
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
| | - Cara M M Williams
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
- WRDM, Inflammation and Immunology Research Unit, Pfizer, Cambridge, MA, USA
| | - Daniel Peter
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
| | - Meera Ramanujam
- Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Jay Fine
- Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Florian Gantner
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Matthew Thomas
- Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany
| | - Peter J Barnes
- Airway Disease, National Heart and Lung Institute, Imperial College London, London, UK
| | - Louise E Donnelly
- Airway Disease, National Heart and Lung Institute, Imperial College London, London, UK
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87
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Smets T, De Keyser T, Tousseyn T, Waelkens E, De Moor B. Correspondence-Aware Manifold Learning for Microscopic and Spatial Omics Imaging: A Novel Data Fusion Method Bringing Mass Spectrometry Imaging to a Cellular Resolution. Anal Chem 2021; 93:3452-3460. [PMID: 33555194 DOI: 10.1021/acs.analchem.0c04759] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
High-dimensional molecular measurements are transforming the field of pathology into a data-driven discipline. While hematoxylin and eosin (H&E) stainings are still the gold standard to diagnose diseases, the integration of microscopic and molecular information is becoming crucial to advance our understanding of tissue heterogeneity. To this end, we propose a data fusion method that integrates spatial omics and microscopic data obtained from the same tissue slide. Through correspondence-aware manifold learning, we can visualize the biological trends observed in the high-dimensional omics data at microscopic resolution. While data fusion enables the detection of elements that would not be detected taking into account the separate data modalities individually, out-of-sample prediction makes it possible to predict molecular trends outside of the measured tissue area. The proposed dimensionality reduction-based data fusion paradigm will therefore be helpful in deciphering molecular heterogeneity by bringing molecular measurements such as mass spectrometry imaging (MSI) to the cellular resolution.
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Affiliation(s)
- Tina Smets
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium
| | | | - Thomas Tousseyn
- Department of Pathology, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Etienne Waelkens
- Department of Cellular and Molecular Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Bart De Moor
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium
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88
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Schmidt TL, Swan T, Chung J, Karl S, Demok S, Yang Q, Field MA, Muzari MO, Ehlers G, Brugh M, Bellwood R, Horne P, Burkot TR, Ritchie S, Hoffmann AA. Spatial population genomics of a recent mosquito invasion. Mol Ecol 2021; 30:1174-1189. [PMID: 33421231 DOI: 10.1111/mec.15792] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 12/20/2020] [Accepted: 01/04/2021] [Indexed: 02/06/2023]
Abstract
Population genomic approaches can characterize dispersal across a single generation through to many generations in the past, bridging the gap between individual movement and intergenerational gene flow. These approaches are particularly useful when investigating dispersal in recently altered systems, where they provide a way of inferring long-distance dispersal between newly established populations and their interactions with existing populations. Human-mediated biological invasions represent such altered systems which can be investigated with appropriate study designs and analyses. Here we apply temporally restricted sampling and a range of population genomic approaches to investigate dispersal in a 2004 invasion of Aedes albopictus (the Asian tiger mosquito) in the Torres Strait Islands (TSI) of Australia. We sampled mosquitoes from 13 TSI villages simultaneously and genotyped 373 mosquitoes at genome-wide single nucleotide polymorphisms (SNPs): 331 from the TSI, 36 from Papua New Guinea (PNG) and four incursive mosquitoes detected in uninvaded regions. Within villages, spatial genetic structure varied substantially but overall displayed isolation by distance and a neighbourhood size of 232-577. Close kin dyads revealed recent movement between islands 31-203 km apart, and deep learning inferences showed incursive Ae. albopictus had travelled to uninvaded regions from both adjacent and nonadjacent islands. Private alleles and a co-ancestry matrix indicated direct gene flow from PNG into nearby islands. Outlier analyses also detected four linked alleles introgressed from PNG, with the alleles surrounding 12 resistance-associated cytochrome P450 genes. By treating dispersal as both an intergenerational process and a set of discrete events, we describe a highly interconnected invasive system.
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Affiliation(s)
- Thomas L Schmidt
- Pest and Environmental Adaptation Research Group, School of BioSciences, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia
| | - Tom Swan
- Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia.,College of Public Health, Medical and Veterinary Sciences, James Cook University, Cairns, QLD, Australia
| | - Jessica Chung
- Pest and Environmental Adaptation Research Group, School of BioSciences, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia.,Melbourne Bioinformatics, University of Melbourne, Parkville, VIC, Australia
| | - Stephan Karl
- Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia.,Vector Borne Diseases Unit, Papua New Guinea Institute of Medical Research, Madang, Papua New Guinea
| | - Samuel Demok
- Vector Borne Diseases Unit, Papua New Guinea Institute of Medical Research, Madang, Papua New Guinea
| | - Qiong Yang
- Pest and Environmental Adaptation Research Group, School of BioSciences, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia
| | - Matt A Field
- Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia.,John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Mutizwa Odwell Muzari
- Medical Entomology, Tropical Public Health Services Cairns, Cairns and Hinterland Hospital & Health Services, Cairns, QLD, Australia
| | - Gerhard Ehlers
- Medical Entomology, Tropical Public Health Services Cairns, Cairns and Hinterland Hospital & Health Services, Cairns, QLD, Australia
| | - Mathew Brugh
- Medical Entomology, Tropical Public Health Services Cairns, Cairns and Hinterland Hospital & Health Services, Cairns, QLD, Australia
| | - Rodney Bellwood
- Medical Entomology, Tropical Public Health Services Cairns, Cairns and Hinterland Hospital & Health Services, Cairns, QLD, Australia
| | - Peter Horne
- Medical Entomology, Tropical Public Health Services Cairns, Cairns and Hinterland Hospital & Health Services, Cairns, QLD, Australia
| | - Thomas R Burkot
- Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia
| | - Scott Ritchie
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Cairns, QLD, Australia.,Institute of Vector-Borne Disease, Monash University, Clayton, VIC, Australia
| | - Ary A Hoffmann
- Pest and Environmental Adaptation Research Group, School of BioSciences, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia
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89
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Johnson NE, Butterfield RJ, Mayne K, Newcomb T, Imburgia C, Dunn D, Duval B, Feldkamp ML, Weiss RB. Population-Based Prevalence of Myotonic Dystrophy Type 1 Using Genetic Analysis of Statewide Blood Screening Program. Neurology 2021; 96:e1045-e1053. [PMID: 33472919 DOI: 10.1212/wnl.0000000000011425] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 10/12/2020] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE To determine whether the genetic prevalence of the CTG expansion in the DMPK gene associated with myotonic dystrophy type 1 (DM1) in an unbiased cohort is higher than previously reported population estimates, ranging from 5 to 20 per 100,000 individuals. METHODS This study used a cross-sectional cohort of deidentified dried blood spots from the newborn screening program in the state of New York, taken from consecutive births from 2013 to 2014. Blood spots were screened for the CTG repeat expansion in the DMPK gene using triplet-repeat primed PCR and melt curve analysis. Melt curve morphology was assessed by 4 blinded reviewers to identify samples with possible CTG expansion. Expansion of the CTG repeat was validated by PCR fragment sizing using capillary electrophoresis for samples classified as positive or premutation to confirm the result. Prevalence was calculated as the number of samples with CTG repeat size ≥50 repeats compared to the overall cohort. RESULTS Of 50,382 consecutive births, there were 24 with a CTG repeat expansion ≥50, consistent with a diagnosis of DM1. This represents a significantly higher DM1 prevalence of 4.76 per 10,000 births (95% confidence interval 2.86-6.67) or 1 in every 2,100 births. There were an additional 96 samples (19.1 per 10,000 or 1 in 525 births) with a CTG expansion in the DMPK gene in the premutation range (CTG)35-49. CONCLUSION The prevalence of individuals with CTG repeat expansions in DMPK is up to 5 times higher than previous reported estimates. This suggests that DM1, with multisystemic manifestations, is likely underdiagnosed in practice.
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Affiliation(s)
- Nicholas E Johnson
- From the Department of Neurology (N.E.J.), Virginia Commonwealth University, Richmond; and Departments of Pediatrics (R.J.B., K.M., T.N., C.I., M.L.F.) and .,Human Genetics (D.D., B.D., R.B.W.), University of Utah, Salt Lake City.
| | - Russell J Butterfield
- From the Department of Neurology (N.E.J.), Virginia Commonwealth University, Richmond; and Departments of Pediatrics (R.J.B., K.M., T.N., C.I., M.L.F.) and.,Human Genetics (D.D., B.D., R.B.W.), University of Utah, Salt Lake City
| | - Katie Mayne
- From the Department of Neurology (N.E.J.), Virginia Commonwealth University, Richmond; and Departments of Pediatrics (R.J.B., K.M., T.N., C.I., M.L.F.) and.,Human Genetics (D.D., B.D., R.B.W.), University of Utah, Salt Lake City
| | - Tara Newcomb
- From the Department of Neurology (N.E.J.), Virginia Commonwealth University, Richmond; and Departments of Pediatrics (R.J.B., K.M., T.N., C.I., M.L.F.) and.,Human Genetics (D.D., B.D., R.B.W.), University of Utah, Salt Lake City
| | - Carina Imburgia
- From the Department of Neurology (N.E.J.), Virginia Commonwealth University, Richmond; and Departments of Pediatrics (R.J.B., K.M., T.N., C.I., M.L.F.) and.,Human Genetics (D.D., B.D., R.B.W.), University of Utah, Salt Lake City
| | - Diane Dunn
- From the Department of Neurology (N.E.J.), Virginia Commonwealth University, Richmond; and Departments of Pediatrics (R.J.B., K.M., T.N., C.I., M.L.F.) and.,Human Genetics (D.D., B.D., R.B.W.), University of Utah, Salt Lake City
| | - Brett Duval
- From the Department of Neurology (N.E.J.), Virginia Commonwealth University, Richmond; and Departments of Pediatrics (R.J.B., K.M., T.N., C.I., M.L.F.) and.,Human Genetics (D.D., B.D., R.B.W.), University of Utah, Salt Lake City
| | - Marcia L Feldkamp
- From the Department of Neurology (N.E.J.), Virginia Commonwealth University, Richmond; and Departments of Pediatrics (R.J.B., K.M., T.N., C.I., M.L.F.) and.,Human Genetics (D.D., B.D., R.B.W.), University of Utah, Salt Lake City
| | - Robert B Weiss
- From the Department of Neurology (N.E.J.), Virginia Commonwealth University, Richmond; and Departments of Pediatrics (R.J.B., K.M., T.N., C.I., M.L.F.) and.,Human Genetics (D.D., B.D., R.B.W.), University of Utah, Salt Lake City
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90
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Battey CJ, Coffing GC, Kern AD. Visualizing population structure with variational autoencoders. G3 (BETHESDA, MD.) 2021; 11:jkaa036. [PMID: 33561250 PMCID: PMC8022710 DOI: 10.1093/g3journal/jkaa036] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 12/15/2020] [Indexed: 11/13/2022]
Abstract
Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here we explore the utility of variational autoencoders (VAEs)-generative machine learning models in which a pair of neural networks seek to first compress and then recreate the input data-for visualizing population genetic variation. VAEs incorporate nonlinear relationships, allow users to define the dimensionality of the latent space, and in our tests preserve global geometry better than t-SNE and UMAP. Our implementation, which we call popvae, is available as a command-line python program at github.com/kr-colab/popvae. The approach yields latent embeddings that capture subtle aspects of population structure in humans and Anopheles mosquitoes, and can generate artificial genotypes characteristic of a given sample or population.
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Affiliation(s)
- C J Battey
- Department of Biology, University of Oregon Institute of Ecology and Evolution, Eugene, Oregon, 97403
| | - Gabrielle C Coffing
- Department of Biology, University of Oregon Institute of Ecology and Evolution, Eugene, Oregon, 97403
| | - Andrew D Kern
- Department of Biology, University of Oregon Institute of Ecology and Evolution, Eugene, Oregon, 97403
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91
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Orzechowska M, Anusewicz D, Bednarek AK. Functional Gene Expression Differentiation of the Notch Signaling Pathway in Female Reproductive Tract Tissues-A Comprehensive Review With Analysis. Front Cell Dev Biol 2021; 8:592616. [PMID: 33384996 PMCID: PMC7770115 DOI: 10.3389/fcell.2020.592616] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/11/2020] [Indexed: 12/13/2022] Open
Abstract
The Notch pathway involves evolutionarily conserved signaling regulating the development of the female tract organs such as breast, ovary, cervix, and uterine endometrium. A great number of studies revealed Notch aberrancies in association with their carcinogenesis and disease progression, the management of which is still challenging. The present study is a comprehensive review of the available literature on Notch signaling during the normal development and carcinogenesis of the female tract organs. The review has been enriched with our analyses of the TCGA data including breast, cervical, ovarian, and endometrial carcinomas concerning the effects of Notch signaling at two levels: the core components and downstream effectors, hence filling the lack of global overview of Notch-driven carcinogenesis and disease progression. Phenotype heterogeneity regarding Notch signaling was projected in two uniform manifold approximation and projection algorithm dimensions, preceded by the principal component analysis step reducing the data burden. Additionally, overall and disease-free survival analyses were performed with the optimal cutpoint determination by Evaluate Cutpoints software to establish the character of particular Notch components in tumorigenesis. In addition to the review, we demonstrated separate models of the examined cancers of the Notch pathway and its targets, although expression profiles of all normal tissues were much more similar to each other than to its cancerous compartments. Such Notch-driven cancerous differentiation resulted in a case of opposite association with DFS and OS. As a consequence, target genes also show very distinct profiles including genes associated with cell proliferation and differentiation, energy metabolism, or the EMT. In conclusion, the observed Notch associations with the female tract malignancies resulted from differential expression of target genes. This may influence a future analysis to search for new therapeutic targets based on specific Notch pathway profiles.
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Affiliation(s)
| | - Dorota Anusewicz
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
| | - Andrzej K Bednarek
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
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92
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Quintero A, Hübschmann D, Kurzawa N, Steinhauser S, Rentzsch P, Krämer S, Andresen C, Park J, Eils R, Schlesner M, Herrmann C. ShinyButchR: Interactive NMF-based decomposition workflow of genome-scale datasets. Biol Methods Protoc 2020; 5:bpaa022. [PMID: 33376806 PMCID: PMC7750682 DOI: 10.1093/biomethods/bpaa022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 11/12/2022] Open
Abstract
Non-negative matrix factorization (NMF) has been widely used for the analysis of genomic data to perform feature extraction and signature identification due to the interpretability of the decomposed signatures. However, running a basic NMF analysis requires the installation of multiple tools and dependencies, along with a steep learning curve and computing time. To mitigate such obstacles, we developed ShinyButchR, a novel R/Shiny application that provides a complete NMF-based analysis workflow, allowing the user to perform matrix decomposition using NMF, feature extraction, interactive visualization, relevant signature identification, and association to biological and clinical variables. ShinyButchR builds upon the also novel R package ButchR, which provides new TensorFlow solvers for algorithms of the NMF family, functions for downstream analysis, a rational method to determine the optimal factorization rank and a novel feature selection strategy.
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Affiliation(s)
| | | | | | | | - Philipp Rentzsch
- Computational Genome Biology, Berlin Institute of Health (BIH), Berlin, Germany
| | - Stephen Krämer
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Carolin Andresen
- Computational Oncology, Molecular Diagnostics Program, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM), Heidelberg, Germany
| | - Jeongbin Park
- Center for Digital Health, Berlin Institute of Health (BIH) and Charité – Universitätsmedizin Berlin, Germany
| | - Roland Eils
- Health Data Science Unit, Medical Faculty and BioQuant, University Heidelberg, Heidelberg, Germany
- Center for Digital Health, Berlin Institute of Health (BIH) and Charité – Universitätsmedizin Berlin, Germany
| | - Matthias Schlesner
- Present address: Chair of Biomedical Informatics, Data Mining and Data Analytics, Faculty of Computer Science, University of Augsburg, Germany
| | - Carl Herrmann
- Correspondence address. Health Data Science Unit, Medical Faculty and BioQuant, University Heidelberg, Heidelberg, Germany. Tel: +49 6221 5451249; Fax: +49 6221 545148. E-mail:
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93
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Fodoulian L, Tuberosa J, Rossier D, Boillat M, Kan C, Pauli V, Egervari K, Lobrinus JA, Landis BN, Carleton A, Rodriguez I. SARS-CoV-2 Receptors and Entry Genes Are Expressed in the Human Olfactory Neuroepithelium and Brain. iScience 2020; 23:101839. [PMID: 33251489 DOI: 10.1101/2020.03.31.013268] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 09/25/2020] [Accepted: 11/18/2020] [Indexed: 05/23/2023] Open
Abstract
Reports indicate an association between COVID-19 and anosmia, as well as the presence of SARS-CoV-2 virions in the olfactory bulb. To test whether the olfactory neuroepithelium may represent a target of the virus, we generated RNA-seq libraries from human olfactory neuroepithelia, in which we found substantial expression of the genes coding for the virus receptor angiotensin-converting enzyme-2 (ACE2) and for the virus internalization enhancer TMPRSS2. We analyzed a human olfactory single-cell RNA-seq dataset and determined that sustentacular cells, which maintain the integrity of olfactory sensory neurons, express ACE2 and TMPRSS2. ACE2 protein was highly expressed in a subset of sustentacular cells in human and mouse olfactory tissues. Finally, we found ACE2 transcripts in specific brain cell types, both in mice and humans. Sustentacular cells thus represent a potential entry door for SARS-CoV-2 in a neuronal sensory system that is in direct connection with the brain.
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Affiliation(s)
- Leon Fodoulian
- Department of Genetics and Evolution, Faculty of Sciences, University of Geneva, quai Ernest-Ansermet 30, 1211 Geneva, Switzerland
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, 1 rue Michel-Servet, 1211 Geneva, Switzerland
| | - Joël Tuberosa
- Department of Genetics and Evolution, Faculty of Sciences, University of Geneva, quai Ernest-Ansermet 30, 1211 Geneva, Switzerland
| | - Daniel Rossier
- Department of Genetics and Evolution, Faculty of Sciences, University of Geneva, quai Ernest-Ansermet 30, 1211 Geneva, Switzerland
| | - Madlaina Boillat
- Department of Genetics and Evolution, Faculty of Sciences, University of Geneva, quai Ernest-Ansermet 30, 1211 Geneva, Switzerland
| | - Chenda Kan
- Department of Genetics and Evolution, Faculty of Sciences, University of Geneva, quai Ernest-Ansermet 30, 1211 Geneva, Switzerland
| | - Véronique Pauli
- Department of Genetics and Evolution, Faculty of Sciences, University of Geneva, quai Ernest-Ansermet 30, 1211 Geneva, Switzerland
| | - Kristof Egervari
- Service of Clinical Pathology, Department of Genetic Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, 1 rue Michel-Servet, 1211 Geneva, Switzerland
| | - Johannes A Lobrinus
- Service of Clinical Pathology, Department of Genetic Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Basile N Landis
- Rhinology-Olfactology Unit, Department of Otorhinolaryngology, Head and Neck Surgery, Geneva University Hospitals, Geneva, Switzerland
| | - Alan Carleton
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, 1 rue Michel-Servet, 1211 Geneva, Switzerland
| | - Ivan Rodriguez
- Department of Genetics and Evolution, Faculty of Sciences, University of Geneva, quai Ernest-Ansermet 30, 1211 Geneva, Switzerland
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94
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Lu S, Qiao X. Single-cell profiles of human bone marrow-derived mesenchymal stromal cells after IFN-γ and TNF-α licensing. Gene 2020; 771:145347. [PMID: 33333228 DOI: 10.1016/j.gene.2020.145347] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 10/12/2020] [Accepted: 12/01/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Pre-licensing mesenchymal stromal cells (MSCs) with IFN-γ and TNF-α can empower their immune fate and induce a more effective immune regulation. However, the cellular heterogeneity of MSCs limits our understanding of this inflammatory licensing. METHODS The publicly available Gene Expression Omnibus single-cell RNA sequencing (scRNA-seq) data of human bone marrow-derived MSCs with or without IFN-γ and TNF-α licensing were analyzed. Based on the scRNA-seq data and related marker genes, the cell-cycle, stemness, differentiative potencies, and immunomodulate capability of unlicensed and licensed MSCs were compared. RESULTS After removing low-quality cells and regressing out the ribosomal gene effects, high-quality data reflecting IFN-γ and TNF-α effect on MSCs were chosen for further analysis. Despite the heterogeneity, pre-licensing didn't influence the cell-cycle and stemness of human bone marrow-derived MSCs. The osteogenesis potencies were decreased, the chondrogenesis potencies were increased while the adipogenesis potencies were stable in licensed MSCs. Licensed MSCs also showed more effective immunomodulate capability including expression of related chemokines, cytokines, surface molecules, and receptors. CONCLUSION Collectively, our study showed the expression profiles of human bone marrow-derived unlicensed and licensed MSCs about the cell cycle, stemness, differentiative potencies, and immunomodulate capability at single-cell resolution, which may help the comprehensive understanding about the inflammatory licensing of human bone marrow-derived MSCs and their further clinical application.
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Affiliation(s)
- Shuanglong Lu
- Department of Pediatrics, Tongji Hospital, Tongji University School of Medicine, 389 Xincun Road, Shanghai 200065, China
| | - Xiaohong Qiao
- Department of Pediatrics, Tongji Hospital, Tongji University School of Medicine, 389 Xincun Road, Shanghai 200065, China.
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95
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Qorri B, Tsay M, Agrawal A, Au R, Gracie J. Using machine intelligence to uncover Alzheimer’s disease progression heterogeneity. EXPLORATION OF MEDICINE 2020. [DOI: 10.37349/emed.2020.00026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Aim: Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Through a proprietary interactive ML system, several underlying biological mechanisms associated with AD pathology were uncovered. This paper is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD.
Methods: A public AD data set (GSE84422) consisting of transcriptomic data of postmortem brain samples from healthy controls (n = 121) and AD (n = 380) subjects was analyzed. Data were processed by an artificial intelligence platform designed to discover potential drug repurposing candidates, followed by an interactive augmented intelligence program.
Results: Using perspective analytics, six perspective classes were identified: Class I is defined by TUBB1, ASB4, and PDE5A; Class II by NRG2 and ZNF3; Class III by IGF1, ASB4, and GTSE1; Class IV is defined by cDNA FLJ39269, ITGA1, and CPM; Class V is defined by PDE5A, PSEN1, and NDUFS8; and Class VI is defined by DCAF17, cDNA FLJ75819, and SLC33A1. It is hypothesized that these classes represent biological mechanisms that may act alone or in any combination to manifest an Alzheimer’s pathology.
Conclusions: Using a limited transcriptomic public database, six different classes that drive AD were uncovered, supporting the premise that AD is a heterogeneously complex disorder. The perspective classes highlighted genetic pathways associated with vasculogenesis, cellular signaling and differentiation, metabolic function, mitochondrial function, nitric oxide, and metal ion metabolism. The interplay among these genetic factors reveals a more profound underlying complexity of AD that may be responsible for the confluence of several biological factors. These results are not exhaustive; instead, they demonstrate that even within a relatively small study sample, next-generation machine intelligence can uncover multiple genetically driven subtypes. The models and the underlying hypotheses generated using novel analytic methods may translate into potential treatment pathways.
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Affiliation(s)
- Bessi Qorri
- Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Mike Tsay
- NetraMark Corp, Toronto, ON M4E 1G8, Canada
| | | | - Rhoda Au
- Department of Anatomy & Neurobiology, Neurology and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA 02218, USA
| | - Joseph Gracie
- NetraMark Corp, Toronto, ON M4E 1G8, Canada 5Department of Pathology and Molecular Medicine, Queen’s University, Kingston, ON K7L 3N6, Canada
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96
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Li Y, Xu Q, Wu D, Chen G. Exploring Additional Valuable Information From Single-Cell RNA-Seq Data. Front Cell Dev Biol 2020; 8:593007. [PMID: 33335900 PMCID: PMC7736616 DOI: 10.3389/fcell.2020.593007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 10/26/2020] [Indexed: 12/28/2022] Open
Abstract
Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.
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Affiliation(s)
- Yunjin Li
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China
| | - Qiyue Xu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China
| | - Duojiao Wu
- Institute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Geng Chen
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China
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97
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Kautt AF, Kratochwil CF, Nater A, Machado-Schiaffino G, Olave M, Henning F, Torres-Dowdall J, Härer A, Hulsey CD, Franchini P, Pippel M, Myers EW, Meyer A. Contrasting signatures of genomic divergence during sympatric speciation. Nature 2020; 588:106-111. [PMID: 33116308 PMCID: PMC7759464 DOI: 10.1038/s41586-020-2845-0] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 07/23/2020] [Indexed: 01/25/2023]
Abstract
The transition from 'well-marked varieties' of a single species into 'well-defined species'-especially in the absence of geographic barriers to gene flow (sympatric speciation)-has puzzled evolutionary biologists ever since Darwin1,2. Gene flow counteracts the buildup of genome-wide differentiation, which is a hallmark of speciation and increases the likelihood of the evolution of irreversible reproductive barriers (incompatibilities) that complete the speciation process3. Theory predicts that the genetic architecture of divergently selected traits can influence whether sympatric speciation occurs4, but empirical tests of this theory are scant because comprehensive data are difficult to collect and synthesize across species, owing to their unique biologies and evolutionary histories5. Here, within a young species complex of neotropical cichlid fishes (Amphilophus spp.), we analysed genomic divergence among populations and species. By generating a new genome assembly and re-sequencing 453 genomes, we uncovered the genetic architecture of traits that have been suggested to be important for divergence. Species that differ in monogenic or oligogenic traits that affect ecological performance and/or mate choice show remarkably localized genomic differentiation. By contrast, differentiation among species that have diverged in polygenic traits is genomically widespread and much higher overall, consistent with the evolution of effective and stable genome-wide barriers to gene flow. Thus, we conclude that simple trait architectures are not always as conducive to speciation with gene flow as previously suggested, whereas polygenic architectures can promote rapid and stable speciation in sympatry.
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Affiliation(s)
- Andreas F Kautt
- Department of Biology, University of Konstanz, Konstanz, Germany
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | | | - Alexander Nater
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Gonzalo Machado-Schiaffino
- Department of Biology, University of Konstanz, Konstanz, Germany
- Department of Functional Biology, Area of Genetics, University of Oviedo, Oviedo, Spain
| | - Melisa Olave
- Department of Biology, University of Konstanz, Konstanz, Germany
- Argentine Dryland Research Institute of the National Council for Scientific Research (IADIZA-CONICET), Mendoza, Argentina
| | - Frederico Henning
- Department of Biology, University of Konstanz, Konstanz, Germany
- Department of Genetics, Institute of Biology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | | | - Andreas Härer
- Department of Biology, University of Konstanz, Konstanz, Germany
- Division of Biological Sciences, Section of Ecology, Behavior & Evolution, University of California San Diego, La Jolla, CA, USA
| | - C Darrin Hulsey
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Paolo Franchini
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Martin Pippel
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- Center for Systems Biology Dresden, Dresden, Germany
| | - Eugene W Myers
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- Center for Systems Biology Dresden, Dresden, Germany
| | - Axel Meyer
- Department of Biology, University of Konstanz, Konstanz, Germany.
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98
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Liu Y, Ye G, Huang L, Zhang C, Sheng Y, Wu B, Han L, Wu C, Dong B, Qi Y. Single-cell transcriptome analysis demonstrates inter-patient and intra-tumor heterogeneity in primary and metastatic lung adenocarcinoma. Aging (Albany NY) 2020; 12:21559-21581. [PMID: 33170151 PMCID: PMC7695431 DOI: 10.18632/aging.103945] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/08/2020] [Indexed: 12/15/2022]
Abstract
In this study, we performed single-cell transcriptome data analysis of fifty primary and metastatic lung adenocarcinoma (LUAD) samples from the GSE123902 and GSE131907 datasets to determine the landscape of inter-patient and intra-tumoral heterogeneity. The gene expression profiles and copy number variations (CNV) showed significant heterogeneity in the primary and metastatic LUAD samples. We observed upregulation of pathways related to translational initiation, endoplasmic reticulum stress, exosomes, and unfolded protein response in the brain metastasis samples as compared to the primary tumor samples. Pathways related to exosomes, cell adhesion and metabolism were upregulated and the epithelial-to-mesenchymal-transition (EMT) pathway was downregulated in brain metastasis samples from chemotherapy-treated LUAD patients as compared to those from the untreated LUAD patients. Tumor cell subgroups in the brain metastasis samples showed differential expression of genes related to type II alveolar cells, chemoresistance, glycolysis and oxidative phosphorylation (metabolic reprogramming), and EMT. Thus, single-cell transcriptome analysis demonstrated intra-patient and intra-tumor heterogeneity in the regulation of pathways related to tumor progression, chemoresistance and metabolism in the primary and metastatic LUAD tissues. Moreover, our study demonstrates that single cell transcriptome analysis is a potentially useful tool for accurate diagnosis and personalized targeted treatment of LUAD patients.
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Affiliation(s)
- Yafei Liu
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Guanchao Ye
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Lan Huang
- Biotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Chunyang Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yinliang Sheng
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Bin Wu
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Lu Han
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Chunli Wu
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Bo Dong
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yu Qi
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
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99
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Simon A, Fraïsse C, El Ayari T, Liautard-Haag C, Strelkov P, Welch JJ, Bierne N. How do species barriers decay? Concordance and local introgression in mosaic hybrid zones of mussels. J Evol Biol 2020; 34:208-223. [PMID: 33045123 DOI: 10.1111/jeb.13709] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 08/20/2020] [Accepted: 09/16/2020] [Indexed: 12/19/2022]
Abstract
The Mytilus complex of marine mussel species forms a mosaic of hybrid zones, found across temperate regions of the globe. This allows us to study 'replicated' instances of secondary contact between closely related species. Previous work on this complex has shown that local introgression is both widespread and highly heterogeneous, and has identified SNPs that are outliers of differentiation between lineages. Here, we developed an ancestry-informative panel of such SNPs. We then compared their frequencies in newly sampled populations, including samples from within the hybrid zones, and parental populations at different distances from the contact. Results show that close to the hybrid zones, some outlier loci are near to fixation for the heterospecific allele, suggesting enhanced local introgression, or the local sweep of a shared ancestral allele. Conversely, genomic cline analyses, treating local parental populations as the reference, reveal a globally high concordance among loci, albeit with a few signals of asymmetric introgression. Enhanced local introgression at specific loci is consistent with the early transfer of adaptive variants after contact, possibly including asymmetric bi-stable variants (Dobzhansky-Muller incompatibilities), or haplotypes loaded with fewer deleterious mutations. Having escaped one barrier, however, these variants can be trapped or delayed at the next barrier, confining the introgression locally. These results shed light on the decay of species barriers during phases of contact.
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Affiliation(s)
- Alexis Simon
- ISEM, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Christelle Fraïsse
- ISEM, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France.,Institute of Science and Technology Austria, Klosterneuburg, Austria, Austria
| | - Tahani El Ayari
- ISEM, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | | | - Petr Strelkov
- St. Petersburg State University, St. Petersburg, Russia.,Laboratory of Monitoring and Conservation of Natural Arctic Ecosystems, Murmansk Arctic State University, Murmansk, Russia
| | - John J Welch
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Nicolas Bierne
- ISEM, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
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100
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Tüshaus J, Müller SA, Kataka ES, Zaucha J, Sebastian Monasor L, Su M, Güner G, Jocher G, Tahirovic S, Frishman D, Simons M, Lichtenthaler SF. An optimized quantitative proteomics method establishes the cell type-resolved mouse brain secretome. EMBO J 2020; 39:e105693. [PMID: 32954517 PMCID: PMC7560198 DOI: 10.15252/embj.2020105693] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/12/2020] [Accepted: 08/14/2020] [Indexed: 12/21/2022] Open
Abstract
To understand how cells communicate in the nervous system, it is essential to define their secretome, which is challenging for primary cells because of large cell numbers being required. Here, we miniaturized secretome analysis by developing the "high-performance secretome protein enrichment with click sugars" (hiSPECS) method. To demonstrate its broad utility, hiSPECS was used to identify the secretory response of brain slices upon LPS-induced neuroinflammation and to establish the cell type-resolved mouse brain secretome resource using primary astrocytes, microglia, neurons, and oligodendrocytes. This resource allowed mapping the cellular origin of CSF proteins and revealed that an unexpectedly high number of secreted proteins in vitro and in vivo are proteolytically cleaved membrane protein ectodomains. Two examples are neuronally secreted ADAM22 and CD200, which we identified as substrates of the Alzheimer-linked protease BACE1. hiSPECS and the brain secretome resource can be widely exploited to systematically study protein secretion and brain function and to identify cell type-specific biomarkers for CNS diseases.
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Affiliation(s)
- Johanna Tüshaus
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Stephan A Müller
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Evans Sioma Kataka
- Department of BioinformaticsWissenschaftszentrum WeihenstephanTechnical University of MunichFreisingGermany
| | - Jan Zaucha
- Department of BioinformaticsWissenschaftszentrum WeihenstephanTechnical University of MunichFreisingGermany
| | | | - Minhui Su
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- Institute of Neuronal Cell BiologyTechnical University MunichMunichGermany
| | - Gökhan Güner
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Georg Jocher
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Sabina Tahirovic
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
| | - Dmitrij Frishman
- Department of BioinformaticsWissenschaftszentrum WeihenstephanTechnical University of MunichFreisingGermany
| | - Mikael Simons
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- Institute of Neuronal Cell BiologyTechnical University MunichMunichGermany
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
| | - Stefan F Lichtenthaler
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
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