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Although many studies have elucidated the mechanisms by which different wavelengths of light (blue, red, far-red, or ultraviolet-B [UV-B]) regulate plant development, whether and how green light regulates plant development remains largely unknown. Previous studies reported that green light participates in regulating growth and development in land plants, but these studies have reported conflicting results, likely due to technical problems. For example, commercial green light-emitting diode light sources emit a little blue or red light. Here, using a pure green light source, we determined that unlike blue, red, far-red, or UV-B light, which inhibits hypocotyl elongation, green light promotes hypocotyl elongation in Arabidopsis thaliana and several other plants during the first 2–3 d after planting. Phytochromes, cryptochromes, and other known photoreceptors do not mediate green-light-promoted hypocotyl elongation, but the brassinosteroid (BR) signaling pathway is involved in this process. Green light promotes the DNA binding activity of BRI1-EMS-SUPPRESSOR 1 (BES1), a master transcription factor of the BR pathway, thus regulating gene transcription to promote hypocotyl elongation. Our results indicate that pure green light promotes elongation via BR signaling and acts as a shade signal to enable plants to adapt their development to a green-light-dominant environment under a canopy. [ABSTRACT FROM AUTHOR]

Simple Summary: Smoking is the commonest cause of oral cavity squamous cell carcinoma (OC-SCC), but the cause of OC-SCC in nonsmokers is unknown. Our primary goal was to use metagenomic shotgun sequencing (MSS) to directly define the taxonomic composition and functional potential of oral metagenome in nonsmokers with OC-SCC. We found three bacterial phyla, one genus, and one species were enriched in OC-SCC while two phyla, five genera, and 18 species were enriched in controls. Pathways related to metabolism of flavin, biotin, thiamine, heme, sugars, fatty acids, peptidoglycans, and tRNA were more abundant in OC-SCC while those related to metabolism of nucleotides and essential amino acids were more abundant in controls. The MSS method achieved similar results to the commonly used 16S rRNA gene survey in compositional differentiation but differed greatly from prediction using 16S rRNA genes in functional differentiation of microbiomes in OC-SCC and controls. Objectives: Smoking is the commonest cause of oral cavity squamous cell carcinoma (OC-SCC), but the etiology of OC-SCC in nonsmokers is unknown. Our primary goal was to use metagenomic shotgun sequencing (MSS) to define the taxonomic composition and functional potential of oral metagenome in nonsmokers with OC-SCC. Methods: We conducted a case–control study with 42 OC-SCC case and 45 control nonsmokers. MSS was performed on DNA extracted from mouthwash samples. Taxonomic analysis and pathway analysis were done using MetaPhlAn2 and HUMAnN2, respectively. Statistical difference was determined using the Mann–Whitney test controlling false discovery rate. Results: There was no significant difference in age, sex, race, or alcohol consumption between OC-SCC and control patients. There was a significant difference in beta diversity between OC-SCC and controls. At the phylum level, Bacteroidetes and Synergistetes were overly represented in OC-SCC while Actinobacteria and Firmicutes were overly represented in controls. At the genus level, Fusobacterium was overly represented in OC-SCC compared with controls, while Corynebacterium, Streptococcus, Actinomyces, Cryptobacterium, and Selenomonas were overly represented in controls. Bacterial pathway analysis identified overrepresentation in OC-SCC of pathways related to metabolism of flavin, biotin, thiamin, heme, sugars, fatty acids, peptidoglycans, and tRNA and overrepresentation of nucleotides and essential amino acids in controls. Conclusions: The oral microbiome in nonsmoker patients with OC-SCC is significantly different from that of nonsmoker control patients in taxonomic compositions and functional potentials. Our study's MSS findings matched with previous 16S-based methods in taxonomic differentiation but varied greatly in functional differentiation of microbiomes in OC-SCC and controls. [ABSTRACT FROM AUTHOR]

The incidence of esophageal adenocarcinoma (EA) has drastically increased in the United States since 1970s for unclear reasons. We hypothesized that the widespread usage of antibiotics has increased the procarcinogenic potential of the orodigestive microbiota along the sequence of gastroesophageal reflux (GR), Barrett's esophagus (BE) and EA phenotypes. This case control study included normal controls (NC) and three disease phenotypes GR, BE and EA. Microbiota in the mouth, esophagus, and stomach, and rectum were analyzed using 16S rRNA gene sequencing. Overall, we discovered 44 significant pairwise differences in abundance of microbial taxa between the four phenotypes, with 12 differences in the mouth, 21 in the esophagus, two in the stomach, and nine in the rectum. Along the GR→BE→EA sequence, oral and esophageal microbiota were more diversified, the dominant genus Streptococcus was progressively depleted while six other genera Atopobium, Actinomyces, Veillonella, Ralstonia, Burkholderia and Lautropia progressively enriched. In NC, Streptococcus appeared to control populations of other genera in the foregut via numerous negative and positive connections, while in disease states, the rich network was markedly simplified. Inferred gene functional content showed a progressive enrichment through the stages of EA development in genes encoding antibiotic resistance, ligands of Toll‐like and NOD‐like receptors, nitrate‐nitrite‐nitric oxide pathway and acetaldehyde metabolism. The orodigestive microbiota is in a progressive dysbiotic state along the GR‐BE‐EA sequence. The increasing dysbiosis and antibiotic and procarcinogenic genes in the disease states warrants further study to define their roles in EA pathogenesis. [ABSTRACT FROM AUTHOR]

The mutualistic relationship of gut-resident microbiota and the host immune system promotes homeostasis that ensures maintenance of the microbial community and of a largely non-aggressive immune cell compartment1,2. The consequences of disturbing this balance include proximal inflammatory conditions, such as Crohn’s disease, and systemic illnesses. This equilibrium is achieved in part through the induction of both effector and suppressor arms of the adaptive immune system. Helicobacter species induce T regulatory (Treg) and T follicular helper (TFH) cells under homeostatic conditions, but induce inflammatory T helper 17 (TH17) cells when induced Treg (iTreg) cells are compromised3,4. How Helicobacter and other gut bacteria direct T cells to adopt distinct functions remains poorly understood. Here we investigated the cells and molecular components required for iTreg cell differentiation. We found that antigen presentation by cells expressing RORγt, rather than by classical dendritic cells, was required and sufficient for induction of Treg cells. These RORγt+ cells—probably type 3 innate lymphoid cells and/or Janus cells5—require the antigen-presentation machinery, the chemokine receptor CCR7 and the TGFβ activator αv integrin. In the absence of any of these factors, there was expansion of pathogenic TH17 cells instead of iTreg cells, induced by CCR7-independent antigen-presenting cells. Thus, intestinal commensal microbes and their products target multiple antigen-presenting cells with pre-determined features suited to directing appropriate T cell differentiation programmes, rather than a common antigen-presenting cell that they endow with appropriate functions.Induction of T regulatory cells by gut microbes is mediated by antigen-presenting RORγt+ cells, unlike that of T follicular helper and T helper 17 cells, which requires different cell types. [ABSTRACT FROM AUTHOR]

Evidence suggests that Helicobacter pylori plays a role in gastric cancer (GC) initiation. However, epidemiologic studies on the specific role of other bacteria in the development of GC are lacking. We conducted a case‐control study of 89 cases with gastric intestinal metaplasia (IM) and 89 matched controls who underwent upper gastrointestinal endoscopy at three sites affiliated with NYU Langone Health. We performed shotgun metagenomic sequencing using oral wash samples from 89 case‐control pairs and antral mucosal brushing samples from 55 case‐control pairs. We examined the associations of relative abundances of bacterial taxa and functional pathways with IM using conditional logistic regression with and without elastic‐net penalty. Compared with controls, oral species Peptostreptococcus stomatis, Johnsonella ignava, Neisseria elongata and Neisseria flavescens were enriched in cases (odds ratios [ORs] = 1.29‐1.50, P =.004‐.01) while Lactobacillus gasseri, Streptococcus mutans, S parasanguinis and S sanguinis were under‐represented (ORs = 0.66‐0.76, P =.006‐.042) in cases. Species J ignava and Filifactor alocis in the gastric microbiota were enriched (ORs = 3.27 and 1.43, P =.005 and.035, respectively), while S mutans, S parasanguinis and S sanguinis were under‐represented (ORs = 0.61‐0.75, P =.024‐.046), in cases compared with controls. The lipopolysaccharide and ubiquinol biosynthesis pathways were more abundant in IM, while the sugar degradation pathways were under‐represented in IM. The findings suggest potential roles of certain oral and gastric microbiota, which are correlated with regulation of pathways associated with inflammation, in the development of gastric precancerous lesions. What's new? There's not much evidence for how gut bacteria, besides H. pylori, contribute to gastric cancer risk. Here, the authors used metagenome sequencing to characterize the differences in bacterial microbiota in gastric precancerous lesion cases compared with healthy controls. In gastric intestinal metaplasias, they found higher proportions of several species related to periodontal disease as well as opportunistic pathogens, but lower levels of certain commensals and probiotic species. Metagenomic analysis revealed pathways that were associated with gastric intestinal metaplasias, suggesting a possible mechanism for how the bacteria influence disease risk. [ABSTRACT FROM AUTHOR]

Low socioeconomic status is associated with a high stroke risk. However, few studies have quantitatively assessed the relationship between stroke burden and national economic development indicators. We explored the quantitative association between macroeconomic development and stroke burden in rural China. In this population-based, prospective study (1992–2016), we collected data on annual registrations of stroke events and deaths in Tianjin, China. Economic development over the period was represented by gross domestic product annually adjusted for purchasing power parity (PPP-aGDP) and per capita net income (PCNI) of rural residents in China. We assessed the association of first-ever stroke incidence with PPP-aGDP and PCNI. During the 25-year study period, there were 1,185 stroke events and 362,296 person years of surveillance. First-ever stroke incidence increased by an average of 10.7% per 1,000 USD increase in overall PPP-aGDP and by 12.0% per 1,000 Yuan increase in PCNI; respectively, the mean increases were 9.6 and 10.8% in men and 13.0 and 14.4% in women (all, P < 0.001). These same changes in PPP-aGDP and PCNI also resulted in increases in the incidence of ischemic stroke (12.6 and 14.3%, respectively; P < 0.05), and intracerebral hemorrhage (both, 6.2%; P < 0.05). Similarly, in men, the age of onset of intracerebral hemorrhage decreased by 0.96-years (P = 0.002) for each 1,000 USD increase in PPP-aGDP and by 1.08-years (P = 0.003) for each 1,000 Yuan increase in PCNI. Macroeconomic development was positively associated with stroke incidence in rural China. Thus, enhancing health-care investments is crucial for containing the stroke burden during this remarkable economic development in China. Our findings could guide other developing countries with information regarding the timely control of stroke risk factors and reductions in stroke burden during the initial stages of economic development. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a change in the epidemiology of oral cavity squamous cell cancer (OC‐SCC). Many new cases of OC‐SCC lack the recognized risk factors of smoking, alcohol and human papilloma virus. The aim of this study was to determine if the oral microbiome may be associated with OC‐SCC in nonsmoking HPV negative patients. We compared the oral microbiome of HPV‐negative nonsmoker OC‐SCC(n = 18), premalignant lesions(PML) (n = 8) and normal control patients (n = 12). Their oral microbiome was sampled by oral wash and defined by 16S rRNA gene sequencing. We report that the periodontal pathogens Fusobacterium, Prevotella, Alloprevotella were enriched while commensal Streptococcus depleted in OC‐SCC. Based on the four genera plus a marker genus Veillonella for PML, we classified the oral microbiome into two types. Gene/pathway analysis revealed a progressive increase of genes encoding HSP90 and ligands for TLRs 1, 2 and 4 along the controls→PML → OC‐SCC progression sequence. Our findings suggest an association between periodontal pathogens and OC‐SCC in non smoking HPV negative patients. What's new? Many recent cases of oral‐cavity squamous‐cell cancer (OC‐SCC) lack known risk factors such as smoking or HPV infection. In this study, the authors found that the oral microbiome of patients with OC‐SCC differed from normal controls. They also found that periodontal pathogens may contribute to the development of oral cancer by increasing inflammation, via increased expression of HSP90 and TLR ligands. These results have important public‐health implications for more efficient screening and early detection of OCSCC. Such patients might even be treated with antibiotics or probiotic agents to prevent further cancer progression. [ABSTRACT FROM AUTHOR]

In the context of computing and informatics, Cognitive Diversity (CD) has been proposed to characterize the degree of dissimilarity between multiple scoring systems (MSS). As such, CD serves a role in informatics analogous to that of Pearson's Correlation in classical statistics. Here we review MSS and explore CD's utility in relation to the notions of correlation and distance in machine learning, ensemble methods, rank aggregation, and combinatorial fusion in both parametric score space and non-parametric rank space. Finally, we survey applications of CD in combining MSS in a variety of domains in science, technology, society, business, and management. Our study provides a new data science framework for discovery in data-rich environments. [ABSTRACT FROM AUTHOR]

Motivation: Shotgun DNA sequencing provides sensitive detection of all 182 HPV types in tissue and body fluid. However, existing computational methods either produce false positives misidentifying HPV types due to shared sequences among HPV, human and prokaryotes, or produce false negative since they identify HPV by assembled contigs requiring large abundant of HPV reads. Results: We designed HPViewer with two custom HPV reference databases masking simple repeats and homology sequences respectively and one homology distance matrix to hybridize these two databases. It directly identified HPV from short DNA reads rather than assembled contigs. Using 100 100 simulated samples, we revealed that HPViewer was robust for samples containing either high or low number of HPV reads. Using 12 shotgun sequencing samples from respiratory papillomatosis, HPViewer was equal to VirusTAP, and Vipie and better than HPVDetector with the respect to specificity and was the most sensitive method in the detection of HPV types 6 and 11. We demonstrated that contigs-based approaches had disadvantages of detection of HPV. In 1573 sets of metagenomic data from 18 human body sites, HPViewer identified 104 types of HPV in a body-site associated pattern and 89 types of HPV co-occurring in one sample with other types of HPV. We demonstrated HPViewer was sensitive and specific for HPV detection in metagenomic data. [ABSTRACT FROM AUTHOR]

Objective: Pertussis toxin uncoupling-based studies have shown that Gαi and Gαo can inhibit insulin secretion in pancreatic β-cells. Yet it is unclear whether Gαi and Gαo operate through identical mechanisms and how these G-protein-mediated signals inhibit insulin secretion in vivo. Our objective is to examine whether/how Gαo regulates islet development and insulin secretion in β-cells.Research Design and Methods: Immunoassays were used to analyze the Gαo expression in mouse pancreatic cells. Gαo was specifically inactivated in pancreatic progenitor cells by pancreatic cell-specific gene deletion. Hormone expression and insulin secretion in response to different stimuli were assayed in vivo and in vitro. Electron microscope and total internal reflection fluorescence-based assays were used to evaluate how Gαo regulates insulin vesicle docking and secretion in response to glucose stimulation.Results: Islet cells differentiate properly in Gαo(-/-) mutant mice. Gαo inactivation significantly enhances insulin secretion both in vivo and in isolation. Gαo nullizygous β-cells contain an increased number of insulin granules docked on the cell plasma membrane, although the total number of vesicles per β-cell remains unchanged.Conclusions: Gαo is not required for endocrine islet cell differentiation, but it regulates the number of insulin vesicles docked on the β-cell membrane. [ABSTRACT FROM AUTHOR]

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity. [Display omitted] • "Weighted nearest neighbor" analysis integrates multimodal single-cell data • A multimodal reference "atlas" of the circulating human immune system • Identification and validation of novel sources of lymphoid heterogeneity • "Reference-based" mapping of query datasets onto a multimodal atlas A framework that allows for the integration of multiple data types using single cells is applied to understand distinct immune cell states, previously unidentified immune populations, and to interpret immune responses to vaccinations. [ABSTRACT FROM AUTHOR]

Background Current methods used for annotating metagenomics shotgun sequencing (MGS) data rely on a computationally intensive and low-stringency approach of mapping each read to a generic database of proteins or reference microbial genomes. Results We developed MGS-Fast, an analysis approach for shotgun whole-genome metagenomic data utilizing Bowtie2 DNA-DNA alignment of reads that is an alternative to using the integrated catalog of reference genes database of well-annotated genes compiled from human microbiome data. This method is rapid and provides high-stringency matches (>90% DNA sequence identity) of the metagenomics reads to genes with annotated functions. We demonstrate the use of this method with data from a study of liver disease and synthetic reads, and Human Microbiome Project shotgun data, to detect differentially abundant Kyoto Encyclopedia of Genes and Genomes gene functions in these experiments. This rapid annotation method is freely available as a Galaxy workflow within a Docker image. Conclusions MGS-Fast can confidently transfer functional annotations from gene databases to metagenomic reads, with speed and accuracy. [ABSTRACT FROM AUTHOR]

Targeting glycolysis has been considered therapeutically intractable owing to its essential housekeeping role. However, the context-dependent requirement for individual glycolytic steps has not been fully explored. We show that CRISPR-mediated targeting of glycolysis in T cells in mice results in global loss of Th17 cells, whereas deficiency of the glycolytic enzyme glucose phosphate isomerase (Gpi1) selectively eliminates inflammatory encephalitogenic and colitogenic Th17 cells, without substantially affecting homeostatic microbiota-specific Th17 cells. In homeostatic Th17 cells, partial blockade of glycolysis upon Gpi1 inactivation was compensated by pentose phosphate pathway flux and increased mitochondrial respiration. In contrast, inflammatory Th17 cells experience a hypoxic microenvironment known to limit mitochondrial respiration, which is incompatible with loss of Gpi1. Our study suggests that inhibiting glycolysis by targeting Gpi1 could be an effective therapeutic strategy with minimum toxicity for Th17-mediated autoimmune diseases, and, more generally, that metabolic redundancies can be exploited for selective targeting of disease processes. • PPP and OXPHOS compensate for Gpi1 loss in Th17 cells in normoxic environment • Gpi1 is essential in hypoxic inflamed tissue due to reduced OXPHOS compensation • Metabolic redundancy can vary according to the microenvironment • Targeting Gpi1 could be a tolerable approach for therapeutic glycolysis inhibition Metabolic redundancy differs according to microenvironments, making the glycolysis gene Gpi1 dispensable for homeostatic Th17 cells in normal tissue but essential for pathogenic Th17 cells in hypoxic inflamed tissue. [ABSTRACT FROM AUTHOR]

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets. • Seurat v3 identifies correspondences between cells in different experiments • These "anchors" can be used to harmonize datasets into a single reference • Reference labels and data can be projected onto query datasets • Extends beyond RNA-seq to single-cell protein, chromatin, and spatial data A computational approach to integrate diverse modalities associated with single-cell sequencing datasets can be used to better understand cellular identity and function. [ABSTRACT FROM AUTHOR]