Genomic Features of Mutualistic Plant Bacteria

Author(s):  
Pablo R. Hardoim ◽  
Cristiane Cassiolato Pires Hardoim
Keyword(s):  
2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Hooi-Leng Ser ◽  
Wai-Fong Yin ◽  
Kok-Gan Chan ◽  
Nurul-Syakima Ab Mutalib ◽  
Learn-Han Lee

Novosphingobium malaysiense strain MUSC 273T is a recently identified Gram-negative, aerobic alpha-proteobacterium. The strain was isolated from intertidal soil with strong catalase activity. The genome sequence comprises 5,027,021 bp, with 50 tRNA and 3 rRNA genes. Further analysis identified presence of secondary metabolite gene clusters within genome of MUSC 273T. Knowledge of the genomic features of the strain may allow further biotechnological exploitation, particularly for production of secondary metabolites as well as production of industrially important enzymes


2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.


2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Jaewon Lim ◽  
Hong-Tae Park ◽  
Seyoung Ko ◽  
Hyun-Eui Park ◽  
Gyumin Lee ◽  
...  

AbstractMycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic granulomatous enteropathy in ruminants. Determining the genetic diversity of MAP is necessary to understand the epidemiology and biology of MAP, as well as establishing disease control strategies. In the present study, whole genome-based alignment and comparative analysis were performed using 40 publicly available MAP genomes, including newly sequenced Korean isolates. First, whole genome-based alignment was employed to identify new genomic structures in MAP genomes. Second, the genomic diversity of the MAP population was described by pangenome analysis. A phylogenetic tree based on the core genome and pangenome showed that the MAP was differentiated into two major types (C- and S-type), which was in keeping with the findings of previous studies. However, B-type strains were discriminated from C-type strains. Finally, functional analysis of the pangenome was performed using three virulence factor databases (i.e., PATRIC, VFDB, and Victors) to predict the phenotypic diversity of MAP in terms of pathogenicity. Based on the results of the pangenome analysis, we developed a real-time PCR technique to distinguish among S-, B- and C-type strains. In conclusion, the results of our study suggest that the phenotypic differences between MAP strains can be explained by their genetic polymorphisms. These results may help to elucidate the diversity of MAP, extending from genomic features to phenotypic traits.


Author(s):  
Ehsan Kayal ◽  
David R Smith

Abstract Mitochondrial DNA (mtDNA) is a universal hallmark of aerobic eukaryotes. That is why the recent suggestion by John et al. (2019) that the aerobic dinoflagellate Amoebophrya sp. strain AT5 (Syndiniales) lacks mtDNA was so remarkable. Here, by reanalysing recently published genomic and transcriptomic data from three Amoebophrya strains, we provide evidence of a cryptic, highly reduced mtDNA in this clade. More work is needed before one can definitively say if Amoebophrya has or does not have a mtDNA, but for now the data are pointing towards the existence of one. Ultimately, we urge caution when basing supposedly absent genomic features on single line evidences.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos

AbstractHigh-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A150-A150
Author(s):  
Christina Yu ◽  
Brian Walker ◽  
G David Roodman ◽  
Kun Huang ◽  
Michel Sadelain ◽  
...  

BackgroundMultiple Myeloma (MM) is an incurable disease, with a particularly poor prognosis for patients with refractory/relapsed MM or high-risk cytogenetics. Chimeric Antigen Receptor (CAR) T-cell therapy targeting BCMA can induce deep responses in highly pretreated RRMM; however, remissions are not sustained, and the majority of patients eventually relapse. We hypothesized that genomic determinants of MM play a role in dictating the expression of surface targets that can be of use for immune targeting.MethodsWe analyzed the gene expression of 24 immunotherapeutic targets in a combined dataset of 1900 MM patients from three independent expression datasets obtained from the Multiple Myeloma Research Foundation CoMMpass study and Gene Expression Omnibus. Given that CAR T-cell therapy may be especially important for patients with high-risk myeloma, we defined the expression of each target in high-risk MM patients by stratifying patients based on several genomic features impacting prognosis. Additionally, we conducted a gene co-expression network analysis and identified 30 gene modules highly correlated with 16 cell surface targets from our panel, further suggesting that genetic determinants of MM may shape a targetable cell surfaceome. In order to determine whether targeting any of these candidate antigens might cause major toxicity to normal cells, we utilized several repositories providing protein data1 to annotate their expression in several normal cell types.ResultsWe determined that a number of genomic factors could stratify the 24 targets into three general groups: 1) targets that show consistent overexpression in high-risk patients: IGF1R, ITGB7, GPRC5D and CD70, and are thus suitable for most high-risk patients; 2) targets that are down-regulated in patients with high-risk genomic features: CD200, CD19, CD40, CD1D and IGKC, perhaps playing a role in cancer immune escape; and 3) targets associated with one specific genetic abnormality, i.e. t(4;14): FUT3, SLAMF7, CD56, CD138 and BCMA, thus of use for precision CAR therapy in this high-risk patient subset.ConclusionsOur work provides a means of target selection for precision CAR therapy, by considering both patient genomic backgrounds and cancer cell surface profiles. Furthermore, our results provide a roadmap for immunotherapy of MM by unbiasedly comparing the expression of top MM cell surface targets in patient data and normal cells and suggest that the genetic landscape of MM may predict the expression of specific targets for precision immunotherapy. The quest for novel MM targets for immunotherapies remains open, and CAR target discovery driven by specific genetic events remains an active area of investigation.ReferencePerna F, Berman SH, Soni RK, et al. Integrating proteomics and transcriptomics for systematic combinatorial chimeric antigen receptor therapy of AML. Cancer Cell 2017;32(4):506–19.


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