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Microbial symbioses can mitigate drought stress in crops but harnessing these beneficial interactions will require an in-depth understanding of root microbiome responses to drought cycles. Here, by detailed temporal characterization of root-associated microbiomes of rice plants during drought stress and recovery, we find that endosphere communities remained compositionally altered after rewatering, with prolonged droughts leading to decreased resilience. Several endospheric Actinobacteria were significantly enriched during drought and for weeks after rewatering. Notably, the most abundant endosphere taxon during this period was a Streptomyces, and a corresponding isolate promoted root growth. Additionally, drought stress disrupted the temporal dynamics of late-colonizing microorganisms, permanently altering the normal successional trends of root microbiota. These findings reveal that severe drought results in enduring impacts on rice root microbiomes, including enrichment of taxonomic groups that could shape the recovery response of the host, and have implications relevant to drought protection strategies using root microbiota.
A geographically isolated maize landrace cultivated on nitrogen-depleted fields without synthetic fertilizer in the Sierra Mixe region of Oaxaca, Mexico utilizes nitrogen derived from the atmosphere and develops an extensive network of mucilage-secreting aerial roots that harbors a diazotrophic microbiota. Targeting these diazotrophs, we selected nearly 600 microbes from a collection isolated from these plants and confirmed their ability to incorporate heavy nitrogen ( ¹⁵ N 2 ) metabolites in vitro . Sequencing their genomes and conducting comparative bioinformatic analyses showed that these genomes had substantial phylogenetic diversity. We examined each diazotroph genome for the presence of nif genes essential to nitrogen fixation ( nif HDKENB) and carbohydrate utilization genes relevant to the mucilage polysaccharide digestion. These analyses identified diazotrophs that possessed canonical nif gene operons, as well as many other operon configurations with concomitant fixation and release of >700 different ¹⁵ N labeled metabolites. We further demonstrated that many diazotrophs possessed alternative nif gene operons and confirmed their genomic potential to derive chemical energy from mucilage polysaccharide to fuel nitrogen fixation. These results confirm that some diazotrophic bacteria associated with Sierra Mixe maize were capable of incorporating atmospheric nitrogen into their small molecule extracellular metabolites through multiple nif gene configurations while others were able to fix nitrogen without the canonical ( nif HDKENB) genes. Data Summary Genetic resources, including biological materials and nucleic acid sequences, were accessed under an Access and Benefit Sharing (ABS) Agreement between the Sierra Mixe community and the Mars Corporation, and with authorization from the Mexican government. An internationally recognized certificate of compliance has been issued by the Mexican government under the Nagoya Protocol for such activities (ABSCH-IRCC-MX-207343-3). Any party seeking access to the nucleic acid sequences underlying the analysis reported here is subject to the full terms and obligations of the ABS agreement and the authorization from the government of Mexico. Individuals wishing to access nucleic acid sequence data for scientific research activities should contact Mars Incorporated Chief Science Officer at CSO@effem.com .
Genome methylation in bacteria is an area of intense interest because it has broad implications for bacteriophage resistance, replication, genomic diversity via replication fidelity, response to stress, gene expression regulation, and virulence. Increasing interest in bacterial DNA modification is coming about with investigation of host/microbe interactions and the microbiome association and coevolution with the host organism. Since the recognition of DNA methylation being important in Escherichia coli and bacteriophage resistance using restriction/modification systems, more than 43,600 restriction enzymes have been cataloged in more than 3600 different bacteria. While DNA sequencing methods have made great advances there is a dearth of method advances to examine these modifications in situ. However, the large increase in whole genome sequences has led to advances in defining the modification status of single genomes as well as mining new restriction enzymes, methyltransferases, and modification motifs. These advances provide the basis for the study of pan-epigenomes, population-scale comparisons among pangenomes to link replication fidelity and methylation status along with mutational analysis of mutLS. Newer DNA sequencing methods that include SMRT and nanopore sequencing will aid the detection of DNA modifications on the ever-increasing whole genome and metagenome sequences that are being produced. As more sequences become available, larger analyses are being done to provide insight into the role and guidance of bacterial DNA modification to bacterial survival and physiology.
Highly dimensional data generated from bacterial whole genome sequencing is providing unprecedented scale of information that requires appropriate statistical frameworks of analysis to infer biological function from bacterial genomic populations. Application of genome wide association study (GWAS) methods is an emerging approach with bacterial population genomics that yields a list of genes associated with a phenotype with an undefined importance among the candidates in the list. Here, we validate the combination of GWAS, machine learning, and pathogenic bacterial population genomics as a novel scheme to identify SNPs and rank allelic variants to determine associations for accurate estimation of disease phenotype. This approach parsed a dataset of 1.2 million SNPs that resulted in a ranked importance of associated alleles of Campylobacter jejuni porA using multiple spatial locations over a 30-year period. We validated this approach using previously proven laboratory experimental alleles from an in vivo guinea pig abortion model. This approach, termed BioML, defined intestinal and extraintestinal groups that have differential allelic variants that cause abortion. Divergent variants containing indels that defeated gene callers were rescued using biological context and knowledge that resulted in defining rare and divergent variants that were maintained in the population over two continents and 30 years. This study defines the capability of machine learning coupled to GWAS and population genomics to simultaneously identify and rank alleles to define their role in abortion, and more broadly infectious disease.
The goal of the project is to determine novel biomarkers of abortion in ruminants due to Campylobacter jejuni infection using predictive modeling of whole genome sequencing and machine learning. Determining the genomic basis for phenotypes is going to impact infectious disease surveillance, vaccine design and public health. Here we applied a novel approach to population genomics of infectious disease using machine learning.
Non-jejuni Campylobacter species identification is quite challenging for a diagnostic microbiology laboratory due to suboptimal isolation protocols and lack of differentiating biochemical features. Here we applied whole genome sequencing in an outbreak investigation of a cryptic pathogen Campylobacter hyointestinalis subsp. hyointestinalis in great apes in zoological collection facility in US.
RNA viruses are hypermutable. Using reovirus as model system for hypermutable virus evolution and reassortment. Avian reovirus (ARV) in meat type chickens manifests as a plethora of clinical signs ranging from runting and stunting to a severe disease characterized by viral tenosynovitis, pericarditis and myocarditis. The strategy to control the disease in meat type chickens entails breeder live virus vaccination using conventional S1133-like strains, followed by autogenous vaccines using prevalent isolates obtained from the field. However, the rate of change in the virus hinders our ability to obtain vaccines that provide persistent protection.
Importance: Listeria monocytogenes is the causative agent of listeriosis, a disease which manifests as gastroenteritis, meningoencephalitis, and abortion. Amongst Salmonella, E. coli, Campylobacter, and Listeria-the most prevalent foodborne illnesses-infection by L. monocytogenes carries the highest mortality rate. The ability of L. monocytogenes to regulate its response to various harsh environments enables its persistence and transmission. Small scale comparisons of L. monocytogenes focusing solely on genome contents reveal a highly syntenic genome yet fail to address the observed diversity in phenotypic regulation. This study provides a large scale comparison of 302 L. monocytogenes, revealing the importance of the epigenome and restriction-modification systems as major determinants of L. monocytogenes phylogenetic grouping and subsequent phenotypic expression. Further examination of virulence genes of select outbreak strains reveals an unprecedented diversity in methylation statuses despite high degrees of genome conservation.
Listeria monocytogenes is a food-associated bacterium that is responsible for food-related illnesses worldwide. This is the initial public release of 306 L. monocytogenes genome sequences as part of the 100K Pathogen Genome Project. These isolates represent global genomic diversity in L. monocytogenes .