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Journal articles on the topic 'Metatranscriptomica'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Klingenberg, Heiner, and Peter Meinicke. "How to normalize metatranscriptomic count data for differential expression analysis." PeerJ 5 (October 17, 2017): e3859. http://dx.doi.org/10.7717/peerj.3859.

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Background Differential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it has not been clear whether and how the transcriptomic approach can be used for differential expression analysis in metatranscriptomics. Methods We propose a model for differential expression in metatranscriptomics that explicitly accounts for variations in the taxonomic composition of transcripts across different samples. As a main consequence the correct normalization of metatranscriptomic count data under this model requires the taxonomic separation of the data into organism-specific bins. Then the taxon-specific scaling of organism profiles yields a valid normalization and allows us to recombine the scaled profiles into a metatranscriptomic count matrix. This matrix can then be analyzed with statistical tools for transcriptomic count data. For taxon-specific scaling and recombination of scaled counts we provide a simple R script. Results When applying transcriptomic tools for differential expression analysis directly to metatranscriptomic data with an organism-independent (global) scaling of counts the resulting differences may be difficult to interpret. The differences may correspond to changing functional profiles of the contributing organisms but may also result from a variation of taxonomic abundances. Taxon-specific scaling eliminates this variation and therefore the resulting differences actually reflect a different behavior of organisms under changing conditions. In simulation studies we show that the divergence between results from global and taxon-specific scaling can be drastic. In particular, the variation of organism abundances can imply a considerable increase of significant differences with global scaling. Also, on real metatranscriptomic data, the predictions from taxon-specific and global scaling can differ widely. Our studies indicate that in real data applications performed with global scaling it might be impossible to distinguish between differential expression in terms of transcriptomic changes and differential composition in terms of changing taxonomic proportions. Conclusions As in transcriptomics, a proper normalization of count data is also essential for differential expression analysis in metatranscriptomics. Our model implies a taxon-specific scaling of counts for normalization of the data. The application of taxon-specific scaling consequently removes taxonomic composition variations from functional profiles and therefore provides a clear interpretation of the observed functional differences.

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Mehta, Subina, Marie Crane, Emma Leith, et al. "ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework." F1000Research 10 (April 19, 2021): 103. http://dx.doi.org/10.12688/f1000research.28608.2.

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The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

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Mehta, Subina, Marie Crane, Emma Leith, et al. "ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework." F1000Research 10 (February 11, 2021): 103. http://dx.doi.org/10.12688/f1000research.28608.1.

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The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

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4

Sadeghpour Heravi, Fatemah, Martha Zakrzewski, Karen Vickery, David G. Armstrong, and Honghua Hu. "Bacterial Diversity of Diabetic Foot Ulcers: Current Status and Future Prospectives." Journal of Clinical Medicine 8, no. 11 (2019): 1935. http://dx.doi.org/10.3390/jcm8111935.

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Diabetic foot ulcers (DFUs) and diabetic foot infections (DFIs) are associated with reduced patient quality of life, lower-extremity amputation, hospitalization, and high morbidity and mortality. Diverse bacterial communities have been identified in DFUs/DFIs, playing a significant role in infection prognosis. However, due to the high heterogeneity of bacterial communities colonized in DFUs/DFIs, culture-based methods may not isolate all of the bacterial population or unexpected microorganisms. Recently, high sensitivity and specificity of DNA (metagenomics) and RNA (metatranscriptomics) technologies have addressed limitations of culture-based methods and have taken a step beyond bacterial identification. As a consequence, new advances obtained from DNA- and RNA-based techniques for bacterial identification can improve therapeutic approaches. This review evaluated the current state of play in aetiology of DFUs/DFIs on culture and molecular approaches, and discussed the impact of metagenomic and metatranscriptomic methods in bacterial identification approaches.

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5

Sapp, Philip, Regina Lamendella, Penny Kris-Etherton, and Kristina Petersen. "Peanut Intake Enriches Butyrate Producing Bacteria and Expression of a Gene Associated With Butyrate Production in Adults With Elevated Fasting Glucose: An RCT." Current Developments in Nutrition 5, Supplement_2 (2021): 1178. http://dx.doi.org/10.1093/cdn/nzab054_033.

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Abstract Objectives To assess the effect of consuming 28 g/d of peanuts for 6-weeks, compared to an isocaloric lower fat, higher carbohydrate (LFHC) snack, on gut microbiota composition in adults with elevated fasting glucose. Further, to identify functional and compositional differences in responders using metatranscriptomics. Methods In a randomized, crossover trial, 50 adults (52% male; 42 ± 15 y; BMI 28.3 ± 5.6 kg/m2; glucose 100 ± 8 mg/dL) consumed 28g/d of dry roasted, unsalted, peanuts (160 kcal) or a LFHC snack for 6-wk with a 4-wk washout period. Fecal samples were collected at the baseline and endpoint of each period. Gut microbiota composition was measured using 16 rRNA sequencing and QIIME2 for amplicon sequence variant assignment. Metatranscriptomic sequencing was conducted on baseline and endpoint samples from subjects with the greatest reduction in glucose following the peanut condition (n = 24), to measure gene expression related to microbial metabolic pathways. The NUGEN library preparation method was used to generate cDNA. MetaPhlan2 and HUMAnN2 were used for taxonomic and functional gene annotation, and iPATH3 and Pathview were used for mapping to functional gene pathways. Results No between-condition difference in α or β microbiota diversity was observed. Following peanut intake, roseburia and ruminococcaceae were significantly enriched (LDA &gt; 2; P &lt; 0.05). Metatranscriptomics showed enrichment of the K03518 (aerobic carbon-monoxide dehydrogenase small subunit) gene following peanut intake (P &lt; 0.05). Conclusions Enrichment of roseburia was observed following consumption of 28 g/d of peanuts in adults with elevated fasting glucose. Metatranscriptomics revealed enrichment of the K03518 gene, which is associated with short chain fatty acid production and degradation of β-mannans. These results suggest peanut intake enriches a known butyrate producer and the increased expression of a gene implicated in butyrate production adds further support for peanut-induced gut microbiome modulation. Funding Sources The Peanut Institute and the National Center for Advancing Translational Sciences, National Institutes of Health (1UL1TR002014-01).

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Ranjan, Ravi, Asha Rani, Patricia W. Finn, and David L. Perkins. "Multiomic Strategies Reveal Diversity and Important Functional Aspects of Human Gut Microbiome." BioMed Research International 2018 (November 14, 2018): 1–13. http://dx.doi.org/10.1155/2018/6074918.

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It is well accepted that dysbiosis of microbiota is associated with disease; however, the biological mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data to complement the interpretation of metagenomics (bacterial abundance). The purpose of this study was twofold, first to evaluate the bacterial diversity and differential gene expression of gut microbiota using complementary shotgun metagenomics (MG) and metatranscriptomics (MT) from same fecal sample. Second, to compare sequence data using different Illumina platforms and with different sequencing parameters as new sequencers are introduced, and to determine if the data are comparable on different platforms. In this study, we perform ultradeep metatranscriptomic shotgun sequencing for a sample that we previously analyzed with metagenomics shotgun sequencing. We performed sequencing analysis using different Illumina platforms, with different sequencing and analysis parameters. Our results suggest that use of different Illumina platform did not lead to detectable bias in the sequencing data. The analysis of the sample using MG and MT approach shows that some species genes are highly represented in the MT than in the MG, indicating that some species are highly metabolically active. Our analysis also shows that ~52% of the genes in the metagenome are in the metatranscriptome and therefore are robustly expressed. The functions of the low and rare abundance bacterial species remain poorly understood. Our observations indicate that among the low abundant species analyzed in this study some were found to be more metabolically active compared to others, and can contribute distinct profiles of biological functions that may modulate the host-microbiota and bacteria-bacteria interactions.

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Zheng, Yue, Huan Wang, Zheng Yu, Fauzi Haroon, Maria E. Hernández, and Ludmila Chistoserdova. "Metagenomic Insight into Environmentally Challenged Methane-Fed Microbial Communities." Microorganisms 8, no. 10 (2020): 1614. http://dx.doi.org/10.3390/microorganisms8101614.

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In this study, we aimed to investigate, through high-resolution metagenomics and metatranscriptomics, the composition and the trajectories of microbial communities originating from a natural sample, fed exclusively with methane, over 14 weeks of laboratory incubation. This study builds on our prior data, suggesting that multiple functional guilds feed on methane, likely through guild-to-guild carbon transfer, and potentially through intraguild and intraspecies interactions. We observed that, under two simulated dioxygen partial pressures—low versus high—community trajectories were different, with considerable variability among the replicates. In all microcosms, four major functional guilds were prominently present, representing Methylococcaceae (the true methanotrophs), Methylophilaceae (the nonmethanotrophic methylotrophs), Burkholderiales, and Bacteroidetes. Additional functional guilds were detected in multiple samples, such as members of Opitutae, as well as the predatory species, suggesting additional complexity for methane-oxidizing communities. Metatranscriptomic analysis suggested simultaneous expression of the two alternative types of methanol dehydrogenases in both Methylococcaceae and Methylophilaceae, while high expression of the oxidative/nitrosative stress response genes suggested competition for dioxygen among the community members. The transcriptomic analysis further suggested that Burkholderiales likely feed on acetate that is produced by Methylococcaceae under hypoxic conditions, while Bacteroidetes likely feed on biopolymers produced by both Methylococcaceae and Methylophilaceae.

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Wang, Pan, Meng Qi, Perry Barboza, et al. "Isolation of high-quality total RNA from rumen anaerobic bacteria and fungi, and subsequent detection of glycoside hydrolases." Canadian Journal of Microbiology 57, no. 7 (2011): 590–98. http://dx.doi.org/10.1139/w11-048.

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The rumen is one of the most powerful fibrolytic fermentation systems known. Gene expression analyses, such as reverse transcription PCR (RT-PCR), microarrays, and metatranscriptomics, are techniques that could significantly expand our understanding of this ecosystem. The ability to isolate and stabilize representative RNA samples is critical to obtaining reliable results with these procedures. In this study, we successfully isolated high-quality total RNA from the solid phase of ruminal contents by using an improved RNA extraction method. This method is based on liquid nitrogen grinding of whole ruminal solids without microbial detachment and acid guanidinium – phenol – chloroform extraction combined with column purification. Yields of total RNA were as high as 150 µg per g of fresh ruminal content. The typical large subunit/small subunit rRNA ratio ranged from 1.8 to 2.0 with an RNA integrity number (Agilent Technologies) greater than 8.5. By eliminating the detachment step, the resulting RNA was more representative of the complete ecosystem. Our improved method removed a major barrier limiting analysis of rumen microbial function from a gene expression perspective. The polyA-tailed eukaryotic mRNAs obtained have successfully been applied to next-generation sequencing, and metatranscriptomic analysis of the solid fraction of rumen contents revealed abundant sequences related to rumen fungi.

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Moran, Mary Ann, Brandon Satinsky, Scott M. Gifford, et al. "Sizing up metatranscriptomics." ISME Journal 7, no. 2 (2012): 237–43. http://dx.doi.org/10.1038/ismej.2012.94.

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Shrestha, Pravin Malla, Amelia-Elena Rotaru, Zarath M. Summers, Minita Shrestha, Fanghua Liu, and Derek R. Lovley. "Transcriptomic and Genetic Analysis of Direct Interspecies Electron Transfer." Applied and Environmental Microbiology 79, no. 7 (2013): 2397–404. http://dx.doi.org/10.1128/aem.03837-12.

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ABSTRACTThe possibility that metatranscriptomic analysis could distinguish between direct interspecies electron transfer (DIET) and H2interspecies transfer (HIT) in anaerobic communities was investigated by comparing gene transcript abundance in cocultures in whichGeobacter sulfurreducenswas the electron-accepting partner for eitherGeobacter metallireducens, which performs DIET, orPelobacter carbinolicus, which relies on HIT. Transcript abundance forG. sulfurreducensuptake hydrogenase genes was 7-fold lower in cocultures withG. metallireducensthan in cocultures withP. carbinolicus, consistent with DIET and HIT, respectively, in the two cocultures. Transcript abundance for the pilus-associated cytochrome OmcS, which is essential for DIET but not for HIT, was 240-fold higher in the cocultures withG. metallireducensthan in cocultures withP. carbinolicus. The pilin genepilAwas moderately expressed despite a mutation that might be expected to represspilAexpression. Lower transcript abundance forG. sulfurreducensgenes associated with acetate metabolism in the cocultures withP. carbinolicuswas consistent with the repression of these genes by H2during HIT. Genes for the biogenesis of pili and flagella and severalc-type cytochrome genes were among the most highly expressed inG. metallireducens. Mutant strains that lacked the ability to produce pili, flagella, or the outer surfacec-type cytochrome encoded by Gmet_2896 were not able to form cocultures withG. sulfurreducens. These results demonstrate that there are unique gene expression patterns that distinguish DIET from HIT and suggest that metatranscriptomics may be a promising route to investigate interspecies electron transfer pathways in more-complex environments.

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Liao, Hui-Ling, Gregory Bonito, J. Alejandro Rojas, et al. "Fungal Endophytes of Populus trichocarpa Alter Host Phenotype, Gene Expression, and Rhizobiome Composition." Molecular Plant-Microbe Interactions® 32, no. 7 (2019): 853–64. http://dx.doi.org/10.1094/mpmi-05-18-0133-r.

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Mortierella and Ilyonectria genera include common species of soil fungi that are frequently detected as root endophytes in many plants, including Populus spp. However, the ecological roles of these and other endophytic fungi with respect to plant growth and function are still not well understood. The functional ecology of two key taxa from the P. trichocarpa rhizobiome, M. elongata PMI93 and I. europaea PMI82, was studied by coupling forest soil bioassays with environmental metatranscriptomics. Using soil bioassay experiments amended with fungal inoculants, M. elongata was observed to promote the growth of P. trichocarpa. This response was cultivar independent. In contrast, I. europaea had no visible effect on P. trichocarpa growth. Metatranscriptomic studies revealed that these fungi impacted rhizophytic and endophytic activities in P. trichocarpa and induced shifts in soil and root microbial communities. Differential expression of core genes in P. trichocarpa roots was observed in response to both fungal species. Expression of P. trichocarpa genes for lipid signaling and nutrient uptake were upregulated, and expression of genes associated with gibberellin signaling were altered in plants inoculated with M. elongata, but not I. europaea. Upregulation of genes for growth promotion, downregulation of genes for several leucine-rich repeat receptor kinases, and alteration of expression of genes associated with plant defense responses (e.g., jasmonic acid, salicylic acid, and ethylene signal pathways) also suggest that M. elongata manipulates plant defenses while promoting plant growth.

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Muers, Mary. "Microbial metatranscriptomics goes deep." Nature Reviews Genetics 10, no. 7 (2009): 426–27. http://dx.doi.org/10.1038/nrg2616.

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Ismail, Wazim, Yuzhen Ye, and Haixu Tang. "Gene finding in metatranscriptomic sequences." BMC Bioinformatics 15, Suppl 9 (2014): S8. http://dx.doi.org/10.1186/1471-2105-15-s9-s8.

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Carvalhais, Lilia C., Paul G. Dennis, Gene W. Tyson, and Peer M. Schenk. "Application of metatranscriptomics to soil environments." Journal of Microbiological Methods 91, no. 2 (2012): 246–51. http://dx.doi.org/10.1016/j.mimet.2012.08.011.

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Bashiardes, Stavros, Gili Zilberman-Schapira, and Eran Elinav. "Use of Metatranscriptomics in Microbiome Research." Bioinformatics and Biology Insights 10 (January 2016): BBI.S34610. http://dx.doi.org/10.4137/bbi.s34610.

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Sichertz Pontén, Thomas. "Metatranscriptomics of the human gut microbiome." Genome Biology 12, Suppl 1 (2011): I15. http://dx.doi.org/10.1186/gb-2011-12-s1-i15.

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17

Moran, Mary Ann. "Metatranscriptomics: Eavesdropping on Complex Microbial Communities." Microbe Magazine 4, no. 7 (2009): 329–35. http://dx.doi.org/10.1128/microbe.4.329.1.

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Duran‐Pinedo, Ana E. "Metatranscriptomic analyses of the oral microbiome." Periodontology 2000 85, no. 1 (2020): 28–45. http://dx.doi.org/10.1111/prd.12350.

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Geisen, Stefan, Alexander T. Tveit, Ian M. Clark, et al. "Metatranscriptomic census of active protists in soils." ISME Journal 9, no. 10 (2015): 2178–90. http://dx.doi.org/10.1038/ismej.2015.30.

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Tveit, Alexander T., Tim Urich, and Mette M. Svenning. "Metatranscriptomic Analysis of Arctic Peat Soil Microbiota." Applied and Environmental Microbiology 80, no. 18 (2014): 5761–72. http://dx.doi.org/10.1128/aem.01030-14.

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ABSTRACTRecent advances in meta-omics and particularly metatranscriptomic approaches have enabled detailed studies of the structure and function of microbial communities in many ecosystems. Molecular analyses of peat soils, ecosystems important to the global carbon balance, are still challenging due to the presence of coextracted substances that inhibit enzymes used in downstream applications. We sampled layers at different depths from two high-Arctic peat soils in Svalbard for metatranscriptome preparation. Here we show that enzyme inhibition in the preparation of metatranscriptomic libraries can be circumvented by linear amplification of diluted template RNA. A comparative analysis of mRNA-enriched and nonenriched metatranscriptomes showed that mRNA enrichment resulted in a 2-fold increase in the relative abundance of mRNA but biased the relative distribution of mRNA. The relative abundance of transcripts for cellulose degradation decreased with depth, while the transcripts for hemicellulose debranching increased, indicating that the polysaccharide composition of the peat was different in the deeper and older layers. Taxonomic annotation revealed thatActinobacteriaandBacteroideteswere the dominating polysaccharide decomposers. The relative abundances of 16S rRNA and mRNA transcripts of methanogenicArchaeaincreased substantially with depth. Acetoclastic methanogenesis was the dominating pathway, followed by methanogenesis from formate. The relative abundances of 16S rRNA and mRNA assigned to the methanotrophicMethylococcaceae, primarilyMethylobacter, increased with depth. In conclusion, linear amplification of total RNA and deep sequencing constituted the preferred method for metatranscriptomic preparation to enable high-resolution functional and taxonomic analyses of the active microbiota in Arctic peat soil.

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Santos, Fernando, Mercedes Moreno-Paz, Inmaculada Meseguer, et al. "Metatranscriptomic analysis of extremely halophilic viral communities." ISME Journal 5, no. 10 (2011): 1621–33. http://dx.doi.org/10.1038/ismej.2011.34.

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Bailly, Julie, Laurence Fraissinet-Tachet, Marie-Christine Verner, et al. "Soil eukaryotic functional diversity, a metatranscriptomic approach." ISME Journal 1, no. 7 (2007): 632–42. http://dx.doi.org/10.1038/ismej.2007.68.

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Ojala, Teija, Andrew Lindford, Kirsi Savijoki, et al. "Metatranscriptomic assessment of burn wound infection clearance." Clinical Microbiology and Infection 27, no. 1 (2021): 144–46. http://dx.doi.org/10.1016/j.cmi.2020.07.021.

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Hatch, Andrew, James Horne, Ryan Toma, et al. "A Robust Metatranscriptomic Technology for Population-Scale Studies of Diet, Gut Microbiome, and Human Health." International Journal of Genomics 2019 (October 1, 2019): 1–9. http://dx.doi.org/10.1155/2019/1718741.

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A functional readout of the gut microbiome is necessary to enable precise control of the gut microbiome’s functions, which support human health and prevent or minimize a wide range of chronic diseases. Stool metatranscriptomic analysis offers a comprehensive functional view of the gut microbiome, but despite its usefulness, it has rarely been used in clinical studies due to its complexity, cost, and bioinformatic challenges. This method has also received criticism due to potential intrasample variability, rapid changes, and RNA degradation. Here, we describe a robust and automated stool metatranscriptomic method, called Viomega, which was specifically developed for population-scale studies. Viomega includes sample collection, ambient temperature sample preservation, total RNA extraction, physical removal of ribosomal RNAs (rRNAs), preparation of directional Illumina libraries, Illumina sequencing, taxonomic classification based on a database of >110,000 microbial genomes, and quantitative microbial gene expression analysis using a database of ~100 million microbial genes. We applied this method to 10,000 human stool samples and performed several small-scale studies to demonstrate sample stability and consistency. In summary, Viomega is an inexpensive, high-throughput, automated, and accurate sample-to-result stool metatranscriptomic technology platform for large-scale studies and a wide range of applications.

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Aguiar-Pulido, Vanessa, Wenrui Huang, Victoria Suarez-Ulloa, Trevor Cickovski, Kalai Mathee, and Giri Narasimhan. "Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis." Evolutionary Bioinformatics 12s1 (January 2016): EBO.S36436. http://dx.doi.org/10.4137/ebo.s36436.

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Lavelle, Aonghus, and Harry Sokol. "Beyond metagenomics, metatranscriptomics illuminates microbiome functionality in IBD." Nature Reviews Gastroenterology & Hepatology 15, no. 4 (2018): 193–94. http://dx.doi.org/10.1038/nrgastro.2018.15.

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Bejerman, Nicolás, Ralf G. Dietzgen, and Humberto Debat. "Illuminating the Plant Rhabdovirus Landscape through Metatranscriptomics Data." Viruses 13, no. 7 (2021): 1304. http://dx.doi.org/10.3390/v13071304.

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Rhabdoviruses infect a large number of plant species and cause significant crop diseases. They have a negative-sense, single-stranded unsegmented or bisegmented RNA genome. The number of plant-associated rhabdovirid sequences has grown in the last few years in concert with the extensive use of high-throughput sequencing platforms. Here, we report the discovery of 27 novel rhabdovirus genomes associated with 25 different host plant species and one insect, which were hidden in public databases. These viral sequences were identified through homology searches in more than 3000 plant and insect transcriptomes from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) using known plant rhabdovirus sequences as the query. The identification, assembly and curation of raw SRA reads resulted in sixteen viral genome sequences with full-length coding regions and ten partial genomes. Highlights of the obtained sequences include viruses with unique and novel genome organizations among known plant rhabdoviruses. Phylogenetic analysis showed that thirteen of the novel viruses were related to cytorhabdoviruses, one to alphanucleorhabdoviruses, five to betanucleorhabdoviruses, one to dichorhaviruses and seven to varicosaviruses. These findings resulted in the most complete phylogeny of plant rhabdoviruses to date and shed new light on the phylogenetic relationships and evolutionary landscape of this group of plant viruses. Furthermore, this study provided additional evidence for the complexity and diversity of plant rhabdovirus genomes and demonstrated that analyzing SRA public data provides an invaluable tool to accelerate virus discovery, gain evolutionary insights and refine virus taxonomy.

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Zhang, Yancong, Kelsey N. Thompson, Curtis Huttenhower, and Eric A. Franzosa. "Statistical approaches for differential expression analysis in metatranscriptomics." Bioinformatics 37, Supplement_1 (2021): i34—i41. http://dx.doi.org/10.1093/bioinformatics/btab327.

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Abstract Motivation Metatranscriptomics (MTX) has become an increasingly practical way to profile the functional activity of microbial communities in situ. However, MTX remains underutilized due to experimental and computational limitations. The latter are complicated by non-independent changes in both RNA transcript levels and their underlying genomic DNA copies (as microbes simultaneously change their overall abundance in the population and regulate individual transcripts), genetic plasticity (as whole loci are frequently gained and lost in microbial lineages) and measurement compositionality and zero-inflation. Here, we present a systematic evaluation of and recommendations for differential expression (DE) analysis in MTX. Results We designed and assessed six statistical models for DE discovery in MTX that incorporate different combinations of DNA and RNA normalization and assumptions about the underlying changes of gene copies or species abundance within communities. We evaluated these models on multiple simulated and real multi-omic datasets. Models adjusting transcripts relative to their encoding gene copies as a covariate were significantly more accurate in identifying DE from MTX in both simulated and real datasets. Moreover, we show that when paired DNA measurements (metagenomic data) are not available, models normalizing MTX measurements within-species while also adjusting for total-species RNA balance sensitivity, specificity and interpretability of DE detection, as does filtering likely technical zeros. The efficiency and accuracy of these models pave the way for more effective MTX-based DE discovery in microbial communities. Availability and implementation The analysis code and synthetic datasets used in this evaluation are available online at http://huttenhower.sph.harvard.edu/mtx2021. Supplementary information Supplementary data are available at Bioinformatics online.

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Gómez, Giovan F., Juan P. Isaza, Juan A. Segura, Juan F. Alzate, and Lina A. Gutiérrez. "Metatranscriptomic virome assessment of Rhipicephalus microplus from Colombia." Ticks and Tick-borne Diseases 11, no. 5 (2020): 101426. http://dx.doi.org/10.1016/j.ttbdis.2020.101426.

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Jones, Daniel S., Beverly E. Flood, and Jake V. Bailey. "Metatranscriptomic insights into polyphosphate metabolism in marine sediments." ISME Journal 10, no. 4 (2015): 1015–19. http://dx.doi.org/10.1038/ismej.2015.169.

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Knapik, Kamila, Andrea Bagi, Adriana Krolicka, and Thierry Baussant. "Metatranscriptomic Analysis of Oil-Exposed Seawater Bacterial Communities Archived by an Environmental Sample Processor (ESP)." Microorganisms 8, no. 5 (2020): 744. http://dx.doi.org/10.3390/microorganisms8050744.

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The use of natural marine bacteria as “oil sensors” for the detection of pollution events can be suggested as a novel way of monitoring oil occurrence at sea. Nucleic acid-based devices generically called genosensors are emerging as potentially promising tools for in situ detection of specific microbial marker genes suited for that purpose. Functional marker genes are particularly interesting as targets for oil-related genosensing but their identification remains a challenge. Here, seawater samples, collected in tanks with oil addition mimicking a realistic oil spill scenario, were filtered and archived by the Environmental Sample Processor (ESP), a fully robotized genosensor, and the samples were then used for post-retrieval metatranscriptomic analysis. After extraction, RNA from ESP-archived samples at start, Day 4 and Day 7 of the experiment was used for sequencing. Metatranscriptomics revealed that several KEGG pathways were significantly enriched in samples exposed to oil. However, these pathways were highly expressed also in the non-oil-exposed water samples, most likely as a result of the release of natural organic matter from decaying phytoplankton. Temporary peaks of aliphatic alcohol and aldehyde dehydrogenases and monoaromatic ring-degrading enzymes (e.g., ben, box, and dmp clusters) were observed on Day 4 in both control and oil-exposed and non-exposed tanks. Few alkane 1-monooxygenase genes were upregulated on oil, mostly transcribed by families Porticoccaceae and Rhodobacteraceae, together with aromatic ring-hydroxylating dioxygenases, mostly transcribed by Rhodobacteraceae. Few transcripts from obligate hydrocarbonoclastic genera of Alcanivorax, Oleispira and Cycloclasticus were significantly enriched in the oil-treated exposed tank in comparison to control the non-exposed tank, and these were mostly transporters and genes involved in nitrogen and phosphorous acquisition. This study highlights the importance of seasonality, i.e., phytoplankton occurrence and senescence leading to organic compound release which can be used preferentially by bacteria over oil compounds, delaying the latter process. As a result, such seasonal effect can reduce the sensitivity of genosensing tools employing bacterial functional genes to sense oil. A better understanding of the use of natural organic matter by bacteria involved in oil-biodegradation is needed to develop an array of functional markers enabling the rapid and specific in situ detection of anthropogenic pollution.

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Simón-Soro, Aurea, Miriam Guillen-Navarro, and Alex Mira. "Metatranscriptomics reveals overall active bacterial composition in caries lesions." Journal of Oral Microbiology 6, no. 1 (2014): 25443. http://dx.doi.org/10.3402/jom.v6.25443.

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Kuske, Cheryl R., Cedar N. Hesse, Jean F. Challacombe, et al. "Prospects and challenges for fungal metatranscriptomics of complex communities." Fungal Ecology 14 (April 2015): 133–37. http://dx.doi.org/10.1016/j.funeco.2014.12.005.

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Campos, Gubio S., Silvia I. Sardi, Melissa B. Falcao, et al. "Ion torrent-based nasopharyngeal swab metatranscriptomics in COVID-19." Journal of Virological Methods 282 (August 2020): 113888. http://dx.doi.org/10.1016/j.jviromet.2020.113888.

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Mojib, N., M. Thimma, M. Kumaran, R. Sougrat, and X. Irigoien. "Comparative metatranscriptomics reveals decline of a neustonic planktonic population." Limnology and Oceanography 62, no. 1 (2016): 299–310. http://dx.doi.org/10.1002/lno.10395.

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Stewart, Frank J., Osvaldo Ulloa, and Edward F. DeLong. "Microbial metatranscriptomics in a permanent marine oxygen minimum zone." Environmental Microbiology 14, no. 1 (2011): 23–40. http://dx.doi.org/10.1111/j.1462-2920.2010.02400.x.

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Ivanova, Anastasia A., Carl-Eric Wegner, Yongkyu Kim, Werner Liesack, and Svetlana N. Dedysh. "Metatranscriptomics reveals the hydrolytic potential of peat-inhabiting Planctomycetes." Antonie van Leeuwenhoek 111, no. 6 (2017): 801–9. http://dx.doi.org/10.1007/s10482-017-0973-9.

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Mou, Xiaozhen, Maria Vila-Costa, Shulei Sun, Weidong Zhao, Shalabh Sharma, and Mary Ann Moran. "Metatranscriptomic signature of exogenous polyamine utilization by coastal bacterioplankton." Environmental Microbiology Reports 3, no. 6 (2011): 798–806. http://dx.doi.org/10.1111/j.1758-2229.2011.00289.x.

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Wang, Ying, Lin Liu, Lina Chen, Ting Chen, and Fengzhu Sun. "Comparison of Metatranscriptomic Samples Based on k-Tuple Frequencies." PLoS ONE 9, no. 1 (2014): e84348. http://dx.doi.org/10.1371/journal.pone.0084348.

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Ottesen, Elizabeth A., Roman Marin, Christina M. Preston, et al. "Metatranscriptomic analysis of autonomously collected and preserved marine bacterioplankton." ISME Journal 5, no. 12 (2011): 1881–95. http://dx.doi.org/10.1038/ismej.2011.70.

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Gosalbes, María José, Ana Durbán, Miguel Pignatelli, et al. "Metatranscriptomic Approach to Analyze the Functional Human Gut Microbiota." PLoS ONE 6, no. 3 (2011): e17447. http://dx.doi.org/10.1371/journal.pone.0017447.

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Wang, Ziyi, Achal Neupane, Jiuhuan Feng, Connor Pedersen, and Shin-Yi Lee Marzano. "Direct Metatranscriptomic Survey of the Sunflower Microbiome and Virome." Viruses 13, no. 9 (2021): 1867. http://dx.doi.org/10.3390/v13091867.

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Sunflowers (Helianthus annuus L.) are susceptible to multiple diseases in field production. In this study, we collected diseased sunflower leaves in fields located in South Dakota, USA, for virome investigation. The leaves showed visible symptoms on the foliage, indicating phomopsis and rust infections. To identify the viruses potentially associated with the disease diagnosed, symptomatic leaves were obtained from diseased plants. Total RNA was extracted corresponding to each disease diagnosed to generate libraries for paired-end high throughput sequencing. Short sequencing reads were assembled de novo and the contigs with similarities to viruses were identified by aligning against a custom protein database. We report the discovery of two novel mitoviruses, four novel partitiviruses, one novel victorivirus, and nine novel totiviruses based on similarities to RNA-dependent RNA polymerases and capsid proteins. Contigs similar to bean yellow mosaic virus and Sclerotinia sclerotiorum hypovirulence-associated DNA virus were also detected. To the best of our knowledge, this is the first report of direct metatranscriptomics discovery of viruses associated with fungal infections of sunflowers bypassing culturing. These newly discovered viruses represent a natural genetic resource from which we can further develop potential biopesticide to control sunflower diseases.

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Ma, Anjun, Minxuan Sun, Adam McDermaid, Bingqiang Liu, and Qin Ma. "MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome." Bioinformatics 35, no. 21 (2019): 4474–77. http://dx.doi.org/10.1093/bioinformatics/btz414.

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Abstract Motivation Metagenomic and metatranscriptomic analyses can provide an abundance of information related to microbial communities. However, straightforward analysis of this data does not provide optimal results, with a required integration of data types being needed to thoroughly investigate these microbiomes and their environmental interactions. Results Here, we present MetaQUBIC, an integrated biclustering-based computational pipeline for gene module detection that integrates both metagenomic and metatranscriptomic data. Additionally, we used this pipeline to investigate 735 paired DNA and RNA human gut microbiome samples, resulting in a comprehensive hybrid gene expression matrix of 2.3 million cross-species genes in the 735 human fecal samples and 155 functional enriched gene modules. We believe both the MetaQUBIC pipeline and the generated comprehensive human gut hybrid expression matrix will facilitate further investigations into multiple levels of microbiome studies. Availability and implementation The package is freely available at https://github.com/OSU-BMBL/metaqubic. Supplementary information Supplementary data are available at Bioinformatics online.

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Marzano, Shin-Yi Lee, and Leslie L. Domier. "Novel mycoviruses discovered from metatranscriptomics survey of soybean phyllosphere phytobiomes." Virus Research 213 (February 2016): 332–42. http://dx.doi.org/10.1016/j.virusres.2015.11.002.

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He, Shaomei, Omri Wurtzel, Kanwar Singh, et al. "Validation of two ribosomal RNA removal methods for microbial metatranscriptomics." Nature Methods 7, no. 10 (2010): 807–12. http://dx.doi.org/10.1038/nmeth.1507.

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Wilson, John-James, Guo-Jie Brandon-Mong, Han-Ming Gan, and Kong-Wah Sing. "High-throughput terrestrial biodiversity assessments: mitochondrial metabarcoding, metagenomics or metatranscriptomics?" Mitochondrial DNA Part A 30, no. 1 (2018): 60–67. http://dx.doi.org/10.1080/24701394.2018.1455189.

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Sheik, Cody S., Sunit Jain, and Gregory J. Dick. "Metabolic flexibility of enigmatic SAR324 revealed through metagenomics and metatranscriptomics." Environmental Microbiology 16, no. 1 (2013): 304–17. http://dx.doi.org/10.1111/1462-2920.12165.

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Hilton, Jason A., Brandon M. Satinsky, Mary Doherty, Brian Zielinski, and Jonathan P. Zehr. "Metatranscriptomics of N2-fixing cyanobacteria in the Amazon River plume." ISME Journal 9, no. 7 (2014): 1557–69. http://dx.doi.org/10.1038/ismej.2014.240.

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Zhang, Yancong, Kelsey N. Thompson, Tobyn Branck, et al. "Metatranscriptomics for the Human Microbiome and Microbial Community Functional Profiling." Annual Review of Biomedical Data Science 4, no. 1 (2021): 279–311. http://dx.doi.org/10.1146/annurev-biodatasci-031121-103035.

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Shotgun metatranscriptomics (MTX) is an increasingly practical way to survey microbial community gene function and regulation at scale. This review begins by summarizing the motivations for community transcriptomics and the history of the field. We then explore the principles, best practices, and challenges of contemporary MTX workflows: beginning with laboratory methods for isolation and sequencing of community RNA, followed by informatics methods for quantifying RNA features, and finally statistical methods for detecting differential expression in a community context. In thesecond half of the review, we survey important biological findings from the MTX literature, drawing examples from the human microbiome, other (nonhuman) host-associated microbiomes, and the environment. Across these examples, MTX methods prove invaluable for probing microbe–microbe and host–microbe interactions, the dynamics of energy harvest and chemical cycling, and responses to environmental stresses. We conclude with a review of open challenges in the MTX field, including making assays and analyses more robust, accessible, and adaptable to new technologies; deciphering roles for millions of uncharacterized microbial transcripts; and solving applied problems such as biomarker discovery and development of microbial therapeutics.

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Falk, N., T. Reid, A. Skoyles, A. Grgicak-Mannion, K. Drouillard, and C. G. Weisener. "Microbial metatranscriptomic investigations across contaminant gradients of the Detroit River." Science of The Total Environment 690 (November 2019): 121–31. http://dx.doi.org/10.1016/j.scitotenv.2019.06.451.

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