scholarly journals Regulatory non-coding small RNAs are diverse and abundant in an extremophilic microbial community

2019 ◽  
Author(s):  
Diego R. Gelsinger ◽  
Gherman Uritskiy ◽  
Rahul Reddy ◽  
Adam Munn ◽  
Katie Farney ◽  
...  
Keyword(s):  
Small Rnas ◽  
Large Scale ◽  
Field Experiments ◽  
Target Prediction ◽  
Model Organisms ◽  
Natural Environments ◽  
Cellular Processes ◽  
Non Coding Rna ◽  
Domains Of Life ◽  
Gene Regulatory

ABSTRACTRegulatory small RNAs (sRNAs) represent a major class of regulatory molecules that play large-scale and essential roles in many cellular processes across all domains of life. Microbial sRNAs have been primarily investigated in a few model organisms and little is known about the dynamics of sRNA synthesis in natural environments, and the roles of these short transcripts at the community level. Analyzing the metatranscriptome of a model extremophilic community inhabiting halite nodules (salt rocks) from the Atacama Desert with SnapT – a new sRNA annotation pipeline – we discovered hundreds of intergenic (itsRNAs) and antisense (asRNAs) sRNAs. The halite sRNAs were taxonomically diverse with the majority expressed by members of the Halobacteria. We found asRNAs with expression levels negatively correlated with that of their putative overlapping target, suggesting a potential gene regulatory mechanism. A number of itsRNAs were conserved and significantly differentially expressed (FDR<5%) between 2 sampling time points allowing for stable secondary structure modeling and target prediction. This work demonstrates that metatranscriptomic field experiments link environmental variation with changes in RNA pools and have the potential to provide new insights into environmental sensing and responses in natural microbial communities through non-coding RNA mediated gene regulation.

Development and Testing of an Unmanned Aerial Vehicle for Large Scale Particle Image Velocimetry

2014 ◽  
Author(s):  
Christopher Pagano ◽  
Flavia Tauro ◽  
Salvatore Grimaldi ◽  
Maurizio Porfiri
Keyword(s):  
Particle Image Velocimetry ◽  
Large Scale ◽  
Field Experiments ◽  
Particle Image ◽  
Low Cost ◽  
Surface Flow ◽  
Natural Environments ◽  
Natural Stream ◽  
Image Velocimetry ◽  
Scale Particle

Large scale particle image velocimetry (LSPIV) is a nonintrusive environmental monitoring methodology that allows for continuous characterization of surface flows in natural catchments. Despite its promise, the implementation of LSPIV in natural environments is limited to areas accessible to human operators. In this work, we propose a novel experimental configuration that allows for unsupervised LSPIV over large water bodies. Specifically, we design, develop, and characterize a lightweight, low cost, and stable quadricopter hosting a digital acquisition system. An active gimbal maintains the camera lens orthogonal to the water surface, thus preventing severe image distortions. Field experiments are performed to characterize the vehicle and assess the feasibility of the approach. We demonstrate that the quadricopter can hover above an area of 1×1m2 for 4–5 minutes with a payload of 500g. Further, LSPIV measurements on a natural stream confirm that the methodology can be reliably used for surface flow studies.

DNA microarrays: a bridge between genome sequence information and biological understanding

2002 ◽  
Vol 10 (3) ◽  
pp. 389-408 ◽  
Author(s):  
KEITH HARSHMAN ◽  
CARLOS MARTÍNEZ-A
Keyword(s):  
Large Scale ◽  
Dna Microarrays ◽  
Expression Patterns ◽  
Dna Polymorphisms ◽  
Model Organisms ◽  
Sequence Information ◽  
Single Experiment ◽  
Cellular Processes ◽  
Large Scale Analysis ◽  
Sequence Variations

The development, refinement and increasingly widespread use of DNA microarrays have been important responses to the explosion of sequence information produced by genome science. The high sample densities possible with DNA microarrays, coupled with the complete or nearly complete genome sequences available for humans and model organisms, provide a powerful analytical method to measure both qualitative and quantitative variations in RNA and DNA. Principal among the applications of microarrays is the large-scale analysis of RNA expression, often referred to as expression profiling. The power of this application lies in its ability to determine the expression patterns of tens of thousands of genes in a single experiment. Additionally, the ability to detect DNA polymorphisms makes microarrays useful in studies designed to correlate DNA sequence variations with variations in phenotype. The unprecedented scale on which microarrays allow both experimentation and generation of results should make possible a more complete and comprehensive understanding of cells and cellular processes.

scholarly journals Regulatory Noncoding Small RNAs Are Diverse and Abundant in an Extremophilic Microbial Community

mSystems ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Diego R. Gelsinger ◽  
Gherman Uritskiy ◽  
Rahul Reddy ◽  
Adam Munn ◽  
Katie Farney ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Microbial Community ◽  
Small Rnas ◽  
Noncoding Rna ◽  
Field Experiments ◽  
Natural World ◽  
Community Level ◽  
Model Organisms ◽  
Rain Event ◽  
Expression Levels

ABSTRACT Regulatory small RNAs (sRNAs) play large-scale and essential roles in many cellular processes across all domains of life. Microbial sRNAs have been extensively studied in model organisms, but very little is known about the dynamics of sRNA synthesis and their roles in the natural environment. In this study, we discovered hundreds of intergenic (itsRNAs) and antisense (asRNAs) sRNAs expressed in an extremophilic microbial community inhabiting halite nodules (salt rocks) in the Atacama Desert. For this, we built SnapT, a new sRNA annotation pipeline that can be applied to any microbial community. We found asRNAs with expression levels negatively correlated with that of their overlapping putative target and itsRNAs that were conserved and significantly differentially expressed between 2 sampling time points. We demonstrated that we could perform target prediction and correlate expression levels between sRNAs and predicted target mRNAs at the community level. Functions of putative mRNA targets reflected the environmental challenges members of the halite communities were subjected to, including osmotic adjustments to a major rain event and competition for nutrients. IMPORTANCE Microorganisms in the natural world are found in communities, communicating and interacting with each other; therefore, it is essential that microbial regulatory mechanisms, such as gene regulation affected by small RNAs (sRNAs), be investigated at the community level. This work demonstrates that metatranscriptomic field experiments can link environmental variation with changes in RNA pools and have the potential to provide new insights into environmental sensing and responses in natural microbial communities through noncoding RNA-mediated gene regulation.

NAD+ capping of RNA in Archaea and Mycobacteria

2021 ◽  
Author(s):  
Olatz Ruiz-Larrabeiti ◽  
Roberto Benoni ◽  
Viacheslav Zemlianski ◽  
Nikola Hanisakova ◽  
Marek Schwarz ◽  
...  
Keyword(s):  
Nicotinamide Adenine Dinucleotide ◽  
Mycobacterium Smegmatis ◽  
Small Rnas ◽  
Methanosarcina Barkeri ◽  
Model Organisms ◽  
Rna Modification ◽  
Essential Properties ◽  
Domains Of Life ◽  
Living Organisms ◽  
Rna Capping

Chemical modifications of RNA affect essential properties of transcripts, such as their translation, localization and stability. 5-end RNA capping with the ubiquitous redox cofactor nicotinamide adenine dinucleotide (NAD+) has been discovered in organisms ranging from bacteria to mammals. However, the hypothesis that NAD+ capping might be universal in all domains of life has not been proven yet, as information on this RNA modification is missing for Archaea. Likewise, this RNA modification has not been studied in the clinically important Mycobacterium genus. Here, we demonstrate that NAD+ capping occurs in the archaeal and mycobacterial model organisms Methanosarcina barkeri and Mycobacterium smegmatis. Moreover, we identify the NAD+-capped transcripts in M. smegmatis, showing that this modification is more prevalent in stationary phase, and revealing that mycobacterial NAD+-capped transcripts include non-coding small RNAs, such as Ms1. Furthermore, we show that mycobacterial RNA polymerase incorporates NAD+ into RNA, and that the genes of NAD+-capped transcripts are preceded by promoter elements compatible with SigA/SigF dependent expression. Taken together, our findings demonstrate that NAD+ capping exists in the archaeal domain of life, suggesting that it is universal to all living organisms, and define the NAD+-capped RNA landscape in mycobacteria, providing a basis for its future exploration.

scholarly journals sRNA-target Prediction Organizing Tool (SPOT) integrates computational and experimental data to facilitate functional characterization of bacterial small RNAs

2018 ◽  
Author(s):  
Alisa M. King ◽  
Carin K. Vanderpool ◽  
Patrick H. Degnan
Keyword(s):  
Experimental Data ◽  
Small Rnas ◽  
Fatty Acid Synthase ◽  
Functional Characterization ◽  
Target Prediction ◽  
Model Organisms ◽  
Structure Stability ◽  
Cyclopropane Fatty Acid ◽  
Mrna Targets

ABSTRACTSmall RNAs (sRNAs) post-transcriptionally regulate mRNA targets, typically under conditions of environmental stress. Although hundreds of sRNAs have been discovered in diverse bacterial genomes, most sRNAs remain uncharacterized, even in model organisms. Identification of mRNA targets directly regulated by sRNAs is rate-limiting for sRNA functional characterization. To address this, we developed a computational pipeline that we named SPOT for sRNA-targetPredictionOrganizingTool. SPOT incorporates existing computational tools to search for sRNA binding sites, allows filtering based on experimental data, and organizes the results into a standardized report. SPOT sensitivity (Correctly Predicted Targets/Total Known Targets) was equal to or exceeded any individual method when used on 12 characterized sRNAs. Using SPOT, we generated a set of target predictions for the sRNA RydC, which was previously shown to positively regulatecfamRNA, encoding cyclopropane fatty acid synthase. SPOT identifiedcfaalong with additional putative mRNA targets, which we then tested experimentally. Our results demonstrated that in addition tocfamRNA, RydC also regulatestrpEandpheAmRNAs, which encode aromatic amino acid biosynthesis enzymes. Our results suggest that SPOT can facilitate elucidation of sRNA target regulons to expand our understanding of the many regulatory roles played by bacterial sRNAs.IMPORTANCESmall RNAs (sRNAs) regulate gene expression in diverse bacteria by interacting with mRNAs to change their structure, stability or translation. Hundreds of sRNAs have been identified in bacteria, but characterization of their regulatory functions is limited by difficulty with sensitive and accurate identification of mRNA targets. Thus, new robust methods of bacterial sRNA target identification are in demand. Here, we describe ourSmall RNA-targetPredictionOrganizingTool, which streamlines the process of sRNA target prediction by providing a single pipeline that combines available computational prediction tools with customizable results filtering based on experimental data. SPOT allows the user to rapidly produce a prioritized list of predicted sRNA-target mRNA interactions that serves as a basis for further experimental characterization. This tool will facilitate elucidation of sRNA regulons in bacteria, allowing new discoveries regarding the roles of sRNAs in bacterial stress responses and metabolic regulation.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

scholarly journals Role of Long Non-Coding RNA Polymorphisms in Cancer Chemotherapeutic Response

2021 ◽  
Vol 11 (6) ◽  
pp. 513
Author(s):  
Zheng Zhang ◽  
Meng Gu ◽  
Zhongze Gu ◽  
Yan-Ru Lou
Keyword(s):  
Molecular Mechanisms ◽  
Neurological Diseases ◽  
Cellular Processes ◽  
Dna And Rna ◽  
Chemotherapeutic Response ◽  
Non Coding Rna ◽  
Response To Chemotherapy ◽  
Personalized Oncology ◽  
Long Non Coding Rna

Genetic polymorphisms are defined as the presence of two or more different alleles in the same locus, with a frequency higher than 1% in the population. Since the discovery of long non-coding RNAs (lncRNAs), which refer to a non-coding RNA with a length of more than 200 nucleotides, their biological roles have been increasingly revealed in recent years. They regulate many cellular processes, from pluripotency to cancer. Interestingly, abnormal expression or dysfunction of lncRNAs is closely related to the occurrence of human diseases, including cancer and degenerative neurological diseases. Particularly, their polymorphisms have been found to be associated with altered drug response and/or drug toxicity in cancer treatment. However, molecular mechanisms are not yet fully elucidated, which are expected to be discovered by detailed studies of RNA–protein, RNA–DNA, and RNA–lipid interactions. In conclusion, lncRNAs polymorphisms may become biomarkers for predicting the response to chemotherapy in cancer patients. Here we review and discuss how gene polymorphisms of lncRNAs affect cancer chemotherapeutic response. This knowledge may pave the way to personalized oncology treatments.

scholarly journals Large-scale transcriptomics to dissect 2 years of the life of a fungal phytopathogen interacting with its host plant

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Elise J. Gay ◽  
Jessica L. Soyer ◽  
Nicolas Lapalu ◽  
Juliette Linglin ◽  
Isabelle Fudal ◽  
...  
Keyword(s):  
Host Plant ◽  
Large Scale ◽  
Field Experiments ◽  
Plant Residues ◽  
Plant Infection ◽  
Effector Genes ◽  
Controlled Conditions ◽  
Niche Adaptation ◽  
Depth Analysis ◽  
Fungal Genes

Abstract Background The fungus Leptosphaeria maculans has an exceptionally long and complex relationship with its host plant, Brassica napus, during which it switches between different lifestyles, including asymptomatic, biotrophic, necrotrophic, and saprotrophic stages. The fungus is also exemplary of “two-speed” genome organisms in the genome of which gene-rich and repeat-rich regions alternate. Except for a few stages of plant infection under controlled conditions, nothing is known about the genes mobilized by the fungus throughout its life cycle, which may last several years in the field. Results We performed RNA-seq on samples corresponding to all stages of the interaction of L. maculans with its host plant, either alive or dead (stem residues after harvest) in controlled conditions or in field experiments under natural inoculum pressure, over periods of time ranging from a few days to months or years. A total of 102 biological samples corresponding to 37 sets of conditions were analyzed. We show here that about 9% of the genes of this fungus are highly expressed during its interactions with its host plant. These genes are distributed into eight well-defined expression clusters, corresponding to specific infection lifestyles or to tissue-specific genes. All expression clusters are enriched in effector genes, and one cluster is specific to the saprophytic lifestyle on plant residues. One cluster, including genes known to be involved in the first phase of asymptomatic fungal growth in leaves, is re-used at each asymptomatic growth stage, regardless of the type of organ infected. The expression of the genes of this cluster is repeatedly turned on and off during infection. Whatever their expression profile, the genes of these clusters are enriched in heterochromatin regions associated with H3K9me3 or H3K27me3 repressive marks. These findings provide support for the hypothesis that part of the fungal genes involved in niche adaptation is located in heterochromatic regions of the genome, conferring an extreme plasticity of expression. Conclusion This work opens up new avenues for plant disease control, by identifying stage-specific effectors that could be used as targets for the identification of novel durable disease resistance genes, or for the in-depth analysis of chromatin remodeling during plant infection, which could be manipulated to interfere with the global expression of effector genes at crucial stages of plant infection.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

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