Preferential regulation of stably expressed genes in the human genome suggests a widespread expression buffering role of microRNAs

In this study, we comprehensively explored the stably expressed genes (SE genes) and fluctuant genes (FL genes) in the human genome by a meta-analysis of large scale microarray data. We found that these genes have distinct function distributions. miRNA targets are shown to be significantly enriched in SE genes by using propensity analysis of miRNA regulation, supporting the hypothesis that miRNAs can buffer whole genome expression fluctuation. The expression-buffering effect of miRNA is independent of the target site number within the 3'-untranslated region. In addition, we found that gene expression fluctuation is positively correlated with the number of transcription factor binding sites in the promoter region, which suggests that coordination between transcription factors and miRNAs leads to balanced responses to external perturbations

Architecture and dynamics of the jasmonic acid gene regulatory network

Jasmonic acid (JA) is a critical hormonal regulator of plant growth and defense. To advance our understanding of the architecture and dynamic regulation of the JA gene regulatory network, we performed a high-resolution RNA-seq time series of methyl JA-treated Arabidopsis thaliana at 15 time points over a 16-h period. Computational analysis showed that methyl JA (MeJA) induces a burst of transcriptional activity, generating diverse expression patterns over time that partition into distinct sectors of the JA response targeting specific biological processes. The presence of transcription factor (TF) DNA binding motifs correlated with specific TF activity during temporal MeJA-induced transcriptional reprogramming. Insight into the underlying dynamic transcriptional regulation mechanisms was captured in a chronological model of the JA gene regulatory network. Several TFs, including MYB59 and bHLH27, were uncovered as early network components with a role in pathogen and insect resistance. Analysis of subnetworks surrounding the TFs ORA47, RAP2.6L, MYB59, and ANAC055, using transcriptome profiling of overexpressors and mutants, provided insights into their regulatory role in defined modules of the JA network. Collectively, our work illuminates the complexity of the JA gene regulatory network, pinpoints and validates previously unknown regulators, and provides a valuable resource for functional studies on JA signaling components in plant defense and development

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables

The construction of a Solanum habrochaites LYC4 introgression line population and the identification of QTLs for resistance to Botrytis cinerea

Tomato (Solanum lycopersicum) is susceptible to grey mold (Botrytis cinerea). Partial resistance to this fungus has been identified in accessions of wild relatives of tomato such as Solanum habrochaites LYC4. In a previous F2 mapping study, three QTLs conferring resistance to B. cinerea (Rbcq1, Rbcq2 and Rbcq4a) were identified. As it was probable that this study had not identified all QTLs involved in resistance we developed an introgression line (IL) population (n = 30), each containing a S. habrochaites introgression in the S. lycopersicum cv. Moneymaker genetic background. On average each IL contained 5.2% of the S. habrochaites genome and together the lines provide an estimated coverage of 95%. The level of susceptibility to B. cinerea for each of the ILs was assessed in a greenhouse trial and compared to the susceptible parent S. lycopersicum cv. Moneymaker. The effect of the three previously identified loci could be confirmed and seven additional loci were detected. Some ILs contains multiple QTLs and the increased resistance to B. cinerea in these ILs is in line with a completely additive model. We conclude that this set of QTLs offers good perspectives for breeding of B. cinerea resistant cultivars and that screening an IL population is more sensitive for detection of QTLs conferring resistance to B. cinerea than the analysis in an F2 population

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing

The Saharan isolate Saccharothrix algeriensis NRRL B-24137 induces systemic resistance in Arabidopsis thaliana seedlings against Botrytis cinerea

Background and aim Saccharothrix algeriensis NRRL B-24137, isolated from a Saharan soil, has been described as a potential biocontrol agent against Botrytis cinerea and other phytopathogens. However, the plant protection mechanisms involved still need to be described. The aim of this study was to determine this protection phenomenon as well as parts of the mechanisms involved, using Arabidopsis thaliana seedlings and B. cinerea. Methods The bacterial colonization process was evaluated on A. thaliana seedlings using fluorescence in situ hybridization. Protection of A. thaliana seedlings inoculated with NRRL B-24137 against B. cinerea was then evaluated. Parts of the mechanisms involved in the systemic protection against B. cinerea were evaluated using known mutants of genes involved in jasmonate (JA)/ethylene (ET)/salicylic acid (SA) signaling. Other Arabidopsis mutants, AtrhbohD-3, AtrhbohF-3, and ups1-1 were also screened to determine other parts of the mechanisms involved. Results The results showed that the strain NRRL B-24137 colonized, epi- and endophytically, the roots of Arabidopsis seedlings but the strain was not a systemic colonizer during the time of the experiment. The strain NRRL B-24137 also reduced B. cinerea symptoms and the protection was linked to known mechanisms of induced systemic resistance (ISR; JA/ET signaling), as well as to functionality of AtrbohF oxidase and of UPS1. Crosstalk between ET/JA and SA signaling could also be involved. Conclusions The isolate NRRL B-24137, after colonizing the root systems of A. thaliana, induces an ISR against B. cinerea, which is JA/ET dependent, but could also require SA crosstalk and protection could also require NAPDH oxidases and UPS1 functionalities