Description: The overproduction of reactive oxygen species (ROS) in cells can lead to the development of diseases associated with aging. We have previously shown that C. elegans BRAP-2 (Brca1 associated binding protein 2) regulates phase II detoxification genes such as gst-4 , by increasing SKN-1 activity. Previously, a transcription factor (TF) RNAi screen was conducted to identify potential activators that are required to induce gst-4 expression in brap-2 ( ok1492 ) mutants. The lipid metabolism regulator NHR-49 /HNF4 was among 18 TFs identified. Here, we show that knockdown of nhr-49 suppresses the activation of gst-4 caused by brap-2 inactivation and that gain-of-function alleles of nhr-49 promote gst-4 expression. We also demonstrate that nhr-49 and its cofactor mdt-15 are required to express phase II detoxification enzymes upon exposure to chemicals that induce oxidative stress. Furthermore, we show that NHR-49 and MDT-15 enhance expression of skn-1a/c . These findings identify a novel role for NHR-49 in ROS detoxification by regulating expression of SKN-1C and phase II detoxification genes.

Description: Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of genomic selection (GS). Multi-trait models are more complex than univariate models and usually require more computational resources, but they are preferred because they can exploit the correlation between traits, which many times helps improve prediction accuracy. For this reason, in this paper we explore the power of multi-trait deep learning (MTDL) models in terms of prediction accuracy. The prediction performance of MTDL models was compared to the performance of the Bayesian multi-trait and multi-environment (BMTME) model proposed by Montesinos-López et al. (2016) , which is a multi-trait version of the genomic best linear unbiased prediction (GBLUP) univariate model. Both models were evaluated with predictors with and without the genotype x environment interaction term. The prediction performance of both models was evaluated in terms of Pearson’s correlation using cross-validation. We found that the best predictions in two of the three data sets were found under the BMTME model, but in general the predictions of both models, BTMTE and MTDL, were similar. Among models without the genotype x environment interaction, the MTDL model was the best, while among models with genotype x environment interaction, the BMTME model was superior. These results indicate that the MTDL model is very competitive for performing predictions in the context of GS, with the important practical advantage that it requires less computational resources than the BMTME model.

Description: Next generation biofuels including longer-chain alcohols such as butanol are attractive as renewable, high-energy fuels. A barrier to microbial production of butanols is the increased toxicity compared to ethanol; however, the cellular targets and microbial defense mechanisms remain poorly understood, especially under anaerobic conditions used frequently in industry. Here we took a comparative approach to understand the response of Saccharomyces cerevisiae to 1-butanol, isobutanol, or ethanol, across three genetic backgrounds of varying tolerance in aerobic and anaerobic conditions. We find that strains have different growth properties and alcohol tolerances with and without oxygen availability, as well as unique and common responses to each of the three alcohols. Our results provide evidence for strain-by-alcohol-by-oxygen interactions that moderate how cells respond to alcohol stress.

Description: Cassava production in the central, southern and eastern parts of Africa is under threat by cassava brown streak virus (CBSV). Yield losses of up to 100% occur in cases of severe infections of edible roots. Easy illegal movement of planting materials across African countries, and long-range movement of the virus vector ( Bemisia tabaci ) may facilitate spread of CBSV to West Africa. Thus, effort to pre-emptively breed for CBSD resistance in W. Africa is critical. Genomic selection (GS) has become the main approach for cassava breeding, as costs of genotyping per sample have declined. Using phenotypic and genotypic data (genotyping-by-sequencing), followed by imputation to whole genome sequence (WGS) for 922 clones from National Crops Resources Research Institute, Namulonge, Uganda as a training population (TP), we predicted CBSD symptoms for 35 genotyped W. African clones, evaluated in Uganda. The highest prediction accuracy (r = 0.44) was observed for cassava brown streak disease severity scored at three months (CBSD3s) in the W. African clones using WGS-imputed markers. Optimized TPs gave higher prediction accuracies for CBSD3s and CBSD6s than random TPs of the same size. Inclusion of CBSD QTL chromosome markers as kernels, increased prediction accuracies for CBSD3s and CBSD6s. Similarly, WGS imputation of markers increased prediction accuracies for CBSD3s and for cassava brown streak disease root severity (CBSDRs), but not for CBSD6s. Based on these results we recommend TP optimization, inclusion of CBSD QTL markers in genomic prediction models, and the use of high-density (WGS-imputed) markers for CBSD predictions across population.

Description: Aspergillus flavus , the primary causal agent for aflatoxin contamination on crops, consists of isolates with two distinct morphologies: isolates of the S morphotype produce numerous s mall sclerotia and lower numbers of conidia while isolates of the L morphotype produce fewer l arge sclerotia and abundant conidia. The morphotypes also differ in aflatoxin production with S isolates consistently producing high concentrations of aflatoxin, whereas L isolates range from atoxigenic to highly toxigenic. The production of abundant sclerotia by the S morphotype suggests adaptation for long-term survival in the soil, whereas the production of abundant conidia by the L morphotype suggests adaptation for aerial dispersal to the phyllosphere. To identify genomic changes that support differential niche adaption, the sequences of three S and three L morphotype isolates were compared. Differences in genome structure and gene content were identified between the morphotypes. A 〉530 kb inversion between the morphotypes affect a secondary metabolite gene cluster and a cutinase gene. The morphotypes also differed in proteins predicted to be involved in carbon/nitrogen metabolism, iron acquisition, antimicrobial defense, and evasion of host immunity. The S morphotype genomes contained more intact secondary metabolite clusters indicating there is higher selection pressure to maintain secondary metabolism in the soil and that it is not limited to aflatoxin production. The L morphotype genomes were enriched in amino acid transporters, suggesting efficient nitrogen transport may be critical in the nutrient limited phyllosphere. These findings indicate the genomes of the two morphotypes differ beyond developmental genes and have diverged as they adapted to their respective niches.

Description: We present an RNA-Seq based approach to map 3' end sequences of mature 16S rRNA (3' TAIL) in bacteria with single-base specificity. Our results show that 3' TAILs are heterogeneous among species; they contain the core CCUCC anti-Shine-Dalgarno motif, but vary in downstream lengths. Importantly, our findings rectify the mis-annotated 16S rRNAs in 11 out of 13 bacterial species studied herein (covering Cyanobacteria, Deinococcus-Thermus, Firmicutes, Proteobacteria, Tenericutes, and Spirochaetes). Furthermore, our results show that species-specific 3' TAIL boundaries are retained due to their high complementarity with preferred Shine-Dalgarno sequences, suggesting that 3' TAIL bases downstream of the canonical CCUCC motif play a more important role in translation initiation than previously reported.

Description: The cucumber ( Cucumis sativus ) is characterized by its diversity and seasonal plasticity in sexual type. A long day length condition significantly decreased the cucumber female flower ratio by 17.7–52.9%, and the effect of photoperiod treatment is more significant under low temperature than under high temperature. Transcriptome analysis indicates that the photoperiod treatment preferentially significantly influenced flower development processes, particularly MADS-box genes in shoot apices. The long-day treatment resulted in predominantly transposable element (TE)- and gene-associated CHH-types of DNA methylation changes. Nevertheless, there was significant enrichment of CG- and CHG-types of DNA methylation changes nearing transcription start sites (TSSs)/transcription end sites (TESs) and gene bodies, respectively. Predominantly negative association between differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were observed which implied epiregulation of DEGs. Two MADS-box genes that were significantly downregulated by long photoperiod showed significant hypermethylation in promoter regions that is essentially TE-rich. This study indicates MADS-box genes which are partially regulated by promoter methylation state may mediate photoperiod-dependent regulation of cucumber sex expression.

Description: Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of genomic selection. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. We compared DL methods with densely connected network architecture to one of the most often used genome-enabled prediction models: Genomic Best Linear Unbiased Prediction (GBLUP). We used nine published real genomic data sets to compare a fraction of all possible deep learning models to obtain a "meta picture" of the performance of DL methods with densely connected network architecture. In general, the best predictions were obtained with the GBLUP model when genotype x environment interaction (G x E) was taken into account (8 out of 9 data sets); when the interactions were ignored, the DL method was better than the GBLUP in terms of prediction accuracy in 6 out of the 9 data sets. For this reason, we believe that DL should be added to the data science toolkit of scientists working on animal and plant breeding. This study corroborates the view that there are no universally best prediction machines.

Description: Short linear motifs (SLiMs) can play pivotal functional roles in proteins, such as targeting proteins to specific subcellular localizations, modulating the efficiency of translation and tagging proteins for degradation. Until recently we had little knowledge about SLiM evolution. Only a few amino acids in these motifs are functionally important, making them likely to evolve ex nihilo and suggesting that they can play key roles in protein evolution. Several reports now suggest that these motifs can appear and disappear while their function in the protein is preserved, a process sometimes referred to as "turnover". However, there has been a lack of specific experiments to determine whether independently evolved motifs do indeed have the same function, which would conclusively determine whether the process of turnover actually occurs. In this study, we experimentally detected evidence for such a mutational turnover process for nuclear localization signals (NLS) during the post-duplication divergence of the Complementary sex determiner (Csd) and Feminizer (Fem) proteins in the honeybee ( Apis mellifera ) lineage. Experiments on the nuclear transport activity of protein segments and those of the most recent common ancestor (MRCA) sequences revealed that three new NLS motifs evolved in the Csd protein during the post-duplication divergence while other NLS motifs were lost that existed before duplication. A screen for essential and newly evolved amino acids revealed that new motifs in the Csd protein evolved by one or two missense mutations coding for lysine. Amino acids that were predating the duplication were also essential in the acquisition of the C1 motif suggesting that the ex nihilo origin was constrained by preexisting amino acids in the physical proximity. Our data support a model in which stabilizing selection maintains the constancy of nuclear transport function but allowed mutational turnover of the encoding NLS motifs.