Translational control is extremely important in all organisms, and some of its aspects are highly conserved among all primary kingdoms, such as those related to the translation elongation step. The previously classified translation initiation factor 5A (eIF5A) and its bacterial homologue elongation factor P (EF-P) were discovered in the late 70's and have recently been the object of many studies. eIF5A and EF-P are the only cellular proteins that undergo hypusination and lysinylation, respectively, both of which are unique posttranslational modifications. Herein, we review all the important discoveries related to the biochemical and functional characterization of these factors, highlighting the implication of eIF5A in translation elongation instead of initiation. The findings that eIF5A and EF-P are important for specific cellular processes and play a role in the relief of ribosome stalling caused by specific amino acid sequences, such as those containing prolines reinforce the hypothesis that these factors are involved in specialized translation. Although there are some divergences between these unique factors, recent studies have clarified that they act similarly during protein synthesis. Further studies may reveal their precise mechanism of ribosome activity modulation as well as the mRNA targets that require eIF5A and EF-P for their proper translation.

Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% when using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. As a result of this approach, we achieved an accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

Profiling the transcriptome that underlies biomass degradation by the fungus Trichoderma harzianum allows the identification of gene sequences with potential application in enzymatic hydrolysis processing. In the present study, the transcriptome of T. harzianum IOC-3844 was analyzed using RNA-seq technology. The sequencing generated 14.7 Gbp for downstream analyses. De novo assembly resulted in 32,396 contigs, which were submitted for identification and classified according to their identities. This analysis allowed us to define a principal set of T. harzianum genes that are involved in the degradation of cellulose and hemicellulose and the accessory genes that are involved in the depolymerization of biomass. An additional analysis of expression levels identified a set of carbohydrate-active enzymes that are upregulated under different conditions. The present study provides valuable information for future studies on biomass degradation and contributes to a better understanding of the role of the genes that are involved in this process.