Cassava (Manihot esculenta Crantz) is a root crop that accumulates large quantities of starch, and it is an important source of carbohydrate. Study on gene expressions during storage root development provides important information on storage root formation and starch accumulation as well as unlock new traits for improving of starch yield. cDNA-Amplified Fragment Length Polymorphism (AFLP) was used to compare gene expression profiles in fibrous and storage roots of cassava cultivar Kasetsart 50. Total of 155 differentially expressed transcript-derived fragments with undetectable or low expression in leaves were characterized and classified into 11 groups regarding to their functions. The four major groups were no similarity (20%), hypothetical or unknown proteins (17%), cellular metabolism and biosynthesis (17%) and cellular communication and signaling (14%). Interestingly, sulfite reductase (MeKD82), calcium-dependent protein kinase (CDPK) (MeKD83), ent-kaurene synthase (KS) (MeKD106) and hexose transporter (HT) (MeKD154) showed root-specific expression patterns. This finding is consistent with previously reported genes involved in the initiation of potato tuber. Semi-quantitative reverse transcription polymerase chain reaction of early-developed root samples confirmed that those four genes exhibited significant expression with similar pattern in the storage root initiation and early developmental stages. We proposed that KS and HT may involve in transient induction of CDPK expression, which may play an important role in the signaling pathway of storage root initiation. Sulfite reductase, on the other hand, may involve in storage root development by facilitating sulfur-containing protein biosynthesis or detoxifying the cyanogenic glucoside content through aspartate biosynthesis.

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Increasing cassava production could mitigate one of the global food insecurity challenges by providing a sustainable food source. To improve the yield potential, physiological strategies (i.e., the photosynthetic efficiency, source-to-sink carbon partitioning, and intracellular carbon metabolism) can be applied in breeding to screen for superior genotypes. However, the influences of source-to-sink carbon partitioning and carbon metabolism on the storage root development of cassava are relatively little understood. We hypothesized that carbon partitioning and utilization vary modulating the distinctive storage root yields of high and low-yielding cassava varieties, represented in this study by varieties Kasetsart 50 (KU50) and Hanatee (HN), respectively. Plant growth, photosynthesis measurements, soluble sugars, and starch contents of individual tissues were analyzed at different developmental stages. Also, the diurnal patterns of starch accumulation and degradation in leaves were investigated through iodine staining. Despite a comparable photosynthetic rate, KU50 grew better and yielded greater storage roots than HN. Interestingly, both varieties differed in their carbon partitioning strategies. KU50 had a high photosynthetic capacity and was better efficient in converting photoassimilates to carbon substrates and allocating them to sink organs for their growth. In contrast, HN utilized the photoassimilates at a high metabolic cost, in terms of respiration, and inefficiently allocated carbon to stems rather than storage roots. These results highlighted that carbon assimilation and allocation are genetic potential characteristics of individual varieties, which in effect determine plant growth and storage root yield of cassava. The knowledge gained from this study sheds light on potential strategies for developing new high-yielding genotypes in cassava breeding programs.

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RNA-RNA interactions play a crucial role in gene regulation in living organisms. They have gained increasing interest in the field of synthetic biology because of their potential applications in medicine and biotechnology. However, few novel regulators based on RNA-RNA interactions with desired structures and functions have been developed due to the challenges of developing design tools. Recently, we proposed a novel tool, called iDoDe, for designing RNA-RNA interacting sequences by first decomposing RNA structures into interacting domains and then designing each domain using a stochastic algorithm. However, iDoDe did not provide an optimal solution because it still lacks a mechanism to optimize the design. In this work, we have further developed the tool by incorporating a genetic algorithm (GA) to find an RNA solution with maximized structural similarity and minimized hybridized RNA energy, and renamed the tool iDoRNA. A set of suitable parameters for the genetic algorithm were determined and found to be a weighting factor of 0.7, a crossover rate of 0.9, a mutation rate of 0.1, and the number of individuals per population set to 8. We demonstrated the performance of iDoRNA in comparison with iDoDe by using six RNA-RNA interaction models. It was found that iDoRNA could efficiently generate all models of interacting RNAs with far more accuracy and required far less computational time than iDoDe. Moreover, we compared the design performance of our tool against existing design tools using forty-four RNA-RNA interaction models. The results showed that the performance of iDoRNA is better than RiboMaker when considering the ensemble defect, the fitness score and computation time usage. However, it appears that iDoRNA is outperformed by NUPACK and RNAiFold 2.0 when considering the ensemble defect. Nevertheless, iDoRNA can still be an useful alternative tool for designing novel RNA-RNA interactions in synthetic biology research. The source code of iDoRNA can be downloaded from the site http://synbio.sbi.kmutt.ac.th.

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