DNA signals at isoform promoters

Roles and regulatory patterns of protein isoforms in plant adaptation and development

Protein isoforms (PIs) play pivotal roles in regulating plant growth and development that confer adaptability to diverse environmental conditions. PIs are widely present in plants and generated through alternative splicing (AS), alternative polyadenylation (APA), alternative initiation (AI), and ribosomal frameshifting (RF) events. The widespread presence of PIs not only significantly increases the complexity of genomic information but also greatly enriches regulatory networks and enhances their flexibility. PIs may also play important roles in phenotypic diversity, ecological niche differentiation, and speciation, thereby increasing the dimensions of research in molecular ecology. However, PIs pose new challenges for the quantitative analysis, annotation, and identification of genetic regulatory mechanisms. Thus, focus on PIs make genomic and epigenomic studies both more powerful and more challenging. This review summarizes the origins, functions, regulatory patterns of isoforms, and the challenges they present for future research in molecular ecology and molecular biology.

Regulatory Element Analysis and Comparative Genomics Study of Heavy Metal-Resistant Genes in the Complete Genome of Cupriavidus gilardii CR3

Environmental pollution has become a worldwide concern that requires rigorous efforts from all sectors of society to monitor, control, and remediate it. In environmental pollution control, Cupriavidus gilardii CR3 has become a model organism to study resistance to heavy metals as a means of bacterial bioremediation. This research aimed to single out regulatory element analysis and conduct a comparative genome study of the heavy metal resistance genes in the complete genome of C gilardii CR3 using bioinformatics and omics tools. Comparative genome analysis, promoter prediction, common motif identification, transcriptional start site identification, gene annotation, and transcription factor identification search are the major steps to understanding gene expression and regulation. MEME Suit, TOMTOM, Prokka, Rapid Annotation utilizing Subsystem Technology (RAST), Orthologous Average Nucleotide Identity Software Tool (OAT), and EziBio databases or programs were the major tools used in this study. Fourteen transcriptional factors were identified and predicted from the most credible and lowest candidate motifs with an e-value of 3.0e-009, which was statistically the utmost remarkable candidate motif. A detailed evaluation was further performed, and 14 transcriptional factors were identified as in activation, repression, and dual functions. The data revealed that most transcriptional factors identified were used for activation rather than repression. The C gilardii CR3 genome contains many genes responsible for resisting heavy metals such as mercury, cadmium, zinc, copper, and arsenate. As a result, regulatory elements will lay a solid basis for understanding genes responsible for heavy metal bioremediation. It was concluded that further studies with wet lab support could be conducted for confirmation. Moreover, other advanced bioinformatics and omics technologies are needed to strengthen the results.

In silico analysis of promoter region and regulatory elements of mitogenome co-expressed trn gene clusters encoding for bio-pesticide in entomopathogenic fungus, Metarhizium anisopliae: strain ME1

Background

Pest control strategies almost entirely rely on chemical insecticides, which cause environmental problems such as biosphere deterioration and emergence of resistant pests. Bio-pesticide is an alternative approach, which uses organisms such as entomopathogenic fungi, Metarhizium anisopliae, to control pests. Screening such potential organism at a molecular level and understanding their gene regulation mechanism is an important approach to reduce emergence of pesticide resistance and worsening of the biosphere. Understanding promoter regions which play a pivotal role in gene regulation is crucial. In particular, identification of the promoter regions in M. anisopliae Strain ME1 remains poorly understood. To our knowledge, the mitogenome trn gene clusters of M. anisopliae Strain ME1 were not characterized. Here, we used machine learning approach to identify and characterize the promoter regions, regulatory elements, and CpG island densities of 15 protein coding genes of entomopathogenic fungi, M. anisolpliae Strain ME1.

Results

The current analysis revealed multiple transcription start sites (TSS) for all utilized sequences, except for promoter region genes of Pro-cob and Pro-nad5. With reference to the start codon (ATG), 85.3% of TSS was located above – 500 bp. Based on the standard predictive score at cut off value of 0.8^a, the current study revealed 54.7% of predictive score greater than or equal from 0.9 promoter prediction score. Expectation maximization algorithm output identified five candidate motifs. Nonetheless, of all candidate motifs, MtrnI was revealed as the common promoter region motif with a value of 76.9% both in terms of size of binding sites and with an E value of 9.1E−054. Accordingly, we perceived that MtrnI serve as the binding site for tryptophan cluster with P value 0.0044 and C4 type zinc fingers functions as the binding site to regulate gene expression of M. anisopliae Strain ME1. The analysis revealed that mitogenome trn gene clusters of M. anisopliae Strain ME1 showed homologues evolutionary ancestor supported with a bootstrap value of 100%.

Conclusion

Identified common candidate motifs and binding transcription factors through in silico approach are likely expected to contribute for better understanding of gene expression and strain improvement of M. anisopliae Strain ME1 for its bio-pesticides role.