Current Genomics - Volume 21, Issue 7, 2020

Interest in the efficient production of soybean, as one of the most important crop plants, is significantly increasing worldwide. Soybean symbioses, the most important biological process affecting soybean yield and protein content, were revitalized due to the need for sustainable agricultural practices. Similar to many crop species, soybean can establish symbiotic associations with the soil bacteria rhizobia, and with the soil fungi, arbuscular mycorrhizal fungi, and other beneficial rhizospheric microorganisms are often applied as biofertilizers. Microbial interactions may importantly affect soybean production and plant health by activating different genomic pathways in soybean. Genomic research is an important tool, which may be used to elucidate and enhance the mechanisms controlling such actions and interactions. This review presents the available details on the genomic research favoring higher soybean production. Accordingly, new technologies applied to plant rhizosphere and symbiotic microbiota, root-plant endophytes, and details about the genetic composition of soybean inoculant strains are highlighted. Such details may be effectively used to enhance soybean growth and yield, under different conditions, including stress, resulting in a more sustainable production.

Plants inherently show resistance to pathogen attack but are susceptible to multiple bacteria, viruses, fungi, and phytoplasmas. Diseases as a result of such infection leads to the deterioration of crop yield. Several pathogen-sensitive gene activities, promoters of such genes, associated transcription factors, and promoter elements responsible for crosstalk between the defense signaling pathways are involved in plant resistance towards a pathogen. Still, only a handful of genes and their promoters related to plant resistance have been identified to date. Such pathogen-sensitive promoters are accountable for elevating the transcriptional activity of certain genes in response to infection. Also, a suitable promoter is a key to devising successful crop improvement strategies as it ensures the optimum expression of the required transgene. The study of the promoters also helps in mining more details about the transcription factors controlling their activities and helps to unveil the involvement of new genes in the pathogen response. Therefore, the only way out to formulate new solutions is by analyzing the molecular aspects of these promoters in detail. In this review, we provided an overview of the promoter motifs and cis-regulatory elements having specific roles in pathogen attack response. To elaborate on the importance and get a vivid picture of the pathogen-sensitive promoter sequences, the key motifs and promoter elements were analyzed with the help of PlantCare and interpreted with available literature. This review intends to provide useful information for reconstructing the gene networks underlying the resistance of plants against pathogens.

Background: In this study, whole genome re-sequencing of rust resistant soybean genotype EC241780 was performed to understand the genomic landscape involved in the resistance mechanism. Methods: A total of 374 million raw reads were obtained with paired-end sequencing performed with Illumina HiSeq 2500 instrument, out of which 287.3 million high quality reads were mapped to Williams 82 reference genome. Comparative sequence analysis of EC241780 with rust susceptible cultivars Williams 82 and JS 335 was performed to identify sequence variation and to prioritise the candidate genes. Results: Comparative analysis indicates that genotype EC241780 has high sequence similarity with rust resistant genotype PI 200492 and the resistance in EC241780 is conferred by the Rpp1 locus. Based on the sequence variations and functional annotations, three genes Glyma18G51715, Glyma18G51741 and Glyma18G51765 encoding for NBS-LRR family protein were identified as the most prominent candidate for Rpp1 locus. Conclusion: The study provides insights of genome-wide sequence variation more particularly at Rpp1 loci which will help to develop rust resistant soybean cultivars through efficient exploration of the genomic resource.

Background: Microorganisms are an important component of an aquatic ecosystem and play a critical role in the biogeochemical cycle which influences the circulation of the materials and maintains the balance in aquatic ecosystems. Objective: The seasonal variation along with the impact of anthropogenic activities, water quality, bacterial community composition and dynamics in the Loktak Lake, the largest freshwater lake of North East India, located in the Indo-Burma hotspot region was assessed during post-monsoon and winter season through metagenome analysis. Methods: Five soil samples were collected during Post-monsoon and winter season from the Loktak Lake that had undergone different anthropogenic impacts. The metagenomic DNA of the soil samples was extracted using commercial metagenomic DNA extraction kits following the manufacturer’s instruction. The extracted DNA was used to prepare the NGS library and sequenced in the Illumina MiSeq platform. Results: Metagenomics analysis reveals Proteobacteria as the predominant community followed by Acidobacteria and Actinobacteria. The presence of these groups of bacteria indicates nitrogen fixation, oxidation of iron, sulfur, methane, and source of novel antibiotic candidates. The bacterial members belonging to different groups were involved in various biogeochemical processes, including fixation of carbon and nitrogen, producing streptomycin, gramicidin and perform oxidation of sulfur, sulfide, ammonia, and methane. Conclusion: The outcome of this study provides a valuable dataset representing a seasonal profile across various land use and analysis, targeting at establishing an understanding of how the microbial communities vary across the land use and the role of keystone taxa. The findings may contribute to searches for microbial bio-indicators as biodiversity markers for improving the aquatic ecosystem of the Loktak Lake.

Background: The ability of the human body to produce metabolic energy from light modifies fundamental concepts of biochemistry. Objective: This review discusses the relationships between the long-accepted concept is that glucose has a unique dual role as an energy source and as the main source of carbon chains that are precursors of all organic matter. The capability of melanin to produce energy challenges this premise. Methods: The prevalent biochemical concept, therefore, needs to be adjusted to incorporate a newly discovered state of Nature based on melanin’s ability to dissociate water to produce energy and to reform water from molecular hydrogen and oxygen. Results and Discussion: Our findings regarding the potential implication of QIAPI-1 as a melanin precursor that has bioenergetics capabilities. Conclusion: Specifically, we reported its promising application as a means for treating retinopathy of prematurity (ROP). The instant report focuses on the long-term treatment medical effects of melanin in treating ROP.

Hypothetical Proteins [HP] are the transcripts predicted to be expressed in an organism, but no evidence of it exists in gene banks. On the other hand, long non-coding RNAs [lncRNAs] are the transcripts that might be present in the 5’ UTR or intergenic regions of the genes whose lengths are above 200 bases. With the known unknown [KU] regions in the genomes rapidly existing in gene banks, there is a need to understand the role of open reading frames in the context of annotation. In this commentary, we emphasize that HPs could indeed be the predecessors of lncRNAs.

Introduction: Hydroxylation is one of the most important post-translational modifications (PTM) in cellular functions and is linked to various diseases. The addition of one of the hydroxyl groups (OH) to the lysine sites produces hydroxylysine when undergoes chemical modification. Methods: The method which is used in this study for identifying hydroxylysine sites based on powerful mathematical and statistical methodology incorporating the sequence-order effect and composition of each object within protein sequences. This predictor is called "iHyd-LysSite (EPSV)" (identifying hydroxylysine sites by extracting enhanced position and sequence variant technique). The prediction of hydroxylysine sites by experimental methods is difficult, laborious and highly expensive. In silico technique is an alternative approach to identify hydroxylysine sites in proteins. Results: The experimental results require that the predictive model should have high sensitivity and specificity values and must be more accurate. The self-consistency, independent, 10-fold crossvalidation and jackknife tests are performed for validation purposes. These tests are resulted by using three renowned classifiers, Neural Networks (NN), Random Forest (RF) and Support Vector Machine (SVM) with the demanding prediction rate. The overall predictive outcomes are extraordinarily superior to the results obtained by previous predictors. The proposed model contributed an excellent prediction rate in the system for NN, RF, and SVM classifiers. The sensitivity and specificity results using all these classifiers for jackknife test are 96.08%, 94.99%, 98.16% and 97.52%, 98.52%, 80.95%. Conclusion: The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.

Aims: To develop a tool that can annotate subcellular localization of human proteins. Background: With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/ compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research. Objective: To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision. Methods: In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset. Results: The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit). Conclusion: In conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.