Current Genomics - Volume 21, Issue 3, 2020

Microorganisms that are capable of live and adapt in hostile habitats of different environmental factors such as extremes temperature, salinity, nutrient availability and pressure are known as extremophiles. Exposure to xenobiotic compounds is global concern influencing the world population as a health hazard. Hence their removal is warranted using biological means that is very sustainable, potentially cost-effective and eco-friendly. Due to adaptation in extreme environments and unique defense mechanisms, they are receiving more attention for the bioremediation of the xenobiotic compounds. They possess robust enzymatic and biocatalytic systems that make them suitable for the effective removal of pollutants from the contaminated environment. Additionally, the extremophiles act as microfactories having specific genetic and biotechnological potential for the production of biomolecules. This mini review will provide an overview of microbial degradation metabolic pathways for bioremediation along with the molecular and physiological properties of diverse extremophiles from variety of habitats. Furthermore, the factors affecting the bioremediation process is also summarized.

Plant-microbe interactions are both symbiotic and antagonistic, and the knowledge of both these interactions is equally important for the progress of agricultural practice and produce. This review gives an insight into the recent advances that have been made in the plant-microbe interaction study in the post-genomic era and the application of those for enhancing agricultural production. Adoption of next-generation sequencing (NGS) and marker assisted selection of resistant genes in plants, equipped with cloning and recombination techniques, has progressed the techniques for the development of resistant plant varieties by leaps and bounds. Genome-wide association studies (GWAS) of both plants and microbes have made the selection of desirable traits in plants and manipulation of the genomes of both plants and microbes effortless and less time-consuming. Stress tolerance in plants has been shown to be accentuated by association of certain microorganisms with the plant, the study and application of the same have helped develop stress-resistant varieties of crops. Beneficial microbes associated with plants are being extensively used for the development of microbial consortia that can be applied directly to the plants or the soil. Next-generation sequencing approaches have made it possible to identify the function of microbes associated in the plant microbiome that are both culturable and non-culturable, thus opening up new doors and possibilities for the use of these huge resources of microbes that can have a potential impact on agriculture.

Oilseed brassicas stand as the second most valuable source of vegetable oil and the third most traded one across the globe. However, the yield can be severely affected by infections caused by phytopathogens. White rust is a major oomycete disease of oilseed brassicas resulting in up to 60% yield loss globally. So far, success in the development of oomycete resistant Brassicas through conventional breeding has been limited. Hence, there is an imperative need to blend conventional and frontier biotechnological means to breed for improved crop protection and yield. This review provides a deep insight into the white rust disease and explains the oomycete-plant molecular events with special reference to Albugo candida describing the role of effector molecules, A. candida secretome, and disease response mechanism along with nucleotide-binding leucine-rich repeat receptor (NLR) signaling. Based on these facts, we further discussed the recent progress and future scopes of genomic approaches to transfer white rust resistance in the susceptible varieties of oilseed brassicas, while elucidating the role of resistance and susceptibility genes. Novel genomic technologies have been widely used in crop sustainability by deploying resistance in the host. Enrichment of NLR repertoire, over-expression of R genes, silencing of avirulent and disease susceptibility genes through RNA interference and CRSPR-Cas are technologies which have been successfully applied against pathogen-resistance mechanism. The article provides new insight into Albugo and Brassica genomics which could be useful for producing high yielding and WR resistant oilseed cultivars across the globe.

A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often laborintensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphorylation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, window size, and software utility. Initially, we review the currently available phosphorylation site databases of the microbiome, the state-of-the-art ML approaches, working principles, and their performances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.

Background: As a new type of protein acylation modification, lysine glutarylation has been found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are treated as positive samples and non-verified lysine sites as the negative samples to train predictors. However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally identified yet. Methods: In this study, experimentally verified glutarylation sites are treated as the positive samples, whereas the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm. Results: Experimental results show that PUL-GLU significantly outperforms the current glutarylation site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation sites. Conclusion: A user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.

Background: Cicer reticulatum L. is the wild progenitor of chickpea Cicer arietinum L., the fourth most important pulse crop in the world. Iron (Fe) and zinc (Zn) are vital micronutrients that play crucial roles in sustaining life by acting as co-factors for various proteins. Aims and Objectives: In order to improve micronutrient-dense chickpea lines, this study aimed to investigate variability and detect DNA markers associated with Fe and Zn concentrations in the seeds of 73 cultivated (C. arietinum L.) and 107 C. reticulatum genotypes. Methods: A set of 180 accessions was genotyped using 20,868 single nucleotide polymorphism (SNP) markers obtained from genotyping by sequencing analysis. Results: The results revealed substantial variation in the seed Fe and Zn concentration of the surveyed population. Using STRUCTURE software, the population structure was divided into two groups according to the principal component analysis and neighbor-joining tree analysis. A total of 23 and 16 associated SNP markers related to Fe and Zn concentrations, respectively were identified in TASSEL software by the mixed linear model method. Significant SNP markers found in more than two environments were accepted as more reliable than those that only existed in a single environment. Conclusion: The identified markers can be used in marker-assisted selection in chickpea breeding programs for the improvement of seed Fe and Zn concentrations in the chickpea.

Background: Migraine is a polygenetic disease, considered as a channelopathy. The dysregulation of ion functioning due to genetic changes may activate the trigeminovascular system and induce migraine attack both migraine with aura (MA) and without aura (MO). Objectives: The aim of the study was to analyze the following variants of genes encoding ion channels and associated protein: c.3199G>A SCN1A, c.56G>A SCN2A, c.28A>G and c.328T>C KCNK18, c.3053A>G TRPA1, c.31-1811C>T STX1A in migraine patients. Patients and Methods: The study included 170 migraine patients and 173 controls. HRMA and Sanger sequencing were used for genotyping. Meta-analysis was performed for c.28A>G, c.328T>C KCNK18, and c.31-1811C>T STX1A. Results: AA genotype of c.56G>A SCN2A was found only in migraine patients. Patients with c.328T>C KCNK18 mutation had an increased risk of developing migraine before the age of 18. Moreover, individuals with AA/TC haplotype of KCNK18 had higher attack frequency than those with AA/TT (p<0.05). T allele of c.31-1811C>T STX1A was more frequent in MA patients than MO (p<0.05). The c.3053A>G TRPA1 polymorphism was more common in patients with migraine onset before the age of 15 (p<0.05), while c.31-1811C>T STX1A and c.3199G>A SCN1A before the age of 10 (p<0.01). Meta-analysis showed a significant association of c.31-1811C>T STX1A polymorphism with migraine overall (OR=1.22, p=0.0086), MA, and MO. No association was found for c.28A>G KCNK18, c.328T>C KCNK18, and migraine overall. Conclusion: Changes in genes encoding ion channels or proteins regulating their functioning may increase the risk of migraines and correlate with clinical features of disease, e.g. age of onset and attack frequency.