Symbiotic Nitrogen Fixation-A Bioinformatics Perspective

Bioengineering Techniques to Improve Nitrogen Transformation and Utilization: Implications for Nitrogen Use Efficiency and Future Sustainable Crop Production

Nitrogen (N) is crucial for plant growth and development, especially in physiological and biochemical processes such as component of different proteins, enzymes, nucleic acids, and plant growth regulators. Six categories, such as transporters, nitrate absorption, signal molecules, amino acid biosynthesis, transcription factors, and miscellaneous genes, broadly encompass the genes regulating NUE in various cereal crops. Herein, we outline detailed research on bioengineering modifications of N metabolism to improve the different crop yields and biomass. We emphasize effective and precise molecular approaches and technologies, including N transporters, transgenics, omics, etc., which are opening up fascinating opportunities for a complete analysis of the molecular elements that contribute to NUE. Moreover, the detection of various types of N compounds and associated signaling pathways within plant organs have been discussed. Finally, we highlight the broader impacts of increasing NUE in crops, crucial for better agricultural yield and in the greater context of global climate change.

Mycobacterium abscessus: insights from a bioinformatic perspective

Mycobacterium abscessus is a nontuberculous mycobacterium, associated with broncho-pulmonary infections in individuals suffering from cystic fibrosis, bronchiectasis, and pulmonary diseases. The risk factors for transmission include biofilms, contaminated water resources, fomites, and infected individuals. M. abscessus is extensively resistant to antibiotics. To date, there is no vaccine and combination antibiotic therapy is followed. However, drug toxicities, low cure rates, and high cost of treatment make it imperfect. Over the last 20 years, bioinformatic studies on M. abscessus have advanced our understanding of the pathogen. This review integrates knowledge from the analysis of genomes, microbiomes, genomic variations, phylogeny, proteome, transcriptome, secretome, antibiotic resistance, and vaccine design to further our understanding. The utility of genome-based studies in comprehending disease progression, surveillance, tracing transmission routes, and epidemiological outbreaks on a global scale has been highlighted. Furthermore, this review underlined the importance of using computational methodologies for pinpointing factors responsible for pathogen survival and resistance. We reiterate the significance of interdisciplinary research to fight M. abscessus. In a nutshell, the outcome of computational studies can go a long way in creating novel therapeutic avenues to control M. abscessus mediated pulmonary infections.

Root-Derived Endophytic Diazotrophic Bacteria Pantoea cypripedii AF1 and Kosakonia arachidis EF1 Promote Nitrogen Assimilation and Growth in Sugarcane

Excessive, long-term application of chemical fertilizers in sugarcane crops disrupts soil microbial flora and causes environmental pollution and yield decline. The role of endophytic bacteria in improving crop production is now well-documented. In this study, we have isolated and identified several endophytic bacterial strains from the root tissues of five sugarcane species. Among them, eleven Gram-negative isolates were selected and screened for plant growth-promoting characteristics, i.e., production of siderophores, indole-3-acetic acid (IAA), ammonia, hydrogen cyanide (HCN), and hydrolytic enzymes, phosphorus solubilization, antifungal activity against plant pathogens, nitrogen-fixation, 1-aminocyclopropane-1-carboxylic acid deaminase activity, and improving tolerance to different abiotic stresses. These isolates had nifH (11 isolates), acdS (8 isolates), and HCN (11 isolates) genes involved in N-fixation, stress tolerance, and pathogen biocontrol, respectively. Two isolates Pantoea cypripedii AF1and Kosakonia arachidis EF1 were the most potent strains and they colonized and grew in sugarcane plants. Both strains readily colonized the leading Chinese sugarcane variety GT42 and significantly increased the activity of nitrogen assimilation enzymes (glutamine synthetase, NADH glutamate dehydrogenase, and nitrate reductase), chitinase, and endo-glucanase and the content of phytohormones gibberellic acid, indole-3-acetic acid, and abscisic acid. The gene expression analysis of GT42 inoculated with isolates of P. cypripedii AF1 or K. arachidis EF1 showed increased activity of nifH and nitrogen assimilation genes. Also, the inoculated diazotrophs significantly increased plant nitrogen content, which was corroborated by the 15N isotope dilution analysis. Collectively, these findings suggest that P. cypripedii and K. arachidis are beneficial endophytes that could be used as a biofertilizer to improve plant nitrogen nutrition and growth of sugarcane. To the best of our knowledge, this is the first report of sugarcane growth enhancement and nitrogen fixation by Gram-negative sugarcane root-associated endophytic bacteria P. cypripedii and K. arachidis. These strains have the potential to be utilized as sugarcane biofertilizers, thus reducing nitrogen fertilizer use and improving disease management.

Exploiting Biological Nitrogen Fixation: A Route Towards a Sustainable Agriculture

For all living organisms, nitrogen is an essential element, while being the most limiting in ecosystems and for crop production. Despite the significant contribution of synthetic fertilizers, nitrogen requirements for food production increase from year to year, while the overuse of agrochemicals compromise soil health and agricultural sustainability. One alternative to overcome this problem is biological nitrogen fixation (BNF). Indeed, more than 60% of the fixed N on Earth results from BNF. Therefore, optimizing BNF in agriculture is more and more urgent to help meet the demand of the food production needs for the growing world population. This optimization will require a good knowledge of the diversity of nitrogen-fixing microorganisms, the mechanisms of fixation, and the selection and formulation of efficient N-fixing microorganisms as biofertilizers. Good understanding of BNF process may allow the transfer of this ability to other non-fixing microorganisms or to non-leguminous plants with high added value. This minireview covers a brief history on BNF, cycle and mechanisms of nitrogen fixation, biofertilizers market value, and use of biofertilizers in agriculture. The minireview focuses particularly on some of the most effective microbial products marketed to date, their efficiency, and success-limiting in agriculture. It also highlights opportunities and difficulties of transferring nitrogen fixation capacity in cereals.

nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine

As inorganic nitrogen compounds are essential for basic building blocks of life (e.g., nucleotides and amino acids), the role of biological nitrogen-fixation (BNF) is indispensible. All nitrogen fixing microbes rely on the same nitrogenase enzyme for nitrogen reduction, which is in fact an enzyme complex consists of as many as 20 genes. However, the occurrence of six genes viz., nifB, nifD, nifE, nifH, nifK, and nifN has been proposed to be essential for a functional nitrogenase enzyme. Therefore, identification of these genes is important to understand the mechanism of BNF as well as to explore the possibilities for improving BNF from agricultural sustainability point of view. Further, though the computational tools are available for the annotation and phylogenetic analysis of nifH gene sequences alone, to the best of our knowledge no tool is available for the computational prediction of the above mentioned six categories of nitrogen-fixation (nif) genes or proteins. Thus, we proposed an approach, which is first of its kind for the computational identification of nif proteins encoded by the six categories of nif genes. Sequence-derived features were employed to map the input sequences into vectors of numeric observations that were subsequently fed to the support vector machine as input. Two types of classifier were constructed: (i) a binary classifier for classification of nif and non-nitrogen-fixation (non-nif) proteins, and (ii) a multi-class classifier for classification of six categories of nif proteins. Higher accuracies were observed for the combination of composition-transition-distribution (CTD) feature set and radial kernel, as compared to the other feature-kernel combinations. The overall accuracies were observed >90% in both binary and multi-class classifications. The developed approach further achieved >92% accuracy, while evaluated with blind (independent) test datasets. The developed approach also produced higher accuracy in identifying nif proteins, while evaluated using proteome-wide datasets of several species. Furthermore, we established a prediction server nifPred (http://webapp.cabgrid.res.in/nifPred) to assist the scientific community for proteome-wide identification of six categories of nif proteins. Besides, the source code of nifPred is also available at https://github.com/PrabinaMeher/nifPred. The developed web server is expected to supplement the transcriptional profiling and comparative genomics studies for the identification and functional annotation of genes related to BNF.