Towards Prediction of Metabolic Products of Polyketide Synthases: An In Silico Analysis

Sequence data arising from an increasing number of partial and complete genome projects is revealing the presence of the polyketide synthase (PKS) family of genes not only in microbes and fungi but also in plants and other eukaryotes. PKSs are huge multifunctional megasynthases that use a variety of biosynthetic paradigms to generate enormously diverse arrays of polyketide products that posses several pharmaceutically important properties. The remarkable conservation of these gene clusters across organisms offers abundant scope for obtaining novel insights into PKS biosynthetic code by computational analysis. We have carried out a comprehensive in silico analysis of modular and iterative gene clusters to test whether chemical structures of the secondary metabolites can be predicted from PKS protein sequences. Here, we report the success of our method and demonstrate the feasibility of deciphering the putative metabolic products of uncharacterized PKS clusters found in newly sequenced genomes. Profile Hidden Markov Model analysis has revealed distinct sequence features that can distinguish modular PKS proteins from their iterative counterparts. For iterative PKS proteins, structural models of iterative ketosynthase (KS) domains have revealed novel correlations between the size of the polyketide products and volume of the active site pocket. Furthermore, we have identified key residues in the substrate binding pocket that control the number of chain extensions in iterative PKSs. For modular PKS proteins, we describe for the first time an automated method based on crucial intermolecular contacts that can distinguish the correct biosynthetic order of substrate channeling from a large number of non-cognate combinatorial possibilities. Taken together, our in silico analysis provides valuable clues for formulating rules for predicting polyketide products of iterative as well as modular PKS clusters. These results have promising potential for discovery of novel natural products by genome mining and rational design of novel natural products.     

Effect of different Mg/Ca ratios and electrolyte concentrations in irrigation water on the nutrient content of wheat crop

Summary A study conducted in pots to evaluate the effect of different Mg/Ca ratios (2, 4, 8 and 16) and electrolyte concentrations (20 and 80 meq/l) at SAR 10 in irrigation water on the nutrient uptake and yield of wheat crop in two soils revealed that the average grain and dry matter yields of wheat decreased significantly with an increase in Mg/Ca ratio in irrigation water, but the magnitude of decrease was greater at higher electrolyte concentration than at lower electrolyte concentration. The concentration of Na in both straw and grain of wheat increased and that of K decreased with an increase in Mg/Ca ratio and electrolyte concentration of irrigation water, which led to higher Na/Ca and Na/K ratios in the plant. Further, the concentration of Ca and Mg both in straw as well as in grain increased with increasing electrolyte concentration of the irrigation water. An increasing proportion of Mg in saline irrigation water resulted in decreased concentration of Ca and increased concentration of Mg in both straw and grain of wheat crop. It was also noticed that the increasing proportion of Mg over Ca in the poor quality irrigation water increased the P content of both straw and grain of wheat crop.     

Holliday junction resolution by At-HIGLE: an SLX1 lineage endonuclease from Arabidopsis thaliana with a novel in-built regulatory mechanism

Holliday junction is the key homologous recombination intermediate, resolved by structure-selective endonucleases (SSEs). SLX1 is the most promiscuous SSE of the GIY-YIG nuclease superfamily. In fungi and animals, SLX1 nuclease activity relies on a non-enzymatic partner, SLX4, but no SLX1-SLX4 like complex has ever been characterized in plants. Plants exhibit specialized DNA repair and recombination machinery. Based on sequence similarity with the GIY-YIG nuclease domain of SLX1 proteins from fungi and animals, At-HIGLE was identified to be a possible SLX1 like nuclease from plants. Here, we elucidated the crystal structure of the At-HIGLE nuclease domain from Arabidopsis thaliana, establishing it as a member of the SLX1-lineage of the GIY-YIG superfamily with structural changes in DNA interacting regions. We show that At-HIGLE can process branched-DNA molecules without an SLX4 like protein. Unlike fungal SLX1, At-HIGLE exists as a catalytically active homodimer capable of generating two coordinated nicks during HJ resolution. Truncating the extended C-terminal region of At-HIGLE increases its catalytic activity, changes the nicking pattern, and monomerizes At-HIGLE. Overall, we elucidated the first structure of a plant SLX1-lineage protein, showed its HJ resolving activity independent of any regulatory protein, and identified an in-built novel regulatory mechanism engaging its C-terminal region.     

Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer

Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.     

Association of Chlamydia trachomatis,Neisseria gonorrhoeae,Mycoplasma genitalium and Ureaplasma species infection and organism load with cervicitis in north Indian population

Cervicitis is predominantly caused by Neisseria gonorrhoeae and Chlamydia trachomatis, which accounts for almost half of all the cases of cervicitis. The role of newer organisms like Mycoplasma genitalium and Ureaplasma sp. and association of bacterial load with cervicitis are also not well established. So the study aimed to determine the relative frequency of these organisms and their load in association with cervicitis cases from north India. A case–control study involving 300 women was conducted using quantitative real-time PCR from endocervical swabs for identification of organisms and quantification of bacterial load. Among 150 cervicitis cases, C. trachomatis, N. gonorrhoeae, M. genitalium and Ureaplasma parvum were detected in 5 (3·3%), 10 (6·6%), 37(24·6%) and 47 (31·3%) respectively. Old age (<0·001, chi-squared test) and irregular menstrual cycles (<0·001, chi-squared test) were significantly associated with cervicitis. M genitalium was the only organism to be associated significantly with cervicitis with regard to age (<0·031) and symptoms like discharge (P < 0·033, chi-squared test) and dysuria (P < 0·044, chi-squared test) in multivariate analysis. Our finding suggests that the bacterial load of these organisms is not significantly associated with cervicitis. However, we found significant association of M. genitalium infection with clinical characteristics of cervicitis cases.     

Diversity of Essential Oil-Secretory Cells and Oil Composition in Flowers and Buds of Magnolia sirindhorniae and Its Biological Activities

Magnolia sirindhorniae Noot. & Chalermglin produces fragrant flowers. The volatile oil secretary cells, quantity and quality as well as antioxidant and antimicrobial activities of the oils extracted from buds and flowers, have been investigated. The distribution of essential oil secretory cell in bud and flower revealed that the density and size of the oil cells were significantly higher in flowers compared to buds. In different floral parts, carpel has a higher oil cell density followed by gynophore and tepal. The histochemical analysis revealed the essential oil is synthesized in oil secretory cells. The volatile oil yield was 0.25 % in the buds and 0.50 % in flowers. GC/FID and GC/MS analysis identified 33 compounds contributing 83.2–83.5 % of the total essential oil composition. Linalool is the main constituent contributing 58.9 % and 51.0 % in the buds and flowers oils, respectively. The essential oil extracted from the flowers showed higher antimicrobial efficacy against Klebsiella pneumoniae and Staphylococcus aureus. Similarly, the essential oil isolated from the flowers depicts higher free radical scavenging, and antioxidant activity compared to buds’ oil.     

The dependency of autophagy and ubiquitin proteasome system during skeletal muscle atrophy

Among the four proteolytic systems in the cell, autophagy and the ubiquitin-proteasome system (UPS) are the main proteolytic events that allow for the removal of cell debris and proteins to maintain cellular homeostasis. Previous studies have revealed that these systems perform their functions independently of each other. However, recent studies indicate the existence of regulatory interactions between these proteolytic systems via ubiquitinated tags and a reciprocal regulation mechanism with several crosstalk points. UPS plays an important role in the elimination of short-lived/soluble misfolded proteins, whereas autophagy eliminates defective organelles and persistent insoluble protein aggregates. Both of these systems seem to act independently; however, disruption of one pathway affects the activity of the other pathway and contributes to different pathological conditions. This review summarizes the recent findings on direct and indirect dependencies of autophagy and UPS and their execution at the molecular level along with the important drug targets in skeletal muscle atrophy.     

Recent trends in stem cell-based therapies and applications of artificial intelligence in regenerative medicine

Stem cells are undifferentiated cells that can self-renew and differentiate into diverse types of mature and functional cells while maintaining their original identity. This profound potential of stem cells has been thoroughly investigated for its significance in regenerative medicine and has laid the foundation for cell-based therapies. Regenerative medicine is rapidly progressing in healthcare with the prospect of repair and restoration of specific organs or tissue injuries or chronic disease conditions where the body’s regenerative process is not sufficient to heal. In this review, the recent advances in stem cell-based therapies in regenerative medicine are discussed, emphasizing mesenchymal stem cell-based therapies as these cells have been extensively studied for clinical use. Recent applications of artificial intelligence algorithms in stem cell-based therapies, their limitation, and future prospects are highlighted.     

Cloning,expression analysis and In silico characterization of HSP101: a potential player conferring heat stress in Aegilops speltoides (Tausch) Gren

Heat shock protein (HSP101) function as molecular chaperones and confer thermotolerance to plants. In the present investigation, identification, comprehensive expression analysis, phylogeny and protein modelling of HSP101 gene has been done in Aegilops speltoides accession Pau3583. In the present study, we cloned and in silico characterized a HSP101C gene designated as AsHSP101C-Pau3583. AsHSP101C-Pau3583 is 4180 bp long with seven exons and six introns and encoded a polypeptide of 910 amino acids predicted by FGENESH. We have identified 58 SNPs between the AsHSP101C-Pau3583 and reference gene sequence extracted from Ae. speltoides TGAC assembly. Real-time RT-PCR analysis of expression levels of HSP101 gene in two wheat genotypes under heat stress revealed that gene namely HSP101C was up-regulated in Aegilops speltoides acc. Pau3583 by > fourfold in comparison to Triticum aestivum cv. PBW343 under heat stress signifies that it plays a role in conferring heat tolerance. Sequence comparison and phylogenetic analysis of AsHSP101C-Pau3583 with seven wheat homologs Triticum aestivum, Aegilops speltoides (TGAC), Triticum durum cv Cappelli, Triticum durum cv Strongfield, Triticum monococcum, Aegilops tauschii and Triticum urartu showed significant similarities with highly conserved coding regions and functional domains (AAA, AAA + 2, ClpB domains), suggesting the conserved function of HSP101C in different species. The illustration of the protein models of HSP101C in homologs provided information for the ATP-binding motifs within the nucleotide binding domains (NBD), specific for the chaperone activity. These findings are important and identified SNPs could be used for designing markers for ensuring the transfer of AsHSP101C-Pau3583 gene into hexaploid wheat and its role in heat tolerance.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12298-021-01005-2.