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Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets. [ABSTRACT FROM AUTHOR]

The vital tissue homeostasis regulator p53 forms a tetramer when it binds to DNA and regulates the genes that mediate essential biological processes such as cell-cycle arrest, senescence, DNA repair, and apoptosis. Missense mutations in the core DNA-binding domain (109–292) simultaneously cause the loss of p53 tumor suppressor function and accumulation of the mutant p53 proteins that are carcinogenic. The most common p53 hotspot mutation at codon 248 in the DNA-binding region, where arginine (R) is substituted by tryptophan (W), glycine (G), leucine (L), proline (P), and glutamine (Q), is reported in various cancers. However, it is unclear how the p53 Arg248 mutation with distinct amino acid substitution affects the structure, function, and DNA binding affinity. Here, we characterized the pathogenicity and protein stability of p53 hotspot mutations at codon 248 using computational tools PredictSNP, Align GVGD, HOPE, ConSurf, and iStable. We found R248W, R248G, and R248P mutations highly deleterious and destabilizing. Further, we subjected all five R248 mutant-p53–DNA and wt-p53–DNA complexes to molecular dynamics simulation to investigate the structural stability and DNA binding affinity. From the MD simulation analysis, we observed increased RMSD, RMSF, and Rg values and decreased protein–DNA intermolecular hydrogen bonds in the R248-p53–DNA than the wt-p53–DNA complexes. Likewise, due to high SASA values, we observed the shrinkage of proteins in R248W, R248G, and R248P mutant-p53–DNA complexes. Compared to other mutant p53–DNA complexes, the R248W, R248G, and R248P mutant-p53–DNA complexes showed more structural alteration. MM-PBSA analysis showed decreased binding energies with DNA in all five R248-p53–DNA mutants than the wt-p53–DNA complexes. Henceforth, we conclude that the amino acid substitution of Arginine with the other five amino acids at codon 248 reduces the p53 protein's affinity for DNA and may disrupt cell division, resulting in a gain of p53 function. The proposed study influences the development of rationally designed molecular-targeted treatments that improve p53-based therapeutic outcomes in cancer. [ABSTRACT FROM AUTHOR]

New evidence strongly discloses the pathogenesis of host-associated microbiomes in respiratory diseases. The microbiome dysbiosis modulates the lung's behavior and deteriorates the respiratory system's effective functioning. Several exogenous and environmental factors influence the development of asthma and chronic lung disease. The relationship between asthma and microbes is reasonably understood and yet to be investigated for more substantiation. The comorbidities such as SARS-CoV-2 further exacerbate the health condition of the asthma-affected individuals. This study examines the raw 16S rRNA sequencing data collected from the saliva and nasopharyngeal regions of pre-existing asthma (23) and non-asthma patients (82) infected by SARS-CoV-2 acquired from the public database. The experiment is designed in a two-fold pattern, analyzing the associativity between the samples collected from the saliva and nasopharyngeal regions. Later, investigates the microbial pathogenesis, its role in exacerbations of respiratory disease, and deciphering the diagnostic biomarkers of the target condition. LEfSE analysis identified that Actinobacteriota and Pseudomonadota are enriched in the SARS-CoV-2-non-asthma group and SARS-CoV-2 asthma group of the salivary microbiome, respectively. Random forest algorithm is trained with amplicon sequence variants (ASVs) attained better classification accuracy, ROC scores on nasal (84% and 87%) and saliva datasets (93% and 97.5%). Rothia mucilaginosa is less abundant, and Corynebacterium tuberculostearicum showed higher abundance in the SARS-CoV-2 asthma group. The increase in Streptococcus at the genus level in the SARS-CoV-2-asthma group is evidence of discriminating the subgroups. [ABSTRACT FROM AUTHOR]

The pace of anti-diabetic drug discovery is very slow in spite of increasing rate of prevalence of Type 2 Diabetes which remains a major public health concern. Though extensive research steps are taken in the past decade, yet craves for better new treatment strategies to overcome the current scenario. One such general finding is the evolution of gliptins which discriminately inhibits DPP4 (Dipeptidyl peptidase-4) enzyme. Although the mechanism of action of gliptin is highly target oriented and accurate, still its long-term use stands unknown. This step calls for a fast, flexible, and cost-effective strategies to meet the demands of producing arrays of high-content lead compounds with improved efficiency for better clinical success. The present review highlights the available gliptins in the market and also other naturally occurring DPP4 enzyme inhibitors. Along with describing the known inhibitors and their origin in this review, we attempted to identify a probable new lead compounds using advanced computational techniques. In this context, computational methods that integrate the knowledge of proteins and drug responses were utilized in prioritizing targets and designing drugs towards clinical trials with better efficacy. The compounds obtained as a result of virtual screening were compared with the commercially available gliptin in the market to have better efficiency in the identification and validation of the potential DPP4 inhibitors. The combinatorial computational methods used in the present study identified Compound 1: 25022354 as promising inhibitor. [ABSTRACT FROM AUTHOR]

Some individuals with non-small-cell lung cancer (NSCLC) benefit from therapies targeting epidermal growth factor receptor (EGFR), and the characterization of a new mechanism of resistance to the EGFR-specific antibody gefitinib will provide valuable insight into how therapeutic strategies might be designed to overcome this particular resistance mechanism. The G719S and T790M mutations and their combination were involved in causing different conformational redistribution of EGFR. In the present computational study, we analyzed the impact and structural influence of G719S/T790M double mutation (DM) in EGFR with ligand (gefitinib) through molecular dynamic simulation (50 ns) and docking analysis. We observed the escalation in distance between the functional loop and activation loop with respect to T790M mutation compared to the G719S mutation. Furthermore, we confirmed that the G719S mutation causes the ligand to move closer to the hinge region, whereas T790M makes the ligand escape from the binding pocket. Obtained results provide with an explanation for the resistance induced by T790M and a vital clue for the design of drugs to combat gefitinib resistance. [ABSTRACT FROM AUTHOR]

Background: Understanding and predicting molecular basis of disease is one of the major challenges in modern biology and medicine. SNPs associated with complex disorders can create, destroy, or modify protein coding sites. Single amino acid substitutions in the ATM gene are the most common forms of genetic variations that account for various forms of cancer. However, the extent to which SNPs interferes with the gene regulation and affects cancer susceptibility remains largely unknown. Principal findings: We analyzed the deleterious nsSNPs associated with ATM gene based on different computational methods. An integrative scoring system and sequence conservation of amino acid residues was adapted for a priori nsSNP analysis of variants associated with cancer. We further extended our approach on SNPs that could potentially influence protein Post Translational Modifications in ATM gene. Significance: In the lack of adequate prior reports on the possible deleterious effects of nsSNPs, we have systematically analyzed and characterized the functional variants in both coding and non coding region that can alter the expression and function of ATM gene. In silico characterization of nsSNPs affecting ATM gene function can aid in better understanding of genetic differences in disease susceptibility. [ABSTRACT FROM AUTHOR]

Background: Elucidating the molecular dynamic behavior of Protein-DNA complex upon mutation is crucial in current genomics. Molecular dynamics approach reveals the changes on incorporation of variants that dictate the structure and function of Protein-DNA complexes. Deleterious mutations in APE1 protein modify the physicochemical property of amino acids that affect the protein stability and dynamic behavior. Further, these mutations disrupt the binding sites and prohibit the protein to form complexes with its interacting DNA. Principal Findings: In this study, we developed a rapid and cost-effective method to analyze variants in APE1 gene that are associated with disease susceptibility and evaluated their impacts on APE1-DNA complex dynamic behavior. Initially, two different in silico approaches were used to identify deleterious variants in APE1 gene. Deleterious scores that overlap in these approaches were taken in concern and based on it, two nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were taken further for structural analysis. Significance: Different parameters such as RMSD, RMSF, salt bridge, H-bonds and SASA applied in Molecular dynamic study reveals that predicted deleterious variants I64T and P311S alters the structure as well as affect the stability of APE1-DNA interacting functions. This study addresses such new methods for validating functional polymorphisms of human APE1 which is critically involved in causing deficit in repair capacity, which in turn leads to genetic instability and carcinogenesis. [ABSTRACT FROM AUTHOR]

Background: A central focus of cancer genetics is the study of mutations that are causally implicated in tumorigenesis. The identification of such causal mutations not only provides insight into cancer biology but also presents anticancer therapeutic targets and diagnostic markers. Missense mutations are nucleotide substitutions that change an amino acid in a protein, the deleterious effects of these mutations are commonly attributed to their impact on primary amino acid sequence and protein structure. Methods: The method to identify functional SNPs from a pool, containing both functional and neutral SNPs is challenging by experimental protocols. To explore possible relationships between genetic mutation and phenotypic variation, we employed different bioinformatics algorithms like Sorting Intolerant from Tolerant (SIFT), Polymorphism Phenotyping (PolyPhen), and PupaSuite to predict the impact of these amino acid substitutions on protein activity of mismatch repair (MMR) genes causing hereditary nonpolyposis colorectal cancer (HNPCC). Results: SIFT classified 22 of 125 variants (18%) as 'Intolerant." PolyPhen classified 40 of 125 amino acid substitutions (32%) as "Probably or possibly damaging". The PupaSuite predicted the phenotypic effect of SNPs on the structure and function of the affected protein. Based on the PolyPhen scores and availability of three-dimensional structures, structure analysis was carried out with the major mutations that occurred in the native protein coded by MSH2 and MSH6 genes. The amino acid residues in the native and mutant model protein were further analyzed for solvent accessibility and secondary structure to check the stability of the proteins. Conclusion: Based on this approach, we have shown that four nsSNPs, which were predicted to have functional consequences (MSH2-Y43C, MSH6-Y538S, MSH6-S580L, and MSH6-K854M), were already found to be associated with cancer risk. Our study demonstrates the presence of other deleterious mutations and also endorses with in vivo experimental studies. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variations in humans. Understanding the functions of SNPs can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. The method to identify functional SNPs from a pool, containing both functional and neutral SNPs is challenging by experimental protocols. To explore possible relationships between genetic mutation and phenotypic variation, different computational algorithm tools like Sorting Intolerant from Tolerant, Polymorphism Phenotyping, UTRscan, FASTSNP, and PupaSuite were used for prioritization of high-risk SNPs in coding region (exonic nonsynonymous SNPs) and noncoding regions (intronic and exonic 5’ and 3’-untranslated region (UTR) SNPs). In this work, we have analyzed the SNPs that can alter the expression and function of transcriptional factor TP53 as a pipeline and for providing a guide to experimental work. We identified the possible mutations and proposed modeled structure for the mutant proteins and compared them with the native protein. These nsSNPs play a critical role in cancer association studies aiming to explain the disparity in cancer treatment responses as well as to improve the effectiveness of the cancer treatments. Our results endorse the study with in vivo experimental protocols. [ABSTRACT FROM AUTHOR]

Mucopolysaccharidosis (MPS) IIIA, also known as Sanfilippo syndrome type A, is a severe, progressive disease that affects the central nervous system (CNS). MPS IIIA is inherited in an autosomal recessive manner and is caused by a deficiency in the lysosomal enzyme sulfamidase, which is required for the degradation of heparan sulfate. The sulfamidase is produced by the N-sulphoglucosamine sulphohydrolase (SGSH) gene. In MPS IIIA patients, the excess of lysosomal storage of heparan sulfate often leads to mental retardation, hyperactive behavior, and connective tissue impairments, which occur due to various known missense mutations in the SGSH, leading to protein dysfunction. In this study, we focused on three mutations (R74C, S66W, and R245H) based on in silico pathogenic, conservation, and stability prediction tool studies. The three mutations were further subjected to molecular dynamic simulation (MDS) analysis using GROMACS simulation software to observe the structural changes they induced, and all the mutants exhibited maximum deviation patterns compared with the native protein. Conformational changes were observed in the mutants based on various geometrical parameters, such as conformational stability, fluctuation, and compactness, followed by hydrogen bonding, physicochemical properties, principal component analysis (PCA), and salt bridge analyses, which further validated the underlying cause of the protein instability. Additionally, secondary structure and surrounding amino acid analyses further confirmed the above results indicating the loss of protein function in the mutants compared with the native protein. The present results reveal the effects of three mutations on the enzymatic activity of sulfamidase, providing a molecular explanation for the cause of the disease. Thus, this study allows for a better understanding of the effect of SGSH mutations through the use of various computational approaches in terms of both structure and functions and provides a platform for the development of therapeutic drugs and potential disease treatments. [ABSTRACT FROM AUTHOR]

A systematic search was implemented using four literature databases (PubMed, Embase, Science Direct and Web of Science) to capture all the causative mutations of Glucose-6-phosphate dehydrogenase (G6PD) deficiency (G6PDD) in the 22 Arab countries. Our search yielded 43 studies that captured 33 mutations (23 missense, one silent, two deletions, and seven intronic mutations), in 3,430 Arab patients with G6PDD. The 23 missense mutations were then subjected to phenotypic classification using in silico prediction tools, which were compared to the WHO pathogenicity scale as a reference. These in silico tools were tested for their predicting efficiency using rigorous statistical analyses. Of the 23 missense mutations, p.S188F, p.I48T, p.N126D, and p.V68M, were identified as the most common mutations among Arab populations, but were not unique to the Arab world, interestingly, our search strategy found four other mutations (p.N135T, p.S179N, p.R246L, and p.Q307P) that are unique to Arabs. These mutations were exposed to structural analysis and molecular dynamics simulation analysis (MDSA), which predicting these mutant forms as potentially affect the enzyme function. The combination of the MDSA, structural analysis, and in silico predictions and statistical tools we used will provide a platform for future prediction accuracy for the pathogenicity of genetic mutations. [ABSTRACT FROM AUTHOR]

Microbial Dysbiosis is associated with the etiology and pathogenesis of diseases. The studies on the vaginal microbiome in cervical cancer are essential to discern the cause and effect of the condition. The present study characterizes the microbial pathogenesis involved in developing cervical cancer. Relative species abundance assessment identified Firmicutes, Actinobacteria, and Proteobacteria dominating the phylum level. A significant increase in Lactobacillus iners and Prevotella timonensis at the species level revealed its pathogenic influence on cervical cancer progression. The diversity, richness, and dominance analysis divulges a substantial decline in cervical cancer compared to control samples. The β diversity index proves the homogeneity in the subgroups' microbial composition. The association between enriched Lactobacillus iners at the species level, Lactobacillus, Pseudomonas, and Enterococcus genera with cervical cancer is identified by Linear discriminant analysis Effect Size (LEfSe) prediction. The functional enrichment corroborates the microbial disease association with pathogenic infections such as aerobic vaginitis, bacterial vaginosis, and chlamydia. The dataset is trained and validated with repeated k-fold cross-validation technique using a random forest algorithm to determine the discriminative pattern from the samples. SHapley Additive exPlanations (SHAP), a game theoretic approach, is employed to analyze the results predicted by the model. Interestingly, SHAP identified that the increase in Ralstonia has a higher probability of predicting the sample as cervical cancer. New evidential microbiomes identified in the experiment confirm the presence of pathogenic microbiomes in cervical cancer vaginal samples and their mutuality with microbial imbalance. [ABSTRACT FROM AUTHOR]

Von Hippel-Lindau [VHL] disease, an autosomal dominant hereditary cancer syndrome, is well known for its complex genotype-phenotype correlations. We looked for germline mutations in the VHL gene in an affected multiplex family with Type 1 VHL disease. Real-Time quantitative PCR for deletions and Sanger sequencing of coding regions along with flanking intronic regions were performed in two affected individuals and one related individual. Direct sequencing identified a novel heterozygous single nucleotide base substitution in both the affected members tested, segregating with VHL phenotype in this family. This variant in exon 3, c.473T>A, results in substitution of leucine, a highly conserved acid, to glutamine at position 158 [p.L158Q] and has not been reported thus far as a variant associated with disease causation. Further, this variant was not observed in 50 age and ethnicity matched healthy individuals. Extensive in silico prediction analysis along with molecular dynamics simulation revealed significant deleterious nature of the substitution L158Q on pVHL. The results of this study when collated support the view that the missense variation p.L158Q in the Elongin C binding domain of pVHL may be disease causing. [ABSTRACT FROM AUTHOR]

This paper depicts the first report from an Indian population on the association between the variant Arg399Gln of XRCC1 locus in the DNA repair system and schizophrenia, the debilitating disease that affects 1% of the world population. Genotypic analysis of a total of 523 subjects (260 patients and 263 controls) revealed an overwhelming presence of Gln399Gln in the case subjects against the controls (P < 0.0068), indicating significant level of association of this nsSNP with schizophrenia; the Gln399 allele frequency was also perceptibly more in cases than in controls (p < 0.003; OR = 1.448). The results of the genotypic studies were further validated using pathogenicity and stability prediction analysis employing computational tools [I-Mutant Suite, iStable, PolyPhen2, SNAP, and PROVEAN], with a view toassess the magnitude of deleteriousness of the mutation. The pathogenicity analysis reveals that the nsSNP could be deleterious inasmuch as it could affect the functionality of the gene, and interfere with protein function. Molecular dynamics simulation of 60ns was performed using GROMACS to analyse structural change due to a mutation (Arg399Gln) that was never examined before. RMSD, RMSF, hydrogen bonds, radius of gyration and SASA analysis showedthe existence of asignificant difference between the native and the mutant protein. The present study gives astrong indication that the XRCC1 locus deserves serious attention, as it could be a potential candidatecontributing to the etio-pathogenesis of the disease. [ABSTRACT FROM AUTHOR]

In this work we have analyzed the genetic variation that can alter the expression and the function of the VHL gene using computational methods. Of 110 single nucleotide polymorphisms (SNPs), 33 were found to be nonsynonymous (nsSNPs) and 23 SNPs were found in untranslated regions. Of the 33 nsSNPs investigated, 36.3% were found to be deleterious by both SIFT and PolyPhen servers. An untranslated region (UTR) resource tool suggested that two SNPs in the 5′ UTR region and six SNPs in the 3′ UTR region might change the protein expression levels. It was found by both SIFT and PolyPhen servers that a mutation from histidine to arginine at position 115 of the native protein of the VHL gene was most deleterious. A structural analysis of this mutated protein and the native protein was performed and had a root mean square deviation (RMSD) of 2.78 Å. Based on this work, we propose that the nsSNP with a SNPid of rs5030812 is an important candidate for the cause of von Hippel–Lindau syndrome via the VHL gene. [ABSTRACT FROM AUTHOR]

Abstract: Single-nucleotide polymorphisms (SNPs) play a major role in the understanding of the genetic basis of many complex human diseases. Also, the genetics of human phenotype variation could be understood by knowing the functions of these SNPs. It is still a major challenge to identify the functional SNPs in a disease-related gene. In this work, we have analyzed the genetic variation that can alter the expression and the function of the BRCA1 gene using computational methods. Of the total 477 SNPs, 65 were found to be nonsynonymous (ns) SNPs. Among the 14 SNPs in the untranslated region, 4 were found in the 5′ and 10 were found in the 3′ untranslated region (UTR). It was found that 16.9% of the nsSNPs were damaging, by both the SIFT and the PolyPhen servers. The UTR Resource tool suggested that 2 of 4 SNPs in the 5′ UTR and 3 of 10 SNPs in the 3′ UTR might change the protein expression levels. We identified major mutations from proline to serine at positions 1776 and 1812 of the native protein of the BRCA1 gene. From a comparison of the stabilizing residues of the native and mutant proteins, we propose that an nsSNP (rs1800751) could be an important candidate for the breast cancer caused by the BRCA1 gene. [Copyright &y& Elsevier]

The rise of pollution due to the dye industries and textile wastes are evolving rapidly every day. The dyes are used in different trade names by the textile industries. The actual chemistry of dye is vague and difficult to understand even today though we are equipped technically. The toxic effects of the dyes and the reasons behind the acute toxicity are also an undiscovered mystery; therefore, no effective measures can be employed to degrade dyes. Deploying physical or chemical methods to pre-treat the azo dyes are expensive, extremely energy-consuming, and are not environment friendly. Hence, the use of microbes for textile dye degradation will be eco-friendly and is probably a cost-effective alternative to physicochemical methods. The present study was conducted to investigate the degradation of azo dyes isolated from textile effluent contaminated soil by employing the bacterial strains for degradation. The bacterial strains could degrade the optimum concentration of mixed azo dyes (200 mg/L) with an incubation up to 5 days. The decolourization of the dyes was expressed in terms of percentage of decolourization, and was found that about 87.35% of degradation by Bacillus subtilis strain. The enzyme responsible was analyzed as intracellular azoreductase involved in the degradation of mixed azo dyes. The enzymatic pathway and 1-phenyl-2–4(4-methyl phenyl)-diazene 1-oxide was observed as the major metabolite by GC–MS analysis. The in silico study determined the binding of mixed azo dye with azoreductase and hypothesized that their linking could be the main reason for the degradation of mixed azo dye. [ABSTRACT FROM AUTHOR]

Matrix metalloproteinases (MMPs) are pivotal for cancer cell migration and metastasis which are generally over-expressed in such cell types. Many drugs targeting MMPs do so by binding to the conserved catalytic domains and thus exhibit poor selectivity due to domain-similarities with other proteases. We report herein the binding of a novel compound [3-(E-3,4-dihydroxycinnamaoyloxyl)-2-hydroxypropyl 9Z, 12Z-octadeca-9, 12-dienoate; Mol. wt: 516.67 Da], (C1), isolated from a seagrass, Cymodocea serrulata to the unconserved hemopexin-like (PEX) domain of MMP2 (− 9.258 kcal/mol). MD simulations for 25 ns, suggest stable ligand-target binding. In addition, C1 killed an ovarian cancer cell line, PA1 at IC50: 5.8 μM (lesser than Doxorubicin: 8.6 µM) and formed micronuclei, apoptotic bodies and nucleoplasmic bridges whilst causing DNA laddering, S and G2/M phase dual arrests and MMP disturbance, suggesting intrinsic apoptosis. The molecule increased mRNA transcripts of BAX and BAD and down-regulated cell survival genes, Bcl-xL, Bcl-2, MMP2 and MMP9. The chemical and structural details of C1 were deduced through FT-IR, GC–MS, ESI–MS, 1H and 13C NMR [both 1D and 2D] spectra. [ABSTRACT FROM AUTHOR]

Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield. • Improved photosyntheticactivity enhances the crop productivity. • Machine learning should be used effectively in photosynthesis research. • There is a vastpotential for application of ML in photosynthetic pigment studies. [ABSTRACT FROM AUTHOR]

The effect of salt stress on pigment synthesis and antioxidant enzyme activity as well as in the genes involved in the biosynthetic pathway of bixin was studied. The 14-day germinated seedlings of Bixa orellana were induced into the various NaCl concentration (0, 25, 50, 75, 100 mM). After 45 days, leaves were taken for pigment analysis, antioxidant assays, and gene expression analysis to study the response of salt stress. The pigment content such as chlorophyll level was increased upon salt stress with a reduction in total carotenoid clearly indicating the adaptability of plants towards the stressed state. The level of β-carotene was increased in the highest concentration of salt stress treatment. The secondary metabolites such as bixin and abscisic acid (ABA) content were also high in elevated concentration of salt-treated seedling than control. The antioxidant enzyme activity was increased with the highest dose of salt stress suggesting the antioxidant enzymes to protect the plant from the deleterious effects. The mRNA transcript gene of the carotenoid biosynthetic pathway such as phytoene synthase (PSY), 1-deoxyxylulose-5-phosphate synthase (DXS), phytoene desaturase (PDS), beta-lycopene cyclase (LCY-β), epsilon lycopene cyclase (LCY-ε), carboxyl methyl transferase (CMT), aldehyde dehydrogenase (ADH), lycopene cleavage dioxygenase (LCD), and carotenoid cleavage dioxygenase (CCD) showed differential expression pattern under salt stress. In addendum, we studied the co-expression network analysis of gene to assess the co-related genes associated in the biosynthesis pathway of carotenoid. From the co-expression analysis result showed, the LCY, PDS, and PSY genes were closely correlated with other genes. These finding may provide insight to the plants to exist in the stress condition and to improve the industrially important pigment production. [ABSTRACT FROM AUTHOR]

Aims: To conduct a systematic review and meta-analysis of retinopathy prevalence in patients with type 1 diabetes (T1D) in 22 Arab countries. Methods: We systematically searched 4 different literature databases (PubMed, Science Direct, Web of Science and Embase), from the date of inception until December 2017, to collect all the information about patients with T1D who developed retinopathy complications; for statistical analysis, we used MetaXL to evaluate the pooled prevalence estimate and the subgroup prevalence estimates employing double arcsine transformation and inverse variance heterogeneity models. Results: Our search strategy returned 475 studies, of which 39 met our inclusion criteria; of those, 16 were eligible for meta-analysis that were captured only in 15 Arab countries, through 45 years (1969–2014). The number of retinopathy patients was 396 out of 1,931 patients with T1D. The prevalence of retinopathy was 19% (95% CI 10–28%). Substantial heterogeneity was observed (Q 240.78, p < 0.0001, I2 93.77%, 95% CI 91.35–95.52%); however, no single study considerably affected the overall pooled prevalence estimate. Conclusion: Almost one fifth of T1D patients in 15 Arab countries have diabetic retinopathy, therefore it is important to improve the care of patients with T1D and in Arab countries to avoid the development of such a devastating complication. [ABSTRACT FROM AUTHOR]

Carotenoids are isoprenoid pigments synthesized exclusively by plants and microorganisms and play critical roles in light harvesting, photoprotection, attracting pollinators and phytohormone production. In recent years, carotenoids have been used for their health benefits due to their high antioxidant activity and are extensively utilized in food, pharmaceutical, and nutraceutical industries. Regulation of carotenoid biosynthesis occurs throughout the life cycle of plants, with vibrant changes in composition based on developmental needs and responses to external environmental stimuli. With advancements in metabolic engineering techniques, there has been tremendous progress in the production of industrially valuable secondary metabolites such as carotenoids. Application of metabolic engineering and synthetic biology has become essential for the successful and improved production of carotenoids. Synthetic biology is an emerging discipline; metabolic engineering approaches may provide insights into novel ideas for biosynthetic pathways. In this review, we discuss the current knowledge on carotenoid biosynthetic pathways and genetic engineering of carotenoids to improve their nutritional value. In addition, we investigated synthetic biological approaches for the production of carotenoids. Theoretical biology approaches that may aid in understanding the biological sciences are discussed in this review. A combination of theoretical knowledge and experimental strategies may improve the production of industrially relevant secondary metabolites. [ABSTRACT FROM AUTHOR]

Objective: Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin). Materials and Methods: During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals'+H-bond+desolvo energy (EVHD) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server. Results: Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively. Conclusion: Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines. [ABSTRACT FROM AUTHOR]

In human, Wnt/β-catenin signaling pathway plays a significant role in cell growth, cell development, and disease pathogenesis. Four human (Rspo)s are known to activate canonicalWnt/β-catenin signaling pathway. Presently, (Rspo)s serve as therapeutic target for several human diseases. Henceforth, basic understanding about themolecular properties of (Rspo)s is essential. We approached this issue by interpreting the biochemical and biophysical properties alongwithmolecular evolution of (Rspo)s thorough computational algorithm methods. Our analysis shows that signal peptide length is roughly similar in (Rspo)s family along with similarity in aa distribution pattern. In Rspo3, four N-glycosylation sites were noted. All members are hydrophilic in nature and showed alike GRAVY values, approximately. Conversely, Rspo3 contains the maximum positively charged residues while Rspo4 includes the lowest. Four highly aligned blocks were recorded through Gblocks. Phylogenetic analysis shows Rspo4 is being rooted with Rspo2 and similarly Rspo3 and Rspo1 have the common point of origin. Through phylogenomics study, we developed a phylogenetic tree of sixty proteins (n= 60) with the orthologs and paralogs seed sequences. Protein-protein network was also illustrated. Results demonstrated in our study may help the future researchers to unfold significant physiological and therapeutic properties of (Rspo)s in various disease models. [ABSTRACT FROM AUTHOR]

Advancement in the gene delivery system have resulted in clinical successes in gene therapy for patients with several genetic diseases, such as immunodeficiency diseases, X-linked adrenoleukodystrophy (X-ALD) blindness, thalassemia, and many more. Among various delivery systems, liposomal mediated gene delivery route is offering great promises for gene therapy. This review is an attempt to depict a portrait about the polymer based liposomal gene delivery systems and their future applications. Herein, we have discussed in detail the characteristics of liposome, importance of polymer for liposome formulation, gene delivery, and future direction of liposome based gene delivery as a whole. [ABSTRACT FROM AUTHOR]

Plastic pollution in the current scenario requires a sustainable and eco-friendly treatment process. Single-use plastics accumulate more than recyclable plastic wastes. Low-Density Polyethylene (LDPE) is one among the plastic family with inert characteristics. The traditional method, such as landfilling, develops pollution resistant micro-organisms. It is involved in the exploitation of the native microbes to the fullest. The soil of the Kodungaiyur, agriculture site, and Otteri dumpyard were used, which resulted in nearly 22.97 ± 2.7115%, 15.91667 ± 2.73775%, and 10.74 ± 0.502925% of LDPE degradation in 30 days without nutrient supplements. The enrichment of the column by organic nutrients increased the degradation of LDPE. The column enrichment was confirmed by the sulfur oxidizing bacteria (SOB) Escherichia coli and Pseudomonas stutzeri, which produced 195 mg/mL of sulfate ions. The FTIR of the LDPE degradation showed the polymer's oxygenation, while the electron microscopic images revealed cracks. In addition, an attempt was made to fit the experimental time-series data into suitable mathematical models to look at prediction and elementary forecasting. Three mathematical models, namely the customized moving averages model (CMAM), simple liinear regression model (SLRM), and a modified linear regression model (MLRM) with a lag, were able to represent the real experimental data complementarily. [Display omitted] • Winogradsky Column (Traditional method) enhanced the LDPE degradation using native microorganisms. • The microbes in the Kodungaiyur dumpsite soil effectively degraded LDPE compared to other sampling sites. • The presence of Sulfur Oxidizing Bacteria confirms the enrichment of the column constructed. • The mathematical model represents the data complimentarily to the biological system. [ABSTRACT FROM AUTHOR]

Background : CHARGE syndrome, caused by mutation of CHD7, is inherited in an autosomal dominant manner. Heterozygous mutations in CHD7 cause CHARGE syndrome in 70% to 90% of patients which is often diagnosed with the assistance of radiologists. Currently, a vast literature exists reporting possible associations between mutations associated with CHARGE syndrome but the structural and functional impact is very less explored. Objectives: Examining the kind of effects of mutations/variations over the protein structure and function using various experimental analyses, sometimes could be time and cost consuming. In this study, we formulated a computational pipeline effectively to analyze the impact of the mutations at a structural and functional level. Methods: A series of computational analysis such as analyzing the pathogenic impact, stability impact and evolutionary impact occurred due to the mutation (201 missense mutations) as the first line of screening. As a final step, molecular dynamics simulation analysis will be carried out for the understanding of the structural impact of the mutation upon the protein. Results:Based on the availability of 3D structure and deleteriousness by in silico prediction methods,two missense mutations P2683 and R2702C were considered for further analysis. Based on Conservation and physiochemical property analysis mutant R2702C was designated as highly deleterious, highly unstable and high functional effect when compared to P2683S. Molecular Dynamics simulation analysis using Gromacs demonstrated higher RMSD and radius of gyration values in 2702C and less compactness than that of the native and P2683S structure. Conclusion: Hence forth, mutant R2702C is determined to have a huge impact to cause CHARGE syndrome in individuals harboring this mutation. [ABSTRACT FROM AUTHOR]

Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual's susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application of in silico prediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient's drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians and in silico resources in tailoring treatments to the patients' specific genotype. [ABSTRACT FROM AUTHOR]