Priestia megaterium Metabolism: Isolation, Identification of Naringenin Analogues and Genes Elevated Associated with Nanoparticle Intervention.

The impact of gold nanoparticles (AuNPs) on the biosynthetic manipulation of Priestia megaterium metabolism where an existing gene cluster is enhanced to produce and enrich bioactive secondary metabolites has been studied previously. In this research, we aimed to isolate and elucidate the structure of metabolites of compounds 1 and 2 which have been analyzed previously in P. megaterium crude extract. This was achieved through a PREP-ODS C18 column with an HPLC-UV/visible detector. Then, the compounds were subjected to nuclear magnetic resonance (NMR), electrospray ionization mass spectrometry (ESI-MS), and Fourier-transform infrared spectroscopy (FT-IR) techniques. Furthermore, bioinformatics and transcriptome analysis were used to examine the gene expression for which the secondary metabolites produced in the presence of AuNPs showed significant enhancement in transcriptomic responses. The metabolites of compounds 1 and 2 were identified as daidzein and genistein, respectively. The real-time polymerase chain reaction (RT-PCR) technique was used to assess the expression of three genes (csoR, CHS, and yjiB) from a panel of selected genes known to be involved in the biosynthesis of the identified secondary metabolites. The expression levels of two genes (csoR and yijB) increased in response to AuNP intervention, whereas CHS was unaffected.

Draft genome sequence of potato crop bacterial isolates and nanoparticles-intervention for the induction of secondary metabolites biosynthesis.

Introduction: Insights about the effects of gold nanoparticles (AuNPs) on the biosynthetic manipulation of unknown microbe secondary metabolites could be a promising technique for prospective research on nano-biotechnology. Aim: In this research, we aimed to isolate a fresh, non-domesticated unknown bacterium strain from a common scab of potato crop located in Saudi Arabia and study the metabolic profile. Methodology: This was achieved through genomic DNA (gDNA) sequencing using Oxford Nanopore Technology. The genomic data were subjected to several bioinformatics tools, including canu-1.9 software, Prokka, DFAST, Geneious Prime, and AntiSMASH. We exposed the culture of the bacterial isolate with different concentrations of AuNPs and investigated the effects of AuNPs on secondary metabolites biosynthesis using several analytical techniques. Furthermore, Tandem-mass spectrometric (MS/MS) technique was optimized for the characterization of several significant sub-classes. Results: The genomic draft sequence assembly, alignment, and annotation have verified the bacterial isolate as Priestia megaterium. This bacterium has secondary metabolites related to different biosynthetic gene clusters. AuNPs intervention showed an increase in the production of compounds with the molecular weights of 254 and 270 Da in a direct-dependent manner with the increase of the AuNPs concentrations. Conclusion: The increase in the yields of compound 1 and 2 concomitantly with the increase in the concentration of the added AuNPs provide evidences about the effects of nanoparticles on the biosynthesis of the secondary metabolites. It contributes to the discovery of genes involved in different biosynthetic gene clusters (BGCs) and prediction of the structures of the natural products.

Body composition in very preterm infants before discharge is associated with macronutrient intake.

Very preterm infants experience poor postnatal growth relative to intra-uterine growth rates but have increased percentage body fat (%fat). The aim of the present study was to identify nutritional and other clinical predictors of infant %fat, fat mass (FM) (g) and lean mass (LM) (g) in very preterm infants during their hospital stay. Daily intakes of protein, carbohydrate, lipids and energy were recorded from birth to 34 weeks postmenstrual age (PMA) in fifty infants born <32 weeks. Clinical illness variables and anthropometric data were also collected. Body composition was assessed at 34-37 weeks PMA using the PEA POD Infant Body Composition System. Multiple regression analysis was used to identify independent predictors of body composition (%fat, FM or LM). Birth weight, birth weight z-score and PMA were strong positive predictors of infant LM. After adjustment for these factors, the strongest nutrient predictors of LM were protein:carbohydrate ratios (102-318 g LM/0·1 increase in ratio, P = 0·006-0·015). Postnatal age (PNA) and PMA were the strongest predictors of infant FM or %fat. When PNA and PMA were accounted for a higher intake of energy (-1·41 to -1·61 g FM/kJ per kg per d, P = 0·001-0·012), protein (-75·5 to -81·0 g FM/g per kg per d, P = 0·019-0·038) and carbohydrate (-27·2 to -30·0 g FM/g per kg per d, P = 0·012-0·019) were associated with a lower FM at 34-37 weeks PMA. Higher intakes of energy, protein and carbohydrate may reduce fat accumulation in very preterm infants until at least 34-37 weeks PMA.