Identification of microRNAs from medicinal plant Murraya Koenigii by high-throughput sequencing and their functional implications in secondary metabolite biosynthesis

MicroRNAs (miRNAs) are small noncoding RNA molecules that play crucial post-transcriptional regulatory roles in plants, including development and stress-response signaling. However, information about their involvement in secondary metabolism is still limited. Murraya koenigii is a popular medicinal plant, better known as curry leaves, that possesses pharmaceutically active secondary metabolites. The present study utilized high-throughput sequencing technology to investigate the miRNA profile of M. koenigii and their association with secondary metabolite biosynthesis. A total of 343,505 unique reads with lengths ranging from 16 to 40 nt were obtained from the sequencing data, among which 142 miRNAs were identified as conserved and 7 as novel miRNAs. Moreover, 6078 corresponding potential target genes of M. koenigii miRNAs were recognized in this study. Interestingly, several conserved and novel miRNAs of M. koenigii were found to target key enzymes of the terpenoid backbone and the flavonoid biosynthesis pathways. Furthermore, to validate the sequencing results, the relative expression of eight randomly selected miRNAs was determined by qPCR. To the best of our knowledge, this is the first report of the M. koenigii miRNA profile that may provide useful information for further elucidation of the involvement of miRNAs in secondary metabolism. These findings might be crucial in the future to generate artificial-miRNA-based, genetically engineered M. koenigii plants for the overproduction of medicinally highly valuable secondary metabolites.publishedVersio

Updated Guidance Regarding The Risk ofAllergic Reactions to COVID-19 Vaccines and Recommended Evaluation and Management: A GRADE Assessment, and International Consensus Approach

This guidance updates 2021 GRADE (Grading of Recommendations Assessment, Development and Evaluation) recommendations regarding immediate allergic reactions following coronavirus disease 2019 (COVID-19) vaccines and addresses revaccinating individuals with first-dose allergic reactions and allergy testing to determine revaccination outcomes. Recent meta-analyses assessed the incidence of severe allergic reactions to initial COVID-19 vaccination, risk of mRNA-COVID-19 revaccination after an initial reaction, and diagnostic accuracy of COVID-19 vaccine and vaccine excipient testing in predicting reactions. GRADE methods informed rating the certainty of evidence and strength of recommendations. A modified Delphi panel consisting of experts in allergy, anaphylaxis, vaccinology, infectious diseases, emergency medicine, and primary care from Australia, Canada, Europe, Japan, South Africa, the United Kingdom, and the United States formed the recommendations. We recommend vaccination for persons without COVID-19 vaccine excipient allergy and revaccination after a prior immediate allergic reaction. We suggest against \u3e 15-minute postvaccination observation. We recommend against mRNA vaccine or excipient skin testing to predict outcomes. We suggest revaccination of persons with an immediate allergic reaction to the mRNA vaccine or excipients be performed by a person with vaccine allergy expertise in a properly equipped setting. We suggest against premedication, split-dosing, or special precautions because of a comorbid allergic history

Metallomic profiling and linkage map analysis of early Parkinson’s disease: a new insight to aluminum marker for the possible diagnosis

Background: Parkinson’s disease (PD) is the most common neurodegenerative disorder. The diagnosis of PD is challenging and currently none of the biochemical tests have proven to help in diagnosis. Serum metallomic analysis may suggest the possibility of diagnosis of PD. Methodology/Results: The metallomic analysis was targeted on 31 elements obtained from 42 healthy controls and 45 drug naive PD patients using ICP-AES and ICP-MS to determine the concentration variations of elements between PD and normal. The targeted metallomic analysis showed the significant variations in 19 elements of patients compared to healthy control (p,0.04). The partial least squares discriminant analysis (PLS-DA) showed aluminium, copper, iron, manganese and zinc are the key elements, contributes the separation of PD patients from control samples. The correlation coefficient analysis and element-element ratio confirm the imbalance of inter-elements relationship in PD patients ’ serum. Furthermore, elements linkage map analysis showed aluminium is a key element involved in triggering of phosphorus, which subsequently lead to imbalance of homeostatic in PD serum. The execution of neural network using elements concentrations provides 95 % accuracy in detection of disease. Conclusions/Significance: These results suggest that there is a disturbance in the elements homeostasis and inter-elements relationship in PD patients ’ serum. The analysis of serum elements helps in linking the underlying cellular processes such a

PLS-DA analysis of normal and PD serum elements.

<p>PLS-DA score's plot displays a significant separation between control subjects (n = 42) and unmedicated PD patients (n = 45) using complete digital maps. The observations coded according to class membership: triangle is normal and rhombus is PD. Each data point on a plot represents one individual.</p

Machine learning algorithm.

<p>The performance of 21 machine learning algorithms for the prediction of HIA (panel A) and caco-2 (panel B) data sets were measured as averaged accuracy of 10-fold cross-validation analysis (the algorithm showing highest predictive accuracy indicated in blue). The predictive accuracy of the logistic algorithm was based on individual descriptors compared with the combined descriptors of HIA (panel C) and caco-2 data sets (panel D).</p

Systems biological framework for developing molecular descriptors connectivity maps.

<p>The framework consists of five major components: (i) compilation and curation of data sets, (ii) generation of descriptors (iii) multivariate analysis (iv) machine learning and (v) statistical analysis. The first component takes the inputs from literature and outputs the curated data sets. The second component takes the input from the curated data sets and generates molecular descriptors using E-dragon software. In the third component, HIA and caco-2 permeability data sets were subjected to multivariate analysis to obtain the most contributing descriptors involved in classification of groups against each data set. In the fourth component, the contributing descriptors were subjected to machine learning approach to determine the predictive accuracy of the models. The final statistical component was generated for the descriptors associated between data sets showing the interdependence between the descriptors.</p

Systems Biological Approach of Molecular Descriptors Connectivity: Optimal Descriptors for Oral Bioavailability Prediction

<div><h3>Background</h3><p>Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. <em>In silico</em> prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.</p> <h3>Results</h3><p>The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/−bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.</p> <h3>Conclusion</h3><p>The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.</p> </div

Biomarker detection: Elements which are dominating the separation of disease from normal.

<p>The loading coefficient map showing that aluminum, copper, iron, manganese, and zinc were predominantly responsible for the classification of groups. The blackened triangle represents elements; grey circle represents normal (N) and Parkinson's disease (P).</p

Neural network predication.

<p>Neural network classification of 23 individuals (x-axis) with known clinical information. Values (y-axis) are predicted over the trained network and are 0.1 to 1; values (x) ≤0.54 reflect a neural-network classification of “normal,” and values (x) ≥0.55 reflect a neural-network classification of PD. Individuals denoted by a triangle  =  normal, circle  =  PD and square  =  misdiagnosed individual.</p