Novel cost-effective design for bio-volatilization studies in photosynthetic microalgae exposed to arsenic with emphasis on growth and glutathione modulation.

A novel laboratory model was designed to study the arsenic (As) biotransformation potential of the microalgae Chlorella vulgaris and Nannochloropsis sp. and the cyanobacterium Anabaena doliolum. The Algae were treated under different concentrations of As(III) to check their growth, toxicity optimization, and volatilization potential. The results revealed that the alga Nannochloropsis sp. was better adopted in term of growth rate and biomass than C. vulgaris and A. doliolum. Algae grown under an As(III) environment can tolerate up to 200 µM As(III) with moderate toxicity impact. Further, the present study revealed the biotransformation capacity of the algae A. doliolum, Nannochloropsis sp., and Chlorella vulgaris. The microalga Nannochloropsis sp. volatilized a large maximum amount of As (4,393 ng), followed by C. vulgaris (4382.75 ng) and A. doliolum (2687.21 ng) after 21 days. The present study showed that As(III) stressed algae-conferred resistance and provided tolerance through high production of glutathione content and As-GSH chemistry inside cells. Thus, the biotransformation potential of algae may contribute to As reduction, biogeochemistry, and detoxification at a large scale.

Drummondin E and Flinderole B are potential inhibitors of RNA-dependent RNA polymerase of SARS-CoV-2: an in silico study.

Coronavirus disease 2019 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected 235.6 million people worldwide. In the present study, RNA-dependent RNA polymerase (RdRp) (PDB Id: 6M71) of SARS-CoV-2, an essential enzyme needed for subgenomic replication and amplification of RNA, was selected. Similar to other RdRps, it is a conserved protein and a popular target for antiviral drug therapy. Based on a computational approach, potential RdRp inhibitors were identified. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of selected molecules were determined using computation tools. The potential inhibitors were docked to the RdRp and later confirmed by Molecular Dynamics (MD) using the "Flare" module of Cresset software. Drummondin E and Flinderole B had higher drug similarity scores among the compounds selected in this study. Both these compounds are noncarcinogenic, nonirritant, nontumorigenic, and nonmutagenic. Molecular docking studies showed that both compounds can bind to RdRp. The best ligand interaction patterns were validated by MD using the "Flare" module. MD was performed for the period of 100 ns with the time step of 1 fs. The simulation results suggest that Thr-680, Arg-624, Lys-676, and Val-557 are key interacting partners in the Drummondin E-RdRp complex, while Asp-618, Asp-760, Asp-623, Arg-624, and Asp-761 are the interacting partners in the Flinderole B-RdRp complex. Based on the in silico drug-likeness score; ADMET properties; and molecular simulation result, we surmise that Flinderole B and Drummondin E could impede SARS-CoV-2 genome replication and transcription by targeting the RdRp protein.

A Review of Chemistry and Pharmacology of Piperidine Alkaloids of Pinus and Related Genera.

BACKGROUND: Pinus belongs to the family Pinaceae, represented by several species across the globe. Various parts of the plant including needles are rich in biologically active compounds, such as thunbergol, 3-carene, cembrene, α-pinene, quercetin, xanthone. Of all the alkaloids, the piperidine group is one of the important component and holds considerable medicinal importance. METHODS: The group of alkaloids was initially identified from the genus Piper through which a large variety of piperidine molecules have been extracted. The planar structure of this heterocyclic nucleus enables acetamide groups to be added at various ring configurations. RESULTS: Piperidines have gained considerable importance. The broad range of its therapeutic application has paved a way for researchers to implant the nucleus from time to time in diversified pharmacophores and establish new profile. DISCUSSION: Biological functions of piperidine metabolites have been mostly examined on a limited scale, and that most of the findings are preliminary. We have tried to present various clinical applications of piperidine alkaloids in this study that researchers have already attempted to demystify with time. CONCLUSION: We have also illustrated different types of piperidine structures and their sources in different members of the family Pinaceae with special emphasis on Pinus. Given the importance of the piperidine nucleus, the study will enable the researchers to produce scaffolds of highest therapeutic efficacy.

A knowledge-driven protocol for prediction of proteins of interest with an emphasis on biosynthetic pathways.

This protocol describes a stepwise process to identify proteins of interest from a query proteome derived from NGS data. We implemented this protocol on Moringa oleifera transcriptome to identify proteins involved in secondary metabolite and vitamin biosynthesis and ion transport. This knowledge-driven protocol identifies proteins using an integrated approach involving sensitive sequence search and evolutionary relationships. We make use of functionally important residues (FIR) specific for the query protein family identified through its homologous sequences and literature. We screen protein hits based on the clustering with true homologues through phylogenetic tree reconstruction complemented with the FIR mapping. The protocol was validated for the protein hits through qRT-PCR and transcriptome quantification. Our protocol demonstrated a higher specificity as compared to other methods, particularly in distinguishing cross-family hits. This protocol was effective in transcriptome data analysis of M. oleifera as described in Pasha et al.•Knowledge-driven protocol to identify secondary metabolite synthesizing protein in a highly specific manner.•Use of functionally important residues for screening of true hits.•Beneficial for metabolite pathway reconstruction in any (species, metagenomics) NGS data.

Dataset for the combined transcriptome assembly of M. oleifera and functional annotation.

In this paper, we present the data acquired during transcriptome analysis of the plant Moringa oleifera [1] from five different tissues (root, stem, leaf, flower and seed) by RNA sequencing. A total of 271 million reads were assembled with an N50 of 2094 bp. The combined transcriptome was assessed for transcript abundance across five tissues. The protein coding genes identified from the transcripts were annotated and used for orthology analysis. Further, enzymes involved in the biosynthesis of select medicinally important secondary metabolites, vitamins and ion transporters were identified and their expression levels across tissues were examined. The data generated by RNA sequencing has been deposited to NCBI public repository under the accession number PRJNA394193 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA394193).

The transcriptome enables the identification of candidate genes behind medicinal value of Drumstick tree (Moringa oleifera).

Moringa oleifera is a plant well-known for its nutrition value, drought resistance and medicinal properties. cDNA libraries from five different tissues (leaf, root, stem, seed and flower) of M. oleifera cultivar Bhagya were generated and sequenced. We developed a bioinformatics pipeline to assemble transcriptome, along with the previously published M. oleifera genome, to predict 17,148 gene models. Few candidate genes related to biosynthesis of secondary metabolites, vitamins and ion transporters were identified. Expressions were further confirmed by real-time quantitative PCR experiments for few promising leads. Quantitative estimation of metabolites, as well as elemental analysis, was also carried out to support our observations. Enzymes in the biosynthesis of vitamins and metabolites like quercetin and kaempferol are highly expressed in leaves, flowers and seeds. The expression of iron transporters and calcium storage proteins were observed in root and leaves. In general, leaves retain the highest amount of small molecules of interest.

Genome sequencing of herb Tulsi (Ocimum tenuiflorum) unravels key genes behind its strong medicinal properties.

BACKGROUND: Krishna Tulsi, a member of Lamiaceae family, is a herb well known for its spiritual, religious and medicinal importance in India. The common name of this plant is 'Tulsi' (or 'Tulasi' or 'Thulasi') and is considered sacred by Hindus. We present the draft genome of Ocimum tenuiflurum L (subtype Krishna Tulsi) in this report. The paired-end and mate-pair sequence libraries were generated for the whole genome sequenced with the Illumina Hiseq 1000, resulting in an assembled genome of 374 Mb, with a genome coverage of 61 % (612 Mb estimated genome size). We have also studied transcriptomes (RNA-Seq) of two subtypes of O. tenuiflorum, Krishna and Rama Tulsi and report the relative expression of genes in both the varieties. RESULTS: The pathways leading to the production of medicinally-important specialized metabolites have been studied in detail, in relation to similar pathways in Arabidopsis thaliana and other plants. Expression levels of anthocyanin biosynthesis-related genes in leaf samples of Krishna Tulsi were observed to be relatively high, explaining the purple colouration of Krishna Tulsi leaves. The expression of six important genes identified from genome data were validated by performing q-RT-PCR in different tissues of five different species, which shows the high extent of urosolic acid-producing genes in young leaves of the Rama subtype. In addition, the presence of eugenol and ursolic acid, implied as potential drugs in the cure of many diseases including cancer was confirmed using mass spectrometry. CONCLUSIONS: The availability of the whole genome of O.tenuiflorum and our sequence analysis suggests that small amino acid changes at the functional sites of genes involved in metabolite synthesis pathways confer special medicinal properties to this herb.

3PFDB+: improved search protocol and update for the identification of representatives of protein sequence domain families.

Protein domain families are usually classified on the basis of similarity of amino acid sequences. Selection of a single representative sequence for each family provides targets for structure determination or modeling and also enables fast sequence searches to associate new members to a family. Such a selection could be challenging since some of these domain families exhibit huge variation depending on the number of members in the family, the average family sequence length or the extent of sequence divergence within a family. We had earlier created 3PFDB database as a repository of best representative sequences, selected from each PFAM domain family on the basis of high coverage. In this study, we have improved the database using more efficient strategies for the initial generation of sequence profiles and implement two independent methods, FASSM and HMMER, for identifying family members. HMMER employs a global sequence similarity search, while FASSM relies on motif identification and matching. This improved and updated database, 3PFDB+ generated in this study, provides representative sequences and profiles for PFAM families, with 13 519 family representatives having more than 90% family coverage. The representative sequence is also highlighted in a two-dimensional plot, which reflects the relative divergence between family members. Representatives belonging to small families with short sequences are mainly associated with low coverage. The set of sequences not recognized by the family representative profiles, highlight several potential false or weak family associations in PFAM. Partial domains and fragments dominate such cases, along with sequences that are highly diverged or different from other family members. Some of these outliers were also predicted to have different secondary structure contents, which reflect different putative structure or functional roles for these domain sequences. Database URL: http://caps.ncbs.res.in/3pfdbplus/.