Does enterovirus 71 urge for effective vaccine control strategies? Challenges and current opinion.

Enterovirus 71 (EV71) is an infectious virus affecting all age groups of people around the world. It is one of the major aetiologic agents for HFMD (hand, foot and mouth disease) identified globally. It has led to many outbreaks and epidemics in Asian countries. Infection caused by this virus that can lead to serious psychological problems, heart diseases and respiratory issues in children younger than 10 years of age. Many studies are being carried out on the pathogenesis of the virus, but little is known. The host immune response and other molecular responses against the virus are also not clearly determined. This review deals with the interaction between the host and the EV71 virus. We discuss how the virus makes use of its proteins to affect the host's immunity and how the viral proteins help their replication. Additionally, we describe other useful resources that enable the virus to evade the host's immune responses. The knowledge of the viral structure and its interactions with host cells has led to the discovery of various drug targets for the treatment of the virus. Additionally, this review focusses on the antiviral drugs and vaccines developed by targeting various viral surface molecules during their infectious period. Furthermore, it is asserted that the improvement of prevailing vaccines will be the simplest method to manage EV71 infection swiftly. Therefore, we summarise numerous vaccines candidate for the EV71, such as the use of an inactivated complete virus, recombinant VP1 protein, artificial peptides, VLPs (viral-like particles) and live attenuated vaccines for combating the viral outbreaks promptly.

Mannose: a potential saccharide candidate in disease management.

There are a plethora of antibiotic resistance cases and humans are marching towards another big survival test of evolution along with drastic climate change and infectious diseases. Ever since the first antibiotic [penicillin], and the myriad of vaccines, we were privileged to escape many infectious disease threats. The survival technique of pathogens seems rapidly changing and sometimes mimicking our own systems in such a perfect manner that we are left unarmed against them. Apart from searching for natural alternatives, repurposing existing drugs more effectively is becoming a familiar approach to new therapeutic opportunities. The ingenious use of revolutionary artificial intelligence-enabled drug discovery techniques is coping with the speed of such alterations. D-Mannose is a great hope as a nutraceutical in drug discovery, against CDG, diabetes, obesity, lung disease, and autoimmune diseases and recent findings of anti-tumor activity make it interesting along with its role in drug delivery enhancing techniques. A very unique work done in the present investigation is the collection of data from the ChEMBL database and presenting the targetable proteins on pathogens as well as on humans. It shows Mannose has 50 targets and the majority of them are on human beings. The structure and conformation of certain monosaccharides have a decisive role in receptor pathogen interactions and here we attempt to review the multifaceted roles of Mannose sugar, its targets associated with different diseases, as a natural molecule having many success stories as a drug and future hope for disease management.

A core and pan gene map of Leptospira genus and its interactions with human host.

Leptospira species are the etiological agent of an emerging zoonotic disease known as "Leptospirosis" that substantially affects both human health and economy across the globe. Despite the global importance of the disease, pathogenetic features, host-adaptation and proper diagnosis of this bacteria remains lacking. To accomplish these gaps, pan-genome of Leptospira genus was explored in the present study. The pan-genome of Leptospira genus was comprised of core (692) and accessory parts (softcore:1804, shell:6432, cloud:16,600). The functional analysis revealed the abundancy of "Translation, ribosomal structure and biogenesis" COG class in core-genes; whereas in accessory parts, genes involved in signal transduction was the most abundant. Furthermore, pathogen-host interaction (PHI) analysis of core and accessory proteins with human proteins showed the presence of a total of 599 and 510 interactions, respectively. There were eight hubs in core PHI network and five hubs in PHI network of accessory proteins. The human's proteins involved in these interactions were found functionally enriched in metabolic processes, responses to stimulus and immune system processes. Further, pan-genome based phylogeny separated the Leptospira genus in three major clades (belonging to P1, P2 and S) which relates with their pathogenicity level. Additionally, pathogenic and saprophytic clade specific genes of Leptospira have also been identified and functionally annotated for COG, KEGG and virulence factors. The results revealed the presence of 102 pathogenic and 215 saprophytic group specific gene clusters. The COG functional annotation of pathogen specific genes showed that defence mechanism followed by signal transduction mechanisms category were most significantly enriched COG categories; whereas in saprophytic group, signal transduction mechanisms was the most abundant COG, suggesting their role in adaptation and hence important for microbe's evolution and survival. In conclusion, this study provides a new insight of genomic features of Leptospira genus which may further be implemented for development of better control actions of the disease.

miRDetect: A combinatorial approach for automated detection of novel miRNA precursors from plant EST data using homology and Random Forest classification.

Identification of microRNAs from plants is a crucial step for understanding the mechanisms of pathways and regulation of genes. A number of tools have been developed for the detection of microRNAs from small RNA-seq data. However, there is a lack of pipeline for detection of miRNA from EST dataset even when a huge resource is publicly available and the method is known. Here we present miRDetect, a python implementation to detect novel miRNA precursors from plant EST data using homology and machine learning approach. 10-fold cross validation was applied to choose best classifier based on ROC, accuracy, MCC and F1-scores using 112 features. miRDetect achieved a classification accuracy of 93.35% on a Random Forest classifier and outperformed other precursor detection tools in terms of performance. The miRDetect pipeline aids in identifying novel plant precursors using a mixed approach and will be helpful to researchers with less informatics background.

Cross-Kingdom Regulation of Putative miRNAs Derived from Happy Tree in Cancer Pathway: A Systems Biology Approach.

MicroRNAs (miRNAs) are well-known key regulators of gene expression primarily at the post-transcriptional level. Plant-derived miRNAs may pass through the gastrointestinal tract, entering into the body fluid and regulate the expression of endogenous mRNAs. Camptotheca acuminata, a highly important medicinal plant known for its anti-cancer potential was selected to investigate cross-kingdom regulatory mechanism and involvement of miRNAs derived from this plant in cancer-associated pathways through in silico systems biology approach. In this study, total 33 highly stable putative novel miRNAs were predicted from the publically available 53,294 ESTs of C. acuminata, out of which 14 miRNAs were found to be regulating 152 target genes in human. Functional enrichment, gene-disease associations and network analysis of these target genes were carried out and the results revealed their association with prominent types of cancers like breast cancer, leukemia and lung cancer. Pathways like focal adhesion, regulation of lipolysis in adipocytes and mTOR signaling pathways were found significantly associated with the target genes. The regulatory network analysis showed the association of some important hub proteins like GSK3B, NUMB, PEG3, ITGA2 and DLG2 with cancer-associated pathways. Based on the analysis results, it can be suggested that the ingestion of the C. acuminata miRNAs may have a functional impact on tumorigenesis in a cross-kingdom way and may affect the physiological condition at genetic level. Thus, the predicted miRNAs seem to hold potentially significant role in cancer pathway regulation and therefore, may be further validated using in vivo experiments for a better insight into their mechanism of epigenetic action of miRNA.

Epitopes mapping for identification of potential cross-reactive peptide against leptospirosis.

Leptospira, the pathogenic helical spirochetes that cause leptospirosis, is an emerging zoonotic disease with effective dissemination tactics in the host and can infect humans and animals with moderate or severe illnesses. Thus, peptide-based vaccines may be the most effective strategy to manage the immune response against Leptospira to close these gaps. In the current investigation, highly immunogenic proteins from the proteome of Leptospira interorgan serogroup Icterohaemorrhagie serovar Lai strain 56601 were identified using immunoinformatic methods. It was discovered that the conserved and most immunogenic outer membrane Lepin protein was both antigenic and non-allergenic by testing 15 linear B-cells and the ten best T-cell (Helper-lymphocyte (HTL) with the most significant number of HLA-DR binding alleles and the eight cytotoxic T lymphocyte (CTL)) epitopes. Furthermore, a 3D structural model of CTL epitopes was created using the Pep-Fold3 platform. Using the Autodock 4.2 docking server, research was conducted to determine how well the top-ranked CTL peptide models attach to HLA-A*0201 (PDB ID: 4U6Y). With HLA-A*0201, the epitope SSGTGNLHV binds with a binding energy of -1.29 kcal/mol. Utilizing molecular dynamics modeling, the projected epitope-allele docked complex structure was optimized, and the stability of the complex system was assessed. Therefore, this epitope can trigger an immunological response and produce effective Leptospira vaccine candidates. Overall, this study offers a unique vaccination candidate and may encourage additional research into leptospirosis vaccines.Communicated by Ramaswamy H. Sarma.

Artificial Neural Network-Based Study Predicts GS-441524 as a Potential Inhibitor of SARS-CoV-2 Activator Protein Furin: a Polypharmacology Approach.

Furin, a pro-protein convertase, plays a significant role as a biological scissor in bacterial, viral, and even mammalian substrates which in turn decides the fate of many viral and bacterial infections along with the numerous ailments caused by cancer, diabetes, inflammations, and neurological disorders. In the wake of the current pandemic caused by the virus SARS-CoV-2, furin has become the center of attraction for researchers as the spike protein contains a polybasic furin cleavage site. In the present work, we have searched for novel inhibitors against this interesting human target from FDA-approved antiviral. To enhance the selection of new inhibitors, we employed Kohonen's artificial neural network-based self-organizing maps for ligand-based virtual screening. Promising results were obtained which can help in drug repurposing and network pharmacology studies can address the errors generated due to promiscuity/polypharmacology. We found 15 existing FDA antiviral drugs having the potential to inhibit furin. Among these, six compounds have targets on important human proteins (LDLR, FCGR1A, PCK1, TLR7, DNA, and PNP). The role of these 15 drugs inhibiting furin can be established by studying further on patients infected with number of viruses including SARS-CoV-2. Here we propose two promising candidate FDA drugs GS-441524 and Grazoprevir (MK-5172) for repurposing as inhibitors of furin. The best results were observed with GS-441524.

Unravelling Vitamin B12 as a potential inhibitor against SARS-CoV-2: A computational approach.

The new severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) is the etiological agent of Coronavirus disease 2019 (COVID-19), which becomes an eventual pandemic outbreak. Lack of proper therapeutic management has accelerated the researchers to repurpose existing drugs with known preclinical and toxicity profiles, which can easily enter Phase 3 or 4 or can be used directly in clinical settings. Vitamins are necessary nutrients for cell growth, function, and development. Furthermore, they play an important role in pathogen defence via cell-mediated responses and boost immunity. Using a computational approach, we intend to identify the probable inhibitory effect of all vitamins on the drug targets of COVID-19. The computational analysis demonstrated that vitamin B12 resulted in depicting suitable significant binding with furin, RNA dependent RNA polymerase (RdRp), Main proteases (Mpro), ORF3a and ORF7a and Vitamin D3 with spike protein and vitamin B9 with non structural protein 3 (NSP3). A detailed examination of vitamins suggests that vitamin B12 may be the component that reduces virulence by blocking furin which is responsible for entry of virus in the host cell. Details from the Molecular Dynamics (MD) simulation study aided in determining vitamin B12 as a possible furin inhibitor.

High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next-generation sequencing.

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance, and determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding, and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for the detection of SARS-CoV-2, with an additional advantage of enabling the genetic epidemiology of SARS-CoV-2.

Mycobacterium lepromatosis genome exhibits unusually high CpG dinucleotide content and selection is key force in shaping codon usage.

Mycobacterium lepromatosis was identified as a causative agent for leprosy in the year 2008 in the United States and later more cases were identified in Canada, Singapore, Brazil, and Myanmar. It is known to cause diffuse lepromatosis leprosy among humans. Since it is invasive, the mortality rates are higher in comparison to the M. leprae. At genomic level, there exists 90.9% similarity between M. lepromatosis and M. leprae. Codon usage analysis based on analyses of 228 coding sequences (CDSs) of M. lepromatosis, revealed that the genome is GC rich. Among the total 16 dinucleotides, CpG dinucleotide possesses the highest dinucleotide frequency in M. lepromatosis, that is strikingly an unobvious observation since higher CpG is associated with higher proinflammatory cytokine production and NF-κB activation that eventually leads to high pathogenicity. To evade immune response, CpG content is generally less in pathogens. The unusually high CpG content can be explained by the fact that the nucleotide composition of M. lepromatosis is CG rich. Various forces interplay to shape codon usage pattern of any organism including selection; mutation, nucleotide composition as well as GC biased gene conversion. To understand the interplay between various forces; neutrality, parity, Nc-GC3 (Effective number of codons-GC content at 3rd position of the codon), aromaticity (AROMO) and the general average hydropathicity score (GRAVY) analyses have been carried out. The analyses revealed that selection force is the major contributory force. Along with the selection; mutation, nucleotide composition as well as GC biased gene conversion also play role in shaping codon usage bias in M. lepromatosis. This is the first report on the codon usage in M. lepromatosis.

Vaccine Design Against Leptospirosis Using an Immunoinformatic Approach.

Vaccination is the best way to prevent the spread of emerging or reemerging infectious disease. Current research for vaccine development is mainly focused on recombinant-, subunit-, and peptide-based vaccine. At this point, immunoinformatics has been proven as a powerful method for identification of potential vaccine candidates, by analyzing immunodominat B- and T-cell epitopes. This method can reduce the time and cost of experiment to a great extent, by reducing the number of vaccine candidates for experimental testing for their efficacy. This chapter describes the use of immunoinformatics and molecular docking methods to screen potential vaccine candidates by taking Leptospira as a model.

Whole genome sequencing and de novo assembly of three virulent Indian isolates of Leptospira.

Leptospirosis is a re-emerging bacterial zoonosis caused by pathogenic Leptospira, with a worldwide distribution and becoming a major public health concern. Prophylaxis of this disease is difficult due to several factors such as non-specific variable clinical manifestation, presence of a large number of serovar, species and asymptomatic reservoir hosts, lack of proper diagnostics and vaccines. Despite its global importance and severity of the disease, knowledge about the molecular mechanism of pathogenesis and evolution of pathogenic species of Leptospira remains limited. In this study, we sequenced and analyzed three highly pathogenic species of Indian isolates of Leptospira (interrogans, santarosai, and kirschneri). Additionally, we identified some virulence-related and CRISPR-Cas genes. The virulent analysis showed 232 potential virulence factors encoding proteins in L. interrogans strain Salinem and L. santarosai strain M-4 genome. While the genome of L. kirschneri strain Wumalasena was predicted to encode 198 virulence factor proteins. The variant calling analysis revealed 1151, 19,786, and 22,996 single nucleotide polymorphisms (SNPs) for L. interrogans strain Salinem, L. kirschneri strain Wumalasena and L. santarosai strain M-4, respectively, with a maximum of 5315 missense and 12,221 synonymous mutations for L. santarosai strain M-4. The structural analyses of genomes indicated potential evidence of inversions and structural rearrangment in all three genomes. The availability of these genome sequences and in silico analysis of Leptospira will provide a basis for a deeper understanding of their molecular diversity and pathogenesis mechanism, and further pave a way towards proper management of the disease.

Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory.

Leptospirosis is the most emerging zoonotic disease of epidemic potential caused by pathogenic species of Leptospira. The bacterium invades the host system and causes the disease by interacting with the host proteins. Analyzing these pathogen-host protein interactions (PHPIs) may provide deeper insight into the disease pathogenesis. For this analysis, inter-species as well as intra-species protein interactions networks of Leptospira interrogans and human were constructed and investigated. The topological analyses of these networks showed lesser connectivity in inter-species network than intra-species, indicating the perturbed nature of the inter-species network. Hence, it can be one of the reasons behind the disease development. A total of 35 out of 586 PHPIs were identified as key interactions based on their sub-cellular localization. Two outer membrane proteins (GpsA and MetXA) and two periplasmic proteins (Flab and GlyA) participating in PHPIs were found conserved in all pathogenic, intermediate and saprophytic spp. of Leptospira. Furthermore, the bacterial membrane proteins involved in PHPIs were found playing major roles in disruption of the immune systems and metabolic processes within host and thereby causing infectious disease. Thus, the present results signify that the membrane proteins participating in such interactions hold potential to serve as effective immunotherapeutic candidates for vaccine development.

Author Correction: Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Analysis of Nipah Virus Codon Usage and Adaptation to Hosts.

A recent outbreak of Nipah virus (NiV) in India has caused 17 deaths in people living in districts of Kerala state. Its zoonotic nature, as well as high rate of human-to-human transmission, has led researchers worldwide to work toward understanding the different aspects of the NiV. We performed a codon usage analysis, based on publicly available nucleotide sequences of NiV and its host adaptation, along with other members of the Henipavirus genus in ten hosts. NiV genome encodes nine open reading frames; and overall, no significant bias in codon usage was observed. Aromaticity of proteins had no impact on codon usage. An analysis of preferred codons used by NiV and the tRNA pool in human cells indicated that NiV prefers codons from a suboptimal anticodon tRNA pool. We observed that codon usage by NiV is mainly constrained by compositional and selection pressures, not by mutational forces. Parameters that define NiV and host relatedness in terms of codon usage were analyzed, with a codon adaptation index (CAI), relative codon deoptimization index (RCDI), and similarity index calculations; which indicated that, of all hosts analyzed, NiV was best adapted to African green monkeys. A comparative analysis based on the relative codon deoptimization index (RCDI) for host adaptation of NiV, Hendra virus (HeV), Cedar virus (CedV), and Hendra like Mojiang virus (MojV) revealed that except for dogs and ferrets, all evaluated hosts were more susceptible to HeV than NiV.

Differential gene expression analysis of HNSCC tumors deciphered tobacco dependent and independent molecular signatures.

Head and neck cancer is the sixth most common cancer worldwide, with tobacco as the leading cause. However, it is increasing in non-tobacco users also, hence limiting our understanding of its underlying molecular mechanisms. RNA-seq analysis of cancers has proven as effective tool in understanding disease etiology. In the present study, RNA-Seq of 86 matched Tumor/Normal pairs, of tobacco smoking (TOB) and non-smokers (N-TOB) HNSCC samples analyzed, followed by validation on 375 similar datasets. Total 2194 and 2073 differentially expressed genes were identified in TOB and N-TOB tumors, respectively. GO analysis found muscle contraction as the most enriched biological process in both TOB and N-TOB tumors. Pathway analysis identified muscle contraction and salivary secretion pathways enriched in both categories, whereas calcium signaling and neuroactive ligand-receptor pathway was more enriched in TOB and N-TOB tumors respectively. Network analysis identified muscle development related genes as hub node i. e. ACTN2, MYL2 and TTN in both TOB and N-TOB tumors, whereas EGFR and MYH6, depicts specific role in TOB and N-TOB tumors. Additionally, we found enriched gene networks possibly be regulated by tumor suppressor miRNAs such as hsa-miR-29/a/b/c, hsa-miR-26b-5p etc., suggestive to be key riboswitches in regulatory cascade of HNSCC. Interestingly, three genes PKLR, CST1 and C17orf77 found to show opposite regulation in each category, hence suggested to be key genes in separating TOB from N-TOB tumors. Our investigation identified key genes involved in important pathways implicated in tobacco dependent and independent carcinogenesis hence may help in designing precise HNSCC diagnostics and therapeutics strategies.

De novo transcriptome of Gymnema sylvestre identified putative lncRNA and genes regulating terpenoid biosynthesis pathway.

Gymnema sylvestre is a highly valuable medicinal plant in traditional Indian system of medicine and used in many polyherbal formulations especially in treating diabetes. However, the lack of genomic resources has impeded its research at molecular level. The present study investigated functional gene profile of G. sylvestre via RNA sequencing technology. The de novo assembly of 88.9 million high quality reads yielded 23,126 unigenes, of which 18116 were annotated against databases such as NCBI nr database, gene ontology (GO), KEGG, Pfam, CDD, PlantTFcat, UniProt & GreeNC. Total 808 unigenes mapped to 78 different Transcription Factor families, whereas 39 unigenes assigned to CYP450 and 111 unigenes coding for enzymes involved in the biosynthesis of terpenoids including transcripts for synthesis of important compounds like Vitamin E, beta-amyrin and squalene. Among them, presence of six important enzyme coding transcripts were validated using qRT-PCR, which showed high expression of enzymes involved in methyl-erythritol phosphate (MEP) pathway. This study also revealed 1428 simple sequence repeats (SSRs), which may aid in molecular breeding studies. Besides this, 8 putative long non-coding RNAs (lncRNAs) were predicted from un-annotated sequences, which may hold key role in regulation of essential biological processes in G. sylvestre. The study provides an opportunity for future functional genomic studies and to uncover functions of the lncRNAs in G. sylvestre.

Exploring Leptospiral proteomes to identify potential candidates for vaccine design against Leptospirosis using an immunoinformatics approach.

Leptospirosis is the most widespread zoonotic disease, estimated to cause severe infection in more than one million people each year, particularly in developing countries of tropical areas. Several factors such as variable and nonspecific clinical manifestation, existence of large number of serovars and asymptomatic hosts spreading infection, poor sanitation and lack of an effective vaccine make prophylaxis difficult. Consequently, there is an urgent need to develop an effective vaccine to halt its spread all over the world. In this study, an immunoinformatics approach was employed to identify the most vital and effective immunogenic protein from the proteome of Leptospira interrogans serovar Copenhageni strain L1-130 that may be suitable to stimulate a significant immune response aiding in the development of peptide vaccine against leptospirosis. Both B-cell and T-cell (Helper T-lymphocyte (HTL) and cytotoxic T lymphocyte (CTL)) epitopes were predicted for the conserved and most immunogenic outer membrane lipoprotein. Further, the binding interaction of CTL epitopes with Major Histocompatibility Complex class I (MHC-I) was evaluated using docking techniques. A Molecular Dynamics Simulation study was also performed to evaluate the stability of the resulting epitope-MHC-I complexes. Overall, this study provides novel vaccine candidates and may prompt further development of vaccines against leptospirosis.

LeptoDB: an integrated database of genomics and proteomics resource of Leptospira.

Leptospirosis is a potentially fatal zoo-anthroponosis caused by pathogenic species of Leptospira belonging to the family of Leptospiraceae, with a worldwide distribution and effect, in terms of its burden and risk to human health. The 'LeptoDB' is a single window dedicated architecture (5 948 311 entries), modeled using heterogeneous data as a core resource for global Leptospira species. LeptoDB facilitates well-structured knowledge of genomics, proteomics and therapeutic aspects with more than 500 assemblies including 17 complete and 496 draft genomes encoding 1.7 million proteins for 23 Leptospira species with more than 250 serovars comprising pathogenic, intermediate and saprophytic strains. Also, it seeks to be a dynamic compendium for therapeutically essential components such as epitope, primers, CRISPR/Cas9 and putative drug targets. Integration of JBrowse provides elaborated locus centric description of sequence or contig. Jmol for structural visualization of protein structures, MUSCLE for interactive multiple sequence alignment annotation and analysis. The data on genomic islands will definitely provide an understanding of virulence and pathogenicity. Phylogenetics analysis integrated suggests the evolutionary division of strains. Easily accessible on a public web server, we anticipate wide use of this metadata on Leptospira for the development of potential therapeutics.Database URL: http://leptonet.org.in.

Terpenoid compositions and antioxidant activities of two Indian valerian oils from the Khasi Hills of north-east India.

The volatile constituents of Valeriana jatamansi Jones and V. hardwickii Wall. (Valerianaceae) collected from the Khasi Hills of north-east India were analyzed by GC and GC/MS. Twenty-seven and twenty-one compounds were characterized and identified from V. jatamansi and V. hardwickii samples, representing 90.6% and 82.7% of the total oil, respectively. Sesquiterpenes were shown to be the main constituents in both the oil samples. Maaliol (26.1%), patchouli alcohol (9.3%) and a-gurjunene (8.7%) were the major components of V. jatamansi oil, whereas valeracetate (21.3%), methyl linoleate (14.1%), bornyl acetate (13.8%) and cuparene (7.1%) were the main constituents of V. hardwickii oil. Both Indian valerian essential oils were studied for their antioxidant activities using the free radical-scavanging activity (DPPH) and ferric reducing antioxidant power (FRAP) assays. V. hardwickii oil exhibited a higher antioxidant capacity than V. jatamansi in both assays. For both the valerian oil samples, there was a concentration-dependent increase in free radical scavenging activity and ferric reducing capacity. Both valerian oils and their ingredients are potential sources of natural antioxidants.