Biology subjects

Kaur, R.

Publications and source records attributed to Kaur, R..

52 records · Page 3Linked to original sources

bioRxiv2023

Harnessing genetic variations improving seedling vigor for successful crop establishment in deep sown direct-seeded rice

Improving seedling vigour remains key objective for breeders working with direct-seeded rice (DSR). To understand the genetic control of seedling vigour in deep sown DSR, combined genome-wide association mapping, linkage mapping, fine mapping, RNA-sequencing to identify candidate genes and validation of putative candidate genes was performed. Significant phenotypic variations were observed among genotypes in both F3:4:5 and BC2F2:3 populations. The mesocotyl length showed significant positive correlation with %germination, root and shoot length. The 3.79 Mb region on chromosome 7 reported to be associated with mesocotyl elongation. RNA-seq data and RT-PCR results identified and validated seven potential candidate genes. The potential candidate gene, LOC_Os07g25150 codes for Myb-30-related transcription factor. The LOC_Os07g25150 codes for Myb 30-related transcription factor, LOC_Os07g17689 annotated as protein translation factor, LOC_Os07g17770 as tryptophanyl-tRNA synthetase, LOC_Os07g15440 as alanyl-tRNA synthetase family protein, LOC_Os07g24100 as retrotransposon protein and the remaining others as expressed proteins. The antagonistic relationship between Myb-30 and ethylene-mediated signalling (EIN-3) in regulating mesocotyl elongation have been observed. The functional characterization and knocking of LOC_Os07g25150 codes for Myb 30-related transcription factor may provide better understanding of the mechanism behind mesocotyl elongation. The four promising introgression lines with longer mesocotyl length, longer root length and semi-dwarf plant height have been identified.

genomics↗

bioRxiv2022

Application of Aligned-UMAP to longitudinal biomedical studies

Longitudinal multi-dimensional biological datasets are ubiquitous and highly abundant. These datasets are essential to understanding disease progression, identifying subtypes, and drug discovery. Discovering meaningful patterns or disease pathophysiologies in these datasets is challenging due to their high dimensionality, making it difficult to visualize hidden patterns. Several methods have been developed for dimensionality reduction, but they are limited to cross-sectional datasets. Recently proposed Aligned-UMAP, an extension of the UMAP algorithm, can visualize high-dimensional longitudinal datasets. In this work, we applied Aligned-UMAP on a broad spectrum of clinical, imaging, proteomics, and single-cell datasets. Aligned-UMAP reveals time-dependent hidden patterns when color-coded with the metadata. We found that the algorithm parameters also play a crucial role and must be tuned carefully to utilize the algorithms potential fully. Altogether, based on its ease of use and our evaluation of its performance on different modalities, we anticipate that Aligned-UMAP will be a valuable tool for the biomedical community. We also believe our benchmarking study becomes more important as more and more high-dimensional longitudinal data in biomedical research becomes available. Highlights- explored the utility of Aligned-UMAP in longitudinal biomedical datasets - offer insights on optimal uses for the technique - provide recommendations for best practices In BriefHigh-dimensional longitudinal data is prevalent yet understudied in biological literature. High-dimensional data analysis starts with projecting the data to low dimensions to visualize and understand the underlying data structure. Though few methods are available for visualizing high dimensional longitudinal data, they are not studied extensively in real-world biological datasets. A recently developed nonlinear dimensionality reduction technique, Aligned-UMAP, analyzes sequential data. Here, we give an overview of applications of Aligned-UMAP on various biomedical datasets. We further provide recommendations for best practices and offer insights on optimal uses for the technique.

bioinformatics↗

bioRxiv2022

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

BackgroundThe clinical manifestations of Parkinsons disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need. Methods and FindingsWe used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinsons Disease Progression Marker Initiative (PPMI) (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinsons Disease Biomarker Program (PDBP) (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression five years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 {+/-} 0.01 for the slower progressing group (PDvec1), 0.87 {+/-} 0.03 for moderate progressors, and 0.95 {+/-} 0.02 for the fast progressing group (PDvec3). We identified serum neurofilament light (Nfl) as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent validation cohort, released the analytical code, and developed models in an open science manner. ConclusionsOur data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources, and ultimately individualized patient care.

bioinformatics↗

bioRxiv2022

Loss of PRMT2 in myeloid cells in normoglycemic mice phenocopies impaired regression of atherosclerosis in diabetic mice

Regression of atherosclerosis is impaired in diabetes. However, the factors mediating this effect remain incomplete. We identified Protein Arginine Methyltransferase 2 (PRMT2) as a protein whose expression in macrophages is reduced in hyperglycemia and diabetes. PRMT2 catalyzes arginine methylation to target proteins to modulate gene expression. Because PRMT2 expression is reduced in cells in hyperglycemia, we wanted to determine whether PRMT2 plays a causal role in the impairment of atherosclerosis regression in diabetes. We therefore examined the consequence of deleting PRMT2 in myeloid cells during regression of atherosclerosis in normal and diabetic mice. Remarkably, we found a significant impairment of atherosclerosis regression under normoglycemic conditions in mice lacking PRMT2 (Prmt2-/-) in myeloid cells that mimics the decrease in regression of atherosclerosis in WT mice under diabetic conditions. This was associated with increased plaque macrophage retention, as well as increased apoptosis and necrosis. PRMT2-deficient plaque CD68+ cells under normoglycemic conditions showed increased expression of genes involved in cytokine signaling and inflammation compared to WT. Consistently Prmt2-/- bone marrow derived macrophages (BMDMs) showed an increased response of pro-inflammatory genes to LPS, and a decreased response of inflammation resolving genes to IL-4. This increased response to LPS in Prmt2-/- BMDMs is via enhanced NF-kappa B activity. Thus, the loss of PRMT2 is causally linked to impaired atherosclerosis regression via a heightened inflammatory response in macrophages. That PRMT2 expression was lower in myeloid cells in plaques from human subjects with diabetes supports the relevance of our findings to human atherosclerosis.

genetics↗

bioRxiv2022

Nuclease proteins CifA and CifB promote spermatid DNA damage associated with symbiont-induced cytoplasmic incompatibility

The worldwide endosymbiosis between arthropods and Wolbachia bacteria is an archetype for reproductive parasitism. This parasitic strategy rapidly increases the proportion of symbiont-transmitting mothers, and the most common form, cytoplasmic incompatibility (CI), impacts insect evolution and arboviral control strategies. During CI, sperms from symbiotic males kill embryos of aposymbiotic females via two nuclear-targeting proteins, CifA and CifB, that alter sperm chromatin organization in Drosophila melanogaster. Here we hypothesize that Cif proteins metabolize nucleic acids of developing sperm to initiate genome integrity changes. Using in vitro and in situ transgenic, mutant, enzymatic, and cytochemical assays, we show that CifA is a previously-unrecognized DNase and RNase, and CifB is a DNase. Notably, in vitro nuclease activity translates to in situ spermatid DNA damage at the canoe stage of spermiogenesis. Evolution-guided mutations ablate Cif enzymatic activity. Nucleic acid metabolism by Cif enzymes expands a fundamental understanding of the mechanism of symbiont-mediated reproductive parasitism.

biochemistry↗

bioRxiv2022

The cytoplasmic incompatibility Cif proteins from prophage WO modify sperm genome integrity

Inherited microorganisms can selfishly manipulate host reproduction to drive through populations. In Drosophila melanogaster, germline expression of the native Wolbachia prophage WO proteins CifA and CifB cause cytoplasmic incompatibility (CI) in which embryos from infected males and uninfected females suffer catastrophic mitotic defects and lethality; however, in infected females, CifA expression rescues the embryonic lethality and thus imparts a fitness advantage to the maternally-transmitted Wolbachia. Despite widespread relevance to sex determination, evolution, and vector control, the mechanisms underlying when and how CI impairs male reproduction remain unknown and a topic of debate. Here we use cytochemical, microscopic, and transgenic assays in D. melanogaster to demonstrate that CifA and CifB proteins of wMel localize to nuclear DNA throughout the process of spermatogenesis. Cif proteins cause abnormal histone retention in elongating spermatids and protamine deficiency in mature sperms that travel to the female reproductive tract with Cif proteins. Notably, protamine gene knockouts enhance wild type CI. In ovaries, CifA localizes to germ cell nuclei and cytoplasm of early-stage egg chambers, however Cifs are absent in late-stage oocytes and subsequently in fertilized embryos. Finally, CI and rescue are contingent upon a newly annotated CifA bipartite nuclear localization sequence. Together, our results strongly support the Host Modification model of CI in which Cifs initially modify the paternal and maternal gametes to bestow CI-defining embryonic lethality and rescue.

cell biology↗

bioRxiv2021

Targeted resurrection of chromosomal arm 1RS in two elite wheat lines with 1BL/1RS translocation for improved end-use quality

1BL/1RS translocation is widely used around the world to enhance wheat yield potential, resistance to various diseases, and adaptation. However, the translocation is combined with inherent quality problems associated with reduced dough strength and dough stickiness due to the presence of Sec-1 on proximal end and absence of GluB3/GliB1 on distal end. Two NILs, one carrying the distal (1RSRW) and the other carrying the proximal (1RSWR) fragment from 1BS, in background of Pavon were used for transferring these two loci in yellow rust resistant version of two elite wheat varieties PBW550+Yr5 and DBW17+Yr5. Foreground and background marker assisted selection was done for the Sec-1- and GluB3+ alongwith Lr26/Yr9/Sr31, Pm8 and 1RS loci in the advancing generation. BC2F5:6 NILs with absence of Secalin and presence of GluB3/GliB1 loci were evaluated for two years in replicated yield trial. A positive correlation of thousand grain weight (TGW), harvest index (HI), and tiller number per meter (TNpM) with yield (YD) with significant GxE effect was observed. Further multivariate analysis of these traits contributed maximum to the effective yield. Thirty promising NILs were identified with Sec-1-/GluB3+ alongwith with high yield contributing parameters.

genetics↗

bioRxiv2020

In silico validation and fabrication of matrix diffusion based polymeric transdermal films for repurposing gabapentin hydrochloride in oncogenic neuropathic pain.

BackgroundGabapentin (GBP) is an FDA approved drug for the treatment of partial and secondary generalized seizures, apart from also being used for diabetic neuropathy. GBP displays highly intricate mechanism of action and its inhibitory response in elevated antagonism of NMDA (N-methyl-D-aspartate receptor) receptor and has potential in controlling neuropathic pain of cancer origin. ObjectiveTherefore, in the present study, we have selected BCATc (Pyridoxal 5-phosphate dependent branched-chain aminotransferase cytosolic) enzyme that is highly expressed in neuropathic stress conditions and have analysed the GBP as its competitive inhibitor by modeling, docking and checking its pharmacokinetic suitability through ADMET. Though in this study the results exhibited higher efficacy of GBP in controlling neuropathic pain, the drug shows certain potential therapeutic limitations like shorter half-life, repetitive dosing, high inter subjective variability. MethodsTherefore, a suitable and equally efficacious drug delivery method was also designed and developed by loading GBP transdermal patches (GBP-TDP) by solvent evaporation method using PVP and HPMC in ratio of 2:1 as a polymer base for reservoir type of TDP. Also, PEG 400 was used as a plasticizer and PVA (4%) was taken for backing membrane preparation and then the optimized GBP-TDP was subjected for physical characterization, optimization and ex vivo release kinetics. Results and conclusionThe results showed desired specifications with uneven and flaky surface appearance giving avenue for controlled release of the drugs with 75.58% of drug release in 12 hrs., further suggesting that GBP-TDP can be used as an effective tool against diabetic neuropathy pain.

neuroscience↗

bioRxiv2020

A soluble ACE2 microbody protein fused to a single immunoglobulin Fc domain is a potent inhibitor of SARS-CoV-2 infection in cell culture

Soluble forms of ACE2 have recently been shown to inhibit SARS-CoV-2 infection. We report on an improved soluble ACE2, termed a "microbody" in which the ACE2 ectodomain is fused to Fc domain 3 of the immunoglobulin heavy chain. The protein is smaller than previously described ACE2-Ig Fc fusion proteins and contains an H345A mutation in the ACE2 catalytic active site that inactivates the enzyme without reducing its affinity for the SARS-CoV-2 spike. The disulfide-bonded ACE2 microbody protein inhibited entry of lentiviral SARS-CoV-2 spike protein pseudotyped virus and live SARS-CoV-2 with a potency 10-fold higher than unmodified soluble ACE2 and was active after initial virus binding to the cell. The ACE2 microbody inhibited the entry of ACE2-specific {beta} coronaviruses and viruses with the high infectivity variant D614G spike. The ACE2 microbody may be a valuable therapeutic for COVID-19 that is active against SARS-CoV-2 variants and future coronaviruses that may arise.

microbiology↗

bioRxiv2020

Transient delivery of A-C/EBP protein perturbs differentiation of 3T3-L1 cells and induces preadipocyte marker genes

Transformation of committed 3T3-L1 preadipocytes to lipid-laden adipocytes involves timely appearance of numerous transcription factors (TFs), foremost among them C/EBP{beta}, is expressed during early phases of differentiation. Here we describe liposome-mediated protein transfection approach to rapidly downregulate C/EBP{beta} by A-C/EBP protein inhibitor. Signals from tagged A-C/EBP were observed in 3T3-L1 cells within 2hrs of protein inhibitor transfections whereas for gene transfection signals appeared in 48hrs. Following transient transfections, expression profiles of 24 marker genes belonging to pro- and anti-adipogenic, cell cycle, and preadipocytes pathways was analyzed. mRNA and protein expression profiles of adipocyte-marker genes showed lower expression in both A-C/EBP protein and gene transfected samples. Interestingly, for preadipocytes and cell fate determinant genes, striking differences were observed between protein and gene transfected samples. Preadipocyte differentiation factors Stat5a and Creb were downregulated in A-C/EBP protein samples. Five preadipocyte markers, namely, Pdgfr, Pdgfr{beta}, Ly6A, CD34 Itgb1 showed high expression in protein samples whereas only Ly6A and CD34 were expressed in gene transfected samples. Pdgfr and Pdgfr{beta}, two known cell fate markers were expressed in protein transfected samples 5-days post-differentiation suggesting a possible reversal of differentiation. Our study provides evidences for the robust and efficient knockdown of C/EBP{beta} protein to understand time-dependent gene regulation during adipogenesis.

molecular biology↗

bioRxiv2020

Structural basis to design multi-epitope vaccines against Novel Coronavirus 19 (COVID19) infection, the ongoing pandemic emergency: an in silico approach

The 2019 novel coronavirus (COVID19 / Wuhan coronavirus), officially named as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a positive-sense single-stranded RNA coronavirus. SARS-CoV-2 causes the contagious COVID19 disease also known as 2019-nCoV acute respiratory disease and has led to the ongoing 2019-20 pandemic COVID19 outbreak. The effective counter measures against SARS-CoV-2 infection require the design and development of specific and effective vaccine candidate. In the present study, we have screened and shortlisted 38 CTL, 33 HTL and 12 B cell epitopes from the eleven Protein sequences of SARS-CoV-2 by utilizing different in silico tools. The screened epitopes were further validated for their binding with their respective HLA allele binders and TAP (Transporter associated with antigen processing) molecule by molecular docking. The shortlisted screened epitopes were further utilized to design novel two multi-epitope vaccines (MEVs) composed of CTL, HTL and B cell epitopes overlaps with potential to elicit humoral as well as cellular immune response against SARS-CoV-2. To enhance the immune response for our vaccine design, truncated (residues 10-153) Onchocerca volvulus activation-associated secreted protein-1 (Ov-ASP-1) has been utilized as an adjuvant at N terminal of both the MEVs. Further molecular models for both the MEVs were prepared and validated for their stable molecular interactions with Toll-Like Receptor 3 (TLR 3). The codon-optimized cDNA of both the MEVs were further analyzed for their potential of high level of expression in a human cell line. The present study is very significant in terms of molecular designing of prospective CTL and HTL vaccine against SARS-CoV-2 infection with the potential to elicit cellular as well as humoral immune response. (SARS-CoV-2), Coronavirus, Human Transporter associated with antigen processing (TAP), Toll-Like Receptor (TLR), Epitope, Immunoinformatics, Molecular Docking, Molecular dynamics simulation, Multi-epitope Vaccine Graphical abstractThe designed CTL (Cytotoxic T lymphocyte) and HTL (Helper T lymphocyte) multi-epitope vaccines (MEV) against COVID19 infection. Both the CTL and HTL MEV models show a very stable and well fit conformational complex formation tendency with the Toll like receptor 3. CTL and HTL MEVs: ribbon; Toll like receptor 3: gray cartoon; Adjuvant [truncated (residues 10-153) Onchocerca volvulus activation-associated secreted protein-1]: orange ribbon regions; Epitopes: cyan ribbons regions; 6xHis Tag: magenta ribbon regions. O_FIG O_LINKSMALLFIG WIDTH=87 HEIGHT=200 SRC="FIGDIR/small/019299v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@6cb749org.highwire.dtl.DTLVardef@1752d54org.highwire.dtl.DTLVardef@1f2fc16org.highwire.dtl.DTLVardef@18415a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

BackgroundAlzheimers disease (AD) is a common, age-related, neurodegenerative disease that impairs a persons ability to perform day-to-day activities. Diagnosing AD is challenging, especially in the early stages. Many patients still go undiagnosed, partly due to the complex heterogeneity in disease progression. This highlights a need for early prediction of the disease course to assist its treatment and tailor therapy options to the disease progression rate. Recent developments in machine learning techniques provide the potential to not only predict disease progression and trajectory of AD but also to classify the disease into different etiological subtypes. Methods and findingsThe work shown here clusters participants in distinct and multifaceted progression subgroups of AD and discusses an approach to predict the progression rate from baseline diagnosis. We observed that the myriad of clinically reported symptoms summarized in the proposed AD progression space corresponds directly with memory and cognitive measures, which are routinely used to monitor disease onset and progression. Our analysis demonstrated accurate prediction of disease progression after four years from the first 12 months of post-diagnosis clinical data (Area Under the Curve of 0.96 (95% confidence interval (CI), 0.92-1.0), 0.81 (95% CI, 0.74-0.88) and 0.98 (95% CI, 0.96-1.0) for slow, moderate and fast progression rate patients respectively). Further, we explored the long short-term memory (LSTM) neural networks to predict the trajectory of an individual patients progression. ConclusionThe machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries. With additional information about the progression rate of AD at hand, providers may further individualize the treatment plans. The predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the characterization of distinct AD subtypes relating to trajectories of disease progression. These findings are a crucial step forward for early disease detection. These models can be used to design improved clinical trials for AD research.

neuroscience↗

bioRxiv2018

Expression based biomarkers and models to classify early and late stage samples of Papillary Thyroid Carcinoma

In this study, we describe the key transcripts and machine learning models developed for classifying the early and late stage samples of Papillary Thyroid Cancer (PTC), using transcripts expression data from The Cancer Genome Atlas (TCGA). First, we rank all the transcripts on the basis of area under receiver operating characteristic curve, (AUROC) value to discriminate the early and late stage, based on an expression threshold. With the expression of a single transcript DCN, we can classify the stage samples with a 68.5% accuracy and AUROC of 0.66. Then we implemented various combination of multiple gene panels, selected using various gold standard feature selection techniques. The model based on the expression of 36 multiple transcripts (protein coding and non-coding) selected using SVC-L1 achieves the maximum accuracy of 74.51% with AUROC of 0.75 on independent validation dataset with balanced sensitivity and specificity. Further, these signatures also performed well on external microarray data obtained from GEO, predicting nearly 70% (12 samples out of 17 samples) early stage samples correctly. Further, multiclass model, classifying the normal, early and late stage samples achieves the accuracy of 75.43% with AUROC of 0.80 on independent validation dataset. With correlation analysis, we found that transcripts with maximum change in correlation of their expression in both the stages are significantly enriched in neuroactive ligand receptor interaction pathway. We also propose a panel of five protein coding transcripts, which on the basis of their expression, can segregate cancer and normal samples with 97.32% accuracy and AUROC of 0.99 on independent validation dataset. All the models and dataset used in this study are available from the web server CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).

bioinformatics↗

bioRxiv2018

Wolbachia modifies thermal preference in Drosophila melanogaster

Environmental variation can have profound and direct effects on fitness, fecundity, and host symbiont interactions. Replication rates of microbes within arthropod hosts, for example, are correlated with incubation temperature but less is known about the influence of host-symbiont dynamics on environmental preference. Hence, we conducted thermal preference (Tp) assays and tested if infection status and genetic variation in endosymbiont bacterium Wolbachia affected temperature choice of Drosophila melanogaster. We demonstrate that isogenic flies infected with Wolbachia preferred lower temperatures compared to uninfected Drosophila. Moreover, Tp varied with respect to three investigated Wolbachia variants (wMel, wMelCS and wMelPop). While uninfected individuals preferred 24.4{degrees}C, we found significant shifts of - 1.2{degrees}C in wMel- and -4{degrees}C in flies infected either with wMelCS or wMelPop. We, therefore, postulate that Wolbachia-associated Tp variation within a host species might represent a behavioral accommodation to host-symbiont interactions and trigger behavioral self-medication and bacterial titer regulation by the host.

microbiology↗

bioRxiv2017

The elephant in the room- Universal coverage for Costly treatments in an upper middle income country

BackgroundUpper middle income countries have made substantial progress towards universal health coverage. We investigated whether the coverage extended to diseases that incur catastrophic health spending, the contribution of pooled financing and the factors driving it in Malaysia.\n\nMethodsWe adapted the WHO definition of catastrophic health spending to define costly treatment as one that cost, at prevailing market price, more than 10% of the median annual household income in Malaysia. Coverage is defined as the proportion of patients in a year who were in need of a treatment and who received it. Data to estimate coverage and financing were extracted from the published and grey literature, as well as secondary data sources available on disease epidemiology and healthcare in Malaysia.\n\nResultsWe found coverage varies from universal for dialysis, cataract surgery, medicines for organ transplant and CML, to practically none for HCV, stroke, psoriasis and epilepsy surgery. Coverage of targeted therapies for solid cancers, knee replacement surgery, anti-TNF for arthritis and coagulation factors for haemophilia were poor while iron chelation for thalassemia, coronary revacularization, epoetin and anti-retrovirals were barely adequate. Coverage correlates negatively (r=-0.82) with health benefits foregone, and is entirely driven by the contribution of pooled financing (r=0.99 p<0.0001). The relative effectiveness of a treatment, its budget impact, media coverage and political influence of the disease area have little influence on financing. Only effectiveness of the leadership representing the therapy area is influential; an increase in one point on the leadership effectiveness scale is associated with 30% increase in the contribution of pooled financing.\n\nConclusionCoverage for catastrophically costly treatments is uneven and inequitable in Malaysia, despite most of these are affordable. Decisions on coverage are driven by political-economic consideration.

epidemiology↗

bioRxiv2017

Common Garlic (Allium sativum) has Potent Anti-Bacillus anthracis Activity among Various Commonly Used Spices and Herbs

Ethnopharmacological RelevanceGastrointestinal anthrax, a disease caused by Bacillus anthracis, remains an important but relatively neglected endemic disease of animals and humans in remote areas of the Indian subcontinent and some parts of Africa. Its initial symptoms include diarrhea and stomachache. In the current study, several common plants indicated for diarrhea, dysentery, stomachache or as stomachic as per traditional knowledge in the Indian subcontinent, i.e., Aegle marmelos (L.) Correa (Bael), Allium cepa L. (Onion), Allium sativum L. (Garlic), Azadirachta indica A. Juss. (Neem), Berberis asiatica Roxb. ex DC. (Daruharidra), Coriandrum sativum L. (Coriander), Curcuma longa L. (Turmeric), Cynodon dactylon (L.) Pers. (Bermuda grass), Mangifera indica L. (Mango), Morus indica L. (Black mulberry), Ocimum tenuiflorum L. (Ocimum sanctum L., Holy Basil), Ocimum gratissimum L. (Ram Tulsi), Psidium guajava L. (Guava), Zingiber officinale Roscoe (Ginger), were evaluated for their anti-Bacillus anthracis property. The usage of Azadirachta indica A. Juss. and Curcuma longa L. by Santals (India), and Allium sp. by biblical people to alleviate anthrax-like symptoms is well documented, but the usage of other plants is traditionally only indicated for different gastrointestinal disturbances/conditions. Aim of the StudyEvaluate the above listed commonly available edible plants from the Indian subcontinent that are used in the traditional medicine to treat gastrointestinal diseases including those also indicated for anthrax-like symptoms for the presence of potent anti-B. anthracis activity in a form amenable to use by the general population in the endemic areas. Materials and MethodsAqueous extracts made from fourteen plants indicated above were screened for their anti-B. anthracis activity using agar-well diffusion assay (AWDA) and broth microdilution methods. The Aqueous Garlic Extract (AGE) that displayed most potent anti-B. anthracis activity was assessed for its thermostability, stability under pH extremes encountered in the gastrointestinal tract, and potential antagonistic interaction with bile salts as well as the FDA-approved antibiotics used for anthrax control. The bioactive fractions from the AGE were isolated by TLC coupled bioautography followed by their characterization using GC-MS. ResultsGarlic (Allium sativum L.) extract was identified as the most promising candidate with bactericidal activity against B. anthracis. It consistently inhibited the growth of B. anthracis in AWDA and decreased the viable colony-forming unit counts in liquid-broth cultures by 6-logs within 6-12 h. The AGE displayed acceptable thermostability (>80% anti-B. anthracis activity retained on incubation at 50{degrees}C for 12 h) and stability in gastric pH range (2-8). It did not antagonize the activity of FDA-approved antibiotics used for anthrax control. GC-MS analysis of the TLC separated bioactive fractions of AGE indicated the presence of previously unreported constituents such as phthalic acid derivatives, acid esters, phenyl group-containing compounds, steroids etc. ConclusionThe Aqueous Garlic Extract (AGE) displayed potent anti-B. anthracis activity. It was better than that displayed by Azadirachta indica A. Juss. (Neem) and Mangifera indica L. while Curcuma longa L. (Turmeric) did not show any activity under the assay conditions used. Further work should be undertaken to explore the possible application of AGE in preventing anthrax incidences in endemic areas. O_FIG O_LINKSMALLFIG WIDTH=161 HEIGHT=200 SRC="FIGDIR/small/162214v3_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@682faaorg.highwire.dtl.DTLVardef@114d64org.highwire.dtl.DTLVardef@1efe980org.highwire.dtl.DTLVardef@1caea0f_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗