Late afternoon administration of melatonin is prosomatotrophic and exerts androgen independent effects on erythropoiesis in male house sparrow Passer domesticus.

In the third week of September 1989, birds were purchased locally and acclimated to their housing conditions in a room fully exposed to natural day length (average: 11.96 hr) and temperature (26 degrees +/- 2 degrees C) for 2 weeks. Birds were in the regressive phase of their annual gonadal cycle. In the first experiment 24 birds were selected randomly and were divided into 3 groups of 8 birds each. Initial body weight and bill color score were recorded. The birds of group-I and group-II were injected daily with 5 and 10 micrograms of melatonin in 0.1 ml of vehicle, respectively. The birds of group-III were injected with vehicle only and treated as control. Injections were given daily between 1700 and 1730 hrs over a period of 10 days. At the termination of the experiment, the birds were weighed, sacrificed, bill color scored, blood collected and immediately processed to determine the number of erythrocytes and hemoglobin concentration. The mean body weight loss amounted to 9.6% in vehicle-treated house sparrow. Birds receiving low and high doses of melatonin maintained their initial body weight. Melatonin significantly accelerated the rate of bleaching of bill color. Results clearly indicate that in house sparrow, melatonin produces prosomatotrophic and antigonadotrophic effects. The low dose of melatonin stimulated erythropoiesis significantly. In the second experiment, melatonin nullified the castration-induced decline in the number of circulating red cells. This clearly suggests that the influence of melatonin on erythropoietic machinery appears to be independent of testicular hormone(s).     

Synthesis of azatricyclodiones & octahydro-benzo[f]isoindoles and their antimicrobial evaluation

A series of azatricyclodiones and octahydro-benzo[f]isoindoles have been synthesized by (4+2) Diels-Alder cycloaddition of maleimides with furfuryl amine. Reaction of azatricyclodiones with isocyanates led to the respective ureides. All of the compounds were screened against a number of bacteria and fungi. One of the compounds (2) displayed moderate antitubercular activity while two compounds (2) and (4) inhibited the fungal growth at 25 μg/mL.     

Synthesis and biological evaluation of potential modulators of malarial glutathione-S-transferase(s)

Glutathione-S-transferase(s) (E.C.2.5.1.18, GSTs) have been investigated in parasitic protozoans with respect to their biochemistry and they have been identified as potential vaccine candidates in protozoan parasites and as a target in the synthesis of new antiparasitic agents. In a search towards the identification of novel biochemical targets for antimalarial drug design, the area of Plasmodium glutathione metabolism provides a number of promising chemotherapeutic targets. GST activity was determined in various subcellular fractions of malarial parasites Plasmodium yoelii and was found to be localized mainly in the cytosolic fraction (specific activity, c. 0.058 ± 0.016 μmol/min/mg protein). Hemin, a known inhibitor of mammalian GST(s), maximally inhibited this enzyme from P. yoelii to nearly 86%. In a search towards synthetic modulators of malarial GST(s), 575 compounds belonging to various chemical classes were screened for their effect on crude GST from P. yoelii and 92 compounds belonging to various chemical classes were studied on recombinant GST from P. falciparum. Among all the compounds screened, 83 compounds inhibited/stimulated the enzyme from P. yoelii/P. falciparum to the extent of 40% or more.     

Aquaporin 2 Mutations in Trypanosoma brucei gambiense Field Isolates Correlate with Decreased Susceptibility to Pentamidine and Melarsoprol

The predominant mechanism of drug resistance in African trypanosomes is decreased drug uptake due to loss-of-function mutations in the genes for the transporters that mediate drug import. The role of transporters as determinants of drug susceptibility is well documented from laboratory-selected Trypanosoma brucei mutants. But clinical isolates, especially of T. b. gambiense, are less amenable to experimental investigation since they do not readily grow in culture without prior adaptation. Here we analyze a selected panel of 16 T. brucei ssp. field isolates that (i) have been adapted to axenic in vitro cultivation and (ii) mostly stem from treatment-refractory cases. For each isolate, we quantify the sensitivity to melarsoprol, pentamidine, and diminazene, and sequence the genomic loci of the transporter genes TbAT1 and TbAQP2. The former encodes the well-characterized aminopurine permease P2 which transports several trypanocides including melarsoprol, pentamidine, and diminazene. We find that diminazene-resistant field isolates of T. b. brucei and T. b. rhodesiense carry the same set of point mutations in TbAT1 that was previously described from lab mutants. Aquaglyceroporin 2 has only recently been identified as a second transporter involved in melarsoprol/pentamidine cross-resistance. Here we describe two different kinds of TbAQP2 mutations found in T. b. gambiense field isolates: simple loss of TbAQP2, or loss of wild-type TbAQP2 allele combined with the formation of a novel type of TbAQP2/3 chimera. The identified mutant T. b. gambiense are 40- to 50-fold less sensitive to pentamidine and 3- to 5-times less sensitive to melarsoprol than the reference isolates. We thus demonstrate for the first time that rearrangements of the TbAQP2/TbAQP3 locus accompanied by TbAQP2 gene loss also occur in the field, and that the T. b. gambiense carrying such mutations correlate with a significantly reduced susceptibility to pentamidine and melarsoprol.     

A novel familial case of diffuse leukodystrophy related to NDUFV1 compound heterozygous mutations

NDUFV1 mutations have been related to encephalopathic phenotypes due to mitochondrial energy metabolism disturbances. In this study, we report two siblings affected by a diffuse leukodystrophy, who carry the NDUFV1 c.1156C>T (p.Arg386Cys) missense mutation and a novel 42-bp deletion. Bioinformatic and molecular analysis indicated that this deletion lead to the synthesis of mRNA molecules carrying a premature stop codon, which might be degraded by the nonsense-mediated decay system. Our results add information on the molecular basis and the phenotypic features of mitochondrial disease caused by NDUFV1 mutations.     

Auxin: a master regulator in plant root development

The demand for increased crop productivity and the predicted challenges related to plant survival under adverse environmental conditions have renewed the interest in research in root biology. Various physiological and genetic studies have provided ample evidence in support of the role of plant growth regulators in root development. The biosynthesis and transport of auxin and its signaling play a crucial role in controlling root growth and development. The univocal role of auxin in root development has established it as a master regulator. Other plant hormones, such as cytokinins, brassinosteroids, ethylene, abscisic acid, gibberellins, jasmonic acid, polyamines and strigolactones interact either synergistically or antagonistically with auxin to trigger cascades of events leading to root morphogenesis and development. In recent years, the availability of biological resources, development of modern tools and experimental approaches have led to the advancement of knowledge in root development. Research in the areas of hormone signal perception, understanding network of events involved in hormone action and the transport of plant hormones has added a new dimension to root biology. The present review highlights some of the important conceptual developments in the interplay of auxin and other plant hormones and associated downstream events affecting root development.     

Hospitalization-induced exacerbation of the ill effects of chemotherapy on rest-activity rhythm and quality of life of breast cancer patients: a prospective and comparative cross-sectional follow-up study

ABSTRACT

Chemotherapy administration may result in the disruption of circadian rhythms and impairment of quality of life (QoL) of cancer patients. Nevertheless, we have little knowledge on the long-term consequences of chemotherapy and the effects of hospitalization. In the present study, we employed the two-factor repeated-measure cross-sectional design to determine the effects of chemotherapy and hospitalization on rest-activity (RA) rhythm and QoL of breast cancer patients. Initially, we randomly selected 39 inpatients and 42 outpatients, scheduled to receive six cycles of chemotherapy, from the Regional Cancer Center (RCC), Raipur, India. Finally, 30 patients in each group were included in the current study. We monitored circadian RA rhythm and QoL using wrist actigraphy and QLQ-C30 and QLQ-BR23, respectively, during the 1st (C1), 3rd (C3) and 6th (C6) chemotherapy cycles. Results revealed that with the progression of chemotherapy cycles (from C1 to C6), all rhythm parameters, namely mesor, amplitude, acrophase, rhythm quotient (RQ), circadian quotient (CQ), peak activity (PA), dichotomy index and autocorrelation coefficient, significantly decreased in both cancer in- and outpatients. In both groups of patients and during C1–C6, all functional and global QoL measures of QLQ-C30 and QLQ-BR23 significantly decreased and the symptoms significantly increased, except constipation, body image, sexual functioning and future perspectives in outpatients. The hospitalization exacerbated the problems associated with the RA rhythm and the QoL of the patients. In conclusion, the current study highlighted the negative consequences of hospitalization among inpatients, irrespective of the stage of cancer. We, therefore, recommend that cancer patients should be administered with chemotherapy as outpatients. The proposed protocol might have a covert bearing on the expression of better physiological state leading to satisfactory treatment outcomes.     

Patient navigation pathway and barriers to treatment seeking in cancer in India: A qualitative inquiry

Cancer is a leading cause of mortality worldwide. Early diagnosis and treatment of cancer may curb the growing burden of the disease. Understanding cancer patients’ navigation pathways for seeking treatment is important in order to facilitate early diagnosis and treatment. With this background we conducted a hospital-based cross-sectional study comprising 68 randomly selected cancer inpatients in a tertiary cancer specialty hospital in Odisha, India, to explore the treatment-seeking pathways of the cancer patients and the barriers and enablers in seeking treatment. Financial constraint is one of the major reasons for the delay in accessing treatment, even when patients are suspected of or diagnosed with cancer. Low awareness of the presenting signs and symptoms of cancer and limited knowledge of the availability of cancer diagnosis and treatment facilities are major factors contributing to delay. Family and friends’ support is found to be the major enabling factor toward seeking treatment. Generation of awareness of cancer among the general population and primary-care practitioners – including those in alternative systems of medicine – is important. Information on diagnostic and treatment services appears to be a felt need.     

ProDeGe: a computational protocol for fully automated decontamination of genomes

Single amplified genomes and genomes assembled from metagenomes have enabled the exploration of uncultured microorganisms at an unprecedented scale. However, both these types of products are plagued by contamination. Since these genomes are now being generated in a high-throughput manner and sequences from them are propagating into public databases to drive novel scientific discoveries, rigorous quality controls and decontamination protocols are urgently needed. Here, we present ProDeGe (Protocol for fully automated Decontamination of Genomes), the first computational protocol for fully automated decontamination of draft genomes. ProDeGe classifies sequences into two classes—clean and contaminant—using a combination of homology and feature-based methodologies. On average, 84% of sequence from the non-target organism is removed from the data set (specificity) and 84% of the sequence from the target organism is retained (sensitivity). The procedure operates successfully at a rate of ~0.30 CPU core hours per megabase of sequence and can be applied to any type of genome sequence.Recent technological advancements have enabled the large-scale sampling of genomes from uncultured microbial taxa, through the high-throughput sequencing of single amplified genomes (SAGs; Rinke et al., 2013; Swan et al., 2013) and assembly and binning of genomes from metagenomes (GMGs; Cuvelier et al., 2010; Sharon and Banfield, 2013). The importance of these products in assessing community structure and function has been established beyond doubt (Kalisky and Quake, 2011). Multiple Displacement Amplification (MDA) and sequencing of single cells has been immensely successful in capturing rare and novel phyla, generating valuable references for phylogenetic anchoring. However, efforts to conduct MDA and sequencing in a high-throughput manner have been heavily impaired by contamination from DNA introduced by the environmental sample, as well as introduced during the MDA or sequencing process (Woyke et al., 2011; Engel et al., 2014; Field et al., 2014). Similarly, metagenome binning and assembly often carries various errors and artifacts depending on the methods used (Nielsen et al., 2014). Even cultured isolate genomes have been shown to lack immunity to contamination with other species (Parks et al., 2014; Mukherjee et al., 2015). As sequencing of these genome product types rapidly increases, contaminant sequences are finding their way into public databases as reference sequences. It is therefore extremely important to define standardized and automated protocols for quality control and decontamination, which would go a long way towards establishing quality standards for all microbial genome product types.Current procedures for decontamination and quality control of genome sequences in single cells and metagenome bins are heavily manual and can consume hours/megabase when performed by expert biologists. Supervised decontamination typically involves homology-based inspection of ribosomal RNA sequences and protein coding genes, as well as visual analysis of k-mer frequency plots and guanine–cytosine content (Clingenpeel, 2015). Manual decontamination is also possible through the software SmashCell (Harrington et al., 2010), which contains a tool for visual identification of contaminants from a self-organizing map and corresponding U-matrix. Another existing software tool, DeconSeq (Schmieder and Edwards, 2011), automatically removes contaminant sequences, however, the contaminant databases are required input. The former lacks automation, whereas the latter requires prior knowledge of contaminants, rendering both applications impractical for high-throughput decontamination.Here, we introduce ProDeGe, the first fully automated computational protocol for decontamination of genomes. ProDeGe uses a combination of homology-based and sequence composition-based approaches to separate contaminant sequences from the target genome draft. It has been pre-calibrated to discard at least 84% of the contaminant sequence, which results in retention of a median 84% of the target sequence. The standalone software is freely available at http://prodege.jgi-psf.org//downloads/src and can be run on any system that has Perl, R (R Core Team, 2014), Prodigal (Hyatt et al., 2010) and NCBI Blast (Camacho et al., 2009) installed. A graphical viewer allowing further exploration of data sets and exporting of contigs accompanies the web application for ProDeGe at http://prodege.jgi-psf.org, which is open to the wider scientific community as a decontamination service (Supplementary Figure S1).The assembly and corresponding NCBI taxonomy of the data set to be decontaminated are required inputs to ProDeGe (Figure 1a). Contigs are annotated with genes following which, eukaryotic contamination is removed based on homology of genes at the nucleotide level using the eukaryotic subset of NCBI''s Nucleotide database as the reference. For detecting prokaryotic contamination, a curated database of reference contigs from the set of high-quality genomes within the Integrated Microbial Genomes (IMG; Markowitz et al., 2014) system is used as the reference. This ensures that errors in public reference databases due to poor quality of sequencing, assembly and annotation do not negatively impact the decontamination process. Contigs determined as belonging to the target organism based on nucleotide level homology to sequences in the above database are defined as ‘Clean'', whereas those aligned to other organisms are defined as ‘Contaminant''. Contigs whose origin cannot be determined based on alignment are classified as ‘Undecided''. Classified clean and contaminated contigs are used to calibrate the separation in the subsequent 5-mer based binning module, which classifies undecided contigs as ‘Clean'' or ‘Contaminant'' using principal components analysis (PCA) of 5-mer frequencies. This parameter can also be specified by the user. When data sets do not have taxonomy deeper than phylum level, or a single confident taxonomic bin cannot be detected using sequence alignment, solely 9-mer based binning is used due to more accurate overall classification. In the absence of a user-defined cutoff, a pre-calibrated cutoff for 80% or more specificity separates the clean contigs from contaminated sequences in the resulting PCA of the 9-mer frequency matrix. Details on ProDeGe''s custom database, evaluation of the performance of the system and exploration of the parameter space to calibrate ProDeGe for a high accurate classification rate are provided in the Supplementary Material.Open in a separate windowFigure 1(a) Schematic overview of the ProDeGe engine. (b) Features of data sets used to validate ProDeGe: SAGs from the Arabidopsis endophyte sequencing project, MDM project, public data sets found in IMG but not sequenced at the JGI, as well as genomes from metagenomes. All the data and results can be found in Supplementary Table S3.The performance of ProDeGe was evaluated using 182 manually screened SAGs (Figure 1b,Supplementary Table S1) from two studies whose data sets are publicly available within the IMG system: genomes of 107 SAGs from an Arabidopsis endophyte sequencing project and 75 SAGs from the Microbial Dark Matter (MDM) project* (only 75/201 SAGs from the MDM project had 1:1 mapping between contigs in the unscreened and the manually screened versions, hence these were used; Rinke et al., 2013). Manual curation of these SAGs demonstrated that the use of ProDeGe prevented 5311 potentially contaminated contigs in these data sets from entering public databases. Figure 2a demonstrates the sensitivity vs specificity plot of ProDeGe results for the above data sets. Most of the data points in Figure 2a cluster in the top right of the box reflecting a median retention of 89% of the clean sequence (sensitivity) and a median rejection of 100% of the sequence of contaminant origin (specificity). In addition, on average, 84% of the bases of a data set are accurately classified. ProDeGe performs best when the target organism has sequenced homologs at the class level or deeper in its high-quality prokaryotic nucleotide reference database. If the target organism''s taxonomy is unknown or not deeper than domain level, or there are few contigs with taxonomic assignments, a target bin cannot be assessed and thus ProDeGe removes contaminant contigs using sequence composition only. The few samples in Figure 2a that demonstrate a higher rate of false positives (lower specificity) and/or reduced sensitivity typically occur when the data set contains few contaminant contigs or ProDeGe incorrectly assumes that the largest bin is the target bin. Some data sets contain a higher proportion of contamination than target sequence and ProDeGe''s performance can suffer under this condition. However, under all other conditions, ProDeGe demonstrates high speed, specificity and sensitivity (Figure 2). In addition, ProDeGe demonstrates better performance in overall classification when nucleotides are considered than when contigs are considered, illustrating that longer contigs are more accurately classified (Supplementary Table S1).Open in a separate windowFigure 2ProDeGe accuracy and performance scatterplots of 182 manually curated single amplified genomes (SAGs), where each symbol represents one SAG data set. (a) Accuracy shown by sensitivity (proportion of bases confirmed ‘Clean'') vs specificity (proportion of bases confirmed ‘Contaminant'') from the Endophyte and Microbial Dark Matter (MDM) data sets. Symbol size reflects input data set size in megabases. Most points cluster in the top right of the plot, showing ProDeGe''s high accuracy. Median and average overall results are shown in Supplementary Table S1. (b) ProDeGe completion time in central processing unit (CPU) core hours for the 182 SAGs. ProDeGe operates successfully at an average rate of 0.30 CPU core hours per megabase of sequence. Principal components analysis (PCA) of a 9-mer frequency matrix costs more computationally than PCA of a 5-mer frequency matrix used with blast-binning. The lack of known taxonomy for the MDM data sets prevents blast-binning, thus showing longer finishing times than the endophyte data sets, which have known taxonomy for use in blast-binning.All SAGs used in the evaluation of ProDeGe were assembled using SPAdes (Bankevich et al., 2012). In-house testing has shown that reads assembled with SPAdes from different strains or even slightly divergent species of the same genera may be combined into the same contig (Personal communications, KT and Robert Bowers). Ideally, the DNA in a well that gets sequenced belongs to a single cell. In the best case, contaminant sequences need to be at least from a different species to be recognized as such by the homology-based screening stage. In the absence of closely related sequenced organisms, contaminant sequences need to be at least from a different genus to be recognized as such by the composition-based screening stage (Supplementary Material). Thus, there is little risk of ProDeGe separating sequences from clonal populations or strains. We have found species- and genus-level contamination in MDA samples to be rare.To evaluate the quality of publicly available uncultured genomes, ProDeGe was used to screen 185 SAGs and 14 GMGs (Figure 1b). Compared with CheckM (Parks et al., 2014), a tool which calculates an estimate of genome sequence contamination using marker genes, ProDeGe generally marks a higher proportion of sequence as ‘Contaminant'' (Supplementary Table S2). This is because ProDeGe has been calibrated to perform at high specificity levels. The command line version of ProDeGe allows users to conduct their own calibration and specify a user-defined distance cutoff. Further, CheckM only outputs the proportion of contamination, but ProDeGe actually labels each contig as ‘Clean'' or ‘Contaminant'' during the process of automated removal.The web application for ProDeGe allows users to export clean and contaminant contigs, examine contig gene calls with their corresponding taxonomies, and discover contig clusters in the first three components of their k-dimensional space. Non-linear approaches for dimensionality reduction of k-mer vectors are gaining popularity (van der Maaten and Hinton, 2008), but we observed no systematic advantage of using t-Distributed Stochastic Neighbor Embedding over PCA (Supplementary Figure S2).ProDeGe is the first step towards establishing a standard for quality control of genomes from both cultured and uncultured microorganisms. It is valuable for preventing the dissemination of contaminated sequence data into public databases, avoiding resulting misleading analyses. The fully automated nature of the pipeline relieves scientists of hours of manual screening, producing reliably clean data sets and enabling the high-throughput screening of data sets for the first time. ProDeGe, therefore, represents a critical component in our toolkit during an era of next-generation DNA sequencing and cultivation-independent microbial genomics.