Orexinergic Input to Dopaminergic Neurons of the Human Ventral Tegmental Area

The mesolimbic reward pathway arising from dopaminergic (DA) neurons of the ventral tegmental area (VTA) has been strongly implicated in reward processing and drug abuse. In rodents, behaviors associated with this projection are profoundly influenced by an orexinergic input from the lateral hypothalamus to the VTA. Because the existence and significance of an analogous orexigenic regulatory mechanism acting in the human VTA have been elusive, here we addressed the possibility that orexinergic neurons provide direct input to DA neurons of the human VTA. Dual-label immunohistochemistry was used and orexinergic projections to the VTA and to DA neurons of the neighboring substantia nigra (SN) were analyzed comparatively in adult male humans and rats. Orexin B-immunoreactive (IR) axons apposed to tyrosine hydroxylase (TH)-IR DA and to non-DA neurons were scarce in the VTA and SN of both species. In the VTA, 15.062.8% of TH-IR perikarya in humans and 3.260.3% in rats received orexin B-IR afferent contacts. On average, 0.2460.05 and 0.0560.005 orexinergic appositions per TH-IR perikaryon were detected in humans and rats, respectively. The majority(86–88%) of randomly encountered orexinergic contacts targeted the dendritic compartment of DA neurons. Finally, DA neurons of the SN also received orexinergic innervation in both species. Based on the observation of five times heavierorexinergic input to TH-IR neurons of the human, compared with the rat, VTA, we propose that orexinergic mechanism acting in the VTA may play just as important roles in reward processing and drug abuse in humans, as already established well in rodents

A Genetic Screen for Attenuated Growth Identifies Genes Crucial for Intraerythrocytic Development of Plasmodium falciparum

A majority of the Plasmodium falciparum genome codes for genes with unknown functions, which presents a major challenge to understanding the parasite's biology. Large-scale functional analysis of the parasite genome is essential to pave the way for novel therapeutic intervention strategies against the disease and yet difficulties in genetic manipulation of this deadly human malaria parasite have been a major hindrance for functional analysis of its genome. Here, we used a forward functional genomic approach to study P. falciparum and identify genes important for optimal parasite development in the disease-causing, intraerythrocytic stages. We analyzed 123 piggyBac insertion mutants of P. falciparum for proliferation efficiency in the intraerythrocytic stages, in vitro. Almost 50% of the analyzed mutants showed significant reduction in proliferation efficiency, with 20% displaying severe defects. Functional categorization of genes in the severely attenuated mutants revealed significant enrichment for RNA binding proteins, suggesting the significance of post-transcriptional gene regulation in parasite development and emphasizing its importance as an antimalarial target. This study demonstrates the feasibility of much needed forward genetics approaches for P. falciparum to better characterize its genome and accelerate drug and vaccine development

Discovery: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria

<p>Abstract</p> <p>Background</p> <p>Up to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties.</p> <p>Methods</p> <p>A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available.</p> <p>Results</p> <p>An <it>in silico</it> system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from <it>Plasmodium falciparum</it>.</p> <p>Conclusion</p> <p>The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties.</p

Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment

<p>Abstract</p> <p>Background</p> <p>A widely-used approach for discovering functional and physical interactions among proteins involves phylogenetic profile comparisons (PPCs). Here, proteins with similar profiles are inferred to be functionally related under the assumption that proteins involved in the same metabolic pathway or cellular system are likely to have been co-inherited during evolution.</p> <p>Results</p> <p>Our experimentation with <it>E. coli </it>and yeast proteins with 16 different carefully composed reference sets of genomes revealed that the phyletic patterns of proteins in prokaryotes alone could be adequate enough to make reasonably accurate functional linkage predictions. A slight improvement in performance is observed on adding few eukaryotes into the reference set, but a noticeable drop-off in performance is observed with increased number of eukaryotes. Inclusion of most parasitic, pathogenic or vertebrate genomes and multiple strains of the same species into the reference set do not necessarily contribute to an improved sensitivity or accuracy. Interestingly, we also found that evolutionary histories of individual pathways have a significant affect on the performance of the PPC approach with respect to a particular reference set. For example, to accurately predict functional links in carbohydrate or lipid metabolism, a reference set solely composed of prokaryotic (or bacterial) genomes performed among the best compared to one composed of genomes from all three super-kingdoms; this is in contrast to predicting functional links in translation for which a reference set composed of prokaryotic (or bacterial) genomes performed the worst. We also demonstrate that the widely used random null model to quantify the statistical significance of profile similarity is incomplete, which could result in an increased number of false-positives.</p> <p>Conclusion</p> <p>Contrary to previous proposals, it is not merely the number of genomes but a careful selection of informative genomes in the reference set that influences the prediction accuracy of the PPC approach. We note that the predictive power of the PPC approach, especially in eukaryotes, is heavily influenced by the primary endosymbiosis and subsequent bacterial contributions. The over-representation of parasitic unicellular eukaryotes and vertebrates additionally make eukaryotes less useful in the reference sets. Reference sets composed of highly non-redundant set of genomes from all three super-kingdoms fare better with pathways showing considerable vertical inheritance and strong conservation (e.g. translation apparatus), while reference sets solely composed of prokaryotic genomes fare better for more variable pathways like carbohydrate metabolism. Differential performance of the PPC approach on various pathways, and a weak positive correlation between functional and profile similarities suggest that caution should be exercised while interpreting functional linkages inferred from genome-wide large-scale profile comparisons using a single reference set.</p

Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy

Background A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets. Methods Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis. Results A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001). Conclusion We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

Comparative Genomics of Cell Envelope Components in Mycobacteria

Mycobacterial cell envelope components have been a major focus of research due to their unique features that confer intrinsic resistance to antibiotics and chemicals apart from serving as a low-permeability barrier. The complex lipids secreted by Mycobacteria are known to evoke/repress host-immune response and thus contribute to its pathogenicity. This study focuses on the comparative genomics of the biosynthetic machinery of cell wall components across 21-mycobacterial genomes available in GenBank release 179.0. An insight into survival in varied environments could be attributed to its variation in the biosynthetic machinery. Gene-specific motifs like ‘DLLAQPTPAW’ of ufaA1 gene, novel functional linkages such as involvement of Rv0227c in mycolate biosynthesis; Rv2613c in LAM biosynthesis and Rv1209 in arabinogalactan peptidoglycan biosynthesis were detected in this study. These predictions correlate well with the available mutant and coexpression data from TBDB. It also helped to arrive at a minimal functional gene set for these biosynthetic pathways that complements findings using TraSH