Subcompartmentalisation of Proteins in the Rhoptries Correlates with Ordered Events of Erythrocyte Invasion by the Blood Stage Malaria Parasite

Host cell infection by apicomplexan parasites plays an essential role in lifecycle progression for these obligate intracellular pathogens. For most species, including the etiological agents of malaria and toxoplasmosis, infection requires active host-cell invasion dependent on formation of a tight junction - the organising interface between parasite and host cell during entry. Formation of this structure is not, however, shared across all Apicomplexa or indeed all parasite lifecycle stages. Here, using an in silico integrative genomic search and endogenous gene-tagging strategy, we sought to characterise proteins that function specifically during junction-dependent invasion, a class of proteins we term invasins to distinguish them from adhesins that function in species specific host-cell recognition. High-definition imaging of tagged Plasmodium falciparum invasins localised proteins to multiple cellular compartments of the blood stage merozoite. This includes several that localise to distinct subcompartments within the rhoptries. While originating from the same organelle, however, each has very different dynamics during invasion. Apical Sushi Protein and Rhoptry Neck protein 2 release early, following the junction, whilst a novel rhoptry protein PFF0645c releases only after invasion is complete. This supports the idea that organisation of proteins within a secretory organelle determines the order and destination of protein secretion and provides a localisation-based classification strategy for predicting invasin function during apicomplexan parasite invasion. © 2012 Zuccala et al

Multiple sclerosis risk variants regulate gene expression in innate and adaptive immune cells

At least 200 single-nucleotide polymorphisms (SNPs) are associated with multiple sclerosis (MS) risk. A key function that could mediate SNP-encoded MS risk is their regulatory effects on gene expression. We performed microarrays using RNA extracted from purified immune cell types from 73 untreated MS cases and 97 healthy controls and then performed Cis expression quantitative trait loci mapping studies using additive linear models. We describe MS risk expression quantitative trait loci associations for 129 distinct genes. By extending these models to include an interaction term between genotype and phenotype, we identify MS risk SNPs with opposing effects on gene expression in cases compared with controls, namely, rs2256814 MYT1 in CD4 cells (q = 0.05) and rs12087340 RF00136 in monocyte cells (q = 0.04). The rs703842 SNP was also associated with a differential effect size on the expression of the METTL21B gene in CD8 cells of MS cases relative to controls (q = 0.03). Our study provides a detailed map of MS risk loci that function by regulating gene expression in cell types relevant to MS

Differential splicing using whole-transcript microarrays

<p>Abstract</p> <p>Background</p> <p>The latest generation of Affymetrix microarrays are designed to interrogate expression over the entire length of every locus, thus giving the opportunity to study alternative splicing genome-wide. The Exon 1.0 ST (sense target) platform, with versions for Human, Mouse and Rat, is designed primarily to probe every known or predicted exon. The smaller Gene 1.0 ST array is designed as an expression microarray but still interrogates expression with probes along the full length of each well-characterized transcript. We explore the possibility of using the Gene 1.0 ST platform to identify differential splicing events.</p> <p>Results</p> <p>We propose a strategy to score differential splicing by using the auxiliary information from fitting the statistical model, RMA (robust multichip analysis). RMA partitions the probe-level data into probe effects and expression levels, operating robustly so that if a small number of probes behave differently than the rest, they are downweighted in the fitting step. We argue that adjacent poorly fitting probes for a given sample can be evidence of <it>differential </it>splicing and have designed a statistic to search for this behaviour. Using a public tissue panel dataset, we show many examples of tissue-specific alternative splicing. Furthermore, we show that evidence for putative alternative splicing has a strong correspondence between the Gene 1.0 ST and Exon 1.0 ST platforms.</p> <p>Conclusion</p> <p>We propose a new approach, FIRMAGene, to search for differentially spliced genes using the Gene 1.0 ST platform. Such an analysis complements the search for differential expression. We validate the method by illustrating several known examples and we note some of the challenges in interpreting the probe-level data.</p> <p>Software implementing our methods is freely available as an <monospace>R</monospace> package.</p

Metric for Measuring the Effectiveness of Clustering of DNA Microarray Expression

BACKGROUND: The recent advancement of microarray technology with lower noise and better affordability makes it possible to determine expression of several thousand genes simultaneously. The differentially expressed genes are filtered first and then clustered based on the expression profiles of the genes. A large number of clustering algorithms and distance measuring matrices are proposed in the literature. The popular ones among them include hierarchal clustering and k-means clustering. These algorithms have often used the Euclidian distance or Pearson correlation distance. The biologists or the practitioners are often confused as to which algorithm to use since there is no clear winner among algorithms or among distance measuring metrics. Several validation indices have been proposed in the literature and these are based directly or indirectly on distances; hence a method that uses any of these indices does not relate to any biological features such as biological processes or molecular functions. RESULTS: In this paper we have proposed a metric to measure the effectiveness of clustering algorithms of genes by computing inter-cluster cohesiveness and as well as the intra-cluster separation with respect to biological features such as biological processes or molecular functions. We have applied this metric to the clusters on the data set that we have created as part of a larger study to determine the cancer suppressive mechanism of a class of chemicals called retinoids. We have considered hierarchal and k-means clustering with Euclidian and Pearson correlation distances. Our results show that genes of similar expression profiles are more likely to be closely related to biological processes than they are to molecular functions. The findings have been supported by many works in the area of gene clustering. CONCLUSION: The best clustering algorithm of genes must achieve cohesiveness within a cluster with respect to some biological features, and as well as maximum separation between clusters in terms of the distribution of genes of a behavioral group across clusters. We claim that our proposed metric is novel in this respect and that it provides a measure of both inter and intra cluster cohesiveness. Best of all, computation of the proposed metric is easy and it provides a single quantitative value, which makes comparison of different algorithms easier. The maximum cluster cohesiveness and the maximum intra-cluster separation are indicated by the metric when its value is 0. We have demonstrated the metric by applying it to a data set with gene behavioral groupings such as biological process and molecular functions. The metric can be easily extended to other features of a gene such as DNA binding sites and protein-protein interactions of the gene product, special features of the intron-exon structure, promoter characteristics, etc. The metric can also be used in other domains that use two different parametric spaces; one for clustering and the other one for measuring the effectiveness

Early immune pressure initiated by tissue-resident memory T cells sculpts tumor evolution in non-small cell lung cancer

Tissue-resident memory T (TRM) cells provide immune defense against local infection and can inhibit cancer progression. However, it is unclear to what extent chronic inflammation impacts TRM activation and whether TRM cells existing in tissues before tumor onset influence cancer evolution in humans. We performed deep profiling of healthy lungs and lung cancers in never-smokers (NSs) and ever-smokers (ESs), finding evidence of enhanced immunosurveillance by cells with a TRM-like phenotype in ES lungs. In preclinical models, tumor-specific or bystander TRM-like cells present prior to tumor onset boosted immune cell recruitment, causing tumor immune evasion through loss of MHC class I protein expression and resistance to immune checkpoint inhibitors. In humans, only tumors arising in ES patients underwent clonal immune evasion, unrelated to tobacco-associated mutagenic signatures or oncogenic drivers. These data demonstrate that enhanced TRM-like activity prior to tumor development shapes the evolution of tumor immunogenicity and can impact immunotherapy outcomes

Speeding up the Consensus Clustering methodology for microarray data analysis

<p>Abstract</p> <p>Background</p> <p>The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e.g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of <monospace>Consensus</monospace> (Consensus Clustering), a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset, together with a dissimilarity matrix (the consensus matrix) that can be used by clustering algorithms. As detailed in the remainder of the paper, <monospace>Consensus</monospace> is a natural candidate for a speed-up.</p> <p>Results</p> <p>Since the time-precision performance of <monospace>Consensus</monospace> depends on two parameters, our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for <monospace>Consensus</monospace>. That is, the closely related algorithm <monospace>FC</monospace> (Fast Consensus) that would have the same precision as <monospace>Consensus</monospace> with a substantially better time performance. The performance of <monospace>FC</monospace> has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications, such as cancer studies, gene expression with up and down patterns, and a full spectrum of dimensionality up to over a thousand. Based on their outcome, compared with previous benchmarking results available in the literature, <monospace>FC</monospace> turns out to be among the fastest internal validation methods, while retaining the same outstanding precision of <monospace>Consensus</monospace>. Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix, guaranteeing the same performance as the corresponding matrix produced by <monospace>Consensus</monospace>. We have also experimented with the use of <monospace>Consensus</monospace> and <monospace>FC</monospace> in conjunction with <monospace>NMF</monospace> (Nonnegative Matrix Factorization), in order to identify the correct number of clusters in a dataset. Although <monospace>NMF</monospace> is an increasingly popular technique for biological data mining, our results are somewhat disappointing and complement quite well the state of the art about <monospace>NMF</monospace>, shedding further light on its merits and limitations.</p> <p>Conclusions</p> <p>In summary, <monospace>FC</monospace> with a parameter setting that makes it robust with respect to small and medium-sized datasets, i.e, number of items to cluster in the hundreds and number of conditions up to a thousand, seems to be the internal validation measure of choice. Moreover, the technique we have developed here can be used in other contexts, in particular for the speed-up of stability-based validation measures.</p

Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance

BACKGROUND: Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC). METHODS: We collected ED CCs from 2/1/94 – 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 – 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies (simulated and real). RESULTS: Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets. CONCLUSION: We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results

ELECTROENCEPHALOGRAPHY (EEG) AND ITS USE IN MOTOR LEARNING AND CONTROL

Electroencephalography (EEG) is a non-invasive technique of measuring electric currents generated from active brain regions and is a useful tool for researchers interested in motor control. The study of motor learning and control seeks to understand the way the brain understands, plans and executes movement both physical and imagined. Thus, the purpose of this study was to better understand the ways in which electroencephalography can be used to measure regions of the brain involved with motor control and learning. For this purpose, two independent studies were completed using EEG to monitor brain activity during both executed and imagined actions. The first study sought to understand the cognitive demand of altering a running gait and provides EEG evidence of motor learning. 13 young healthy runners participated in a 6-week in-field gait-retraining program that altered running gait by increasing step rate (steps per minute) by 5-10%. EEG was collected while participants ran on a treadmill with their original gait as a baseline measurement. After the baseline collection, participants ran for one minute at the same speed with a 5-10% step rate increase while EEG was collected. Participants then participated in a 6-week in-field gait-retraining program in which they received bandwith feedback while running in order to learn the new gait. After completing the 6-week training protocol, participants returned to the lab for post training EEG collection while running with the new step rate. Power spectral density plots were generated to measure frequency band power in all gait-retraining phases. Results in the right prefrontal cortex showed a significant increase in beta (13-30 Hz) while initially running with the new gait compared to the baseline step rate. Previous work suggests the right prefrontal cortex is involved with the inhibition of a previously learned behavior and thus, our results suggest an increase in cognitive load to inhibit the previous full stride motion. After training, this increase in beta over the right prefrontal cortex decreased, suggesting motor adaptations had occurred as a result of motor learning. These results give promising evidence for a new method of ensuring permanent changes in performance that will benefit rehabilitation and athletic performance training programs. The second study in this project sought to understand differences in right and left-handers as they mentally simulate movement. 24 right and left-handed individuals (12 right-handers, 12 left-handers) were shown pictures of individual hands on a screen while EEG was collected. Previous research has shown than while solving this task, participants mentally rotate a mental representation of their own hand to determine the handedness of the image. Event-related potential results showed that right-handers had an earlier and greater activation in the parietal regions than left-handers, whereas left-handers had a later and greater activation in the motor related brain regions compared to right-handers. These results suggest differing strategies while mentally solving motor related tasks between right and left-handers. We speculate this is a result of left-handers' need to adapt to a majorly right-hand dominant environment. Both these studies show the benefits of using EEG to understand the motor system in physically executed and imagined actions

Covert Genetic Selections to Optimize Phenotypes

In many high complexity systems (cells, organisms, institutions, societies, economies, etc.), it is unclear which components should be regulated to affect overall performance. To identify and prioritize molecular targets which impact cellular phenotypes, we have developed a selection procedure (“SPI”–single promoting/inhibiting target identification) which monitors the abundance of ectopic cDNAs. We have used this approach to identify growth regulators. For this purpose, complex pools of S. cerevisiae cDNA transformants were established and we quantitated the evolution of the spectrum of cDNAs which was initially present. These data emphasized the importance of translation initiation and ER-Golgi traffic for growth. SPI provides functional insight into the stability of cellular phenotypes under circumstances in which established genetic approaches cannot be implemented. It provides a functional “synthetic genetic signature” for each state of the cell (i.e. genotype and environment) by surveying complex genetic libraries, and does not require specialized arrays of cDNAs/shRNAs, deletion strains, direct assessment of clonal growth or even a conditional phenotype. Moreover, it establishes a hierarchy of importance of those targets which can contribute, either positively or negatively, to modify the prevailing phenotype. Extensions of these proof-of-principle experiments to other cell types should provide a novel and powerful approach to analyze multiple aspects of the basic biology of yeast and animal cells as well as clinically-relevant issues

Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk.

Variation in the human leukocyte antigen (HLA) genes accounts for one-half of the genetic risk in type 1 diabetes (T1D). Amino acid changes in the HLA-DR and HLA-DQ molecules mediate most of the risk, but extensive linkage disequilibrium complicates the localization of independent effects. Using 18,832 case-control samples, we localized the signal to 3 amino acid positions in HLA-DQ and HLA-DR. HLA-DQβ1 position 57 (previously known; P = 1 × 10(-1,355)) by itself explained 15.2% of the total phenotypic variance. Independent effects at HLA-DRβ1 positions 13 (P = 1 × 10(-721)) and 71 (P = 1 × 10(-95)) increased the proportion of variance explained to 26.9%. The three positions together explained 90% of the phenotypic variance in the HLA-DRB1-HLA-DQA1-HLA-DQB1 locus. Additionally, we observed significant interactions for 11 of 21 pairs of common HLA-DRB1-HLA-DQA1-HLA-DQB1 haplotypes (P = 1.6 × 10(-64)). HLA-DRβ1 positions 13 and 71 implicate the P4 pocket in the antigen-binding groove, thus pointing to another critical protein structure for T1D risk, in addition to the HLA-DQ P9 pocket.This research utilizes resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD), and Juvenile Diabetes Research Foundation International (JDRF) and supported by U01 DK062418. This work is supported in part by funding from the National Institutes of Health (5R01AR062886-02 (PIdB), 1R01AR063759 (SR), 5U01GM092691-05 (SR), 1UH2AR067677-01 (SR), R01AR065183 (PIWdB)), a Doris Duke Clinical Scientist Development Award (SR), the Wellcome Trust (JAT) and the National Institute for Health Research (JAT and JMMH), and a Vernieuwingsimpuls VIDI Award (016.126.354) from the Netherlands Organization for Scientific Research (PIWdB). TLL was supported by the German Research Foundation (LE 2593/1-1 and LE 2593/2-1).This is the accepted manuscript. The final version is available at http://www.nature.com/ng/journal/v47/n8/full/ng.3353.html