Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.     

Renal Function in Children Suffering from Sickle Cell Disease: Challenge of Early Detection in Highly Resource-Scarce Settings

Background

The prevalence of Sickle cell disease is extremely high in Democratic Republic of Congo. Despite this high prevalence of the disease, data on renal abnormalities in children are rare.

Method

The study proposed to assess blood pressure, glomerular function, urea and uric acid levels in 65 steady state Congolese children with homozygous sickle cell disease and 67 normal controls.

Results

In Hb-SS group, blood pressure level tended to be lower than Hb-AA groups but there was no statistically significant difference (p>0.05) between the two groups. The absolute values for GFR corrected for BSA were significantly higher in Hb-SS group compared to Hb-AA group (130.5±34.1 ml/min/1.73 m² vs 113.7±24.5 ml/min/1.73 m²; p = 0.004). Children with Hb-SS were more likely to hyperfiltrate (30.8% of subjects) than children with Hb-AA (6.1% of subjects). Proteinuria was found in 4 (6.2%) children with Hb-SS. Uric acid level was significantly increased in children with Hb-SS compared to corresponding values in control group (4.4±1.3 mg/dl vs 3.5±1.1 mg/dl; p<0.001). Urea level was significantly decreased compared to corresponding values in Hb-AA group (15.3±8.3 mg/dl vs 22.9±10.1 mg/dl; p<0.001).

Conclusion

Hyperfiltration, low creatinine, lower urea and high uric acid are more common in children with sickle cell disease than in normal controls.     

Impact of Insecticide Resistance on the Effectiveness of Pyrethroid-Based Malaria Vectors Control Tools in Benin: Decreased Toxicity and Repellent Effect

Since the first evidence of pyrethroids resistance in 1999 in Benin, mutations have rapidly increased in mosquitoes and it is now difficult to design a study including a control area where malaria vectors are fully susceptible. Few studies have assessed the after effect of resistance on the success of pyrethroid based prevention methods in mosquito populations. We therefore assessed the impact of resistance on the effectiveness of pyrethroids based indoor residual spraying (IRS) in semi-field conditions and long lasting insecticidal nets (LLINs) in laboratory conditions. The results observed showed low repulsion and low toxicity of pyrethroids compounds in the test populations. The toxicity of pyrethroids used in IRS was significantly low with An. gambiae s.l (< 46%) but high for other predominant species such as Mansonia africana (93% to 97%). There were significant differences in terms of the repellent effect expressed as exophily and deterrence compared to the untreated huts (P<0.001). Furthermore, mortality was 23.71% for OlyseNet® and 39.06% for PermaNet®. However, with laboratory susceptible “Kisumu”, mortality was 100% for both nets suggesting a resistance within the wild mosquito populations. Thus treatment with pyrethroids at World Health Organization recommended dose will not be effective at reducing malaria in the coming years. Therefore it is necessary to study how insecticide resistance decreases the efficacy of particular pyrethroids used in pyrethroid-based vector control so that a targeted approach can be adopted.     

GenePattern flow cytometry suite

Background

Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.

Results

In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines.

Conclusions

GenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.

     

Pathogenicity of diatraea saccharalis Densovirus to Host Insets and Characterization of its Viral Genome

Pathogenicity of the Diatraea saccharalis densovirus (DsDNV) was tested on its host larvae. The results showed that up to 4 days after inoculation, no larvae mortality was observed and the infected larvae started to exhibit the infection symptoms from the fourth day. After 5 days of infection, the cumulative mortality of infected larvae increased significantly and reached 60% after 12 days and 100% after 21 days of infection, whereas that of the control group was only 10% and 20%, respectively, after same periods of infection, suggesting that the high mortality of infected larvae groups was due to the high pathogenicity of DsDNV. The size of the DsDNA was determined by Electron microscopy visualization of viral DNA molecules and gel electrophoresis of both native and endonuclease digested DNA fragments. The total length of the native DsDNA was about 5.95 kb. The DsDNV DNA was digested with 16 restriction enzymes and a restriction map of those enzymes was constructed with 41 restriction sites. Comparison of the restriction map of the DsDNV genome with those of the genomes ofJunonia coenia densovirus (JcDNV) and Galleria mellonella densovirus (GmDNV) indicated that the three densovirus genomes were found to share many identical restriction sites. Thus, most of the restriction sites of the following endonucleases Bam H I, Hha I, Xba I, Cla I, Asp 700, Spe I, Nco I and Bcl I, were found to be conserved among the three densovirus genomes. Symmetrical cleavage sites mapped at the both ends of the genome suggested the presence of inverted terminal repeats (ITRs) whose size was estimated to be about 500 bp. The similar genome size, almost identical restriction sites and presence of an ITR of about 500 bp for these three densoviruses suggested that they belong to the same group of ambisense densoviruses.     

Pathogenicity of diatraea saccharalis densovirus to host insets and characterization of its viral genome

Pathogenicity of the Diatraea saccharalis densovirus (DsDNV) was tested on its host larvae.The results showed that up to 4 days after inoculation,no larvae mortality was observed and the infected larvae started to exhibit the infection symptoms from the fourth day.After 5 days of infection,the cumulative mortality of infected larvae increased significantly and reached 60% after 12 days and 100% after 21 days of infection,whereas that of the control group was only 10% and 20%,respectively,after same periods of infection,suggesting that the high mortality of infected larvae groups was due to the high pathogenicity of DsDNV.The size of the DsDNA was determined by Electron microscopy visualization of viral DNA molecules and gel electrophoresis of both native and endonuclease digested DNA fragments.The total length of the native DsDNA was about 5.95 kb.The DsDNV DNA was digested with 16 restriction enzymes and a restriction map of those enzymes was constructed with 41 restriction sites.Comparison of the restriction map of the DsDNV genome with those of the genomes ofJunonia coenia densovirus (JcDNV) and Galleria mellonella densovirus (GmDNV) indicated that the three densovirus genomes were found to share many identical restriction sites.Thus,most of the restriction sites of the following endonucleases Bam H Ⅰ,Hha Ⅰ,Xba Ⅰ,Cla Ⅰ,Asp 700,Spe Ⅰ,Nco Ⅰ and Bcl Ⅰ,were found to be conserved among the three densovirus genomes.Symmetrical cleavage sites mapped at the both ends of the genome suggested the presence of inverted terminal repeats (ITRs) whose size was estimated to be about 500 bp.The similar genome size,almost identical restriction sites and presence of an ITR of about 500 bp for these three densoviruses suggested that they belong to the same group of ambisense densoviruses.