PseudoGA: cell pseudotime reconstruction based on genetic algorithm.

Dynamic regulation of gene expression is often governed by progression through transient cell states. Bulk RNA-seq analysis can only detect average change in expression levels and is unable to identify this dynamics. Single cell RNA-seq presents an unprecedented opportunity that helps in placing the cells on a hypothetical time trajectory that reflects gradual transition of their transcriptomes. This continuum trajectory or 'pseudotime', may reveal the developmental pathway and provide us with information on dynamic transcriptomic changes and other biological processes. Existing approaches to build pseudotime heavily depend on reducing huge dimension to extremely low dimensional subspaces and may lead to loss of information. We propose PseudoGA, a genetic algorithm based approach to order cells assuming that gene expressions vary according to a smooth curve along the pseudotime trajectory. We observe superior accuracy of our method in simulated as well as benchmarking real datasets. Generality of the assumption behind PseudoGA and no dependence on dimensionality reduction technique make it a robust choice for pseudotime estimation from single cell transcriptome data. PseudoGA is also time efficient when applied to a large single cell RNA-seq data and adaptable to parallel computing. R code for PseudoGA is freely available at https://github.com/indranillab/pseudoga.

DNA methylome in pancreatic cancer identified novel promoter hyper-methylation in NPY and FAIM2 genes associated with poor prognosis in Indian patient cohort.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading cancers worldwide and has a poor survival, with a 5-year survival rate of only 8.5%. In this study we investigated altered DNA methylation associated with PDAC severity and prognosis. METHODS: Methylome data, generated using 450 K bead array, was compared between paired PDAC and normal samples in the TCGA cohort (n = 9) and our Indian cohort (n = 7). The total Indian Cohort (n = 75) was split into cohort 1 (n = 7), cohort 2 (n = 22), cohort 3 (n = 26) and cohort 4 (n = 20).Validation of differential methylation (6 selected CpG loci) and associated gene expression for differentially methylated genes (10 selected gDMs) were carried out in separate validation cohorts, using MSP, RT-PCR and IHC correlations between methylation and gene expression were observed in TCGA, GTEx cohorts and in validation cohorts. Kaplan-Meier survival analysis was done to study differential prognosis, during 2-5 years of follow-up. RESULTS: We identified 156 DMPs, mapped to 91 genes (gDMs), in PDAC; 68 (43.5%) DMPs were found to be differentially methylated both in TCGA cohort and our cohort, with significant concordance at hypo- and hyper-methylated loci. Enrichments of "regulation of ion transport", "Interferon alpha/beta signalling", "morphogenesis and development" and "transcriptional dysregulation" pathways were observed among 91 gDMs. Hyper-methylation of NPY and FAIM2 genes with down-regulated expression in PDAC, were significantly associated with poor prognosis in the Indian patient cohort. CONCLUSIONS: Ethnic variations among populations may determine the altered epigenetic landscape in the PDAC patients of the Indian cohort. Our study identified novel differentially methylated genes (mainly NPY and FAIM2) and also validated the previously identified differentially methylated CpG sites associated with PDAC cancer patient's survival. Comparative analysis of our data with TCGA and CPTAC cohorts showed that both NPY and FAIM2 hyper-methylation and down-regulations can be novel epigenetically regulated genes in the Indian patient population, statistically significantly associated with poor survival and advanced tumour stages.

Correction: Phytoplankton nutrient dynamics and flow cytometry based population study of a eutrophic wetland habitat in eastern India, a Ramsar site.

[This corrects the article DOI: 10.1039/C7RA12761H.].

Reciprocal interplay between asporin and decorin: Implications in gastric cancer prognosis.

Effective patient prognosis necessitates identification of novel tumor promoting drivers of gastric cancer (GC) which contribute to worsened conditions by analysing TCGA-gastric adenocarcinoma dataset. Small leucine-rich proteoglycans, asporin (ASPN) and decorin (DCN), play overlapping roles in development and diseases; however, the mechanisms underlying their interplay remain elusive. Here, we investigated the complex interplay of asporin, decorin and their interaction with TGFß in GC tumor and corresponding normal tissues. The mRNA levels, protein expressions and cellular localizations of ASPN and DCN were analyzed using real-time PCR, western blot and immunohistochemistry, respectively. The protein-protein interaction was predicted by in-silico interaction analysis and validated by co-immunoprecipitation assay. The correlations between ASPN and EMT proteins, VEGF and collagen were achieved using western blot analysis. A significant increase in expression of ASPN in tumor tissue vs. normal tissue was observed in both TCGA and our patient cohort. DCN, an effective inhibitor of the TGFß pathway, was negatively correlated with stages of GC. Co-immunoprecipitation demonstrated that DCN binds with TGFß, in normal gastric epithelium, whereas in GC, ASPN preferentially binds TGFß. Possible activation of the canonical TGFß pathway by phosphorylation of SMAD2 in tumor tissues suggests its role as an intracellular tumor promoter. Furthermore, tissues expressing ASPN showed unregulated EMT signalling. Our study uncovers ASPN as a GC-promoting gene and DCN as tumor suppressor, suggesting that ASPN can act as a prognostic marker in GC. For the first time, we describe the physical interaction of TGFß with ASPN in GC and DCN with TGFß in GC and normal gastric epithelium respectively. This study suggests that prevention of ASPN-TGFß interaction or overexpression of DCN could serve as promising therapeutic strategies for GC patients.

Family-based genome-wide association of inflammation biomarkers and fenofibrate treatment response in the GOLDN study.

In this paper we analyzed whole-genome genetic information provided by GAW20 from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study for family data. Lipid levels such as triglycerides (TGs) and high-density lipoprotein (HDL) are measured at different time points before and after administration of an anti-inflammatory drug fenofibrate. Apart from that, the data contain some covariates and whole-genome genotype information. We propose 2 novel approaches based on Henderson's iterative mixed model to identify associated loci corresponding to (a) inflammatory biomarkers like TGs and HDLs together over time, and (b) the response to fenofibrate treatment. We developed a mixed-model approach using both TG and HDL phenotypes at all 4 time points for a genetic association study whereas we used TGs only to study genetic association with response to the drug. We expect that use of complete family data in a longitudinal framework under a single model involving the appropriate correlation structures would be able to exploit the maximum possible information contained in the sample. Our analysis of whole-genome single nucleotide polymorphisms (SNPs) and genomic regions corresponding to drug treatment finds no significant locus after multiple correction. Arguably, the moderately small sample size of the data set, as compared to the sample size usually used in genome-wide association studies (GWAS), could be a reason for such a result. Nevertheless, we report the top 20 SNPs associated with the phenotypes, and the top 20 SNPs and genomic regions associated with a response to fenofibrate treatment. Application of our methods to larger GWAS and further functional validation of the reported top SNPs and genomic regions might provide important biological insight into the genetic constitution of the trait.

Phytoplankton nutrient dynamics and flow cytometry based population study of a eutrophic wetland habitat in eastern India, a Ramsar site.

Phytoplankton diversity, their abundance based on flow cytometric (FCM) analysis and seasonal nutrient dynamics were investigated from a waste water fed wetland of Eastern India (88° 24.641'E and 22° 33.115'N). The primary objective of the study was to correlate the seasonal fluctuations in phytoplankton abundance to the environmental variables. Total chlorophyll content and FCM based cell counts were used to characterize and quantify the phytoplankton population. Multivariate statistical methods were employed in predicting the possible relationships between biotic and abiotic variables. Distinct seasonal variations characterized by high abundance during the pre-summer period compared to other seasons were detected. The results indicated that environmental factors like water temperature and nutrients, such as various forms of nitrogen and phosphate, influenced the seasonal phytoplankton accumulation. Cluster analysis and non-metric multidimensional scaling helped analyze the seasonal distribution of phytoplankton based on their composition. The dominant genera among the entire phytoplankton community were Scenedesmus spp. of Chlorophyta, followed by Merismopedia spp. of Cyanoprokaryota. Around 3.7 × 105 phytoplankton mL-1 were recorded during the study period. Due to the very high count of individual species in the community, FCM based counting was applied for determination of Species Diversity Index. The entire population was divided into 13 subpopulations based on the cell sorting method and the seasonal abundance in each sub-population was illustrated.