Study of a tri-trophic prey-dependent food chain model of interacting populations

The current paper accounts for the influence of intra-specific competition among predators in a prey dependent tri-trophic food chain model of interacting populations. We offer a detailed mathematical analysis of the proposed food chain model to illustrate some of the significant results that has arisen from the interplay of deterministic ecological phenomena and processes. Biologically feasible equilibria of the system are observed and the behaviours of the system around each of them are described. In particular, persistence, stability (local and global) and bifurcation (saddle-node, transcritical, Hopf–Andronov) analysis of this model are obtained. Relevant results from previous well known food chain models are compared with the current findings. Global stability analysis is also carried out by constructing appropriate Lyapunov functions. Numerical simulations show that the present system is capable enough to produce chaotic dynamics when the rate of self-interaction is very low. On the other hand such chaotic behaviour disappears for a certain value of the rate of self interaction. In addition, numerical simulations with experimented parameters values confirm the analytical results and shows that intra-specific competitions bears a potential role in controlling the chaotic dynamics of the system; and thus the role of self interactions in food chain model is illustrated first time. Finally, a discussion of the ecological applications of the analytical and numerical findings concludes the paper.     

Disentangling the role of soil in structuring tropical tree communities at Tarap Hill Reserve of Bangladesh

Tarap Hill Reserve, the largest upland reserve of Bangladesh, is situated along the Indo-Burma Biodiversity Hotspot. It encompasses the last remaining patches of natural vegetation in the Northeastern Tarap Mountain System and harbors 87 % of the nationally declared red-listed vascular plant species. Despite requiring high conservation priority, this is one of the least studied reserves in the tropics. In this study, we collected vegetation and soil (eight variables) data from 68 sample plots. We identified the tree communities by cluster analysis and verified them using the multi-response permutation procedure and detrended correspondence analysis. Species richness, diversity, and compositional similarity between the communities were also estimated. In total, 116 tree species representing 69 genera were recorded within the four identified tree community types. Finally, canonical correspondence analysis with associated Monte Carlo permutation tests (499 permutations) was performed to explore the patterns of variation in tree species distribution explained by the soil variables. Soil phosphorus, organic matter content, and pH were most closely correlated with tree compositional variation. Thus, conservation strategies that take into account variations in these influential soil factors may aid in the conservation of trees in the reserve.     

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only ''pair-wise interactions''. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the ''Apriori algorithm'' for deriving ''microbial association rules'' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various ''(subsets of) features/items'' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between ''subsets/subgroups'' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving ''microbial association rules'' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.     

Vikodak - A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets

Background

The overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Consequently, prior (collated) information pertaining to genes, enzymes encoded by the resident microbes can aid in indirectly (re)constructing/ inferring the metabolic/ functional potential of a given microbial community (given its taxonomic abundance profile). In this study, we present Vikodak—a multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. With the underlying assumptions of co-metabolism and independent contributions of different microbes in a community, a concerted effort has been made to accommodate microbial co-existence patterns in various modules incorporated in Vikodak.

Results

Validation experiments on over 1400 metagenomic samples have confirmed the utility of Vikodak in (a) deciphering enzyme abundance profiles of any KEGG metabolic pathway, (b) functional resolution of distinct metagenomic environments, (c) inferring patterns of functional interaction between resident microbes, and (d) automating statistical comparison of functional features of studied microbiomes. Novel features incorporated in Vikodak also facilitate automatic removal of false positives and spurious functional predictions.

Conclusions

With novel provisions for comprehensive functional analysis, inclusion of microbial co-existence pattern based algorithms, automated inter-environment comparisons; in-depth analysis of individual metabolic pathways and greater flexibilities at the user end, Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.

Availability and Implementation

A web implementation of Vikodak can be publicly accessed at: http://metagenomics.atc.tcs.com/vikodak. This web service is freely available for all categories of users (academic as well as commercial).     

Stunted at 10 Years. Linear Growth Trajectories and Stunting from Birth to Pre-Adolescence in a Rural Bangladeshi Cohort

Background

Few studies in low-income settings analyse linear growth trajectories from foetal life to pre-adolescence. The aim of this study is to describe linear growth and stunting from birth to 10 years in rural Bangladesh and to analyse whether maternal and environmental determinants at conception are associated with linear growth throughout childhood and stunting at 10 years.

Methods and Findings

Pregnant women participating in the MINIMat trial were identified in early pregnancy and a birth cohort (n = 1054) was followed with 19 growth measurements from birth to 10 years. Analyses of baseline predictors and mean height-for-age Z-scores (HAZ) over time were modelled using GLMM. Logistic regression analysis was used to investigate the associations between baseline predictors and stunting (HAZ<-2) at 10 years. HAZ decreased to 2 years, followed by an increase up to 10 years, while the average height-for-age difference in cm (HAD) to the WHO reference median continued to increase up to 10 years. Prevalence of stunting was highest at 2 years (50%) decreasing to 29% at 10 years. Maternal height, maternal educational level and season of conception were all independent predictors of HAZ from birth to pre-adolescence (p<0.001) and stunting at 10 years. The highest probability to be stunted at 10 years was for children born by short mothers (<147.5 cm) (OR_adj 2.93, 95% CI: 2.06–4.20), mothers with no education (OR_adj 1.74, 95% CI 1.17–2.81) or those conceived in the pre-monsoon season (OR_adj 1.94, 95% CI 1.37–2.77).

Conclusions

Height growth trajectories and prevalence of stunting in pre-adolescence showed strong intergenerational associations, social differentials, and environmental influence from foetal life. Targeting women before and during pregnancy is needed for the prevention of impaired child growth.     

Blimp-1-Dependent IL-10 Production by Tr1 Cells Regulates TNF-Mediated Tissue Pathology

Tumor necrosis factor (TNF) is critical for controlling many intracellular infections, but can also contribute to inflammation. It can promote the destruction of important cell populations and trigger dramatic tissue remodeling following establishment of chronic disease. Therefore, a better understanding of TNF regulation is needed to allow pathogen control without causing or exacerbating disease. IL-10 is an important regulatory cytokine with broad activities, including the suppression of inflammation. IL-10 is produced by different immune cells; however, its regulation and function appears to be cell-specific and context-dependent. Recently, IL-10 produced by Th1 (Tr1) cells was shown to protect host tissues from inflammation induced following infection. Here, we identify a novel pathway of TNF regulation by IL-10 from Tr1 cells during parasitic infection. We report elevated Blimp-1 mRNA levels in CD4⁺ T cells from visceral leishmaniasis (VL) patients, and demonstrate IL-12 was essential for Blimp-1 expression and Tr1 cell development in experimental VL. Critically, we show Blimp-1-dependent IL-10 production by Tr1 cells prevents tissue damage caused by IFNγ-dependent TNF production. Therefore, we identify Blimp-1-dependent IL-10 produced by Tr1 cells as a key regulator of TNF-mediated pathology and identify Tr1 cells as potential therapeutic tools to control inflammation.     

Overexpression of ACC gene from oleaginous yeast Lipomyces starkeyi enhanced the lipid accumulation in Saccharomyces cerevisiae with increased levels of glycerol 3-phosphate substrates

The conversion of acetyl-CoA to malonyl-CoA by acetyl-CoA carboxylase (ACC) is the rate-limiting step in fatty acid biosynthesis. In this study, a gene coding for ACC was isolated and characterized from an oleaginous yeast, Lipomyces starkeyi. Real-time quantitative PCR (qPCR) analysis of L. starkeyi acetyl-CoA carboxylase gene (LsACC1) showed that the expression levels were upregulated with the fast accumulation of lipids. The LsACC1 was co-overexpressed with the glycerol 3-phosphate dehydrogenase gene (GPD1), which regulates lipids biosynthesis by supplying another substrates glycerol 3-phosphate for storage lipid assembly, in the non-oleaginous yeast Saccharomyces cerevisiae. Further, the S. cerevisiae acetyl-CoA carboxylase (ScACC1) was transferred with GPD1 and its function was analyzed in comparison with LsACC1. The results showed that overexpressed LsACC1 and GPD1 resulted in a 63% increase in S. cerevisiae. This study gives new data in understanding of the molecular mechanisms underlying the regulation of fatty acids and lipid biosynthesis in yeasts.     

Lipidomic Analysis of α-Synuclein Neurotoxicity Identifies Stearoyl CoA Desaturase as a Target for Parkinson Treatment

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