Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

A predictive model for freshwater bioassessment (Mondego River,Portugal)

We sampled macroinvertebrates at 75 locations in the Mondego river catchment, Central Portugal, and developed a predictive model for water quality assessment of this basin, based on the Reference Condition Approach. Sampling was done from June to September 2001. Fifty-five sites were identified as “Reference sites” and 20 sites were used as “Test sites” to test the model. At each site we also measured 40 habitat variables to characterize water physics and chemistry, habitat type, land use, stream hydrology and geographic location. Macroinvertebrates were generally identified to species or genus level; a total of 207 taxa were found. By Unweighted Pair Group Method with Arithmetic mean (UPGMA) clustering and analysis of species contribution to similarities percentage (SIMPER), two groups of reference sites were established. Using Discriminant Analysis (stepwise forward), four variables correctly predicted 78% of the reference sites to the appropriate group: stream order, pool quality, substrate quality and current velocity. Test sites’ environmental quality was established from their relative distance to reference sites, in MDS ordination space, using a series of bands (BEAST methodology). The model performed well at upstream sites, but at downstream sites it was compromised by the lack of reference sites. As with the English RIVPACS predictive model, the Mondego model should be continually improved with the addition of new reference sites. The adaptation of the Mondego model methodology to the Water Framework Directive is possible and would consist mainly of the integration of the WFD typology and increasing the number of ellipses that define quality bands. Handling editor: K. Martens     

Flood Risk Analysis Based on Information Diffusion Theory

Due to the fact that the flood data series of small drainage basins is relatively short, available data are often not sufficient for flood risk analysis. This presents the problem of risk analysis using very small data samples. One method that can be applied is to regard the available small samples as fuzzy information and optimize them using information diffusion technology to yield analytical results with greater reliability. In this article a risk analysis method based on information diffusion theory is applied to create a new flood risk analysis model. Application of the model is illustrated taking the Jinhuajiang and Qujiang drainage basins as examples. This is a new attempt at applying information diffusion theory in flood risk analysis. Computations based on this analytical flood risk model can yield an estimated flood damage value that is relatively accurate. This study indicates that the aforementioned model exhibits fairly stable analytical results, even when using a small set of sample data. The results also indicate that information diffusion technology is highly capable of extracting useful information and therefore improves system recognition accuracy. This method can be easily applied and the analytical results produced are easy to understand. Results are accurate enough to act as a guide in disaster situations.     

Effects of grazing intensity and grazing exclusion on litter decomposition in the temperate steppe of Nei Mongol,China

Aims Grazing intensity and grazing exclusion affect ecosystem carbon cycling by changing the plant community and soil micro-environment in grassland ecosystems. The aims of this study were: 1) to determine the effects of grazing intensity and grazing exclusion on litter decomposition in the temperate grasslands of Nei Mongol; 2) to compare the difference between above-ground and below-ground litter decomposition; 3) to identify the effects of precipitation on litter production and decomposition. Methods We measured litter production, quality, decomposition rates and soil nutrient contents during the growing season in 2011 and 2012 in four plots, i.e. light grazing, heavy grazing, light grazing exclusion and heavy grazing exclusion. Quadrate surveys and litter bags were used to measure litter production and decomposition rates. All data were analyzed with ANOVA and Pearson’s correlation procedures in SPSS. Important findings Litter production and decomposition rates differed greatly among four plots. During the two years of our study, above-ground litter production and decomposition in heavy-grazing plots were faster than those in light-grazing plots. In the dry year, below-ground litter production and decomposition in light-grazing plots were faster than those in heavy-grazing plots, which is opposite to the findings in the wet year. Short-term grazing exclusion could promote litter production, and the exclusion of light-grazing could increase litter decomposition and nutrient cycling. In contrast, heavy-grazing exclusion decreased litter decomposition. Thus, grazing exclusion is beneficial to the restoration of the light-grazing grasslands, and more human management measures are needed during the restoration of heavy-grazing grasslands. Precipitation increased litter production and decomposition, and below-ground litter was more vulnerable to the inter-annual change of precipitation than above-ground litter. Compared to the light-grazing grasslands, heavy-grazing grasslands had higher sensitivity to precipitation. The above-ground litter decomposition was strongly positively correlated with the litter N content (R² = 0.489, p < 0.01) and strongly negatively correlated with the soil total N content (R² = 0.450, p < 0.01), but it was not significantly correlated with C:N and lignin:N. Below-ground litter decomposition was negatively correlated with the litter C (R² = 0.263, p < 0.01), C:N (R² = 0.349, p < 0.01) and cellulose content (R² = 0.460, p < 0.01). Our results will provide a theoretical basis for ecosystem restoration and the research of carbon cycling.     

An Efficient Regression Type Estimator in Survey Sampling

For the estimation of population mean in simple random sampling, an efficient regression-type estimator is proposed which is more efficient than the conventional regression estimator and hence than mean per unit estimator, ratio and product estimators and many other estimators proposed by various authors. Some numerical examples are included for illustration.     

Assessing water scarcity by simultaneously considering environmental flow requirements,water quantity,and water quality

Water scarcity is a widespread problem in many parts of the world. Most previous methods of water scarcity assessment only considered water quantity, and ignored water quality. In addition, the environmental flow requirement (EFR) was commonly not explicitly considered in the assessment. In this study, we developed an approach to assess water scarcity by considering both water quantity and quality, while at the same time explicitly considering EFR. We applied this quantity–quality-EFR (QQE) approach for the Huangqihai River Basin in Inner Mongolia, China. We found that to keep the river ecosystem health at a “good” level (i.e., suitable for swimming, fishing, and aquaculture), 26% of the total blue water resources should be allocated to meet the EFR. When such a “good” level is maintained, the quantity- and quality-based water scarcity indicators were 1.3 and 14.2, respectively; both were above the threshold of 1.0. The QQE water scarcity indicator thus can be expressed as 1.3(26%)|14.2, indicating that the basin was suffering from scarcity problems related to both water quantity and water quality for a given rate of EFR. The current water consumption has resulted in degradation of the basin's river ecosystems, and the EFR cannot be met in 3 months of a year. To reverse this situation, future policies should aim to reduce water use and pollution discharge, meet the EFR for maintaining healthy river ecosystems, and substantially improve pollution treatment.     

RNA‐seq analysis reveals new candidate genes for drip loss in a Pietrain × Duroc × Landrace × Yorkshire population

Drip loss, one of the most important meat quality traits, is characterized by low heritability. To date, the genetic factors affecting the drip loss trait have not been clearly elucidated. The objective of this study was to identify critical candidate genes affecting drip loss. First, we generated a Pietrain × Duroc × Landrace × Yorkshire commercial pig population and obtained phenotypic values for the drip loss trait. Furthermore, we constructed two RNA libraries from pooled samples of longissimus dorsi muscles with the highest (H group) and lowest (L group) drip loss and identified the differentially expressed genes (DEGs) between these extreme phenotypes using RNA‐seq technology. In total, 25 883 genes were detected in the H and L group libraries, and none was specifically expressed in only one library. Comparative analysis of gene expression levels found that 150 genes were differentially expressed, of which 127 were upregulated and 23 were downregulated in the H group relative to the L group. In addition, 68 drip loss quantitative trait loci (QTL) overlapping with 63 DEGs were identified, and these QTL were distributed mainly on chromosomes 1, 2, 5 and 6. Interestingly, the triadin (TRDN) gene, which is involved in muscle contraction and fat deposition, and the myostatin (MSTN) gene, which has a role in muscle growth, were localized to more than two drip loss QTL, suggesting that both are critical candidate genes responsible for drip loss.     

Joint multiple quantitative trait loci mapping for allometries of body compositions and metabolic traits to body weights in broiler

In order to map quantitative trait loci (QTLs) for allometries of body compositions and metabolic traits in chicken, we phenotypically characterize the allometric growths of multiple body components and metabolic traits relative to BWs using joint allometric scaling models and then establish random regression models (RRMs) to fit genetic effects of markers and minor polygenes derived from the pedigree on the allometric scalings. Prior to statistically inferring the QTLs for the allometric scalings by solving the RRMs, the LASSO technique is adopted to rapidly shrink most of marker genetic effects to zero. Computer simulation analysis confirms the reliability and adaptability of the so-called LASSO-RRM mapping method. In the F₂ population constructed by multiple families, we formulate two joint allometric scaling models of body compositions and metabolic traits, in which six of nine body compositions are tested as significant, while six of eight metabolic traits are as significant. For body compositions, a total of 14 QTLs, of which 9 dominant, were detected to be associated with the allometric scalings of drumstick, fat, heart, shank, liver and spleen to BWs; while for metabolic traits, a total of 19 QTLs also including 9 dominant be responsible for the allometries of T4, IGFI, IGFII, GLC, INS, IGR to BWs. The detectable QTLs or highly linked markers can be used to regulate relative growths of the body components and metabolic traits to BWs in marker-assisted breeding of chickens.