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A central goal of systems biology is to develop models that are both predictive and accurately describe the biological system. One complexity to this endeavor is that it is possible to develop models that appear predictive even if they use far fewer components than the biological system itself uses for the same process. This problem also occurs in quantitative genetics where it is often possible to describe the variation in a system using fewer genes than are actually variable due to the complications of linkage between causal polymorphisms and population structure. Thus, there is a crucial need to begin an empirical investigation into the true number of components that are used by biological systems to determine a phenotypic outcome. In this study, we use a meta-analysis of directly comparable metabolomics quantitative studies using quantitative trait locus mapping and genome wide association mapping to show that it is currently not possible to estimate how many genetic loci are truly polymorphic within Arabidopsis thaliana. Our analysis shows that it would require the analysis of at least a 1000 line bi-parental population to begin to estimate how many polymorphic loci control metabolic variation within Arabidopsis. Understanding the base number of loci that are actually involved in determining variation in metabolic systems is fundamental to developing systems models that are truly reflective of how metabolism is modulated within a living organism.

Regulation of primary metabolism is a central mechanism by which plants coordinate their various responses to biotic and abiotic challenge. To identify genes responsible for natural variation in primary metabolism, we focused on cloning a locus from Arabidopsis thaliana that influences the level of TCA cycle metabolites in planta. We found that the Met.V.67 locus was controlled by natural variation in METABOLIC NETWORK MODULATOR 1 (MNM1), which encoded an AT-hook motif-containing protein that was unique to the Brassicales lineage. MNM1 had wide ranging effects on plant metabolism and displayed a tissue expression pattern that was suggestive of a function in sink tissues. Natural variation within MNM1 had differential effects during a diurnal time course, and this temporal dependency was supported by analysis of T-DNA insertion and over-expression lines for MNM1. Thus, the cloning of a natural variation locus specifically associated with primary metabolism allowed us to identify MNM1 as a lineage-specific modulator of primary metabolism, suggesting that the regulation of primary metabolism can change during evolution.

Background: The fetal neurodevelopmental microstructural alterations of intrauterine exposure to preeclampsia (PE) or gestational hypertension (GH) remain unknown. Purpose: To evaluate the differences in diffusion‐weighted imaging (DWI) of the fetal brain between normotensive pregnancies and PE/GH pregnancies, with a focus on PE/GH pregnancies with fetal growth restriction (FGR). Study Type: Retrospective matched case–control study. Population: 40 singleton pregnancies with PE/GH complicated by FGR, and 3 paired control groups (PE/GH without FGR, normotensive FGR, normotensive pregnancies) (28–38 gestational weeks). Field Strength/Sequence: DWI with single‐shot echo‐planar imaging at 1.5 Tesla. Assessment: The apparent diffusion coefficient (ADC) values were calculated in the centrum semi‐ovale (CSO), parietal white matter (PWM), frontal white matter (FWM), occipital white matter (OWM), temporal white matter (TWM), basal ganglia, thalamus (THAL), pons, and cerebellar hemisphere. Statistical Tests: Student t test or Wilcoxon matched test was used to reveal the difference of ADC values among the investigated brain regions. A correlation between gestational age (GA) and ADC values was determined by linear regression analysis. Results: Compared with fetuses in PE/GH without FGR and those with normotensive pregnancies, fetuses in the PE/GH with FGR group had significantly lower average ADC measurements of supratentorial regions (1.65 ± 0.09 vs. 1.71 ± 0.10 10−3 mm2/sec; vs. 1.73 ± 0.11 10−3 mm2/sec, respectively). Regions of significantly decreased ADC values in the fetal brain included CSO, FWM, PWM, OWM, TWM and THAL in cases of PE/GH with FGR. ADC values from supratentorial regions in PE/GH pregnancies were not significantly correlated with GA (P = 0.12, 0.26); however, this trend was statistically significant in the normotensive groups. Data Conclusion: ADC values may indicate fetal brain developmental alterations in PE/GH with FGR fetuses but more microscopic and morphological studies are necessary to provide additional evidence to offer a different interpretation of this trend in fetal brain. Level of Evidence: 4 Technical Efficacy Stage: 3 [ABSTRACT FROM AUTHOR]

Inorganic components in the solid electrolyte interphases (SEIs) on lithium (Li) metal play a critical role in Li metal batteries (LMBs) since the presence of numerous inorganic constituents may promote ion diffusion and mechanical strength. However, manipulating inorganic components and acquiring an in-depth understanding of their roles in LMBs remain challenging. Herein, we deliberately introduce different kinds of inorganic components (e.g., LiF, Li3N, and their complexes) into SEIs on Li metal in LMBs by electrolyte engineering. Through in situ atomic force microscopy and electrochemical experiments, the roles of the specific inorganic component in SEIs played in the process of Li nucleation and evolution are revealed, and the detailed synergistic effect of LiF and Li3N is clarified. We demonstrate that the simultaneous presence of LiF and Li3N in SEIs enables homogeneous and dense lithium metal nucleation and growth, improving both the chemical stability and mechanical strength of SEIs for highly stable LMBs. In contrast, individual LiF and Li3N in SEIs induce needle morphology of Li metal and discrete Li deposition, respectively. These findings can provide rational guidelines for optimizing SEIs through electrolyte engineering for stable LMBs. [ABSTRACT FROM AUTHOR]

Understanding genome to phenotype linkages has been greatly enabled by genomic sequencing. However, most genome analysis is typically confined to the nuclear genome. We conducted a metabolomic QTL analysis on a reciprocal RIL population structured to examine how variation in the organelle genomes affects phenotypic variation. This showed that the cytoplasmic variation had effects similar to, if not larger than, the largest individual nuclear locus. Inclusion of cytoplasmic variation into the genetic model greatly increased the explained phenotypic variation. Cytoplasmic genetic variation was a central hub in the epistatic network controlling the plant metabolome. This epistatic influence manifested such that the cytoplasmic background could alter or hide pairwise epistasis between nuclear loci. Thus, cytoplasmic genetic variation plays a central role in controlling natural variation in metabolomic networks. This suggests that cytoplasmic genomes must be included in any future analysis of natural variation. DOI: http://dx.doi.org/10.7554/eLife.00776.001.