What does biologically meaningful mean? A perspective on gene regulatory network validation

Gene regulatory networks (GRNs) are rapidly being delineated, but their quality and biological meaning are often questioned. Here, I argue that biological meaning is challenging to define and discuss reasons why GRN validation should be interpreted cautiously.     

What's in a name; Genetic structure in Solanum section Petota studied using population-genetic tools

Background  

The taxonomy and systematic relationships among species of Solanum section Petota are complicated and the section seems overclassified. Many of the presumed (sub)species from South America are very similar and they are able to exchange genetic material. We applied a population genetic approach to evaluate support for subgroups within this material, using AFLP data. Our approach is based on the following assumptions: (i) accessions that may exchange genetic material can be analyzed as if they are part of one gene pool, and (ii) genetic differentiation among species is expected to be higher than within species.     

Synergizing metabolic flux analysis and nucleotide sugar metabolism to understand the control of glycosylation of recombinant protein in CHO cells

Background

The photorespiratory nitrogen cycle in C₃ plants involves an extensive diversion of carbon and nitrogen away from the direct pathways of assimilation. The liberated ammonia is re-assimilated, but up to 25% of the carbon may be released into the atmosphere as CO₂. Because of the loss of CO₂ and high energy costs, there has been considerable interest in attempts to decrease the flux through the cycle in C₃ plants. Transgenic tobacco plants were generated that contained the genes gcl and hyi from E. coli encoding glyoxylate carboligase (EC 4.1.1.47) and hydroxypyruvate isomerase (EC 5.3.1.22) respectively, targeted to the peroxisomes. It was presumed that the two enzymes could work together and compete with the aminotransferases that convert glyoxylate to glycine, thus avoiding ammonia production in the photorespiratory nitrogen cycle.

Results

When grown in ambient air, but not in elevated CO₂, the transgenic tobacco lines had a distinctive phenotype of necrotic lesions on the leaves. Three of the six lines chosen for a detailed study contained single copies of the gcl gene, two contained single copies of both the gcl and hyi genes and one line contained multiple copies of both gcl and hyi genes. The gcl protein was detected in the five transgenic lines containing single copies of the gcl gene but hyi protein was not detected in any of the transgenic lines. The content of soluble amino acids including glycine and serine, was generally increased in the transgenic lines growing in air, when compared to the wild type. The content of soluble sugars, glucose, fructose and sucrose in the shoot was decreased in transgenic lines growing in air, consistent with decreased carbon assimilation.

Conclusions

Tobacco plants have been generated that produce bacterial glyoxylate carboligase but not hydroxypyruvate isomerase. The transgenic plants exhibit a stress response when exposed to air, suggesting that some glyoxylate is diverted away from conversion to glycine in a deleterious short-circuit of the photorespiratory nitrogen cycle. This diversion in metabolism gave rise to increased concentrations of amino acids, in particular glutamine and asparagine in the leaves and a decrease of soluble sugars.     

Tracking genome engineering outcome at individual DNA breakpoints

Site-specific genome engineering technologies are increasingly important tools in the postgenomic era, where biotechnological objectives often require organisms with precisely modified genomes. Rare-cutting endonucleases, through their capacity to create a targeted DNA strand break, are one of the most promising of these technologies. However, realizing the full potential of nuclease-induced genome engineering requires a detailed understanding of the variables that influence resolution of nuclease-induced DNA breaks. Here we present a genome engineering reporter system, designated 'traffic light', that supports rapid flow-cytometric analysis of repair pathway choice at individual DNA breaks, quantitative tracking of nuclease expression and donor template delivery, and high-throughput screens for factors that bias the engineering outcome. We applied the traffic light system to evaluate the efficiency and outcome of nuclease-induced genome engineering in human cell lines and identified strategies to facilitate isolation of cells in which a desired engineering outcome has occurred.     

Structural analysis of papain-like NlpC/P60 superfamily enzymes with a circularly permuted topology reveals potential lipid binding sites

NlpC/P60 superfamily papain-like enzymes play important roles in all kingdoms of life. Two members of this superfamily, LRAT-like and YaeF/YiiX-like families, were predicted to contain a catalytic domain that is circularly permuted such that the catalytic cysteine is located near the C-terminus, instead of at the N-terminus. These permuted enzymes are widespread in virus, pathogenic bacteria, and eukaryotes. We determined the crystal structure of a member of the YaeF/YiiX-like family from Bacillus cereus in complex with lysine. The structure, which adopts a ligand-induced, "closed" conformation, confirms the circular permutation of catalytic residues. A comparative analysis of other related protein structures within the NlpC/P60 superfamily is presented. Permutated NlpC/P60 enzymes contain a similar conserved core and arrangement of catalytic residues, including a Cys/His-containing triad and an additional conserved tyrosine. More surprisingly, permuted enzymes have a hydrophobic S1 binding pocket that is distinct from previously characterized enzymes in the family, indicative of novel substrate specificity. Further analysis of a structural homolog, YiiX (PDB 2if6) identified a fatty acid in the conserved hydrophobic pocket, thus providing additional insights into possible function of these novel enzymes.     

CD24 Expression Identifies Teratogen-Sensitive Fetal Neural Stem Cell Subpopulations: Evidence from Developmental Ethanol Exposure and Orthotopic Cell Transfer Models

Background

Ethanol is a potent teratogen. Its adverse neural effects are partly mediated by disrupting fetal neurogenesis. The teratogenic process is poorly understood, and vulnerable neurogenic stages have not been identified. Identifying these is a prerequisite for therapeutic interventions to mitigate effects of teratogen exposures.

Methods

We used flow cytometry and qRT-PCR to screen fetal mouse-derived neurosphere cultures for ethanol-sensitive neural stem cell (NSC) subpopulations, to study NSC renewal and differentiation. The identity of vulnerable NSC populations was validated in vivo, using a maternal ethanol exposure model. Finally, the effect of ethanol exposure on the ability of vulnerable NSC subpopulations to integrate into the fetal neurogenic environment was assessed following ultrasound guided, adoptive transfer.

Results

Ethanol decreased NSC mRNAs for c-kit, Musashi-1and GFAP. The CD24⁺ NSC population, specifically the CD24⁺CD15⁺ double-positive subpopulation, was selectively decreased by ethanol. Maternal ethanol exposure also resulted in decreased fetal forebrain CD24 expression. Ethanol pre-exposed CD24⁺ cells exhibited increased proliferation, and deficits in cell-autonomous and cue-directed neuronal differentiation, and following orthotopic transplantation into naïve fetuses, were unable to integrate into neurogenic niches. CD24^depleted cells retained neurosphere regeneration capacity, but following ethanol exposure, generated increased numbers of CD24⁺ cells relative to controls.

Conclusions

Neuronal lineage committed CD24⁺ cells exhibit specific vulnerability, and ethanol exposure persistently impairs this population’s cell-autonomous differentiation capacity. CD24⁺ cells may additionally serve as quorum sensors within neurogenic niches; their loss, leading to compensatory NSC activation, perhaps depleting renewal capacity. These data collectively advance a mechanistic hypothesis for teratogenesis leading to microencephaly.     

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models

Background

Artificial neural networks (ANN) mimic the function of the human brain and are capable of performing massively parallel computations for data processing and knowledge representation. ANN can capture nonlinear relationships between predictors and responses and can adaptively learn complex functional forms, in particular, for situations where conventional regression models are ineffective. In a previous study, ANN with Bayesian regularization outperformed a benchmark linear model when predicting milk yield in dairy cattle or grain yield of wheat. Although breeding values rely on the assumption of additive inheritance, the predictive capabilities of ANN are of interest from the perspective of their potential to increase the accuracy of prediction of molecular breeding values used for genomic selection. This motivated the present study, in which the aim was to investigate the accuracy of ANN when predicting the expected progeny difference (EPD) of marbling score in Angus cattle. Various ANN architectures were explored, which involved two training algorithms, two types of activation functions, and from 1 to 4 neurons in hidden layers. For comparison, BayesCπ models were used to select a subset of optimal markers (referred to as feature selection), under the assumption of additive inheritance, and then the marker effects were estimated using BayesCπ with π set equal to zero. This procedure is referred to as BayesCpC and was implemented on a high-throughput computing cluster.

Results

The ANN with Bayesian regularization method performed equally well for prediction of EPD as BayesCpC, based on prediction accuracy and sum of squared errors. With the 3K-SNP panel, for example, prediction accuracy was 0.776 using BayesCpC, and ranged from 0.776 to 0.807 using BRANN. With the selected 700-SNP panel, prediction accuracy was 0.863 for BayesCpC and ranged from 0.842 to 0.858 for BRANN. However, prediction accuracy for the ANN with scaled conjugate gradient back-propagation was lower, ranging from 0.653 to 0.689 with the 3K-SNP panel, and from 0.743 to 0.793 with the selected 700-SNP panel.

Conclusions

ANN with Bayesian regularization performed as well as linear Bayesian regression models in predicting additive genetic values, supporting the idea that ANN are useful as universal approximators of functions of interest in breeding contexts.     

Identification and optimization of novel 1,3,4-oxadiazole EP1 receptor antagonists

A novel series of oxadiazole EP1 receptor antagonists was identified by replacing the amide of a known glycine sulfonamide derivative with a 1,3,4-oxadiazole. Optimization of the substitution patterns on the three aromatic rings led to the identification of high affinity EP1 receptor antagonists. The derivative with highest affinity displayed a binding IC50 of 2.5 nM (pIC50 8.6).