Filaggrin loss-of-function variants are associated with atopic dermatitis phenotypes in a diverse, early-life prospective cohort.

Loss-of-function (LoF) variants in the filaggrin (FLG) gene are the strongest known genetic risk factor for atopic dermatitis (AD), but the impact of these variants on AD outcomes is poorly understood. We comprehensively identified genetic variants through targeted region sequencing of FLG in children participating in the Mechanisms of Progression of Atopic Dermatitis to Asthma in Children cohort. Twenty FLG LoF variants were identified, including 1 novel variant and 9 variants not previously associated with AD. FLG LoF variants were found in the cohort. Among these children, the presence of 1 or more FLG LoF variants was associated with moderate/severe AD compared with those with mild AD. Children with FLG LoF variants had a higher SCORing for Atopic Dermatitis (SCORAD) and higher likelihood of food allergy within the first 2.5 years of life. LoF variants were associated with higher transepidermal water loss (TEWL) in both lesional and nonlesional skin. Collectively, our study identifies established and potentially novel AD-associated FLG LoF variants and associates FLG LoF variants with higher TEWL in lesional and nonlesional skin.

Effects of IRF5 lupus risk haplotype on pathways predicted to influence B cell functions.

Both genetic and environmental interactions affect systemic lupus erythematosus (SLE) development and pathogenesis. One known genetic factor associated with lupus is a haplotype of the interferon regulatory factor 5 (IRF5) gene. Analysis of global gene expression microarray data using gene set enrichment analysis identified multiple interferon- and inflammation-related gene sets significantly overrepresented in cells with the risk haplotype. Pathway analysis using expressed genes from the significant gene sets impacted by the IRF5 risk haplotype confirmed significant correlation with the interferon pathway, Toll-like receptor pathway, and the B-cell receptor pathway. SLE patients with the IRF5 risk haplotype have a heightened interferon signature, even in an unstimulated state (P = 0.011), while patients with the IRF5 protective haplotype have a B cell interferon signature similar to that of controls. These results identify multiple genes in functionally significant pathways which are affected by IRF5 genotype. They also establish the IRF5 risk haplotype as a key determinant of not only the interferon response, but also other B-cell pathways involved in SLE.

Advances in flux balance analysis.

Biology is going through a paradigm shift from reductionist to holistic, systems-based approaches. The complete genome sequence for a number of organisms is available and the analysis of genome sequence data is proving very useful. Thus, genome sequencing projects and bioinformatic analyses are leading to a complete 'parts catalog' of the molecular components in many organisms. The next challenge will be to reconstruct and simulate overall cellular functions based on the extensive reductionist information. Recent advances have been made in the area of flux balance analysis, a mathematical modeling approach often utilized by metabolic engineers to quantitatively simulate microbial metabolism.

Decreased protein expression and intermittent recoveries in BiP levels result from cellular stress during heterologous protein expression in Saccharomyces cerevisiae.

Cells are inherently robust to environmental perturbations and have evolved to recover readily from short-term exposure to heat, pH changes, and nutrient deprivation during times of stress. The stress of unfolded protein accumulation has been implicated previously in low protein yields during heterologous protein expression. Here we describe the dynamics of the response to this stress, termed the unfolded protein response (UPR), during the expression of the single chain antibody 4-4-20 (scFv) in Saccharomyces cerevisiae. Expression of scFv decreased the growth rate of yeast cells whether the scFv was expressed from single-copy plasmids or integrated into the chromosome. However, the growth rates recovered at longer expression times, and surprisingly, the recovery occurred more quickly in the high-copy integration strains. The presence of a functional UPR pathway was necessary for a recovery of normal growth rates. During the growth inhibition, the UPR pathway appeared to be activated, resulting in decreased intracellular scFv levels and intermittent recovery of the chaperone BiP within the endoplasmic reticulum. Intracellular scFv was observed primarily in the endoplasmic reticulum, consistent with activation of the UPR pathway. Although the intracellular scFv levels dropped over the course of the expression, this was not a result of scFv secretion. A functional UPR pathway was necessary for the drop in intracellular scFv, suggesting that the decrease was a direct response of UPR activation. Taken together, these results suggest that control of heterologous gene expression to avoid UPR activation will result in higher production levels.

Designing experiments that aid in the identification of regulatory networks.

Predictive mathematical models of the interactions of a genetic network can provide insight into the mechanisms of gene regulation, the role of various genes within a network and how multiple genes interact leading to complex traits. However, identification of the parameters and interactions is currently a limiting step in the development of such models. This work reviews the state of the art for design of experiments in biological systems and demonstrates the need for improved design of experiments through the use of a model system. Appropriate design of experiments has a profound impact on the ability to identify a model and on the quality of resulting identified model. Key issues include the selection of appropriate input sequences (e.g. random, independent multivariate inputs) and the selection of the sampling frequencies. This work demonstrates that these issues are especially important in the identification of biochemical networks and that the traditional biochemical approach is incapable of truly identifying the behavior present in such networks.

Description and analysis of metabolic connectivity and dynamics in the human red blood cell.

The human red blood cell (hRBC) metabolic network is relatively simple compared with other whole cell metabolic networks, yet too complicated to study without the aid of a computer model. Systems science techniques can be used to uncover the key dynamic features of hRBC metabolism. Herein, we have studied a full dynamic hRBC metabolic model and developed several approaches to identify metabolic pools of metabolites. In particular, we have used phase planes, temporal decomposition, and statistical analysis to show hRBC metabolism is characterized by the formation of pseudoequilibrium concentration states. Such equilibria identify metabolic "pools" or aggregates of concentration variables. We proceed to define physiologically meaningful pools, characterize them within the hRBC, and compare them with those derived from systems engineering techniques. In conclusion, systems science methods can decipher detailed information about individual enzymes and metabolites within metabolic networks and provide further understanding of complex biological networks.