Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Kinetic Analysis of L1 Homophilic Interaction: ROLE OF THE FIRST FOUR IMMUNOGLOBULIN DOMAINS AND IMPLICATIONS ON BINDING MECHANISM*

L1 is a cell adhesion molecule of the immunoglobulin (Ig) superfamily, critical for central nervous system development, and involved in several neuronal biological events. It is a type I membrane glycoprotein. The L1 ectodomain, composed of six Ig-like and five fibronectin (Fn) type-III domains, is involved in homophilic binding. Here, co-immunoprecipitation studies between recombinant truncated forms of human L1 expressed and purified from insect Spodoptera frugiperda Sf9 cells, and endogenous full-length L1 from human NT2N neurons, showed that the L1 ectodomain (L1/ECD) and L1/Ig1–4 interacted homophilically in trans, contrary to mutants L1/Ig1–3 and L1/Ig2-Fn5. All mutants were correctly folded as evaluated by combination of far-UV CD and fluorescence spectroscopy. Surface plasmon resonance analysis showed comparable dissociation constants of 116 ± 2 and 130 ± 6 nm for L1/ECD-L1/ECD and L1/ECD-L1/Ig1–4, respectively, whereas deletion mutants for Ig1 or Ig4 did not interact. Accordingly, in vivo, Sf9 cells stably expressing L1 were found to adhere only to L1/ECD- and L1/Ig1–4-coated surfaces. Furthermore, only these mutants bound to HEK293 cells overexpressing L1 at the cell surface. Enhancement of neurite outgrowth, which is the consequence of signaling events caused by L1 homophilic binding, was comparable between L1/ECD and L1/Ig1–4. Altogether, these results showed that domains Ig1 to Ig4 are necessary and sufficient for L1 homophilic binding in trans, and that the rest of the molecule does not contribute to the affinity under the conditions of the current study. Furthermore, they are compatible with a cooperative interaction between modules Ig1–Ig4 in a horseshoe conformation

Two Alcohol Binding Residues Interact across a Domain Interface of the L1 Neural Cell Adhesion Molecule and Regulate Cell Adhesion*

Ethanol may cause fetal alcohol spectrum disorders (FASD) in part by inhibiting cell adhesion mediated by the L1 neural cell adhesion molecule. Azialcohols photolabel Glu-33 and Tyr-418, two residues that are predicted by homology modeling to lie within 2.8 Å of each other at the interface between the Ig1 and Ig4 domains of L1 (Arevalo, E., Shanmugasundararaj, S., Wilkemeyer, M. F., Dou, X., Chen, S., Charness, M. E., and Miller, K. W. (2008) Proc. Natl. Acad. Sci. U.S.A. 105, 371–375). Using transient transfection of NIH/3T3 cells with wild type (WT-L1) and mutated L1, we found that cysteine substitution of both residues (E33C/Y418C-L1) significantly increased L1 adhesion above levels observed for WT-L1 or the single cysteine substitutions E33C-L1 or Y418C-L1. The reducing agent β-mercaptoethanol (βME) reversibly decreased the adhesion of E33C/Y418C-L1, but had no effect on WT-L1, E33C-L1, or Y418C-L1. Thus, disulfide bond formation occurs between Cys-33 and Cys-418, confirming both the close proximity of these residues and the importance of Ig1-Ig4 interactions in L1 adhesion. Maximal ethanol inhibition of cell adhesion was significantly lower in cells expressing E33C/Y418C-L1 than in those expressing WT-L1, E33C-L1, or Y418C-L1. Moreover, the effects of βME and ethanol on E33C/Y418C-L1 adhesion were non-additive. The cutoff for alcohol inhibition of WT-L1 adhesion was between 1-butanol and 1-pentanol. Increasing the size of the alcohol binding pocket by mutating Glu-33 to Ala-33, increased the alcohol cutoff from 1-butanol to 1-decanol. These findings support the hypothesis that alcohol binding within a pocket bordered by Glu-33 and Tyr-418 inhibits L1 adhesion by disrupting the Ig1-Ig4 interaction