Small molecule developmental screens reveal the logic and timing of vertebrate development

Much has been learned about vertebrate development by random mutagenesis followed by phenotypic screening and by targeted gene disruption followed by phenotypic analysis in model organisms. Because the timing of many developmental events is critical, it would be useful to have temporal control over modulation of gene function, a luxury frequently not possible with genetic mutants. Here, we demonstrate that small molecules capable of conditional gene product modulation can be identified through developmental screens in zebrafish. We have identified several small molecules that specifically modulate various aspects of vertebrate ontogeny, including development of the central nervous system, the cardiovascular system, the neural crest, and the ear. Several of the small molecules identified allowed us to dissect the logic of melanocyte and otolith development and to identify critical periods for these events. Small molecules identified in this way offer potential to dissect further these and other developmental processes and to identify novel genes involved in vertebrate development.

Logic gates using high Rydberg states

Connected logic gates can be operated on the levels of one molecule by making use of the special properties of high Rydberg states. Explicit experimental results for the NO molecule are provided as an example. A number of other options, including that of several gates concatenated so as to operate as a full adder, are discussed. Specific properties of high Rydberg states that are used are: their autoionization is delayed so that they can be distinguished from direct multiphoton ionization, during their long life such states also can decay by energy transfer to the molecular core in a way that can be controlled by the judicious application of very weak external electrical fields, and the Rydberg states can be detected by the application of an ionizing electrical field. The combination of two (or three) color photons with and without external weak fields allows the construction of quite elaborate logic circuit diagrams and shows that taking advantage of the different intramolecular dynamics of levels that differ by their excitation enables the compounding of logic operations on one molecular frame.

Carbon- and nitrogen-quality signaling to translation are mediated by distinct GATA-type transcription factors

The target of rapamycin (Tor) proteins sense nutrients and control transcription and translation relevant to cell growth. Treating cells with the immunosuppressant rapamycin leads to the intracellular formation of an Fpr1p-rapamycin-Tor ternary complex that in turn leads to translational down-regulation. A more rapid effect is a rich transcriptional response resembling that when cells are shifted from high- to low-quality carbon or nitrogen sources. This transcriptional response is partly mediated by the nutrient-sensitive transcription factors GLN3 and NIL1 (also named GAT1). Here, we show that these GATA-type transcription factors control transcriptional responses that mediate translation by several means. Four observations highlight upstream roles of GATA-type transcription factors in translation. In their absence, processes caused by rapamycin or poor nutrients are diminished: translation repression, eIF4G protein loss, transcriptional down-regulation of proteins involved in translation, and RNA polymerase I/III activity repression. The Tor proteins preferentially use Gln3p or Nil1p to down-regulate translation in response to low-quality nitrogen or carbon, respectively. Functional consideration of the genes regulated by Gln3p or Nil1p reveals the logic of this differential regulation. Besides integrating control of transcription and translation, these transcription factors constitute branches downstream of the multichannel Tor proteins that can be selectively modulated in response to distinct (carbon- and nitrogen-based) nutrient signals from the environment.

Selective response of ternary complex factor Sap1a to different mitogen-activated protein kinase subgroups.

Mitogenic and stres signals results in the activation of extracellular signal-regulated kinases (ERKs) and stress-activated protein kinase/c-Jun N-terminal kinases (SAPK/JNKs), respectively, which are two subgroups of the mitogen-activated protein kinases. A nuclear target of mitogen-activated protein (MAP) kinases is the ternary complex factor Elk-1, which underlies its involvement in the regulation of c-fos gene expression by mitogenic and stress signals. A second ternary complex factor, Sap1a, is coexpressed with Elk-1 in several cell types and shares attributes of Elk-1, the significance of which is not clear. Here we show that Sap1a is phosphorylated efficiently by ERKs but not by SAPK/JNKs. Serum response factor-dependent ternary complex formation by Sap1a is stimulated by ERK phosphorylation but not by SAPK/JNKs. Moreover, Sap1a-mediated transcription is activated by mitogenic signals but not by cell stress. These results suggest that Sap1a and Elk-1 have distinct physiological functions.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.