An exon junction complex-independent function of Barentsz in neuromuscular synapse growth.

The exon junction complex controls the translation, degradation, and localization of spliced mRNAs, and three of its core subunits also play a role in splicing. Here, we show that a fourth subunit, Barentsz, has distinct functions within and separate from the exon junction complex in Drosophila neuromuscular development. The distribution of mitochondria in larval muscles requires Barentsz as well as other exon junction complex subunits and is not rescued by a Barentsz transgene in which residues required for binding to the core subunit eIF4AIII are mutated. In contrast, interactions with the exon junction complex are not required for Barentsz to promote the growth of neuromuscular synapses. We find that the Activin ligand Dawdle shows reduced expression in barentsz mutants and acts downstream of Barentsz to control synapse growth. Both barentsz and dawdle are required in motor neurons, muscles, and glia for normal synapse growth, and exogenous Dawdle can rescue synapse growth in the absence of barentsz. These results identify a biological function for Barentsz that is independent of the exon junction complex.

m6A modulates neuronal functions and sex determination in Drosophila.

N6-methyladenosine RNA (m6A) is a prevalent messenger RNA modification in vertebrates. Although its functions in the regulation of post-transcriptional gene expression are beginning to be unveiled, the precise roles of m6A during development of complex organisms remain unclear. Here we carry out a comprehensive molecular and physiological characterization of the individual components of the methyltransferase complex, as well as of the YTH domain-containing nuclear reader protein in Drosophila melanogaster. We identify the member of the split ends protein family, Spenito, as a novel bona fide subunit of the methyltransferase complex. We further demonstrate important roles of this complex in neuronal functions and sex determination, and implicate the nuclear YT521-B protein as a main m6A effector in these processes. Altogether, our work substantially extends our knowledge of m6A biology, demonstrating the crucial functions of this modification in fundamental processes within the context of the whole animal.

Specific Isoforms of the Guanine-Nucleotide Exchange Factor dPix Couple Neuromuscular Synapse Growth to Muscle Growth.

Developmental growth requires coordination between the growth rates of individual tissues and organs. Here, we examine how Drosophila neuromuscular synapses grow to match the size of their target muscles. We show that changes in muscle growth driven by autonomous modulation of insulin receptor signaling produce corresponding changes in synapse size, with each muscle affecting only its presynaptic motor neuron branches. This scaling growth is mechanistically distinct from synaptic plasticity driven by neuronal activity and requires increased postsynaptic differentiation induced by insulin receptor signaling in muscle. We identify the guanine-nucleotide exchange factor dPix as an effector of insulin receptor signaling. Alternatively spliced dPix isoforms that contain a specific exon are necessary and sufficient for postsynaptic differentiation and scaling growth, and their mRNA levels are regulated by insulin receptor signaling. These findings define a mechanism by which the same signaling pathway promotes both autonomous muscle growth and non-autonomous synapse growth.

A Multi-Laplacian Prior and Augmented Lagrangian Approach to the Exploratory Analysis of Time-Varying Gene and Transcriptional Regulatory Networks for Gene Microarray Data.

This paper proposes a novel multi-Laplacian prior (MLP) and augmented Lagrangian method (ALM) approach for gene interactions and putative transcription factors (TFs) identification from time-course gene microarray data. It employs a non-linear time-varying auto-regressive (N-TVAR) model and the Maximum-A-Posteriori-Probability method for incorporating the multi-Laplacian prior and the continuity constraint. The MLP allows connections to/from a gene to be better preserved for putative TF identification in non-stationarity gene regulatory network as compared with conventional L1-based penalties. Moreover, the ALM allows the resultant non-smooth L1-based penalties to be decoupled from the remaining smooth terms, so that the former and latter can be efficiently solved using a low-complexity proximity operator and smooth optimization technique, respectively. Synthetic and real time-course gene microarray datasets are tested to evaluate the performance of the proposed method. Experimental results show that the proposed method gives better accuracy and higher computational speed than our previous work using smoothed approximation. Moreover, its performance, without the use of ChIP-chip data, is found to be highly comparable with other state-of-the-art methods integrating both ChIP-chip and gene microarray data. It suggests that the proposed method may serve as a useful exploratory tool for putative TF identification with reduced experimental cost.

Combining functional genomics and chemical biology to identify targets of bioactive compounds.

Genome sequencing projects have revealed thousands of suspected genes, challenging researchers to develop efficient large-scale functional analysis methodologies. Determining the function of a gene product generally requires a means to alter its function. Genetically tractable model organisms have been widely exploited for the isolation and characterization of activating and inactivating mutations in genes encoding proteins of interest. Chemical genetics represents a complementary approach involving the use of small molecules capable of either inactivating or activating their targets. Saccharomyces cerevisiae has been an important test bed for the development and application of chemical genomic assays aimed at identifying targets and modes of action of known and uncharacterized compounds. Here we review yeast chemical genomic assays strategies for drug target identification.

Dosage suppression genetic interaction networks enhance functional wiring diagrams of the cell.

Dosage suppression is a genetic interaction in which overproduction of one gene rescues a mutant phenotype of another gene. Although dosage suppression is known to map functional connections among genes, the extent to which it might illuminate global cellular functions is unclear. Here we analyze a network of interactions linking dosage suppressors to 437 essential genes in yeast. For 424 genes, we curated interactions from the literature. Analyses revealed that many dosage suppression interactions occur between functionally related genes and that the majority do not overlap with other types of genetic or physical interactions. To confirm the generality of these network properties, we experimentally identified dosage suppressors for 29 genes from pooled populations of temperature-sensitive mutant cells transformed with a high-copy molecular-barcoded open reading frame library, MoBY-ORF 2.0. We classified 87% of the 1,640 total interactions into four general types of suppression mechanisms, which provided insight into their relative frequencies. This work suggests that integrating the results of dosage suppression studies with other interaction networks could generate insights into the functional wiring diagram of a cell.

A molecular barcoded yeast ORF library enables mode-of-action analysis of bioactive compounds.

We present a yeast chemical-genomics approach designed to identify genes that when mutated confer drug resistance, thereby providing insight about the modes of action of compounds. We developed a molecular barcoded yeast open reading frame (MoBY-ORF) library in which each gene, controlled by its native promoter and terminator, is cloned into a centromere-based vector along with two unique oligonucleotide barcodes. The MoBY-ORF resource has numerous genetic and chemical-genetic applications, but here we focus on cloning wild-type versions of mutant drug-resistance genes using a complementation strategy and on simultaneously assaying the fitness of all transformants with barcode microarrays. The complementation cloning was validated by mutation detection using whole-genome yeast tiling microarrays, which identified unique polymorphisms associated with a drug-resistant mutant. We used the MoBY-ORF library to identify the genetic basis of several drug-resistant mutants and in this analysis discovered a new class of sterol-binding compounds.

Exploring the mode-of-action of bioactive compounds by chemical-genetic profiling in yeast.

Discovering target and off-target effects of specific compounds is critical to drug discovery and development. We generated a compendium of "chemical-genetic interaction" profiles by testing the collection of viable yeast haploid deletion mutants for hypersensitivity to 82 compounds and natural product extracts. To cluster compounds with a similar mode-of-action and to reveal insights into the cellular pathways and proteins affected, we applied both a hierarchical clustering and a factorgram method, which allows a gene or compound to be associated with more than one group. In particular, tamoxifen, a breast cancer therapeutic, was found to disrupt calcium homeostasis and phosphatidylserine (PS) was recognized as a target for papuamide B, a cytotoxic lipopeptide with anti-HIV activity. Further, the profile of crude extracts resembled that of its constituent purified natural product, enabling detailed classification of extract activity prior to purification. This compendium should serve as a valuable key for interpreting cellular effects of novel compounds with similar activities.