bpRNA-align: improved RNA secondary structure global alignment for comparing and clustering RNA structures.

Ribonucleic acid (RNA) is a polymeric molecule that is fundamental to biological processes, with structure being more highly conserved than primary sequence and often key to its function. Advances in RNA structure characterization have resulted in an increase in the number of accurate secondary structures. The task of uncovering common RNA structural motifs with a collective function through structural comparison, providing a level of similarity, remains challenging and could be used to improve RNA secondary structure databases and discover new RNA families. In this work, we present a novel secondary structure alignment method, bpRNA-align. bpRNA-align is a customized global structural alignment method, utilizing an inverted (gap extend costs more than gap open) and context-specific affine gap penalty along with a structural, feature-specific substitution matrix to provide similarity scores. We evaluate our similarity scores in comparison to other methods, using affinity propagation clustering, applied to a benchmarking data set of known structure types. bpRNA-align shows improvement in clustering performance over a broad range of structure types.

Improving deep models of protein-coding potential with a Fourier-transform architecture and machine translation task.

Ribosomes are information-processing macromolecular machines that integrate complex sequence patterns in messenger RNA (mRNA) transcripts to synthesize proteins. Studies of the sequence features that distinguish mRNAs from long noncoding RNAs (lncRNAs) may yield insight into the information that directs and regulates translation. Computational methods for calculating protein-coding potential are important for distinguishing mRNAs from lncRNAs during genome annotation, but most machine learning methods for this task rely on previously known rules to define features. Sequence-to-sequence (seq2seq) models, particularly ones using transformer networks, have proven capable of learning complex grammatical relationships between words to perform natural language translation. Seeking to leverage these advancements in the biological domain, we present a seq2seq formulation for predicting protein-coding potential with deep neural networks and demonstrate that simultaneously learning translation from RNA to protein improves classification performance relative to a classification-only training objective. Inspired by classical signal processing methods for gene discovery and Fourier-based image-processing neural networks, we introduce LocalFilterNet (LFNet). LFNet is a network architecture with an inductive bias for modeling the three-nucleotide periodicity apparent in coding sequences. We incorporate LFNet within an encoder-decoder framework to test whether the translation task improves the classification of transcripts and the interpretation of their sequence features. We use the resulting model to compute nucleotide-resolution importance scores, revealing sequence patterns that could assist the cellular machinery in distinguishing mRNAs and lncRNAs. Finally, we develop a novel approach for estimating mutation effects from Integrated Gradients, a backpropagation-based feature attribution, and characterize the difficulty of efficient approximations in this setting.

Discovery and Visualization of Age-Dependent Patterns in the Diurnal Transcriptome of Drosophila.

Many critical life processes are regulated by input from 24-hour external light/dark cycles, such as metabolism, cellular homeostasis, and detoxification. The circadian clock, which helps coordinate the response to these diurnal light/dark cycles, remains rhythmic across lifespan; however, rhythmic transcript expression is altered during normal aging. To better understand how aging impacts diurnal expression, we present an improved Fourier-based method for detecting and visualizing rhythmicity that is based on the relative power of the 24-hour period compared to other periods (RP24). We apply RP24 to transcript-level expression profiles from the heads of young (5-day) and old (55-day) Drosophila melanogaster, and reveal novel age-dependent rhythmicity changes that may be masked at the gene level. We show that core clock transcripts phase advance during aging, while most rhythmic transcripts phase delay. Transcripts rhythmic only in young flies tend to peak before lights on, while transcripts only rhythmic in old peak after lights on. We show that several pathways, including glutathione metabolism, gain or lose coordinated rhythmic expression with age, providing insight into possible mechanisms of age-onset neurodegeneration. Remarkably, we find that many pathways show very robust coordinated rhythms across lifespan, highlighting their putative roles in promoting neural health. We investigate statistically enriched transcription factor binding site motifs that may be involved in these rhythmicity changes.

DELAY OF GERMINATION 1-LIKE 4 acts as an inducer of seed reserve accumulation.

More than 70% of global food supply depends on seeds. The major seed reserves, such as proteins, lipids, and polysaccharides, are produced during seed maturation. Here, we report that DELAY OF GERMINATION 1-LIKE 4 (DOGL4) is a major inducer of reserve accumulation during seed maturation. The DOGL family proteins are plant-specific proteins of largely unknown biochemical function. DOGL4 shares only limited homology in amino acid sequence with DOG1, a major regulator of seed dormancy. DOGL4 was identified as one of the outstanding abscisic acid (ABA)-induced genes in our RNA sequencing analysis, whereas DOG1 was not induced by ABA. Induction of DOGL4 caused the expression of 70 seed maturation-specific genes, even in germinating seeds, including the major seed reserves ALBUMIN, CRUCIFERIN and OLEOSIN. Although DOG1 affects the expression of many seed maturation genes, the major seed reserve genes induced by DOGL4 are not altered by the dog1 mutation. Furthermore, the reduced dormancy and longevity phenotypes observed in the dog1 seeds were not observed in the dogl4 mutants, suggesting that these two genes have limited functional overlap. Taken together, these results suggest that DOGL4 is a central factor mediating reserve accumulation in seeds, and that the two DOG1 family proteins have diverged over the course of evolution into independent regulators of seed maturation, but retain some overlapping function.

Pan-tissue transcriptome analysis of long noncoding RNAs in the American beaver Castor canadensis.

BACKGROUND: Long noncoding RNAs (lncRNAs) have roles in gene regulation, epigenetics, and molecular scaffolding and it is hypothesized that they underlie some mammalian evolutionary adaptations. However, for many mammalian species, the absence of a genome assembly precludes the comprehensive identification of lncRNAs. The genome of the American beaver (Castor canadensis) has recently been sequenced, setting the stage for the systematic identification of beaver lncRNAs and the characterization of their expression in various tissues. The objective of this study was to discover and profile polyadenylated lncRNAs in the beaver using high-throughput short-read sequencing of RNA from sixteen beaver tissues and to annotate the resulting lncRNAs based on their potential for orthology with known lncRNAs in other species. RESULTS: Using de novo transcriptome assembly, we found 9528 potential lncRNA contigs and 187 high-confidence lncRNA contigs. Of the high-confidence lncRNA contigs, 147 have no known orthologs (and thus are putative novel lncRNAs) and 40 have mammalian orthologs. The novel lncRNAs mapped to the Oregon State University (OSU) reference beaver genome with greater than 90% sequence identity. While the novel lncRNAs were on average shorter than their annotated counterparts, they were similar to the annotated lncRNAs in terms of the relationships between contig length and minimum free energy (MFE) and between coverage and contig length. We identified beaver orthologs of known lncRNAs such as XIST, MEG3, TINCR, and NIPBL-DT. We profiled the expression of the 187 high-confidence lncRNAs across 16 beaver tissues (whole blood, brain, lung, liver, heart, stomach, intestine, skeletal muscle, kidney, spleen, ovary, placenta, castor gland, tail, toe-webbing, and tongue) and identified both tissue-specific and ubiquitous lncRNAs. CONCLUSIONS: To our knowledge this is the first report of systematic identification of lncRNAs and their expression atlas in beaver. LncRNAs-both novel and those with known orthologs-are expressed in each of the beaver tissues that we analyzed. For some beaver lncRNAs with known orthologs, the tissue-specific expression patterns were phylogenetically conserved. The lncRNA sequence data files and raw sequence files are available via the web supplement and the NCBI Sequence Read Archive, respectively.

Nanoengineered Light-Activatable Polybubbles for On-Demand Therapeutic Delivery.

Vaccine coverage is severely limited in developing countries due to inefficient protection of vaccine functionality as well as lack of patient compliance to receive the additional booster doses. Thus, there is an urgent need to design a thermostable vaccine delivery platform that also enables release of the bolus after predetermined time. Here, the formation of injectable and light-activatable polybubbles for vaccine delivery is reported. In vitro studies show that polybubbles enable delayed burst release, irrespective of cargo types, namely small molecule and antigen. The extracorporeal activation of polybubbles is achieved by incorporating near-infrared (NIR)-sensitive gold nanorods (AuNRs). Interestingly, light-activatable polybubbles can be used for on-demand burst release of cargo. In vitro, ex vivo, and in vivo studies demonstrate successful activation of AuNR-loaded polybubbles. Overall, the light-activatable polybubble technology can be used for on-demand delivery of various therapeutics including small molecule drugs, immunologically relevant protein, peptide antigens, and nucleic acids.

LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search.

MOTIVATION: Predicting the secondary structure of an ribonucleic acid (RNA) sequence is useful in many applications. Existing algorithms [based on dynamic programming] suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. RESULTS: We present a novel alternative O(n3)-time dynamic programming algorithm for RNA folding that is amenable to heuristics that make it run in O(n) time and O(n) space, while producing a high-quality approximation to the optimal solution. Inspired by incremental parsing for context-free grammars in computational linguistics, our alternative dynamic programming algorithm scans the sequence in a left-to-right (5'-to-3') direction rather than in a bottom-up fashion, which allows us to employ the effective beam pruning heuristic. Our work, though inexact, is the first RNA folding algorithm to achieve linear runtime (and linear space) without imposing constraints on the output structure. Surprisingly, our approximate search results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart), both of which are well known to be challenging for the current models. AVAILABILITY AND IMPLEMENTATION: Our source code is available at https://github.com/LinearFold/LinearFold, and our webserver is at http://linearfold.org (sequence limit: 100 000nt). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sulforaphane Bioavailability and Chemopreventive Activity in Men Presenting for Biopsy of the Prostate Gland: A Randomized Controlled Trial.

Previous studies suggest compounds such as sulforaphane (SFN) derived from cruciferous vegetables may prevent prostate cancer development and progression. This study evaluated the effect of broccoli sprout extract (BSE) supplementation on blood histone deacetylase (HDAC) activity, prostate RNA gene expression, and tissue biomarkers (histone H3 lysine 18 acetylation (H3K18ac), HDAC3, HDAC6, Ki67, and p21). A total of 98 men scheduled for prostate biopsy were allocated into either BSE (200 µmol daily) or a placebo in our double-blind, randomized controlled trial. We used nonparametric tests to evaluate the differences of blood HDAC activity and prostate tissue immunohistochemistry biomarkers between treatment groups. Further, we performed RNA-Seq analysis on the prostate biopsies and identified 40 differentially expressed genes correlated with BSE treatment, including downregulation of two genes previously implicated in prostate cancer development, AMACR and ARLNC1. Although urine and plasma SFN isothiocyanates and individual SFN metabolites were statistically higher in the treatment group, our results did not show a significant difference in HDAC activity or prostate tissue biomarkers. This study indicates BSE supplementation correlates with changes in gene expression but not with several other prostate cancer biomarkers. More research is required to fully understand the chemopreventive effects of BSE supplementation on prostate cancer.

miRWoods: Enhanced precursor detection and stacked random forests for the sensitive detection of microRNAs.

MicroRNAs are conserved, endogenous small RNAs with critical post-transcriptional regulatory functions throughout eukaryota, including prominent roles in development and disease. Despite much effort, microRNA annotations still contain errors and are incomplete due especially to challenges related to identifying valid miRs that have small numbers of reads, to properly locating hairpin precursors and to balancing precision and recall. Here, we present miRWoods, which solves these challenges using a duplex-focused precursor detection method and stacked random forests with specialized layers to detect mature and precursor microRNAs, and has been tuned to optimize the harmonic mean of precision and recall. We trained and tuned our discovery pipeline on data sets from the well-annotated human genome, and evaluated its performance on data from mouse. Compared to existing approaches, miRWoods better identifies precursor spans, and can balance sensitivity and specificity for an overall greater prediction accuracy, recalling an average of 10% more annotated microRNAs, and correctly predicts substantially more microRNAs with only one read. We apply this method to the under-annotated genomes of Felis catus (domestic cat) and Bos taurus (cow). We identified hundreds of novel microRNAs in small RNA sequencing data sets from muscle and skin from cat, from 10 tissues from cow and also from human and mouse cells. Our novel predictions include a microRNA in an intron of tyrosine kinase 2 (TYK2) that is present in both cat and cow, as well as a family of mirtrons with two instances in the human genome. Our predictions support a more expanded miR-2284 family in the bovine genome, a larger mir-548 family in the human genome, and a larger let-7 family in the feline genome.

bpRNA: large-scale automated annotation and analysis of RNA secondary structure.

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here, we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, 'bpRNA-1m', of over 100 000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential.

The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, feature-based methods for RNA classification are limited by current scientific knowledge, deep learning methods can independently discover complex biological rules in the data de novo. We trained a gated recurrent neural network (RNN) on human messenger RNA (mRNA) and long noncoding RNA (lncRNA) sequences. Our model, mRNA RNN (mRNN), surpasses state-of-the-art methods at predicting protein-coding potential despite being trained with less data and with no prior concept of what features define mRNAs. To understand what mRNN learned, we probed the network and uncovered several context-sensitive codons highly predictive of coding potential. Our results suggest that gated RNNs can learn complex and long-range patterns in full-length human transcripts, making them ideal for performing a wide range of difficult classification tasks and, most importantly, for harvesting new biological insights from the rising flood of sequencing data.

The polycomb group mutant esc leads to augmented levels of paused Pol II in the Drosophila embryo.

Many developmental control genes contain paused RNA polymerase II (Pol II) and are thereby "poised" for rapid and synchronous activation in the early Drosophila embryo. Evidence is presented that Polycomb group (PcG) repressors can influence paused Pol II. ChIP-Seq and GRO-Seq assays were used to determine the genome-wide distributions of Pol II, H3K27me3, and H3K4me3 in extra sex combs (esc) mutant embryos. ESC is a key component of the Polycomb repressive complex 2 (PRC2), which mediates H3K27me3 modification. Enhanced Pol II occupancy is observed for thousands of genes in esc mutant embryos, including genes not directly regulated by PRC2. Thus, it would appear that silent genes lacking promoter-associated paused Pol II in wild-type embryos are converted into "poised" genes with paused Pol II in esc mutants. We suggest that this conversion of silent genes into poised genes might render differentiated cell types susceptible to switches in identity in PcG mutants.

In Vivo Characterization of an AHR-Dependent Long Noncoding RNA Required for Proper Sox9b Expression.

Xenobiotic activation of the aryl hydrocarbon receptor (AHR) by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) prevents the proper formation of craniofacial cartilage and the heart in developing zebrafish. Downstream molecular targets responsible for AHR-dependent adverse effects remain largely unknown; however, in zebrafish sox9b has been identified as one of the most-reduced transcripts in several target organs and is hypothesized to have a causal role in TCDD-induced toxicity. The reduction of sox9b expression in TCDD-exposed zebrafish embryos has been shown to contribute to heart and jaw malformation phenotypes. The mechanisms by which AHR2 (functional ortholog of mammalian AHR) activation leads to reduced sox9b expression levels and subsequent target organ toxicity are unknown. We have identified a novel long noncoding RNA (slincR) that is upregulated by strong AHR ligands and is located adjacent to the sox9b gene. We hypothesize that slincR is regulated by AHR2 and transcriptionally represses sox9b. The slincR transcript functions as an RNA macromolecule, and slincR expression is AHR2 dependent. Antisense knockdown of slincR results in an increase in sox9b expression during both normal development and AHR2 activation, which suggests relief in repression. During development, slincR was expressed in tissues with sox9 essential functions, including the jaw/snout region, otic vesicle, eye, and brain. Reducing the levels of slincR resulted in altered neurologic and/or locomotor behavioral responses. Our results place slincR as an intermediate between AHR2 activation and the reduction of sox9b mRNA in the AHR2 signaling pathway.

Neural-specific elongation of 3' UTRs during Drosophila development.

The 3' termini of eukaryotic mRNAs influence transcript stability, translation efficiency, and subcellular localization. Here we report that a subset of developmental regulatory genes, enriched in critical RNA-processing factors, exhibits synchronous lengthening of their 3' UTRs during embryogenesis. The resulting UTRs are up to 20-fold longer than those found on typical Drosophila mRNAs. The large mRNAs emerge shortly after the onset of zygotic transcription, with several of these genes acquiring additional, phased UTR extensions later in embryogenesis. We show that these extended 3' UTR sequences are selectively expressed in neural tissues and contain putative recognition motifs for the translational repressor, Pumilio, which also exhibits the 3' lengthening phenomenon documented in this study. These findings suggest a previously unknown mode of posttranscriptional regulation that may contribute to the complexity of neurogenesis or neural function.

Improving deep models of protein-coding potential with a Fourier-transform architecture and machine translation task.

Ribosomes are information-processing macromolecular machines that integrate complex sequence patterns in messenger RNA (mRNA) transcripts to synthesize proteins. Studies of the sequence features that distinguish mRNAs from long noncoding RNAs (lncRNAs) may yield insight into the information that directs and regulates translation. Computational methods for calculating protein-coding potential are important for distinguishing mRNAs from lncRNAs during genome annotation, but most machine learning methods for this task rely on previously known rules to define features. Sequence-to-sequence (seq2seq) models, particularly ones using transformer networks, have proven capable of learning complex grammatical relationships between words to perform natural language translation. Seeking to leverage these advancements in the biological domain, we present a seq2seq formulation for predicting protein-coding potential with deep neural networks and demonstrate that simultaneously learning translation from RNA to protein improves classification performance relative to a classification-only training objective. Inspired by classical signal processing methods for gene discovery and Fourier-based image-processing neural networks, we introduce LocalFilterNet (LFNet). LFNet is a network architecture with an inductive bias for modeling the three-nucleotide periodicity apparent in coding sequences. We incorporate LFNet within an encoder-decoder framework to test whether the translation task improves the classification of transcripts and the interpretation of their sequence features. We use the resulting model to compute nucleotide-resolution importance scores, revealing sequence patterns that could assist the cellular machinery in distinguishing mRNAs and lncRNAs. Finally, we develop a novel approach for estimating mutation effects from Integrated Gradients, a backpropagation-based feature attribution, and characterize the difficulty of efficient approximations in this setting.

An improved assembly of the "Cascade" hop (Humulus lupulus) genome uncovers signatures of molecular evolution and refines time of divergence estimates for the Cannabaceae family.

We present a chromosome-level assembly of the Cascade hop (Humulus lupulus L. var. lupulus) genome. The hop genome is large (2.8 Gb) and complex, and early attempts at assembly were fragmented. Recent advances have made assembly of the hop genome more tractable, transforming the extent of investigation that can occur. The chromosome-level assembly of Cascade was developed by scaffolding the previously reported Cascade assembly generated with PacBio long-read sequencing and polishing with Illumina short-read DNA sequencing. We developed gene models and repeat annotations and used a controlled bi-parental mapping population to identify significant sex-associated markers. We assessed molecular evolution in gene sequences, gene family expansion and contraction, and time of divergence from Cannabis sativa and other closely related plant species using Bayesian inference. We identified the putative sex chromosome in the female genome based on significant sex-associated markers from the bi-parental mapping population. While the estimate of repeat content (~64%) is similar to the estimate for the hemp genome, syntenic blocks in hop contain a greater percentage of LTRs. Hop is enriched for disease resistance-associated genes in syntenic gene blocks and expanded gene families. The Cascade chromosome-level assembly will inform cultivation strategies and serve to deepen our understanding of the hop genomic landscape, benefiting hop researchers and the Cannabaceae genomics community.

RNA structure and multiple weak interactions balance the interplay between RNA binding and phase separation of SARS-CoV-2 nucleocapsid.

The nucleocapsid (N) protein of SARS-CoV-2 binds viral RNA, condensing it inside the virion, and phase separating with RNA to form liquid-liquid condensates. There is little consensus on what differentiates sequence-independent N-RNA interactions in the virion or in liquid droplets from those with specific genomic RNA (gRNA) motifs necessary for viral function inside infected cells. To identify the RNA structures and the N domains responsible for specific interactions and phase separation, we use the first 1,000 nt of viral RNA and short RNA segments designed as models for single-stranded and paired RNA. Binding affinities estimated from fluorescence anisotropy of these RNAs to the two-folded domains of N (the NTD and CTD) and comparison to full-length N demonstrate that the NTD binds preferentially to single-stranded RNA, and while it is the primary RNA-binding site, it is not essential to phase separation. Nuclear magnetic resonance spectroscopy identifies two RNA-binding sites on the NTD: a previously characterized site and an additional although weaker RNA-binding face that becomes prominent when binding to the primary site is weak, such as with dsRNA or a binding-impaired mutant. Phase separation assays of nucleocapsid domains with double-stranded and single-stranded RNA structures support a model where multiple weak interactions, such as with the CTD or the NTD's secondary face promote phase separation, while strong, specific interactions do not. These studies indicate that both strong and multivalent weak N-RNA interactions underlie the multifunctional abilities of N.

Age-dependent effects of blue light exposure on lifespan, neurodegeneration, and mitochondria physiology in Drosophila melanogaster.

Blue light is a predominant component of light emitting devices (LEDs), which are increasingly present in our environment. There is already accumulating evidence that blue light exposure causes damage to retinal cells in vitro and in vivo; however, much less is known about potential effects of blue light on non-retinal cells. That blue light may be detrimental at the organismal level independent from retinal effect was recently shown by findings that it reduces lifespan in worms and also in flies with genetically ablated retinas. Here, we investigated the effects of blue light exposure across the fly lifespan and found that susceptibility to blue light stress is strongly age-dependent. The blue light of the same intensity and duration reduced survival and increased neurodegeneration more significantly in old flies than in young flies. These differences appear to be caused, at least in part, by impairments of mitochondrial respiratory function. We report that blue light significantly reduces the activity of Complex II in the electron transport system and decrease the biochemical activity of succinate dehydrogenase in both young and old flies. In addition, complex I and complex IV activities are reduced by age, as are ATP levels. We therefore propose that older flies are more sensitive to blue light because the light-induced mitochondrial damage potentiates the age-related impairments in energy metabolism that occurs even in darkness. Taken together, our results show that damaging effects of blue light at the organismal level are strongly age dependent and are associated with reduced activity of specific components of energy producing pathways in mitochondria.

Chronic blue light leads to accelerated aging in Drosophila by impairing energy metabolism and neurotransmitter levels.

Blue light (BL) is becoming increasingly prevalent in artificial illumination, raising concerns about its potential health hazard to humans. In fact, there is evidence suggesting that acute BL exposure may lead to oxidative stress and death of retinal cells specialized for photoreception. On the other hand, recent studies in Drosophila melanogaster demonstrated that chronic BL exposure across lifespan leads to accelerated aging manifested in reduced lifespan and brain neurodegeneration even in flies with genetically ablated eyes, suggesting that BL can damage cells and tissues not specialized for light perception. At the physiological level, BL exposure impairs mitochondria function in flies, but the metabolic underpinnings of these effects have not been studied. Here, we investigated effects of chronic BL on metabolic pathways in heads of eyes absent (eya 2 ) mutant flies in order to focus on extra-retinal tissues. We compared metabolomic profiles in flies kept for 10 or 14 days in constant BL or constant darkness, using LC-MS and GC-MS. Data analysis revealed significant alterations in the levels of several metabolites suggesting that critical cellular pathways are impacted in BL-exposed flies. In particular, dramatic metabolic rearrangements are observed in heads of flies kept in BL for 14 days, including highly elevated levels of succinate but reduced levels of pyruvate and citrate, suggesting impairments in energy production. These flies also show onset of neurodegeneration and our analysis detected significantly reduced levels of several neurotransmitters including glutamate and Gamma-aminobutyric acid (GABA), suggesting that BL disrupts brain homeostasis. Taken together, these data provide novel insights into the mechanisms by which BL interferes with vital metabolic pathways that are conserved between fly and human cells.

How the Dorsal gradient works: insights from postgenome technologies.

Gradients of extracellular signaling molecules and transcription factors are used in a variety of developmental processes, including the patterning of the Drosophila embryo, the establishment of diverse neuronal cell types in the vertebrate neural tube, and the anterior-posterior patterning of vertebrate limbs. Here, we discuss how a gradient of the maternal transcription factor Dorsal produces complex patterns of gene expression across the dorsal-ventral (DV) axis of the early Drosophila embryo. The identification of 60-70 Dorsal target genes, along with the characterization of approximately 35 associated regulatory DNAs, suggests that there are at least six different regulatory codes driving diverse DV expression profiles.