pubmedKB: an interactive web server for exploring biomedical entity relations in the biomedical literature.

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

iTARGEX analysis of yeast deletome reveals novel regulators of transcriptional buffering in S phase and protein turnover.

Integrating omics data with quantification of biological traits provides unparalleled opportunities for discovery of genetic regulators by in silico inference. However, current approaches to analyze genetic-perturbation screens are limited by their reliance on annotation libraries for prioritization of hits and subsequent targeted experimentation. Here, we present iTARGEX (identification of Trait-Associated Regulatory Genes via mixture regression using EXpectation maximization), an association framework with no requirement of a priori knowledge of gene function. After creating this tool, we used it to test associations between gene expression profiles and two biological traits in single-gene deletion budding yeast mutants, including transcription homeostasis during S phase and global protein turnover. For each trait, we discovered novel regulators without prior functional annotations. The functional effects of the novel candidates were then validated experimentally, providing solid evidence for their roles in the respective traits. Hence, we conclude that iTARGEX can reliably identify novel factors involved in given biological traits. As such, it is capable of converting genome-wide observations into causal gene function predictions. Further application of iTARGEX in other contexts is expected to facilitate the discovery of new regulators and provide observations for novel mechanistic hypotheses regarding different biological traits and phenotypes.

Genome-wide identification of associations between enhancer and alternative splicing in human and mouse.

BACKGROUND: Alternative splicing (AS) increases the diversity of transcriptome and could fine-tune the function of genes, so that understanding the regulation of AS is vital. AS could be regulated by many different cis-regulatory elements, such as enhancer. Enhancer has been experimentally proved to regulate AS in some genes. However, there is a lack of genome-wide studies on the association between enhancer and AS (enhancer-AS association). To bridge the gap, here we developed an integrative analysis on a genome-wide scale to identify enhancer-AS associations in human and mouse. RESULT: We collected enhancer datasets which include 28 human and 24 mouse tissues and cell lines, and RNA-seq datasets which are paired with the selected tissues. Combining with data integration and statistical analysis, we identified 3,242 human and 7,716 mouse genes which have significant enhancer-AS associations in at least one tissue. On average, for each gene, about 6% of enhancers in human (5% in mouse) are associated to AS change and for each enhancer, approximately one gene is identified to have enhancer-AS association in both human and mouse. We found that 52% of the human significant (34% in mouse) enhancer-AS associations are the co-existence of homologous genes and homologous enhancers. We further constructed a user-friendly platform, named Visualization of Enhancer-associated Alternative Splicing (VEnAS, http://venas.iis.sinica.edu.tw/ ), to provide genomic architecture, intuitive association plot, and contingency table of the significant enhancer-AS associations. CONCLUSION: This study provides the first genome-wide identification of enhancer-AS associations in human and mouse. The results suggest that a notable portion of enhancers are playing roles in AS regulations. The analyzed results and the proposed platform VEnAS would provide a further understanding of enhancers on regulating alternative splicing.

Identification and comparative analysis of long non-coding RNAs in the brain of fire ant queens in two different reproductive states.

BACKGROUND: Many long non-coding RNAs (lncRNAs) have been extensively identified in higher eukaryotic species. The function of lncRNAs has been reported to play important roles in diverse biological processes, including developmental regulation and behavioral plasticity. However, there are no reports of systematic characterization of long non-coding RNAs in the fire ant Solenopsis invicta. RESULTS: In this study, we performed a genome-wide analysis of lncRNAs in the brains of S. invicta from RNA-seq. In total, 1,393 novel lncRNA transcripts were identified in the fire ant. In contrast to the annotated lncRNA transcripts having at least two exons, novel lncRNAs are monoexonic transcripts with a shorter length. Besides, the transcriptome from virgin alate and dealate mated queens were analyzed and compared. The results showed 295 differentially expressed mRNA genes (DEGs) and 65 differentially expressed lncRNA genes (DELs) between virgin and mated queens, of which 17 lncRNAs were highly expressed in the virgin alates and 47 lncRNAs were highly expressed in the mated dealates. By identifying the DEL:DEG pairs with a high association in their expression (Spearman's |rho|> 0.8 and p-value < 0.01), many DELs were co-regulated with DEGs after mating. Furthermore, several remarkable lncRNAs (MSTRG.6523, MSTRG.588, and nc909) that were found to associate with particular coding genes may play important roles in the regulation of brain gene expression in reproductive transition in fire ants. CONCLUSION: This study provides the first genome-wide identification of S. invicta lncRNAs in the brains in different reproductive states. It will contribute to a fuller understanding of the transcriptional regulation underpinning reproductive changes.

Genome-wide association study reveals ethnicity-specific SNPs associated with ankylosing spondylitis in the Taiwanese population.

BACKGROUND: Ankylosing spondylitis (AS) is an autoimmune disease affecting mainly spine and sacroiliac joints and adjacent soft tissues. Genome-wide association studies (GWASs) are used to evaluate genetic associations and to predict genetic risk factors that determine the biological basis of disease susceptibility. We aimed to explore the race-specific SNP susceptibility of AS in Taiwanese individuals and to investigate the association between HLA-B27 and AS susceptibility SNPs in Taiwan. METHODS: Genotyping data were collected from a medical center participating in the Taiwan Precision Medicine Initiative (TPMI) in the northern district of Taiwan. We designed a case-control study to identify AS susceptibility SNPs through GWAS. We searched the genome browser to find the corresponding susceptibility genes and used the GTEx database to confirm the regulation of gene expression. A polygenic risk score approach was also applied to evaluate the genetic variants in the prediction of developing AS. RESULTS: The results showed that the SNPs located on the sixth chromosome were related to higher susceptibility in the AS group. There was no overlap between our results and the susceptibility SNPs found in other races. The 12 tag SNPs located in the MHC region that were found through the linkage disequilibrium method had higher gene expression. Furthermore, Taiwanese people with HLA-B27 positivity had a higher proportion of minor alleles. This might be the reason that the AS prevalence is higher in Taiwan than in other countries. We developed AS polygenic risk score models with six different methods in which those with the top 10% polygenic risk had a fivefold increased risk of developing AS compared to the remaining group with low risk. CONCLUSION: A total of 147 SNPs in the Taiwanese population were found to be statistically significantly associated with AS on the sixth pair of chromosomes and did not overlap with previously published sites in the GWAS Catalog. Whether those genes mapped by AS-associated SNPs are involved in AS and what the pathogenic mechanism of the mapped genes is remain to be further studied.

Genome-wide analyses identify novel risk loci for cluster headache in Han Chinese residing in Taiwan.

BACKGROUND: Cluster headache is a highly debilitating neurological disorder with considerable inter-ethnic differences. Genome-wide association studies (GWAS) recently identified replicable genomic loci for cluster headache in Europeans, but the genetic underpinnings for cluster headache in Asians remain unclear. The objective of this study is to investigate the genetic architecture and susceptibility loci of cluster headache in Han Chinese resided in Taiwan. METHODS: We conducted a two-stage genome-wide association study in a Taiwanese cohort enrolled from 2007 through 2022 to identify the genetic variants associated with cluster headache. Diagnosis of cluster headache was retrospectively ascertained with the criteria of International Classification of Headache Disorders, third edition. Control subjects were enrolled from the Taiwan Biobank. Genotyping was conducted with the Axiom Genome-Wide Array TWB chip, followed by whole genome imputation. A polygenic risk score was developed to differentiate patients from controls. Downstream analyses including gene-set and tissue enrichment, linkage disequilibrium score regression, and pathway analyses were performed. RESULTS: We enrolled 734 patients with cluster headache and 9,846 population-based controls. We identified three replicable loci, with the lead SNPs being rs1556780 in CAPN2 (odds ratio = 1.59, 95% CI 1.42‒1.78, p = 7.61 × 10-16), rs10188640 in MERTK (odds ratio = 1.52, 95% CI 1.33‒1.73, p = 8.58 × 10-13), and rs13028839 in STAB2 (odds ratio = 0.63, 95% CI 0.52‒0.78, p = 2.81 × 10-8), with the latter two replicating the findings in European populations. Several previously reported genes also showed significant associations with cluster headache in our samples. Polygenic risk score differentiated patients from controls with an area under the receiver operating characteristic curve of 0.77. Downstream analyses implicated circadian regulation and immunological processes in the pathogenesis of cluster headache. CONCLUSIONS: This study revealed the genetic architecture and novel susceptible loci of cluster headache in Han Chinese residing in Taiwan. Our findings support the common genetic contributions of cluster headache across ethnicities and provide novel mechanistic insights into the pathogenesis of cluster headache.

CATANA: a tool for generating comprehensive annotations of alternative transcript events.

SUMMARY: In higher eukaryotes, the generation of transcript isoforms from a single gene through alternative splicing (AS) and alternative transcription (AT) mechanisms increases functional and regulatory diversities. Annotating these alternative transcript events is essential for genomic studies. However, there are no existing tools that generate comprehensive annotations of all these alternative transcript events including both AS and AT events. In the present study, we develop CATANA, with the encoded exon usage patterns based on the flattened gene model, to identify ten types of AS and AT events. We demonstrate the power and versatility of CATANA by showing greater depth of annotations of alternative transcript events according to either genome annotation or RNA-seq data. AVAILABILITY AND IMPLEMENTATION: CATANA is available on https://github.com/shiauck/CATANA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cellular and molecular remodelling of a host cell for vertical transmission of bacterial symbionts.

Various insects require intracellular bacteria that are restricted to specialized cells (bacteriocytes) and are transmitted vertically via the female ovary, but the transmission mechanisms are obscure. We hypothesized that, in the whitefly Bemisia tabaci, where intact bacteriocytes (and not isolated bacteria) are transferred to oocytes, the transmission mechanism would be evident as cellular and molecular differences between the nymph (pre-adult) and adult bacteriocytes. We demonstrate dramatic remodelling of bacteriocytes at the developmental transition from nymph to adulthood. This transition involves the loss of cell-cell adhesion, high division rates to constant cell size and onset of cell mobility, enabling the bacteriocytes to crawl to the ovaries. These changes are accompanied by cytoskeleton reorganization and changes in gene expression: genes functioning in cell-cell adhesion display reduced expression and genes involved in cell division, cell motility and endocytosis/exocytosis have elevated expression in adult bacteriocytes, relative to nymph bacteriocytes. This study demonstrates, for the first time, how developmentally orchestrated remodelling of gene expression and correlated changes in cell behaviour underpin the capacity of bacteriocytes to mediate the vertical transmission and persistence of the symbiotic bacteria on which the insect host depends.

Consumption of dietary sugar by gut bacteria determines Drosophila lipid content.

Gut microorganisms are essential for the nutritional health of many animals, but the underlying mechanisms are poorly understood. This study investigated how lipid accumulation by adult Drosophila melanogaster is reduced in flies associated with the bacterium Acetobacter tropicalis which displays oral-faecal cycling between the gut and food. We demonstrate that the lower lipid content of A. tropicalis-colonized flies relative to bacteria-free flies is linked with a parallel bacterial-mediated reduction in food glucose content; and can be accounted for quantitatively by the amount of glucose acquired by the flies, as determined from the feeding rate and assimilation efficiency of bacteria-free and A. tropicalis-colonized flies. We recommend that nutritional studies on Drosophila include empirical quantification of food nutrient content, to account for likely microbial-mediated effects on diet composition. More broadly, this study demonstrates that selective consumption of dietary constituents by microorganisms can alter the nutritional balance of food and, thereby, influence the nutritional status of the animal host.

Broad-complex functions in postembryonic development of the cockroach Blattella germanica shed new light on the evolution of insect metamorphosis.

BACKGROUND: Insect metamorphosis proceeds in two modes: hemimetaboly, gradual change along the life cycle; and holometaboly, abrupt change from larvae to adult mediated by a pupal stage. Both are regulated by 20-hydroxyecdysone (20E), which promotes molts, and juvenile hormone (JH), which represses adult morphogenesis. Expression of Broad-complex (BR-C) is induced by 20E and modulated by JH. In holometabolous species, like Drosophila melanogaster, BR-C expression is inhibited by JH in young larvae and enhanced in mature larvae, when JH declines and BR-C expression specifies the pupal stage. METHODS: Using Blattella germanica as a basal hemimetabolous model, we determined the patterns of expression of BR-C mRNAs using quantitative RT-PCR, and we studied the functions of BR-C factors using RNA interference approaches. RESULTS: We found that BR-C expression is enhanced by JH and correlates with JH hemolymph concentration. BR-C factors appear to be involved in cell division and wing pad growth, as well as wing vein patterning. CONCLUSIONS: In B. germanica, expression of BR-C is enhanced by JH, and BR-C factors appear to promote wing growth to reach the right size, form and patterning, which contrast with the endocrine regulation and complex functions observed in holometabolous species. GENERAL SIGNIFICANCE: Our results shed new light to the evolution from hemimetaboly to holometaboly regarding BR-C, whose regulation and functions were affected by two innovations: 1) a shift in JH action on BR-C expression during young stages, from stimulatory to inhibitory, and 2) an expansion of functions, from regulating wing development, to determining pupal morphogenesis.

scDrug: From single-cell RNA-seq to drug response prediction.

Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment.

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

TRIPBASE: a database for identifying the human genomic DNA and lncRNA triplexes.

Long-non-coding RNAs (lncRNAs) are defined as RNA sequences which are >200 nt with no coding capacity. These lncRNAs participate in various biological mechanisms, and are widely abundant in a diversity of species. There is well-documented evidence that lncRNAs can interact with genomic DNAs by forming triple helices (triplexes). Previously, several computational methods have been designed based on the Hoogsteen base-pair rule to find theoretical RNA-DNA:DNA triplexes. While powerful, these methods suffer from a high false-positive rate between the predicted triplexes and the biological experiments. To address this issue, we first collected the experimental data of genomic RNA-DNA triplexes from antisense oligonucleotide (ASO)-mediated capture assays and used Triplexator, the most widely used tool for lncRNA-DNA interaction, to reveal the intrinsic information on true triplex binding potential. Based on the analysis, we proposed six computational attributes as filters to improve the in-silico triplex prediction by removing most false positives. Further, we have built a new database, TRIPBASE, as the first comprehensive collection of genome-wide triplex predictions of human lncRNAs. In TRIPBASE, the user interface allows scientists to apply customized filtering criteria to access the potential triplexes of human lncRNAs in the cis-regulatory regions of the human genome. TRIPBASE can be accessed at https://tripbase.iis.sinica.edu.tw/.

Efficiently Predicting Vancomycin Resistance of Enterococcus Faecium From MALDI-TOF MS Spectra Using a Deep Learning-Based Approach.

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has recently become a useful analytical approach for microbial identification. The presence and absence of specific peaks on MS spectra are commonly used to identify the bacterial species and predict antibiotic-resistant strains. However, the conventional approach using few single peaks would result in insufficient prediction power without using complete information of whole MS spectra. In the past few years, machine learning algorithms have been successfully applied to analyze the MALDI-TOF MS peaks pattern for rapid strain typing. In this study, we developed a convolutional neural network (CNN) method to deal with the complete information of MALDI-TOF MS spectra for detecting Enterococcus faecium, which is one of the leading pathogens in the world. We developed a CNN model to rapidly and accurately predict vancomycin-resistant Enterococcus faecium (VREfm) samples from the whole mass spectra profiles of clinical samples. The CNN models demonstrated good classification performances with the average area under the receiver operating characteristic curve (AUROC) of 0.887 when using external validation data independently. Additionally, we employed the score-class activation mapping (CAM) method to identify the important features of our CNN models and found some discriminative signals that can substantially contribute to detecting the ion of resistance. This study not only utilized the complete information of MALTI-TOF MS data directly but also provided a practical means for rapid detection of VREfm using a deep learning algorithm.

Termite Pest Identification Method Based on Deep Convolution Neural Networks.

Several species of drywood termites, subterranean termites, and fungus-growing termites cause extensive economic losses annually worldwide. Because no universal method is available for controlling all termites, correct species identification is crucial for termite management. Despite deep neural network technologies' promising performance in pest recognition, a method for automatic termite recognition remains lacking. To develop an automated deep learning classifier for termite image recognition suitable for mobile applications, we used smartphones to acquire 18,000 original images each of four termite pest species: Kalotermitidae: Cryptotermes domesticus (Haviland); Rhinotermitidae: Coptotermes formosanus Shiraki and Reticulitermes flaviceps (Oshima); and Termitidae: Odontotermes formosanus (Shiraki). Each original image included multiple individuals, and we applied five image segmentation techniques for capturing individual termites. We used 24,000 individual-termite images (4 species × 2 castes × 3 groups × 1,000 images) for model development and testing. We implemented a termite classification system by using a deep learning-based model, MobileNetV2. Our models achieved high accuracy scores of 0.947, 0.946, and 0.929 for identifying soldiers, workers, and both castes, respectively, which is not significantly different from human expert performance. We further applied image augmentation techniques, including geometrical transformations and intensity transformations, to individual-termite images. The results revealed that the same classification accuracy can be achieved by using 1,000 augmented images derived from only 200 individual-termite images, thus facilitating further model development on the basis of many fewer original images. Our image-based identification system can enable the selection of termite control tools for pest management professionals or homeowners.

NK cell receptor and ligand composition influences the clearance of SARS-CoV-2.

To explore how the immune system controls clearance of SARS-CoV-2, we used a single-cell, mass cytometry-based proteomics platform to profile the immune systems of 21 patients who had recovered from SARS-CoV-2 infection without need for admission to an intensive care unit or for mechanical ventilation. We focused on receptors involved in interactions between immune cells and virus-infected cells. We found that the diversity of receptor repertoires on natural killer (NK) cells was negatively correlated with the viral clearance rate. In addition, NK subsets expressing the receptor DNAM1 were increased in patients who more rapidly recovered from infection. Ex vivo functional studies revealed that NK subpopulations with high DNAM1 expression had cytolytic activities in response to target cell stimulation. We also found that SARS-CoV-2 infection induced the expression of CD155 and nectin-4, ligands of DNAM1 and its paired coinhibitory receptor TIGIT, which counterbalanced the cytolytic activities of NK cells. Collectively, our results link the cytolytic immune responses of NK cells to the clearance of SARS-CoV-2 and show that the DNAM1 pathway modulates host-pathogen interactions during SARS-CoV-2 infection.

Inferring the transcriptional regulatory mechanism of signal-dependent gene expression via an integrative computational approach.

Eukaryotic transcription factors (TFs) coordinate different upstream signals to regulate the expression of their target genes. To unveil this regulatory network in B-cell receptor signaling, we developed a computational pipeline to systematically analyze the extracellular signal-regulated kinase (ERK)- and IκB kinase (IKK)-dependent transcriptome responses. We combined a bilinear regression method and kinetic modeling to identify the signal-to-TF and TF-to-gene dynamics, respectively. We input a set of time-course experimental data for B cells and concentrated on transcriptional activators. The results show that the combination of TFs differentially controlled by ERK and IKK could contribute divergent expression dynamics in orchestrating the B-cell response. Our findings provide insights into the regulatory mechanisms underlying signal-dependent gene expression in eukaryotic cells.

Practical Use of RNA Interference: Oral Delivery of Double-stranded RNA in Liposome Carriers for Cockroaches.

RNA interference (RNAi) has been widely applied for uncovering the biological functions of numerous genes, and has been envisaged as a pest control tool operating by disruption of essential gene expression. Although different methods, such as injection, feeding, and soaking, have been reported for successful delivery of double-stranded RNA (dsRNA), the efficiency of RNAi through oral delivery of dsRNA is highly variable among different insect groups. The German cockroach, Blattella germanica, is highly sensitive to the injection of dsRNA, as shown by many studies published previously. The present study describes a method to demonstrate that the dsRNA encapsulated with liposome carriers is sufficient to retard the degradation of dsRNA by midgut juice. Notably, the continuous feeding of dsRNA encapsulated by liposomes significantly reduces the tubulin expression in the midgut, and led to the death of cockroaches. In conclusion, the formulation and utilization of dsRNA lipoplexes, which protect dsRNA against nucleases, could be a practical use of RNAi for insect pest control in the future.

Borders of Cis-Regulatory DNA Sequences Preferentially Harbor the Divergent Transcription Factor Binding Motifs in the Human Genome.

Changes in cis-regulatory DNA sequences and transcription factor (TF) repertoires provide major sources of phenotypic diversity that shape the evolution of gene regulation in eukaryotes. The DNA-binding specificities of TFs may be diversified or produce new variants in different eukaryotic species. However, it is currently unclear how various levels of divergence in TF DNA-binding specificities or motifs became introduced into the cis-regulatory DNA regions of the genome over evolutionary time. Here, we first estimated the evolutionary divergence levels of TF binding motifs and quantified their occurrence at DNase I-hypersensitive sites. Results from our in silico motif scan and experimentally derived chromatin immunoprecipitation (TF-ChIP) show that the divergent motifs tend to be introduced in the edges of cis-regulatory regions, which is probably accompanied by the expansion of the accessible core of promoter-associated regulatory elements during evolution. We also find that the genes neighboring the expanded cis-regulatory regions with the most divergent motifs are associated with functions like development and morphogenesis. Accordingly, we propose that the accumulation of divergent motifs in the edges of cis-regulatory regions provides a functional mechanism for the evolution of divergent regulatory circuits.

Physiological responses of insects to microbial fermentation products: Insights from the interactions between Drosophila and acetic acid.

Acetic acid is a fermentation product of many microorganisms, including some that inhabit the food and guts of Drosophila. Here, we investigated the effect of dietary acetic acid on oviposition and larval performance of Drosophila. At all concentrations tested (0.34-3.4%), acetic acid promoted egg deposition by mated females in no-choice assays; and females preferred to oviposit on diet with acetic acid relative to acetic acid-free diet. However, acetic acid depressed larval performance, particularly extending the development time of both larvae colonized with the bacterium Acetobacter pomorum and axenic (microbe-free) larvae. The larvae may incur an energetic cost associated with dissipating the high acid load on acetic acid-supplemented diets. This effect was compounded by suppressed population growth of A. pomorum on the 3.4% acetic acid diet, such that the gnotobiotic Drosophila on this diet displayed traits characteristic of axenic Drosophila, specifically reduced developmental rate and elevated lipid content. It is concluded that acetic acid is deleterious to larval Drosophila, and hypothesized that acetic acid may function as a reliable cue for females to oviposit in substrates bearing microbial communities that promote larval nutrition.