Phenotypic differences between closely related species are thought to arise primarily from changes in gene expression due to mutations in cis-regulatory sequences (enhancers). However, it has remained unclear how frequently mutations alter enhancer activity or create functional enhancers de novo. Here we use STARR-seq, a recently developed quantitative enhancer assay, to determine genome-wide enhancer activity profiles for five Drosophila species in the constant trans-regulatory environment of Drosophila melanogaster S2 cells. We find that the functions of a large fraction of D. melanogaster enhancers are conserved for their orthologous sequences owing to selection and stabilizing turnover of transcription factor motifs. Moreover, hundreds of enhancers have been gained since the D. melanogaster–Drosophila yakuba split about 11 million years ago without apparent adaptive selection and can contribute to changes in gene expression in vivo. Our finding that enhancer activity is often deeply conserved and frequently gained provides functional insights into regulatory evolution.

RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (<8 time points) in terms of overall performance and robustness to noise, mostly because of high number of false positives, with the exception of ImpulseDE2. Overlapping of candidate lists between tools improved this shortcoming, as the majority of false-positive, but not true-positive, candidates were unique for each method. On longer time series, pairwise approach was less efficient on the overall performance compared with splineTC and maSigPro, which did not identify any false-positive candidate.

Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.

Neuronal L-type voltage-gated calcium channels (LTCCs) are involved in several physiological functions, but increased activity of LTCCs has been linked to pathology. Due to the coupling of LTCC-mediated Ca2+ influx to Ca2+-dependent conductances, such as KCa or non-specific cation channels, LTCCs act as important regulators of neuronal excitability. Augmentation of after-hyperpolarizations may be one mechanism that shows how elevated LTCC activity can lead to neurological malfunctions. However, little is known about other impacts on electrical discharge activity. We used pharmacological up-regulation of LTCCs to address this issue on primary rat hippocampal neurons. Potentiation of LTCCs with Bay K8644 enhanced excitatory postsynaptic potentials to various degrees and eventually resulted in paroxysmal depolarization shifts (PDS). Under conditions of disturbed Ca2+ homeostasis, PDS were evoked frequently upon LTCC potentiation. Exposing the neurons to oxidative stress using hydrogen peroxide also induced LTCC-dependent PDS. Hence, raising LTCC activity had unidirectional effects on brief electrical signals and increased the likeliness of epileptiform events. However, long-lasting seizure-like activity induced by various pharmacological means was affected by Bay K8644 in a bimodal manner, with increases in one group of neurons and decreases in another group. In each group, isradipine exerted the opposite effect. This suggests that therapeutic reduction in LTCC activity may have little beneficial or even adverse effects on long-lasting abnormal discharge activities. However, our data identify enhanced activity of LTCCs as one precipitating cause of PDS. Because evidence is continuously accumulating that PDS represent important elements in neuropathogenesis, LTCCs may provide valuable targets for neuroprophylactic therapy.Electronic supplementary materialThe online version of this article (doi:10.1007/s12017-013-8234-1) contains supplementary material, which is available to authorized users.

MicroRNA (miRNA) loaded Argonaute (AGO) complexes regulate gene expression via direct base-pairing with their mRNA targets. Current prediction approaches identified that between 20 to 60% of mammalian transcriptomes are regulated by miRNAs, but it remains largely unknown which fraction of these interactions are functional in a specific cellular context. Here, we integrated transcriptome data from a set of miRNA-depleted mouse embryonic stem cell (mESC) lines with published miRNA interaction predictions and AGO-binding profiles. This integrative approach, combined with molecular validation data, identified that only 6% of expressed genes are functionally and directly regulated by miRNAs in mESCs. In addition, analyses of the stem cell-specific miR-290-295 cluster target genes identified TFAP4 as an important transcription factor for early development. The extensive datasets developed in this study will support the development of improved predictive models for miRNA-mRNA functional interactions.

Transcriptional and translational control are key determinants of gene expression, however, to what extent these two processes can be collectively coordinated is still poorly understood. Here, we use Nanopore long-read sequencing and cap analysis of gene expression (CAGE-seq) to document the landscape of 5′ and 3′ untranslated region (UTR) isoforms and transcription start sites of epidermal stem cells, wild-type keratinocytes and squamous cell carcinomas. Focusing on squamous cell carcinomas, we show that a small cohort of genes with alternative 5′UTR isoforms exhibit overall increased translational efficiencies and are enriched in ribosomal proteins and splicing factors. By combining polysome fractionations and CAGE-seq, we further characterize two of these UTR isoform genes with identical coding sequences and demonstrate that the underlying transcription start site heterogeneity frequently results in 5′ terminal oligopyrimidine (TOP) and pyrimidine-rich translational element (PRTE) motif switches to drive mTORC1-dependent translation of the mRNA. Genome-wide, we show that highly translated squamous cell carcinoma transcripts switch towards increased use of 5′TOP and PRTE motifs, have generally shorter 5′UTRs and expose decreased RNA secondary structures. Notably, we found that the two 5′TOP motif-containing, but not the TOP-less, RPL21 transcript isoforms strongly correlated with overall survival in human head and neck squamous cell carcinoma patients. Our findings warrant isoform-specific analyses in human cancer datasets and suggest that switching between 5′UTR isoforms is an elegant and simple way to alter protein synthesis rates, set their sensitivity to the mTORC1-dependent nutrient-sensing pathway and direct the translational potential of an mRNA by the precise 5′UTR sequence.

Low flows can impact water use and instream ecology. Therefore, reliable predictions of low-flow metrics are crucial. In this study, we assess which catchment characteristics (climate, topography, geology and landcover) can explain the spatial variability of lowflow metrics at two different scales: the regional scale and the small headwater catchment scale. For the regional-scale analysis, we calculated the mean 7-day annual minimum flow (q min ), the mean of the flow that is exceeded 95% of the year (q 95 ), and the master recession constant (C) for 280 independent gauging stations across the Swiss Plateau and the Swiss Alps for the 2000-2018 period. We assessed the relation between 44 catchment characteristics and the three low-flow metrics based on correlation analysis and a random forest model. Low-flow magnitudes across the Swiss Plateau were positively correlated with the fraction of the area covered by sandstone bedrock or alluvium, and with the area that has a slope between 10 and 30 . Across the Swiss Alps, low-flow magnitudes were positively correlated with the fraction of area with slopes between 30 and 60 , and the area with glacial deposits and debris cover. There was good agreement between observations and predictions by the random forest regression model with the top 11 catchment characteristics for both regions: for 80% of the Swiss Plateau catchments and 60% of the Swiss Alpine catchments, we could predict the three low-flow metrics within an error of 30%. The residuals of the regression model, however, varied across short distances, suggesting that local catchment characteristics affect the variability of low-flow metrics. For the local-scale headwater catchments, we conducted 1-day snapshot field campaigns in 16 catchments during low-flow periods in 2015 and 2016. The measurements in these sub-catchments also showed that areas with sandstone bedrock and a good storage-to-river connectivity had above average low-flow magnitudes. Including knowledge on local catchment characteristics may help to improve regional low-flow predictions, however, not all local catchment characteristics were useful descriptors at larger scales.

MicroRNA (miRNA) loaded Argonaute (AGO) complexes regulate gene expression via direct base pairing with their mRNA targets. Previous works suggest that up to 60% of mammalian transcripts might be subject to miRNA-mediated regulation, but it remains largely unknown which fraction of these interactions are functional in a specific cellular context. Here, we integrate transcriptome data from a set of miRNA-depleted mouse embryonic stem cell (mESC) lines with published miRNA interaction predictions and AGO-binding profiles. Using this integrative approach, combined with molecular validation data, we present evidence that < 10% of expressed genes are functionally and directly regulated by miRNAs in mESCs. In addition, analyses of the stem cell-specific miR-290-295 cluster target genes identify TFAP4 as an important transcription factor for early development. The extensive datasets developed in this study will support the development of improved predictive models for miRNA-mRNA functional interactions.