Search

Evidence is emerging that immune responses not only play a part in the central nervous system (CNS) in diseases but may also be relevant for healthy conditions. We discovered a major role for the interleukin-4 (IL-4)/IL-4 receptor alpha (IL-4Rα) signaling pathway in synaptic processes, as indicated by transcriptome analysis in IL-4Rα–deficient mice and human neurons with/without IL-4 treatment. Moreover, IL-4Rα is expressed presynaptically, and locally available IL-4 regulates synaptic transmission. We found reduced synaptic vesicle pools, altered postsynaptic currents, and a higher excitatory drive in cortical networks of IL-4Rα–deficient neurons. Acute effects of IL-4 treatment on postsynaptic currents in wild-type neurons were mediated via PKCγ signaling release and led to increased inhibitory activity supporting the findings in IL-4Rα–deficient neurons. In fact, the deficiency of IL-4Rα resulted in increased network activity in vivo, accompanied by altered exploration and anxiety-related learning behavior; general learning and memory was unchanged. In conclusion, neuronal IL-4Rα and its presynaptic prevalence appear relevant for maintaining homeostasis of CNS synaptic function.

Background Fast and accurate diagnostics are key for personalised medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies, which can prolong lives. In this work, we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalised therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep-learning method for scoring microscopy images of GEA for the presence of HER2 overexpression. Methods Our method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1602 patient samples and tested on an independent set of 307 patient samples. We additionally verified the CNN’s generalisation capabilities with an independent dataset with 653 samples from a separate clinical centre. We incorporated an attention mechanism in the network architecture to identify the tissue regions, which are important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridisation (ISH) tests. Results We show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve and that require additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical centre. The attention-based CNN exploits morphological information in microscopy images and is superior to a predictive model based on the staining intensity only. Conclusions We demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.

Significance We show that bees locate their queen by performing a cascade of “scenting” events, where individual bees direct their pheromone signals by fanning their wings. The bees create a dynamic spatiotemporal network that recruits new broadcasting bees over time, as the pheromones travel a distance that is orders of magnitude the length of an individual. We develop high-throughput machine learning tools to identify the locations and timings of scenting events, and demonstrate that these events integrate into a global “map” that leads to the queen. We use these results to build an agent-based model that illustrates the advantage of the directional signaling in amplifying the pheromones, thus leading to an effective search and aggregation process., Honeybee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones. But how can distant individuals exploit these chemical signals, which decay rapidly in space and time? Here, we combine a behavioral assay with the machine vision detection of organism location and scenting (pheromone propagation via wing fanning) behavior to track the search and aggregation dynamics of the honeybee Apis mellifera L. We find that bees collectively create a scenting-mediated communication network by arranging in a specific spatial distribution where there is a characteristic distance between individuals and directional signaling away from the queen. To better understand such a flow-mediated directional communication strategy, we developed an agent-based model where bee agents obeying simple, local behavioral rules exist in a flow environment in which the chemical signals diffuse and decay. Our model serves as a guide to exploring how physical parameters affect the collective scenting behavior and shows that increased directional bias in scenting leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic (nondirectional and axisymmetric) communication, such as small bee clusters that persist throughout the simulation. Our results highlight an example of extended classical stigmergy: Rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

Cockayne syndrome (CS) is a congenital syndrome characterized by growth and mental retardation, and premature ageing. The complexity of CS and mammalian models warrants simpler metazoan models that display CS-like phenotypes that could be studied in the context of a live organism. Here, we provide a characterization of neuronal and mitochondrial aberrations caused by a mutation in the csb-1 gene in Caenorhabditis elegans. We report a progressive neurodegeneration in adult animals that is enhanced upon UV-induced DNA damage. The csb-1 mutants show dysfunctional hyperfused mitochondria that degrade upon DNA damage, resulting in diminished respiratory activity. Our data support the role of endogenous DNA damage as a driving factor of CS-related neuropathology and underline the role of mitochondrial dysfunction in the disease.

The kidneys constantly filter enormous amounts of fluid, with almost complete retention of albumin and other macromolecules in the plasma. Diseases of podocytes at the kidney filtration barrier reduce the glomerular capillary surface area available for filtration and alter the intrinsic permeability of the capillary wall resulting in albuminuria, however, direct quantitative assessment of the underlying morphological changes has not been possible so far. Here we developed a deep learning-based approach for segmentation of foot processes in images acquired with super-resolved stimulated emission depletion (STED) microscopy or confocal microscopy. Our method, Automatic Morphological Analysis of Podocytes (AMAP), detected 87-95% manually-annotated foot processes and additionally recognized 1.3 - 2.17-fold more. It also robustly determined morphometric parameters, at a Pearson correlation of r > 0.71 with a previously published semi-automated approach, across a large set of mouse tissue samples. The artificial intelligence algorithm was applied to a set of human kidney disease conditions allowing comprehensive quantifications of various underlying morphometric parameters. These data confirmed that when podocytes are injured, they take on a more simplified architecture and the slit-diaphragm length is much reduced, resulting in a reduction in the filtration slit area and a loss of the buttress force of podocytes which increases the permeability of the GBM to albumin.

From cells in tissue, to bird flocks, to human crowds, living systems display a stunning variety of collective behaviors. Yet quantifying such phenomena first requires tracking a significant fraction of the group members in natural conditions, a substantial and ongoing challenge. We present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. These fluctuations include ~24 h cycles in the counted detections, negative correlation between bee and brood, and nightly enhancement of bees inside comb cells. We combine detected positions with visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over 5 min timespans. The trajectories reveal important individual behaviors, including waggle dances and crawling inside comb cells. Our results provide opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems., Honey bee colonies are hard to automatically monitor due to the high number of visually similar members which move rapidly and whose numbers change over time. Here, the authors report a method for markerless tracking of a bee colony by adapting convolutional neural networks for detection and tracking.

Data related to thestudyMarkerless tracking of an entire insect colony. The dataset comprises detection and trajectory information from five beehive recordings at 10 fps 5 min segments. Tracking pipeline is described in ourgithub repositoryandsample datawas prepared to run the pipeline.

Data related to thestudyMarkerless tracking of an entire insect colony. The dataset comprises detection and trajectory information from five beehive recordings at 10 fps 5 min segments. Tracking pipeline is described in ourgithub repositoryandsample datawas prepared to run the pipeline. We include also manuscript Supplementary Movies in this dataset: M1Background extracted from hive L1. Each frame represents 12 h of the original recording. M2Background extracted from hive L5. Each frame represents 12 h of the original recording, predicted brood cells are marked in red. M3-M7. Example bee trajectories with their corresponding trajectories through the space of visual features. The originally 64-dimensional vectors of visual features representing each bee instance are projected into 3D with the use of t-Distributed Stochastic Neighbor Embedding (t-SNE). Analogous to the plot 3B, 10 most recent representations of the tracked bee are marked in red dots and representations of all other bees from three most recent video frames are marked in yellow dots. Examples are from hives S1-S5, respectively. M8-M12. Short video snippets provided to visually illustrate the proportion of tracked individuals in each hives. All reference trajectories in hives: S1-S5, respectively. M13-M15. Bees performing waggle dance, whose trajectories are shown in Fig. 4D. M16-M18. Bees visiting multiple comb cells, whose trajectories are shown in Fig. 4F.

Honey bee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones, but how can distant individuals exploit these chemical signals which decay rapidly in space and time? Here, we combine a novel behavioral assay with the machine vision detection of organism location and scenting behavior to track the search and aggregation dynamics of the honey bee Apis mellifera L. We find that bees collectively create a communication network to propagate pheromone signals, by arranging in a specific spatial distribution where there is a characteristic distance between individuals and a characteristic direction in which individuals broadcast the signals. To better understand such a flow–mediated directional communication strategy, we connect our experimental results to an agent–based model where virtual bees with simple, local behavioral rules, exist in a flow environment. Our model shows that increased directional bias leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic communication, such as small bee clusters that persist throughout the simulation. Our results highlight a novel example of extended classical stigmergy: rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

We present a comprehensive, computational method for tracking an entire colony of the honey beeApis melliferausing high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. We combine extracted positions with rich visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over a span of 5 minutes. The resulting trajectories reveal important behaviors, including fast motion, comb-cell activity, and waggle dances. Our results provide new opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.

Maximal lifespan of mammalian species, even if closely related, may differ more than 10-fold, however the nature of the mechanisms that determine this variability is unresolved. Here, we assess the relationship between maximal lifespan duration and concentrations of more than 20,000 lipid compounds, measured in 669 tissue samples from 6 tissues of 35 species representing three mammalian clades: primates, rodents and bats. We identify lipids associated with species' longevity across the three clades, uncoupled from other parameters, such as basal metabolic rate, body size, or body temperature. These lipids clustered in specific lipid classes and pathways, and enzymes linked to them display signatures of greater stabilizing selection in long-living species, and cluster in functional groups related to signaling and protein-modification processes. These findings point towards the existence of defined molecular mechanisms underlying variation in maximal lifespan among mammals.

Lipids are essential structural and functional components of cells. Little is known, however, about the evolution of lipid composition in different tissues. Here, we report a large-scale analysis of the lipidome evolution in six tissues of 32 species representing primates, rodents, and bats. While changes in genes’ sequence and expression accumulate proportionally to the phylogenetic distances

The original version of this Article omitted from the author list the fourth author Alexander S. Mikheyev, who is from the Ecology and Evolution Unit, OIST Graduate University, Okinawa, Japan, and the Research School of Biology, Australian National University, Canberra, Australia. The third author Yoann Portugal has the following additional affiliation: Ecology and Evolution Unit, OIST Graduate University, Okinawa, Japan. The fourth author Alexander S. Mikheyev and the fifth author Greg J. Stephens declare equal contributions. Consequently, the Acknowledgements, which formerly read “We thank Michael Iuzzolino, Dieu My thanh Nguyen, Orit Peleg, and Michael Smith for comments on the manuscript and code testing. This work was supported by the Okinawa Institute of Science and Technology Graduate University”, have been corrected to “We are grateful to Takahashi Ikemiya for maintaining the experimental bee colonies. We thank Michael Iuzzolino, Dieu My Thanh Nguyen, Orit Peleg, and Michael Smith for comments on the manuscript and code testing. This work was supported by the Okinawa Institute of Science and Technology Graduate University. Additional funding was provided by KAKENHI grants 16H06209 and 16KK0175 from the Japan Society for the Promotion of Science to AM”. Additionally, the Author Contributions, which formerly read “Y.P. performed the bee work and devised the imaging setup, L. H. devised the labeling tool, K.B. performed method development and data analysis, K.B. and G.S. designed the study and wrote the manuscript”, has been corrected to “Y.P. performed the bee work, Y.P. and A.M. devised the imaging setup, L.H. devised the labeling tool, K.B. performed method development and data analysis, K.B., A.M., and G.S. designed the study, K.B. and G.S. wrote the manuscript”. This has been corrected in both the PDF and HTML versions of the Article. The original version of the Supplementary information associated with this Article contained an error in the description of Supplementary Table 2, which incorrectly read “All imaging data in this study were collected in 2019”. The correct version states “2018” in place of “2019”. The HTML has been updated to include a corrected version of the Supplementary information.

Lipids are essential components of the brain. Here, we conducted a comprehensive mass spectrometry-based analysis of lipidome composition in the prefrontal cortex of 40 humans, 40 chimpanzees, and 40 rhesus monkeys over postnatal development and adulthood. Of the 11,772 quantified lipid peaks, 7,589 change significantly along the lifespan. More than 60% of these changes occur prior to adulthood, with less than a quarter associated with myelination progression. Evolutionarily, 36% of the age-dependent lipids exhibit concentration profiles distinct to one of the three species; 488 (18%) of them were unique to humans. In both humans and chimpanzees, the greatest extent of species-specific differences occurs in early development. Human-specific lipidome differences, however, persist over most of the lifespan and reach their peak from 20 to 35 years of age, when compared with chimpanzee-specific ones.

Maximal lifespan of mammalian species, even if closely related, may differ more than 10-fold, however the nature of the mechanisms that determine this variability is unresolved. Here, we assess the relationship between maximal lifespan duration and concentrations of more than 20,000 lipid compounds, measured in 669 tissue samples from 6 tissues of 35 species representing three mammalian clades: primates, rodents and bats. We identify lipids associated with species’ longevity across the three clades, uncoupled from other parameters, such as basal metabolic rate, body size, or body temperature. These lipids clustered in specific lipid classes and pathways, and enzymes linked to them display signatures of greater stabilizing selection in long-living species, and cluster in functional groups related to signaling and protein-modification processes. These findings point towards the existence of defined molecular mechanisms underlying variation in maximal lifespan among mammals.

Tracking large numbers of densely-arranged, interacting objects is challenging due to occlusions and the resulting complexity of possible trajectory combinations, as well as the sparsity of relevant, labeled datasets. Here we describe a novel technique of collective tracking in the model environment of a 2D honeybee hive in which sample colonies consist of $N\sim10^3$ highly similar individuals, tightly packed, and in rapid, irregular motion. Such a system offers universal challenges for multi-object tracking, while being conveniently accessible for image recording. We first apply an accurate, segmentation-based object detection method to build initial short trajectory segments by matching object configurations based on class, position and orientation. We then join these tracks into full single object trajectories by creating an object recognition model which is adaptively trained to recognize honeybee individuals through their visual appearance across multiple frames, an attribute we denote as pixel personality. Overall, we reconstruct ~46% of the trajectories in 5 min recordings from two different hives and over 71% of the tracks for at least 2 min. We provide validated trajectories spanning 3000 video frames of 876 unmarked moving bees in two distinct colonies in different locations and filmed with different pixel resolutions, which we expect to be useful in the further development of general-purpose tracking solutions., 13 pages, 4 main and 9 supplementary figures as well as a link to supplementary movies

Sudden biotic pressures, such as those from novel diseases and pathogens, require populations to respond rapidly or face potential extinction. How this response process takes place remains poorly understood, particularly in natural environments. In this study we take advantage of unique decade-long data sets of two wild honey bee (Apis mellifera) populations in the United States to reconstruct the evolution of tolerance to a novel parasite, the ectoparasitic mite Varroa destructor. Upon the arrival of Varroa, the two geographically isolated populations simultaneously suffered massive Varroa-induced mortality, but stabilized within two years. Here we sequenced and phased genomes of 465 bees sampled from both populations annually over the decade that spanned Varroa's arrival. Remarkably, we found that genetic changes were strongly correlated between the populations, suggesting parallel selective responses to Varroa parasitization. The arrival of Varroa was also correlated with an influx of genes of African origin into both populations, though surprisingly it did not substantially contribute to the overall similarity of the evolutionary response between them. Genes involved in metabolic, protein processing and developmental pathways were under particularly strong selection. It is possible that interactions among highly connected gene groups in these pathways may help channelize selective responses to novel parasites, causing completely unrelated populations to exhibit parallel evolutionary trajectories when faced with the same biotic pressure. Our analyses illustrate that ecologically relevant traits emerge from highly polygenic selection involving thousands of genes contributing to complex patterns of evolutionary change.

Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.

Circadian rhythms regulate a wide range of cellular, physiological, metabolic and behavioral activities in mammals. The complexity of tissue- and day-time specific regulation of thousands of clock controlled genes (CCGs) suggests that many transcriptional regulators are involved. Our bioinformatic analysis is based on two published DNA-array studies from mouse liver. We search overrepresented transcription factor binding sites in promoter regions of CCGs using GC-matched controls. Analyzing a large set of CCG promoters, we find known motifs such as E-boxes, D-boxes and cAMP responsive elements. In addition, we find overrepresented GC-rich motifs (Sp1, ETF, Nrf1), AT-rich motifs (TBP, Fox04, MEF-2), Y-box motifs (NF-Y, C/EBP) and cell cycle regulators (E2F, Elk-1). In a subset of system-driven genes, we find overrepresented motifs of the serum response factor SRF and the estrogen receptor ER. The analysis of published ChIP data reveals that some of our predicted regulators (C/EBP, E2F, HNF-1, Myc, MEF-2) target relatively many clock controlled genes. Our analysis of CCG promoters contributes to an understanding of the complex transcriptional regulation of circadian rhythms in liver.

Variable region 1 (V1) of the SPRY domain of TRIM5alpha is a major determinant for species-specific virus restriction in primates. We previously reported that a chimeric TRIM5alpha containing baboon V1 in the background of cynomolgus monkey TRIM5alpha showed potent anti-human immunodeficiency virus type 2 (HIV-2) activity. Since baboons are reportedly sensitive to HIV-2 infection, there was a discrepancy between the ability of baboon TRIM5alpha V1 to restrict HIV-2 and baboon sensitivity to HIV-2. In the study presented here, we examined the roles of V2 and V3 of the baboon TRIM5alpha SPRY domain in its anti-HIV-2 activity. A chimeric TRIM5alpha containing the entire baboon SPRY domain showed weak anti-HIV-2 activity. This attenuation of activity was caused by a single serine-to-proline substitution in baboon TRIM5alpha V2. These findings indicate that the combination of V1 with other variable regions of SPRY is important in anti-HIV-2 activity of primate TRIM5alpha.

Accelerated evolution of the human brain and muscle metabolomes reflects our unique cognitive and physical capacities., Metabolite concentrations reflect the physiological states of tissues and cells. However, the role of metabolic changes in species evolution is currently unknown. Here, we present a study of metabolome evolution conducted in three brain regions and two non-neural tissues from humans, chimpanzees, macaque monkeys, and mice based on over 10,000 hydrophilic compounds. While chimpanzee, macaque, and mouse metabolomes diverge following the genetic distances among species, we detect remarkable acceleration of metabolome evolution in human prefrontal cortex and skeletal muscle affecting neural and energy metabolism pathways. These metabolic changes could not be attributed to environmental conditions and were confirmed against the expression of their corresponding enzymes. We further conducted muscle strength tests in humans, chimpanzees, and macaques. The results suggest that, while humans are characterized by superior cognition, their muscular performance might be markedly inferior to that of chimpanzees and macaque monkeys., Author Summary Physiological processes that maintain our tissues' functionality involve the generation of multiple products and intermediates known as metabolites—small molecules with a weight of less than 1,500 Daltons. Changes in concentrations of these metabolites are thought to be closely related to changes in phenotype. Here, we assessed concentrations of more than 10,000 metabolites in three brain regions and two non-neural tissues (skeletal muscle and kidney) of humans, chimpanzees, macaque monkeys, and mice using mass spectrometry-based approaches. We found that the evolution of the metabolome largely reflects genetic divergence between species and is not greatly affected by environmental factors. In the human lineage, however, we observed an exceptional acceleration of metabolome evolution in the prefrontal cortical region of the brain and in skeletal muscle. Based on additional behavioral tests, we further show that metabolic changes in human muscle seem to be paralleled by a drastic reduction in muscle strength. The observed rapid metabolic changes in brain and muscle, together with the unique human cognitive skills and low muscle performance, might reflect parallel mechanisms in human evolution.

Although Neanderthals are extinct, fragments of their genomes persist in contemporary humans. Here we show that while the genome-wide frequency of Neanderthal-like sites is approximately constant across all contemporary out-of-Africa populations, genes involved in lipid catabolism contain more than threefold excess of such sites in contemporary humans of European descent. Evolutionally, these genes show significant association with signatures of recent positive selection in the contemporary European, but not Asian or African populations. Functionally, the excess of Neanderthal-like sites in lipid catabolism genes can be linked with a greater divergence of lipid concentrations and enzyme expression levels within this pathway, seen in contemporary Europeans, but not in the other populations. We conclude that sequence variants that evolved in Neanderthals may have given a selective advantage to anatomically modern humans that settled in the same geographical areas., Modern human genomes contain Neanderthal sequences, but it is unclear whether these were selected. Here, Khrameeva et al. show that Neanderthal sequences associated with lipid catabolism are three times more frequent in Europe, suggesting that these sequences might have been beneficial to Europeans.

Human immunodeficiency virus type 2 (HIV-2) and simian immunodeficiency virus isolated from a macaque monkey (SIVmac) are assumed to have originated from simian immunodeficiency virus isolated from sooty mangabey (SIVsm). Despite their close similarity in genome structure, HIV-2 and SIVmac show different sensitivities to TRIM5α, a host restriction factor against retroviruses. The replication of HIV-2 strains is potently restricted by rhesus (Rh) monkey TRIM5α, while that of SIVmac strain 239 (SIVmac239) is not. Viral capsid protein is the determinant of this differential sensitivity to TRIM5α, as the HIV-2 mutant carrying SIVmac239 capsid protein evaded Rh TRIM5α-mediated restriction. However, the molecular determinants of this restriction mechanism are unknown. Electrostatic potential on the protein-binding site is one of the properties regulating protein–protein interactions. In this study, we investigated the electrostatic potential on the interaction surface of capsid protein of HIV-2 strain GH123 and SIVmac239. Although HIV-2 GH123 and SIVmac239 capsid proteins share more than 87% amino acid identity, we observed a large difference between the two molecules with the HIV-2 GH123 molecule having predominantly positive and SIVmac239 predominantly negative electrostatic potential on the surface of the loop between α-helices 4 and 5 (L4/5). As L4/5 is one of the major determinants of Rh TRIM5α sensitivity of these viruses, the present results suggest that the binding site of the Rh TRIM5α may show complementarity to the HIV-2 GH123 capsid surface charge distribution.

The complexity of tissue- and day time-specific regulation of thousands of clock-controlled genes (CCGs) suggests that many regulatory mechanisms contribute to the transcriptional output of the circadian clock. We aim to predict these mechanisms using a large scale promoter analysis of CCGs.Our study is based on a meta-analysis of DNA-array data from rodent tissues. We searched in the promoter regions of 2065 CCGs for highly overrepresented transcription factor binding sites. In order to compensate the relatively high GC-content of CCG promoters, a novel background model to avoid a bias towards GC-rich motifs was employed. We found that many of the transcription factors with overrepresented binding sites in CCG promoters exhibit themselves circadian rhythms. Among the predicted factors are known regulators such as CLOCKratioBMAL1, DBP, HLF, E4BP4, CREB, RORalpha and the recently described regulators HSF1, STAT3, SP1 and HNF-4alpha. As additional promising candidates of circadian transcriptional regulators PAX-4, C/EBP, EVI-1, IRF, E2F, AP-1, HIF-1 and NF-Y were identified. Moreover, GC-rich motifs (SP1, EGR, ZF5, AP-2, WT1, NRF-1) and AT-rich motifs (MEF-2, HMGIY, HNF-1, OCT-1) are significantly overrepresented in promoter regions of CCGs. Putative tissue-specific binding sites such as HNF-3 for liver, NKX2.5 for heart or Myogenin for skeletal muscle were found. The regulation of the erythropoietin (Epo) gene was analysed, which exhibits many binding sites for circadian regulators. We provide experimental evidence for its circadian regulated expression in the adult murine kidney. Basing on a comprehensive literature search we integrate our predictions into a regulatory network of core clock and clock-controlled genes. Our large scale analysis of the CCG promoters reveals the complexity and extensiveness of the circadian regulation in mammals. Results of this study point to connections of the circadian clock to other functional systems including metabolism, endocrine regulation and pharmacokinetics.

SummaryLipids are prominent components of the nervous system. Here we performed a large-scale mass spectrometry-based analysis of the lipid composition of three brain regions as well as kidney and skeletal muscle of humans, chimpanzees, rhesus macaques, and mice. The human brain shows the most distinct lipid composition: 76% of 5,713 lipid compounds examined in our study are either enriched or depleted in the human brain. Concentration levels of lipids enriched in the brain evolve approximately four times faster among primates compared with lipids characteristic of non-neural tissues and show further acceleration of change in human neocortical regions but not in the cerebellum. Human-specific concentration changes are supported by human-specific expression changes for corresponding enzymes. These results provide the first insights into the role of lipids in human brain evolution.Video Abstract

Background Human immunodeficiency virus type 1 (HIV-1) productively infects only humans and chimpanzees but not Old World monkeys, such as rhesus and cynomolgus (CM) monkeys. To establish a monkey model of HIV-1/AIDS, several HIV-1 derivatives have been constructed. We previously reported that efficient replication of HIV-1 in CM cells was achieved after we replaced the loop between α-helices 6 and 7 (L6/7) of the capsid protein (CA) with that of SIVmac239 in addition to the loop between α-helices 4 and 5 (L4/5) and vif. This virus (NL-4/5S6/7SvifS) was supposed to escape from host restriction factors cyclophilin A, CM TRIM5α, and APOBEC3G. However, the replicative capability of NL-4/5S6/7SvifS in human cells was severely impaired. Results By long-term cultivation of human CEMss cells infected with NL-4/5S6/7SvifS, we succeeded in rescuing the impaired replicative capability of the virus in human cells. Sequence analysis of the CA region of the adapted virus revealed a G-to-E substitution at the 116th position of the CA (G116E). Introduction of this substitution into the molecular DNA clone of NL-4/5S6/7SvifS indeed improved the virus' replicative capability in human cells. Although the G116E substitution occurred during long-term cultivation of human cells infected with NL-4/5S6/7SvifS, the viruses with G116E unexpectedly became resistant to CM, but not human TRIM5α-mediated restriction. The 3-D model showed that position 116 is located in the 6th helix near L4/5 and L6/7 and is apparently exposed to the protein surface. The amino acid substitution at the 116th position caused a change in the structure of the protein surface because of the replacement of G (which has no side chain) with E (which has a long negatively charged side chain). Conclusions We succeeded in rescuing the impaired replicative capability of NL-4/5S6/7SvifS and report a mutation that improved the replicative capability of the virus. Unexpectedly, HIV-1 with this mutation became resistant to CM TRIM5α-mediated restriction.