Hierarchical Compression Reveals Sub-Second to Day-Long Structure in Larval Zebrafish Behaviour, bioRxiv, 2019-07-08
AbstractAnimal behaviour is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioural data. Here, we develop computational tools and study the behaviour of hundreds of larval zebrafish tracked continuously across multiple 24-hour daynight cycles. We extracted millions of movements and pauses, termed bouts, and used unsupervised learning to reduce each larva’s behaviour to an alternating sequence of active and inactive bout types, termed modules. Through hierarchical compression, we identified recurrent behavioural patterns, termed motifs. Module and motif usage varied across the daynight cycle, revealing structure at sub-second to day-long timescales. We further demonstrate that module and motif analysis can uncover novel pharmacological and genetic mutant phenotypes. Overall, our work reveals the organisation of larval zebrafish behaviour at multiple timescales and provides tools to identify structure from large-scale behavioural datasets.
biorxiv neuroscience 0-100-users 2019Structural basis for recognition of RALF peptides by LRX proteins during pollen tube growth, bioRxiv, 2019-07-08
AbstractPlant reproduction relies on the highly regulated growth of the pollen tube for proper sperm delivery. This process is controlled by secreted RALF signaling peptides, which have been previously shown to be perceived by CrRLK1Ls membrane receptor-kinases and leucine-rich (LRR) extensin proteins (LRXs). Here we demonstrate that RALF peptides are active as folded, disulfide bond-stabilized proteins, which can bind to the LRR domain of LRX proteins with nanomolar affinity. Crystal structures of the LRX-RALF signaling complexes reveal LRX proteins as constitutive dimers. The N-terminal LRR domain containing the RALF binding site is tightly linked to the extensin domain via a cysteine-rich tail. Our biochemical and structural work reveals a complex signaling network by which RALF ligands may instruct different signaling proteins – here CrRLK1Ls and LRXs – through structurally different binding modes to orchestrate cell wall remodeling in rapidly growing pollen tubes.SignificancePlant reproduction relies on proper pollen tube growth to reach the female tissue and release the sperm cells. This process is highly regulated by a family of secreted signaling peptides that are recognized by cell-wall monitoring proteins to enable plant fertilization. Here, we report the crystal structure of the LRX-RALF cell-wall complex and we demonstrate that RALF peptides are active as folded proteins. RALFs are autocrine signaling proteins able to instruct LRX cell-wall modules and CrRKL1L receptors, through structurally different binding modes to coordinate pollen tube integrity.
biorxiv plant-biology 0-100-users 2019Deep learning detects virus presence in cancer histology, bioRxiv, 2019-07-06
AbstractOncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.
biorxiv cancer-biology 0-100-users 2019NINscope a versatile miniscope for multi-region circuit investigations, bioRxiv, 2019-07-01
AbstractMiniaturized fluorescence microscopes (miniscopes) have been instrumental to monitor neural activity during unrestrained behavior and their open-source versions have helped to distribute them at an affordable cost. Generally, the footprint and weight of open-source miniscopes is sacrificed for added functionality. Here, we present NINscope a light-weight, small footprint, open-source miniscope that incorporates a high-sensitivity image sensor, an inertial measurement unit (IMU), and an LED driver for an external optogenetic probe. We highlight the advantages of NINscope by performing the first simultaneous cellular resolution (dual scope) recordings from cerebellum and cerebral cortex in unrestrained mice, revealing that the activity of both regions generally precedes the onset of behavioral acceleration. We further demonstrate the optogenetic stimulation capabilities of NINscope and show that cerebral cortical activity can be driven strongly by cerebellar stimulation. To validate the performance of our miniscope to image from deep-brain regions, we recorded in the dorsal striatum and, using the IMU to assess turning movements, replicate previous studies that show encoding of action space in this subcortical region. Finally, we combine optogenetic stimulation of distinct cortical regions projecting to the dorsal striatum, to probe functional connectivity. In combination with cross-platform control software, NINscope is a versatile addition to the expanding toolbox of open-source miniscopes and will aid multi-region circuit investigations during unrestrained behavior.
biorxiv neuroscience 0-100-users 2019Direct evidence for transport of RNA from the mouse brain to the germline and offspring, bioRxiv, 2019-06-29
AbstractThe traditional concept that heritability occurs exclusively from the transfer of germline-restricted genetics is being challenged by the increasing accumulation of evidence confirming the existence of experience-dependent transgenerational inheritance. Transgenerational inheritance is emerging as a powerful mechanism for robustly transmitting phenotypic adaptations to offspring. However, questions remain unanswered as to how this heritable information is passed from somatic cells. Previous studies have implicated the critical involvement of RNA in heritable transgenerational effects and the high degree of mobility and genomic impact of RNAs in all organisms is an attractive model for the efficient transfer of genetic information. Here we show, for the first time, robust transport of RNA from the brain of an adult male mouse to sperm, and subsequently to offspring. Our observation of heritable genetic information originating from a somatic tissue may reveal a mechanism for how transgenerational effects are transmitted to offspring.
biorxiv evolutionary-biology 0-100-users 2019MitoFinder efficient automated large-scale extraction of mitogenomic data in target enrichment phylogenomics, bioRxiv, 2019-06-28
AbstractThanks to the development of high-throughput sequencing technologies, target enrichment sequencing of nuclear ultraconserved DNA elements (UCEs) now allows routinely inferring phylogenetic relationships from thousands of genomic markers. Recently, it has been shown that mitochondrial DNA (mtDNA) is frequently sequenced alongside the targeted loci in such capture experiments. Despite its broad evolutionary interest, mtDNA is rarely assembled and used in conjunction with nuclear markers in capture-based studies. Here, we developed MitoFinder, a user-friendly bioinformatic pipeline, to efficiently assemble and annotate mitogenomic data from hundreds of UCE libraries. As a case study, we used ants (Formicidae) for which 501 UCE libraries have been sequenced whereas only 29 mitogenomes are available. We compared the efficiency of four different assemblers (IDBA-UD, MEGAHIT, MetaSPAdes, and Trinity) for assembling both UCE and mtDNA loci. Using MitoFinder, we show that metagenomic assemblers, in particular MetaSPAdes, are well suited to assemble both UCEs and mtDNA. Mitogenomic signal was successfully extracted from all 501 UCE libraries allowing confirming species identification using COI barcoding. Moreover, our automated procedure retrieved 296 cases in which the mitochondrial genome was assembled in a single contig, thus increasing the number of available ant mitogenomes by an order of magnitude. By leveraging the power of metagenomic assemblers, MitoFinder provides an efficient tool to extract complementary mitogenomic data from UCE libraries, allowing testing for potential mito-nuclear discordance. Our approach is potentially applicable to other sequence capture methods, transcriptomic data, and whole genome shotgun sequencing in diverse taxa.
biorxiv evolutionary-biology 0-100-users 2019