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 2019Systems-level immunomonitoring using self-sampled capillary blood, bioRxiv, 2019-07-08
AbstractComprehensive profiling of the human immune system in patients with cancer, autoimmune disease and during infections are providing valuable information that help us understand disease states and discriminate productive from inefficient immune responses and identify possible targets for immune modulation. Recent technical advances now allow for all immune cell populations and hundreds of plasma proteins to be detected using small volume blood samples. To democratize such systems-immunological analyses, further simplified blood sampling and preservation will be important. Here we describe that blood obtained via a nearly painless self-sampling device of 100 microliter of capillary blood that is preserved and frozen, can simplify systems-level immunomonitoring studies.
biorxiv immunology 100-200-users 2019Transcriptome assembly from long-read RNA-seq alignments with StringTie2, bioRxiv, 2019-07-08
AbstractRNA sequencing using the latest single-molecule sequencing instruments produces reads that are thousands of nucleotides long. The ability to assemble these long reads can greatly improve the sensitivity of long-read analyses. Here we present StringTie2, a reference-guided transcriptome assembler that works with both short and long reads. StringTie2 includes new computational methods to handle the high error rate of long-read sequencing technology, which previous assemblers could not tolerate. It also offers the ability to work with full-length super-reads assembled from short reads, which further improves the quality of assemblies. On 33 short-read datasets from humans and two plant species, StringTie2 is 47.3% more precise and 3.9% more sensitive than Scallop. On multiple long read datasets, StringTie2 on average correctly assembles 8.3 and 2.6 times as many transcripts as FLAIR and Traphlor, respectively, with substantially higher precision. StringTie2 is also faster and has a smaller memory footprint than all comparable tools.
biorxiv genomics 100-200-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 2019Aging is associated with a systemic length-driven transcriptome imbalance, bioRxiv, 2019-07-04
AbstractAging manifests itself through a decline in organismal homeostasis and a multitude of cellular and physiological functions1. Efforts to identify a common basis for vertebrate aging face many challenges; for example, while there have been documented changes in the expression of many hundreds of mRNAs, the results across tissues and species have been inconsistent2. We therefore analyzed age-resolved transcriptomic data from 17 mouse organs and 51 human organs using unsupervised machine learning3–5 to identify the architectural and regulatory characteristics most informative on the differential expression of genes with age. We report a hitherto unknown phenomenon, a systemic age-dependent length-driven transcriptome imbalance that for older organisms disrupts the homeostatic balance between short and long transcript molecules for mice, rats, killifishes, and humans. We also demonstrate that in a mouse model of healthy aging, length-driven transcriptome imbalance correlates with changes in expression of splicing factor proline and glutamine rich (Sfpq), which regulates transcriptional elongation according to gene length6. Furthermore, we demonstrate that length-driven transcriptome imbalance can be triggered by environmental hazards and pathogens. Our findings reinforce the picture of aging as a systemic homeostasis breakdown and suggest a promising explanation for why diverse insults affect multiple age-dependent phenotypes in a similar manner.
biorxiv systems-biology 100-200-users 2019