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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

KCC2 regulates neuronal excitability and hippocampal activity via interaction with Task-3 channels

The K+/Cl- co-transporter KCC2 (SLC12A5) regulates neuronal transmembrane chloride gradients and thereby controls GABA signaling in the brain. KCC2 downregulation is observed in several neurological and psychiatric disorders including epilepsy, neuropathic pain and autism spectrum disorders. Paradoxical, excitatory GABA signaling is usually assumed to contribute to abnormal network activity underlying the pathology. We tested this hypothesis and explored the functional impact of chronic KCC2 downregulation in the rat dentate gyrus. Although the reversal potential of GABAA receptor currents was depolarized in KCC2 knockdown neurons, this shift was fully compensated by depolarization of their resting membrane potential. This effect was due to downregulation of Task-3 leak potassium channels that we show require KCC2 for membrane trafficking. Increased neuronal excitability upon KCC2 suppression altered dentate gyrus rhythmogenesis that could be normalized by chemogenetic hyperpolarization. Our data reveal KCC2 downregulation engages complex synaptic and cellular alterations beyond GABA signaling that concur to perturb network activity, thus offering novel targets for therapeutic intervention.

neuroscience↗

bioRxiv2018

An In-Silico Mammalian Whole-Cell Model Reveals the Influence of Spatial Organization on RNA Splicing Efficiency

Spatial organization is a characteristic of eukaryotic cells, achieved by utilizing both membrane-bound and non-bound organelles. We model the effects of this organization and of organelle heterogeneity on RNA splicing (the process of making translationally-ready messenger RNA) and on splicing particles (the building blocks of splicing machinery) in mammalian cells. We constructed a spatially-resolved whole HeLa cell model from various experimental data and developed reaction networks to describe the RNA splicing processes. We incorporated these networks into our whole-cell model and performed stochastic simulations for up to 15 minutes of biological time. We find that the number of nuclear pore complexes affects the number of assembled splicing particles; that a slight increase of splicing particle localization in nuclear speckles (non-membrane-bound or- ganelles) leads to disproportionate enhancement in the mRNA splicing and reduction in the transcript noise; and that compartmentalization is critical for a correctly-assembled particle yield. Our model also predicts that the distance between genes and speckles has a considerable effect on effective mRNA production rate, further emphasizing the importance of genome organization around speckles. The HeLa cell model, including organelles and subcompartments, provides an adaptable foundation to study other cellular processes which are strongly modulated by spatio-temporal heterogeneity.\n\nSignificance StatementThe spliceosome is one of the most complex cellular machineries that cuts and splices the RNA code in eukaryotic cells. It dynamically assembles, disassembles, and its components are formed in multiple compartments. The efficiency of splicing process depends on localization of its components in nuclear membrane-less organelles. Therefore, a computational model of spliceosomal function must contain a spatial model of the entire cell. However, building such a model is a challenging task, mainly due to the lack of homogeneous experimental data and a suitable computational framework. Here, we overcome these challenges and present a whole HeLa cell model, with nuclear, subnuclear, and extensive cytoplasmic structures. The three-dimensional model is supplemented by reaction-diffusion processes to shed light on the function of the spliceosome.

biophysics↗

bioRxiv2018

High local mutual information drives the response in the human language network

The fronto-temporal language network responds robustly and selectively to sentences. But the features of linguistic input that drive this response and the computations these language areas support remain debated. Two key features of sentences are typically confounded in natural linguistic input: words in sentences a) are semantically and syntactically combinable into phrase- and clause-level meanings, and b) occur in an order licensed by the languages grammar. Inspired by recent psycholinguistic work establishing that language processing is robust to word order violations, we hypothesized that the core linguistic computation is composition, and, thus, can take place even when the word order violates the grammatical constraints of the language. This hypothesis predicts that a linguistic string should elicit a sentence-level response in the language network as long as the words in that string can enter into dependency relationships as in typical sentences. We tested this prediction across two fMRI experiments (total N=47) by introducing a varying number of local word swaps into naturalistic sentences, leading to progressively less syntactically well-formed strings. Critically, local dependency relationships were preserved because combinable words remained close to each other. As predicted, word order degradation did not decrease the magnitude of the BOLD response in the language network, except when combinable words were so far apart that composition among nearby words was highly unlikely. This finding demonstrates that composition is robust to word order violations, and that the language regions respond as strongly as they do to naturalistic linguistic input as long as composition can take place.

neuroscience↗

bioRxiv2018

Evolved phenological cueing strategies show variable responses to climate change

Several studies have documented a global pattern of phenological advancement that is consistent with ongoing climate change. However, the magnitude of these phenological shifts is highly variable across taxa and locations. This variability of phenological responses has been difficult to explain mechanistically. To examine how the evolution of multi-trait cueing strategies could produce variable responses to climate change, we constructed a model in which organisms evolve strategies that integrate multiple environmental cues to inform anticipatory phenological decisions. We simulated the evolution of phenological cueing strategies in multiple environments, using historic climate data from 78 locations in North America and Hawaii to capture features of climatic correlation structures in the real world. Organisms in our model evolved diverse strategies that were spatially autocorrelated across locations on a continental scale, showing that similar strategies tend to evolve in similar climates. Within locations, organisms often evolved a wide range of strategies that showed similar response phenotypes and fitness outcomes under historical conditions. However, these strategies responded differently to novel climatic conditions, with variable fitness consequences. Our model shows how the evolution of phenological cueing strategies can explain observed variation in phenological shifts and unexpected responses to climate change.

ecology↗

bioRxiv2018

Genomic characterization of additional cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modeling distribution of background mutations, existing statistical methods are often underpowered to discriminate driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), to detect cancer-driver genes showing an excess of somatic mutations. By solving the problem of inaccurately modeling background mutations, this approach works even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in all tested cancers. Applying this approach, we estimated 178 driver genes in 26 different cancers types. In silico validation confirmed 90.5% of predicted genes as likely known drivers and 7 genes unique for individual cancers as likely new drivers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects, rescuing more genes missed by alternative tools.

bioinformatics↗

bioRxiv2018

An open microscopy framework suited for tracking dCas9 in live bacteria

Super-resolution microscopy is frequently employed in the life sciences, but the number of freely accessible and affordable microscopy frameworks, especially for single particle tracking photo-activation localization microscopy (sptPALM), remains limited. To this end, we designed the miCube: a versatile super-resolution capable fluorescence microscope, which combines high spatiotemporal resolution, good adaptability, low price, and easy installation. By providing all details, we hope to enable interested researchers to build an identical or derivative instrument. The capabilities of the miCube are assessed with a novel sptPALM assay relying on the heterogeneous expression of target genes. Here, we elucidate mechanistic details of catalytically inactive Cas9 (dead Cas9) in live Lactococcus lactis. We show that, lacking specific DNA target sites, the binding and unbinding of dCas9 to DNA can be described using simplified rate constants of kbound[->]free = 30-80 s-1 and kfree[->]bound = 15-40 s-1. Moreover, after providing specific DNA target sites via DNA plasmids, the plasmid-bound dCas9 population size decreases with increasing dCas9 copy number via a mono-exponential decay, indicative of simple disassociation kinetics.

biophysics↗

bioRxiv2018

BIN1 genetic risk factor for Alzheimer is sufficient to induce early structural tract alterations in entorhinal cortex-dentate gyrus pathway and related hippocampal multi-scale impairments

Genetic factors are known to contribute to Late Onset Alzheimers disease (LOAD) but their contribution to pathophysiology, specially to prodomic phases accessible to therapeutic approaches are far to be understood. To translate genetic risk of Alzheimers disease (AD) into mechanistic insight, we generated transgenic mouse lines that express a [~]195 kbp human BAC that includes only BIN1, a gene associated to LOAD. This model gives a modest BIN1 overexpression, dependent of the number of BAC copies. At 6 months of age, we detected impaired entorhinal cortex (EC)-hippocampal pathways with specific impairments in EC-dentate gyrus synaptic long-term potentiation, dendritic spines of granular cells and recognition episodic memory. Structural changes were quantified using MRI. Their whole-brain functional impact were analyzed using resting state fMRI with a hypoconnectivity centered on entorhinal cortex. These early phenotype defects independent of any changes in A-beta can be instrumental in the search for new AD drug targets.

neuroscience↗

bioRxiv2018

Cytoskeletal tension actively sustains the T cell immunological synapse

When migratory T cells encounter antigen presenting cells (APCs), they arrest and form radially symmetric, stable intercellular junctions termed immunological synapses which facilitate exchange of crucial biochemical information and are critical for T cell immunity. While the cellular processes underlying synapse formation have been well-characterized, those that maintain the symmetry, and thereby the stability of the synapse remain unknown. Here we identify an antigen-triggered mechanism that actively promotes T cell synapse symmetry by generating cytoskeletal tension in the plane of the synapse through focal nucleation of actin via Wiskott -Aldrich syndrome Protein (WASP), and contraction of the resultant actin filaments by myosin II. Following T cell activation, WASP is degraded, leading to cytoskeletal rearrangement and tension decay, which result in synapse breaking. Thus, our study identifies and characterizes a mechanical program within otherwise highly motile T cells that sustains the symmetry and stability of the T cell-APC synaptic contact.

cell biology↗

bioRxiv2018

Initiation of droxidopa during hospital admission for management of refractory neurogenic orthostatic hypotension in severely ill patients

IntroductionOrthostatic hypotension (OH) is a common cause of hospitalization, particularly in the elderly. Hospitalized patients with OH are often severely ill, with complex medical comorbidities and high rates of disability. Droxidopa is a norepinephrine precursor approved for the treatment of neurogenic OH (nOH) associated with autonomic failure that is commonly used in the outpatient setting, but there is currently no data regarding the safety and efficacy of droxidopa initiation in medically complex patients.\n\nMethodsWe performed a retrospective review of patients started on droxidopa for refractory nOH while hospitalized at Vanderbilt University Medical Center between October 2014 and May 2017. Primary outcome measures were safety, change in physician global impression of illness severity from admission to discharge, and persistence on medication after 180-day follow-up.\n\nResultsA total of 20 patients were identified through chart review. Patients were medically complex with high rates of cardiovascular comorbidities and a diverse array of underlying autonomic diagnoses. Rapid titration of droxidopa was safe and well-tolerated in this cohort, with no cardiovascular events or new onset arrhythmias. Supine hypertension requiring treatment occurred in four patients. One death occurred during hospital admission due to organ failure associated with end-stage amyloidosis. Treating physicians noted improvements in presyncopal symptoms in 80% of patients. After 6 months, 13 patients (65%) continued on droxidopa therapy.\n\nConclusionIn a retrospective cohort of hospitalized, severely ill patients with refractory nOH, supervised rapid titration of droxidopa was safe and effective. Treatment persistence was high, suggesting that symptomatic benefit extended beyond acute intervention.

pharmacology and toxicology↗

bioRxiv2018

Transection of the ventral hippocampal commissure impairs spatial but not contextual memory in split-brain mice

Plasticity is a neural phenomenon in which experience induces long-lasting changes to neuronal circuits and is at the center of most neurobiological theories of learning and memory. However, too much plasticity is maladaptive and must be balanced with substrate stability. Area CA3 of the hippocampus is lateralized with the left hemisphere dominant in plasticity and the right specialized for stability. Left and right CA3 project bilaterally to CA1; however, it is not known whether this downstream merging of lateralized plasticity and stability is functional. We hypothesized that interhemispheric integration of input from these pathways is essential for integrating spatial memory stored in the left CA3 with spatial working memory facilitated by the right CA3. To test this, we severed interhemispheric connections between the left and right hippocampi in mice and assessed learning and memory. Despite damage to this major hippocampal fiber tract, hippocampus-dependent spatial working memory and short- and long-term memory were both spared. However, tasks that required the integration of information retrieved from memory with ongoing spatial working memory and navigation were impaired. We propose that one function of interhemispheric communication in the mouse hippocampus is to integrate lateralized processing of plastic and stable circuits to facilitate memory-guided spatial navigation.

neuroscience↗

bioRxiv2018

Cortical microinfarcts caused by single penetrating vessel occlusion lead to widespread reorganization across the entire brain in a CX3CR1 dependent manner

1. Introduction 1. Introduction 2. Results 3. Discussion 4. Methods and Materials 6. Author Contribution 7. Competing interests References Dementia, and the associated cognitive impairment that goes along with it, have been attributed to a plethora of different mechanisms. As proper brain function is tightly coupled with the on-demand supply of oxygen and nutrients to active areas, perturbation to blood flow and the resulting neurovas-cular uncoupling play a crucial role in the ethology of these pathologies [1, 2, 3], even before the onset of the clinical symptoms [4]. Although research has traditionally focused on systemic and large-scale perturbations such as hypertension and stroke, cortical microinfarcts emerge nowaday ...

neuroscience↗

bioRxiv2018

Comprehensive Benchmarking and Integration of Tumour Microenvironment Cell Estimation Methods

The tumour microenvironment comprises complex cellular compositions and interactions between cancer, immune, and stromal components which all play crucial roles in cancer. Various computational approaches have been developed during the last decade that estimate the relative abundance of different cell types in an unbiased manner using bulk tumour RNA data. However, a comparison that objectively evaluates the performance of these approaches against one another has not been conducted. Here we benchmarked six widely used tools and gene sets: Bindea et al. gene sets, Davoli et al. gene sets, CIBERSORT, MCP-counter, TIMER, and xCell. We also introduce ConsensusTME, a consensus approach that uses the union of genes that the six tools used for cell estimation, and corrects for tumour-type specificity. We benchmarked the seven tools using TCGA DNA-derived purity scores (33 tumour-types), methylation-derived leukocyte scores (30 tumour-types), and H&E deep learning derived lymphocyte counts (13 tumour types), and individual benchmark data sets (PBMCs and 2 tumour types). Although none of the seven tools outperformed others in every benchmark, ConsensusTME ranked consistently well in all cancer-related benchmarks making it the top performing method overall. Computational methods that provide robust and accurate estimates of non-cancerous cell populations in the tumour microenvironment from tumour bulk expression data are important tools that can advance our understanding of tumour, immune, and stroma interactions, with potential clinical application if high accuracy estimates are achieved.

bioinformatics↗

bioRxiv2018

Ontology based mining of pathogen-disease associations from literature

BackgroundInfectious diseases claim millions of lives especially in the developing countries each year, and resistance to drugs is an emerging threat worldwide. Identification of causative pathogens accurately and rapidly plays a key role in the success of treatment. To support infectious disease research and mechanisms of infection, there is a need for an open resource on pathogen-disease associations that can be utilized in computational studies. A large number of pathogen-disease associations is available from the literature in unstructured form and we need automated methods to extract the data.\n\nResultsWe developed a text mining system designed for extracting pathogen-disease relations from literature. Our approach utilizes background knowledge from an ontology and statistical methods for extracting associations between pathogens and diseases. In total, we extracted a total of 3,420 pathogen-disease associations from literature. We integrated our literature-derived associations into a database which links pathogens to their phenotypes for supporting infectious disease research.\n\nConclusionsTo the best of our knowledge, we present the first study focusing on extracting pathogen-disease associations from publications. We believe the text mined data can be utilized as a valuable resource for infectious disease research. All the data is publicly available from https://github.com/bio-ontology-research-group/padimi and through a public SPARQL endpoint from http://patho.phenomebrowser.net/.

bioinformatics↗

bioRxiv2018

Integrative Network and Brain Expression Analysis reveals Mechanistic Modules in Ataxia

Background: Genetic forms of ataxia are a heterogenous group of degenerative diseases of the cerebellum. Many causative genes have been identified, but a systematic investigation of these genes to understand ataxia pathophysiology has not been performed. Methods: A manually curated catalogue of 71 genes involved in disorders with progressive ataxias as a major clinical feature was subjected to an integrated gene ontology (GO), protein network, and brain gene expression profiling analysis. Results: We found that ataxia genes operate in networks with significantly enriched protein connectivity, demonstrating coherence on a global level, independent of inheritance mode. Moreover, elevated expression specifically in the cerebellum predisposes to ataxia. Genes expressed in this pattern are significantly overrepresented among ataxia genes and are enriched for ion homeostasis/synaptic functions. The majority of ataxia genes, however, does not show elevated cerebellar expression that could account for region-specific degeneration. For these, we identified defective cellular stress responses as a major common biological theme, suggesting that the defense pathways against stress are more critical to maintain cerebellar integrity than integrity of other brain regions. Approximately half of the ataxia genes, mostly part of the stress module, show higher expression at embryonic stages, which argues for a developmental predisposition. Conclusion: Genetic defects in ataxia predominantly affect neuronal homeostasis, to which the cerebellum appears to be excessively susceptible. Based on the identified modules, it is conceivable to propose common therapeutic interventions that target deregulated calcium and ROS levels, or mechanisms that can decrease the harmful downstream effects of these deleterious insults.

neuroscience↗

bioRxiv2018

Optimised DNA extraction and library preparation for minute arthropods: application to target enrichment in chalcid wasps used for biocontrol.

Enriching subsets of the genome prior to sequencing allows focusing effort on regions that are relevant to answer specific questions. As experimental design can be adapted to sequence many samples simultaneously, using such approach also contributes to reduce cost. In the field of ecology and evolution, target enrichment is increasingly used for genotyping of plant and animal species or to better understand the evolutionary history of important lineages through the inference of statistically robust phylogenies. Limitations to routine target enrichment by research laboratories are both the complexity of current protocols and low input DNA quantity. Thus, working with tiny organisms such as micro-arthropods can be challenging. Here, we propose easy to set up optimisations for DNA extraction and library preparation prior to target enrichment. Prepared libraries were used to capture 1432 Ultra-Conserved Elements (UCEs) from microhymenoptera (Chalcidoidea), which are among the tiniest insects on Earth and the most commercialized worldwide for biological control purposes. Results show no correlation between input DNA quantities (1.8-250ng, 0.4 ng with an extra whole genome amplification step) and the number of sequenced UCEs. Phylogenetic inferences highlight the potential of UCEs to solve relationships within the families of chalcid wasps, which has not been achieved so far. The protocol (library preparation + target enrichment), allows processing 96 specimens in five working days, by a single person, without requiring the use of expensive robotic molecular biology platforms, which could help to generalize the use of target enrichment for minute specimens.

evolutionary biology↗

bioRxiv2018

Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurode-generative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions.\n\nIn this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature, with respect to K-nearest neighbour algorithm currently used for lesion probability map estimation in BIANCA. Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort and a vascular cohort.\n\nWe observed that including population-level parametric lesion probabilities with re-spect to age and using alternative machine learning techniques provided negligible im-provement. However, LOCATE provided a substantial improvement in the lesion segmentation performance when compared to the global thresholding currently used in BIANCA. We further validated LOCATE on a cohort of CADASIL (Cerebral autoso-mal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease characterised by extensive WMH burden, and healthy controls showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.

neuroscience↗

bioRxiv2018

Horizontal acquisition of a patchwork Calvin cycle by symbiotic and free-living Campylobacterota (formerly Epsilonproteobacteria)

Although the majority of known autotrophs use the Calvin-Benson-Bassham (CBB) cycle for carbon fixation, all currently described autotrophs from the Campylobacterota (previously Epsilonproteobacteria) use the reductive tricarboxylic acid cycle (rTCA) instead. We discovered campylobacterotal epibionts (\"Candidatus Thiobarba\") of deep-sea mussels that have acquired a complete CBB cycle and lost key genes of the rTCA cycle. Intriguingly, the phylogenies of campylobacterotal CBB genes suggest they were acquired in multiple transfers from Gammaproteobacteria closely related to sulfur-oxidizing endosymbionts associated with the mussels, as well as from Betaproteobacteria. We hypothesize that \"Ca. Thiobarba\" switched from the rTCA to a fully functional CBB cycle during its evolution, by acquiring genes from multiple sources, including co-occurring symbionts. We also found key CBB cycle genes in free-living Campylobacterota, suggesting that the CBB cycle may be more widespread in this phylum than previously known. Metatranscriptomics and metaproteomics confirmed high expression of CBB cycle genes in mussel-associated \"Ca. Thiobarba\". Direct stable isotope fingerprinting showed that \"Ca. Thiobarba\" has typical CBB signatures, additional evidence that it uses this cycle for carbon fixation. Our discovery calls into question current assumptions about the distribution of carbon fixation pathways across the tree of life, and the interpretation of stable isotope measurements in the environment.

microbiology↗

bioRxiv2018

Exploring the Correlation between the Cognitive Benefits of Drug Combinations in a Clinical Alzheimer Disease Database and the Efficacies of the Same Drug Combinations Predicted from a Computational Model

INTRODUCTIONIdentification of drug combinations that could be effective in Alzheimers treatment is made difficult by the number of possible combinations. This analysis identifies as potentially therapeutic those drug combinations that rank highest when their efficacy is determined jointly from two independent data sources.\n\nMETHODSEstimates of the efficacy of the same drug combinations were derived from a clinical dataset and from pre-clinical data, in the form of a computational model of neuroinflammation. Standard linear regression was used to show that the two sets of estimates were correlated, and to rule out possible confounds.\n\nRESULTSThe ten highest ranking, jointly determined drug combinations most frequently consisted of COX2 inhibitors and aspirin, along with various antihypertensive medications.\n\nDISCUSSIONTen combinations of from five to nine drugs, and the three-drug combination of a COX2 inhibitor, aspirin, and a calcium-channel blocker, are discussed as candidates for consideration in future clinical and pre-clinical studies.

pharmacology and toxicology↗