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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Social isolation-induced epigenetic and transcriptional changes in Drosophila dopaminergic neurons

Epigenetic mechanisms play fundamental roles in brain function and behavior and stressors such as social isolation can alter animal behavior via epigenetic mechanisms. However, due to cellular heterogeneity, identifying cell-type-specific epigenetic changes in the brain is challenging. Here we report first use of a modified INTACT method in behavioral epigenetics of Drosophila: a method we call mini-INTACT. Using ChIP-seq on mini-INTACT purified dopaminergic nuclei, we identified epigenetic signatures in socially-isolated and socially-enriched Drosophila males. Social experience altered the epigenetic landscape in clusters of genes involved in transcription and neural function. Some of these alterations were predicted by expression changes of four transcription factors and the prevalence of their binding sites in several clusters. These transcription factors were previously identified as activity-regulated genes and their knockdown in dopaminergic neurons reduced the effects of social experience on sleep. Our work enables the use of Drosophila as a model for cell-type-specific behavioral epigenetics.

neuroscience↗

bioRxiv2018

A flexible and generalizable model of online latent-state learning

Many models of classical conditioning fail to describe important phenomena, notably the rapid return of fear after extinction. To address this shortfall, evidence converged on the idea that learning agents rely on latent-state inferences, i.e. an ability to index disparate associations from cues to rewards (or penalties) and infer which index (i.e. latent state) is presently active. Our goal was to develop a model of latent-state inferences that uses latent states to predict rewards from cues efficiently and that can describe behavior in a diverse set of experiments. The resulting model combines a Rescorla-Wagner rule, for which updates to associations are proportional to prediction error, with an approximate Bayesian rule, for which beliefs in latent states are proportional to prior beliefs and an approximate likelihood based on current associations. In simulation, we demonstrate the models ability to reproduce learning effects both famously explained and not explained by the Rescorla-Wagner model, including rapid return of fear after extinction, the Hall-Pearce effect, partial reinforcement extinction effect, backwards blocking, and memory modification. Lastly, we derive our model as an online algorithm to maximum likelihood estimation, demonstrating it is an efficient approach to outcome prediction. Establishing such a framework is a key step towards quantifying normative and pathological ranges of latent-state inferences in various contexts.\n\nAuthor summaryComputational researchers are increasingly interested in a structured form of learning known as latent-state inferences. Latent-state inferences is a type of learning that involves categorizing, generalizing, and recalling disparate associations between observations in ones environment and is used in situations when the correct association is latent or unknown. This type of learning has been used to explain overgeneralization of a fear memory and the cognitive role of certain brain regions important to cognitive neuroscience and psychiatry. Accordingly, latent-state inferences are an important area of inquiry. Through simulation and theory, we establish a new model of latent-state inferences. Moving forward, we aim to use this framework to measure latent-state inferences in healthy and psychiatric populations.

neuroscience↗

bioRxiv2018

Microplate Assay for Denatured Collagen using CollagenHybridizing Peptides

The purpose of this study was to develop a microplate assay for quantifying denatured collagen by measuring the fluorescence of carboxyfluorescein bound collagen hybridizing peptides (F-CHP). We have shown that F-CHP binds selectively with denatured collagen, and that mechanical overload of tendon fascicles causes collagen denaturation. Proteinase K was used to homogenize tissue samples after F-CHP staining, allowing fluorescence measurement using a microplate reader. We compared our new assay to our previous image analysis method and the trypsin-hydroxyproline assay, which is the only other available method to directly quantify denatured collagen. Relative quantification of denatured collagen was performed in rat tail tendon fascicles subjected to incremental tensile overload, and normal and ostoeoarthritic guinea pig cartilage. In addition, the absolute amount of denatured collagen was determined in rat tail tendon by correlating F-CHP fluorescence with percent denatured collagen as determined by the trypsin-hydroxyproline assay. Rat tail tendon fascicles stretched to low strains (<7.5%) exhibited minimal denature collagen, but values rapidly increased at medium strains (7.5-10.5%) and plateaued at high strains ([≥]12%). Osteoarthritic cartilage had higher F-CHP fluorescence than healthy cartilage. Both of these outcomes are consistent with previous studies. With the calibration curve, the microplate assay was able to absolutely quantify denatured collagen in mechanically damaged rat tail tendon fascicles as reliably as the trypsin-hydroxyproline assay. Further, we achieved these results more efficiently than current methods in a rapid, high-throughput manner, with multiple types of collagenous tissue while maintaining accuracy.

bioengineering↗

bioRxiv2018

The effects of training population design on genomic prediction accuracy in wheat

Genomic selection offers several routes for increasing genetic gain or efficiency of plant breeding programs. In various species of livestock there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeders equation. Accurate predictions of genomic breeding value are central to this and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable accurate predictions.\n\nTo quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and triparental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25,000 segregating single nucleotide polymorphism markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Within cross genomic prediction accuracies of yield BLUEs were 0.125 - 0.127 using two different cross-validation approaches, and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasize the importance of the training set design in relation to the genetic material to which the resulting prediction model is to be applied.

genetics↗

bioRxiv2018

A role for long-range, through-lattice coupling in microtubule catastrophe

Microtubules are cylindrical polymers of {beta}-tubulin that play critical roles in fundamental processes like chromosome segregation and vesicular transport. Microtubules display dynamic instability, switching stochastically between growing and rapid shrinking as a consequence of GTPase activity in the lattice. The molecular mechanisms behind microtubule catastrophe, the switch from growing to rapid shrinking, remain poorly defined. Indeed, two-state stochastic models that seek to describe microtubule dynamics purely in terms of the biochemical properties of GTP- and GDP-bound {beta}-tubulin incorrectly predict the concentration-dependence of microtubule catastrophe. Recent studies provided evidence for three distinct conformations of {beta}-tubulin in the lattice that likely correspond to GTP, GDP.Pi, and GDP. The incommensurate lattices observed for these different conformations raises the possibility that in a mixed nucleotide state lattice, neighboring tubulin dimers might modulate each others conformations and hence their biochemistry. We explored whether incorporating a GDP.Pi state or the likely effects of conformational accommodation can improve predictions of catastrophe. Adding a GDP.Pi intermediate did not improve the model. In contrast, adding neighbor-dependent modulation of tubulin biochemistry improved predictions of catastrophe. Conformational accommodation should propagate beyond nearest-neighbor contacts, and consequently our modeling demonstrates that long-range, through-lattice effects are important determinants of microtubule catastrophe.

cell biology↗

bioRxiv2018

Interfacial actin protrusions mechanically potentiate killing by cytotoxic T cells

Cytotoxic T lymphocytes (CTLs) kill by forming immunological synapses with target cells and secreting toxic proteases and the pore forming protein perforin into the intercellular space. Immunological synapses are highly dynamic structures that potentiate perforin activity by applying mechanical force against the target cell. Here, we employed high-resolution imaging and microfabrication to investigate how CTLs exert synaptic forces and coordinate their mechanical output with perforin secretion. Using micropatterned stimulatory substrates that enable synapse growth in three dimensions, we found that perforin release occurs at the base of actin-rich protrusions that extend from central and intermediate locations within the synapse. These protrusions, which depended on the cytoskeletal regulator WASP and the Arp2/3 actin nucleation complex, were required for synaptic force exertion and efficient killing. They also mediated physical distortion of the target cell surface during CTL-target cell interactions. Our results reveal the mechanical basis of cellular cytotoxicity and highlight the functional importance of dynamic, three-dimensional architecture in immune cell-cell interfaces.\n\nOne sentence summaryCytotoxic T lymphocytes use F-actin-rich protrusions at the immunological synapse to potentiate perforin-and granzyme-mediated target cell killing.

immunology↗

bioRxiv2018

Computational modeling of the crosstalk between macrophage polarization and tumor cell plasticity in the tumor microenvironment

Tumor microenvironments contain multiple cell types interacting among one another via different signaling pathways. Furthermore, both cancer cells and different immune cells can display phenotypic plasticity in response to these communicating signals, thereby leading to complex spatiotemporal patterns that can impact therapeutic response. Here, we investigate the crosstalk between cancer cells and macrophages in a tumor microenvironment through in silico (computational) co-culture models. In particular, we investigate how macrophages of different polarization (M1 vs. M2) can interact with epithelial-mesenchymal plasticity of cancer cells, and conversely, how cancer cells exhibiting different phenotypes (epithelial vs. mesenchymal) can influence the polarization of macrophages. Based on interactions documented in the literature, an interaction network of cancer cells and macrophages is constructed. The steady states of the network are then analyzed. Various interactions were removed or added into the constructed-network to test the functions of those interactions. Also, parameters in the mathematical models were varied to explore their effects on the steady states of the network. In general, the interactions between cancer cells and macrophages can give rise to multiple stable steady-states for a given set of parameters and each steady state is stable against perturbations. Importantly, we show that the system can often reach one type of stable steady states where cancer cells go extinct. Our results may help inform efficient therapeutic strategies.

systems biology↗

bioRxiv2018

A 117 year retrospective analysis of Pennsylvania tick community dynamics

BackgroundTick-borne diseases have been increasing at the local, national, and global levels. Researchers studying ticks and tick-borne disease need a thorough knowledge of the pathogens, vectors, and epidemiology of disease spread. Three surveillance approaches are commonly used to provide insight into tick-borne disease risk: human disease case surveillance, active tick surveillance, and passive tick surveillance. Long-term passive surveillance can provide up-to-date data on the spatial variability and temporal dynamics of ectoparasite communities and shed light into the ecology of rarer tick species. We present a retrospective analysis on compiled data of ticks from Pennsylvania over the last 117 years.\n\nMethodsWe compiled data from ticks collected during tick surveillance research, and from citizen-based submissions to the Penn State University Department of Entomology (PSUEnt). Specimens were deposited at the PSUEnt arthropod collections that eventually became The Frost Entomological Museum. While most of the specimens were submitted by the public, a subset of the data were collected through active methods (flagging or dragging, or removal of ticks from wildlife). We analyzed all data from 1900-2017 for tick community composition, host associations, and spatio-temporal dynamics.\n\nResultsIn total there were 4,491 submission lots consisting of 7,132 tick specimens. Twenty-four different species were identified, with the large proportion of submissions represented by five tick species. We observed a shift in tick community composition in which the dominant species of tick (Ixodes cookei) was overtaken in abundance by Dermacentor variabilis in the early 1990s, and then replaced in abundance by I. scapularis. We analyzed host data and identified overlaps in host range amongst tick species, suggesting potential hubs of pathogen transfer between different tick vectors and their reservoir hosts.\n\nConclusionsWe highlight the importance of long-term passive tick surveillance in investigating the ecology of both common and rare tick species. Information on the geographic distribution, host-association, and seasonality of the tick community can help researchers and health-officials to identify high-risk areas.

ecology↗

bioRxiv2018

A free boundary model of epithelial dynamics

In this work we analyse a one-dimensional, cell-based model of an epithelial sheet. In this model, cells interact with their nearest neighbouring cells and move deterministically. Cells also proliferate stochastically, with the rate of proliferation specified as a function of the cell length. This mechanical model of cell dynamics gives rise to a free boundary problem. We construct a corresponding continuum-limit description where the variables in the continuum limit description are expanded in powers of the small parameter 1/N, where N is the number of cells in the population. By carefully constructing the continuum limit description we obtain a free boundary partial differential equation description governing the density of the cells within the evolving domain, as well as a free boundary condition that governs the evolution of the domain. We show that care must be taken to arrive at a free boundary condition that conserves mass. By comparing averaged realisations of the cell-based model with the numerical solution of the free boundary partial differential equation, we show that the new mass-conserving boundary condition enables the coarsegrained partial differential equation model to provide very accurate predictions of the behaviour of the cell-based model, including both evolution of the cell density, and the position of the free boundary, across a range of interaction potentials and proliferation functions in the cell based model.

systems biology↗

bioRxiv2018

Cleft lip/palate and educational attainment: cause, consequence, or correlation? A Mendelian randomization study

ImportancePrevious studies have found that children born with a non-syndromic form of cleft lip and/or palate have lower-than-average educational attainment. These differences could be due to a genetic predisposition to low intelligence and academic performance, factors arising due to the cleft phenotype (such as school absence, social stigmatization and impaired speech and language development), or confounding by the prenatal environment. A clearer understanding of this mechanism will inform development of interventions to improve educational attainment in individuals born with a cleft, which could have wide-ranging knock-on effects on their quality of life.\n\nObjectiveTo assess evidence for the hypothesis that common variant genetic liability to non-syndromic cleft lip with or without cleft palate (nsCL/P) influences educational attainment.\n\nDesignUsing summary data from genome-wide association studies (GWAS), we performed Linkage Disequilibrium (LD)-score regression and two-sample Mendelian randomization to evaluate the relationship between genetic liability to nsCL/P (GWAS n=3,987) and educational attainment (GWAS n=766,345), and intelligence (GWAS n=257,828).\n\nResultsThere was little evidence for shared genetic aetiology between nsCL/P and educational attainment (rg -0.03, 95% CI -0.14 to 0.08, P 0.58; {beta}MR 0.002, 95% CI -0.001 to 0.005, P 0.417) or intelligence (rg -0.01, 95% CI -0.12 to 0.10, P 0.85; {beta}MR 0.002, 95% CI -0.010 to 0.014, P 0.669).\n\nConclusions and relevanceCommon genetic variants are unlikely to predispose individuals born with nsCL/P to low educational attainment or intelligence. This information will help tailor clinical-, school-, social- and family-level interventions to improve educational attainment in this group.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSDo children born with a non-syndromic cleft lip with or without palate (nsCL/P) have lower-than average academic achievement because of an underlying genetic predisposition to educational attainment and/or intelligence?\n\nFindingsThere was little evidence for shared common variant genetic correlation between nsCL/P, educational attainment and intelligence.\n\nMeaningCommon genetic variants are unlikely to predispose individuals born with nsCL/P to low educational attainment or intelligence. This information will help tailor clinical-, school-, social- and family-level interventions to improve educational attainment in this group.

genetics↗

bioRxiv2018

Evolutionarily conserved anatomical and physiological properties of olfactory pathway till fourth order neurons in a species of grasshopper (Hieroglyphus banian)

Olfactory systems of different species show variations in structure and physiology despite some conserved characteristics. We characterized the olfactory circuit of the grasshopper Hieroglyphus banian of family Acrididae (subfamily: Hemiacridinae) and compared it to a well-studied species of locust, Schistocerca americana (subfamily: Cyrtacanthacridinae), also belonging to family Acrididae. We used in vivo electrophysiological, immunohistochemical and anatomical (bulk tract tracing) methods to elucidate the olfactory pathway from the second order neurons in antennal lobe to the fourth order neurons in {beta}-lobe of H. banian.\n\nWe observe highly conserved anatomical and physiological characteristics till the fourth order neurons in the olfactory circuit of H. banian and S. americana, though they are evolutionarily divergent (~57 million years ago). However, we found one major difference between the two species-there are four antennal lobe tracts in H. banian while only one is reported in S. americana. Besides, we are reporting for the first time, a new class of bilateral neurons which respond weakly to olfactory stimuli even though they innervate densely downstream of Kenyon cells.

neuroscience↗

bioRxiv2018

The first draft genomes of the ant Formica exsecta, and its Wolbachia endosymbiont reveal extensive gene transfer from endosymbiont to host.

The wood ant Formica exsecta (Formicidae; Hymenoptera), is a common ant species throughout the Palearctic region. The species is a well established model for studies of ecological characteristics and evolutionary conflict. In this study, we sequenced and assembled draft genomes for Formica exsecta and its endosymbiont Wolbachia. The draft F. exsecta genome is 277.7 Mb long; we identify 13,767 protein coding genes for which we provide gene ontology, and protein domain annotations. This is also the first report of a Wolbachia genome from ants, and provides insights into the phylogenetic position of this endosymbiont. We also identified multiple horizontal gene transfer events (HGTs) from Wolbachia to F. exsecta. Some of these HGTs have also occurred in parallel in multiple other insect genomes, highlighting the extent of HGTs in eukaryotes. We expect that the F. exsecta genome will be valuable resource in further exploration of the molecular basis of the evolution of social organization.

genomics↗

bioRxiv2018

XTalkiiS: a tool for finding data-driven cross-talks between intra-/inter-species pathways

Cell-cell communication via pathway cross-talks within a single species have been studied in silico recently to decipher various disease phenotype. However, computational prediction of pathway cross-talks among multiple species in a data-driven manner is yet to be explored. In this article, I present XTalkiiS (Cross-talks between inter-/intra species pathways), a tool to automatically predict pathway cross-talks from data-driven models of pathway network, both within the same organism (intra-species) and between two organisms (inter-species). XTalkiiS starts with retrieving and listing up-to-date pathway information in all the species available in KEGG database using RESTful APIs (exploiting KEGG web services) and an in-house built web crawler. I hypothesize that data-driven network models can be built by simultaneously quantifying co-expression of pathway components (i.e. genes/proteins) in matched samples in multiple organisms. Next, XTalkiiS loads a data-driven pathway network and applies a novel cross-talk modelling approach to determine interactions among known KEGG pathways in selected organisms. The potentials of XTalkiiS are huge as it paves the way of finding novel insights into mechanisms how pathways from two species (ideally host-parasite) may interact that may contribute to the various phenotype of interests such as malaria disease. XTalkiiS is made open sourced at https://github.com/Akmazad/XTalkiiS and its binary files are freely available for downloading from https://sourceforge.net/projects/xtalkiis/.

bioinformatics↗

bioRxiv2018

Gorilla in our Midst: An online behavioral experiment builder

Behavioural researchers are increasingly conducting their studies online to gain access to large and diverse samples that would be difficult to get in a laboratory environment. However, there are technical access barriers to building experiments online, and web-browsers can present problems for consistent timing - an important issue with reaction time-sensitive measures. For example, to ensure accuracy and test-retest reliability in presentation and response recording, experimenters need a working knowledge of programming languages such as JavaScript. We review some of the previous and current tools for online behavioural research, and how well they address the issues of usability and timing. We then present The Gorilla Experiment Builder (gorilla.sc) a fully tooled experiment authoring and deployment platform, designed to resolve many timing issues, and make reliable online experimentation open and accessible to a wider range of technical abilities. In order to demonstrate the platforms aptitude for accessible, reliable and scalable research, we administered the task with a range of participant groups (primary school children and adults), settings (without supervision, at home, and under supervision, in schools and public engagement events), equipment (own computers, computer supplied by researcher), and connection types (personal internet connection, mobile phone 3G/4G). We used a simplified flanker task, taken from the Attentional Networks Task (Rueda, Posner, & Rothbart, 2004). We replicated the conflict network effect in all these populations, demonstrating the platforms capability to run reaction time-sensitive experiments. Unresolved limitations of running experiments online are then discussed, along with potential solutions, and some future features of the platform.

neuroscience↗

bioRxiv2018

Differentiating Genomic SNPs Using Allele Depth and Predicted Genotype

Differentiating between genomic SNPs and other types of single nucleotide variants becomes a key issue in research aimed at studying the importance of these variants of a particular type in biological processes. Here we present an R based method for differentiating between genomic single nucleotide polymorphisms (SNPs) and RNA editing sites. We use data from an earlier study of ours and target only the known dbsnp SNPs that we found in our study. Our method involves calculating the ratio of allele depth for ref and alt alleles and comparing that to the predicted genotype. We use the concept that editing levels should be different for each allele and thus should not reflect the ratio predicted by the genotype. The study yielded an accuracy rate ranging from 86 to over 90 percent at successfully predicted dbsnp entries as SNPs. Albeit this is in the absence of known RNA editing site vcf data to compare as a reference.

genomics↗

bioRxiv2018

A Universal Algorithm to Detect Rare or Novel Cell Types in High-Throughput Single-Cell Gene Expression Data

Detecting rare cell types would allow early disease detection of cancers and infections, identification of new cell types, and a deepened understanding of cell differentiation. We developed a universal algorithm to identify rare cell types from a wide variety of single-cell omics data. We validated our algorithm on single-cell qPCR data from mouse hematopoietic cells and single-cell RNA-seq data from human glioblastoma tumors cells, both with expression values from an ample number of genes. We then applied our algorithm to seq-FISH data from mouse hippocampus cells containing expression values from only 121 genes. Our algorithm detected rare cell types including a putative new hippocampal cell type.\n\nAuthor summaryRare cell type detection would advance early disease diagnosis (e.g., cancer, infection), allow identification of new cell types, and increase understanding of cell differentiation. Current computational methods can detect common cell types, but it remains a challenge to detect rare cell types within cell populations, especially with expression data from a relatively small number of genes. We created a powerful algorithm to detect rare cell types in a population of cells. We validated our algorithm on data from mouse blood stem cells and human glioblastoma tumors cells. When we applied our algorithm to mouse hippocampus data containing expression values from only 121 genes, we detected a putative new brain cell type that no previous algorithm has identified. Our universal algorithm can now be applied to a wide range of data to detect the early onset of diseases and discover new cell types.

bioinformatics↗

bioRxiv2018

Marked point process filter for clusterless and adaptive encoding-decoding of multiunit activity

Real-time, closed-loop experiments can uncover causal relationships between specific neural activity and behavior. An important advance in realizing this is the marked point process filtering framework which utilizes the \"mark\" or the waveform features of unsorted spikes, to construct a relationship between these features and behavior, which we call the encoding model. This relationship is not fixed, because learning changes coding properties of individual neurons, and electrodes can physically move during the experiment, changing waveform characteristics. We introduce a sequential, Bayesian encoding model which allows incorporation of new information on the fly to adapt the model in real time. A possible application of this framework is to the decoding of the contents of hippocampal ripples in rats exploring a maze. During physical exploration, we observe the marks and positions at which they occur, to update the encoding model, which is employed to decode contents of ripples when rats stop moving, and switch back to updating the model once the rat starts moving again.

neuroscience↗

bioRxiv2018

Mindfulness meditators do not show differences in electrophysiological measures of error processing

Mindfulness meditation may improve attention and self-regulation. One component of attention and self-regulation that may allow these improvements is performance monitoring. Neural correlates of performance monitoring can be objectively measured with electroencephalogram (EEG) via the error related negativity (ERN) and error positivity (Pe). Previous research assessing the ERN and Pe in meditators has resulted in inconsistent findings; some have reported alteration in peak amplitudes from both very brief meditation practice and long-term meditation practice, while others have failed to provide evidence for differences in the ERN or Pe. However, recently developed EEG analysis techniques allow for more rigorous analyses than have been used in past investigations. The current study measured the ERN and Pe, as well as post-error alpha suppression, during a Go/Nogo task, and emotional and colour Stroop tasks. The measures were compared between 22 experienced meditators (mean of 8 years of practice) and 20 healthy controls. The results suggested no differences in the ERN, Pe, or post-error alpha suppression (all p > 0.05), even when varying multiple analysis parameters. The study showed equivalent statistical power to previous research, and > 85% power to detect medium effect sizes present in previous research. Bayes Factor analysis indicated the null hypotheses were > 3.5 more likely than any of the alternative hypotheses for the ERN or Pe. These results suggest that meditation may not alter neural activity related to error processing, despite prior research suggesting that it does.

neuroscience↗