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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

An inexpensive air stream temperature controller and its use to facilitate temperature controlled behavior in living Drosophila

Controlling the environment of an organism has many biologically relevant applications. Temperature-dependent inducible biological reagents have proven invaluable for elucidating signaling cascades and dissection of neural circuits. Here we develop a simple and affordable system for rapidly changing temperature in a chamber housing adult Drosophila melanogaster. Utilizing flies expressing the temperature inducible channel dTrpA1 in dopaminergic neurons, we show rapid and reproducible changes in locomotor behavior. This device should have wide application to temperature modulated biological reagents.\n\nMethod SummaryWe develop widely applicable and affordable solution to rapidly changing temperature within an enclosed chamber using commercially available components.

animal behavior and cognition↗

bioRxiv2018

A multifaceted approach for analyzing complex phenotypic data in rodent models of autism

AutDB features a modular framework that aims at collating multifactorial risk factors associated with autism spectrum disorder (ASD). The animal model (AM) module of AutDB was first developed for mouse models of genes and CNVs associated with ASD (Kumar et al., 2011). Subsequently, environmentally induced rodent models were introduced to capture the full spectrum of risk-factors associated with ASD, along with idiopathic models represented by inbred strains. Using the data systematically annotated in AutDB, we depict the intricate trends in the research findings based on rodent models of ASD. We identify the top 30 most frequently studied phenotypes extracted from 911 genetic, 269 induced and 17 inbred rodent models of ASD extracted from 787 publications. As expected, many of these include animal model equivalents of the core phenotypes associated with ASD, as well as several comorbid features of ASD including anxiety, seizures and motor-control deficits. Uniquely, AutDB curates rescue models where various treatment strategies were used in rodent ASD models to alleviate ASDrelevant phenotypes. We further examine ASD models based on 52 genes and 2 CNV loci to identify 24 pharmaceutical agents that were used in 2 or more paradigms for testing their efficacy. As a case study, we analyze various Shank3 mouse models providing a highresolution view of the in vivo role of this high-confidence ASD gene. Together, this resource provides a snapshot of genetic and induced models of ASD within a shared annotation platform to examine the complex meshing of diverse ASD-associated risk-factors.

bioinformatics↗

bioRxiv2018

Antibodies against egg- and cell-grown influenza A(H3N2) viruses in adults hospitalized during the 2017-2018 season

BackgroundThe 2017-2018 US influenza season was severe with low vaccine effectiveness. Circulating A(H3N2) viruses from multiple genetic groups were antigenically similar to cell-grown vaccine strains. However, most influenza vaccines are egg-propagated.\n\nMethodsSerum was collected shortly after illness onset from 15 PCR confirmed A(H3N2) infected cases and 15 uninfected (controls) hospitalized adults enrolled in an influenza vaccine effectiveness study.\n\nGeometric mean titers against egg- and cell-grown A/Hong Kong/4801/2014 A(H3N2) vaccine strains and representative circulating viruses (including A/Washington/16/2017) were determined by microneutralization (MN) assays. Independent effects of strain-specific titers on susceptibility were estimated by logistic regression.\n\nResultsMN titers against egg-A/Hong Kong were significantly higher among those who were vaccinated (MN GMT: 173 vs 41; P = 0.01). However, antibody titers to cell-grown viruses were much lower in all individuals (P>0.05) regardless of vaccination. In unadjusted models, a 2-fold increase in MN titers against egg-A/Hong Kong was not significantly protective against infection (29% reduction; p=0.09), but a similar increase in cell-A/Washington titer (3C.2a2) was protective (60% reduction; p=0.02). A similar increase in egg-A/Hong Kong titer was not significantly associated with odds of infection when adjusting for MN titers against A/Washington (15% reduction; P=0.61). A 54% reduction of odds of infection was observed with a 2-fold increase in A/Washington (not significant; P=0.07), adjusted for egg-A/Hong Kong titer.\n\nConclusionAlthough individuals vaccinated in 2017-2018 had high antibody titers against the egg-adapted vaccine strain, antibody responses to cell-grown circulating viruses may not be sufficient to provide protection, likely due to egg-adaptation in the vaccine.

epidemiology↗

bioRxiv2018

Multi-allele species reconstruction using ASTRAL

Genome-wide phylogeny reconstruction is becoming increasingly common, and one driving factor behind these phylogenomic studies is the promise that the potential discordance between gene trees and the species tree can be modeled. Incomplete lineage sorting is one cause of discordance that bridges population genetic and phylogenetic processes. ASTRAL is a species tree reconstruction method that seeks to find the tree with minimum quartet distance to an input set of inferred gene trees. However, the published ASTRAL algorithm only works with one sample per species. To account for polymorphisms in present-day species, one can sample multiple individuals per species to create multi-allele datasets. Here, we introduce how ASTRAL can handle multi-allele datasets. We show that the quartet-based optimization problem extends naturally, and we introduce heuristic methods for building the search space specifically for the case of multi-individual datasets. We study the accuracy and scalability of the multi-individual version of ASTRAL-III using extensive simulation studies and compare it to NJst, the only other scalable method that can handle these datasets. We do not find strong evidence that using multiple individuals dramatically improves accuracy. When we study the trade-off between sampling more genes versus more individuals, we find that sampling more genes is more effective than sampling more individuals, even under conditions that we study where trees are shallow (median length: {approx} 1Ne) and ILS is extremely high.

bioinformatics↗

bioRxiv2018

A phylum-wide survey reveals multiple independent gains of head regeneration ability in Nemertea

Animals vary widely in their ability to regenerate, suggesting that regenerative abilities have a rich evolutionary history. However, our understanding of this history remains limited because regeneration ability has only been evaluated in a tiny fraction of species. Available comparative regeneration studies have identified losses of regenerative ability, yet clear documentation of gains is lacking. We surveyed regenerative ability in 34 species spanning the phylum Nemertea, assessing the ability to regenerate heads and tails either through our own experiments or from literature reports. Our sampling included representatives of the 10 most diverse families and all three orders comprising this phylum. We generated a phylogenetic framework using sequence data to reconstruct the evolutionary history of head and tail regeneration ability across the phylum and found that while all evaluated species can remake a posterior end, surprisingly few could regenerate a complete head. Our analysis reconstructs a nemertean ancestor unable to regenerate a head and indicates at least four separate lineages have independently gained head regeneration ability, one such gains reconstructed as taking place within the last 10-15 mya. Our study highlights nemerteans as a valuable group for studying evolution of regeneration and identifying mechanisms associated with repeated gains of regenerative ability.

evolutionary biology↗

bioRxiv2018

Crop Information Engine and Research Assistant (CIERA) for managing genealogy, phenotypic and genotypic data for breeding programs

BackgroundWith the advent of next-generation marker platforms and phenomics in crop breeding programs, the volume of both the genotypic and phenotypic data produced has increased exponentially. Often the data remain underutilized if not properly collated, managed and accessed. Effective management of the data is paramount to making sound and timely decision on cross planning in order to accelerate genetic gain ({Delta}G) in crops for disease resistance, agronomic and end-use quality traits.\n\nResultsTo address the challenges in managing and efficient utilization of the sheer volume of data generated in a crop breeding program, we developed an electronic information system called the Crop Information Engine and Research Assistant (CIERA). The CIERA, written in Visual Basic, runs on the Microsoft Windows operating system and requires the .Net Framework 4.7 as well as the MySQL Community Server 5.7. The highly intuitive graphical user interface of CIERA includes user-friendly query tools to facilitate the collation of data across relevant phenotypic environments from its phenotypic data management database and can combine that information with the genealogy and genetic data from its genealogy management and genetic data management databases, respectively.\n\nConclusionsUsing CIERA, breeders can build a comprehensive profile of germplasm, within a few minutes, to assist them in planning crosses for enhancing genetic gain by selecting superior lines for crosses.

bioinformatics↗

bioRxiv2018

NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information

Automated function prediction (AFP) of proteins is of great significance in biology. In essence, AFP is a large-scale multi-label classification over pairs of proteins and GO terms. Existing AFP approaches, however, have their limitations on both sides of proteins and GO terms. Using various sequence information and the robust learning to rank (LTR) framework, we have developed GOLabeler, a state-of-the-art approach of CAFA3, which overcomes the limitation of the GO term side, such as imbalanced GO terms. Unfortunately, for the protein side issue, available abundant protein information, except for sequences, have not been effectively used for large-scale AFP in CAFA. We propose NetGO that is able to improve large-scale AFP with massive network information. The novelties of NetGO have threefold in using network information: 1) the powerful LTR framework of NetGO efficiently and effectively integrates both sequence and network information, which can easily make large-scale AFP; 2) NetGO can use whole and massive network information of all species (>2000) in STRING (other than only high confidence links and/or some specific species); and 3) NetGO can still use network information to annotate a protein by homology transfer even if it is not covered in STRING. Under numerous experimental settings, we examined the performance of NetGO, such as general performance comparison, species-specific prediction, and prediction on difficult proteins, by using training and test data separated by time-delayed settings of CAFA. Experimental results have clearly demonstrated that NetGO outperforms GOLabeler, DeepGO, and other compared baseline methods significantly. In addition, several interesting findings from our experiments on NetGO would be useful for future AFP research.

bioinformatics↗

bioRxiv2018

Differential Metabolic and Multi-tissue Transcriptomic Responses to Fructose Consumption among Genetically Diverse Mice

High fructose intake is a major risk for metabolic syndrome; however, its effects seem to vary across individuals. To determine main factors involved in the inter-individual responses to fructose, we fed inbred mouse strains C57BL/6J (B6), DBA/2J (DBA) and FVB/NJ (FVB) with fructose. DBA mice showed the highest susceptibility to gain adiposity and glucose intolerance. Elevated insulin was found in DBA and FVB mice, and cholesterol levels were uniquely elevated in B6 mice. The transcriptional profiles of liver, hypothalamus, and adipose tissues showed strain- and tissue-specific pathways altered by fructose, such as fatty acid and cholesterol pathways for B6 and PPAR signaling for DBA in liver, and oxidative phosphorylation for B6 and protein processing for DBA in hypothalamus. Using network modeling, we predicted potential strain-specific key regulators of fructose response such as Fgf21 (DBA) and Lss (B6) in liver, and validated strain-biased responses as well as the regulatory actions of Fgf21 and Lss in primary hepatocytes. Our findings support that fructose perturbs individualized tissue networks and pathways and associates with distinct features of metabolic dysfunctions across genetically diverse mice. Our results elucidate the molecular pathways and gene regulatory mechanisms underlying inter-individual variability in response to high fructose diet.

systems biology↗

bioRxiv2018

Microbial contamination screening and interpretation for biological laboratory environments

Advances in microbiome researches have led us to the realization that the composition of microbial communities of indoor environment is profoundly affected by the function of buildings, and in turn may bring detrimental effects to the indoor environment and the occupants. Thus investigation is warranted for a deeper understanding of the potential impact of the indoor microbial communities. Among these environments, the biological laboratories stand out because they are relatively clean and yet are highly susceptible to microbial contaminants. In this study, we assessed the microbial compositions of samples from the surfaces of various sites across different types of biological laboratories. We have qualitatively and quantitatively assessed these possible microbial contaminants, and found distinct differences in their microbial community composition. We also found that the type of laboratories has a larger influence than the sampling site in shaping the microbial community, in terms of both structure and richness. On the other hand, the public areas of the different types of laboratories share very similar sets of microbes. Tracing the main sources of these microbes, we identified both environmental and human factors that are important factors in shaping the diversity and dynamics of these possible microbial contaminations in biological laboratories. These possible microbial contaminants that we have identified will be helpful for people who aim to eliminate them from samples.\n\nImportanceMicrobial communities from biological laboratories might hamper the conduction of molecular biology experiments, yet these possible contaminations are not yet carefully investigated. In this work, a metagenomic approach has been applied to identify the possible microbial contaminants and their sources, from the surfaces of various sites across different types of biological laboratories. We have found distinct differences in their microbial community compositions. We have also identified the main sources of these microbes, as well as important factors in shaping the diversity and dynamics of these possible microbial contaminations. The identification and interpretation of these possible microbial contaminants in biological laboratories would be helpful for alleviate their potential detrimental effects.

microbiology↗

bioRxiv2018

Comparative whole-genome analysis reveals genetic adaptation of the invasive pinewood nematode

Genetic adaptation to new environments is essential for invasive species. To explore the genetic underpinnings of invasiveness of a dangerous invasive species, the pinewood nematode (PWN) Bursaphelenchus xylophilus, we analysed the genome-wide variations of a large cohort of 55 strains isolated from both the native and introduced regions. Comparative analysis showed abundant genetic diversity existing in the nematode, especially in the native populations. Phylogenetic relationships and principal component analysis indicate a dominant invasive population/group (DIG) existing in China and expansion beyond, with few genomic variations. Putative origin and migration paths at a global scale were traced by targeted analysis of rDNA sequences. A progressive loss of genetic diversity was observed along spread routes. We focused on variations with a low frequency allele (<50%) in the native USA population but fixation in DIG, and a total of 25,992 single nuclear polymorphisms (SNPs) were screened out. We found that a clear majority of these fixation alleles originated from standing variation. Functional annotation of these SNP-harboured genes showed that adaptation-related genes are abundant, such as genes that encode for chemoreceptors, proteases, detoxification enzymes, and proteins involved in signal transduction and in response to stresses and stimuli. Some genes under positive selection were predicted. Our results suggest that adaptability to new environments plays essentially roles in PWN invasiveness. Genetic drift, mutation and strong selection drive the nematode to rapidly evolve in adaptation to new environments, which including local pine hosts, vector beetles, commensal microflora and other new environmental factors, during invasion process.

genomics↗

bioRxiv2018

Benchmarking network propagation methods for disease gene identification

BackgroundIn-silico identification of potential disease genes has become an essential aspect of drug target discovery. Recent studies suggest that one powerful way to identify successful targets is through the use of genetic and genomic information. Given a known disease gene, leveraging intermolecular connections via networks and pathways seems a natural way to identify other genes and proteins that are involved in similar biological processes, and that can therefore be analysed as additional targets.\n\nResultsHere, we systematically tested the ability of 12 varied network-based algorithms to identify target genes and cross-validated these using gene-disease data from Open Targets on 22 common diseases. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. We also compared several cross-validation schemes and showed that different choices had a remarkable impact on the performance estimates. When seeding biological networks with known drug targets, we found that machine learning and diffusion-based methods are able to find novel targets, showing around 2-4 true hits in the top 20 suggestions. Seeding the networks with genes associated to disease by genetics resulted in poorer performance, below 1 true hit on average. We also observed that the use of a larger network, although noisier, improved overall performance.\n\nConclusionsWe conclude that machine learning and diffusion-based prioritisers are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large effect of several factors on prediction performance, especially the validation strategy, input biological network, and definition of seed disease genes.

genetics↗

bioRxiv2018

In vivo phosphoproteomics reveals pathogenic signaling changes in diabetic islets

Progressive decline of pancreatic beta cells function is key to the pathogenesis of type 2 diabetes. Protein phosphorylation is the central mechanism controlling glucose-stimulated insulin secretion in beta cells. However, if and how signaling networks are remodeled in diabetic islets in vivo remain unknowns. Here we applied high-sensitivity mass spectrometry-based proteomics and quantified the levels of about 6,500 proteins and 13,000 phosphopeptides in islets of obese diabetic mice and matched controls. This highlighted drastic remodeling of key kinase hubs and signaling pathways. We integrated our phosphoproteomic dataset with a literature-derived signaling network, which revealed a crucial and conserved role of GSK3 kinase in the control of the beta cells-specific transcription factor PDX1 and insulin secretion, which we functionally verified. Our resource will enable the community to investigate potential mechanisms and drug targets in type 2 diabetes.

systems biology↗

bioRxiv2018

Factors influencing leaf- and root-associated communities of bacteria and fungi across 33 plant orders in a grassland

In terrestrial ecosystems, plants interact with diverse taxonomic groups of bacteria and fungi in the phyllosphere and rhizosphere. Although recent studies based on high-throughput DNA sequencing have drastically increased our understanding of plant-associated microbiomes, we still have limited knowledge of how plant species in a species-rich community differ in their leaf and root microbiome compositions. In a cool-temperate semi-natural grassland in Japan, we compared leaf- and root-associated microbiomes across 138 plant species belonging to 33 plant orders. Based on the whole-microbiome inventory data, we analyzed how sampling season as well as the taxonomy, nativeness (native or alien), lifeform (herbaceous or woody), and mycorrhizal type of host plants could contribute to variation in microbiome compositions among co-occurring plant species. The data also allowed us to explore prokaryote and fungal lineages showing preferences for specific host characteristics. The list of microbial taxa showing significant host preferences involved those potentially having some impacts on survival, growth, or environmental resistance of host plants. Overall, this study provides a platform for understanding how plant and microbial communities are linked with each other at the ecosystem level.

ecology↗

bioRxiv2018

Existence and implications of population variance structure

Identifying the genetic and environmental factors underlying phenotypic differences between populations is fundamental to multiple research communities. To date, studies have focused on the relationship between population and phenotypic mean. Here we consider the relationship between population and phenotypic variance, i.e., \"population variance structure.\" In addition to gene-gene and gene-environment interaction, we show that population variance structure is a direct consequence of natural selection. We develop the ancestry double generalized linear model (ADGLM), a statistical framework to jointly model population mean and variance effects. We apply ADGLM to several deeply phenotyped datasets and observe ancestry-variance associations with 12 of 44 tested traits in ~113K British individuals and 3 of 14 tested traits in ~3K Mexican, Puerto Rican, and African-American individuals. We show through extensive simulations that population variance structure can both bias and reduce the power of genetic association studies, even when principal components or linear mixed models are used. ADGLM corrects this bias and improves power relative to previous methods in both simulated and real datasets. Additionally, ADGLM identifies 17 novel genotype-variance associations across six phenotypes.

genetics↗

bioRxiv2018

Bacterial strain displacement in inflammatory bowel diseases after fecal microbiota transplantation

Fecal microbiota transplantation (FMT), which is thought to have the potential to correct dysbiosis of gut microbiota, has recently been used to treat inflammatory bowel disease (IBD). To elucidate the extent and principles of microbiota engraftment in IBD patients after FMT treatment, we conducted an interventional prospective cohort study. The cohort included two categories of patients: (1) patients with moderate to severe Crohns disease (CD) (Harvey-Bradshaw Index [≥] 7, n = 11, and (2) patients with ulcerative colitis (UC) (Montreal classification, S2 and S3, n = 4). All patients were treated with a single FMT (via mid-gut, from healthy donors) and follow-up visits were performed at baseline, 3 days, one week, and one month after FMT (missing time points included). At each follow-up time point, fecal samples of the participants were collected along with their clinical metadata. For comparative analysis, 10 fecal samples from 10 healthy people were included to represent the diversity level of normal gut microbiota. Additionally, the metagenomic data of 25 fecal samples from 5 individuals with metabolic syndrome who underwent autologous FMT treatment were downloaded from a previous published paper to represent natural microbiota shifts during FMT. All fecal samples underwent shotgun metagenomic sequencing. We found that 3 days after FMT, 11 out of 15 recipients were in remission (3 out of 4 UC recipients; 8 out of 11 CD recipients). Generally, bacterial colonization was observed to be lower in CD recipients than in UC recipients at both species and strain levels. Furthermore, across species, different strains displayed disease-specific displacement advantages under two-disease status. Finally, most post-FMT species (> 80%) could be properly predicted (AUC > 85%) using a random forest classification model, with the gut microbiota composition and clinical parameters of pre-FMT recipients acting as the most contributive factors for prediction accuracy.

clinical trials↗

bioRxiv2018

Extended dengue virus panel: application to antiviral compounds studies

Dengue fever is the most widespread of the human arbovirus diseases, with approximately one third of the worlds population at risk of infection. Dengue viruses are members of the genus Flavivirus (family Flaviviridae) and, antigenically, they separate as four closely related serotypes (1-4) that share 60 to 75 % amino acid homology. This genetic diversity complicates the process of antiviral drug discovery. Thus, currently no approved dengue-specific therapeutic treatments are available. With the aim of providing an efficient tool for dengue virus drug discovery, a collection of nineteen dengue viruses, representing the genotypic diversity within the four serotypes, was developed. After phylogenetic analysis of the full-length genomes, we selected relevant strains from the EVAg collection at Aix-Marseille University and completed the virus collection, using a reverse genetic system based on the infectious sub-genomic amplicons technique. Finally, we evaluated this dengue virus collection against three published dengue inhibitory compounds. NITD008, which targets the highly conserved active site of the viral NS5 polymerase enzyme, exhibited similar antiviral potencies against each of the different dengue genotypes in the panel. Compounds targeting less conserved protein subdomains, such as the capsid inhibitor ST-148, or SDM25N, a {partial} opioid receptor antagonist which indirectly targets NS4B, exhibited larger differences in potency against the various genotypes of dengue viruses. These results illustrate the importance of a phylogenetically based dengue virus reference panel for dengue antiviral research. The collection developed in this study, which includes such representative dengue viruses, has been made available to the scientific community through the European Virus Archive to evaluate novel DENV antiviral candidates.

microbiology↗

bioRxiv2018

Ablation of NMDA receptors in dopamine neurons disrupts attribution of incentive salience to reward-paired stimuli

Midbrain dopamine (DA) neurons play a crucial role in the formation of conditioned associations between environmental cues and appetitive events. Activation of N-methyl-D-aspartate (NMDA) receptors is a key mechanism responsible for the generation of conditioned responses of DA neurons to reward cues. Here, we tested the effects of the cell type-specific inactivation of NMDA receptors in DA neurons in adult mice on stimulus-reward learning. Animals were trained in a Pavlovian learning paradigm in which they had to learn the predictive value of two conditioned stimuli, one of which (CS+) was paired with the delivery of a water reward. Over the course of conditioning, mutant mice learned that the CS+ predicted reward availability, and they approached the reward receptacle more frequently during CS+ trials than CS- trials. However, conditioned responses to the CS+ were weaker in the mutant mice, possibly indicating that they did not attribute incentive salience to the CS+. To further assess whether the attribution of incentive salience was impaired by the mutation, animals were tested in a conditioned reinforcement test. The test revealed that mutant mice made fewer instrumental responses paired with CS+ presentation, confirming that the CS+ had a weaker incentive value. Taken together, these results indicate that reward prediction learning does occur in the absence of NMDA receptors in DA neurons, but the ability of reward-paired cues to invigorate and reinforce behavior is lost.

animal behavior and cognition↗

bioRxiv2018

Growth-factor like gene regulation is separable from survival and maturation in antibody secreting cells

Recurrent mutational activation of the MAP kinase pathway in plasma cell myeloma implicates growth factor-like signaling responses in the biology of antibody secreting cells (ASCs). Physiological ASCs survive in niche microenvironments, but how niche signals are propagated and integrated is poorly understood. Here we dissect such a response in human ASCs using an in vitro model. Applying time course expression data and parsimonious gene correlation networking analysis (PGCNA), we map expression changes that occur during the maturation of proliferating plasmablast to quiescent plasma cell under survival conditions including the potential niche signal TGFB3. This analysis demonstrates a convergent pattern of differentiation, linking UPR/ER stress to secretory optimization, co-ordinated with cell cycle exit. TGFB3 supports ASC survival while having a limited effect on gene expression including up-regulation of CXCR4. This is associated with a significant shift in response to SDF1 in ASCs with amplified ERK1/2 activation, growth factor-like immediate early gene regulation and EGR1 protein expression. Similarly, ASCs responding to survival conditions initially induce partially overlapping sets of immediate early genes, without sustaining the response. Thus, in human ASCs growth factor-like gene regulation is transiently imposed by niche signals but is not sustained during subsequent survival and maturation.

immunology↗