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bioRxiv2016

Particularity of “Universal resilience patterns in complex networks”

In a recent Letter to Nature,Gao, Barzel and Barabasi 1 describe an elegant procedure to reduce the dimensionality of complex dynamical networks, which they claim reveals \"universal patterns of network resilience\", offering \"ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes\". However, Gao et al restrict their attention to systems for which all interactions between nodes are mutualistic. Since antagonism is ubiquitous in natural and social networks, we clarify why this stringent hypothesis is necessary and what happens when it is relaxed. By analyzing broad classes of competitive and predator-prey networks we provide novel insights into the underlying mechanisms at work in Gao et als theory, and novel predictions for dynamical systems that are not purely mutualistic.

Ecology↗

bioRxiv2016

Microbial Mat Functional and Compositional Sensitivity to Environmental Disturbance

The ability of ecosystems to adapt to environmental perturbations depends on the duration and intensity of change and the overall biological diversity of the system. While studies have indicated that rare microbial taxa may provide a biological reservoir that supports long-term ecosystem stability, how this dynamic population is influenced by environmental parameters remains unclear. In this study, a microbial mat ecosystem located on San Salvador Island, The Bahamas was used as a model to examine how environmental disturbance affects the activity of rare and abundant archaeal and bacterial communities and how these changes impact potential biogeochemical processes. While this ecosystem undergoes a range of seasonal variation, it experienced a large shift in salinity (230 to 65 g kg-1) during 2011-2012 following the landfall of Hurricane Irene on San Salvador Island. High throughput sequencing and analysis of 16S rRNA and rRNA genes from samples before and after the pulse disturbance showed significant changes in the diversity and activity of abundant and rare taxa, suggesting overall functional and compositional sensitivity to environmental change. In both archaeal and bacterial communities, while the majority of taxa showed low activity across conditions, the total number of active taxa and overall activity increased postdisturbance, with significant shifts in activity occurring among abundant and rare taxa across and within phyla. Broadly, following the post-disturbance reduction in salinity, taxa within Halobacteria decreased while those within Crenarchaeota, Thaumarchaeota, Thermoplasmata, Cyanobacteria, and Proteobacteria, increased in abundance and activity. Quantitative PCR of genes and transcripts involved in nitrogen and sulfur cycling showed concomitant shifts in biogeochemical cycling potential. Post-disturbance conditions increased the expression of genes involved in N-fixation, nitrification, denitrification, and sulfate reduction. Together, our findings show complex community adaptation to environmental change and help elucidate factors connecting disturbance, biodiversity, and ecosystem function that may enhance ecosystem models.

Microbiology↗

bioRxiv2017

Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data.

bioinformatics↗

bioRxiv2017

Prediction of fluoroquinolone susceptibility directly from whole genome sequence data using liquid chromatography-tandem mass spectrometry to identify mutant genotypes.

Fluoroquinolone resistance in bacteria is multifactorial, involving target site mutations, reductions in fluoroquinolone entry due to reduced porin production, increased fluoroquinolone efflux, enzymes that modify fluoroquinolones, and Qnr, a DNA mimic that protects the drug target from fluoroquinolone binding. Here we report a comprehensive analysis using transformation and in vitro mutant selection, of the relative importance of each of these mechanisms in fluoroquinolone resistance and non-susceptibility, using Klebsiella pneumoniae, one of the most clinically important multi-drug resistant bacterial species known, as a model system. Our improved biological understanding was then used to generate rules that could be predict fluoroquinolone susceptibility in K. pneumoniae clinical isolates. Key to the success of this predictive process was the use of liquid chromatography tandem mass spectrometry to measure the abundance of proteins in extracts of cultured bacteria, identifying which sequence variants seen in the whole genome sequence data were functionally important in the context of fluoroquinolone susceptibility.

microbiology↗

bioRxiv2017

WebMeV: A Cloud Platform for Analyzing and Visualizing Cancer Genomic Data

Although large, complex genomic data sets are increasingly easy to generate, and the number of publicly available data sets in cancer and other diseases is rapidly growing, the lack of intuitive, easy to use analysis tools has remained a barrier to the effective use of such data. WebMeV (https://mev.tm4.org) is an open-source, web-based tool that gives users access to sophisticated tools for analysis of RNA-Seq and other data in an interface designed to democratize data access. WebMeV combines cloud-based technologies with a simple user interface to allow users to access large public data sets such as that from The Cancer Genome Atlas (TCGA) or to upload their own. The interface allows users to visualize data and to apply advanced data mining analysis methods to explore the data and draw biologically meaningful conclusions. We provide an overview of WebMeV and demonstrate two simple use cases that illustrate the value of putting data analysis in the hands of those looking to explore the underlying biology of the systems being studied.

bioinformatics↗

bioRxiv2017

Structural basis of the Cope rearrangement and C-C bond-forming cascade in hapalindole/fischerindole biogenesis

STRUCTURESThe atomic coordinates and structure factors for:\n\nHpiC1 W73M/K132M SeMet (P212121) -1.7 [A]\n\nHpiC1 native (C2) -1.5 [A]\n\nHpiC1 native (P42) -2.1 [A]\n\nHpiC1 Y101F (C2) -1.4 [A]\n\nHpiC1 Y101S (C2) -1.4 [A]\n\nHpiC1 F138S (P21) -1.7 [A]\n\nHpiC1 Y101F/F138S (P21 -1.65 [A] have been deposited with the Research Collaboratory for Structural Bioinformatics as Protein Data Bank entries 5WPP, 5WPR, 6AL6, 5WPR, 5WPU, 6AL7, and 6AL8 (www.rcsb.org).\n\nGRANTSThis work was supported by: The authors thank the National Science Foundation under the CCI Center for Selective C-H Functionalization (CHE-1205646), the National Institutes of Health (CA70375 to RMW and DHS), R35 GM118101, R01 GM076477 and the Hans W. Vahlteich Professorship (to DHS) for financial support. M.G-B. thanks the Ramon Areces Foundation for a postdoctoral fellowship. J.N.S. acknowledges the support of the National Institute of General Medical Sciences of the National Institutes of Health under Award Number F32GM122218. Computational resources were provided by the UCLA Institute for Digital Research and Education (IDRE) and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the NSF (OCI-1053575). The content does not necessarily represent the official views of the National Institutes of Health.\n\nABSTRACTHapalindole alkaloids are a structurally diverse class of cyanobacterial natural products defined by their varied polycyclic ring systems and diverse biological activities. These polycyclic scaffolds are generated from a common biosynthetic intermediate by the Stig cyclases in three mechanistic steps, including a rare Cope-rearrangement, 6-exo-trig cyclization, and electrophilic aromatic substitution. Here we report the structure of HpiC1, a Stig cyclase that catalyzes the formation of 12-epi-hapalindole U in vitro. The 1.5 [A] structure reveals a dimeric assembly with two calcium ions per monomer and the active sites located at the distal ends of the protein dimer. Mutational analysis and computational methods uncovered key residues for an acid catalyzed [3,3]-sigmatropic rearrangement and specific determinants that control the position of terminal electrophilic aromatic substitution leading to a switch from hapalindole to fischerindole alkaloids.

biochemistry↗

bioRxiv2017

Computational Re-Design of Synthetic Genetic Oscillators for Independent Amplitude and Frequency Modulation

Engineering robust and tuneable genetic clocks is a topic of current interest in Systems and Synthetic Biology with wide applications in biotechnology. Synthetic genetic oscillators share a common structure based on a negative feedback loop with a time delay, and generally display only limited tuneability. Recently, the dual-feedback oscillator was demonstrated to be robust and tuneable, to some extent, by the use of chemical inducers. Yet no engineered genetic oscillator currently allows for the independent modulation of amplitude and period. In this work, we demonstrate computationally how recent advances in tuneable synthetic degradation can be used to decouple the frequency and amplitude modulation in synthetic genetic oscillators. We show how the range of tuneability can be increased by connecting additional input dials, e.g. orthogonal transcription factors that respond to chemical, temperature or even light signals. Modelling and numerical simulations predict that our proposed re-designs enable amplitude tuning without period modulation, coupled modulation of both period and amplitude, or period adjustment with near-constant amplitude. We illustrate our work through computational re-designs of both the dual-feedback oscillator and the repressilator, and show that the repressilator is more flexible and can allow for independent amplitude and near-independent period modulation.

synthetic biology↗

bioRxiv2018

Effect of eco-remediation and microbial community using multilayer solar planted floating island (MS-PFI) in the drainage channel

A multilayer solar planted floating island (MS-PFI) planted with Eichhornia crassipes are potential alternatives to traditional PFI. The highest removal rates of suspended solids, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand was 86%, 75%, 80%, 95% and 84%, respectively. Proteobacteria (average 43.4% of total sequences) and Actinobacteria (19.9%) were the dominant phyla. Numerous genus had obvious differences between influent and effluent water, for instance, 13, 12 and 7 % in effluent water were assigned to the hgcl_clade, Norank_c_Cyanobacteria, and Rhizorhapis, while their relative abundances were decreased to 5, 3 and 0 %. In contrast, a distinct increase among Flavobacterium (10%), Limnohabitans (7%), Alpinimonas (4%), norank_p_Saccharibacteria (4%), Erwinia (3%) after MS-PFI treatment. MS-PFI brings various bacteria involved in contaminant degradation and nutrient removal in biological wastewater treatment systems. An amount of {yen} 1,843 was totally inputted to construct floating bed, which was rarely needed operation and maintenance costs.\n\nImportanceIn-situ micro-polluted water ecological remediation, microorganisms and plants are effective to improve environmental quality and provide essential ecosystem services. Recently, we invent a new multilayer solar with an excellent pollutant removal efficiency. Microbes can decompose or mineralize organic matter effectively, also provide food for aquatic animals and increase nutrients or substances for plants, it is an important part of biogeochemical cycles and energy flows in aquatic ecological systems. However, few study explain the bacteria diversity and its responses between influent and effluent water in a planted floating island. The significance of our study is in identifying-in greater detail-the responses of bacteria in the new MS-PFI. This will greatly enhance our knowledge of bacteria communities, and can be widely used in micro-polluted water remediation.

bioengineering↗

bioRxiv2018

Measures of Possible Allostatic Load in Comorbid Cocaine and Alcohol use Disorder: Brain White Matter Integrity, Telomere Length, and Anti-Saccade Performance

Chronic cocaine and alcohol use impart significant stress on biological and cognitive systems, resulting in changes consistent with an allostatic load model of neurocognitive impairment. The present study measured potential markers of allostatic load in individuals with comorbid cocaine/alcohol use disorders (CUD/AUD) and control subjects. Measures of brain white matter (WM) integrity, telomere length, and impulsivity/attentional bias were obtained. WM integrity (CUD/AUD only) was indexed by diffusion tensor imaging metrics, including radial diffusivity (RD) and fractional anisotropy (FA). Telomere length was indexed by T/S ratio. Impulsivity and attentional bias to drug cues were measured via eye-tracking, and were also modeled using the Hierarchical Diffusion Drift Model (HDDM). Average whole-brain RD and FA were associated with years of cocaine use (R2 = 0.56 and 0.51, both p < .005) but not years of alcohol use. CUD/AUD subjects showed more anti-saccade errors (p < .01), greater attentional bias scores (p < .001), and higher HDDM drift rates on cocaine-cue trials (Bayesian probability CUD/AUD > control = p > 0.99). Telomere length was shorter in CUD/AUD, but the difference was not statistically significant. Within the CUD/AUD group, exploratory regression using an elastic-net model determined that more years of cocaine use, older age, larger HDDM drift rate differences and shorter telomere length were all predictive of white matter integrity as measured by RD (model R2 = 0.79). Collectively, the results provide modest support linking CUD/AUD to putative markers of allostatic load.

neuroscience↗

bioRxiv2018

A computational observer model of spatial contrast sensitivity: Effects of wavefront-based optics, cone mosaic structure, and inference engine

We present a computational observer model of the human spatial contrast sensitivity (CSF) function based on the Image Systems EngineeringTools for Biology (ISETBio) simulation framework. We demonstrate that ISETBio-derived CSFs agree well with CSFs derived using traditional ideal observer approaches, when the mosaic, optics, and inference engine are matched. Further simulations extend earlier work by considering more realistic cone mosaics, more recent measurements of human physiological optics, and the effect of varying the inference engine used to link visual representations to psy-chohysical performance. Relative to earlier calculations, our simulations show that the spatial structure of realistic cone mosaics reduces upper bounds on performance at low spatial frequencies, whereas realistic optics derived from modern wavefront measurements lead to increased upper bounds high spatial frequencies. Finally, we demonstrate that the type of inference engine used has a substantial effect on the absolute level of predicted performance. Indeed, the performance gap between an ideal observer with exact knowledge of the relevant signals and human observers is greatly reduced when the inference engine has to learn aspects of the visual task. ISETBio-derived estimates of stimulus representations at different stages along the visual pathway provide a powerful tool for computing the limits of human performance.

neuroscience↗

bioRxiv2018

Telescope: Characterization of the retrotranscriptome by accurate estimation of transposable element expression

Characterization of Human Endogenous Retrovirus (HERV) expression within the transcriptomic landscape using RNA-seq is complicated by uncertainty in fragment assignment because of sequence similarity. We present Telescope, a computational software tool that provides accurate estimation of transposable element expression (retrotranscriptome) resolved to specific genomic locations. Telescope directly addresses uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model. We demonstrate the utility of our approach through single locus analysis of HERV expression in 13 ENCODE cell types. When examined at this resolution, we find that the magnitude and breadth of the retrotranscriptome can be vastly different among cell types. Furthermore, our approach is robust to differences in sequencing technology, and demonstrates that the retrotranscriptome has potential to be used for cell type identification. Telescope performs highly accurate quantification of the retrotranscriptomic landscape in RNA-seq experiments, revealing a differential complexity in the transposable element biology of complex systems not previously observed. Telescope is available at github.com/mlbendall/telescope.\n\nAuthor SummaryAlmost half of the human genome is composed of Transposable elements (TEs), but their contribution to the transcriptome, their cell-type specific expression patterns, and their role in disease remains poorly understood. Recent studies have found many elements to be actively expressed and involved in key cellular processes. For example, human endogenous retroviruses (HERVs) are reported to be involved in human embryonic stem cell differentiation. Discovering which exact HERVs are differentially expressed in RNA-seq data would be a major advance in understanding such processes. However, because HERVs have a high level of sequence similarity it is hard to identify which exact HERV is differentially expressed. To solve this problem, we developed a computer program which addressed uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model. We call this program, \"Telescope\". We then used Telescope to identify HERV expression in 13 well-studied cell types from the ENCODE consortium and found that different cell types could be characterized by enrichment for different HERV families, and for locus specific expression. We also showed that Telescope performed better than other methods currently used to determine TE expression. The use of this computational tool to examine new and existing RNA-seq data sets may lead to new understanding of the roles of TEs in health and disease.

genomics↗

bioRxiv2014

Modes of interaction between individuals dominate the topologies of real world networks

We find that the topologies of real world networks, such as those formed within human societies, by the Internet, or among cellular proteins, are dominated by the mode of the interactions considered among the individuals. Consequently, a major dichotomy in previously studied networks arises from modeling networks in terms of pairwise versus group tasks. The former often intrinsically give rise to scale-free, disassortative, hierarchical networks, whereas the latter often give rise to broad-scale, assortative, nonhierarchical networks. These dependencies explain contrasting observations among previous topological analyses of real world complex systems. We also observe this trend in systems with natural hierarchies, in which alternate representations of the same networks, but which capture different levels of the hierarchy, manifest these signature topological differences. For example, in both the Internet and cellular proteomes, networks of lower-level system components (routers within domains or proteins within biological processes) are assortative and nonhierarchical, whereas networks of upper-level system components (internet domains or biological processes) are disassortative and hierarchical. Our results demonstrate that network topologies of complex systems must be interpreted in light of their hierarchical natures and interaction types.

Systems Biology↗

bioRxiv2017

Network integration of multi-tumour omics data suggests novel targeting strategies

We characterize different tumour types in the search for multi-tumour drug targets, in particular aiming for drug repurposing or novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allowed us to define three cluster-specific signatures, with genes belonging to NF-B signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we defined new pharmacological strategies validated by in vitro experiments that showed inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways with the potential to be used, singularly or in combination, for the design of novel treatment strategies.

systems biology↗

bioRxiv2016

PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal \"virtual laboratory\" for such multicellular systems simulates both the biochemical microenvironment (the \"stage\") and many mechanically and biochemically interacting cells (the \"players\" upon the stage).\n\nPhysiCell--physics-based multicellular simulator--is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility \"out of the box.\" The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes.\n\nWe demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a \"cellular cargo delivery\" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.\n\nAuthor SummaryThis paper introduces PhysiCell: an open source, agent-based modeling framework for 3-D multicellular simulations. It includes a standard library of sub-models for cell fluid and solid volume changes, cycle progression, apoptosis, necrosis, mechanics, and motility. PhysiCell is directly coupled to a biotransport solver to simulate many diffusing substrates and cell-secreted signals. Each cell can dynamically update its phenotype based on its microenvironmental conditions. Users can customize or replace the included sub-models.\n\nPhysiCell runs on a variety of platforms (Linux, OSX, and Windows) with few software dependencies. Its computational cost scales linearly in the number of cells. It is feasible to simulate 500,000 cells on quad-core desktop workstations, and millions of cells on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on hanging drop tumor spheroids (HDS) and ductal carcinoma in situ (DCIS) of the breast. We demonstrate contact- and chemokine-based interactions among multiple cell types with examples in synthetic multicellular bioengineering, cancer heterogeneity, and cancer immunology.\n\nWe developed PhysiCell to help the scientific community tackle multicellular systems biology problems involving many interacting cells in multi-substrate microenvironments. PhysiCell is also an independent, cross-platform codebase for replicating results from other simulators.

systems biology↗

bioRxiv2017

Identifying Core Biological Processes Distinguishing Human Eye Tissues With Systems-Level Gene Expression Analyses And Weighted Correlation Networks

The human eye is built from several specialized tissues which direct, capture, and pre-process information to provide vision. The gene expression of the different eye tissues has been extensively profiled with RNA-seq across numerous studies. Large consortium projects have also used RNA-seq to study gene expression patterning across many different human tissues, minus the eye. There has not been an integrated study of expression patterns from multiple eye tissues compared to other human body tissues. We have collated all publicly available healthy human eye RNA-seq datasets as well as dozens of other tissues. We use this fully integrated dataset to probe the biological processes and pan expression relationships between the cornea, retina, RPE-choroid complex, and the rest of the human tissues with differential expression, clustering, and GO term enrichment tools. We also leverage our large collection of retina and RPE-choroid tissues to build the first human weighted gene correlation networks and use them to highlight known biological pathways and eye gene disease enrichment. We also have integrated publicly available single cell RNA-seq data from mouse retina into our framework for validation and discovery. Finally, we make all these data, analyses, and visualizations available via a powerful interactive web application (https://eyeintegration.nei.nih.gov/).

genomics↗

bioRxiv2018

Seamless Assembly of Biological Parts into Functional Devices and Higher Order Multi-Device Systems.

A new method is described for the seamless assembly of independent, prefabricated and functionally tested blunt-end, double strand nucleic acid parts (DNA fragments) into more complex biological devices (vectors) and higher order multi-device systems. Individual parts include bacterial selection markers, bacterial origins of replication, promoters from a variety of different species, transcription terminators, shuttle sequences and a variety of \"N\" and \"C\" terminal solubility/affinity expression tags. Pre-assembly modification of parts with DNA modifying enzymes is not required. Seamless assembly of multiple parts is accomplished in a single step using a specialized thermostable enzyme blend in about 30 minutes. Combinatorial assembly of parts is an inherent feature of the new process, substantially simplifying device and system optimization. To underscore the utility of the new process, parts were assembled into several protein expression devices in order to identify the optimal expression construct for a model target gene, as an example of the utility of the assembly process, and a higher order multi-device system is also described, for the over-expression of a four-enzyme bio-synthetic pathway, and optimized for end-product accumulation in E. coli as a paradigm for how this assembly process could be used to address the assembly of more complex biological pathways.

synthetic biology↗

bioRxiv2014

Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight across multiple dimensions. For this potential to be realized, we need a suitable representation to understand the data. Since a wide range of experiments and the unknown complexity of the underlying system contribute to the heterogeneity of biological data, we propose a method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components when there are corrupted observations. The proposed method provides us a new representation of these data sets in terms of a common and aberrant response. This representation might help users to acquire a new insight from data.\n\nAuthor SummaryOne of the most exciting trends and important themes in science and engineering involves the use of high-throughput measurement data. With different dimensions, for example, various perturbations, different doses of drug or cell lines characteristics, such multidimensional data sets enable us to understand commonalities and differences across multiple dimensions. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. With this notion, we propose an RPCA-based method which models common variations as approximately the low-rank component and anomalies as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses.

Systems Biology↗

bioRxiv2018

Two levels of host-specificity in a fig-associated Caenorhabditis

BackgroundBiotic interactions are ubiquitous and require information from ecology, evolutionary biology, and functional genetics in order to be completely understood. However, study systems that are amenable to investigations across such disparate fields are rare. Figs and fig wasps are a classic system for ecology and evolutionary biology with poor functional genetics; C. elegans is a classic system for functional genetics with poor ecology. In order to help bridge these disciplines, here we describe the natural history of a close relative of C. elegans, C. sp. 34, that is associated with the fig Ficus septica and its pollinating Ceratosolen wasps.\n\nResultsTo understand the natural context of fig-associated Caenorhabditis, fresh F. septica figs from four Okinawan islands were sampled, dissected, and observed under microscopy. C. sp. 34 was found in all islands where F. septica figs were found. C. sp. 34 was routinely found in the fig interior and almost never observed on the outside surface. Caenorhabditis was only found in pollinated figs, and C. sp. 34 was more likely to be observed in figs with more foundress pollinating wasps. Actively reproducing C. sp. 34 dominated younger figs, whereas older figs with emerging wasp progeny harbored C. sp. 34 dispersal larvae. Additionally, C. sp. 34 was observed dismounting from plated Ceratosolen pollinating wasps. C. sp. 34 was never found on non-pollinating, parasitic Philotrypesis wasps. Finally, C. sp. 34 was only observed in F. septica figs among five Okinawan Ficus species sampled.\n\nConclusionThese observations suggest a natural history where C. sp. 34 proliferates in young F. septica figs and disperses from old figs on Ceratosolen pollinating fig wasps. The fig and wasp host specificity of this Caenorhabditis is highly divergent from its close relatives and frames hypotheses for future investigations. This natural co-occurrence of the fig/fig wasp and Caenorhabditis study systems sets the stage for an integrated research program that can help to explain the evolution of interspecific interactions.

ecology↗