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Results for “Synthetic Biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Bayesian Inference Of Phylogenetic Networks From Bi-allelic Genetic Markers

Phylogenetic networks are rooted, directed, acyclic graphs that model reticulate evolutionary histories. Recently, statistical methods were devised for inferring such networks from either gene tree estimates or the sequence alignments of multiple unlinked loci. Bi-allelic markers, most notably single nucleotide polymorphisms (SNPs) and amplified fragment length polymorphisms (AFLPs), provide a powerful source of genome-wide data. In a recent paper, a method called SNAPP was introduced for statistical inference of species trees from unlinked bi-allelic markers. The generative process assumed by the method combined both a model of evolution for the bi-allelic markers, as well as the multispecies coalescent. A novel component of the method was a polynomial-time algorithm for exact computation of the likelihood of a fixed species tree via integration over all possible gene trees for a given marker. Here we report on a method for Bayesian inference of phylogenetic networks from bi-allelic markers. Our method significantly extends the algorithm for exact computation of phylogenetic network likelihood via integration over all possible gene trees. Unlike the case of species trees, the algorithm is no longer polynomial-time on all instances of phylogenetic networks. Furthermore, the method utilizes a reversible-jump MCMC technique to sample the posterior of phylogenetic networks given bi-allelic marker data. Our method has a very good performance in terms of accuracy and robustness as we demonstrate on simulated data, as well as a data set of multiple New Zealand species of the plant genus Ourisia(Plantaginaceae). We implemented the method in the publicly available, open-source PhyloNet software package.\n\nAuthor summaryThe availability of genomic data has revolutionized the study of evolutionary histories and phylogeny inference. Inferring evolutionary histories from genomic data requires, in most cases, accounting for the fact that different genomic regions could have evolutionary histories that differ from each other as well as from that of the species from which the genomes were sampled. In this paper, we introduce a method for inferring evolutionary histories while accounting for two processes that could give rise to such differences across the genomes, namely incomplete lineage sorting and hybridization. We introduce a novel algorithm for computing the likelihood of phylogenetic networks from bi-allelic genetic markers and use it in a Bayesian inference method. Analyses of synthetic and empirical data sets show a very good performance of the method in terms of the estimates it obtains.

evolutionary biology↗
bioRxiv2017

The development of scientific consensus: analyzing conflict and concordance among Avian phylogenies

Recent developments in phylogenetic methods and data acquisition have allowed for the construction of large and comprehensive phylogenetic relationships. Published phylogenies represent an enormous resource that not only facilitate the resolution of questions related to comparative biology, but also provide a resource on which to gauge the development of consensus across the tree of life. From the Open Tree of Life, we gathered 290 avian phylogenies representing all major groups that have been published over the last few decades and analyzed how concordance and conflict develop among these trees through time. Nine large scale backbone trees (including a new synthetic tree from this study) were used for the consensus assessment. We found that conflicts were over-represented both along the backbone (higher-level neoavian relationships) and within the oscine Passeriformes. Importantly, although we have made major strides in our knowledge of major clades, recent published comprehensive trees, as well as trees of individual clades, continue to contribute significantly to the resolution of clades in the avian phylogeny. These findings are somewhat unexpected, given that birds constitute a relatively well-studied and small clade of the tree of life (i.e., Aves). Therefore, our analysis highlights that much work is still needed before we can confidently resolve the less well studied areas of the tree of life.

evolutionary biology↗
bioRxiv2016

Multiple Confounders Correction with Regularized Linear Mixed Effect Models, with Application in Biological Processes

In this paper, we inspect the performance of regularized linear mixed effect models, as an extension of linear mixed effect model, when multiple confounding factors coexist. We first review its parameter estimation algorithms before we introduce three different methods for multiple confounding factors correction, namely concatenation, sequence, and interpolation. Then we investigate the performance on variable selection task and predictive task on three different data sets, synthetic data set, semi-empirical synthetic data set based on genome sequences and brain wave data set connecting to confused mental states. Our results suggest that sequence multiple confounding factors corrections behave the best when different confounders contribute equally to response variables. On the other hand, when various confounders affect the response variable unevenly, results mainly rely on the degree of how the major confounder is corrected.

bioinformatics↗
bioRxiv2018

Efficient Synthesis of Mutants Using Genetic Crosses

The genetic cross is a fundamental, flexible, and widely-used experimental technique to create new mutant strains from existing ones. Surprisingly, the problem of how to efficiently compute a sequence of crosses that can make a desired target mutant from a set of source mutants has received scarce attention. In this paper, we make three contributions to this question.\n\nFirst, we formulate several natural problems related to efficient synthesis of a target mutant from source mutants. Our formulations capture experimentally-useful notions of verifiability (e.g the need to confirm that a mutant contains mutations in the desired genes) and permissibility (e.g., the requirement that no intermediate mutants in the synthesis be inviable).\n\nSecond, we develop combinatorial techniques to solve these problems. We prove that checking the existence of a verifiable, permissible synthesis is NP-complete in general. We complement this result with three polynomial time or fixed-parameter tractable algorithms for optimal synthesis of a target mutant for special cases of the problem that arise in practice.\n\nThird, we apply these algorithms to simulated data and to synthetic data. We use results from simulations of a mathematical model of the cell cycle to replicate realistic experimental scenarios where a biologist may be interested in creating several mutants in order to verify model predictions. Our results show that the consideration of permissible mutants can affect the existence of a synthesis or the number of crosses in an optimal one. Our algorithms gracefully handle the restrictions that permissible mutants impose. Results on synthetic data show that our algorithms scale well with increases in the size of the input and the fixed parameters.

systems biology↗
bioRxiv2017

Implementation and System Identification of a Phosphorylation-Based Insulator in a Cell-Free Transcription-Translation System

An outstanding challenge in the design of synthetic biocircuits is the development of a robust and efficient strategy for interconnecting functional modules. Recent work demonstrated that a phosphorylation-based insulator (PBI) implementing a dual strategy of high gain and strong negative feedback can be used as a device to attenuate retroactivity. This paper describes the implementation of such a biological circuit in a cell-free transcription-translation system and the structural identifiability of the PBI in the system. We first show that the retroactivity also exists in the cell-free system by testing a simple negative regulation circuit. Then we demonstrate that the PBI circuit helps attenuate the retroactivity significantly compared to the control. We consider a complex model that provides an intricate description of all chemical reactions and leveraging specific physiologically plausible assumptions. We derive a rigorous simplified model that captures the output dynamics of the PBI. We perform standard system identification analysis and determine that the model is globally identifiable with respect to three critical parameters. These three parameters are identifiable under specific experimental conditions and we perform these experiments to estimate the parameters. Our experimental results suggest that the functional form of our simplified model is sufficient to describe the reporter dynamics and enable parameter estimation. In general, this research illustrates the utility of the cell-free expression system as an alternate platform for biocircuit implementation and system identification and it can provide helpful insights into future biological circuit designs.

bioengineering↗
bioRxiv2016

Whole organism lineage tracing by combinatorial and cumulative genome editing

Multicellular systems develop from single cells through a lineage, but current lineage tracing approaches scale poorly to whole organisms. Here we use genome editing to progressively introduce and accumulate diverse mutations in a DNA barcode over multiple rounds of cell division. The barcode, an array of CRISPR/Cas9 target sites, records lineage relationships in the patterns of mutations shared between cells. In cell culture and zebrafish, we show that rates and patterns of editing are tunable, and that thousands of lineage-informative barcode alleles can be generated. We find that most cells in adult zebrafish organs derive from relatively few embryonic progenitors. Genome editing of synthetic target arrays for lineage tracing (GESTALT) will help generate large-scale maps of cell lineage in multicellular systems.

Developmental Biology↗
bioRxiv2017

The scent of the fly

(Z)-4-undecenal (Z4-11Al) is the volatile pheromone produced by females of the vinegar fly Drosophila melanogaster. Female flies emit Z4-11Al for species-specific communication and mate-finding. A sensory panel finds that synthetic Z4-11Al has a characteristic flavour, which we perceive even at the small amounts produced by one female fly. Since only females produce Z4-11Al, and not males, we can reliably distinguish between single D. melanogaster males and females, according to their scent. Females release Z4-11Al at 2.4 ng/h and we readily sense 1 ng synthetic Z4-11Al in a glass of wine (0.03 nmol/L), while a tenfold concentration is perceived as a loud off-flavour. This corroborates the observation that a glass of wine is spoilt by a single D. melanogaster fly falling into it, which we here show is caused by Z4-11Al. The biological role of Z4-11Al or structurally related aldehydes in humans and the basis for this semiochemical convergence remains yet unclear.

animal behavior and cognition↗
bioRxiv2018

Modular assembly of polysaccharide-degrading microbial communities in the ocean

Many complex biological systems such as metabolic networks can be divided into functional and organizational subunits, called modules, which provide the flexibility to assemble novel multi-functional hierarchies by a mix and match of simpler components. Here we show that polysaccharide-degrading microbial communities in the ocean can also assemble in a modular fashion. Using synthetic particles made of a variety of polysaccharides commonly found in the ocean, we showed that the particle colonization dynamics of natural bacterioplankton assemblages can be understood as the aggregation of species modules of two main types: a first module type made of narrow niche-range primary degraders, whose dynamics are controlled by particle polysaccharide composition, and a second module type containing broad niche-range, substrate-independent taxa whose dynamics are controlled by interspecific interactions, in particular cross-feeding via organic acids, amino acids and other metabolic byproducts. As a consequence of this modular logic, communities can be predicted to assemble by a sum of substrate-specific primary degrader modules, one for each complex polysaccharide in the particle, connected to a single broad-niche range consumer module. We validate this model by showing that a linear combination of the communities on single-polysaccharide particles accurately predicts community composition on mixed-polysaccharide particles. Our results suggest thus that the assembly of heterotrophic communities that degrade complex organic materials follow simple design principles that can be exploited to engineer heterotrophic microbiomes.

microbiology↗
bioRxiv2018

Translation factor mRNA granules direct protein synthetic capacity to regions of polarized growth

mRNA localization serves key functions in localized protein production making it critical that the translation machinery itself is present at these locations. Here we show that translation factor mRNAs are localized to distinct granules within yeast cells. In contrast to many mRNP granules, such as P-bodies and stress granules, which contain translationally repressed mRNAs, these granules harbor translated mRNAs under active growth conditions. The granules require Pablp for their integrity and are inherited by developing daughter cells in a She2p/ She3p dependent manner. These results point to a model where roughly half the mRNA for certain translation factors are specifically directed in granules toward the tip of the developing daughter cell where protein synthesis is most heavily required, which has particular implications for filamentous forms of growth. Such a feedforward mechanism would ensure adequate provision of the translation machinery where it is to be needed most over the coming growth cycle. SummaryThis study shows that mRNAs encoding a range of translation factors are localized to granules that get transported into the yeast daughter cell using the She2p/She3p machinery. This likely supports an intensification of protein synthetic activity to facilitate apical polarized growth.

cell biology↗
bioRxiv2018

Synthetic Lethality of Wnt Pathway Activation and Asparaginase in Drug-Resistant Acute Leukemias

Resistance to asparaginase, an antileukemic enzyme that depletes asparagine, is a common clinical problem. Using a genome-wide CRISPR/Cas9 screen, we found a synthetic lethal interaction between Wnt pathway activation and asparaginase in acute leukemias resistant to this enzyme. Wnt pathway activation induced asparaginase sensitivity in distinct treatment-resistant subtypes of acute leukemia, including T-lymphoblastic, hypodiploid B-lymphoblastic, and acute myeloid leukemias, but not in normal hematopoietic progenitors. Sensitization to asparaginase was mediated by Wnt-dependent stabilization of proteins (Wnt/STOP), which inhibits GSK3-dependent protein ubiquitination and degradation. Inhibiting the alpha isoform of GSK3 phenocopied this effect, and pharmacologic GSK3 inhibition profoundly sensitized drug-resistant leukemias to asparaginase. Our findings provide a molecular rationale for activation of Wnt/STOP signaling to improve the therapeutic index of asparaginase.\n\nSIGNIFICANCEThe intensification of asparaginase-based therapy has improved outcomes for several subtypes of acute leukemia, but the development of treatment resistance has a poor prognosis. We hypothesized, from the concept of synthetic lethality, that gain-of-fitness alterations in drug-resistant cells had conferred a survival advantage that could be exploited therapeutically. We found a synthetic lethal interaction between activation of Wnt-dependent stabilization of proteins (Wnt/STOP) and asparaginase in acute leukemias resistant to this enzyme. Inhibition of the alpha isoform of GSK3 was sufficient to phenocopy this effect, and the combination of GSK3-selective inhibitors and asparaginase had marked therapeutic activity against leukemias resistant to monotherapy with either agent. These data indicate that drug-drug synthetic lethal interactions can improve the therapeutic index of cancer therapy.

cancer biology↗
bioRxiv2014

Reconstruction of Gene Regulatory Networks based on Repairing Sparse Low-rank Matrices

With the growth of high-throughput proteomic data, in particular time series gene expression data from various perturbations, a general question that has arisen is how to organize inherently heterogenous data into meaningful structures. Since biological systems such as breast cancer tumors respond differently to various treatments, little is known about exactly how these gene regulatory networks (GRNs) operate under different stimuli. For example, when we apply a drug-induced perturbation to a target protein, we often only know that the dynamic response of the specific protein may be affected. We do not know by how much, how long and even whether this perturbation affects other proteins or not. Challenges due to the lack of such knowledge not only occur in modeling the dynamics of a GRN but also cause bias or uncertainties in identifying parameters or inferring the GRN structure. This paper describes a new algorithm which enables us to estimate bias error due to the effect of perturbations and correctly identify the common graph structure among biased inferred graph structures. To do this, we retrieve common dynamics of the GRN subject to various perturbations. We refer to the task as \"repairing\" inspired by \"image repairing\" in computer vision. The method can automatically correctly repair the common graph structure across perturbed GRNs, even without precise information about the effect of the perturbations. We evaluate the method on synthetic data sets and demonstrate advantages over C-regularized graph inference by advancing our understanding of how these networks respond across different targeted therapies. Also, we demonstrate an application to the DREAM data sets and discuss its implications to experiment design.

Systems Biology↗
bioRxiv2015

Spatially-constrained growth enhances conversional meltdown

Cells that mutate or commit to a specialized function (differentiate) often undergo conversions that are effectively irreversible. Slowed growth of converted cells can act as a form of selection, balancing unidirectional conversion to maintain both cell types at a steady-state ratio. However, when one-way conversion is insuffciently counterbalanced by selection, the original cell type will ultimately be lost, often with negative impacts on the populations overall ftness. The critical balance between selection and conversion needed for preservation of unconverted cells and the steady-state ratio between cell types depends on the spatial circumstances under which cells proliferate. We present experimental data on a yeast strain engineered to undergo irreversible conversion: this synthetic system permits cell type-specifc fuorescent labeling and exogenous variation of the relative growth and conversion rates. We fnd that populations confned to grow on a fat agar surface are more susceptible than their well-mixed counterparts to ftness loss via a conversion-induced \"meltdown.\" We then present analytical predictions for growth in several biologically-relevant geometries - well-mixed liquid media, radially-expanding two-dimensional colonies, and linear fronts in two dimensions - by employing analogies to the directed percolation transition from non-equilibrium statistical physics. These simplifed theories are consistent with the experimental results.

Evolutionary Biology↗
bioRxiv2018

EZH2 co-opts gain-of-function p53 mutants to promote cancer growth and metastasis

With the unfolding of more and more cancer-driven gain-of-function (GOF) mutants of p53, it is important to define a common mechanism to systematically target different mutants rather than develop strategies tailored to inhibit each mutant individually. Here, using RNA immunoprecipitation sequencing (RIP-seq) we identified EZH2 as a p53 mRNA-binding protein. EZH2 bound to the internal ribosome entry site (IRES) in the 5 untranslated region (5UTR) of p53 mRNA and enhanced p53 protein translation in a methyltransferase-independent manner. EZH2 augmented p53 GOF mutant-mediated cancer growth and metastasis by increasing p53 GOF mutant protein level. EZH2 overexpression associated with the worse outcome only in patients with p53-mutated cancer. Depletion of EZH2 by antisense oligonucleotides inhibited p53 GOF mutant-mediated cancer growth. Our findings reveal a non-methyltransferase function of EZH2 that controls protein translation of p53 GOF mutants, inhibition of which causes synthetic lethality in cancer cells expressing p53 GOF mutants.

cancer biology↗
bioRxiv2018

A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

Genome-, transcriptome- and proteome-wide measurements provide valuable insights into how biological systems are regulated. However, even fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we have generated a systematic, quantitative and deep proteome and transcriptome abundance atlas from 29 paired healthy human tissues from the Human Protein Atlas Project and representing human genes by 17,615 transcripts and 13,664 proteins. The analysis revealed that few proteins show truly tissue-specific expression, that vast differences between mRNA and protein quantities within and across tissues exist and that the expression levels of proteins are often more stable across tissues than those of transcripts. In addition, only ~2% of all exome and ~7% of all mRNA variants could be confidently detected at the protein level showing that proteogenomics remains challenging, requires rigorous validation using synthetic peptides and needs more sophisticated computational methods. Many uses of this resource can be envisaged ranging from the study of gene/protein expression regulation to protein biomarker specificity evaluation to name a few.

systems biology↗
bioRxiv2017

The largest SWI/SNF polyglutamine domain is a pH sensor

Polyglutamines are known to form aggregates in pathogenic contexts, such as in Huntingtons disease, however little is known about their role in normal biological processes. We found that a polyglutamine domain in the SNF5 subunit of the yeast SWI/SNF complex, histidines within this sequence, and transient intracellular acidification are required for efficient transcriptional regulation during carbon starvation. We hypothesized that a pH-driven oligomerization of the SNF5 polyglutamine region is required for transcriptional reprogramming. In support of this idea, we found that a synthetic spidroin domain from spider silk, which is soluble at pH 7 but oligomerizes at pH ~ 6.3, could partially complement the function of the SNF5 polyglutamine. These results suggest that the SNF5 polyglutamine domain acts as a pH-driven transcriptional regulator.

genetics↗
bioRxiv2018

Metatranscriptomic and thermodynamic insights into medium-chain fatty acid production using an anaerobic microbiome

Biomanufacturing from renewable feedstocks represents a sustainable strategy to offset fossil fuel-based chemical production. One potential biomanufacturing strategy is production of medium chain fatty acids (MCFA) from organic feedstocks using either pure cultures or microbiomes. While the set of microbes in a microbiome can often metabolize more diverse organic materials than a single species, and the role of specific species may be known, knowledge of the carbon and energy flow within and between organisms in MCFA producing microbiomes is only starting to emerge. Here, we integrate metagenomic, metatranscriptomic, and thermodynamic analyses to predict and characterize the metabolic network of an anaerobic microbiome producing MCFA from organic matter derived from lignocellulosic ethanol fermentation conversion residue. A total of 37 high quality (>80% complete, < 10% contamination) metagenome-assembled genomes (MAGs) were recovered from the microbiome and metabolic reconstruction of the 10 most abundant MAGs was performed. Metabolic reconstruction combined with metatranscriptomic analysis predicted that organisms affiliated with Lactobacillus and Coriobacteriaceae degraded carbohydrates and fermented sugars to lactate and acetate. Lachnospiraceae and Eubacteriaceae affiliated organisms were predicted to transform these fermentation products to MCFA. Thermodynamic analyses identified conditions in which H2 is expected to be either produced or consumed, suggesting a potential role of H2 partial pressure on MCFA production. From an integrated systems analysis perspective, we propose that MCFA production could be improved if microbiomes are engineered to use homofermentative instead of heterofermentative Lactobacillus, and if MCFA-producing organisms are engineered to preferentially use a thioesterase instead of a CoA transferase as the terminal enzyme in reverse {beta}-oxidation.\n\nImportanceMixed communities of microbes play important roles in health, the environment, agriculture, and biotechnology. While tapping the combined activity of organisms within microbiomes may allow the utilization of a wider range of substrate over the use of pure cultures for biomanufacturing, harnessing the metabolism of these mixed cultures remains a major challenge. Here, we predict metabolic functions of bacteria in a microbiome that produces medium-chain fatty acids from a renewable feedstock. Our findings lay the foundation to begin addressing how to engineer and control microbiomes for improved biomanufacturing; to build synthetic mixtures of microbes that produce valuable chemicals from renewable resources; and to better understand microbial communities that contribute to health, agriculture, and the environment.

systems biology↗
bioRxiv2016

Active delivery to the brain by chemotaxis

In recent years, scientists have created artificial microscopic and nanoscopic self-propelling particles, often referred to as nano- or micro-swimmers, capable of mimicking biological locomotion and taxis. This active diffusion enables the engineering of complex operations that so far have not been possible at the micro- and nanoscale. One of the most promising task is the ability to engineer nanocarriers that can autonomously navigate within tissues and organs, accessing nearly every site of the human body guided by endogenous chemical gradients. Here we report a fully synthetic, organic, nanoscopic system that exhibits attractive chemotaxis driven by enzymatic conversion of glucose. We achieve this by encapsulating glucose oxidase -- alone or in combination with catalase -- into nanoscopic and biocompatible asymmetric polymer vesicles (known as polymersomes). We show that these vesicles self-propel in response to an external gradient of glucose by inducing a slip velocity on their surface, which makes them move in an extremely sensitive way towards higher concentration regions. We finally demonstrate that the chemotactic behaviour of these nanoswimmers enables a four-fold increase in penetration to the brain compared to non-chemotactic systems.

Bioengineering↗
bioRxiv2017

Redkmer: An assembly-free pipeline for the identification of abundant and specific X-chromosome target sequences for X-shredding by CRISPR endonucleases

CRISPR-based synthetic sex ratio distorters, that operate by shredding the X-chromosome during male meiosis, are promising tools for the area-wide control of harmful insect pest or disease vector species. However, the selection of gRNA targets, in the form of high-copy sequence repeats on the X chromosome of a given species, is difficult since such repeats are not accurately resolved in genome assemblies and cant be assigned to chromosomes with confidence. We have therefore developed the redkmer computational pipeline, designed to identify short and highly-abundant sequence elements occurring uniquely on the X-chromosome. Redkmer was designed to use as input exclusively raw WGS data from males and females. We tested redkmer with suitable short and long read WGS data of An. gambiae, the major vector of human malaria, in which the X-shredding paradigm was originally developed. Redkmer establishes long reads as chromosomal proxies with excellent correlation to the genome assembly and uses them to rank X-candidate kmers for their level of X-specificity and abundance. Redkmer identified a high-confidence set of 25-mers, many of which belong to previously known X-chromosome specific repeats of An. gambiae, including the ribosomal gene array and the selfish genetics elements harbored within it. WGS data from a control strain in which these repeats are also present on the Y chromosome confirmed the elimination of these kmers in the filtering steps. Finally, we show that redkmer output can be linked directly to gRNA selection and can also inform gRNA off-target prediction. The redkmer pipeline is designed to enable the generation of synthetic sex ratio distorters for the control of harmful insect species of medical or agricultural importance. It proceeds from WGS input data to deliver candidate X-specific CRISPR gRNA candidate target sequences. In addition the output of redkmer, including the prediction of chromosomal origin of single-molecule long reads and chromosome specific kmers, could also be used for the characterization of other biologically relevant sex chromosome sequences, a task that is frequently hampered by the repetitiveness of sex chromosome sequence content.

bioinformatics↗