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bioRxiv2017

The role of traction in membrane curvature generation

Curvature of biological membranes can be generated by a variety of molecular mechanisms including protein scaffolding, compositional heterogeneity, and cytoskeletal forces. These mechanisms have the net effect of generating tractions (force per unit length) on the bilayer that are translated into distinct shapes of the membrane. Here, we demonstrate how the local shape of the membrane can be used to infer the traction acting locally on the membrane. We show that buds and tubes, two common membrane deformations studied in trafficking processes, have different traction distributions along the membrane and that these tractions are specific to the molecular mechanism used to generate these shapes. Furthermore, we show that the magnitude of an axial force applied to the membrane as well as that of an effective line tension can be calculated from these tractions. Finally, we consider the sensitivity of these quantities with respect to uncertainties in material properties and follow with a discussion on sources of uncertainty in membrane shape.

biophysics↗
bioRxiv2017

Airway IRF7hi versus IRF7lo molecular response patterns determine clinical phenotypes in children with acute wheezing

Asthma exacerbations are triggered by rhinovirus infections. We employed a systems biology approach to delineate upper airway gene network patterns underlying asthma exacerbation phenotypes in children. Cluster analysis unveiled distinct IRF7hi versus IRF7lo molecular phenotypes, the former exhibiting robust upregulation of Th1/type I interferon responses and the latter an alternative signature marked by upregulation of cytokine and growth factor signalling and downregulation of interferon gamma. The two phenotypes also produced distinct clinical phenotypes. For IRF7lo versus IRF7hi: symptom duration prior to hospital presentation was more than twice as long from initial symptoms (p=0.011) and nearly three times as long for cough (p<0.001); the odds ratio of admission to hospital was increased more than four-fold (p=0.018); and time to recurrence was shorter (p=0.015). In summary, our findings demonstrate that asthma exacerbations in children can be divided into IRF7hi versus IRF7lo phenotypes with associated differences in clinical phenotypes.\n\nAbbreviationsAHR, airway hyperresponsiveness; ARG1, Arginase 1, CSF3, Colony Stimulating Factor 3; CD38, Cluster of Differentiation 38; CD163, Cluster of Differentiation 163; cDCs, conventional (or myeloid) dendritic cells; DDX60, DExD/H-Box Helicase 60; ED, Emergency Department; EGF, Epidermal Growth Factor; ERK, Extracellular signal-Regulated Kinase; FCER1G, Fc Fragment Of IgE Receptor Ig; HMBS, Hydroxymethylbilane Synthase; IFNg, Interferon Gamma; IFNL1, Interferon Lambda 1; IL-1R2, Interleukin 1 Receptor Type 2; IRF7, Interferon Regulatory Factor 7; ISG15, Interferon-stimulated gene 15; MDA5, Melanoma Differentiation-Associated protein 5; MX1, Myxovirus Resistance Protein 1; NAD, nicotinamide adenine dinucleotide; NCR1, Natural cytotoxicity triggering receptor 1; OSM, Oncostatin M; PD-L1, Programmed Death-Ligand 1; PPIA, Peptidylprolyl Isomerase A; PPIB Peptidylprolyl Isomerase B; RSAD2, Radical S-adenosyl methionine domain-containing protein 2; RSV, respiratory syncytial virus; RT-qPCR, quantitative reverse transcription PCR; RV, rhinovirus; sPLA2, secretory Phospholipase A2; TGFb, Transforming Growth Factor beta; THBS1, Thrombospondin 1; TNF, Tumor Necrosis Factor; TLR2, Toll-like Receptor 2.

cell biology↗
bioRxiv2016

Detection of statistically significant network changes in complex biological networks

1 MotivationBiological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localised re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to discovery of novel relevant signatures.\n\n2 ResultsSeveral procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks. In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation. In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates.\n\n3 AvailabilityThe scripts implementing the proposed algorithms are available in R at https://sites.google.com/site/raghvendramallmlresearcher/codes.\n\n4 Contactrmall@qf.org.qa

Bioinformatics↗
bioRxiv2018

Topological data analysis quantifies biological nano-structure from single molecule localization microscopy

The study of complex molecular organisation and nano-structure by localization based microscopy is limited by the available analysis tools. We present a segmentation protocol which, through the application of persistence based clustering, is capable of probing densely packed structures which vary in scale. An increase in segmentation performance over state-of-the-art methods is demonstrated. Moreover we employ persistent homology to move beyond clustering, and quantify the topological structure within data. This provides new information about the preserved shapes formed by molecular architecture. Our methods are flexible and we demonstrate this by applying them to receptor clustering in platelets, nuclear pore components and endocytic proteins. Both 2D and 3D implementations are provided within RSMLM, an R package for pointillist based analysis and batch processing of localization microscopy data.

bioinformatics↗
bioRxiv2016

The evolution of coercive policing in genetically mixed groups: the case of plasmid copy number control

Policing is a widespread mechanism regulating cooperation in both human and animal social groups. Policing can promote the evolution and maintenance of cooperation among non-relatives by tying the reproductive success of individuals to the efficiency and success of the group. In this paper, we investigate the evolution of reproductive policing using a multi-scale computational model inspired by the copy number control system of conjugative bacterial plasmids. Our results show that the repression of competition through policing can evolve across a very broad range of migration (plasmid conjugation) rates, improving system-level performance and bringing efficiency gains to the group beyond those achievable by pure self-restraint. Reproductive policing acts to increase genetic relatedness by reducing variation in group size which, in turn, reduces the heterogeneity of the plasmid population. When among-group migration is high, coercive policing strategies are favoured, characterized by high levels of policing coupled with relatively lower obedience. Coercive policing strategies preferentially limit the reproduction of rival lineages while, at the same time, maintaining effective collective reproductive control.\n\nAuthor SummaryThe emergence and maintenance of cooperation is a topic of great importance in evolutionary biology. The evolution of cooperation has been explained in the context of kin selection when there is sufficient genetic relatedness among interacting individuals. When there is insufficient relatedness, the presence of alternative mechanisms, such as mutual policing, can promote the evolution and maintenance of cooperation by tying the reproductive success of individual to the efficiency and success of the group. In this paper, we investigate the evolution of reproductive policing using an agent-based computational model inspired by a simple and elegant biological example: replication control among conjugative plasmids, a class of molecular symbionts of bacterial hosts. Our results show that the repression of competition through policing evolves and improves plasmid group performance beyond levels achievable by self-restraint, across a very broad range of migration rates. Under conditions of high migration (frequent conjugation), we observe the evolution of coercive policing strategies that limit the reproduction of rival lineages by investing disproportionately in policing relative to their obedience to the policing trait.

Evolutionary Biology↗
bioRxiv2018

A bHLH-PAS protein regulates light-dependent rhythmic processes in the marine diatom Phaeodactylum tricornutum

Periodic light-dark cycles govern the timing of basic biological processes in organisms inhabiting land as well as the sea, where life evolved. Although prominent marine phytoplanktonic organisms such as diatoms show robust diurnal rhythms in growth, cell cycle and gene expression, the molecular foundations controlling these processes are still obscure. By exploring the regulatory landscape of diatom diurnal rhythms, we unveil the function of a Phaeodactylum tricornutum bHLH-PAS protein, PtbHLH1a, in the regulation of light-dependent diurnal rhythms. Peak expression of PtbHLH1a mRNA occurs toward the end of the light period and it adjusts to photoperiod changes. Ectopic over-expression of PtbHLH1a results in lines showing a phase shift in diurnal cell fluorescence, compared to the wild-type cells, and with altered cell cycle progression and gene expression. Reduced oscillations in gene expression are also observed in overexpression lines compared to wild-type in continuous darkness, showing that the regulation of rhythmicity by PtbHLH1a is not directly dependent on light inputs and cell division. PtbHLH1a homologs are widespread in diatom genomes which may indicate a common function in many species. This study adds new elements to understand diatom biology and ecology and offers new perspectives to elucidate timekeeping mechanisms in marine organisms belonging to a major, but underinvestigated branch of the tree of life.\n\nSIGNIFICANCE STATEMENTMost organisms experience diurnal light-dark changes and show rhythms of basic biological processes such that they occur at optimal times of the day. The ocean harbours a huge diversity of organisms showing light-dependent rhythms, but their molecular foundations are still largely unknown. In this study, we discover a novel protein, PtbHLH1a that regulates cell division, gene expression and the diurnal timing of these events in the marine diatom Phaedoactylum tricornutum. The identification of PtbHLH1a-like genes in many diatom species suggests a conserved function in diurnal rhythm regulation in the most species-rich group of algae in the ocean. This study unveils critical features of diatom biology and advances the field of marine rhythms and their environmental regulation.

molecular biology↗
bioRxiv2018

Genome-wide disruption of DNA methylation by 5-aza-2’-deoxycytidine in the parasitioid wasp Nasonia vitripennis

DNA methylation of cytosine residues across the genome influences how genes and phenotypes are regulated in a wide range of organisms. As such, understanding the role of DNA methylation and other epigenetic mechanisms has become very much a part of mapping genotype to phenotype, a major question in evolutionary biology. Ideally, we would like to manipulate DNA methylation patterns on a genome-wide scale, to help us to elucidate the role that epigenetic modifications play in phenotypic expression. Recently, the demethylating agent 5-aza-2-deoxycytidine (5-aza-dC; commonly used in the epigenetic treatment of certain cancers), has been deployed to explore the epigenetic regulation of a number of traits of interest to evolutionary ecologists, including facultative sex allocation in the parasitoid wasp Nasonia vitripennis. In a recent study, we showed that treatment with 5-aza-dC did not ablate the facultative sex allocation response in Nasonia, but shifted the patterns of sex allocation in a way predicted by genomic conflict theory. This was the first (albeit indirect) experimental evidence for genomic conflict over sex allocation facilitated by DNA methylation. However, that work lacked direct evidence of the effects of 5-aza-dC on DNA methylation, and indeed the effect of the chemical has since been questioned in Nasonia. Here, using whole-genome bisulphite sequencing of more than 4 million CpGs, across more than 11,000 genes, we demonstrate unequivocally that 5-aza-dC disrupts methylation on a large scale across the Nasonia vitripennis genome. We show that the disruption can lead to both hypo- and hyper-methylation, may vary across tissues and time of sampling, and that the effects of 5-aza-dC are context- and sequence specific. We conclude that 5-aza-dC does indeed have the potential to be repurposed as a tool for studying the role of DNA methylation in evolutionary ecology, whilst many details of its action remain to be discovered. Author SummaryShedding light on the mechanistic basis of phenotypes is a major aim in the field of evolutionary biology. If we understand how phenotypes are controlled at the molecular level, we can begin to understand how evolution has shaped that phenotype and conversely, how genetic architecture may constrain trait evolution. Epigenetic markers (such as DNA methylation) also influence phenotypic expression by regulating how and when genes are expressed. Recently, 5-aza-2-deoxycytidine (5-aza-dC), a hypomethylating agent used in the treatment of certain cancers, has been used to explore the epigenetic regulation of traits of interest to evolutionary ecologists. Previously, we used 5-aza-dC to validate a role for DNA methylation in facultative sex allocation behaviour in the parasitoid wasp Nasonia vitripennis. However, the direct effects of the chemical were not examined at that point and its efficacy in insects was questioned. Here, we demonstrate that 5-aza-dC disrupts DNA methylation on a genome-wide scale in a context- and sequence-specific manner and results in both hypo- and hyper-methylation. Our work demonstrates that 5-aza-dC has the potential to be repurposed as a tool for studying the role of DNA methylation in phenotypic expression.

evolutionary biology↗
bioRxiv2017

Learning causal biological networks with generalized Mendelian randomization

Although large amounts of genomic data are available, it remains a challenge to reliably infer causal (i.e., regulatory) relationships among molecular phenotypes (such as gene expression), especially when many phenotypes are involved. We extend the interpretation of the Principle of Mendelian randomization (PMR) and present MRPC, a novel machine learning algorithm that incorporates the PMR in classical algorithms for learning causal graphs in computer science. MRPC learns a causal biological network efficiently and robustly from integrating genotype and molecular phenotype data, in which directed edges indicate causal directions. We demonstrate through simulation that MRPC outperforms existing general-purpose network inference methods and other PMR-based methods. We apply MRPC to distinguish direct and indirect targets among multiple genes associated with expression quantitative trait loci.

bioinformatics↗
bioRxiv2018

Ultrasensitive molecular controllers for quasi-integral feedback

Feedback control has enabled the success of automated technologies by mitigating the effects of variability, unknown disturbances, and noise. Similarly, feedback loops in biology reduce the impact of noise and help shape kinetic responses, but it is still unclear how to rationally design molecular controllers that approach the performance of controllers in traditional engineering applications, in particular the performance of integral controllers. Here, we describe a strategy to build molecular quasi-integral controllers by following two design principles: (1) a highly ultrasensitive response, which guarantees a small steady-state error, and (2) a tunable ultrasensitivity threshold, which determines the system equilibrium point (reference). We describe a molecular reaction network, which we name Brink motif, that satisfies these requirements by combining sequestration and an activation/deactivation cycle. We show that if ultrasensitivity conditions are satisfied, this motif operates as a quasi-integral controller and promotes homeostatic behavior of the closed-loop system (robust tracking of the input reference while rejecting disturbances). We propose potential biological implementations of Brink controllers and we illustrate different example applications with computational models.

synthetic biology↗
bioRxiv2017

The ubiquity of directional and reciprocating motion in enzymes out of equilibrium

Molecular motors are thought to generate force and directional motion via nonequilibrium switching between energy surfaces. Because all enzymes can undergo such switching, we hypothesized that the ability to generate rotary motion and torque is not unique to highly adapted biological motor proteins, but is instead a common feature of enzymes. We used molecular dynamics simulations to compute energy surfaces for hundreds of torsions in three enzymes, adenosine kinase, protein kinase A, and HIV-1 protease, and used these energy surfaces within a kinetic model that accounts for intersurface switching and intrasurface probability flows. When substrate is out of equilibrium with product, we find computed torsion rotation rates up ~140 cycle s-1, with stall torques up to ~2 kcal mol-1 cycle-1, and power outputs up to ~50 kcal mol-1 s-1. We argue that these enzymes are instances of a general phenomenon of directional probability flows on asymmetric energy surfaces for systems out of equilibrium. Thus, we conjecture that cyclic probability fluxes, corresponding to rotations of torsions and higher-order collective variables, exist in any chiral molecule driven between states in a non-equilibrium manner; we call this the Asymmetry-Directionality conjecture. This is expected to apply as well to synthetic chiral molecules switched in a nonequilibrium manner between energy surfaces by light, redox chemistry, or catalysis.

biophysics↗
bioRxiv2018

Calcium negatively regulates secretion from dense granules in Toxoplasma gondii.

Apicomplexan parasites including Toxoplasma gondii and Plasmodium spp. manufacture a complex arsenal of secreted proteins used to interact with and manipulate their host environment. These proteins are organised into three principle exocytotic compartment types according to their functions: micronemes for extracellular attachment and motility, rhoptries for host cell penetration, and dense granules for subsequent manipulation of the host intracellular environment. The order and timing of these events during the parasites invasion cycle dictates when exocytosis from each compartment occurs. Tight control of compartment secretion is, therefore, an integral part of apicomplexan biology. Control of microneme exocytosis is best understood, where cytosolic intermediate molecular messengers cGMP and Ca2+ act as positive signals. The mechanisms for controlling secretion from rhoptries and dense granules, however, are virtually unknown. Here, we present evidence that dense granule exocytosis is negatively regulated by cytosolic Ca2+, and we show that this Ca2+-mediated response is contingent on the function of calcium-dependent protein kinases TgCDPK1 and TgCDPK3. Reciprocal control of micronemes and dense granules provides an elegant solution to the mutually exclusive functions of these exocytotic compartments in parasite invasion cycles and further demonstrates the central role that Ca2+ signalling plays in the invasion biology of apicomplexan parasites.

cell biology↗
bioRxiv2017

A large kinome in a large cell: Stentor coeruleus possesses highly expanded kinase families and novel domain architectures

BackgroundStentor coeruleus is a large ciliated protist, about 1mm in length, with the extraordinary ability to fully regenerate each fragment after being cut into pieces, perfectly restoring cell polarity and morphology. Single-cell regeneration in Stentor remains one of the greatest long-standing mysteries of biology, but the recently published Stentor genome now enables studies on this organism at the molecular and genetic levels. Here we characterize the complete complement of kinases, or kinome, of Stentor, in order to begin to understand the signaling capacities that underlie Stentors unique biology.\n\nResultsThe genome of S. coeruleus contains over 2000 kinases, representing 6% of the predicted proteome. Classification of the kinase genes reveals large expansions in several kinase families, particularly in the CDPKs, the DYRKs, and in several mitotic kinase families including the PLKs, NEKs, and Auroras. The large expansion of the CDPK and DYRK kinase families is an unusual feature of the Stentor kinome compared to other ciliates with sequenced genomes. The DYRK family in Stentor, notably, contains only a single pseudokinase which may suggest an important role in Stentor growth and survival, while the smaller PEK family contains a novel pseudokinase subfamily. The Stentor kinome also has examples of new domain architectures that have not been previously observed in other organisms.\n\nConclusionOur analysis provides the first gene-level view into the signaling capabilities of Stentor and will lay the foundation for unraveling how this organism can coordinate processes as complex as regeneration throughout a giant cell.

genomics↗
bioRxiv2016

Stochasticity in the Genotype-Phenotype Map: Implications for the Robustness and Persistence of Bet-Hedging

For the last few decades modern biology has focused on quantifying, understanding and mapping the genetic characteristics of cells. This genotype-driven perspective has led to significant advances in our understanding and treatment of diseases such as cancer e.g. the discovery of driver mutations and the development of molecularly-targeted therapeutics. However, this perspective has largely ignored the functional outcome of genetic changes: the cellular phenotype. In part, this is simply because phenotypes are neither easy to define or measure as they critically depend on both genotype and context. Heterogeneity at the gene scale has been known for sometime, and there has been significant effort invested in trying to find patterns within it, but much less is understood about how this heterogeneity manifests itself in phenotypic change, i.e. the genotype-phenotype map (GP-map). This mapping is not one-to-one but many-to-many and is fundamentally the junction at which both genes and environment meet to produce phenotypes. Many genotypes produce similar phenotypes, and multiple phenotypes can emerge from a single genotype. To further complicate matters, genetically identical cells in uniform environments still exhibit phenotypic heterogeneity. Therefore a central open question in biology today is how can we connect the abundance of genomic data with cell phenotypic behaviour, this is especially pertinent to the issue of treatment resistance as many therapies act on cellular phenotypes.\n\nOur focus here is to tackle the GP-map question through the use of the simplest functional mapping we can define that also captures phenotypic heterogeneity: a molecular switch. Molecular switches are ubiquitous in biology, observed in many organisms and naturally map molecular components to decisions (i.e. phenotypes). Often stochastic in nature, such switches can be the difference between life or death in environments that fluctuate unpredictably, since they will ensure that at least some offspring are adapted to future environments. For convenience we use Chemical Reaction Networks (CRNs) to define the map of gene products to phenotypes, allowing us to investigate the impact of distinct mappings (CRNs) and perturbations to them. We observe that key biological properties naturally emerge, including both robustness and persistence. Robustness may explain why such bet hedging strategies are common in biology, and not readily destroyed through mutation. Whereas persistence may explain the apparent paradox of bet-hedging - why does phenotypic hedging exist in environments beneficial to only one of the phenotypes, when selection necessarily acts against it? The structure of the molecular switch, itself subject to selection, can slow the loss of hedging to ensure a survival mechanism even against environmental catastrophes which are very rare. Critically, these properties when taken together have profound and significant implications for the emergence of treatment resistance, since the timescale of extinction depends heavily on the underlying GP-map.

Evolutionary Biology↗
bioRxiv2018

Methylation patterns reveal cryptic structure and a pathway for adaptation in a panmictic carnivore

Determining the molecular signatures of adaptive differentiation is a fundamental component of evolutionary biology. A key challenge remains for identifying such signatures in wild organisms, particularly between populations of highly mobile species that undergo substantial gene flow. The Canada lynx (Lynx canadensis) is one species where mainland populations appear largely undifferentiated at traditional genetic markers, despite inhabiting diverse environments and displaying phenotypic variation. Here, we used high-throughput sequencing to investigate both neutral genetic structure and epigenetic differentiation across the distributional range of Canada lynx. Using a customized bioinformatics pipeline we scored both neutral SNPs and methylated nucleotides across the lynx genome. Newfoundland lynx were identified as the most differentiated population at neutral genetic markers, with diffusion approximations of allele frequencies indicating that divergence from the panmictic mainland occurred at the end of the last glaciation, with minimal contemporary admixture. In contrast, epigenetic structure revealed hidden levels of differentiation across the range coincident with environmental determinants including winter conditions, particularly in the peripheral Newfoundland and Alaskan populations. Several biological pathways related to morphology were differentially methylated between populations, with Newfoundland being disproportionately methylated for genes that could explain the observed island dwarfism. Our results indicate that epigenetic modifications, specifically DNA methylation, are powerful markers to investigate population differentiation and functional plasticity in wild and non-model systems.\n\nSIGNIFICANCEPopulations experiencing high rates of gene flow often appear undifferentiated at neutral genetic markers, despite often extensive environmental and phenotypic variation. We examined genome-wide genetic differentiation and DNA methylation between three interconnected regions and one insular population of Canada lynx (Lynx canadensis) to determine if epigenetic modifications characterized climatic associations and functional molecular plasticity. Demographic approximations indicated divergence of Newfoundland during the last glaciation, while cryptic epigenetic structure identified putatively functional differentiation that might explain island dwarfism. Our study suggests that DNA methylation is a useful marker for differentiating wild populations, particularly when faced with functional plasticity and low genetic differentiation.

genomics↗
bioRxiv2013

A filter-flow perspective of hematogenous metastasis offers a non-genetic paradigm for personalized cancer therapy

Translational RelevanceSince the discovery of circulating tumor cells (CTC), we have struggled for ways to use them to inform treatment. The only currently accepted method for this is a more is worse paradigm by which clinicians measure CTC burden before and after treatment to assess efficacy. Research efforts are currently focused almost entirely on genetic classification of these cells, which has yet to bear any fruit translationally. We suggest that we should shift the focus of our investigation to one driven by a physical sciences perspective. Specifically, by understanding the vascular system as a network of interconnected organs and capillary beds as filters that capture CTCs. By ascertaining the distribution of CTCs in this network for individual patients, information about the existence of subclinical metastatic disease, and therefore metastatic propensity, will come to light, and allow for better staging, prognostication and rational use of organ-directed therapy in the setting of oligometastatic disease.\n\nAbstractO_ST_ABSPurposeC_ST_ABSResearch into mechanisms of hematogenous metastasis has largely become genetic in focus, attempting to understand the molecular basis of seed-soil relationships. However, preceding this biological mechanism is the physical process of dissemination of circulating tumour cells (CTCs) in the circulatory network. We utilize a novel, network perspective of hematogenous metastasis and a large dataset on metastatic patterns to shed new light on this process.\n\nExperimental DesignThe metastatic efficiency index (MEI), previously suggested by Weiss, quantifies the process of hematogenous metastasis by taking the ratio of metastatic incidence for a given primary-target organ pair and the relative blood flow between the two sites. In this paper we extend the methodology by taking into account the reduction in CTC number that occurs in capillary beds and a novel network model of CTC flow.\n\nResultsBy applying this model to a dataset of metastatic incidence, we show that the MEI depends strongly on the assumptions of micrometastatic lesions in the lung and liver. Utilizing this framework we can represent different configurations of metastatic disease and offer a rational method for identifying patients with oligometastatic disease for inclusion in future trials.\n\nConclusionsWe show that our understanding of the dynamics of CTC flow is significantly lacking, and that this specifically precludes our ability to predict metastatic patterns in individual patients. Our formalism suggests an opportunity to go a step further in metastatic disease characterization by including the distribution of CTCs at staging, offering a rational method of trial design for oligometastatic disease.

Cancer Biology↗
bioRxiv2015

Automatic segmentation of Drosophila neural compartments using GAL4 expression data reveals novel visual pathways

We made use of two recent, large-scale Drosophila GAL4 libraries and associated confocal imaging datasets to automatically segment large brain regions into smaller putative functional units such as neuropils and fiber tracts. The method we developed is based on the hypothesis that molecular identity can be used to assign individual voxels to biologically meaningful regions. Our results (available at https://strawlab.org/braincode) are consistent with this hypothesis because regions with well-known anatomy, namely the antennal lobes and central complex, were automatically segmented into familiar compartments. We then applied the algorithm to the central brain regions receiving input from the optic lobes. Based on the automated segmentation and manual validation, we can identify and provide promising driver lines for 10 previously identified and 14 novel types of visual projection neurons and their associated optic glomeruli. The same strategy can be used in other brain regions and likely other species, including vertebrates.

Neuroscience↗
bioRxiv2016

Towards Consensus Gene Ages

Correctly estimating the age of a gene or gene family is important for a variety of fields, including molecular evolution, comparative genomics, and phylogenetics, and increasingly for systems biology and disease genetics. However, most studies use only a point estimate of a genes age, neglecting the substantial uncertainty involved in this estimation. Here, we characterize this uncertainty by investigating the effect of algorithm choice on gene-age inference and calculate consensus gene ages with attendant error distributions for a variety of model eukaryotes. We use thirteen orthology inference algorithms to create gene-age datasets and then characterize the error around each age-call on a per-gene and per-algorithm basis. Systematic error was found to be a large factor in estimating gene age, suggesting that simple consensus algorithms are not enough to give a reliable point estimate. We also found that different sources of error can affect downstream analyses, such as gene ontology enrichment. Our consensus gene-age datasets, with associated error terms, are made fully available at so that researchers can propagate this uncertainty through their analyses (https://github.com/marcottelab/Gene-Ages).

Genomics↗
bioRxiv2017

Millisecond Dynamics Of BTK Reveal Kinome-Wide Conformational Plasticity Within The Apo Kinase Domain

Bruton tyrosine kinase (BTK) is a key enzyme in B-cell development whose improper regulation causes severe immunodeficiency diseases. Design of selective BTK therapeutics would benefit from improved, in-silico structural modeling of the kinases solution ensemble. However, this remains challenging due to the immense computational cost of sampling events on biological timescales. In this work, we combine multi-millisecond molecular dynamics (MD) simulations with Markov state models (MSMs) to report on the thermodynamics, kinetics, and accessible states of BTKs kinase domain. Our conformational landscape links the active state to several inactive states, connected via a structurally diverse intermediate. Our calculations predict a kinome-wide conformational plasticity, and indicate the presence of several new potentially druggable BTK states. We further find that the population of these states and the kinetics of their inter-conversion are modulated by protonation of an aspartate residue, establishing the power of MD & MSMs in predicting effects of chemical perturbations.

biophysics↗