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Results for “Developmental 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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Coupling of Rho family GTPases during mesenchymal-to-epithelial-like transitions

Many metazoan developmental processes require cells to transition between migratory mesenchymal- and adherent epithelial-like states. These transitions require Rho GTPase-mediated actin rearrangements downstream of integrin and cadherin pathways. A regulatory toolbox of GEF and GAP proteins precisely coordinates Rho protein activities, yet defining the involvement of specific regulators within a cellular context remains a challenge due to overlapping and coupled activities. Here we demonstrate that Drosophila dorsal closure is a simple, powerful model for Rho GTPase regulation during leading edge to cadherin contact transitions. During these transitions a Rac GEF elmo-dock complex regulates both lamellipodia and Rho1-dependent, actomyosin-mediated tension at initial cadherin contacts. Moreover, the Drosophila Rho GAP arhgap21 ortholog controls Rac and Rho GTPases during the same processes and genetically regulates the elmo-dock complex. This study presents a fresh framework to understand the inter-relationship between GEF and GAP proteins that tether Rac and Rho cycles during developmental processes.

cell biology↗
bioRxiv2017

Signatures of the evolution of parthenogenesis and cryptobiosis in the genomes of panagrolaimid nematodes

Most animal species reproduce sexually, but parthenogenesis, asexual reproduction of various forms, has arisen repeatedly. Parthenogenetic lineages are usually short lived in evolution; though in some environments parthenogenesis may be advantageous, avoiding the cost of sex. Panagrolaimus nematodes have colonised environments ranging from arid deserts to arctic and antarctic biomes. Many are parthenogenetic, and most have cryptobiotic abilities, being able to survive repeated complete desiccation and freezing. It is not clear which genomic and molecular mechanisms led to the successful establishment of parthenogenesis and the evolution of cryptobiosis in animals in general. At the same time, model systems to study these traits in the laboratory are missing.\n\nWe compared the genomes and transcriptomes of parthenogenetic and sexual Panagrolaimus able to survive crybtobiosis, as well as a non-cryptobiotic Propanogrolaimus species, to identify systems that contribute to these striking abilities. The parthenogens are most probably tripoids originating from hybridisation (allopolyploids). We identified genomic singularities like expansion of gene families, and selection on genes that could be linked to the adaptation to cryptobiosis. All Panagrolaimus have acquired genes through horizontal transfer, some of which are likely to contribute to cryptobiosis. Many genes acting in C. elegans reproduction and development were absent in distant nematode species (including the Panagrolaimids), suggesting molecular pathways cannot directly be transferred from the model system.\n\nThe easily cultured Panagrolaimus nematodes offer a system to study developmental diversity in Nematoda, the molecular evolution of parthenogens, the effects of triploidy on genomes stability, and the origin and biology of cryptobiosis.

evolutionary biology↗
bioRxiv2017

Deciphering the dynamical origin of mixed population during neural stem cell developmental lineage commitment

Neural stem cells (NSC's) often give rise to mixed population of cells during differentiation. However, the dynamical origin of these mixed states is poorly understood. In this article, our mathematical modeling study demonstrates that the bone morphogenetic protein 2 (BMP2) driven differential differentiation dynamics of NSC's in central and peripheral nervous systems essentially function through two distinct bi-stable switches that are mutually interconnected. Stochastic simulations of the model reveal that the mixed population originates due to the existence of these bistable switching regulations while the maintenance of such mixed states depends on the level of stochastic fluctuations of the system. Importantly, the model predicts that by individually altering the expression level of key regulatory proteins, the NSC's can be converted entirely to a preferred phenotype for BMP2 doses that earlier resulted into mixed population. Our findings show that efficient neuronal regeneration can be achieved by systematically maneuvering the differentiation dynamics.\n\nOne-sentence summaryUnraveling the differential dynamical origin, maintenance and escape route of mixed population in the midst of developmental fate commitment in central and peripheral nervous systems

systems biology↗
bioRxiv2018

Highly dynamic chromatin interactions drive neurogenesis through gene regulatory networks

Lineage commitment is a fundamental process that enables the morphogenesis of multicellular organisms from a single pluripotent cell. While many genes involved in the commitment to specific lineages are known, the logic of their joint action is incompletely understood, and predicting the effects of genetic perturbations on lineage commitment is still challenging. Here, we devised a gene regulatory network analysis approach, GRN-loop, to identify key cis-regulatory DNA elements and transcription factors that drive lineage commitment. GRN-loop is based on signal propagation and combines transcription factor binding data with the temporal profiles of gene expression, chromatin state and 3D chromosomal architecture. Applying GRN-loop to a model of morphogen-induced early neural lineage commitment, we discovered a set of driver transcription factors and enhancers, some of them validated in recent data and others hitherto unknown. Our work provides the basis for an integrated understanding of neural lineage commitment, and demonstrates the potential of gene regulatory network analyses informed by 3D chromatin architecture to uncover the key genes and regulatory elements driving developmental processes.

molecular biology↗
bioRxiv2018

Pathway and network analysis of more than 2,500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

cancer biology↗
bioRxiv2018

Pathogenic DDX3X mutations impair RNA metabolism and neurogenesis during fetal cortical development

De novo germline mutations in the RNA helicase DDX3X account for 1-3% of unexplained intellectual disability (ID) cases in females, and are associated with autism, brain malformations, and epilepsy. Yet, the developmental and molecular mechanisms by which DDX3X mutations impair brain function are unknown. Here we use human and mouse genetics, and cell biological and biochemical approaches to elucidate mechanisms by which pathogenic DDX3X variants disrupt brain development. We report the largest clinical cohort to date with DDX3X mutations (n=78), demonstrating a striking correlation between recurrent dominant missense mutations, polymicrogyria, and the most severe clinical outcomes. We show that Ddx3x controls cortical development by regulating neuronal generation and migration. Severe DDX3X missense mutations profoundly disrupt RNA helicase activity and induce ectopic RNA-protein granules and aberrant translation in neural progenitors and neurons. Together, our study demonstrates novel mechanisms underlying DDX3X syndrome, and highlights roles for RNA-protein aggregates in the pathogenesis of neurodevelopmental disease.

genetics↗
bioRxiv2018

Predicting One-Year Outcome in First Episode Psychosis using Machine Learning

Lay SummaryO_ST_ABSEvidence before this studyC_ST_ABSOur knowledge of factors which predict outcome in first episode psychosis (FEP) is incomplete. Poor premorbid adjustment, history of developmental disorder, symptom severity at baseline and duration of untreated psychosis are the most replicated predictors of poor clinical, functional, cognitive, and biological outcomes. Yet, such group level differences are not always replicated in individuals, nor can observational results be clearly equated with causation. Advanced machine learning techniques have potential to revolutionise medicine by looking at causation and the prediction of individual patient outcome. Within psychiatry, Koutsouleris et al employed machine learning to predict 4- and 52-week functional outcome in FEP to a 75% and 73.8% test-fold balanced accuracy on repeated nested internal cross-validation. The authors suggest that before employing a machine learning model \"into real-world care, replication is needed in external first episode samples\".\n\nAdded value of this studyWe believe our study to be the first externally validated evidence, in a temporally and geographically independent cohort, for predictive modelling in FEP at an individual patient level. Our results demonstrate the ability to predict both symptom remission and functioning (in employment, education or training (EET)) at one-year. The performance of our EET model was particularly robust, with an ability to accurately predict the one-year EET outcome in more than 85% of patients. Regularised regression results in sparse models which are uniquely interpretable and identify meaningful predictors of recovery including specific individual PANSS items, and social support. This builds on existing studies of group-level differences and the elegant work of Koutsouleris et al.\n\nImplications of all the available evidenceWe have demonstrated the externally validated ability to accurately predict one-year symptomatic and functional status in individual patients with FEP. External validation in a plausibly related temporally and geographically distinct population assesses model transportability to an untested situation rather than simply reproducibility alone. We propose that our results represent important and exciting progress in unlocking the potential of predictive modelling in psychiatric illness. The next step prior to implementation into routine clinical practice would be to establish whether, by the accurate identification of individuals who will have poor outcomes, we can meaningful intervene to improve their prognosis.\n\nAbstractO_ST_ABSBackgroundC_ST_ABSEarly illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes.\n\nWe use routinely collected baseline demographic and clinical characteristics to predict employment, education or training (EET) status, and symptom remission in patients with first episode psychosis (FEP) at 1 year.\n\nMethods83 FEP patients were recruited from National Health Service (NHS) Glasgow between 2011 and 2014 to a 24-month prospective cohort study with regular assessment of demographic and psychometric measures. An external independent cohort of 79 FEP patients were recruited from NHS Glasgow and Edinburgh during a 12-month study between 2006 and 2009.\n\nElastic net regularised logistic regression models were built to predict binary EET status, period and point remission outcomes at 1 year on 83 Glasgow patients (training dataset). Models were externally validated on an independent dataset of 79 patients from Glasgow and Edinburgh (validation dataset). Only baseline predictors shared across both cohorts were made available for model training and validation.\n\nOutcomesAfter excluding participants with missing outcomes, models were built on the training dataset for EET status, period and point remission outcomes and externally validated on the validation dataset. Models predicted EET status, period and point remission with ROC area under curve (AUC) performances of 0.876 (95%CI: 0.864, 0.887), 0.630 (95%CI: 0.612, 0.647) and 0.652 (95%CI: 0.635, 0.670) respectively. Positive predictors of EET included baseline EET and living with spouse/children. Negative predictors included higher PANSS suspiciousness, hostility and delusions scores. Positive predictors for symptom remission included living with spouse/children, and affective symptoms on the Positive and Negative Syndrome Scale (PANSS). Negative predictors of remission included passive social withdrawal symptoms on PANSS.\n\nInterpretationUsing advanced statistical machine learning techniques, we provide the first externally validated evidence for the ability to predict 1-year EET status and symptom remission in FEP patients.\n\nFundingThe authors acknowledge financial support from NHS Research Scotland, the Chief Scientist Office, the Wellcome Trust, and the Scottish Mental Health Research Network.

neuroscience↗
bioRxiv2018

A robust nonlinear low-dimensional manifold for single cell RNA-seq data

Modern developments in single cell sequencing technologies enable broad insights into cellular state. Single cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, and developmental trajectories to broaden understanding of cell heterogeneity in tissues and organs. Analysis of these sparse, high-dimensional experimental results requires dimension reduction. Several methods have been developed to estimate low-dimensional embeddings for filtered and normalized single cell data. However, methods have yet to be developed for unfiltered and unnormalized count data. We present a nonlinear latent variable model with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensional nonlinear structure in scRNA-seq data. Gene expression in a single cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional latent positions. This model is called the Gaussian process latent variable model (GPLVM). We model residual errors with a heavy-tailed Students t-distribution to estimate a manifold that is robust to technical and biological noise. We compare our approach to common dimension reduction tools to highlight our models ability to enable important downstream tasks, including clustering and inferring cell developmental trajectories, on available experimental data. We show that our robust nonlinear manifold is well suited for raw, unfiltered gene counts from high throughput sequencing technologies for visualization and exploration of cell states.

genomics↗
bioRxiv2018

Dynamic enhancer partitioning instructs activation of a growth regulator during exit from naïve pluripotency

During early mammalian development, the genome undergoes profound transitions in chromatin states, topological organization and recruitment of cis regulatory factors involved in transcriptional control. How these three layers of gene regulation interact is the matter of intense research. The Zdbf2 gene--which is involved in growth control--provides a valuable model to study this question: upon exit from naive pluripotency and prior to tissue differentiation, it undergoes a switch in usage from a distal to a proximal promoter, along with a switch in chromatin states, from polycomb to DNA methylation occupancy. Using an embryonic stem cell (ESC) culture system to mimic this period, we show here that four enhancers contribute to the Zdbf2 promoter switch, concomitantly with dynamic changes in chromosome architecture. Indeed, CTCF plays a key role in partitioning the locus in ESCs, to facilitate enhancer contact with the distal Zdbf2 promoter only. Partition relieving enhances proximal Zdbf2 promoter activity, as observed during differentiation or with mutants that lack local CTCF-based partition. Importantly, we show that CTCF-based regulation occurs independently of the polycomb and DNA methylation pathways. Our study reveals the importance of multi-layered regulatory frameworks to ensure proper spatio-temporal activation of developmentally important genes.

molecular biology↗
bioRxiv2016

Plant Chimeras: the good, the bad, and the Bizzaria

Chimeras - organisms that are composed of cells of more than one genotype - captured the human imagination long before they were formally described and used in the laboratory. These organisms owe their namesake to a fire-breathing monster from Greek mythology that has the head of a lion, the body of a goat, and the tail of a serpent. The first description of a non-fictional chimera dates back to the middle of the seventeenth century when the Florentine gardener Pietro Nati discovered an adventitious shoot growing from the graft junction between sour orange (Citrus aurantium) and citron (C. medica). This perplexing chimera that grows with sectors phenotypically resembling each of the citrus progenitors inspired discussion and wonder from the scientific community and was fittingly named the Bizzaria. Initially, the Bizzaria was believed to be an asexual hybrid that formed from a cellular fusion between the grafted parents; however, in-depth cellular analyses carried out centuries later demonstrated that the Bizzaria, along with other chimeras, owe their unique sectored appearance to a conglomeration of cells from the two donors. Since this pivotal discovery at the turn of the twentieth century, chimeras have served both as tools and as unique biological phenomena that have contributed to our understanding of plant development at the cellular, tissue, and organismal level. Rapid advancements in genome sequencing technologies have enabled the establishment of new model species with novel morphological and developmental features that enable the generation of chimeric organisms. In this review, we show that genetic mosaic and chimera studies provide a technologically simple way to delve into the organismal, genetic, and genomic inner workings underlying the development of diverse model organisms. Moreover, we discuss the unique opportunity that chimeras present to explore universal principles governing intercellular communication and the coordination of organismal biology in a heterogenomic landscape.

Plant Biology↗
bioRxiv2016

Recessive inactivating mutations in TBCK, encoding a Rab GTPase-activating protein that modulates mTOR signaling, cause severe infantile syndromic encephalopathy

Infantile encephalopathies are a group of clinically and biologically heterogeneous disorders for which the genetic basis remains largely unknown. Here, we report a previously unrecognized syndromic neonatal encephalopathy characterized by profound developmental disability, severe hypotonia, seizures, diminished respiratory drive requiring mechanical ventilation, brain atrophy, corpus callosum dysgenesis, cerebellar vermis hypoplasia, and facial dysmorphism. Biallelic inactivating mutations in TBCK (TBC1 domain-containing kinase) were independently identified by Whole-Exome Sequencing (WES) as the cause of this condition in four unrelated families. Matching these families was facilitated by sharing phenotypic profiles and WES data in a recently released web-based tool (Geno2MP) that links phenotypic information to rare variants in families with Mendelian traits. TBCK is a putative GTPase-activating protein (GAP) for small GTPases of the Rab family and has been shown to control cell growth and proliferation, actin cytoskeleton dynamics, and mTOR signaling. Two of the three mutations are predicted to truncate the protein (c.376C>T [p.Arg126*] and c.1363A>T [p.Lys455*]), and loss of the major TBCK isoform was confirmed in primary fibroblasts from one affected individual. The third mutation, c.1532G>A [p.Arg511His], alters a conserved residue within the TBC1 domain. Structural analysis implicates Arg511 as a required residue for Rab-GAP function, and in silico homology modeling predicts impaired GAP function in the corresponding mutant. These results suggest loss of Rab-GAP activity is the underlying mechanism of disease. In contrast to other disorders caused by dysregulated mTOR signaling associated with focal or global brain overgrowth, impaired TBCK function results in progressive loss of brain volume.

Genetics↗
bioRxiv2017

A Comparative Study Of Endoderm Differentiation In Humans And Chimpanzees

There is substantial interest in the evolutionary forces that shaped the regulatory framework that is established in early human development. Progress in this area has been slow because it is difficult to obtain relevant biological samples. Inducible pluripotent stem cells (iPSCs) provide the ability to establish in vitro models of early human and non-human primate developmental stages. Using matched iPSC panels from humans and chimpanzees, we comparatively characterized gene regulatory changes through a four-day timecourse differentiation of iPSCs (day 0) into primary streak (day 1), endoderm progenitors (day 2), and definitive endoderm (day 3). As might be expected, we found that differentiation stage is the major driver of variation in gene expression levels, followed by species. We identified thousands of differentially expressed genes between humans and chimpanzees in each differentiation stage. Yet, when we considered gene-specific dynamic regulatory trajectories throughout the timecourse, we found that 75.. of genes, including nearly all known endoderm developmental markers, have similar trajectories in the two species. Interestingly, we observed a marked reduction of both intra-and inter-species variation in gene expression levels in primitive streak samples compared to the iPSCs, with a recovery of regulatory variation in endoderm progenitors. The reduction of variation in gene expression levels at a specific developmental stage, paired with overall high degree of conservation of temporal gene regulation, is consistent with the dynamics of developmental canalization. Overall, we conclude that endoderm development in iPSC-based models are highly conserved and canalized between humans and our closest evolutionary relative.

genomics↗
bioRxiv2017

How Well Do You Know Your Mutation? Complex Effects Of Genetic Background On Expressivity, Complementation, And Ordering Of Allelic Effects

For a given gene, different mutations influence organismal phenotypes to varying degrees. However, the expressivity of these variants not only depends on the DNA lesion associated with the mutation, but also on factors including the genetic background and rearing environment. The degree to which these factors influence related alleles, genes, or pathways similarly, and whether similar developmental mechanisms underlie variation in the expressivity of a single allele across conditions and variation across alleles is poorly understood. Besides their fundamental biological significance, these questions have important implications for the interpretation of functional genetic analyses, for example, if these factors alter the ordering of allelic series or patterns of complementation. We examined the impact of genetic background and rearing environment for a series of mutations spanning the range of phenotypic effects for both the scalloped and vestigial genes, which influence wing development in Drosophila melanogaster. Genetic background and rearing environment influenced the phenotypic outcome of mutations, including intra-genic interactions, particularly for mutations of moderate expressivity. We examined whether cellular correlates (such as cell proliferation during development) of these phenotypic effects matched the observed phenotypic outcome. While cell proliferation decreased with mutations of increasingly severe effects, surprisingly it did not co-vary strongly with the degree of background dependence. We discuss these findings and propose a phenomenological model to aid in understanding the biology of genes, and how this influences our interpretation of allelic effects in genetic analysis.

genetics↗
bioRxiv2017

Lineage and stage-specific expressed CYCD7;1 coordinates the single symmetric division that creates stomatal guard cells

Plants, with cells fixed in place by rigid walls, often utilize spatial and temporally distinct cell division programs to organize and maintain organs. This leads to the question of how developmental regulators interact with the cell cycle machinery to link cell division events with particular developmental trajectories. In Arabidopsis leaves, the development of stomata, two-celled epidermal valves that mediate plant-atmosphere gas exchange, relies on a series of oriented stem-cell-like asymmetric divisions followed by a single symmetric division. The stomatal lineage is embedded in a tissue whose cells transition from proliferation to post-mitotic differentiation earlier, necessitating stomatal lineage-specific factors to prolong competence to divide. We show that the D-type cyclin, CYCD7;1 is specifically expressed just prior to the symmetric guard-cell forming division, and that it is limiting for this division. Further, we find that CYCD7;1 is capable of promoting divisions in multiple contexts, likely through RBR-dependent promotion of the G1/S transition, but that CYCD7;1 is regulated at the transcriptional level by cell-type specific transcription factors that confine its expression to the appropriate developmental window.

plant biology↗
bioRxiv2018

Developmental Regulation of an Organelle Tether Coordinates Mitochondrial Remodeling in Meiosis

Cellular differentiation involves remodeling cellular architecture to transform one cell type to another. By investigating mitochondrial dynamics during meiotic differentiation in budding yeast, we sought to understand how organelle morphogenesis is developmentally controlled in a system where regulators of differentiation as well as organelle architecture are known, but the interface between them remains unexplored. We found that mitochondria abruptly detach from the cell cortex shortly before segregating into gametes. Mitochondrial detachment is enabled by the programmed destruction of the mitochondria-endoplasmic reticulum-cortex anchor (MECA), an organelle tether that forms contact sites between mitochondria and the plasma membrane. MECA regulation is governed by a meiotic transcription factor, Ndt80, which promotes the activation of a conserved kinase, Ime2. We found that MECA undergoes Ime2-dependent phosphorylation. Furthermore, Ime2 promotes MECA degradation in a temporally controlled manner. Our study defines a key mechanism that coordinates mitochondrial morphogenesis with the landmark events of meiosis and demonstrates that cells can developmentally regulate tethering to induce organelle remodeling.

cell biology↗
bioRxiv2018

The functional genomic circuitry of human glioblastoma stem cells

SummarySuccessful glioblastoma (GBM) therapies have remained elusive due to limitations in understanding mechanisms of growth and survival of the tumorigenic population. Using CRISPR-Cas9 approaches in patient-derived GBM stem cells to interrogate function of the coding genome, we identify diverse actionable pathways responsible for growth that reveal the gene-essential circuitry of GBM stemness. In particular, we describe the Sox developmental transcription factor family; H3K79 methylation by DOT1L; and ufmylation stress responsiveness programs as essential for GBM stemness. Additionally, we find mechanisms of temozolomide resistance and sensitivity that could lead to combination strategies with this standard of care treatment. By reaching beyond static genome analysis of bulk tumors, with a genome wide functional approach, we dive deep into a broad range of biological processes to provide new understanding of GBM growth and treatment resistance.\n\nSignificanceGlioblastoma (GBM) remains an incurable disease despite an increasingly thorough depth of knowledge of the genomic and epigenomic alterations of bulk tumors. Evidence from multiple approaches support that GBM reflects an aberrant developmental hierarchy, with GBM stem cells (GSCs), fueling tumor growth and invasion. The properties of this tumor subpopulation may also in part explain treatment resistance and disease recurrence. Unfortunately, we still have a limited knowledge of the molecular circuitry of these cells and progress has been slow as we have not been able, until recently, to interrogate function at the genome-wide scale. Here, using parallel genome-wide CRISPR-Cas9 screens, we identify the essential genes for GSC growth. Further, by screening in the presence of low and high dose temozolomide, we identify mechanisms of drug resistance and sensitivity. These functional screens in patient derived cells reveal new aspects of GBM biology and identify a diversity of actionable targets such as genes governing stem cell traits, epigenome regulation and the response to stress stimuli.

cancer biology↗
bioRxiv2017

cycleX: multi-dimensional pseudotime reveals cell cycle and differentiation relationship of dendritic cell progenitors

Advances in single-cell RNA-sequencing have helped reveal the previously underappreciated level of cellular heterogeneity present during cellular differentiation. A static snapshot of single-cell transcriptomes provides a good representation of the various stages of differentiation as differentiation is rarely synchronized between cells. Data from numerous single-cell analyses has suggested that cellular differentiation and development can be conceptualized as continuous processes. Consequently, computational algorithms have been developed to infer pseudotimes and re-ordered cells along developmental trajectories. However, existing pseudotime inference methods generate one-dimensional pseudotime in an unsupervised manner, which is inadequate to elucidate the effects of individual biological processes such as cell cycle and differentiation and the links between them. Here we present a method called cycleX which infers multi-dimensional pseudotimes to reveal putative relationship between cell cycle and differentiation during dendritic cell development. cycleX can be also applied to generate multi-dimensional pseudotime for the relationship among cell cycle, differentiation, trafficking, activation, metabolism and etc.

bioinformatics↗
bioRxiv2018

Contribution of structural variation to genome structure: TAD fusion discovery and ranking

The significant contribution of structural variants to function, disease, and evolution is widely reported. However, in many cases, the mechanism by which these variants contribute to the phenotype is not well understood. Recent studies reported structural variants that disrupted the three-dimensional genome structure by fusing two topologically associating domains (TADs), such that enhancers from one TAD interacted with genes from the other TAD, and could cause severe developmental disorders. However, no computational method exists for directly scoring and ranking structural variations based on their effect on the three-dimensional structure such as the TAD disruption to guide further studies of their biological function. In this paper, we formally define TAD fusion and provide a combinatorial approach for assigning a score to quantify the level of TAD fusion for each deletion denoted as TAD fusion score. We also show that our method outperforms the approaches which use predicted TADs and overlay the deletion on them to predict TAD fusion. Furthermore, we show that deletions that cause TAD fusion are rare and under negative selection in general population. Finally, we show that our method correctly gives higher scores to deletions reported to cause various disorders (developmental disorder and cancer) in comparison to the deletions reported in the 1000 genomes project.

bioinformatics↗