bioRxiv Science⌕ Search

SEARCH · bioRxiv Science

Results for “Pathology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14Linked to original sources
bioRxiv2014

Untangling cross-frequency coupling in neuroscience

Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.\n\nHighlightsFundamental caveats and confounds in the methodology of assessing CFC are discussed.\n\nSignificant CFC can be observed without any underlying physiological coupling.\n\nNon-stationarity of a time-series leads to spectral correlations interpreted as CFC.\n\nWe offer practical recommendations, which can relieve some of the current confounds.\n\nFurther theoretical and experimental work is needed to ground the CFC analysis.

Neuroscience↗
bioRxiv2014

MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions

DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.

Bioinformatics↗
bioRxiv2014

How Well Can We Detect Shifts in Rates of Lineage Diversification? A Simulation Study of Sequential AIC Methods

Evolutionary biologists have long been fascinated by the extreme differences in species numbers across branches of the Tree of Life. This has motivated the development of statistical phylogenetic methods for detecting shifts in the rate of lineage diversification (speciation - extinction). One of the most frequently used methods--implemented in the program MEDUSA--explores a set of diversification-rate models, where each model uniquely assigns branches of the phylogeny to a set of one or more diversification-rate categories. Each candidate model is first fit to the data, and the Akaike Information Criterion (AIC) is then used to identify the optimal diversification model. Surprisingly, the statistical behavior of this popular method is completely unknown, which is a concern in light of the poor performance of the AIC as a means of choosing among models in other phylogenetic comparative contexts, and also because of the ad hoc algorithm used to visit models. Here, we perform an extensive simulation study demonstrating that, as implemented, MEDUSA (1) has an extremely high Type I error rate (on average, spurious diversification-rate shifts are identified 42% of the time), and (2) provides severely biased parameter estimates (on average, estimated net-diversification and relative-extinction rates are 183% and 20% of their true values, respectively). We performed simulation experiments to reveal the source(s) of these pathologies, which include (1) the use of incorrect critical thresholds for model selection, and (2) errors in the likelihood function. Understanding the statistical behavior of MEDUSA is critical both to empirical researchers--in order to clarify whether these methods can reliably be applied to empirical datasets--and to theoretical biologists--in order to clarify whether new methods are required, and to reveal the specific problems that need to be solved in order to develop more reliable approaches for detecting shifts in the rate of lineage diversification.

Evolutionary Biology↗
bioRxiv2014

On the scope and limitations of baker’s yeast as a model organism for studying human tissue-specific pathways

Budding yeast, S. cerevisiae, has been used extensively as a model organism for studying cellular processes in evolutionarily distant species, including humans. However, different human tissues, while inheriting a similar genetic code, exhibit distinct anatomical and physiological properties. Specific biochemical processes and associated biomolecules that differentiate various tissues are not completely understood, neither is the extent to which a unicellular organism, such as yeast, can be used to model these processes within each tissue.\n\nWe propose a novel computational and statistical framework to systematically quantify the suitability of yeast as a model organism for different human tissues. We develop a computational method for dissecting the human interactome into tissue-specific cellular networks. Using these networks, we simultaneously partition the functional space of human genes, and their corresponding pathways, based on their conservation both across species and among different tissues. We study these subspaces in detail, and relate them to the overall similarity of each tissue with yeast.\n\nMany complex disorders are driven by a coupling of housekeeping (universally expressed in all tissues) and tissue-selective (expressed only in specific tissues) dysregulated pathways. We show that human-specific subsets of tissue-selective genes are significantly associated with the onset and development of a number of pathologies. Consequently, they provide excellent candidates as drug targets for therapeutic interventions. We also present a novel tool that can be used to assess the suitability of the yeast model for studying tissue-specific physiology and pathophysiology in humans.

Systems Biology↗
bioRxiv2014

Model checking to assess T-helper cell plasticity

Computational modeling constitutes a crucial step towards the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g. stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity.\n\nIn this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions.\n\nAs a case study, we consider the cellular network regulating the differentiation of T-helper cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of T-helper cell differentiation. We then use symbolic model checking to analyze reachability properties between T-helper subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of T-helper cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific T-helper cell polarizing or reprograming events.

Systems Biology↗
bioRxiv2015

From system-wide differential gene expression to perturbed regulatory factors: a combinatorial approach

High-throughput experiments such as microarrays and deep sequencing provide large scale information on the pattern of gene expression, which undergoes extensive remodeling as the cell dynamically responds to varying environmental cues or has its function disrupted under pathological conditions. An important initial step in the systematic analysis and interpretation of genome-scale expression alteration involves identification of a set of perturbed transcriptional regulators whose differential activity can provide a proximate hypothesis to account for these transcriptomic changes. In the present work, we propose an unbiased and logically natural approach to transcription factor enrichment. It involves overlaying a list of experimentally determined differentially expressed genes on a background regulatory network coming from e.g. literature curation or computational motif scanning, and identifying that subset of regulators whose aggregated target set best discriminates between the altered and the unaffected genes. In other words, our methodology entails testing of all possible regulatory subnetworks, rather than just the target sets of individual regulators as is followed in most standard approaches. We have proposed an iterative search method to efficiently find such a combination, and benchmarked it on E. coli microarray and regulatory network data available in the public domain. Comparative analysis carried out on simulated differential expression profiles, as well as empirical factor overexpression data for M. tuberculosis, shows that our methodology provides marked improvement in accuracy of regulatory inference relative to the standard method that involves evaluating factor enrichment in an individual manner.

Systems Biology↗
bioRxiv2015

PREDICTABILITY OF AUTISM, SCHIZOPHRENIC AND OBSESSIVE SPECTRA DIAGNOSIS. TOWARD A DAMAGE NETWORK APPROACH

Schizophrenia, obsessive-compulsive and autistic disorders are traditionally considered as three separate psychiatric conditions each with specific symptoms and pattern of brain alterations. This view can be challenged since these three conditions have the same neurobiological origin, stemming from a common root of a unique neurodevelopmental tree.\n\nThe aim of this meta-analytic study was to determine, from a neuroimaging perspective, whether i) white matter and gray matter alterations are specific for the three different spectrum disorders, and the nosographical differentiation of three spectra is supported by different patterns of brain alterations. ii) it might be possible to define new spectra starting from specific brain damage. iii) it is possible to detect a \"brain damage network\" (a connecting link between the damaged areas that relates areas constantly involved in the disorder).\n\nThree main findings emerged from our meta-analysis: O_LIThe three psychiatric spectra do not appear to have their own specific damage.\nC_LIO_LIIt is possible to define two new damage clusters. The first includes substantial parts of the salience network, and the second is more closely linked to the auditory-visual, auditory and visual somatic areas.\nC_LIO_LIIt is possible to define a \"Damage Network\" and to infer a hierarchy of brain substrates in the pattern of propagation of the damage.\nC_LI\n\nThese results suggest the presence of a common pattern of damage in the three pathologies plus a series of variable alterations that, rather than support the sub-division into three spectra, highlight a two-cluster parcellation with an input-output and more cognitive clusters.

Neuroscience↗
bioRxiv2015

Tandem repeat variation in human and great ape populations and its impact on gene expression divergence

Tandem repeats (TR) are stretches of DNA that are highly variable in length and mutate rapidly, and thus an important source of genetic variation. This variation is highly informative for population and conservation genetics, and has also been associated with several pathological conditions and with gene expression regulation. However, genome-wide surveys of TR variation have been scarce due to the technical difficulties derived from short-read technology.\n\nHere, we explored the genome-wide diversity of TRs in a panel of 83 human and nonhuman great ape genomes, and their impact on gene expression evolution. We found that populations and species diversity patterns can be efficiently captured with short TRs (repeat unit length 1-5 base pairs) with potential applications in conservation genetics. We also examined the potential evolutionary role of TRs in gene expression differences between humans and primates by using 30,275 larger TRs (repeat unit length 2-50 base pairs). About one third of the 13,035 one-to-one orthologous genes contained TRs within 5 kilobase pairs of their transcription start site, and had higher expression divergence than genes without such TRs. The same observation held for genes with repeats in their 3 untranslated region, in introns, and in exons. Using our polymorphism data for the shortest TRs, we found that genes with polymorphic repeats in their promoters showed higher expression divergence in humans and chimpanzees compared to genes with fixed or no TRs in the promoters. Our findings highlight the potential contribution of TRs to recent human evolution through gene regulation.

Evolutionary Biology↗
bioRxiv2015

NADH as a cancer medicine

We propose that NADH will exert a specific kill action against some cancers. NADH is a natural metabolite. We envisage a low side effect profile and that NADH therapy will, additionally, combat the wastage and weakness of cancer patients, which can be the cause of death in some cases. Significantly, NADH can be administered orally and has already cleared clinical trials, all be it for other pathologies.\n\nBackgroundAerobic respiration consists of glycolysis in the cytoplasm and in the mitochondria: the Krebs cycle and oxidative phosphorylation (OXPHOS) [1, 2]. It requires O2 and net yields 30 ATPs from one glucose molecule. Anaerobic respiration, consisting of glycolysis only, does not require O2 but produces merely 2 ATPs from one glucose molecule. When O2 is available, normal animal cells tend to favour aerobic respiration because of its higher ATP yield.

Cancer Biology↗
bioRxiv2015

Myc-induced cell mixing is required for competitive tissue invasion and destruction

Cell-cell intercalation is used in several developmental processes to shape the normal body plan1. There is no clear evidence that intercalation is involved in pathologies. Here, we use the proto-oncogene Myc to study a process analogous to early phase of tumour expansion: Myc-induced cell competition2-7. Cell competition is a conserved mechanism5,6,8,9 driving the elimination of slow proliferating cells (so called losers) by faster proliferating neighbours (so called winners) through apoptosis10 and is important to prevent developmental malformations and maintain tissue fitness11. Using long term live imaging of Myc-driven competition in the Drosophila pupal notum and in the wing imaginal disc, we show that the probability of elimination of loser cells correlates with the surface of contact shared with winners. As such, modifying loser/winner interface morphology can modulate the strength of competition. We further show that elimination of loser clones requires winner/loser cell mixing through cell-cell intercalation. Cell mixing is driven by differential growth and the high tension at winner-winner interfaces relative to winner-loser and loser-loser interfaces, which leads to a preferential stabilisation of winner-loser contacts and reduction of clone compactness over time. Differences in tension are generated by a relative difference of junctional F-actin levels between loser and winner junctions, induced by differential levels of the phosphatidylinositol PIP3. Our results establish the first link between cell-cell intercalation induced by a proto-oncogene and how it promotes invasiveness and destruction of healthy tissues.

Cell Biology↗
bioRxiv2015

The Nature, Extent, and Consequences of Cryptic Genetic Variation in the opa Repeats of Notch in Drosophila

ABTRACTPolyglutamine (pQ) tracts are abundant in many proteins co-interacting on DNA. The lengths of these pQ tracts can modulate their interaction strengths. However, pQ tracts > 40 residues are pathologically prone to amyloidogenic self-assembly. Here, we assess the extent and consequences of variation in the pQ-encoding opa repeats of Notch (N) in Drosophila melanogaster. We use Sanger sequencing to genotype opa sequences (5-CAX repeats), which have resisted assembly using short sequence reads. While the majority of N sequences pertain to reference opa31 (Q13HQ17) and opa32 (Q13HQ18) allelic classes, several rare alleles encode tracts > 32 residues: opa33a (Q14HQ18), opa33b (Q15HQ17), opa34 (Q16HQ17), opa35a1/opa35a2 (Q13HQ21), opa36 (Q13HQ22), and opa37 (Q13HQ23). Only one rare allele encodes a tract < 31 residues: opa23 (Q13-Q10). This opa23 allele shortens the pQ tract while simultaneously eliminating the interrupting histidine. Homozygotes for the short and long opa alleles have defects in sensory bristle organ specification, abdominal patterning, and embryonic survival. Inbred stocks with wild-type opa31 alleles become more viable when outbred, while an inbred stock with the longer opa35 becomes less viable after outcrossing to different backgrounds. In contrast, an inbred stock with the short opa23 allele is semi-viable in both inbred and outbred genetic backgrounds. This opa23 Notch allele also produces notched wings when recombined out of the X chromosome. Importantly, wa-linked X balancers carry the N allele opa33b and suppress AS-C insufficiency caused by the sc8 inversion. Our results demonstrate significant cryptic variation and epistatic sensitivity for the N locus, and the need for long read genotyping of key repeat variables underlying gene regulatory networks.

Genetics↗
bioRxiv2015

The Nature, Extent, and Consequences of Genetic Variation in the opa Repeats of Notch in Drosophila

Polyglutamine (pQ) tracts are abundant in many proteins co-interacting on DNA. The lengths of these pQ tracts can modulate their interaction strengths. However, pQ tracts > 40 residues are pathologically prone to amyloidogenic self-assembly. Here, we assess the extent and consequences of variation in the pQ-encoding opa repeats of Notch (N) in Drosophila melanogaster. We use Sanger sequencing to genotype opa sequences (50-CAX repeats), which have resisted assembly using short sequence reads. While the majority of N sequences pertain to reference opa31 (Q13HQ17) and opa32 (Q13HQ18) allelic classes, several rare alleles encode tracts > 32 residues: opa33a (Q14HQ18), opa33b (Q15HQ17), opa34 (Q16HQ17), opa35a1/opa35a2 (Q13HQ21), opa36 (Q13HQ22), and opa37 (Q13HQ23). Only one rare allele encodes a tract < 31 residues: opa23 (Q13-Q10). This opa23 allele shortens the pQ tract while simultaneously eliminating the interrupting histidine. Homozygotes for the short and long opa alleles have defects in sensory bristle organ specification, abdominal patterning, and embryonic survival. Inbred stocks with wild-type opa31 alleles become more viable when outbred, while an inbred stock with the longer opa35 becomes less viable after outcrossing to different backgrounds. In contrast, an inbred stock with the short opa23 allele is semi-viable in both inbred and outbred genetic backgrounds. This opa23 Notch allele also produces notched wings when recombined out of the X chromosome. Importantly, wa-linked X balancers carry the N allele opa33b and suppress AS-C insufficiency caused by the sc8 inversion. Our results demonstrate potent pQ variation and epistatic sensitivity for the N locus, and the need for long read genotyping of key repeat variables underlying gene regulatory networks.

Evolutionary Biology↗
bioRxiv2015

Mitochondrial DNA Copy Number Variation Across Human Cancers

In cancer, mitochondrial dysfunction, through mutations, deletions, and changes in copy number of mitochondrial DNA (mtDNA), contributes to the malignant transformation and progress of tumors. Here, we report the first large-scale survey of mtDNA copy number variation across 21 distinct solid tumor types, examining over 13,000 tissue samples profiled with next-generation sequencing methods. We find a tendency for cancers, especially of the bladder and kidney, to be significantly depleted of mtDNA, relative to matched normal tissue. We show that mtDNA copy number is correlated to the expression of mitochondrially-localized metabolic pathways, suggesting that mtDNA copy number variation reflect gross changes in mitochondrial metabolic activity. Finally, we identify a subset of tumor-type-specific somatic alterations, including IDH1 and NF1 mutations in gliomas, whose incidence is strongly correlated to mtDNA copy number. Our findings suggest that modulation of mtDNA copy number may play a role in the pathology of cancer.

Genomics↗
bioRxiv2015

Efficient Disruption and Replacement of an Effector Gene in the Oomycete Phytophthora sojae using CRISPR/Cas9

Phytophthora sojae is a pathogenic oomycete that infects soybean seedlings as well as stems and roots of established plants, costing growers $1-2 billion per year. Due to its economic importance, P. sojae has become a model for the study of oomycete genetics, physiology and pathology. Despite the availability of several genome sequences, the lack of efficient techniques for targeted mutagenesis and gene replacement have long hampered genetic studies of pathogenicity in Phytophthora species. Here, we describe a CRISPR/Cas9 system enabling rapid and efficient genome editing in P. sojae. Using the RXLR effector gene Avr4/6 as target, we observed that in the absence of a homologous template, the repair of Cas9-induced double-strand breaks (DSBs) in P. sojae was mediated by non-homologous end joining (NHEJ), primarily resulting in short indels. Most mutants were homozygous, presumably due to gene conversion triggered by Cas9-mediated cleavage of non-mutant alleles. When donor DNA was present, homology directed repair (HDR) was observed, which resulted in the replacement of the target gene with the donor DNA. By testing the specific virulence of several NHEJ mutants and HDR -mediated gene replacements on soybeans, we have validated the contribution of Avr4/6 to recognition by soybean R gene loci, Rps4 and Rps6, but also uncovered additional contributions to resistance by these two loci. Our results establish a powerful tool for studying functional genomics in Phytophthora, which provides new avenues for better control of this pathogen.

Microbiology↗
bioRxiv2015

Differential transcript usage from RNA-seq data: isoform pre-filtering improves performance of count-based methods

Large-scale sequencing of cDNA (RNA-seq) has been a boon to the quantitative analysis of transcriptomes. A notable application is the detection of changes in transcript usage between experimental conditions. For example, discovery of pathological alternative splicing may allow the development of new treatments or better management of patients. From an analysis perspective, there are several ways to approach RNA-seq data to unravel differential transcript usage, such as annotation-based exon-level counting, differential analysis of the percent spliced in measure or quantitative analysis of assembled transcripts. The goal of this research is to compare and contrast current state-of-the-art methods, as well as to suggest improvements to commonly used workflows.\n\nWe assess the performance of representative workflows using synthetic data and explore the effect of using non-standard counting bin definitions as input to a state-of-the-art inference engine (DEXSeq). Although the canonical counting provided the best results overall, several non-canonical approaches were as good or better in specific aspects and most counting approaches outperformed the evaluated event- and assembly-based methods. We show that an incomplete annotation catalog can have a detrimental effect on the ability to detect differential transcript usage in transcriptomes with few isoforms per gene and that isoform-level pre-filtering can considerably improve false discovery rate (FDR) control.\n\nCount-based methods generally perform well in detection of differential transcript usage. Controlling the FDR at the imposed threshold is difficult, mainly in complex organisms, but can be improved by pre-filtering of the annotation catalog.

Bioinformatics↗
bioRxiv2015

Simultaneously inferring T cell fate and clonality from single cell transcriptomes

The heterodimeric T cell receptor (TCR) comprises two protein chains that pair to determine the antigen specificity of each T lymphocyte. The enormous sequence diversity within TCR repertoires allows specific TCR sequences to be used as lineage markers for T cells that derive from a common progenitor. We have developed a computational method, called TraCeR, to reconstruct full-length, paired TCR sequences from T lymphocyte single-cell RNA-seq by combining existing assembly and alignment programs with a \"synthetic genome\" library comprising all possible TCR sequences. We validate this method with PCR to quantify its accuracy and sensitivity, and compare to other TCR sequencing methods. Our inferred TCR sequences reveal clonal relationships between T cells, which we put into the context of each cells functional state from the complete transcriptional landscape quantified from the remaining RNA-seq data. This provides a powerful tool to link T cell specificity with functional response in a variety of normal and pathological conditions. We demonstrate this by determining the distribution of members of expanded T cell clonotypes in response to Salmonella infection in the mouse. We show that members of the same clonotype span early activated CD4+ T cells, as well as mature effector and memory cells.

Bioinformatics↗
bioRxiv2015

Evolutionary assembly patterns of prokaryotic genomes

Evolutionary innovation must occur in the context of some genomic background, which limits available evolutionary paths. For example, protein evolution by sequence substitution is constrained by epistasis between residues. In prokaryotes, evolutionary innovation frequently happens by macrogenomic events such as horizontal gene transfer (HGT). Previous work has suggested that HGT can be influenced by ancestral genomic content, yet the extent of such gene-level constraints has not yet been systematically characterized. Here, we evaluated the evolutionary impact of such constraints in prokaryotes, using probabilistic ancestral reconstructions from 634 extant prokaryotic genomes and a novel framework for detecting evolutionary constraints on HGT events. We identified 8,228 directional dependencies between genes, and demonstrated that many such dependencies reflect known functional relationships, including, for example, evolutionary dependencies of the photosynthetic enzyme RuBisCO. Modeling all dependencies as a network, we adapted an approach from graph theory to establish chronological precedence in the acquisition of different genomic functions. Specifically, we demonstrated that specific functions tend to be gained sequentially, suggesting that evolution in prokaryotes is governed by functional assembly patterns. Finally, we showed that these dependencies are universal rather than clade-specific and are often sufficient for predicting whether or not a given ancestral genome will acquire specific genes. Combined, our results indicate that evolutionary innovation via HGT is profoundly constrained by epistasis and historical contingency, similar to the evolution of proteins and phenotypic characters, and suggest that the emergence of specific metabolic and pathological phenotypes in prokaryotes can be predictable from current genomes.

Evolutionary Biology↗
bioRxiv2015

Population heterogeneity in the epithelial to mesenchymal transition is controlled by NFAT and phosphorylated Sp1

Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor {beta} (TGF-{beta}) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-{beta} induced EMT to better understand the operational hierarchy of this complex molecular program. We used ordinary differential equations (ODEs) to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-{beta} exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was enhanced following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-{beta} and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-{beta} and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-{beta} treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response.\n\nAuthor SummaryTissue formation and remodeling requires a complex and dynamic balance of interactions between epithelial cells, which reside on the surface, and mesenchymal cells that reside in the tissue interior. During embryonic development, wound healing, and cancer, epithelial cells transform into a mesenchymal cell to form new types of tissues. It is important to understand this process so that it can be controlled to generate beneficial effects and limit pathological differentiation. Much research over the past 20 years has identified many different molecular species that are relevant, but these have mainly been studied one at a time. In this study, we developed and implemented a novel computational strategy to interrogate the key players in this transformation process to identify which are the major bottlenecks. We determined that NFATc1 and pSP1 are essential for promoting epithelial or mesenchymal differentiation, respectively. We then predicted the existence of a partially transformed cell that exhibits both epithelial and mesenchymal characteristics. We found this partial cell type develops a network of invasive but stunted vascular structures that may be a unique cell target for understanding cancer progression and angiogenesis.

Systems Biology↗