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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Emerging Evidence of Chromosome Folding by Loop Extrusion

Chromosome organization poses a remarkable physical problem with many biological consequences: how can molecular interactions between proteins at the nanometer scale organize micron-long chromatinized DNA molecules, insulating or facilitating interactions between specific genomic elements? The mechanism of active loop extrusion holds great promise for explaining interphase and mitotic chromosome folding, yet remains difficult to assay directly. We discuss predictions from our polymer models of loop extrusion with barrier elements, and review recent experimental studies that provide strong support for loop extrusion, focusing on perturbations to CTCF and cohesin assayed via Hi-C in interphase. Finally, we discuss a likely molecular mechanism of loop extrusion by SMC complexes.

genomics↗

bioRxiv2018

Pathway maps enable straightforward yet customized and semi-automated yet insightful analyses of omics data

To explore the molecular processes underlying some biological theme of interest based on public data, gene lists are used herein as input for the construction of annotated pathway maps, employing Cytoscape apps, and then high-throughput (\"omics\") gene expression data are overlaid onto these maps. Seeded with a published set of marker genes of the senescence-associated secretory phenotype and the genes of the cellular senescence KEGG pathway, a gene/protein interaction network and annotated clusters (a \"pathway map\") of cellular senescence are derived. The map can be amended, by adding some application-specific genes, and overlaid with gene expression data describing cellular senescence of fibroblasts and with disease-related gene expression data associated with prostate and pancreatic cancer, and with ischemic stroke, allowing insights into the role of cellular senescence in disease. Some gene expression data are derived from the \"Biomarker Benchmark repository\". The pathway map approach can be followed in principle for any biological theme of interest, fostering much-needed independence from the investigator-biased expert networks usually used for overlaying gene expression data.

genomics↗

bioRxiv2018

Environment and HSP90 modulate MAPK stomatal developmental pathway

Stomatal ontogenesis is a key element of plant adaptation aiming to control photosynthetic efficiency and water management under fluctuating environments 1,2,3. Development of stomata is guided by endogenous and environmental cues and is tightly coupled to overall plant growth 1,2,3. YODA signaling pathway is essential to stomatal lineage specification4,5,6 since it regulates the activities of transcription factors such as SPEECHLESS (SPCH)7,8,9,10. Heat-shock proteins 90 (HSP90s) are evolutionarily conserved molecular chaperones implicated in a broad range of signalling pathways being integrated in interaction networks with client proteins11,12,13,14. Herein, based on genetic, molecular, biochemical, and cell biological evidence we report that heat-stress conditions affect phosphorylation and deactivation of SPCH and modulate stomatal density. We show that genetic and physical interactions between HSP90s and YODA control stomatal patterning, distribution and morphology. We provide solid evidence that HSP90s play a major role in transducing the heat-stress response since they act upstream and downstream of YODA signalling, regulate the activity and nucleocytoplasmic distribution of MAPKs, and the activation of SPCH. Thus, HSPs control the stomatal development both under normal temperature and acute heat-stress conditions. Our results demonstrate that HSP90s couple stomatal formation and patterning to environmental cues providing an adaptive mechanism of heat-stress tolerance response and stomatal formation in Arabidopsis.

plant biology↗

bioRxiv2018

In vivo assembly and trafficking of olfactory Ionotropic Receptors

lonotropic Receptors (IRs) are a large, divergent subfamily of ionotropic glutamate receptors(iGluRs), with roles in chemosensation, thermosensation and hygrosensation. Analogous to the synaptic targeting mechanisms of their iGluR ancestors, IRs are thought to form complexes of broadly-expressed co-receptors and selectively-expressed tuning receptors to localise to sensory cilia. While tuning receptors extracellular ligand-binding domain (LBD) defines sensory specificity, the role of this domain in co-receptors is unclear. We identify a coreceptor-specific sequence in the LBD, which contains a single N-glycosylation site. Combining molecular genetic and cell biological analyses, we show that this site is dispensable for assembly of IR complexes in olfactory sensory neurons, but essential for endoplasmic reticulum exit of some,but not all, IR complexes. Our data reveal an important role for the IR co-receptor LBD in control of intracellular transport, provide novel insights into the stoichiometry and assembly of IR complexes, and uncover an unexpected heterogeneity in the trafficking regulation of this sensory receptor family.

neuroscience↗

bioRxiv2018

Efficient Proximal Gradient Algorithm for Inference of Differential Gene Networks

BackgroundGene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the data jointly, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes.\n\nResultsIn this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis of this set of 268 genes identified a number of gene sets related to breast cancer or other types of cancer, which corroborated the gene set identified by ProGAdNet was very informative about the cancer disease status. A software package implementing the ProGAdNet and computer simulations is available upon request.\n\nConclusionWith its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions.

bioinformatics↗

bioRxiv2017

Wide sampling of natural diversity identifies novel molecular signatures of C4 photosynthesis

Introductory paragraphMuch of biology is associated with convergent traits, and it is challenging to determine the extent to which underlying molecular mechanisms are shared across phylogeny. By analyzing plants representing eighteen independent origins of C4 photosynthesis, we quantified the extent to which this convergent trait utilises identical molecular mechanisms. We demonstrate that biochemical changes that characterise C4 species are recovered by this process, and expand the paradigm by four metabolic pathways not previously associated with C4 photosynthesis. Furthermore, we show that expression of many genes that distinguish C3 and C4 species respond to low CO2, providing molecular evidence that reduction in atmospheric CO2 was a driver for C4 evolution. Thus the origin and architecture of complex traits can be derived from transcriptome comparisons across natural diversity.

plant biology↗

bioRxiv2026

Postmortem Alterations of Metabotropic Glutamate Receptors across Neuropsychiatric Disorders: A Systematic Review

Metabotropic glutamate receptors (mGluRs) regulate glutamatergic transmission and have been implicated in diverse neuropsychiatric disorders, but human postmortem evidence remains fragmented. We aimed to map these findings across diagnoses, receptor subtypes, brain regions, and measurement modalities. Following PRISMA guidelines, we systematically searched MEDLINE, EMBASE, and Web of Science from inception to August 8, 2026, for studies assessing GRM transcripts, as well as mGluR protein abundance, localization, assembly, or receptor binding in human postmortem brain tissue. Of 532 records identified, 57 reports met eligibility criteria. Findings were synthesized narratively because of substantial heterogeneity in diagnoses, brain regions, receptor subtypes, and assays. Postmortem evidence was concentrated on mGluR5, mGluR2/3, and mGluR1, and on the prefrontal cortex, anterior cingulate cortex, and hippocampus. mGluR-related alterations were reported across disorders, including schizophrenia, major depressive disorder, Alzheimer disease, autism spectrum disorder, and alcohol use disorder. Although most analyses yielded null findings, the direction and magnitude of mGluR alterations varied across brain regions, receptor subtypes, and molecular endpoints. This inconsistency may partly reflect the distinct biological levels captured by transcript abundance, total protein, receptor assembly, localization, and ligand binding, together with regional, cell-type, disease-stage, and clinical heterogeneity. The available evidence therefore suggests context-dependent alterations in mGluR biology but not a uniform or disorder-specific molecular signature. Integration of postmortem findings with other approaches, including in vivo imaging, may clarify their biological and clinical significance.

neuroscience↗

bioRxiv2013

Unexpected links reflect the noise in networks

Gene regulatory networks are commonly used for modeling biological processes and revealing underlying molecular mechanisms. The reconstruction of gene regulatory networks from observational data is a challenging task, especially, considering the large number of involved players (e.g. genes) and much fewer biological replicates available for analysis. Herein, we proposed a new statistical method of estimating the number of erroneous edges that strongly enhances the commonly used inference approaches. This method is based on special relationship between correlation and causality, and allows to identify and to remove approximately half of erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we established a mathematical foundation for our method. Analyzing real biological datasets, we found a strong correlation between the results of our method and the commonly used false discovery rate (FDR) technique. Furthermore, the simulation analysis demonstrates that in large networks, our new method provides a more precise estimation of the proportion of erroneous links than FDR.

Bioinformatics↗

bioRxiv2016

The role of isoforms in the evolution of cryptic coloration in Peromyscus mice

A central goal of evolutionary biology is to understand the molecular mechanisms underlying phenotypic adaptation. While the contribution of protein-coding and cis-regulatory mutations to adaptive traits have been well documented, additional sources of variation--such as the production of alternative RNA transcripts from a single gene, or isoforms--have been understudied. Here, we focus on the pigmentation gene Agouti, known to express multiple alternative transcripts, to investigate the role of isoform usage in the evolution of cryptic color phenotypes in deer mice (genus Peromyscus). We first characterize the Agouti isoforms expressed in the Peromyscus skin and find two novel isoforms not previously identified in Mus. Next, we show that a locally adapted light-colored population of P. maniculatus living on the Nebraska Sand Hills shows an up-regulation of a single Agouti isoform, termed 1C, compared to their ancestral dark-colored conspecifics. Using in vitro assays, we show that this preference for isoform 1C may be driven by isoform-specific differences in translation. In addition, using an admixed population of wild-caught mice, we find that variation in overall Agouti expression maps to a region near exon 1C, which also has patterns of nucleotide variation consistent with strong positive selection. Finally, we show that the independent evolution of cryptic light pigmentation in a different species, P. polionotus, has been driven by a preference for the same Agouti isoform. Together, these findings present an example of the role of alternative transcript processing in adaptation and demonstrate molecular convergence at the level of isoform regulation.

Evolutionary Biology↗

bioRxiv2017

A Synthetic Microbial Operational Amplifier

Synthetic biology has created oscillators, latches, logic gates, logarithmically linear circuits, and load drivers that have electronic analogs in living cells. The ubiquitous operational amplifier, which allows circuits to operate robustly and precisely has not been built with bio-molecular parts. As in electronics, a biological operational-amplifier could greatly improve the predictability of circuits despite noise and variability, a problem that all cellular circuits face. Here, we show how to create a synthetic 3-stage inducer-input operational amplifier with a differential transcription-factor stage, a CRISPR-based push-pull stage, and an enzymatic output stage with just 5 proteins including dCas9. Our Bio-OpAmp expands the toolkit of fundamental circuits available to bioengineers or biologists, and may shed insight into biological systems that require robust and precise molecular homeostasis and regulation.\n\nOne Sentence SummaryA synthetic bio-molecular operational amplifier that can enable robust, precise, and programmable homeostasis and regulation in living cells with just 5 protein parts is described.

synthetic biology↗

bioRxiv2018

An annual cycle of gene regulation in the red-legged salamander mental gland: from hypertrophy to expression of rapidly evolving pheromones

Cell differentiation is mediated by synchronized waves of coordinated expression for hundreds to thousands of genes, and must be an exquisitely regulated process to produce complex tissues and phenotypes. For many animal species, sexual selection has driven the development of elaborate male ornaments, requiring sex-specific differentiation pathways. One such male ornament is the pheromone-producing mental gland of the red-legged salamander (Plethodon shermani). Mental gland development follows an annual cycle of extreme hypertrophy, production of pheromones for the ~2 month mating season, and then complete resorption before repeating the process in the following year. At the peak of the mating season, the transcriptional and translational machinery of the mental gland are almost exclusively redirected to synthesis of many rapidly evolving pheromones. Of these pheromones, Plethodontid Modulating Factor (PMF) has experienced an unusual history of disjunctive evolution: following gene duplication, positive sexual selection has diversified the protein coding region while the untranslated regions have been conserved by purifying selection. However, the molecular underpinnings that bridge the processes of gland hypertrophy, pheromone synthesis, and disjunctive evolution remain to be determined and are the focus of the present investigation. Using Illumina sequencing, we prepared a de novo transcriptome of the mental gland at six stages of development. Differential expression analysis and immunohistochemistry revealed that the mental gland initially adopts a highly proliferative, almost tumor-like phenotype, followed by a rapid increase in pheromone mRNA and protein levels. One likely player in this transition is Cold Inducible RNA Binding Protein (CIRBP), which selectively and cooperatively binds the highly conserved PMF 3' UTR. CIRBP, along with other stress response proteins, have seemingly been co-opted to aid in mental gland development by helping to regulate pheromone synthesis. The P. shermani mental gland utilizes a complex system of transcriptional and post-transcriptional gene regulation to facilitate its hypertrophication and pheromone synthesis. The data support the evolutionary interplay of both coding and noncoding segments in rapid gene evolution, and necessitate study of the co-evolution of pheromone gene products along with their transcriptional and translational regulators. Additionally, the mental gland could be a powerful emerging model of regulated proliferation and subsequent resorption of a tissue, within the dermis, thus having potential links to skin cancer biology.

molecular biology↗

bioRxiv2018

Network controllability: viruses are driver agents in dynamic molecular systems

In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However in an intra-cellular network it is unclear what control means. To address this limitation we use viral infection, specifically HIV-1 and HCV, as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two dynamic states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamic host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The driver nodes used by the virus are distributed throughout the pathways in specific locations enabling effective control of the cell via the high control centrality of the viral and targeted host nodes. Furthermore, this viral infection of the human system permits discrimination between available network-control models, and demonstrates the minimum-dominating set (MDS) method better accounts for how biological information and signals are transferred than the maximum matching (MM) method as it identified most of the HIV-1 proteins as critical driver nodes and goes beyond identifying receptors as the only critical driver nodes. This is because MDS, unlike MM, accounts for the inherent non-linearity of signalling pathways. Our results demonstrate control-theory gives a more complete and dynamic understanding of the viral hijack mechanism when compared with previous analyses limited to static single-state networks.

systems biology↗

bioRxiv2017

Molecularly barcoded Zika virus libraries to probe in vivo evolutionary dynamics

Defining the complex dynamics of Zika virus (ZIKV) infection in pregnancy and during transmission between vertebrate hosts and mosquito vectors is critical for a thorough understanding of viral transmission, pathogenesis, immune evasion, and potential reservoir establishment. Within-host viral diversity in ZIKV infection is low, which makes it difficult to evaluate infection dynamics. To overcome this biological hurdle, we constructed a molecularly barcoded ZIKV. This virus stock consists of a \"synthetic swarm\" whose members are genetically identical except for a run of eight consecutive degenerate codons, which creates approximately 64,000 theoretical nucleotide combinations that all encode the same amino acids. Deep sequencing this region of the ZIKV genome enables counting of individual barcode clonotypes to quantify the number and relative proportions of viral lineages present within a host. Here we used these molecularly barcoded ZIKV variants to study the dynamics of ZIKV infection in pregnant and non-pregnant macaques as well as during mosquito infection/transmission. The barcoded virus had no discernible fitness defects in vivo, and the proportions of individual barcoded virus templates remained stable throughout the duration of acute plasma viremia. ZIKV RNA also was detected in maternal plasma from a pregnant animal infected with barcoded virus for 64 days. The complexity of the virus population declined precipitously 8 days following infection of the dam, consistent with the timing of typical resolution of ZIKV in non-pregnant macaques, and remained low for the subsequent duration of viremia. Our approach showed that synthetic swarm viruses can be used to probe the composition of ZIKV populations over time in vivo to understand vertical transmission, persistent reservoirs, bottlenecks, and evolutionary dynamics.\n\nAuthor summaryUnderstanding the complex dynamics of Zika virus (ZIKV) infection during pregnancy and during transmission to and from vertebrate host and mosquito vector is critical for a thorough understanding of viral transmission, pathogenesis, immune evasion, and reservoir establishment. We sought to develop a virus model system for use in nonhuman primates and mosquitoes that allows for the genetic discrimination of molecularly cloned viruses. This \"synthetic swarm\" of viruses incorporates a molecular barcode that allows for tracking and monitoring individual viral lineages during infection. Here we infected rhesus macaques with this virus to study the dynamics of ZIKV infection in nonhuman primates as well as during mosquito infection/transmission. We found that the proportions of individual barcoded viruses remained relatively stable during acute infection in pregnant and nonpregnant animals. However, in a pregnant animal, the complexity of the virus population declined precipitously 8 days following infection, consistent with the timing of typical resolution of ZIKV in non-pregnant macaques, and remained low for the subsequent duration of viremia.

microbiology↗

bioRxiv2017

BipartGraph: An interactive application to plotbipartite ecological networks

Interactions between two different guilds of entities are pervasive in biology. They may happen at molecular level, like in a diseasome, or amongst individuals linked by biotic relationships, such as mutualism or parasitism. These sets of interactions are complex bipartite networks. Visualization is a powerful tool to explore and analyse them but the most common plots, the bipartite graph and the interaction matrix, become rather confusing when working with real biological networks. We have developed two new types of visualization that exploit the structural properties of these networks to improve readability. A technique called k-core decomposition identifies groups of nodes that share connectivity properties. With the results of this analysis it is possible to build a plot based on information reduction (Polar Plot) and another which takes the groups as elementary blocks for spatial distribution (Ziggurat plot). We describe the applications of both plots and the software to create them.

ecology↗

bioRxiv2017

Similarity identification in gene expression patterns as a new approach in phenotype classification

Stratifying healthy and malignant phenotypes and identifying their biological states using high-throughput molecular data has been the focus of many computational approaches during the last decade. Using multivariate changes in expression of genes within biological pathways, as fingerprints of complex phenotypes, we developed a new methodology for Similarity Identification in Gene expressioN (SIGN). In this approach, we use centroid classifier to identify phenotype of each biological sample. To obtain similarity of a given biological sample with classes of phenotypes, we defined a new distance measure, transcriptional similarity coefficient (TSC) which captures similarity of gene expression patterns between a biological pathway in two samples or populations. We showed that TSC, as an interpretable and stable distance measure in SIGN, captures all oncogenic hallmarks for breast cancer even with low sample size, by comparing healthy and patient tumor samples in the largest breast cancer dataset. In this study, we demonstrate that SIGN is a flexible, yet robust approach for classification based on transcriptomics data. Comparing early and late relapses within each molecular subtypes of breast cancer, our method enabled subtype-specific stratification of breast cancer patients into groups with significantly different survival. Moreover, we used SIGN to classify with more than 99% specificity the site of extraction of healthy and tumor samples from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets. We showed that SIGN also enables robust identification of hematopoietic stem cell and progenitors within the hematopoietic hierarchy. We further explored chemical perturbation data in the Connectivity Map (CMAP) database and showed that SIGN was able to classify seven classes of drugs based on their mechanism of action. In conclusion, we showed that SIGN can be used to achieve interpretable and robust transcriptomic-based classification of healthy and malignant samples, as well as drugs based on their known mechanism of action, supporting the generalizability and relevance of the method for the analysis of gene expression profiles.

bioinformatics↗

bioRxiv2017

ProGeM: A framework for the prioritisation of candidate causal genes at molecular quantitative trait loci

Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids, and proteins through genome-wide association studies (GWAS) represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here we present a framework for the \"Prioritisation of candidate causal Genes at Molecular QTLs\" (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritised by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of \"true positive\" causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

bioinformatics↗

bioRxiv2017

Correlative three-dimensional fluorescence and refractive index tomography: bridging the gap between molecular specificity and quantitative bioimaging

Optical diffraction tomography (ODT) provides label-free three-dimensional (3D) refractive index (RI) measurement of biological samples. However, due to the nature of the RI values of biological specimens, ODT has limited access to molecular specific information. Here, we present an optical setup combining ODT with three-channel 3D fluorescence microscopy, to enhance the molecular specificity of the 3D RI measurement. The 3D RI distribution and 3D deconvoluted fluorescence images of HeLa cells and NIH-3T3 cells are measured, and the cross-correlative analysis between RI and fluorescence of live cells are presented.

cell biology↗

bioRxiv2016

Network Modules Driving Plant Stress Response, Tolerance and Adaptation: A case study using Abscisic acid Induced Protein-protein Interactome of Arabidopsis thaliana

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding key protein-protein interaction network mediated by genes responsive to biotic and abiotic stress could help to understand the functional modules and network topologies driven genes responsive to stresses. It still remains to be an open question whether distinct protein-protein interaction networks have functional or regulatory role in mediating abiotic or biotic stress response in plants. To address this question we compiled abscisic acid responsive genes from Stress-responsive TranscrIption Factor DataBase (version 2; STIFDB2); derived protein-protein interaction network mediated by the genes from STRING database and performed biological network analyses using Cytoscape plugins. We have used Molecular Complex Detection algorithm for deriving highly connected module from the abscisic acid responsive network. Biological Network Gene Ontology tool was used to derive functional enrichment of abscisic acid responsive interaction network using GOSlim_Plants ontology. GraphletCounter was used to identify graph motifs in the network and NetworkAnalyzer was used to compute various network topological parameters. We found 26S proteasome subunits as a highly clustered module using Molecular Complex Detection algorithm. Enrichment analysis indicates that several biological processes terms including \"flower development\" are associated with the network. Results from this case study can be used to understand network properties of abiotic stress responsive genes and gene products in a model plant system.

Plant Biology↗