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Results for “Molecular Biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Sexually divergent DNA methylation programs with hippocampal aging

DNA methylation is a central regulator of genome function and altered methylation patterns are indicative of biological aging and mortality. Age-related cellular, biochemical, and molecular changes in the hippocampus lead to cognitive impairments and greater vulnerability to neurodegenerative disease that varies between the sexes. The role of hippocampal epigenomic changes with aging in these processes is unknown as no genome-wide analyses of age-related methylation changes have considered the factor of sex in a controlled animal model. High-depth, genome-wide bisulfite sequencing of young (3 month) and old (24 month) male and female mouse hippocampus revealed that while total genomic methylation amounts did not change with aging, specific sites in CG and non-CG (CH) contexts demonstrated age-related increases or decreases in methylation that were predominantly sexually divergent. Differential methylation with age for both CG and CH sites was enriched in intergenic, and intronic regions and under-represented in promoters, CG islands and specific enhancer regions in both sexes suggesting that certain genomic elements are especially labile with aging, even if the exact genomic loci altered are predominantly sex-specific. Life-long sex differences in autosomal methylation at CG and CH sites were also observed. The lack of genome-wide hypomethylation, sexually divergent aging response, and autosomal sex differences at CG sites were confirmed in human data. These data reveal sex as a previously unappreciated central factor of hippocampal epigenomic changes with aging. In total, these data demonstrate an intricate regulation of DNA methylation with aging by sex, cytosine context, genomic location, and methylation level.

neuroscience↗

bioRxiv2018

The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic

Esophageal Adenocarcinoma (EAC) is a poor prognosis cancer type with rapidly rising incidence. Our understanding of genetic events which drive EAC development is limited and there are few molecular biomarkers for prognostication or therapeutics. We have accumulated a cohort of 551 genomically characterised EACs (73% WGS and 27% WES) with clinical annotation and matched RNA-seq. Using a variety of driver gene detection methods, we discover 77 EAC driver genes (73% novel) and 21 non-coding driver elements (95% novel), and describe mutation and CNV types with specific functional impact. We identify a mean of 4.4 driver events per case derived from both copy number events and mutations. We compare driver mutation rates to the exome-wide mutational excess calculated using Non-synonymous vs Synonymous mutation rates (dNdS). We observe mutual exclusivity or co-occurrence of events within and between a number of EAC pathways (GATA factors, Core Cell cycle genes, TP53 regulators and the SWI/SNF complex) suggestive of important functional relationships. These driver variants correlate with tumour differentiation, sex and prognosis. Poor prognostic indicators (SMAD4, GATA4) are verified in independent cohorts with significant predictive value. Over 50% of EACs contain sensitising events for CDK4/6 inhibitors which are highly correlated with clinically relevant sensitivity in a panel EAC cell lines and organoids.

cancer biology↗

bioRxiv2018

Insights on protein thermal stability: a graph representation of molecular interactions

Understanding the molecular mechanisms of thermal stability is a challenge in protein biology. Indeed, knowing the temperature at which proteins are stable has important theoretical implications, which are intimately linked with properties of the native fold, and a wide range of potential applications from drug design to the optimization of enzyme activity.\n\nHere, we present a novel graph-theoretical framework to assess thermal stability based on the structure without any a priori information. In our approach we describe proteins as energy-weighted graphs and compare them using ensembles of interaction networks. Investigating the position of specific interactions within the 3D native structure, we developed a parameter-free network descriptor that permits to distinguish thermostable and mesostable proteins with an accuracy of 76% and Area Under the Roc Curve of 78%.

bioinformatics↗

bioRxiv2018

Transcriptional regulatory mechanisms of fibrosis development in mouse lung tissue exposed to carbon nanotubes

BackgroundCarbon nanotubes (CNTs) usage has rapidly increased in the last few decades due to their unique properties, exploited in various industrial and commercial products. Certain types of CNTs cause adverse health effects, including chronic inflammation and fibrosis. Despite the large number of in vitro and in vivo studies evaluating these effects, many important questions remain unanswered due to a lack of mechanistic understanding of how CNTs induce cellular stress responses. In order to predict CNT toxicity, it is important to understand which transcriptional programs are specifically activated in response to CNTs, and what similarities and differences exist in relation to other toxic inducers exerting similar adverse effects.\n\nResultsA systems biology approach was applied to reveal complex interactions at the molecular level in mouse lung tissue in response to different fibrosis inducers: two types of multi-walled CNTs, NM-401 and NRCWE-26, and bleomycin (BLM). Based on mRNA gene expression profiles, we inferred gene regulatory networks (GRNs) to capture functional hierarchical regulatory structures between genes and their regulators. We found that activities of the transcription factors (TFs) Myc, Arid5a and Mxd1 were associated with the regulation of cytokine transcription in response to CNTs, while in response to BLM treatment, Myc was associated with p53 signaling. TF Litaf was identified as the essential regulator for noncanonical signaling of TLR2/4 driven by CNTs. Despite the different nature of the lung injury caused by CNTs and BLM, we identified common stress response modules, that included DNA damage (TFs: E2f8, E2f1, Foxm1), M1/M2 macrophage polarization (TF: Mafb), Interferon response (TFs: Irf7, Stat2 and Irf9) for all agents.\n\nConclusionsThese results suggest that the reconstruction and analysis of TF-centric gene interaction networks can reveal key targets and regulators of cellular stress responses to toxic agents.

pharmacology and toxicology↗

bioRxiv2015

More than meets the eye: diversity and geographic patterns in sea cucumbers

Estimates for the number of species in the sea vary by orders of magnitude. Molecular taxonomy can greatly speed up screening for diversity and evaluating species boundaries, while gaining insights into the biology of the species. DNA barcoding with a region of cytochrome oxidase 1 (COI) is now widely used as a first pass for molecular evaluation of diversity, as it has good potential for identifying cryptic species and improving our understanding of marine biodiversity. We present the results of a large scale barcoding effort for holothuroids (sea cucumbers). We sequenced 3048 individuals from numerous localities spanning the diversity of habitats in which the group occurs, with a particular focus in the shallow tropics (Indo-Pacific and Caribbean) and the Antarctic region. The number of cryptic species is much higher than currently recognized. The vast majority of sister species have allopatric distributions, with species showing genetic differentiation between ocean basins, and some are even differentiated among archipelagos. However, many closely related and sympatric forms, that exhibit distinct color patterns and/or ecology, show little differentiation in, and cannotbe separatedby, COI sequence data. This pattern is much more common among echinoderms than among molluscs or arthropods, and suggests that echinoderms acquire reproductive isolation at a much faster pace than other marine phyla. Understanding the causes behind such patterns will refine our understanding of diversification and biodiversity in the sea.

Evolutionary Biology↗

bioRxiv2016

Gene tree discordance generates patterns of diminishing convergence over time

Phenotypic convergence is an exciting outcome of adaptive evolution, occurring when species find similar solutions to the same problem. Unraveling the molecular basis of convergence provides a way to link genotype to adaptive phenotypes, but can also shed light on the extent to which evolution is repeatable and predictable. Many recent genome-wide studies have uncovered a striking pattern of diminishing convergence over time, ascribing this pattern to the presence of intramolecular epistatic interactions. Here, we consider gene tree discordance as an alternative driver of convergence levels over time. We demonstrate that gene tree discordance can produce patterns of diminishing convergence by itself, and that controlling for discordance as a cause of apparent convergence makes the pattern disappear. We also show that synonymous substitutions, where neither selection nor epistasis should be prevalent, have the same diminishing pattern of molecular convergence among closely related primate species. Finally, we demonstrate that even in situations where biological discordance is not possible, errors in species tree inference can drive these same patterns. Though intramolecular epistasis is undoubtedly affecting many proteins, our results suggest an additional explanation for this widespread pattern. These results contribute to a growing appreciation not just of the presence of gene tree discordance, but of the unpredictable effects this discordance can have on analyses of molecular evolution.

Evolutionary Biology↗

bioRxiv2018

Ancestral Reconstruction of Protein Interaction Networks

The molecular and cellular basis of novelty is a major open question in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term \"phylogenetic latent variable models\" (PLVMs, pronounced \"plums\") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.

evolutionary biology↗

bioRxiv2016

Network Identification Methods

Recently, network inference algorithms have grown tremendously in the field of systems biology because network identification is essential for understanding relationships between regulation mechanisms for genes, elucidating functional mechanisms underlying cellular processes, as well as identifying molecular targets for discoveries in medicines. This article provides a brief overview of different approaches used to identify biological networks and reviews recent advances in network identification.

Systems Biology↗

bioRxiv2017

LM6-M: a high avidity rat monoclonal antibody to pectic α-1,5-L-arabinan

1,5-arabinan is an abundant structural feature of side chains of pectic rhamnogalacturonan-I which is a matrix constituent of plant cell walls. The study of arabinan in cells and tissues is driven by putative roles for this polysaccharide in the generation of cell wall and organ mechanical properties. The biological function(s) of arabinan is still uncertain and high quality molecular tools are required to detect its occurrence and monitor its dynamics. Here we report a new rat monoclonal antibody, LM6-M, similar in specificity to the published rat monoclonal antibody LM6 (Willats et al. (1998) Carbohydrate Research 308: 149-152). LM6-M is of the IgM immunoglobulin class and has a higher avidity for -1-5-L-arabinan than LM6. LM6-M displays high sensitivity in its detection of arabinan in in-vitro assays such as ELISA and epitope detection chromatography and in in-situ analyses.\n\nAbbreviations

plant biology↗

bioRxiv2017

Multi-omic analysis of a hyper-diverse plant metabolic pathway reveals evolutionary routes to biological innovation

The diversity of life on Earth is a result of continual innovations in molecular networks influencing morphology and physiology. Plant specialized metabolism produces hundreds of thousands of compounds, offering striking examples of these innovations. To understand how this novelty is generated, we investigated the evolution of the Solanaceae family-specific, trichome-localized acylsugar biosynthetic pathway using a combination of mass spectrometry, RNA-seq, enzyme assays, RNAi and phylogenetics in non-model species. Our results reveal that hundreds of acylsugars are produced across the Solanaceae family and even within a single plant, revealing this phenotype to be hyper-diverse. The relatively short biosynthetic pathway experienced repeated cycles of innovation over the last 100 million years that include gene duplication and divergence, gene loss, evolution of substrate preference and promiscuity. This study provides mechanistic insights into the emergence of plant chemical novelty, and offers a template for investigating the [~]300,000 non-model plant species that remain underexplored.

genomics↗

bioRxiv2016

A simple molecular mechanism explains multiple patterns of cell-size regulation

Increasingly accurate and massive data have recently shed light on the fundamental question of how cells maintain a stable size trajectory as they progress through the cell cycle. Microbes seem to use strategies ranging from a pure sizer, where the end of a given phase is triggered when the cell reaches a critical size, to pure adder, where the cell adds a constant size during a phase. Yet the biological origins of the observed spectrum of behavior remain elusive. We analyze a molecular size-control mechanism, based on experimental data from the yeast S. cerevisiae, that gives rise to behaviors smoothly interpolating between adder and sizer. The size-control is obtained from the titration of a repressor protein by an activator protein that accumulates more rapidly with increasing cell size. Strikingly, the size-control is composed of two different regimes: for small initial cell size, the size-control is a sizer, whereas for larger initial cell size, is is an imperfect adder. Our model thus indicates that the adder and critical size behaviors may just be different dynamical regimes of a single simple biophysical mechanism.

biophysics↗

bioRxiv2016

Knowledge Formalization and High-Throughput Data Visualization Using Signaling Network Maps

Generation and usage of high-quality molecular signalling network maps can be augmented by standardising notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps, and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.

systems biology↗

bioRxiv2017

Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

bioinformatics↗

bioRxiv2017

Embryonic Exposure to Valproic Acid Disrupts Social Predispositions in Newly-Hatched Chicks

Biological predispositions to attend to visual cues, such as those associated with face-like stimuli or with biological motion, guide social behavior from the first moments of life and have been documented in human neonates, infant monkeys and newly-hatched domestic chicks. In human neonates at high familial risk of Autism Spectrum Disorder (ASD), a lack of such predispositions has been recently reported. Prompted by these observations, we modeled ASD behavioral deficit in newborn chicks, using embryonic exposure to valproic acid (VPA), the histone deacetylases (HDACs) inhibitor that in humans is associated with an increased risk for developing ASD. We assessed spontaneous predispositions in newly-hatched, visually-naive chicks, by comparing responses to a stuffed hen vs. a scrambled version of it. We found that social predispositions were abolished in VPAtreated chicks. In contrast, experience-dependent learning mechanisms associated with filial imprinting were not affected. Our results indicate a specific effect of VPA on the development of biologically-predisposed social orienting mechanisms, opening new perspectives to investigate the molecular and neurobiological mechanisms involved in early ASD symptoms.

neuroscience↗

bioRxiv2017

A simple and powerful analysis of lateral subdiffusion using single particle tracking

In biological membranes many factors such as cytoskeleton, lipid composition, crowding and molecular interactions deviate lateral diffusion from the expected random walks. These factors have different effects on diffusion but act simultaneously so the observed diffusion is a complex mixture of diffusive behaviors (directed, >Brownian, anomalous or confined). Therefore commonly used approaches to quantify diffusion based on averaging of the displacements, such as the mean square displacement, are not adapted to the analysis of this heterogeneity. We introduce a new parameter, the packing coefficient Pc, which gives an estimate of the degree of free movement that a molecule displays in a period of time independently of its global diffusivity. Applying this approach to two different situations (diffusion of a lipid probe and trapping of receptors at synapses), we show that Pc detected and localized temporary changes of diffusive behavior both in time and in space. More importantly, it allowed the detection of periods with very high confinement (~immobility), their frequency and duration, and thus it can be used to calculate the effective kon and koff of scaffolding interactions such those that immobilize receptors at synapses.

biophysics↗

bioRxiv2018

Switch-like activation of Bruton’s tyrosine kinase by membrane-mediated dimerization

The transformation of molecular binding events into cellular decisions is the basis of most biological signal transduction. A fundamental challenge faced by these systems is that protein-ligand chemical affinities alone generally result in poor sensitivity to ligand concentration, endangering the system to error. Here, we examine the lipid-binding pleckstrin homology and Tec homology (PH-TH) module of Brutons tyrosine kinase (Btk) Using fluorescence correlation spectroscopy (FCS) and membrane-binding kinetic measurements, we identify a self-contained phosphatidylinositol (3,4,5)-trisphosphate (PIP3) sensing mechanism that achieves switch-like sensitivity to PIP3 levels, surpassing the intrinsic affinity discrimination of PIP3:PH binding. This mechanism employs multiple PIP3 binding as well as dimerization of Btk on the membrane surface. Mutational studies in live cells confirm that this mechanism is critical for activation of Btk in vivo. These results demonstrate how a single protein module can institute a minimalist coincidence detection mechanism to achieve high-precision discrimination of ligand concentration.

biophysics↗

bioRxiv2018

The influence of X chromosome variants on trait neuroticism.

Autosomal variants have successfully been associated with trait neuroticism in genome-wide analysis of adequately-powered samples. But such studies have so far excluded the X chromosome from analysis. Here, we report genetic association analyses of X chromosome and XY pseudoautosomal single nucleotide polymorphisms (SNPs) and trait neuroticism using UK Biobank samples (N = 405,274). Significant association was found with neuroticism on the X chromosome for 204 markers found within three independent loci (a further 783 were suggestive). Most of these significant neuroticism-related X chromosome variants were located in intergenic regions (n = 713). Involvement of HS6ST2, which has been previously associated with sociability behaviour in the dog, was supported by single SNP and gene-based tests. We found that the amino acid and nucleotide sequences are highly conserved between dogs and humans. From the suggestive X chromosome variants, there were 19 nearby genes which could be linked to gene ontology information. Molecular function was primarily related to binding and catalytic activity; notable biological processes were cellular and metabolic, and nucleic acid binding and transcription factor protein classes were most commonly involved. X-variant heritability of neuroticism was estimated at 0.34% (SE = 0.07). A polygenic X-variant score created in an independent sample (maximum N {approx} 7300) did not predict significant variance in neuroticism, psychological distress, or depressive disorder. We conclude that the X chromosome harbours significant variants influencing neuroticism, and might prove important for other quantitative traits and complex disorders.

genetics↗

bioRxiv2017

Paleophenotype Reconstruction Of Carbon Fixation Proteins As A Window Into Historic Biological States

Two datasets, the geologic record and the genetic content of extant organisms, provide complementary insights into the history of how key molecular components have shaped or driven global environmental and macroevolutionary trends. Changes in global physicochemical modes over time are thought to be a consistent feature of this relationship between Earth and life, as life is thought to have been optimizing protein functions for the entirety of its [~]3.8 billion years of history on Earth. Organismal survival depends on how well critical genetic and metabolic components can adapt to their environments, reflecting an ability to optimize efficiently to changing conditions. The geologic record provides an array of biologically independent indicators of macroscale atmospheric and oceanic composition, but provides little in the way of the exact behavior of the molecular components that influenced the compositions of these reservoirs. By reconstructing sequences of proteins that might have been present in ancient organisms, we can identify a subset of possible sequences that may have been optimized to these ancient environmental conditions. How can extant life be used to reconstruct ancestral phenotypes? Configurations of ancient sequences can be inferred from the diversity of extant sequences, and then resurrected in the lab to ascertain their biochemical attributes. One way to augment sequence-based, single-gene methods to obtain a richer and more reliable picture of the deep past, is to resurrect inferred ancestral protein sequences in living organisms, where their phenotypes can be exposed in a complex molecular-systems context, and to then link consequences of those phenotypes to biosignatures that were preserved in the independent historical repository of the geological record. As a first-step beyond single molecule reconstruction to the study of functional molecular systems, we present here the ancestral sequence reconstruction of the beta-carbonic anhydrase protein. We assess how carbonic anhydrase proteins meet our selection criteria for reconstructing ancient biosignatures in the lab, which we term paleophenotype reconstruction.

evolutionary biology↗