SEARCH · Search bioRxiv

Results for “Systems Biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 361 records · Page 20Linked to original sources

bioRxiv2017

Rice paddy Nitrospirae encode and express genes related to sulfate respiration: proposal of the new genus Candidatus Sulfobium

Nitrospirae spp. distantly related to thermophilic, sulfate-reducing Thermodesulfovibrio species are regularly observed in environmental surveys of anoxic marine and freshwater habitats. However, little is known about their genetic make-up and physiology. Here, we present the draft genome of Nitrospirae bacterium Nbg-4 as a representative of this clade and analyzed its in situ protein expression under sulfate-enriched and sulfate-depleted conditions in rice paddy soil. The genome of Nbg-4 was assembled from replicated metagenomes of rice paddy soil that was used to grow rice plants in the presence and absence of gypsum (CaSO4x2H2O). Nbg-4 encoded the full pathway of dissimilatory sulfate reduction and showed expression thereof in gypsum-amended anoxic bulk soil as revealed by parallel metaproteomics. In addition, Nbg-4 encoded the full pathway of dissimilatory nitrate reduction to ammonia, which was expressed in bulk soil without gypsum amendment. The relative abundance of Nbg-4-related metagenome reads was similar under both treatments indicating that it maintained stable populations while shifting its energy metabolism. Further genome reconstruction revealed the potential to utilize butyrate, formate, H2, or acetate as electron donor, with the Wood-Ljungdahl pathway being expressed under both conditions. Comparison to publicly available Nitrospirae genome bins confirmed that the pathway for dissimilatory sulfate reduction is also present in related Nitrospirae recovered from groundwater. Subsequent phylogenomics showed that such microorganisms form a novel genus within the phylum Nitrospirae, with Nbg-4 as a representative species. Based on the widespread occurrence of this novel genus, we propose for Nbg-4 the name Candidatus Sulfobium mesophilum, gen. nov., spec. nov.\n\nImportanceRice paddies are indispensable for food supply but are a major source of the greenhouse gas methane. If not counterbalanced by cryptic sulfur cycling, methane emission from rice paddy fields would be even higher. However, the microorganisms involved in this sulfur cycling are little understood. By using an environmental systems biology approach of Italian rice paddy soil, we could retrieve the population genome of a novel member of the phylum Nitrospirae. This microorganism encoded the full pathway of dissimilatory sulfate reduction and expressed it in situ under sulfate-enriched and anoxic conditions. Phylogenomics and comparison to environmental surveys showed that such microorganisms are actually widespread in freshwater and marine environments. At the same time, they represent a yet undiscovered genus within the little explored Nitrospirae. Our results will be important to design enrichment strategies and postgenomic studies to fully understand the contribution of these novel Nitrospirae to the global sulfur cycle.

microbiology↗

bioRxiv2017

MAGI: A Bayesian-like method for metabolite, annotation, and gene integration

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (i.e. gene annotations). Metabolite, Annotation, and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. This limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi.

bioinformatics↗

bioRxiv2017

IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics

Phytochemical constituents of medicinal plants encompass a diverse space of chemical scaffolds which can be used for rational design of novel drugs. India is rich with a flora of indigenous medicinal plants that have been used for centuries in traditional Indian medicine to treat human maladies. A comprehensive online database on the phytochemistry of Indian medicinal plants will enable the application of systems biology and cheminformatic approaches towards natural product based drug discovery. In this direction, we here present, IMPPAT, a manually curated database of Indian Medicinal Plants, Phytochemistry, And Therapeutics. IMPPAT contains 1742 Indian medicinal plants, 9596 phytochemicals and 1124 therapeutic uses which span across 27074 plant-phytochemical associations and 11514 plant-therapeutic associations. Notably, the curation effort led to a non-redundant in silico chemical library of 9596 phytochemicals with standard chemical identifiers and structure information. Using cheminformatic approaches, we have computed the physicochemical properties and drug-likeliness of the phytochemicals in IMPPAT which led to a filtered subset of 960 potential druggable phytochemicals. Moreover, a comparative analysis against FDA approved drugs suggests that majority of the druggable phytochemicals in IMPPAT are good candidates for novel prospective drugs as they have little or no structural similarity with existing drugs. The IMPPAT database is openly accessible at: https://www.imsc.res.in/~asamal/resources/imppat/home.

bioinformatics↗

bioRxiv2017

Genomic Locus Modulating Corneal Thickness in the Mouse Identifies POU6F2 as a Potential Risk of Developing Glaucoma

Purpose: Central corneal thickness (CCT) is one of the most heritable ocular traits and it is also a phenotypic risk factor for primary open angle glaucoma (POAG). The present study uses the BXD Recombinant Inbred (RI) strains to identify novel quantitative trait loci (QTLs) modulating CCT in the mouse with the potential of identifying a molecular link between CCT and risk of developing POAG.\n\nMethods: The BXD RI strain set was used to define mammalian genomic loci modulating CCT, with a total of 818 corneas measured from 61 BXD RI strains (between 60-100 days of age). The mice were anesthetized and the eyes were positioned in front of the lens of the Phoenix Micron IV Image-Guided OCT system or the Bioptigen OCT system. CCT data for each strain was averaged and used to identify quantitative trait loci (QTLs) modulating this phenotype using the bioinformatics tools on GeneNetwork (www.genenetwork.org). The candidate genes and genomic loci identified in the mouse were then directly compared with the summary data from a human primary open-angle glaucoma (POGA) genome wide association study (NEIGHBORHOOD) to determine if any genomic elements modulating mouse CCT are also risk factors for POAG.\n\nResults: This analysis revealed one significant QTL on Chr 13 and a suggestive QTL on Chr 7. The significant locus on Chr 13 (13 to 19 Mb) was examined further to define candidate genes modulating this eye phenotype. For the Chr 13 QTL in the mouse, only one gene in the region (Pou6f2) contained nonsynonymous SNPs. Of these five nonsynonymous SNPs in Pou6f2, two resulted in changes in the amino acid proline which could result in altered secondary structure affecting protein function. The 7 Mb region under the mouse Chr 13 peak distributes over 2 chromosomes in the human: Chr 1 and Chr 7. These genomic loci were examined in the NEIGHBORHOOD database to determine if they are potential risk factors for human glaucoma identified using meta-data from human GWAS. The top 50 hits all resided within one gene (POU6F2), with the highest significance level of p = 10-6 for SNP rs76319873. POU6F2 is found in retinal ganglion cells and in corneal limbal stem cells. To test the effect of POU6F2 on CCT we examined the corneas of a Pou6f2-null mice and the corneas were thinner than those of wild-type littermates. In addition, these POU6F2 RGCs die early in the DBA/2J model of glaucoma than most RGCs.\n\nConclusions: Using a mouse genetic reference panel, we identified a transcription factor, Pou6f2, that modulates CCT in the mouse. POU6F2 is also found in a subset of retinal ganglion cells and these RGCs are sensitive to injury.\n\nAuthors SummaryGlaucoma is a complex group of diseases with several known causal mutations and many known risk factors. One well-known risk factor for developing primary open angle glaucoma is the thickness of the central cornea. The present study leverages a unique blend of systems biology methods using BXD recombinant inbred mice and genome-wide association studies from humans to define a putative molecular link between a phenotypic risk factor (central corneal thickness) and glaucoma. We identified a transcription factor, POU6F2, that is found in the developing retinal ganglion cells and cornea. POU6F2 is also present in a subpopulation of retinal ganglion cells and in stem cells of the cornea. Functional studies reveal that POU6F2 is associated the central corneal thickness and with susceptibility of retinal ganglion cells to injury.

genetics↗

bioRxiv2017

Metaproteomics of colonic microbiota unveils discrete protein functions among colitic mice and control groups

Metaproteomics can greatly assist established high-throughput sequencing methodologies to provide systems biological insights into the alterations of microbial protein functionalities correlated with disease-associated dysbiosis of the intestinal microbiota. Here, we utilized the well-characterized murine T cell transfer model of colitis to find specific changes within the intestinal luminal proteome associated with inflammation. MS proteomic analysis of colonic samples permitted the identification of {bsim}10,000-12,000 unique peptides that corresponded to 5,610 protein clusters identified across three groups, including the colitic Rag1-/- T cell recipients, isogenic Rag1-/- controls, and wild-type mice. We demonstrate that the colitic mice exhibited a significant increase in Proteobacteria and Verrucomicrobia and show that such alterations in the microbial communities contributed to the enrichment of specific proteins with transcription and translation gene ontology terms. In combination with 16S sequencing, our metaproteomics-based microbiome studies provide a foundation for assessing alterations in intestinal luminal protein functionalities in a robust and well-characterized mouse model of colitis, and set the stage for future studies to further explore the functional mechanisms of altered protein functionalities associated with dysbiosis and inflammation.\n\nStatement of significance of the studyThe commensal gut microbiota is essential to maintaining health and has a primary role in digestion/metabolism, homeostasis, and protection from pathogenic bacteria. High-throughput sequencing has established Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria as the four major bacterial phyla that comprise the ecological makeup of the intestinal microbiota. However, the tremendous inter-/intra-variability in microbial composition across individuals, as well as along the length of the intestinal tract has made it difficult to definitively ascertain specific bacterial species associated with health or as drivers of disease states, including inflammatory bowel disease. In this study, we expanded upon the current metaproteomics techniques and use the robust and highly reproducible murine T cell transfer model of colitis as well as a comprehensive database of mouse, human, plant, and all microbial genomes sequenced to date to elucidate alterations in both host and gut microbial proteins associated with intestinal inflammation. Our results show that host genetics, gut microbiota, and inflammation have tremendous influences on the intestinal luminal proteomic landscape.

bioinformatics↗

bioRxiv2017

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

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

cell biology↗

bioRxiv2017

Housekeeping genes, revisited at the single-cell level

BackgroundSingle-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level are intrinsically stochastic and noisy. Yet, on cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how their expression stability can be assessed? We have developed a computational framework for ranking expression stability of genes in single cells. Here we evaluate the proposed framework and characterize SEGs derived from two scRNA-seq datasets that profile early human and mouse development.\n\nResultsHere, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets are highly reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across 10 diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells.\n\nConclusionsSEGs identified in this study have immediate utility both for understanding variation/stability of single-cell transcriptomes and for practical applications including scRNA-seq data normalization, the proposed framework can be applied to identify genes with stable expression in other scRNA-seq datasets.

bioinformatics↗

bioRxiv2017

Application of the fluctuation theorem for non-invasive force measurement in living neuronal axons

Although its importance is recently widely accepted, force measurement has been difficult in living biological systems, mainly due to the lack of the versatile non-invasive force measurement methods. The fluctuation theorem, which represents the thermodynamic properties of small fluctuating non-equilibrium systems, has been applied to the analysis of the thermodynamic properties of motor proteins in vitro. Here, we extend it to the axonal transport (displacement) of endosomes. The distribution of the displacement fluctuation had three or four distinct peaks around multiples of a unit value, which the fluctuation theorem can convert into the drag force exerted on the endosomes. The results demonstrated that a single cargo vesicle is conveyed by one to three or four units of force production.

biophysics↗

bioRxiv2017

Modeling Spatio-temporal Dynamics of Chromatin Marks

To model spatial changes of chromatin mark peaks over time we developed and applied ChromTime, a computational method that predicts regions for which peaks either expand or contract significantly or hold steady between time points. Predicted expanding and contracting peaks can mark regulatory regions associated with transcription factor binding and gene expression changes. Spatial dynamics of peaks provided information about gene expression changes beyond localized signal density changes. ChromTime detected asymmetric expansions and contractions, which for some marks associated with the direction of transcription. ChromTime facilitates the analysis of time course chromatin data in a range of biological systems.

bioinformatics↗

bioRxiv2018

Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes

It is widely assumed that cells must be physically isolated to study their molecular profiles. However, intact tissue samples naturally exhibit variation in cellular composition, which drives covariation of cell-class-specific molecular features. By analyzing transcriptional covariation in 7221 intact CNS samples from 840 individuals representing billions of cells, we reveal the core transcriptional identities of major CNS cell classes in humans. By modeling intact CNS transcriptomes as a function of variation in cellular composition, we identify cell-class-specific transcriptional differences in Alzheimers disease, among brain regions, and between species. Among these, we show that PMP2 is expressed by human but not mouse astrocytes and significantly increases mouse astrocyte size upon ectopic expression in vivo, causing them to more closely resemble their human counterparts. Our work is available as an online resource (http://oldhamlab.ctec.ucsf.edu) and provides a generalizable strategy for determining the core molecular features of cellular identity in intact biological systems.

neuroscience↗

bioRxiv2018

The ERBB-STAT3 Axis Drives Tasmanian Devil Facial Tumor Disease

The marsupial Tasmanian devil (Sarcophilus harrisii) faces extinction due to transmissible devil facial tumor disease (DFTD). To unveil the molecular underpinnings of DFTD, we designed an approach that combines sensitivity to drugs with an integrated systems-biology characterization. Sensitivity to inhibitors of the ERBB family of receptor tyrosine kinases correlated with their overexpression, suggesting a causative link. Proteomic and DNA methylation analyses revealed tumor-specific signatures linked to oncogenic signaling hubs including evolutionary conserved STAT3. Indeed, ERBB inhibition blocked phosphorylation of STAT3 and arrested cancer cells. Pharmacological blockade of ERBB signaling prevented tumor growth in a xenograft model and resulted in recovery of MHC class I gene expression. This link between the hyperactive ERBB-STAT3 axis and MHC class I mediated tumor immunosurveillance provides mechanistic insights into horizontal transmissibility and led us to the proposition of a dual chemo-immunotherapeutic strategy to save Tasmanian devils from DFTD.

cancer biology↗

bioRxiv2018

Growth arrest in the active rare biosphere

Microbial diversity in the environment is mainly concealed within the rare biosphere, which is arbitrarily defined as all species with <0.1% relative abundance. While dormancy explains a low-abundance state very well, the cellular mechanisms leading to rare but active microorganisms are not clear. We used environmental systems biology to genomically and metabolically characterize a cosmopolitan sulfate reducer that is of low abundance but highly active in peat soil, where it contributes to counterbalance methane emissions. We obtained a 98%-complete genome of this low-abundance species, Candidatus Desulfosporosinus infrequens, by metagenomics. To test for environmentally relevant metabolic activity of Ca. D. infrequens, anoxic peat soil microcosms were incubated under diverse in situ-like conditions for 36 days and analyzed by metatranscriptomics. Compared to the no-substrate control, transcriptional activity of Ca. D. infrequens increased 56- to 188-fold in incubations with sulfate and acetate, propionate, lactate, or butyrate, revealing a versatile substrate use. Cellular activation was due to a significant overexpression of genes encoding ribosomal proteins, dissimilatory sulfate reduction, and carbon-degradation pathways, but not of genes encoding DNA or cell replication. We show for the first time that a rare biosphere member transcribes metabolic pathways relevant for carbon and sulfur cycling over prolonged time periods while being growth-arrested in its lag phase.\n\nSignificanceThe microbial rare biosphere represents the largest pool of biodiversity on Earth and constitutes, in sum of all its members, a considerable part of a habitats biomass. Dormancy or starvation are typically used to explain a low-abundance state. We show that low-abundance microorganisms can be highly metabolically active while being growth-arrested over prolonged time periods. We show that this is true for microbial keystone species, such as a cosmopolitan but low-abundance sulfate reducer in wetlands that is involved in counterbalancing greenhouse gas emission. Our results challenge the central dogmas \"metabolic activity translates directly into growth\" as well as \"low abundance equals little ecosystem impact\" and provide an important step forward in understanding rare biosphere members relevant for ecosystem functions.

microbiology↗

bioRxiv2018

Optical Imaging of Metabolic Dynamics in Animals

Direct visualization of metabolic dynamics in living tissues with high spatial and temporal resolution is essential to understanding many biological processes. Here we introduce a platform that combines deuterium oxide (D2O) probing with stimulated Raman scattering microscopy (DO-SRS) to image in situ metabolic activities. Enzymatic incorporation of D2O-derived deuterium into macromolecules generates carbon-deuterium (C-D) bonds, which track biosynthesis in tissues and can be imaged by SRS in situ. Within the broad vibrational spectra of C-D bonds, we discovered lipid-, protein-, and DNA-specific Raman shifts and developed spectral unmixing methods to obtain C-D signals with macromolecular selectivity. DO-SRS enabled us to probe de novo lipogenesis in animals, image protein biosynthesis without tissue bias, and simultaneously visualize lipid and protein metabolism and reveal their different dynamics. DO-SRS, being noninvasive, universally applicable, and cost-effective, can be adapted to a broad range of biological systems to study development, tissue homeostasis, aging, and tumor heterogeneity.

biophysics↗

bioRxiv2018

Iteratively Improving Hi-C Experiments One Step at a Time

The 3D organization of eukaryotic chromosomes affects key processes such as gene expression, DNA replication, cell division, and response to DNA damage. The genome-wide chromosome conformation capture (Hi-C) approach can characterize the landscape of 3D genome organization by measuring interaction frequencies between all genomic regions. Hi-C protocol improvements and rapid advances in DNA sequencing power have made Hi-C useful to diverse biological systems, not only to elucidate the role of 3D genome structure in proper cellular function, but also to characterize genomic rearrangements, assemble new genomes, and consider chromatin interactions as potential biomarkers for diseases. Yet, the Hi-C protocol is still complex and subject to variations at numerous steps that can affect the resulting data. Thus, there is still a need for better understanding and control of factors that contribute to Hi-C experiment success and data quality. Here, we evaluate recently proposed Hi-C protocol modifications as well as often overlooked variables in sample preparation and examine their effects on Hi-C data quality. We examine artifacts that can occur during Hi-C library preparation, including microhomology-based artificial template copying and chimera formation that can add noise to the downstream data. Exploring the mechanisms underlying Hi-C artifacts pinpoints steps that should be further optimized in the future. To improve the utility of Hi-C in characterizing the 3D genome of specialized populations of cells or small samples of primary tissue, we identify steps prone to DNA loss which should be optimized to adapt Hi-C to lower cell numbers.\n\nHighlights3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point)\n\nO_LIVariability in Hi-C libraries can arise from early steps of cell preparation\nC_LIO_LIHi-C 2.0 changes to interaction capture steps also benefit 6-cutter libraries\nC_LIO_LIArtificial molecule fusions can arise during end repair and PCR, increasing noise\nC_LIO_LICommon causes of Hi-C DNA loss identified for future optimization\nC_LI

genomics↗

bioRxiv2018

CAMISIM: Simulating metagenomes and microbial communities

Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Here, we describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series and differential abundance studies, includes real and simulated strain-level diversity, and generates second and third generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT and metaSPAdes, on several thousand small data sets generated with CAMISIM. CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with truth standards for method evaluation. All data sets and the software are freely available at: https://github.com/CAMI-challenge/CAMISIM

bioinformatics↗

bioRxiv2018

An important role for periplasmic storage in Pseudomonas aeruginosa copper homeostasis revealed by a combined experimental and computational modeling study

Biological systems require precise copper homeostasis enabling metallation of cuproproteins while preventing metal toxicity. In bacteria, sensing, transport and storage molecules act in coordination to fulfill these roles. However, there is not yet a kinetic schema explaining the system integration. Here, we report a model emerging from experimental and computational approaches that describes the dynamics of copper distribution in Pseudomonas aeruginosa. Based on copper uptake experiments, a minimal kinetic model describes well the copper distribution in the wild type bacteria but is unable to explain the behavior of the mutant strain lacking CopA1, a key Cu+ efflux ATPase. The model was expanded through an iterative hypothesis-driven approach, arriving to a mechanism that considers the induction of compartmental pools and the parallel function of CopA and Cus efflux systems. Model simulations support the presence of a periplasmic copper storage with a crucial role under dyshomeostasis conditions in P. aeruginosa. Importantly, the model predicts not only the interplay of periplasmic and cytoplasmic pools but also the existence of a threshold in the concentration of external copper beyond which cells lose their ability to control copper levels.

microbiology↗

bioRxiv2018

Identification and biochemical characterization of a novel PP2C-like Ser/Thr phosphatase in E. coli

In bacteria, signaling phosphorylation is thought to occur primarily on His and Asp residues. However, phosphoproteomic surveys in phylogenetically diverse bacteria over the past decade have identified numerous proteins that are phosphorylated on Ser and/or Thr residues. Consistently, genes encoding Ser/Thr kinases are present in many bacterial genomes such as E. coli, which encodes at least three Ser/Thr kinases. Since Ser/Thr phosphorylation is a stable modification, a dedicated phosphatase is necessary to allow reversible regulation. Ser/Thr phosphatases belonging to several conserved families are found in bacteria. One family of particular interest are Ser/Thr phosphatases which have extensive sequence and structural homology to eukaryotic Ser/Thr PP2C phosphatases. These proteins, called eSTPs (eukaryotic-like Ser/Thr phosphatases), have been identified in a number of bacteria, but not in E. coli. Here, we describe a previously unknown eSTP encoded by an E. coli ORF, yegK, and characterize its biochemical properties including its kinetics, substrate specificity and sensitivity to known phosphatase inhibitors. We investigate differences in the activity of this protein in closely related E. colistrains. Finally, we demonstrate that this eSTP acts to dephosphorylate a novel Ser/Thr kinase which is encoded in the same operon.\n\nImportanceRegulatory protein phosphorylation is a conserved mechanism of signaling in all biological systems. Recent phosphoproteomic analyses of phylogenetically diverse bacteria including the model Gram-negative bacterium E. coli demonstrate that many proteins are phosphorylated on serine or threonine residues. In contrast to phosphorylation on histidine or aspartate residues, phosphorylation of serine and threonine residues is stable and requires the action of a partner Ser/Thr phosphatase to remove the modification. Although a number of Ser/Thr kinases have been reported in E. coli, no partner Ser/Thrphosphatases have been identified. Here, we biochemically characterize a novel Ser/Thr phosphatase that acts to dephosphorylate a Ser/Thr kinase that is encoded in the same operon.

microbiology↗

bioRxiv2018

isiKnock: in silico knockouts in biochemical pathways

SummaryisiKnock is a new software that automatically conducts in silico knockouts for mathematical models of biochemical pathways. The software allows for the prediction of the behavior of biological systems after single or multiple knockout. The implemented algorithm applies transition invariants and the novel concept of Manatee invariants. A knockout matrix visualizes the results. The tool enables the analysis of dependencies, for example, in signal flows from the receptor activation to the cell response at steady state.\n\nAvailability and ImplementationisiKnock is an open-source tool, freely available at http://www.bioinformatik.uni-frankfurt.de/tools/isiKnock/index.php. It requires at least Java 8 and runs under Microsoft Windows, Linux, and Mac OS.\n\nContactina.koch@bioinformatik.uni-frankfurt.de

bioinformatics↗