Differential Dynamic Microscopy to characterize Brownian motion and bacteria motility

We have developed a lab work module where we teach undergraduate students how to quantify the dynamics of a suspension of microscopic particles, measuring and analyzing the motion of those particles at the individual level or as a group. Differential Dynamic Microscopy (DDM) is a relatively recent technique that precisely does that and constitutes an alternative method to more classical techniques such as dynamics light scattering (DLS) or video particle tracking (VPT). DDM consists in imaging a particle dispersion with a standard light microscope and a camera. The image analysis requires the students to code and relies on digital Fourier transform to obtain the intermediate scattering function, an autocorrelation function that characterizes the dynamics of the dispersion. We first illustrate DDM on the textbook case of colloids where we measure the diffusion coefficient. Then we show that DDM is a pertinent tool to characterize biologic systems such as motile bacteria i.e.bacteria that can self propel, where we not only determine the diffusion coefficient but also the velocity and the fraction of motile bacteria. Finally, so that our paper can be used as a tutorial to the DDM technique, we have joined to this article movies of the colloidal and bacterial suspensions and the DDM algorithm in both Matlab and Python to analyze the movies

Borrelia persica infection in dogs and cats: clinical manifestations, clinicopathological findings and genetic characterization

Background: Relapsing fever (RF) is an acute infectious disease caused by arthropod-borne spirochetes of the genus Borrelia. The disease is characterized by recurrent episodes of fever that concur with spirochetemia. The RF borrelioses include louse-borne RF caused by Borrelia recurrentis and tick-borne endemic RF transmitted by argasid soft ticks and caused by several Borrelia spp. such as B. crocidurae, B. coriaceae, B. duttoni, B. hermsii, B. hispanica and B. persica. Human infection with B. persica is transmitted by the soft tick Ornithodoros tholozani and has been reported from Iran, Israel, Egypt, India, and Central Asia. Methods: During 2003-2015, five cats and five dogs from northern, central and southern Israel were presented for veterinary care and detected with borrelia spirochetemia by blood smear microscopy. The causative infective agent in these animals was identified and characterized by PCR from blood and sequencing of parts of the flagellin (flab), 16S rRNA and glycerophosphodiester phosphodiestrase (GlpQ) genes. Results: All animals were infected with B. persica genetically identical to the causative agent of human RF. Phylogenetic analysis indicated that DNA sequences from these pet carnivores clustered together with B. persica genotypes I and II from humans and O. tholozani ticks and distinctly from other RF Borrelia spp. The main clinical findings in cats included lethargy, anorexia, anemia in 5/5 cats and thrombocytopenia in 4/5. All dogs were lethargic and anorectic, 4/5 were febrile and anemic and 3/5 were thrombocytopenic. Three dogs were co-infected with Babesia spp. The animals were all treated with antibiotics and the survival rate of both dogs and cats was 80 %. The cat and dog that succumbed to disease died one day after the initiation of antibiotic treatment, while survival in the others was followed by the rapid disappearance of spirochetemia. Conclusions: This is the first report of disease due to B. persica infection in cats and the first case series in dogs. Infection was associated with anemia and thrombocytopenia. Fever was more frequently observed in dogs than cats. Domestic canines and felines suffer from clinical disease due to B. persica infection and may also serve as sentinels for human infection

Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model

Motivation: The availability of modern sequencing techniques has led to a rapid increase in the amount of reconstructed metabolic networks. Using these models as a platform for the analysis of high throughput transcriptomic, proteomic and metabolomic data can provide valuable insight into conditional changes in the metabolic activity of an organism. While transcriptomics and proteomics provide important insights into the hierarchical regulation of metabolic flux, metabolomics shed light on the actual enzyme activity through metabolic regulation and mass action effects. Here we introduce a new method, termed integrative omics-metabolic analysis (IOMA) that quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models, to more accurately predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically derived flux estimations

Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer

Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build > 280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.Toxicolog

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration

Toxicolog

Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling

Systems biology modeling typically requires quantitative experimental data such as intracellular concentrations or copy numbers per cell. In order to convert population-averaging omics measurement data to intracellular concentrations or cellular copy numbers, the total cell volume and number of cells in a sample need to be known. Unfortunately, even for the often studied model bacterium Escherichia coli this information is hardly available and furthermore, certain measures (e.g. cell volume) are also dependent on the growth condition. In this work, we have determined these basic data for E. coli cells when grown in 22 different conditions so that respective data conversions can be done correctly. First, we determine growth-rate dependent cell volumes. Second, we show that in a 1 ml E. coli sample at an optical density (600 nm) of 1 the total cell volume is around 3.6 µl for all conditions tested. Third, we demonstrate that the cell number in a sample can be determined on the basis of the sample's optical density and the cells' growth rate. The data presented will allow for conversion of E. coli measurement data normalized to optical density into volumetric cellular concentrations and copy numbers per cell - two important parameters for systems biology model development

Pragmatic Language and School Related Linguistic Abilities in Siblings of Children with Autism

Siblings of probands with autism spectrum disorders are at higher risk for developing the broad autism phenotype (BAP). We compared the linguistic abilities (i.e., pragmatic language, school achievements, and underling reading processes) of 35 school-age siblings of children with autism (SIBS-A) to those of 42 siblings of children with typical development. Results indicated lower pragmatic abilities in a subgroup of SIBS-A identified with BAP related difficulties (SIBS-A-BAP) whereas school achievements and reading processes were intact. Furthermore, among SIBS-A-BAP, significant negative correlations emerged between the severity scores on the Autism Diagnostic Observation Schedule and full and verbal IQ scores. These results are discussed in the context of the developmental trajectories of SIBS-A and in relation to the BAP

Metabolic Networks of Sodalis glossinidius: A Systems Biology Approach to Reductive Evolution

Background: Genome reduction is a common evolutionary process affecting bacterial lineages that establish symbiotic or pathogenic associations with eukaryotic hosts. Such associations yield highly reduced genomes with greatly streamlined metabolic abilities shaped by the type of ecological association with the host. Sodalis glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few complete genomes available of a bacterium at the initial stages of this process. In the present study, genome reduction is studied from a systems biology perspective through the reconstruction and functional analysis of genome-scale metabolic networks of S. glossinidius. Results: The functional profile of ancestral and extant metabolic networks sheds light on the evolutionary events underlying transition to a host-dependent lifestyle. Meanwhile, reductive evolution simulations on the extant metabolic network can predict possible future evolution of S. glossinidius in the context of genome reduction. Finally, knockout simulations in different metabolic systems reveal a gradual decrease in network robustness to different mutational events for bacterial endosymbionts at different stages of the symbiotic association. Conclusions: Stoichiometric analysis reveals few gene inactivation events whose effects on the functionality of S. glossinidius metabolic systems are drastic enough to account for the ecological transition from a free-living to hostdependent lifestyle. The decrease in network robustness across different metabolic systems may be associated with th

Disentangling the heterogeneity of autism spectrum disorder through genetic findings

Autism spectrum disorder (ASD) represents a heterogeneous group of disorders, which presents a substantial challenge to diagnosis and treatment. Over the past decade, considerable progress has been made in the identification of genetic risk factors for ASD that define specific mechanisms and pathways underlying the associated behavioural deficits. In this Review, we discuss how some of the latest advances in the genetics of ASD have facilitated parsing of the phenotypic heterogeneity of this disorder. We argue that only through such advances will we begin to define endophenotypes that can benefit from targeted, hypothesis-driven treatments. We review the latest technologies used to identify and characterize the genetics underlying ASD and then consider three themes—single-gene disorders, the gender bias in ASD, and the genetics of neurological comorbidities—that highlight ways in which we can use genetics to define the many phenotypes within the autism spectrum. We also present current clinical guidelines for genetic testing in ASD and their implications for prognosis and treatment