Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Visceral leishmaniasis is caused by a parasite called Leishmania donovani, which every year infects about half a million people and claims several thousand lives. Existing treatments are now becoming less effective due to the emergence of drug resistance. Improving our understanding of the mechanisms used by the parasite to adapt to drugs and achieve resistance is crucial for developing future treatment strategies. Unfortunately, the biological mechanism whereby Leishmania acquires drug resistance is poorly understood. Recent years have brought new technologies with the potential to increase greatly our understanding of drug resistance mechanisms. The latest mass spectrometry techniques allow the metabolome of parasites to be studied rapidly and in great detail. We have applied this approach to determine the metabolome of drug-sensitive and drug-resistant parasites isolated from patients with leishmaniasis. The data show that there are wholesale differences between the isolates and that the membrane composition has been drastically modified in drug-resistant parasites compared with drug-sensitive parasites. Our findings demonstrate that untargeted metabolomics has great potential to identify major metabolic differences between closely related parasite strains and thus should find many applications in distinguishing parasite phenotypes of clinical relevance

Computational models for inferring biochemical networks

Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.The Romanian National Authority for Scientific Research, CNDI–UEFISCDI, Project No. PN-II-PT-PCCA-2011-3.2-0917

A gene signature for post-infectious chronic fatigue syndrome

Background: At present, there are no clinically reliable disease markers for chronic fatigue syndrome. DNA chip microarray technology provides a method for examining the differential expression of mRNA from a large number of genes. Our hypothesis was that a gene expression signature, generated by microarray assays, could help identify genes which are dysregulated in patients with post-infectious CFS and so help identify biomarkers for the condition. Methods: Human genome-wide Affymetrix GeneChip arrays (39,000 transcripts derived from 33,000 gene sequences) were used to compare the levels of gene expression in the peripheral blood mononuclear cells of male patients with post-infectious chronic fatigue (n = 8) and male healthy control subjects (n = 7). Results: Patients and healthy subjects differed significantly in the level of expression of 366 genes. Analysis of the differentially expressed genes indicated functional implications in immune modulation, oxidative stress and apoptosis. Prototype biomarkers were identified on the basis of differential levels of gene expression and possible biological significance Conclusion: Differential expression of key genes identified in this study offer an insight into the possible mechanism of chronic fatigue following infection. The representative biomarkers identified in this research appear promising as potential biomarkers for diagnosis and treatment

Expression quantitative trait loci are highly sensitive to cellular differentiation state

Blood cell development from multipotent hematopoietic stem cells to specialized blood cells is accompanied by drastic changes in gene expression for which the triggers remain mostly unknown. Genetical genomics is an approach linking natural genetic variation to gene expression variation, thereby allowing the identification of genomic loci containing gene expression modulators (eQTLs). In this paper, we used a genetical genomics approach to analyze gene expression across four developmentally close blood cell types collected from a large number of genetically different but related mouse strains. We found that, while a significant number of eQTLs (365) had a consistent “static” regulatory effect on gene expression, an even larger number were found to be very sensitive to cell stage. As many as 1,283 eQTLs exhibited a “dynamic” behavior across cell types. By looking more closely at these dynamic eQTLs, we show that the sensitivity of eQTLs to cell stage is largely associated with gene expression changes in target genes. These results stress the importance of studying gene expression variation in well-defined cell populations. Only such studies will be able to reveal the important differences in gene regulation between different ce

Interpreting microarray experiments via co-expressed gene groups analysis

International audienceMicroarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of huge data using different sources of information. We propose a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co- expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology (GO)1. By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments

LOFAR observations of the quiet solar corona

The quiet solar corona emits meter-wave thermal bremsstrahlung. Coronal radio emission can only propagate above that radius, $R_\omega$, where the local plasma frequency eqals the observing frequency. The radio interferometer LOw Frequency ARray (LOFAR) observes in its low band (10 -- 90 MHz) solar radio emission originating from the middle and upper corona. We present the first solar aperture synthesis imaging observations in the low band of LOFAR in 12 frequencies each separated by 5 MHz. From each of these radio maps we infer $R_\omega$, and a scale height temperature, $T$. These results can be combined into coronal density and temperature profiles. We derived radial intensity profiles from the radio images. We focus on polar directions with simpler, radial magnetic field structure. Intensity profiles were modeled by ray-tracing simulations, following wave paths through the refractive solar corona, and including free-free emission and absorption. We fitted model profiles to observations with $R_\omega$ and $T$ as fitting parameters. In the low corona, $R_\omega < 1.5$ solar radii, we find high scale height temperatures up to 2.2e6 K, much more than the brightness temperatures usually found there. But if all $R_\omega$ values are combined into a density profile, this profile can be fitted by a hydrostatic model with the same temperature, thereby confirming this with two independent methods. The density profile deviates from the hydrostatic model above 1.5 solar radii, indicating the transition into the solar wind. These results demonstrate what information can be gleaned from solar low-frequency radio images. The scale height temperatures we find are not only higher than brightness temperatures, but also than temperatures derived from coronograph or EUV data. Future observations will provide continuous frequency coverage, eliminating the need for local hydrostatic density models

Genetical genomics: spotlight on QTL hotspots

No abstract available

Lofar low-band antenna observations of the 3C 295 and boötes fields : Source counts and ultra-steep spectrum sources

© 2018 The American Astronomical Society. All rights reserved.We present Low Frequency Array (LOFAR) Low Band observations of the Boötes and 3C 295 fields. Our images made at 34, 46, and 62 MHz reach noise levels of 12, 8, and 5 mJy beam-1, making them the deepest images ever obtained in this frequency range. In total, we detect between 300 and 400 sources in each of these images, covering an area of 17-52 deg2. From the observations, we derive Euclidean-normalized differential source counts. The 62 MHz source counts agree with previous GMRT 153 MHz and Very Large Array 74 MHz differential source counts, scaling with a spectral index of -0.7. We find that a spectral index scaling of -0.5 is required to match up the LOFAR 34 MHz source counts. This result is also in agreement with source counts from the 38 MHz 8C survey, indicating that the average spectral index of radio sources flattens toward lower frequencies. We also find evidence for spectral flattening using the individual flux measurements of sources between 34 and 1400 MHz and by calculating the spectral index averaged over the source population. To select ultra-steep spectrum (α < -1.1) radio sources that could be associated with massive high-redshift radio galaxies, we compute spectral indices between 62 MHz, 153 MHz, and 1.4 GHz for sources in the Boötes field. We cross-correlate these radio sources with optical and infrared catalogs and fit the spectral energy distribution to obtain photometric redshifts. We find that most of these ultra-steep spectrum sources are located in the 0.7 ≲ z ≲ 2.5 range.Peer reviewe

LOFAR tied-array imaging and spectroscopy of solar S bursts

Context. The Sun is an active source of radio emission that is often associated with energetic phenomena ranging from nanoflares to coronal mass ejections (CMEs). At low radio frequencies (&lt;100 MHz), numerous millisecond duration radio bursts have been reported, such as radio spikes or solar S bursts (where S stands for short). To date, these have neither been studied extensively nor imaged because of the instrumental limitations of previous radio telescopes. Aims. Here, LOw Frequency ARray (LOFAR) observations were used to study the spectral and spatial characteristics of a multitude of S bursts, as well as their origin and possible emission mechanisms. Methods. We used 170 simultaneous tied-array beams for spectroscopy and imaging of S bursts. Since S bursts have short timescales and fine frequency structures, high cadence (~50 ms) tied-array images were used instead of standard interferometric imaging, that is currently limited to one image per second. Results. On 9 July 2013, over 3000 S bursts were observed over a time period of ~8 h. S bursts were found to appear as groups of short-lived (&lt;1 s) and narrow-bandwidth (~2.5 MHz) features, the majority drifting at ~3.5 MHz s-1 and a wide range of circular polarisation degrees (2−8 times more polarised than the accompanying Type III bursts). Extrapolation of the photospheric magnetic field using the potential field source surface (PFSS) model suggests that S bursts are associated with a trans-equatorial loop system that connects an active region in the southern hemisphere to a bipolar region of plage in the northern hemisphere. Conclusions. We have identified polarised, short-lived solar radio bursts that have never been imaged before. They are observed at a height and frequency range where plasma emission is the dominant emission mechanism, however, they possess some of the characteristics of electron-cyclotron maser emission