Accurate Identification of Closely Related Mycobacterium tuberculosis Complex Species by High Resolution Tandem Mass Spectrometry

Rapid and accurate differentiation of Mycobacterium tuberculosis complex (MTBC) species from other mycobacterium is essential for appropriate therapeutic management, timely intervention for infection control and initiation of appropriate health care measures. However, routine clinical characterization methods for Mycobacterium tuberculosis (Mtb) species remain both, time consuming and labor intensive. In the present study, an innovative liquid Chromatography-Mass Spectrometry method for the identification of clinically most relevant Mycobacterium tuberculosis complex species is tested using a model set of mycobacterium strains. The methodology is based on protein profiling of Mycobacterium tuberculosis complex isolates, which are used as markers of differentiation. To test the resolving power, speed, and accuracy of the method, four ATCC type strains and 37 recent clinical isolates of closely related species were analyzed using this new approach. Using different deconvolution algorithms, we detected hundreds of individual protein masses, with a subpopulation of these functioning as species-specific markers. This assay identified 216, 260, 222, and 201 proteoforms for M. tuberculosis ATCC 27294™, M. microti ATCC 19422™, M. africanum ATCC 25420™, and M. bovis ATCC 19210™ respectively. All clinical strains were identified to the correct species with a mean of 95% accuracy. Our study successfully demonstrates applicability of this novel mass spectrometric approach to identify clinically relevant Mycobacterium tuberculosis complex species that are very closely related and difficult to differentiate with currently existing methods. Here, we present the first proof-of-principle study employing a fast mass spectrometry-based method to identify the clinically most prevalent species within the Mycobacterium tuberculosis species complex

Global modeling of transcriptional responses in interaction networks

Motivation: Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions, and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms between physiological conditions, and potentially as yet unknown processes. We introduce a novel approach for organism-wide discovery and analysis of transcriptional responses in interaction networks. The method searches for local, connected regions in a network that exhibit coordinated transcriptional response in a subset of conditions. Known interactions between genes are used to limit the search space and to guide the analysis. Validation on a human pathway network reveals physiologically coherent responses, functional relatedness between physiological conditions, and coordinated, context-specific regulation of the genes. Availability: Implementation is freely available in R and Matlab at http://netpro.r-forge.r-project.orgComment: 19 pages, 13 figure

Radar—CubeSat transionospheric HF propagation observations:Suomi 100 satellite and EISCAT HF facility

Abstract Radio waves provide a useful diagnostic tool to investigate the properties of the ionosphere because the ionosphere affects the transmission and properties of high frequency (HF) electromagnetic waves. We have conducted a transionospheric HF-propagation research campaign with a nanosatellite on a low-Earth polar orbit and the EISCAT HF transmitter facility in Tromsø, Norway, in December 2020. In the active measurement, the EISCAT HF facility transmitted sinusoidal 7.953 MHz signal which was received with the High frEquency rAdio spectRomEteR (HEARER) onboard 1 Unit (size: 10 × 10 × 10 cm) Suomi 100 space weather nanosatellite. Data analysis showed that the EISCAT HF signal was detected with the satellite’s radio spectrometer when the satellite was the closest to the heater along its orbit. Part of the observed variations seen in the signal was identified to be related to the heater’s antenna pattern and to the transmitted pulse shapes. Other observed variations can be related to the spatial and temporal variations of the ionosphere and its different responses to the used transmission frequencies and to the transmitted O- and X-wave modes. Some trends in the observed signal may also be associated to changes in the properties of ionospheric plasma resulting from the heater’s electromagnetic wave energy. This paper is, to authors’ best knowledge, the first observation of this kind of “self-absorption” measured from the transionospheric signal path from a powerful radio source on the ground to the satellite-borne receiver