Identification of Tsetse (Glossina spp.) using matrix-assisted laser desorption/ionisation time of flight mass spectrometry

Glossina (G.) spp. (Diptera: Glossinidae), known as tsetse flies, are vectors of African trypanosomes that cause sleeping sickness in humans and nagana in domestic livestock. Knowledge on tsetse distribution and accurate species identification help identify potential vector intervention sites. Morphological species identification of tsetse is challenging and sometimes not accurate. The matrix-assisted laser desorption/ionisation time of flight mass spectrometry (MALDI TOF MS) technique, already standardised for microbial identification, could become a standard method for tsetse fly diagnostics. Therefore, a unique spectra reference database was created for five lab-reared species of riverine-, savannah- and forest- type tsetse flies and incorporated with the commercial Biotyper 3.0 database. The standard formic acid/acetonitrile extraction of male and female whole insects and their body parts (head, thorax, abdomen, wings and legs) was used to obtain the flies' proteins. The computed composite correlation index and cluster analysis revealed the suitability of any tsetse body part for a rapid taxonomical identification. Phyloproteomic analysis revealed that the peak patterns of G. brevipalpis differed greatly from the other tsetse. This outcome was comparable to previous theories that they might be considered as a sister group to other tsetse spp. Freshly extracted samples were found to be matched at the species level. However, sex differentiation proved to be less reliable. Similarly processed samples of the common house fly Musca domestica (Diptera: Muscidae; strain: Lei) did not yield any match with the tsetse reference database. The inclusion of additional strains of morphologically defined wild caught flies of known origin and the availability of large-scale mass spectrometry data could facilitate rapid tsetse species identification in the futur

Bivalent metal complexes of a novel modified nicotinic acid hydrazide drug: Synthesis, characterization, and anti-tubercular studies

Iron(II) and manganese(II) complexes of N'-(1-(pyridin-2-yl)ethylidene)nicotinohydrazide (LH) have been synthesized and characterized by elemental analysis, IR, and 1H NMR spectroscopy. The crystal structure of the ligand has been determined by single crystal X-ray diffraction and electronic spectroscopic techniques. Crystal data for LH, C13H12N4O: Orthorhombic, space group Pbcn (no. 60), a = 18.0824(3) Å, b = 7.86555(14) Å, c = 16.1614(3) Å, V = 2298.60(7) Å3, Z = 8, T = 103 K, μ(Mo Kα) = 0.093 mm-1, Dcalc = 1.388 g/cm3, 36729 reflections measured (5.042° ≤ 2Θ ≤ 54.966°), 2633 unique (Rint = 0.0224, Rsigma = 0.0124) which were used in all calculations. The final R1 was 0.0383 (F2>2σ(F2)) and wR2 was 0.0988 (all data). The ligand was found to chelate to the metal ions through the azomethine nitrogen and amide oxygen atoms in a bidentate manner. The anti-tubercular activity of the ligand, its iron (II) and manganese (II) complexes were studied against Mycobacterium tuberculosis (ATTC 27294). The results revealed higher activity of the iron (II) complex with MIC value of 8.00±0.83 µM and a moderate activity of the manganese (II) complex having MIC value of 14.20±1.40 µM, compared to the reference drugs having MIC values of 9.41±0.92, 10.74±1.02, 25.34±2.6 µM and parent ligand with MIC value of 17.60±1.80 µM

Optimization of matrix assisted desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) for the characterization of Bacillus and Brevibacillus species

Over the past few decades there has been an increased interest in using various analytical techniques for detecting and identifying microorganisms. More recently there has been an explosion in the application of matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) for bacterial characterization, and here we optimize this approach in order to generate reproducible MS data from bacteria belonging to the genera Bacillus and Brevibacillus. Unfortunately MALDI-TOF-MS generates large amounts of data and is prone to instrumental drift. To overcome these challenges we have developed a preprocessing pipeline that includes baseline correction, peak alignment followed by peak picking that in combination significantly reduces the dimensionality of the MS spectra and corrects for instrument drift. Following this two different prediction models were used which are based on support vector machines and these generated satisfactory prediction accuracies of approximately 90%. © 2014