Unravelling metabolism of Leishmania by metabolomics

The leishmaniases are neglected tropical diseases with an urgent need for effective drugs. Better understanding of the metabolism of the causative parasites will hopefully lead to development of new compounds targeted at critical points of the parasite’s biochemical pathways. In my work I focused on the pentose phosphate pathway of Leishmania, specifically on transketolase, sugar utilisation, and comparison between insect and mammalian infective stages of the parasites. The pentose phosphate pathway (PPP) is the major cellular source of NADPH, an agent critical for oxidative stress defence. The PPP uses glucose, reduces the NADP+ cofactor and produces various sugar phosphates by mutual interconversions. One of the enzymes involved in this latter part is transketolase (TKT). A Leishmania mexicana cell line deleted in transketolase (Δtkt) was assessed regarding viability, sensitivity to a range of drugs, changes in metabolism, and infectivity. The Δtkt cell line had no obvious growth defect in the promastigote stage, but it was more sensitive to an oxidative stress inducing agent and most of the drugs tested. Most importantly, the Δtkt cells were not infective to mice, establishing TKT as a new potential drug target. Metabolomic analyses revealed multiple changes as a consequence of TKT deletion. Levels of the PPP intermediates upstream of TKT increased substantially, and were diverted into additional reactions. The perturbation triggered further changes in metabolism, resembling the ‘stringent metabolic response’ of amastigotes. The Δtkt cells consumed less glucose and glycolytic intermediates were decreased indicating a decrease in flux, and metabolic end products were diminished in production. The decrease in glycolysis was possibly caused by inhibition of fructose-1,6-bisphosphate aldolase by accumulation of the PPP intermediates 6-phosphogluconate and ribose 5-phosphate. The TCA cycle was fuelled by alternative carbon sources, most likely amino acids, instead of glucose. It remains unclear why deletion of TKT is lethal for amastigotes, increased sensitivity to oxidative stress or drop in mannogen levels may contribute, but no definite conclusions can be made. TKT localisation indicated interesting trends too. The WT enzyme is present in the cytosol and glycosomes, whereas a mutant version, truncated by ten amino acids, but retaining a C-terminal targeting sequence, localised solely to glycosomes. Surprisingly, cells expressing purely cytosolic or glycosomal TKT did not have different phenotypes regarding growth, oxidative stress sensitivity or any detected changes in metabolism. Hence, control of the subcellular localisation remains unclear as well as its function. However, these data are in agreement with the presumed semipermeable nature of the glycosome. Further, L. mexicana promastigote cultures were grown in media with different combinations of labelled glucose and ribose and their incorporation into metabolism was followed. Glucose was the preferred carbon source, but when not available, it could be fully replaced with ribose. I also compared metabolic profiles from splenic amastigotes, axenic amastigotes and promastigotes of L. donovani. Metabolomic analysis revealed a substantial drop in amino acids and other indications coherent with a stringent metabolic response in amastigotes. Despite some notable differences, axenic and splenic amastigotes demonstrated fairly similar results both regarding the total metabolic profile and specific metabolites of interest.

Hierarchically grouped 2D local features applied to edge contour localisation

One of the most significant research topics in computer vision is object detection. Most of the reported object detection results localise the detected object within a bounding box, but do not explicitly label the edge contours of the object. Since object contours provide a fundamental diagnostic of object shape, some researchers have initiated work on linear contour feature representations for object detection and localisation. However, linear contour feature-based localisation is highly dependent on the performance of linear contour detection within natural images, and this can be perturbed significantly by a cluttered background. In addition, the conventional approach to achieving rotation-invariant features is to rotate the feature receptive field to align with the local dominant orientation before computing the feature representation. Grid resampling after rotation adds extra computational cost and increases the total time consumption for computing the feature descriptor. Though it is not an expensive process if using current computers, it is appreciated that if each step of the implementation is faster to compute especially when the number of local features is increasing and the application is implemented on resource limited ”smart devices”, such as mobile phones, in real-time. Motivated by the above issues, a 2D object localisation system is proposed in this thesis that matches features of edge contour points, which is an alternative method that takes advantage of the shape information for object localisation. This is inspired by edge contour points comprising the basic components of shape contours. In addition, edge point detection is usually simpler to achieve than linear edge contour detection. Therefore, the proposed localization system could avoid the need for linear contour detection and reduce the pathological disruption from the image background. Moreover, since natural images usually comprise many more edge contour points than interest points (i.e. corner points), we also propose new methods to generate rotation-invariant local feature descriptors without pre-rotating the feature receptive field to improve the computational efficiency of the whole system. In detail, the 2D object localisation system is achieved by matching edge contour points features in a constrained search area based on the initial pose-estimate produced by a prior object detection process. The local feature descriptor obtains rotation invariance by making use of rotational symmetry of the hexagonal structure. Therefore, a set of local feature descriptors is proposed based on the hierarchically hexagonal grouping structure. Ultimately, the 2D object localisation system achieves a very promising performance based on matching the proposed features of edge contour points with the mean correct labelling rate of the edge contour points 0.8654 and the mean false labelling rate 0.0314 applied on the data from Amsterdam Library of Object Images (ALOI). Furthermore, the proposed descriptors are evaluated by comparing to the state-of-the-art descriptors and achieve competitive performances in terms of pose estimate with around half-pixel pose error.