The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k-means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a "gray area" and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k-means nearest neighbor and the best approximation of positioning real zeros.
Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.
l-Arginine is an essential amino acid in Leishmania (Leishmania) amazonensis metabolism. A key enzyme for parasite l-arginine metabolism is arginase (ARG) that uses arginine to produce urea and ornithine, a precursor of polyamine pathway guaranteeing parasite replication in both insect and mammal hosts. There is an alternative pathway to produce ornithine via l-proline and glutamate, but this mechanism is not described in Leishmania. In the mammal host, two enzymes can use l-arginine as substrate, the host ARG and the induced nitric oxide synthase that produces nitric oxide. The competition between induced nitric oxide synthase and both parasite and host ARG can favor the success of the infection or its control. Here, we established the metabolomics profile of the polyamine pathway of wild type (WT) L. (L.) amazonensis, submitted or not to l-arginine starvation, and compared to the ARG-knockout mutant (arg ). Our results indicated that arginine starvation induces a decrease in arginine, ornithine, and putrescine, but we could not detect the significative level changes of spermidine, spermine, or agmatine. However, the absence of ARG on the arg induced an increase of arginine and citrulline levels, but decreased the levels of ornithine and putrescine. Similarly to the WT arginine-starved parasites, the arg parasites presented lower levels of proline when compared to the WT ones. This could be indicative of an alternative pathway to surpass the enzyme or its substrate absence.
With the World Health Organization reporting over 30,000 deaths and 200,000 to 400,000 new cases annually, visceral leishmaniasis is a serious disease affecting some of the world's poorest people. As drug resistance continues to rise, there is a huge unmet need to improve treatment. Miltefosine remains one of the main treatments for leishmaniasis, yet its mode of action (MoA) is still unknown. Understanding the MoA of this drug and parasite response to treatment could help pave the way for new and more successful treatments for leishmaniasis. A novel method has been devised to study the metabolome and lipidome of Leishmania donovani axenic amastigotes treated with miltefosine. Miltefosine caused a dramatic decrease in many membrane phospholipids (PLs), in addition to amino acid pools, while sphingolipids (SLs) and sterols increased. Leishmania major promastigotes devoid of SL biosynthesis through loss of the serine palmitoyl transferase gene (ΔLCB2) were 3-fold less sensitive to miltefosine than wild-type (WT) parasites. Changes in the metabolome and lipidome of miltefosine-treated L. major mirrored those of L. donovani. A lack of SLs in the ΔLCB2 mutant was matched by substantial alterations in sterol content. Together, these data indicate that SLs and ergosterol are important for miltefosine sensitivity and, perhaps, MoA.
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