Disentangling genetic and epigenetic determinants of ultrafast adaptation

A major rationale for the advocacy of epigenetically mediated adaptive responses is that they facilitate faster adaptation to environmental challenges. This motivated us to develop a theoretical–experimental framework for disclosing the presence of such adaptation‐speeding mechanisms in an experimental evolution setting circumventing the need for pursuing costly mutation–accumulation experiments. To this end, we exposed clonal populations of budding yeast to a whole range of stressors. By growth phenotyping, we found that almost complete adaptation to arsenic emerged after a few mitotic cell divisions without involving any phenotypic plasticity. Causative mutations were identified by deep sequencing of the arsenic‐adapted populations and reconstructed for validation. Mutation effects on growth phenotypes, and the associated mutational target sizes were quantified and embedded in data‐driven individual‐based evolutionary population models. We found that the experimentally observed homogeneity of adaptation speed and heterogeneity of molecular solutions could only be accounted for if the mutation rate had been near estimates of the basal mutation rate. The ultrafast adaptation could be fully explained by extensive positive pleiotropy such that all beneficial mutations dramatically enhanced multiple fitness components in concert. As our approach can be exploited across a range of model organisms exposed to a variety of environmental challenges, it may be used for determining the importance of epigenetic adaptation‐speeding mechanisms in general.     

Quantitative Proteomics and Metabolomics Analysis of Normal Human Cerebrospinal Fluid Samples*

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.The analysis of CSF¹ is indispensable in the diagnosis and understanding of various neurodegenerative CNS disorders (1–3). CSF is a fluid that has different functions, such as the protection of the brain from outside forces, transport of biological substances, and excretion of toxic and waste substances. It is in close contact with the extracellular fluid of the brain. Therefore, the composition of CSF can reflect biological processes of the brain (4). By discovering the characterization of the proteome and metabolome of CSF we may gain better insight on the pathogenesis of CNS disorders. This would be significant because, for many of these disorders, the etiology is still unclear.CSF is produced in the ventricles of the brain and in the subarachnoidal spaces. Humans normally produce around 500 mL of CSF each day, and the total volume of CSF at a given time is approximately 150 mL. CSF reflects the composition of blood plasma, although the concentrations of most proteins and metabolites in CSF are lower. However, individual proteins and metabolites can act differently. Active transport from blood and secretion from the brain contribute to the specific composition of CSF. This composition can be disturbed in neurological disorders (5–6). Since CNS-specific proteins and metabolites are typically low in abundance compared with their levels in blood, this change in composition is more likely to be found in CSF because in blood the more abundant plasma proteins can completely mask the signal of the less abundant proteins. Also, if the disease markers do not cross the blood-brain-barrier, then the CSF is the only viable biofluid source. Therefore, CSF might be an excellent source for biomarker discovery for CNS disorders if we follow the hypothesis that neurological diseases induce alterations in CSF protein and metabolite levels.Analysis of metabolites in CSF has been common practice in clinical chemistry for decades to analyze biomarkers for inborn errors of metabolism. The approaches used are either metabolite profiling of CSF using NMR (7), or targeted analysis of one or a few metabolites using specific analytical methods (8). Metabolomics includes the analysis of metabolites in biofluids by NMR or MS-based approaches, i.e. LC-MS or GC-MS. Several metabolite profiling studies were performed on CSF using NMR, some of which were published only recently (9,10). Surprisingly, very few metabolomics studies using MS-based methods have been performed on CSF to date (11,12). One of the reasons is the fact that the human CSF metabolome has not yet been characterized very well. Many CSF metabolites remain unidentified, and for those that have been identified there is not much known about normal concentration ranges. A systematic categorization of the CSF metabolome is necessary and expected to be beneficial for future biomarker discoveries. Recently, Wishart et al. made a good start in exploring the human CSF metabolome with their computer-aided literature survey that resulted in 308 detectable metabolites in human CSF (13).The CSF proteome has been characterized to a much larger extent than the CSF metabolome and is currently the topic of investigations in several research groups worldwide. Recently, studies have been published with numerous identities and quantities of CSF proteins. Pan and co-workers were able to identify 2,594 proteins in well-characterized pooled human CSF samples using strict proteomics criteria with a combination of linear trap quadrupole LTQ-FT (Thermo Fisher Scientific, Bremen, Germany) and MALDI TOF/TOF equipment (14). They were also able to quantify several proteins using a targeted LC MALDI TOF/TOF approach (15). Hu et al. have studied the intra- and inter-individual variation in human CSF and found large variations in protein concentrations in six patients by means of two dimensional–gel electrophoresis (16), focusing mainly on the variations within individuals at two different time-points. Although only a limited number of proteins was analyzed, the variation between the time-points was profound, exceeding 200% for seven proteins.Unique CSF biomarkers may contribute to a deeper understanding of the mechanisms of CNS disorders. However, for this assumption to come true, there are still challenges ahead. Although CSF is not as complex as blood (almost missing the cellular part and the clotting system present in blood), it is expected to consist of thousands of organic- and non-organic salts, sugars, lipids, and proteins. A large part of the CSF consists of a few highly abundant metabolites and proteins, which hamper, if no precautions are undertaken, the identification and quantification of metabolites and proteins that occur in lower amounts. The analysis of the CSF metabolome is complicated because of the diverse chemical nature of metabolites and the lower concentration of metabolites compared with blood. Analytical method development is still required because it is not possible to identify the entire range of CSF metabolites with one single analytical method. Although in proteome research efforts have been made to quantify proteins, metabolomics studies up to now either do not provide quantitative information or they only give information for the most abundant metabolites.Another challenge is the sample amount obtained by lumbar puncture to collect CSF. Lumbar puncture is an invasive method that is not performed as frequently as blood sampling. However, often after the analysis of various clinical parameters, only a limited amount of CSF sample is available for biomarker discovery. Metabolomics studies are hampered by limited CSF sample amount. Therefore, analytical methods are required that are suitable to handle relatively small sample volumes.The main objectives of this study were (1) to analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples by multiple analytical platforms; and (2) to integrate metabolomics and proteomics to present biological variations in metabolite and protein abundances and compare these with technical variations with the currently used analytical methods. The results will facilitate and increase the application of CSF for future biomarker discovery studies in the field of neurodegenerative diseases and neuro-oncology.     

Active site substitution A82W improves the regioselectivity of steroid hydroxylation by cytochrome P450 BM3 mutants as rationalized by spin relaxation nuclear magnetic resonance studies

Cytochrome P450 BM3 from Bacillus megaterium is a monooxygenase with great potential for biotechnological applications. In this paper, we present engineered drug-metabolizing P450 BM3 mutants as a novel tool for regioselective hydroxylation of steroids at position 16β. In particular, we show that by replacing alanine at position 82 with a tryptophan in P450 BM3 mutants M01 and M11, the selectivity toward 16β-hydroxylation for both testosterone and norethisterone was strongly increased. The A82W mutation led to a ≤42-fold increase in V(max) for 16β-hydroxylation of these steroids. Moreover, this mutation improves the coupling efficiency of the enzyme, which might be explained by a more efficient exclusion of water from the active site. The substrate affinity for testosterone increased at least 9-fold in M11 with tryptophan at position 82. A change in the orientation of testosterone in the M11 A82W mutant as compared to the orientation in M11 was observed by T(1) paramagnetic relaxation nuclear magnetic resonance. Testosterone is oriented in M11 with both the A- and D-ring protons closest to the heme iron. Substituting alanine at position 82 with tryptophan results in increased A-ring proton-iron distances, consistent with the relative decrease in the level of A-ring hydroxylation at position 2β.     

A novel harmony search-K means hybrid algorithm for clustering gene expression data

Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms.     

NMR and pattern recognition can distinguish neuroinflammation and peripheral inflammation

Multiple Sclerosis (MScl) is a neurodegenerative disease of the CNS, associated with chronic neuroinflammation. Cerebrospinal fluid (CSF), being in closest interaction with CNS, was used to profile neuroinflammation to discover disease-specific markers. We used the commonly accepted animal model for the neuroinflammatory aspect of MScl: the experimental autoimmune/allergic encephalomyelitis (EAE). A combination of advanced (1)H NMR spectroscopy and pattern recognition methods was used to establish the metabolic profile of CSF of EAE-affected rats (representing neuroinflammation) and of two control groups (healthy and peripherally inflamed) to detect specific markers for early neuroinflammation. We found that the CSF metabolic profile for neuroinflammation is distinct from healthy and peripheral inflammation and characterized by changes in concentrations of metabolites such as creatine, arginine, and lysine. Using these disease-specific markers, we were able to detect early stage neuroinflammation, with high accuracy in a second independent set of animals. This confirms the predictive value of these markers. These findings from the EAE model may help to develop a molecular diagnosis for the early stage MScl in humans.     

Histopathological diagnosis of myocarditis in a dengue outbreak in Sri Lanka, 2009

Background

In 2009, an outbreak of dengue caused high fatality in Sri Lanka. We conducted 5 autopsies of clinically suspected myocarditis cases at the General Hospital, Peradeniya to describe the histopathology of the heart and other organs.

Methods

The diagnosis of dengue was confirmed with specific IgM and IgG ELISA, HAI and RT-PCR techniques. The histology was done in tissue sections stained with hematoxylin and eosin.

Results

Of the 319 cases of dengue fever, 166(52%) had severe infection. Of them, 149 patients (90%) had secondary dengue infection and in 5 patients, DEN-1 was identified as the causative serotype. The clinical diagnosis of myocarditis was considered in 45(27%) patients. The autopsies were done in 5 patients who succumbed to shock (3 females and 2 males) aged 13- 31 years. All had pleural effusions, ascites, bleeding patches in tissue planes and histological evidence of myocarditis. The main histological findings of the heart were interstitial oedema with inflammatory cell infiltration and necrosis of myocardial fibers. One patient had pericarditis. The concurrent pulmonary abnormalities were septal congestion, pulmonary haemorrhage and diffuse alveolar damage; one case showed massive necrosis of liver.

Conclusions

The histology supports occurrence of myocarditis in dengue infection.