Evolution of Plant Nucleotide-Sugar Interconversion Enzymes

Nucleotide-diphospho-sugars (NDP-sugars) are the building blocks of diverse polysaccharides and glycoconjugates in all organisms. In plants, 11 families of NDP-sugar interconversion enzymes (NSEs) have been identified, each of which interconverts one NDP-sugar to another. While the functions of these enzyme families have been characterized in various plants, very little is known about their evolution and origin. Our phylogenetic analyses indicate that all the 11 plant NSE families are distantly related and most of them originated from different progenitor genes, which have already diverged in ancient prokaryotes. For instance, all NSE families are found in the lower land plant mosses and most of them are also found in aquatic algae, implicating that they have already evolved to be capable of synthesizing all the 11 different NDP-sugars. Particularly interesting is that the evolution of RHM (UDP-L-rhamnose synthase) manifests the fusion of genes of three enzymatic activities in early eukaryotes in a rather intriguing manner. The plant NRS/ER (nucleotide-rhamnose synthase/epimerase-reductase), on the other hand, evolved much later from the ancient plant RHMs through losing the N-terminal domain. Based on these findings, an evolutionary model is proposed to explain the origin and evolution of different NSE families. For instance, the UGlcAE (UDP-D-glucuronic acid 4-epimerase) family is suggested to have evolved from some chlamydial bacteria. Our data also show considerably higher sequence diversity among NSE-like genes in modern prokaryotes, consistent with the higher sugar diversity found in prokaryotes. All the NSE families are widely found in plants and algae containing carbohydrate-rich cell walls, while sporadically found in animals, fungi and other eukaryotes, which do not have or have cell walls with distinct compositions. Results of this study were shown to be highly useful for identifying unknown genes for further experimental characterization to determine their functions in the synthesis of diverse glycosylated molecules

Early Energy Deficit in Huntington Disease: Identification of a Plasma Biomarker Traceable during Disease Progression

Huntington disease (HD) is a fatal neurodegenerative disorder, with no effective treatment. The pathogenic mechanisms underlying HD have not been elucidated, but weight loss, associated with chorea and cognitive decline, is a characteristic feature of the disease that is accessible to investigation. We, therefore, performed a multiparametric study exploring body weight and the mechanisms of its loss in 32 presymptomatic carriers and HD patients in the early stages of the disease, compared to 21 controls. We combined this study with a multivariate statistical analysis of plasma components quantified by proton nuclear magnetic resonance (1H NMR) spectroscopy. We report evidence of an early hypermetabolic state in HD. Weight loss was observed in the HD group even in presymptomatic carriers, although their caloric intake was higher than that of controls. Inflammatory processes and primary hormonal dysfunction were excluded. 1H NMR spectroscopy on plasma did, however, distinguish HD patients at different stages of the disease and presymptomatic carriers from controls. This distinction was attributable to low levels of the branched chain amino acids (BCAA), valine, leucine and isoleucine. BCAA levels were correlated with weight loss and, importantly, with disease progression and abnormal triplet repeat expansion size in the HD1 gene. Levels of IGF1, which is regulated by BCAA, were also significantly lower in the HD group. Therefore, early weight loss in HD is associated with a systemic metabolic defect, and BCAA levels may be used as a biomarker, indicative of disease onset and early progression. The decreased plasma levels of BCAA may correspond to a critical need for Krebs cycle energy substrates in the brain that increased metabolism in the periphery is trying to provide

Saturated Fats: A Perspective from Lactation and Milk Composition

For recommendations of specific targets for the absolute amount of saturated fat intake, we need to know what dietary intake is most appropriate? Changing agricultural production and processing to lower the relative quantities of macronutrients requires years to accomplish. Changes can have unintended consequences on diets and the health of subsets of the population. Hence, what are the appropriate absolute amounts of saturated fat in our diets? Is the scientific evidence consistent with an optimal intake of zero? If not, is it also possible that a finite intake of saturated fats is beneficial to overall health, at least to a subset of the population? Conclusive evidence from prospective human trials is not available, hence other sources of information must be considered. One approach is to examine the evolution of lactation, and the composition of milks that developed through millennia of natural selective pressure and natural selection processes. Mammalian milks, including human milk, contain 50% of their total fatty acids as saturated fatty acids. The biochemical formation of a single double bond converting a saturated to a monounsaturated fatty acid is a pathway that exists in all eukaryotic organisms and is active within the mammary gland. In the face of selective pressure, mammary lipid synthesis in all mammals continues to release a significant content of saturated fatty acids into milk. Is it possible that evolution of the mammary gland reveals benefits to saturated fatty acids that current recommendations do not consider

HIV Replication Enhances Production of Free Fatty Acids, Low Density Lipoproteins and Many Key Proteins Involved in Lipid Metabolism: A Proteomics Study

BACKGROUND: HIV-infected patients develop multiple metabolic abnormalities including insulin resistance, lipodystrophy and dyslipidemia. Although progression of these disorders has been associated with the use of various protease inhibitors and other antiretroviral drugs, HIV-infected individuals who have not received these treatments also develop lipid abnormalities albeit to a lesser extent. How HIV alters lipid metabolism in an infected cell and what molecular changes are affected through protein interaction pathways are not well-understood. RESULTS: Since many genetic, epigenetic, dietary and other factors influence lipid metabolism in vivo, we have chosen to study genome-wide changes in the proteomes of a human T-cell line before and after HIV infection in order to circumvent computational problems associated with multiple variables. Four separate experiments were conducted including one that compared 14 different time points over a period of >3 months. By subtractive analyses of protein profiles overtime, several hundred differentially expressed proteins were identified in HIV-infected cells by mass spectrometry and each protein was scrutinized for its biological functions by using various bioinformatics programs. Herein, we report 18 HIV-modulated proteins and their interaction pathways that enhance fatty acid synthesis, increase low density lipoproteins (triglycerides), dysregulate lipid transport, oxidize lipids, and alter cellular lipid metabolism. CONCLUSIONS: We conclude that HIV replication alone (i.e. without any influence of antiviral drugs, or other human genetic factors), can induce novel cellular enzymes and proteins that are significantly associated with biologically relevant processes involved in lipid synthesis, transport and metabolism (p = <0.0002-0.01). Translational and clinical studies on the newly discovered proteins may now shed light on how some of these proteins may be useful for early diagnosis of individuals who might be at high risk for developing lipid-related disorders. The target proteins could then be used for future studies in the development of inhibitors for preventing lipid-metabolic anomalies. This is the first direct evidence that HIV-modulates production of proteins that are significantly involved in disrupting the normal lipid-metabolic pathways

Limiting damage during infection:lessons from infection tolerance for novel therapeutics

The distinction between pathogen elimination and damage limitation during infection is beginning to change perspectives on infectious disease control, and has recently led to the development of novel therapies that focus on reducing the illness caused by pathogens ("damage limitation") rather than reducing pathogen burdens directly ("pathogen elimination"). While beneficial at the individual host level, the population consequences of these interventions remain unclear. To address this issue, we present a simple conceptual framework for damage limitation during infection that distinguishes between therapies that are either host-centric (pro-tolerance) or pathogen-centric (anti-virulence). We then draw on recent developments from the evolutionary ecology of disease tolerance to highlight some potential epidemiological and evolutionary responses of pathogens to medical interventions that target the symptoms of infection. Just as pathogens are known to evolve in response to antimicrobial and vaccination therapies, we caution that claims of "evolution-proof" anti-virulence interventions may be premature, and further, that in infections where virulence and transmission are linked, reducing illness without reducing pathogen burden could have non-trivial epidemiological and evolutionary consequences that require careful examination

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems