Conformational differences between linear alpha (2----8)-linked homosialooligosaccharides and the epitope of the group B meningococcal polysaccharide

The alpha-(2----8)-linked sialic acid oligosaccharides (NeuAc)n exhibit an unusual degree of heterogeneity in the conformation of their linkages. This was diagnosed by observation in their 13C NMR spectra of an equivalent and unique heterogeneity in the chemical shifts of their anomeric carbons and subsequently confirmed by more comprehensive 1H and 13C NMR studies. In these studies both one-dimensional and two-dimensional experiments were carried out on the trisaccharide (NeuAc)3 and colominic acid. In addition to the unambiguous assignment of the signals in the spectra, these experiments demonstrated that both linkages of (NeuAc)3 differed in conformation from each other and from the inner linkages of colominic acid. The NMR data indicate that these conformational differences extend to both terminal disaccharides of oligosaccharides larger than (NeuAc)5, a result that has considerable physical and biological significance. In the context of the group B meningococcal polysaccharide, it provides an explanation for the conformational epitope of the group B meningococcal polysaccharide, which was proposed on the evidence that (NeuAc)10, larger than the optimum size of an antibody site, was the smallest oligosaccharide able to bind to group B polysaccharide specific antibodies. Because the two terminal disaccharides of (NeuAc)10 differ in conformation to its inner residues, the immunologically functional part of (NeuAc)10 resides in its inner six residues. This number of residues is now consistent with the maximum size of an antibody site.     

Interacting forces of predation and fishing affect species’ maturation size

1. Fishing is a strong selective force and is supposed to select for earlier maturation at smaller body size. However, the extent to which fishing‐induced evolution is shaping ecosystems remains debated. This is in part because it is challenging to disentangle fishing from other selective forces (e.g., size‐structured predation and cannibalism) in complex ecosystems undergoing rapid change.
2. Changes in maturation size from fishing and predation have previously been explored with multi‐species physiologically structured models but assumed separation of ecological and evolutionary timescales. To assess the eco‐evolutionary impact of fishing and predation at the same timescale, we developed a stochastic physiologically size‐structured food‐web model, where new phenotypes are introduced randomly through time enabling dynamic simulation of species'' relative maturation sizes under different types of selection pressures.
3. Using the model, we carried out a fully factorial in silico experiment to assess how maturation size would change in the absence and presence of both fishing and predation (including cannibalism). We carried out ten replicate stochastic simulations exposed to all combinations of fishing and predation in a model community of nine interacting fish species ranging in their maximum sizes from 10 g to 100 kg. We visualized and statistically analyzed the results using linear models.
4. The effects of fishing on maturation size depended on whether or not predation was enabled and differed substantially across species. Fishing consistently reduced the maturation sizes of two largest species whether or not predation was enabled and this decrease was seen even at low fishing intensities (F = 0.2 per year). In contrast, the maturation sizes of the three smallest species evolved to become smaller through time but this happened regardless of the levels of predation or fishing. For the four medium‐size species, the effect of fishing was highly variable with more species showing significant and larger fishing effects in the presence of predation.
5. Ultimately our results suggest that the interactive effects of predation and fishing can have marked effects on species'' maturation sizes, but that, at least for the largest species, predation does not counterbalance the evolutionary effect of fishing. Our model also produced relative maturation sizes that are broadly consistent with empirical estimates for many fish species.

     

The distribution of carbohydrate side chains along the polypeptide chain of glucoamylase

Glucoamylase is a starch-hydrolyzing enzyme with a glycoprotein structure, used industrially for the conversion of starch to glucose, citric acid, corn syrups, and high-fructose sweeteners. This enzyme possesses an unusual type of structure in which many carbohydrate side chains are linked O-glycosidically to serine and threonine residues of the polypeptide chain. The carbohydrate side chains may be single monosaccharide residues or oligosaccharides of mannose, glucose, galactose, and in some cases N-acetylglucosamine. New data from experiments on the CNBr fragmentation of glucoamylase followed by chemical and immunological characterization of the fragments show that the carbohydrate side chains are distributed randomly along the polypeptide chain. Such a structure is appropriately termed a random model reprensentation for the glucoamylase molecule.     

Biological and structural properties of MIP-1 alpha expressed in yeast.

The murine macrophage inflammatory proteins-1 alpha (MIP-1 alpha) and MIP-1 beta are distinct but closely related cytokines. Partially purified mixtures of the two proteins affect neutrophil function and cause local inflammation and fever. The particular properties of MIP-1 alpha have not been well studied, although it has been identified as being identical to an inhibitor of haemopoietic stem cell growth. We have expressed MIP-1 alpha in yeast cells and purified it to sequence homogeneity. Structural analysis of this biologically active material by circular dichroism and fluorescence spectroscopy confirms that MIP-1 alpha has a very similar secondary and tertiary structure to platelet factor 4 and interleukin 8 with which it shares limited sequence homology. The in-vitro stem cell inhibitory properties have been confirmed using a range of murine progenitor cells including purified bone marrow progenitor cells (FACS-1), the FDCP-mix A4 cell line, and spleen colony forming unit (CFU-S) populations. Plateau levels of inhibition of stem cell growth were achieved using concentrations of 0.15 micrograms/ml MIP-1 alpha. We have also demonstrated that MIP-1 alpha is active in vivo: 5 micrograms of MIP-1 alpha per mouse given as a bolus injection, protects stem cells from subsequent in-vitro killing by tritiated thymidine. MIP-1 alpha was also shown to enhance the proliferation of more committed progenitor granulocyte macrophage-colony forming cells (GM-CFC) in response to granulocyte macrophage-colony stimulating factor (GM-CSF).     

Genome-wide CRISPR Screens Reveal Host Factors Critical for SARS-CoV-2 Infection

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Relationship of physical fitness, hormone replacement therapy, and hemostatic risk factors in postmenopausal women.

This cross-sectional study evaluated the relationship of physical fitness, hormone replacement therapy (HRT), and hemostatic profiles at rest and after an acute bout of maximal exercise in 48 healthy postmenopausal women. Subjects were categorized by fitness and HRT user status into four groups: unfit nonusers, fit nonusers, unfit users, and fit users. Fibrinolytic variables tissue plasminogen activator (tPA), plasminogen activator inhibitor-1 (PAI-1) activity, and antigen and prothrombin fragment 1 + 2, a molecular marker of in vivo thrombin generation, were measured before and after maximal exercise. Fibrinogen was also measured at rest. Higher tPA and lower PAI-1 activities (P <0.05) were seen in HRT users and fit groups. tPA and PAI-1 antigens were lower in HRT and fit groups (P <0.05) but not after correction for body mass index. Prothrombin fragment 1 + 2 was lower in the fit groups regardless of HRT status (P <0.05). Fibrinogen was similar in all groups. Favorable hemostatic profiles were observed in physically fit compared with unfit women, especially in HRT nonusers. Thus fitness is more strongly related to these hemostatic risk factors compared with HRT since HRT did not affect these hemostatic variables in fit postmenopausal women.