A Statistically Rigorous Method for Determining Antigenic Switching Networks

Many vector-borne pathogens rely on antigenic variation to prolong infections and increase their likelihood of onward transmission. This immune evasion strategy often involves mutually exclusive switching between members of gene families that encode functionally similar but antigenically different variants during the course of a single infection. Studies of different pathogens have suggested that switching between variant genes is non-random and that genes have intrinsic probabilities of being activated or silenced. These factors could create a hierarchy of gene expression with important implications for both infection dynamics and the acquisition of protective immunity. Inferring complete switching networks from gene transcription data is problematic, however, because of the high dimensionality of the system and uncertainty in the data. Here we present a statistically rigorous method for analysing temporal gene transcription data to reconstruct an underlying switching network. Using artificially generated transcription profiles together with in vitro var gene transcript data from two Plasmodium falciparum laboratory strains, we show that instead of relying on data from long-term parasite cultures, accuracy can be greatly improved by using transcription time courses of several parasite populations from the same isolate, each starting with different variant distributions. The method further provides explicit indications about the reliability of the resulting networks and can thus be used to test competing hypotheses with regards to the underlying switching pathways. Our results demonstrate that antigenic switch pathways can be determined reliably from short gene transcription profiles assessing multiple time points, even when subject to moderate levels of experimental error. This should yield important new information about switching patterns in antigenically variable organisms and might help to shed light on the molecular basis of antigenic variation

The role of hypothalamic H1 receptor antagonism in antipsychotic-induced weight gain

Treatment with second generation antipsychotics (SGAs), notably olanzapine and clozapine, causes severe obesity side effects. Antagonism of histamine H1 receptors has been identified as a main cause of SGA-induced obesity, but the molecular mechanisms associated with this antagonism in different stages of SGA-induced weight gain remain unclear. This review aims to explore the potential role of hypothalamic histamine H1 receptors in different stages of SGA-induced weight gain/obesity and the molecular pathways related to SGA-induced antagonism of these receptors. Initial data have demonstrated the importance of hypothalamic H1 receptors in both short- and long-term SGA-induced obesity. Blocking hypothalamic H1 receptors by SGAs activates AMP-activated protein kinase (AMPK), a well-known feeding regulator. During short-term treatment, hypothalamic H1 receptor antagonism by SGAs may activate the AMPK—carnitine palmitoyltransferase 1 signaling to rapidly increase caloric intake and result in weight gain. During long-term SGA treatment, hypothalamic H1 receptor antagonism can reduce thermogenesis, possibly by inhibiting the sympathetic outflows to the brainstem rostral raphe pallidus and rostral ventrolateral medulla, therefore decreasing brown adipose tissue thermogenesis. Additionally, blocking of hypothalamic H1 receptors by SGAs may also contribute to fat accumulation by decreasing lipolysis but increasing lipogenesis in white adipose tissue. In summary, antagonism of hypothalamic H1 receptors by SGAs may time-dependently affect the hypothalamus-brainstem circuits to cause weight gain by stimulating appetite and fat accumulation but reducing energy expenditure. The H1 receptor and its downstream signaling molecules could be valuable targets for the design of new compounds for treating SGA-induced weight gain/obesity

Integer DCT-II by Lifting Steps

In image compression, the discrete cosine transform of type II (DCT-II) is of special interest. In this paper we use a new approach to construct an integer DCT-II first considered in [14]. Our method is based on a factorization of the cosine matrix of type II into a product of sparse, orthogonal matrices. The construction of the integer DCT of length 8 works with lifting steps and rounding{o. We are especially interested in the normwise error and the componentwise error when the integer DCT is compared with the exact DCT-II