Circulating Human Eosinophils Share a Similar Transcriptional Profile in Asthma and Other Hypereosinophilic Disorders

Eosinophils are leukocytes that are released into the peripheral blood in a phenotypically mature state and are capable of being recruited into tissues in response to appropriate stimuli. Eosinophils, traditionally considered cytotoxic effector cells, are leukocytes recruited into the airways of asthma patients where they are believed to contribute to the development of many features of the disease. This perception, however, has been challenged by recent findings suggesting that eosinophils have also immunomodulatory functions and may be involved in tissue homeostasis and wound healing. Here we describe a transcriptome-based approach-in a limited number of patients and controls-to investigate the activation state of circulating human eosinophils isolated by flow cytometry. We provide an overview of the global expression pattern in eosinophils in various relevant conditions, e.g., eosinophilic asthma, hypereosinophilic dermatological diseases, parasitosis and pulmonary aspergillosis. Compared to healthy subjects, circulating eosinophils isolated from asthma patients differed in their gene expression profile which is marked by downregulation of transcripts involved in antigen presentation, pathogen recognition and mucosal innate immunity, whereas up-regulated genes were involved in response to non-specific stimulation, wounding and maintenance of homeostasis. Eosinophils from other hypereosinophilic disorders displayed a very similar transcriptional profile. Taken together, these observations seem to indicate that eosinophils exhibit non-specific immunomodulatory functions important for tissue repair and homeostasis and suggest new roles for these cells in asthma immunobiology

Exact distribution of a pattern in a set of random sequences generated by a Markov source: applications to biological data

<p>Abstract</p> <p>Background</p> <p>In bioinformatics it is common to search for a pattern of interest in a potentially large set of rather short sequences (upstream gene regions, proteins, exons, etc.). Although many methodological approaches allow practitioners to compute the distribution of a pattern count in a random sequence generated by a Markov source, no specific developments have taken into account the counting of occurrences in a set of independent sequences. We aim to address this problem by deriving efficient approaches and algorithms to perform these computations both for low and high complexity patterns in the framework of homogeneous or heterogeneous Markov models.</p> <p>Results</p> <p>The latest advances in the field allowed us to use a technique of optimal Markov chain embedding based on deterministic finite automata to introduce three innovative algorithms. Algorithm 1 is the only one able to deal with heterogeneous models. It also permits to avoid any product of convolution of the pattern distribution in individual sequences. When working with homogeneous models, Algorithm 2 yields a dramatic reduction in the complexity by taking advantage of previous computations to obtain moment generating functions efficiently. In the particular case of low or moderate complexity patterns, Algorithm 3 exploits power computation and binary decomposition to further reduce the time complexity to a logarithmic scale. All these algorithms and their relative interest in comparison with existing ones were then tested and discussed on a toy-example and three biological data sets: structural patterns in protein loop structures, PROSITE signatures in a bacterial proteome, and transcription factors in upstream gene regions. On these data sets, we also compared our exact approaches to the tempting approximation that consists in concatenating the sequences in the data set into a single sequence.</p> <p>Conclusions</p> <p>Our algorithms prove to be effective and able to handle real data sets with multiple sequences, as well as biological patterns of interest, even when the latter display a high complexity (PROSITE signatures for example). In addition, these exact algorithms allow us to avoid the edge effect observed under the single sequence approximation, which leads to erroneous results, especially when the marginal distribution of the model displays a slow convergence toward the stationary distribution. We end up with a discussion on our method and on its potential improvements.</p

Constant time estimation of ranking statistics by analytic combinatorics

We consider i.i.d. increments (or jumps) Xi that are integers in J ⊆ [−c,..., +d] for c, d ∈ N, the partial sums Sj = P 1≤i≤j Xi, and the discrete walks ((j, Sj))1≤j≤n. Late conditionning by a return of the walk to zero at time n provides discrete bridges that we note (Bj)1≤j≤n. We give in this extended abstract the asymptotic law in the central domain of the height (max1≤j≤n Bj) of the bridges as n tends to infinity. As expected, this law converges to the Rayleigh law which is the law of the maximum of a standard Brownian bridge. In the case where c = 1 (only one negative jump), we provide a full expansion of the asymptotic limit which improves upon the rate of convergence O(log(n) / √ n) given by Borisov [4] for lattice jumps; this applies in particular for the case where Xi ∈ {−1, +d}, in which case the expansion is expressible as a function of n, d and of the height of the bridge. Applying this expansion for Xi ∈ {−1, d/c} gives an excellent approximation of the case Xi ∈ {−d, +c} and provides in constant time an indicator used in ranking statistics; this indicator can be used for medical diagnosis and bioinformatics analysis (see Keller et al. [7] who compute it in time O(n × min(c, d)) by use of dynamical programming)

Bounded discrete walks

This article tackles the enumeration and asymptotics of directed lattice paths (that are isomorphic to unidimensional paths) of bounded height (walks below one wall, or between two walls, for any finite set of jumps). Thus, for any lattice paths, we give the generating functions of bridges (“discrete” Brownian bridges) and reflected bridges (“discrete” reflected Brownian bridges) of a given height. It is a new success of the “kernel method ” that the generating functions of such walks have some nice expressions as symmetric functions in terms of the roots of the kernel. These formulae also lead to fast algorithms for computing the n-th Taylor coefficients of the corresponding generating functions. For a large class of walks, we give the discrete distribution of the height of bridges, and show the convergence to a Rayleigh limit law. For the family of walks consisting of a −1 jump and many positive jumps, we give more precise bounds for the speed of convergence. We end our article with a heuristic application to bioinformatics that has a high speed-up relative to previous work

On the political economics of taxation

Previous analysis have found mixed results on the effects of flat taxes, in terms of efficiency, equity and tax complexity. After reviewing some of the main theoretical arguments that suggest that the political process plays a crucial role in shaping tax systems, we apply them to the case of flat taxes in Estonia

BIOINFORMATICS Proteome analysis based on motif statistics

Motivation: Even for the amino acid motifs collected in the Prosite database there may be chance occurences as opposed to those occurences where the motif is involved in fold or function of a protein. With recent mathematical advances in assessing the significance of observing such amotif a particular number of times, we can now study the over- or underrepresentation of particular motifs in a complete genome and attempt to make functional deductions. Results: We demonstrate that statistical over- or underrepresentation of motifs in complete proteomes may be an indicator of whether, in that organism, we are looking at chance occurrences of the motif or whether the occurrences are sufficiently numerous to suggest a systematic, and thus functionally important occurrence. This has important implications on databank annotations. Availability: The complete dataset comprising the plotted statistics of 266 Prosite motifs on 42 proteomes is available a