Big brother is watching - using digital disease surveillance tools for near real-time forecasting

Abstract for the International Journal of Infectious Diseases 79 (S1) (2019).https://www.ijidonline.com/article/S1201-9712(18)34659-9/abstractPublished versio

The fibrin-derived gamma377-395 peptide inhibits microglia activation and suppresses relapsing paralysis in central nervous system autoimmune disease.

Perivascular microglia activation is a hallmark of inflammatory demyelination in multiple sclerosis (MS), but the mechanisms underlying microglia activation and specific strategies to attenuate their activation remain elusive. Here, we identify fibrinogen as a novel regulator of microglia activation and show that targeting of the interaction of fibrinogen with the microglia integrin receptor Mac-1 (alpha(M)beta(2), CD11b/CD18) is sufficient to suppress experimental autoimmune encephalomyelitis in mice that retain full coagulation function. We show that fibrinogen, which is deposited perivascularly in MS plaques, signals through Mac-1 and induces the differentiation of microglia to phagocytes via activation of Akt and Rho. Genetic disruption of fibrinogen-Mac-1 interaction in fibrinogen-gamma(390-396A) knock-in mice or pharmacologically impeding fibrinogen-Mac-1 interaction through intranasal delivery of a fibrinogen-derived inhibitory peptide (gamma(377-395)) attenuates microglia activation and suppresses relapsing paralysis. Because blocking fibrinogen-Mac-1 interactions affects the proinflammatory but not the procoagulant properties of fibrinogen, targeting the gamma(377-395) fibrinogen epitope could represent a potential therapeutic strategy for MS and other neuroinflammatory diseases associated with blood-brain barrier disruption and microglia activation

Role of IL-33 and ST2 signalling pathway in multiple sclerosis: expression by oligodendrocytes and inhibition of myelination in central nervous system

Recent research findings have provided convincing evidence indicating a role for Interleukin-33 (IL-33) signalling pathway in a number of central nervous system (CNS) diseases including multiple sclerosis (MS) and Alzheimer’s disease. However, the exact function of IL-33 molecule within the CNS under normal and pathological conditions is currently unknown. In this study, we have mapped cellular expression of IL-33 and its receptor ST2 by immunohistochemistry in the brain tissues of MS patients and appropriate controls; and investigated the functional significance of these findings in vitro using a myelinating culture system. Our results demonstrate that IL-33 is expressed by neurons, astrocytes and microglia as well as oligodendrocytes, while ST2 is expressed in the lesions by oligodendrocytes and within and around axons. Furthermore, the expression levels and patterns of IL-33 and ST2 in the lesions of acute and chronic MS patient brain samples are enhanced compared with the healthy brain tissues. Finally, our data using rat myelinating co-cultures suggest that IL-33 may play an important role in MS development by inhibiting CNS myelination

Who Watches the Watchmen? An Appraisal of Benchmarks for Multiple Sequence Alignment

Multiple sequence alignment (MSA) is a fundamental and ubiquitous technique in bioinformatics used to infer related residues among biological sequences. Thus alignment accuracy is crucial to a vast range of analyses, often in ways difficult to assess in those analyses. To compare the performance of different aligners and help detect systematic errors in alignments, a number of benchmarking strategies have been pursued. Here we present an overview of the main strategies--based on simulation, consistency, protein structure, and phylogeny--and discuss their different advantages and associated risks. We outline a set of desirable characteristics for effective benchmarking, and evaluate each strategy in light of them. We conclude that there is currently no universally applicable means of benchmarking MSA, and that developers and users of alignment tools should base their choice of benchmark depending on the context of application--with a keen awareness of the assumptions underlying each benchmarking strategy.Comment: Revie

Characteristics of TCR repertoire associated with successful immune checkpoint therapy responses

Immunotherapies have revolutionized cancer treatment. In particular, immune checkpoint therapy (ICT) leads to durable responses in some patients with some cancers. However, the majority of treated patients do not respond. Understanding immune mechanisms that underlie responsiveness to ICT will help identify predictive biomarkers of response and develop treatments to convert non-responding patients to responding ones. ICT primarily acts at the level of adaptive immunity. The specificity of adaptive immune cells, such as T and B cells, is determined by antigen-specific receptors. T cell repertoires can be comprehensively profiled by high-throughput sequencing at the bulk and single-cell level. T cell receptor (TCR) sequencing allows for sensitive tracking of dynamic changes in antigen-specific T cells at the clonal level, giving unprecedented insight into the mechanisms by which ICT alters T cell responses. Here, we review how the repertoire influences response to ICT and conversely how ICT affects repertoire diversity. We will also explore how changes to the repertoire in different anatomical locations can better correlate and perhaps predict treatment outcome. We discuss the advantages and limitations of current metrics used to characterize and represent TCR repertoire diversity. Discovery of predictive biomarkers could lie in novel analysis approaches, such as network analysis of amino acids similarities between TCR sequences. Single-cell sequencing is a breakthrough technology that can link phenotype with specificity, identifying T cell clones that are crucial for successful ICT. The field of immuno-sequencing is rapidly developing and cross-disciplinary efforts are required to maximize the analysis, application, and validation of sequencing data. Unravelling the dynamic behavior of the TCR repertoire during ICT will be highly valuable for tracking and understanding anti-tumor immunity, biomarker discovery, and ultimately for the development of novel strategies to improve patient outcomes

T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension

This article introduces a new interface for T-Coffee, a consistency-based multiple sequence alignment program. This interface provides an easy and intuitive access to the most popular functionality of the package. These include the default T-Coffee mode for protein and nucleic acid sequences, the M-Coffee mode that allows combining the output of any other aligners, and template-based modes of T-Coffee that deliver high accuracy alignments while using structural or homology derived templates. These three available template modes are Expresso for the alignment of protein with a known 3D-Structure, R-Coffee to align RNA sequences with conserved secondary structures and PSI-Coffee to accurately align distantly related sequences using homology extension. The new server benefits from recent improvements of the T-Coffee algorithm and can align up to 150 sequences as long as 10 000 residues and is available from both http://www.tcoffee.org and its main mirror http://tcoffee.crg.cat

Elevated endogenous expression of the dominant negative basic helix-loop-helix protein ID1 correlates with significant centrosome abnormalities in human tumor cells

<p>Abstract</p> <p>Background</p> <p>ID proteins are dominant negative inhibitors of basic helix-loop-helix transcription factors that have multiple functions during development and cellular differentiation. Ectopic (over-)expression of ID1 extends the lifespan of primary human epithelial cells. High expression levels of ID1 have been detected in multiple human malignancies, and in some have been correlated with unfavorable clinical prognosis. ID1 protein is localized at the centrosomes and forced (over-)expression of ID1 results in errors during centrosome duplication.</p> <p>Results</p> <p>Here we analyzed the steady state expression levels of the four ID-proteins in 18 tumor cell lines and assessed the number of centrosome abnormalities. While expression of ID1, ID2, and ID3 was detected, we failed to detect protein expression of ID4. Expression of ID1 correlated with increased supernumerary centrosomes in most cell lines analyzed.</p> <p>Conclusions</p> <p>This is the first report that shows that not only ectopic expression in tissue culture but endogenous levels of ID1 modulate centrosome numbers. Thus, our findings support the hypothesis that ID1 interferes with centrosome homeostasis, most likely contributing to genomic instability and associated tumor aggressiveness.</p

Sequence alignment, mutual information, and dissimilarity measures for constructing phylogenies

Existing sequence alignment algorithms use heuristic scoring schemes which cannot be used as objective distance metrics. Therefore one relies on measures like the p- or log-det distances, or makes explicit, and often simplistic, assumptions about sequence evolution. Information theory provides an alternative, in the form of mutual information (MI) which is, in principle, an objective and model independent similarity measure. MI can be estimated by concatenating and zipping sequences, yielding thereby the "normalized compression distance". So far this has produced promising results, but with uncontrolled errors. We describe a simple approach to get robust estimates of MI from global pairwise alignments. Using standard alignment algorithms, this gives for animal mitochondrial DNA estimates that are strikingly close to estimates obtained from the alignment free methods mentioned above. Our main result uses algorithmic (Kolmogorov) information theory, but we show that similar results can also be obtained from Shannon theory. Due to the fact that it is not additive, normalized compression distance is not an optimal metric for phylogenetics, but we propose a simple modification that overcomes the issue of additivity. We test several versions of our MI based distance measures on a large number of randomly chosen quartets and demonstrate that they all perform better than traditional measures like the Kimura or log-det (resp. paralinear) distances. Even a simplified version based on single letter Shannon entropies, which can be easily incorporated in existing software packages, gave superior results throughout the entire animal kingdom. But we see the main virtue of our approach in a more general way. For example, it can also help to judge the relative merits of different alignment algorithms, by estimating the significance of specific alignments.Comment: 19 pages + 16 pages of supplementary materia