抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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Modern Applied Statistics With S

Microbiota Modulate Behavioral and Physiological Abnormalities Associated with Neurodevelopmental Disorders

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

/pdf/fast-tree-computing-large-minimum-evolution-trees-with-4grq0gwfpy.pdf

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.

/pdf/waste-not-want-not-why-rarefying-microbiome-data-is-4qjzw5084c.pdf

Waste not, want not: why rarefying microbiome data is inadmissible.

Pre-diagnostic dynamic HPV16 IgG seropositivity and risk of oropharyngeal cancer: Methodologic issues.

OBJECTIVE To refine the simulation algorithm used in the Bayesian Aerosol Release Detector (BARD) so that it takes into account the work-related mobility and to compare the refined simulator with the existing one. BACKGROUND An important problem in biosurveillance is the early detection and characterization of outdoor aerosol releases of B. anthracis. The Bayesian Aerosol Release Detector (BARD) [1] is a system for simulating, detecting and characterizing such releases. BARD integrates the analysis of medical surveillance data and meteorological data. The existing version of BARD does not account for the fact that many people might be exposed at a location other than their residence due to mobility. Incorporation of a mobility model in biosurveillance has been investigated by several other researchers [2, 3]. In this paper, we describe a refined version of the BARD simulation algorithm which incorporates a model of work-related mobility and report the results of an experiment to measure the effect of this refinement. METHODS To create the commuting model, we used data from the U.S. Census Bureau. A focused analysis restricted our investigation to 62156 tract-to-tract commuting flows corresponding to a seven-county region around the city of Pittsburgh (Figure 1). Currently, BARD supports the zip-code and block-group modes of simulation. The latter mode was used in this study. In a pre-processing step, we converted the tract-level flows provided by the U.S. Census Bureau to block group-level flows. To refine BARD simulation algorithm first we divided the population of each block group into several sub-populations based on the workplace of its residents. Then we computed the number of cases and the time of ED visit for each case based on the exposure level of a sub-population at its place of work. We applied the existing and the refined versions of BARD simulator to several handpicked locations around the region described earlier. For each release, we measured the change in the number of ED visits per block group produced by the two versions of the simulator. RESULTS Figure 1 illustrates a release from the Northwestern corner of the seven-county region with the wind direction pointing toward downtown Pittsburgh. The darker colors in this map correspond to larger counts generated in the case of simulation with commuting. As shown in Figure 1, the main effect of incorporating the commuting model in our simulations appears to be an increase in the number of simulated ED visits for the block groups located crosswind and further from the release location.

Effect of Work-related Mobility in the Simulation of Aerosol Anthrax Releases with BARD

Disclosed herein are compositions and methods for accurately estimating the absorbed dose of radiation indicated by a subject based on the expression pattern of a panel of radiation-modulated (RM) genes at various time points following exposure of the subject to ionizing radiation

Radiation biodosimetry systems

PROBLEM TO BE SOLVED: To provide a method for breast cancer detection.SOLUTION: The method comprises (a) contacting a suitable bodily fluid sample obtained from a subject at risk of breast cancer with one or more isolated polypeptides selected from a group consisting of ATP6AP1, PDCD6IP, DBT, CSNK1E, FRS3, RAC3, HOXD1, SF3A1, CTBP1, C15orf48, MYOZ2, EIF3E, BAT4, ATF3, BMX, RAB5A, UBAP1, SOX2, GPR157, BDNF, ZMYM6, SLC33A1, TRIM32, ALG10, TFCP2, SERPINH1, SELL, ZNF510, and antigenic fragments thereof under suitable conditions, and (b) detecting the presence of antibodies to the polypeptides in the bodily fluid sample. The presence of antibodies to the one or more polypeptides indicates a likelihood of breast cancer in the subject.SELECTED DRAWING: None

Biomarker for early detection of breast cancer

Background: Basal-like breast cancer (BLBC) is a rare aggressive subtype of breast cancer (∼10-20% of all BC) with high recurrence rates and poor prognosis. BLBC disproportionately affects African American and younger women, has a high growth rate and occurs at young ages. These factors render mammographic screening challenging, necessitating the development of new methods for BLBC9s early detection. Proteins released from small early tumors may only be present intermittently and get diluted to tiny concentrations in the blood, making them difficult to use as biomarkers. However, they can induce an autoantibody (AAb) response, which can amplify the signal and persist in the blood even if the antigen is gone. Accordingly, we assessed whether circulating AAbs could act as early detection biomarkers of BLBC. We have developed a sero-proteomic technology, called Nucleic Acid Programmable Protein Arrays (NAPPA), which tests the immune responses to thousands of proteins simultaneously. Results: Plasma samples were from the Polish Breast Cancer Study. BLBC patients were classified by immunohistochemical staining of ER-, PR-, HER2-, and either EGFR+ or CK5/6+. Case and control plasma were probed against ∼10,000 full-length human proteins on NAPPA to discover informative antigens, which were selected based on the following statistical metrics: 1) sensitivity at 95% specificity; 2) area under receiver operating characteristic curve (AUC); 3) partial AUC above 95% specificity; 4) Welch9s t test. We then identified candidate AAbs in a training study combining the 800 top antigens on a single array. These arrays were screened with an independent training set of plasma from 50 each of BLBC cases and healthy controls, and 30 other BC cases. Preliminary analyses suggest 5 candidate biomarkers with sensitivities ranging from 26-32% at 94% specificity. The antigen list will be refined using AUC, and the sensitivity and partial AUC at 95%, 90% and 80% specificity levels. Permutation-based false discovery rates will be used to assess statistical significance, and the antigens will be combined into a biomarker panel using a random forests algorithm. Finally, we will test the individual markers and the antigen panel in a blinded independent validation set of 50 cases, 50 controls and 30 other BC cases. These tests will use cutoffs and algorithm determined entirely from the training data. We are assessing whether the candidate biomarkers are specific for cancer vs. controls and BLBC vs. other molecular subtypes of breast cancer. Summary: This describes the first immunoproteomic screen for AAb biomarkers of BLBC. Using protein microarrays displaying more than 10,000 folded unique human proteins, controls, BLBC and non-BLBC cases were tested in independent discovery and training stages identifying AAbs with potential diagnostic value. Blinded validation studies are in progress. Citation Format: Jie Wang, Jonine D. Figueroa, Garrick Wallstrom, Joshua Sampson, Eliseo Mendoza Garcia, Jason Steel, Jin Park, Karen S. Anderson, Louise Brinton, Montserrat Garcia-Closas, Jolanta Lissowska, Mark E. Sherman, Ji Qiu, Joshua LaBaer. Autoantibody biomarker discovery in basal-like breast cancer using nucleic acid programmable protein array. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 874. doi:10.1158/1538-7445.AM2014-874

Garrick Wallstrom

Papers

Pre-diagnostic dynamic HPV16 IgG seropositivity and risk of oropharyngeal cancer: Methodologic issues.

Effect of Work-related Mobility in the Simulation of Aerosol Anthrax Releases with BARD

Radiation biodosimetry systems

Biomarker for early detection of breast cancer

Abstract 874: Autoantibody biomarker discovery in basal-like breast cancer using nucleic acid programmable protein array