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

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-β activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC-with clear biological interpretability-and the basis for future clinical stratification and subtype-based targeted interventions.

https://serval.unil.ch/resource/serval:BIB_6206DC2BFE0E.P001/REF.pdf

The consensus molecular subtypes of colorectal cancer

To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.

/pdf/the-advantages-of-the-matthews-correlation-coefficient-mcc-hn3s2s0jsg.pdf

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

https://home.ccr.cancer.gov/ncifdaproteomics/pdf/CancerCell-PanIN.pdf

Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse

Traditional statistical methods often fail to identify biologically meaningful biomarkers from expression data alone. In this paper, we develop a novel strategy, namely knowledge-driven multi-level Independent Component Analysis (ICA), to infer regulatory signals and identify biomarkers based on clustering results and partial prior knowledge. A statistical test is designed to evaluate significance of transcription factor enrichment for extracted gene set based on motif information. The experimental results on an Rsf-1 (HBXAP) induced microarray data set show that our method can successfully extract biologically meaningful biomarkers related to ovarian cancer compared to other gene selection methods with or without prior knowledge.

Biomarker identification by knowledge-driven multilevel ICA and motif analysis

The present disclosure relates to proteomic biomarkers of spontaneous preterm birth, proteomic biomarkers of term birth, and methods of use thereof. In particular, the present disclosure provides tools for determining whether a pregnant subject is at an increased risk for premature delivery, as well as tools for decreasing a pregnant subject's risk for premature delivery.

Use of circulating microparticles to stratify risk of spontaneous preterm birth

The invention provides methods and compositions for distinguishing ovarian cancer from a benign pelvic mass using two or more of the following biomarkers: IL-6, MMP9, tPA, IGFBP2, MMP7, Tenascin, NAP2, glycodelin, MCSF, MMP2, Inhibin A, uPAR, and EGFR. The methods are useful in distinguishing a benign pelvic mass from ovarian cancer in subjects, particularly in subjects identified as having increased CA125 levels.

Compositions and methods for diagnosing ovarian cancer

Acinetobacter baumannii  is currently posing a serious threat to global health. Lipopolysaccharide (LPS) is a potent virulence factor of pathogenic Gram-negative bacteria. To explore the antigenic properties of A. baumannii  LPS, four Kdo-containing inner core glycans from A. baumannii  strain ATCC 17904 were synthesized. A flexible and divergent method based on the use of the orthogonally substituted α-Kdo-(2→5)-Kdo disaccharides was developed. Selective removal of different protecting groups in these key precursors and elongation of sugar chain via α-stereocontrolled coupling with 5,7- O -di- tert -butylsilylene or 5- O -benzoyl protected Kdo thioglycosides and 2-azido-2-deoxyglucosyl thioglycoside allowed efficient assembly of the target molecules. Glycan microarray analysis of sera from infected patients revealed that the 4,5-branched Kdo trimer was a potential antigenic epitope, which is attractive for further immunological research to develop carbohydrate vaccines against  A. baumannii.

Chemical Synthesis and Antigenic Evaluation of Inner Core Oligosaccharides from Acinetobacter baumannii Lipopolysaccharide.

Protein glycosylation has long been recognized as one of the most common post-translational modifications Most membrane proteins and extracellular proteins are N-linked glycosylated, and they account for the majority of current clinical diagnostic markers or therapeutic targets Quantitative proteomic analysis of detectable N-linked glycoproteins from cells or tissues using mass spectrometry has the potential to provide biological basis for disease development and identify disease associated glycoproteins However, the information of low abundance but important peptides is lost due to the lack of MS/MS fragmentation or low quality of MS/MS spectra for low abundance peptides Here, we show the feasibility of formerly N-glycopeptide identification and quantification at MS1 level using genomic N-glycosite prediction (GenoGlyco) coupled with stable isotopic labeling and accurate mass matching The GenoGlyco Analyzer software uses accurate precursor masses of detected N-deglycopeptide peaks to match them to N

/pdf/analysis-of-n-glycoproteins-using-genomic-n-glycosite-5l937lle5f.pdf

Zhen Zhang

Papers

Biomarker identification by knowledge-driven multilevel ICA and motif analysis

Use of circulating microparticles to stratify risk of spontaneous preterm birth

Compositions and methods for diagnosing ovarian cancer

Chemical Synthesis and Antigenic Evaluation of Inner Core Oligosaccharides from Acinetobacter baumannii Lipopolysaccharide.

Analysis of N-glycoproteins using genomic N-glycosite prediction.