Biology subjects

Deem, M. W.

Publications and source records attributed to Deem, M. W..

5 recordsLinked to original sources

bioRxiv2018

Predicting influenza H3N2 vaccine efficacy from evolution of the dominant epitope

We predict vaccine efficacy with a measure of antigenic distance between influenza A(H3N2) and candidate vaccine viruses based on amino acid substitutions in the dominant epitopes. In 2016-2017, our model predicts 19% efficacy compared to 20% observed. This tool assists candidate vaccine selection by predicting human protection against circulating strains.\n\n40-word summary of main pointOur pepitope model predicts the ability of the influenza vaccine to reduce the A(H3N2) disease attack rate, with an r^2=0.77. This fast, sequence-based method compliments strain-to-strain antigenic comparisons from ferret models and provides antigenic comparisons for all circulating sequences.

immunology↗

bioRxiv2017

A Non-Classical Phase Diagram for Virus-Bacterial Co-Evolution Mediated by CRISPR

CRISPR is a newly discovered prokaryotic immune system. Bacteria and archaea with this system incorporate genetic material from invading viruses into their genomes, providing protection against future infection by similar viruses. The conditions for coexistence of prokaryots and viruses is an interesting problem in evolutionary biology. In this work, we show an intriguing phase diagram of the virus extinction probability, which is more complex than that of the classical predator-prey model. As the CRISPR incorporates genetic material, viruses are under pressure to evolve to escape the recognition by CRISPR. When bacteria have a small rate of deleting spacers, a new parameter region in which bacteria and viruses can coexist arises, and it leads to a more complex coexistence patten for bacteria and viruses. For example, when the virus mutation rate is low, the virus extinction probability changes non-montonically with the bacterial exposure rate. The virus and bacteria co-evolution not only alters the virus extinction probability, but also changes the bacterial population structure. Additionally, we show that recombination is a successful strategy for viruses to escape from CRISPR recognition when viruses have multiple proto-spacers, providing support for a recombination-mediated escape mechanism suggested experimentally. Finally, we suggest that the reentrant phase diagram, in which phages can progress through three phases of extinction and two phases of abundance at low spacer deletion rates as a function of exposure rate to bacteria, is an experimentally testable phenomenon.

evolutionary biology↗

bioRxiv2017

Brain Modularity Mediates the Relation of Cognitive Performance to Task Complexity

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model (Chen & Deem, 2015) suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole brain organization from network neuroscience to cognitive processing.

neuroscience↗

bioRxiv2017

Single Molecule Sequencing of Cell-free DNA from Maternal Plasma for Noninvasive Trisomy Detection

The demand of non-invasive prenatal testing for autosomal aneuploidy using cell-free fetal DNA (cffDNA) in maternal plasma is a highly sought-after diagnostic, with a rapidly growing market. Current approaches developed by next generation sequencing (NGS) need PCR amplifcation during sample preparation, which results in amplification bias in GC-rich areas of the human genome. With these approaches, the minimum fetal fraction in maternal plasma is 4% for the small differences in circulating cfDNA between trisomic and disomic pregnancies to be detectable. In this paper, we performed single molecule sequencing of cell-free DNA from maternal plasma for noninvasive trisomy 13, 18 and 21 detections using the GenoCare platform. We found that single molecule sequencing is sensitive enough to detect these chromosome abnormalities when the fetal DNA fraction is as low as 2%. Compared to the Hiseq2500 platform, no significant GC bias was observed. The improved sensitivity and unbiased GC readout make GenoCare a promising platform for autosomal aneuploidy detections, even in the very early stage of pregnancy.

genomics↗

bioRxiv2017

Analysis of hierarchical organization in gene expression networks reveals underlying principles of collective tumor cell dissemination and metastatic aggressiveness of inflammatory breast cancer

BackgroundClusters of circulating tumor cells (CTCs), despite being rare, may account for more than 95% of metastases. Cells in these clusters do not undergo a complete epithelial-to-mesenchymal transition (EMT) but retain some epithelial traits as compared to individually disseminating tumor cells. Determinants of single cell dissemination versus collective dissemination remain elusive. Inflammatory breast cancer (IBC), a highly aggressive breast cancer subtype that chiefly metastasizes via CTC clusters, is a promising model for studying mechanisms of collective tumor cell dissemination. Previous studies on breast cancer and adult acute myeloid leukemia, motivated by a theory that suggests physical systems with hierarchical organization tend to be more adaptable, have found that the expression of metastasis associated genes is more hierarchically organized in cases of successful metastases.\n\nMethodsWe used the cophenetic correlation coefficient (CCC) to quantify the hierarchical organization in the expression networks of two distinct gene sets, collective dissemination associated genes and IBC associated genes, in cancer cell lines and in tumor samples from breast cancer patients. Hypothesizing that a higher CCC for collective dissemination associated genes and for IBC associated genes would be associated with a more evident epithelial phenotype and with worse outcomes in breast cancer patients, we evaluated the correlation of CCC with different phenotypic groups.\n\nResultsThe CCC of both gene networks, the collective dissemination associated gene network and the IBC associated gene network, was higher in (a) epithelial cell lines as compared to mesenchymal cell lines and (b) tumor samples from IBC patients, as compared to samples from non-IBC breast cancer patients. A higher CCC of both networks was also correlated with a higher rate of metastatic relapse in breast cancer patients. Neither the levels of CDH1 gene expression, nor gene set enrichment analysis could provide similar insights.\n\nConclusionsThese results suggest that retention of some epithelial traits in disseminating tumor cells as IBC progresses promotes successful breast cancer metastasis to distant organs. The CCC provides additional information regarding the organizational complexity of gene expression in comparison to differential gene expression analyses. We have shown that the CCC may be a useful metric for investigating the collective dissemination phenotype and a prognostic factor for IBC.

cancer biology↗