Ning Sun Yang
Agricultural Biotechnology Research Centre, Taiwan
Title: Immune cell type or signaling-specific effects of four candidate phytomedicines for cancerous and inflammatory diseases
Biography
Biography: Ning Sun Yang
Abstract
In our recent studies, we showed that many phytochemicals or their derivatives can confer diverse pharmacological activities in
preventing tumor metastasis (See References). These phytochemical activities regulate the immune system or non-malignant
cells in a tissue microenvironment under various in vivo conditions. And these activities cannot be effectively addressed by the
conventionally used cell culture systems in vitro. Our current strategy is to initiate our study through a combination of omics
approaches and specified in vivo tumor model systems. Specifically, we first make predictions for candidate specific pharmacological
activities according to the “omics screening” profile of differential responsive genes, proteins or involved metabolites. Then we make a
list of hypothesis in priority sequence. Finally, we detect/evaluate the candidate mechanistic signaling cells/molecules for suppression
of well-defined tumor metastasis activity. With this strategy, we have been successful in evaluating several pharmacological effects
of nature plant phytochemicals or their derivatives on immune cell systems or the surrounding nonmalignant cells in defined tumor
microenvironment. The omics approaches we used to predict and reveal the specific pharmacological activity of phytochemicals
or medicinal herbal extracts/fractions, including genomics, transcriptomics, proteomics, metabolomics and the next generation
sequencing (NGS) systems. The systematic analysis of the observed data is to “contemplate” the cellular or physiological responses,
according to the various pattern changes detected in different response elements. Technically, in our task on pathway and net-working
analyses, the overall or big trend/pattern of the different responsive elements or/and their signaling systems is the key for predicting
specific pharmacological mechanisms, instead of the “super” inducer or suppressor single gene activities. For instance, the “expression
pattern or trend”, rather than the “fold change”, of specific microRNA species is a much more important factor for predicting their
suppressive effect on target genes. As a result, the understanding and background knowledge of specific targets or signaling networking
pathways of specific disease targets are quite important and need to be carefully reviewed “first” before “searching the omics data” in a
totally randomized way. With this approach, whether the expression trend of their downstream genes can fit the proposed hypothesis
is also considered as a key factor.