The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View

The collection of microbes that live in and on the human body - the human microbiome - can impact on cancer initiation, progression, and response to therapy, including cancer immunotherapy. The mechanisms by which microbiomes impact on cancers can yield new diagnostics and treatments, but much remains unknown. The interactions between microbes, diet, host factors, drugs, and cell-cell interactions within the cancer itself likely involve intricate feedbacks, and no single component can explain all the behavior of the system. Understanding the role of host-associated microbial communities in cancer systems will require a multidisciplinary approach combining microbial ecology, immunology, cancer cell biology, and computational biology - a systems biology approach

Synthesis of N-Glycolylneuraminic Acid (Neu5Gc) and Its Glycosides.

Sialic acids constitute a family of negatively charged structurally diverse monosaccharides that are commonly presented on the termini of glycans in higher animals and some microorganisms. In addition to N-acetylneuraminic acid (Neu5Ac), N-glycolyl neuraminic acid (Neu5Gc) is among the most common sialic acid forms in nature. Nevertheless, unlike most animals, human cells loss the ability to synthesize Neu5Gc although Neu5Gc-containing glycoconjugates have been found on human cancer cells and in various human tissues due to dietary incorporation of Neu5Gc. Some pathogenic bacteria also produce Neu5Ac and the corresponding glycoconjugates but Neu5Gc-producing bacteria have yet to be found. In addition to Neu5Gc, more than 20 Neu5Gc derivatives have been found in non-human vertebrates. To explore the biological roles of Neu5Gc and its naturally occurring derivatives as well as the corresponding glycans and glycoconjugates, various chemical and enzymatic synthetic methods have been developed to obtain a vast array of glycosides containing Neu5Gc and/or its derivatives. Here we provide an overview on various synthetic methods that have been developed. Among these, the application of highly efficient one-pot multienzyme (OPME) sialylation systems in synthesizing compounds containing Neu5Gc and derivatives has been proven as a powerful strategy

Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

The goal of cancer immunotherapy is to boost a patient's immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society Interfac

Determining the Quantitative Principles of T Cell Response to Antigenic Disparity in Stem Cell Transplantation

Alloreactivity compromising clinical outcomes in stem cell transplantation is observed despite HLA matching of donors and recipients. This has its origin in the variation between the exomes of the two, which provides the basis for minor histocompatibility antigens (mHA). The mHA presented on the HLA class I and II molecules and the ensuing T cell response to these antigens results in graft vs. host disease. In this paper, results of a whole exome sequencing study are presented, with resulting alloreactive polymorphic peptides and their HLA class I and HLA class II (DRB1) binding affinity quantified. Large libraries of potentially alloreactive recipient peptides binding both sets of molecules were identified, with HLA-DRB1 generally presenting a greater number of peptides. These results are used to develop a quantitative framework to understand the immunobiology of transplantation. A tensor-based approach is used to derive the equations needed to determine the alloreactive donor T cell response from the mHA-HLA binding affinity and protein expression data. This approach may be used in future studies to simulate the magnitude of expected donor T cell response and determine the risk for alloreactive complications in HLA matched or mismatched hematopoietic cell and solid organ transplantation

Outlook Magazine, Winter 2018

https://digitalcommons.wustl.edu/outlook/1206/thumbnail.jp

National Scientific Facilities and Their Science Impact on Non-Biomedical Research

H-index, proposed by Hirsch is a good indicator of the impact of a scientist's research. When evaluating departments, institutions or labs, the importance of h-index can be further enhanced when properly calibrated for size. Particularly acute is the issue of federally funded facilities whose number of actively publishing scientists frequently dwarfs that of academic departments. Recently Molinari and Molinari developed a methodology that shows the h-index has a universal growth rate for large numbers of papers, allowing for meaningful comparisons between institutions. An additional challenge when comparing large institutions is that fields have distinct internal cultures, with different typical rates of publication and citation; biology is more highly cited than physics, which is more highly cited than engineering. For this reason, this study has focused on the physical sciences, engineering, and technology, and has excluded bio-medical research. Comparisons between individual disciplines are reported here to provide contextual framework. Generally, it was found that the universal growth rate of Molinari and Molinari holds well across all the categories considered, testifying to the robustness of both their growth law and our results. The overall goal here is to set the highest standard of comparison for federal investment in science; comparisons are made with the nations preeminent private and public institutions. We find that many among the national facilities compare favorably in research impact with the nations leading universities.Comment: 22 pages, 7 figure