Tagged: PNAS

Reinhard Research Provides Insights into HIV-1

May 14th, 2012 in Front Page, Publications, Reinhard, Björn, Uncategorized

Professor Bjoern Reinhard

Professor Bjoern Reinhard

Recently reported in PNAS, Bjoern Reinhard and his collaborator at the BU Medical School, Dr. Suryaram Gummuluru, have confirmed a unique HIV-1 DC attachment mechanism using lipoparticles with defined surface composition. The  mechanism is dependent on a host-cell–derived ligand, GM3, and is a unique example of pathogen mimicry of host-cell recognition pathways that drive virus capture and dissemination in vivo.   These insights provide the basis for the development of artificial virus nanoparticles with host-derived surface groups that inhibit the HIV-1 trans-dissemination pathway through dendritic cells. The virus parasite uses these dendritic cells to facilitate its dissemination, while avoiding recognition.

Citation: Puryear, et al., “HIV-1 incorporation of host-cell–derived glycosphingolipid GM3 allows for captureby mature dendritic cells”, Proc Natl Acad USA, 2012, 109 (19), 7475-7480.

Reinhard PNAS Fig 1

Gangliosides with α2–3 NeuNAc linkages are important for HIV-1 capture by mDCs. (A) Gag-eGFP VLPs were mock treated or treated with 0.5 units/μL α2–3, 2–6, 2–8 NA. (B) Gag-eGFP VLPs were derived from siRNA-treated HEK293T cells. NT, nontargeting; UGT8, galactosyl transferase; CERT, ceramide transfer protein; UGCG, glucosyltransferases, ST3, GM3 transferase. Capture of VLPs by mDCs was analyzed by FACS (A and B). Data are reported as percentage of eGFP+ mDCs normalized to NT-treated VLPs. (C and D) Ganglioside-deficient HIVLai was derived from HEK293T cells knocked down for NT, UGCG, or ST3. (C) Virions were labeled for p24gag (green) and GM3 (red). Representative fields are shown and the average mean fluorescent intensity (MFI) of GM3 normalized to p24gag ± SD is reported, *P < 0.001, one-way ANOVA with Dunnett’s multiple comparison. (D) Fold decrease of ganglioside-depleted HIVLai capture relative to NT-treated viruses by mDCs is reported. (E) Fold decrease in HIVLaiΔEnv virus capture treated with 0.5 units/μL α2–3, 2–6, 2–8 NA or α2–3-specific NA relative to mock-treated viruses by mDCs is reported. All capture assays represent averaged data from a minimum of three donors, ±SEM, one-sample t test, *P < 0.05, **P < 0.01, ***P < 0.001.

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Collaborative Research Discovers New Approach in the Treatment of Liver Cancer

April 9th, 2012 in CMLD, Publications, Schaus, Scott

Prof Scott Schaus

Professor Scott Schaus

Until now, there has been no effective, systemic treatment for liver cancer (hepatocellular carcinoma), the fifth most common cancer worldwide. Writing in the Proceedings of the National Academy of Science (PNAS), Professor Scott Schaus (Chemistry) and Professor Ulla Hansen (Biology and Molecular Biology, Cell Biology & Biochemistry) have reported their discovery of a new protein target for chemotherapy in the treatment of liver cancer — the transcription factor LSF. LSF occurs at high levels in the tumor tissue of patients with liver cancer and is known to promote the development of cancer (oncogenesis) in studies using laboratory rodents.

The co-investigators have identified small molecules that effectively inhibit LSF cellular activity, which in turn slows the growth of the cancer. In particular, they found that one such molecule, Factor Quinolinone Inhibitor 1 (FQI1), derived from a lead compound, inhibits the ability of LSF to bind DNA both in extracts (in vitro, as determined by electrophoretic mobility shift assays) and in cells. Consistent with inhibiting LSF activity, FQI1 also eliminates the ability of LSF to turn up transcription. While FQI1 quickly causes cell death in LSF-overexpressing cells, including liver cancer cells, healthy cells are unaffected by the treatment. This phenomenon has been called oncogene addiction, where tumor cells are “addicted” to the activity of an oncogenic factor for their survival, but normal cells can do without it. This characteristic is very encouraging for use
of such compounds clinically.

Structures of LSF inhibitors

Structures of LSF inhibitors

Following in vitro trials, the researchers tested the efficacy of FQI1 in inhibiting liver cancer tumor growth by injecting HCC cell lines into rodent models. FQI1 was observed to significantly inhibit tumor growth with no observable side effects (general tissue cytotoxicity). These dramatic findings support the further development of LSF inhibitors as a promising new chemotherapy treatment for liver cancer.

This work is featured as part of the series, “BU Takes on Cancer,” in BU Today (April 11, 2012).


Citation: T.J. Grant, J. A. Bishop, L.M. Christadore, G. Barot, H.G. Chin, S. Woodson, J. Kavouris, A. Siddiq, R. Gedler, X-N. Shen, J. Sherman, T. Meehan, K. Fitzgerald, S. Pradhan, L.A. Briggs, W.H. Andrews, D. Sarkar, S.E. Schaus, and U. Hansen, “Antiproliferative small-molecule inhibitors of transcription factor LSF reveal oncogene addiction to LSF in hepatocellular carcinoma,” Proc. Natl. Acad. Sci. U.S.A., March 20, 2012, Vol. 109, No. 12, 4503-4508.

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Drug Mechanism of Action Using Bioinformatic Pathway Analysis

August 3rd, 2011 in Faculty, Front Page, Graduate, Publications, Research, Schaus, Scott, Students

Lisa Christadore

Lisa Christadore

Professor Scott Schaus

Professor Scott Schaus

Professor Scott Schaus and Graduate Student Lisa Christadore are co-authors of:

Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis

Published in PNAS in July 2011, the paper represents their collaborative work with researchers in the BU Department of Mathematics and Statistics, Professor Eric Kolaczyk and Graduate Student Lisa Pham.

It reports on the effectiveness of their novel method, latent pathway identification analysis (LPIA), in providing insights into systemic biological pathways and key cellular mechanisms that dictate disease states, drug response, and altered cellular function. The work was supported by NIH, NSF, and DOD.

Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis.

Schematic illustration of the proposed LPIA method.

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