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UT Arlington Researcher Using NIH Funding to Study Protein-Protein Interactions in TLR Signaling

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NEW YORK (GenomeWeb) – While toll-like receptors (TLRs) are widely known as key players in the immune system, recognizing pathogens and triggering the release of inflammatory cytokines, much of the research in this field has focused on the activity of individual signaling proteins.

Aiming to take a systems-level approach to uncovering the protein-protein interactions that comprise TLR signaling, University of Texas at Arlington researcher Saiful Chowdhury has recently kicked off a project to develop mass spectrometry approaches for studying the host-defense interactome.

Supported by a three-year grant from the National Institutes of Health worth $354,749 in its first year, Chowdhury's effort will specifically focus on how external agents, such as cholesterol-lowering statin drugs and alcohol influence TLR networks.

In response to infection, TLR signaling can occur along two distinct pathways. The first involves a cytosolic adaptor protein called myeloid differentiation primary response 88, or MyD88, and is used by every TLR expect TLR3. The second requires toll/interleukin-1 receptor domain-containing adapter-inducing interferon-beta (TRFI) and can be triggered by either double-stranded RNAs, which induce TLR3 signaling, or lipopolysaccharides, which induce TLR4 signaling.

Although some of the proximal signaling proteins that transduce TLR signals in the cell have been identified and, in some cases, are being explored as therapeutic targets for inflammatory diseases, many of these proteins remain undefined — in large part because of technological limitations, Chowdhury told GenomeWeb this week.

Cross-linking allows for both the identification of large-scale protein interactions and proteins in their native biological environments, he explained. However, cross-linking generates an enormous amount of complex cross-linked products that are difficult to manage with existing software.

"Finding [target protein-protein] interactions in large-scale data is equal to finding a needle in a haystack," Chowdhury said.

To address these issues, he and his team are setting out to develop novel mass spectrometry-cleavable cross-linkers designed to be more compact and effective than standard ones, he noted. While he declined to provide specific details, he said that the cross-linkers are expected to produce fragmentation signature markers in tandem mass spectrometry of proteins, and to help reduce the complexity of the mass spec data created during large-scale protein-interactions studies.

"It is very difficult to design compact cross-linkers with mass spectrometry-cleavable features and with enrichment functionality," Chowdhury added. "We are developing cross-linkers with cleavable features in conjunction with affinity support, such as biotin and click-chemistry."

At the same time, he and his team are working on software better suited to analyzing the datasets from systems-level protein-interaction studies.

To demonstrate the utility of these tools, Chowdhury plans to conduct comprehensive analyses of the innate immune signaling pathways for TLR4 and TLR2 following cellular exposure to bacterial ligands, which trigger TLR signaling as part of normal immune function, as well as statins and alcohol. These studies effort will use Shimadzu's MALDI-QIT-TOF and ESI-IT-TOF platforms, as well as Thermo Scientific's LTQ Velos platform.

Although a number of external factors are likely to influence the activity of TLR networks, recent research has specifically linked statins and prolonged alcohol exposure to TLR functioning.

Specifically, data suggests that statins suppress TLR expression, helping explain the drugs' anti-inflammatory properties, while chronic alcohol consumption, which is known to induce hepatic inflammation, appears to increase TLR activity.

Although Chowdhury expects his NIH-funded project to uncover new details about the basic science behind TLR signaling cascades and how external agents shape immune responses, he also sees clinical potential for the research by revealing protein interactions that may prove to be good therapeutic targets for inflammatory diseases such as sepsis, asthma, and cancer.

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