NINDS Clinical Proteomics Unit

National Institute Of Neurological Disorders And Stroke

Investigators

Linked publications, trials & patents

Abstract

The NINDS Clinical Proteomics Unit (CPU) continues a long-term collaboration with Dr. Avindra Nath and colleagues in the Section of Infections of the Nervous System (SINS). SINS has an active program studying endogenous retroviruses and their role in neurological disease. Endogenous retroviruses are the result of viral infections that were integrated into the human genome millions of years ago. Nath and colleagues study one of these viruses called human endogenous retrovirus group K (HERV-K). In particular, Nath and colleagues have shown HERV-K is activated in patients with amyotrophic lateral sclerosis (ALS), and transgenic animals that express the envelope protein of HERV-K develop ALS like symptoms. In 2022, CPU conducted several pilot studies, all of which are in progress. One project seeks to identify proteins that may interact with a consensus HERV-K long terminal repeat (LTR) sequence using on-bead digestion of proteins bound to oligonucleotide immunoaffinity columns. Preliminary results show the technique yields a list of approximately 250 proteins including several transcription factors. A postdoctoral fellow had data showing a pro-inflammatory response after exposure to HERV-K env implicating toll-like receptors (TLR) as potential interactors. Co-IPs were prepared using a 6-His tagged HERV-K env protein and whole cell lysates from TLR-expressing and control cell lines. Several TLRs were identified. These data support the idea that HERV-k env signals through TLR2 to trigger inflammation in the CNS. CPU conducted a pilot isobaric mass tagging studies, using whole tissue lysates from brain samples from control and HERV-K transgenic mice. We have performed 2D LC-MS3 using Thermos TMT 6-plex reagents and TMT 10-plex reagents. We are preparing to repeat the 2D LC-MS3 analysis using Thermos TMTPro 16-plex reagent kit. We expect that increasing the number of samples analyzed will improve the statistical meaningfulness of our data. MaxQuant, a robust bioinformatics software package designed for large, high-resolution datasets, was used to identify protein constituents and quantify expression their levels using peptide level data. Detailed inspection of the peptide and Protein Groups reports for each triplicate analysis showed high precision of quantitation within single multiplexed experiments.