Biology subjects

Chi, A.

Publications and source records attributed to Chi, A..

3 recordsLinked to original sources

bioRxiv2025

Leveraging unbiased LC-MS analysis to identify histone post-translational modifications associated with age-related decline of learning and memory

Age-associated cognitive decline is a significant societal challenge, with several neurodegenerative diseases like Alzheimers disease becoming prevalent. Normal ageing is associated with several molecular, cellular, and metabolic hallmarks, including epigenetic mechanisms. In particular, dysregulation of post-translational modifications on histone tails is emerging as a mechanism associated with functional decline. The hippocampus is a region of the brain that is essential for the formation of episodic and spatial memories, which are particularly susceptible to age-related decline. In this perspective manuscript, we have used mass spectrometry as an unbiased method to profile histone tail post-translational modifications both at baseline and after contextual fear conditioning (CFC) from young and aged mouse hippocampi to identify age- and activity-dependent changes. Using this approach, we identify epigenetic marks not widely studied in ageing and in activity-induced learning. We propose a framework on how to integrate these epigenetic marks into current knowledge and discuss how to use this new analysis to formulate novel hypotheses that will broaden this field of ageing research. We believe that this work will be of interest to scientists, clinicians and the wider public by making a significant contribution to our understanding of the molecular mechanisms responsible for age-associated cognitive decline and by reporting the identification of new mechanisms to focus on.

neuroscience↗

bioRxiv2020

Profiling Active Enzymes for Polysorbate Degradation in Biotherapeutics by Activity-Based Protein Profiling

Polysorbate is widely used to maintain stability of biotherapeutic proteins in formulation development. Degradation of polysorbate can lead to particle formation in drug products, which is a major quality concern and potential patient risk factor. Enzymatic activity from residual enzymes such as lipases and esterases can cause polysorbate degradation. Their high activity, even at low concentration, constitutes a major analytical challenge. In this study, we evaluated and optimized the activity-based protein profiling (ABPP) approach to identify active enzymes responsible for polysorbate degradation. Using chemical probes to enrich active serine hydrolases, more than 80 proteins were identified in harvested cell culture fluid (HCCF) from monoclonal antibodies (mAb) production. A total of 8 known lipases were identified by ABPP, while only 5 lipases were identified by a traditional abundance-based proteomics (TABP) approach. Interestingly, phospholipase B-like 2 (PLBL2), a well-known problematic HCP was not found to be active in process-intermediates from two different mAbs. In a proof-of-concept study, phospholipase A2 group VII (PLA2G7) and sialic acid acetylesterase (SIAE) were identified by ABPP as possible root causes of polysorbate-80 degradation. The established ABBP approach can fill the gap between lipase abundance and activity, which enables more meaningful polysorbate degradation investigations for biotherapeutic development.

bioengineering↗

bioRxiv2017

Consequences Of Natural Perturbations In The Human Plasma Proteome

Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, including trans associations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

genomics↗