First Insight into the Variation of the Milk Serum Proteome within and between Individual Cows

Milk contains all nutrients needed for development of calves. One important group of components responsible for this are the milk proteins. Variation due to feed or animal health, has been studied for the most abundant milk proteins. The aim of this study was to determine the variation between and within cows for their milk serum proteome. Sample Set 1 was collected from Holstein Friesian (HF) cows between November 2011 and March 2012 and prepared using filter aided sample preparation (FASP) followed by LC-MS/MS for protein identification and quantification. The results showed that the milk serum proteome was very constant in mid lactation (four cows at five time points, p > 0.05) between 3 and 6 months in lactation. Sample Set 2 was collected from HF cows in Dec 2012 and analyzed using FASP and dimethyl labeling followed by LC-MS/MS. Significant variation in the milk serum proteome (p < 0.05) between 17 individual cows was found in Sample Set 2. The most variable proteins were immune-related proteins, which may reflect the health status of the individual cow. On the other hand, proteins related to nutrient synthesis and transport were relatively constant, indicating the importance of milk in providing a stable supply of nutrients to the neonate. In conclusion, the milk serum proteome was stable over mid lactation, but differed significantly between individuals, especially in immune-related proteins.

25 Potential role of serum proteome in predicting immune-related adverse events from immunotherapy in non-small cell lung cancer

BackgroundPredicting immune-related adverse events (irAEs) in early stage is being emphasized even more. Host response to disease is reflected in serum proteome level and that allows serum proteome level as a new marker to explore response to immunotherapy. With the help of a serum-based proteomics test, Primary Immune Response (PIR), we are accessing the correlations between developing irAEs and immunotherapy in non-small cell lung cancer (NSCLC) patients.MethodsData of 48 consented NSCLC patients with baseline PIR test done within one week prior to the start of immunotherapy were collected. Samples were grouped into either sensitive or intermediate/resistant (not sensitive) by PIR classification. We analyzed the durations from the immunotherapy initiation to the first episode of irAE. IrAEs were graded according to Common Terminology Criteria for Adverse Events (CTCAE) v5.0.ResultsAmong the 48 NSCLC patients, 19 patients (39%) experienced one or more irAEs with the majority classified as either grade 1 (n=7, 36%) or grade 2 (n=10, 52%). PIR-sensitive group showed no difference in irAE free period compared to PIR-not sensitive (p=0.92, HR=0.95, 95% CI=00.3212 to 2.834). The median ‘Time to first irAE’ were undefined and 24 in PIR-sensitive and PIR-not sensitive, respectively.ConclusionsOur results demonstrated PIR-sensitive patients are not likely to tolerate immunotherapy longer without developing irAEs.

A serum proteome signature to predict mortality in severe COVID-19 patients

Here, we recorded serum proteome profiles of 33 severe COVID-19 patients admitted to respiratory and intensive care units because of respiratory failure. We received, for most patients, blood samples just after admission and at two more later time points. With the aim to predict treatment outcome, we focused on serum proteins different in abundance between the group of survivors and non-survivors. We observed that a small panel of about a dozen proteins were significantly different in abundance between these two groups. The four structurally and functionally related type-3 cystatins AHSG, FETUB, histidine-rich glycoprotein, and KNG1 were all more abundant in the survivors. The family of inter-α-trypsin inhibitors, ITIH1, ITIH2, ITIH3, and ITIH4, were all found to be differentially abundant in between survivors and non-survivors, whereby ITIH1 and ITIH2 were more abundant in the survivor group and ITIH3 and ITIH4 more abundant in the non-survivors. ITIH1/ITIH2 and ITIH3/ITIH4 also showed opposite trends in protein abundance during disease progression. We defined an optimal panel of nine proteins for mortality risk assessment. The prediction power of this mortality risk panel was evaluated against two recent COVID-19 serum proteomics studies on independent cohorts measured in other laboratories in different countries and observed to perform very well in predicting mortality also in these cohorts. This panel may not be unique for COVID-19 as some of the proteins in the panel have previously been annotated as mortality markers in aging and in other diseases caused by different pathogens, including bacteria.