Detection of fucosylated haptoglobin using the 10-7G antibody as a biomarker for evaluating endoscopic remission in ulcerative colitis

Prognostic significance of serum fucosylated pro-haptoglobin in advanced renal cell carcinoma patients treated with immune checkpoint inhibitors

With the widespread use of immune checkpoint inhibitors (ICIs), identifying predictive biomarkers is critical. Recently, serum fucosylated haptoglobin (Fuc-Hp) was thought to play an important role in tumour immunity in several types of cancer. Therefore, evaluating serum Fuc-Hp in the peripheral blood can potentially identify non-invasive predictive biomarkers for the clinical efficacy of ICIs. In this study, 31 patients with advanced renal cell carcinoma (RCC) treated with nivolumab were enrolled and defined as responders or non-responders according to RECIST criteria. Serum samples were collected before and 1 month after treatment initiation, and an ELISA assay was performed using Aleuria Aurantia Lectin (AAL) and 10-7G monoclonal antibodies that recognise Fuc-mature Hp (Fuc-mHp) and Fuc-pro Hp (Fuc-pHp), respectively. We first measured AAL-haptoglobin (Fuc-mHp) and total haptoglobin levels before nivolumab and found that neither value could predict the clinical response. Notably, serum 10-7G levels were significantly lower in the responder group (p = 0.035). We also confirmed the use of serum 10-7G levels for predicting progressive disease after nivolumab (area under the curve, 0.816). Accordingly, low 10-7G levels were significantly correlated with better progression-free survival (p = 0.041). In conclusion, serum Fuc-pHp analysis may identify patients with advanced RCC who benefit from ICIs.

Prohaptoglobin is a possible prognostic biomarker for colorectal cancer

BACKGROUND AND AIMS

Fucosylated haptoglobin is a novel glycan biomarker for colorectal and other cancers, while the significance of its precursor, prohaptoglobin (proHp), remains to be elucidated. In this study, we investigated whether proHp can be a colorectal cancer (CRC) biomarker and the biological functions of proHp in CRC using 10-7G, a monoclonal antibody recently developed in our laboratory.

MATERIALS AND METHODS

Serum proHp level in 74 patients with CRC was semi-quantified by western blotting, and 5-year recurrence-free survival and overall survival were analyzed for groups stratified by proHp status (high vs. low). We also performed immunohistochemical analyses of 17 CRC tissue sections using 10-7G mAb. The biological functions of proHp were evaluated by overexpressing proHp in CRC cell lines.

RESULTS

Serum proHp correlated with the clinical stage and poorer prognosis of CRC. In the primary CRC sections, immune cells were stained positive for 10-7G in ∼50% of the cases. Overexpression of proHp in HCT116 human CRC cells induced epithelial-mesenchymal transition-like changes and promoted cell migration in CRC cells.

CONCLUSION

We provide evidence for the first time that proHp has potential as a prognostic biomarker for CRC and demonstrated specific biological activities of proHp.

Biological, genetic and epigenetic markers in ulcerative colitis

In this review, we have summarized the existing knowledge of ulcerative colitis (UC) markers based on current literature, specifically, the roles of potential new biomarkers, such as circulating, fecal, genetic, and epigenetic alterations, in UC onset, disease activity, and in therapy response. UC is a complex multifactorial inflammatory disease. There are many invasive and non-invasive diagnostic methods in UC, including several laboratory markers which are employed in diagnosis and disease assessment; however, colonoscopy remains the most widely used method. Common laboratory abnormalities currently used in the clinical practice include inflammation-induced alterations, serum autoantibodies, and antibodies against bacterial antigens. Other new serum and fecal biomarkers are supportive in diagnosis and monitoring disease activity and therapy response; and potential salivary markers are currently being evaluated as well. Several UC-related genetic and epigenetic alterations are implied in its pathogenesis and therapeutic response. Moreover, the use of artificial intelligence in the integration of laboratory biomarkers and big data could potentially be useful in clinical translation and precision medicine in UC management.

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CV_r) and herd-out (CV_h). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R² value from 5% to 75% for CV_r and 2% to 139% for CV_h, whereas the stacking ensemble increased the R² value from 4% to 70% for CV_r and 4% to 150% for CV_h. Considering the best model, with the CV_r scenario, good prediction accuracies were obtained for glucose (R² = 0.81), urea (R² = 0.73), albumin (R² = 0.75), total reactive oxygen metabolites (R² = 0.79), total thiol groups (R² = 0.76), ceruloplasmin (R² = 0.74), total proteins (R² = 0.81), globulins (R² = 0.87), and Na (R² = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

Current Trends in IBD-Development of Mucosal-Based Biomarkers and a Novel Minimally Invasive Recoverable Sampling System

Despite recent developments in therapy for inflammatory bowel diseases (IBDs), there have been limited advances in diagnostic tools available to aid in disease management. A growing body of evidence suggests that there are important host-microbe interactions at the mucosal interface that modulate the inflammatory response in patients with IBD. Additionally, the importance of mucosal integrity and its disruption appears to be important in the pathophysiology and perpetuation of the disease. The ability to characterize this interface may provide valuable information for both disease monitoring and identification of new treatment targets. Endoscopy remains the primary tool for disease monitoring, and mucosal healing is the primary therapeutic target in IBD treatment. However, establishing mucosal healing requires repetitive endoscopic procedures, and endoscopy is limited by factors such as invasiveness, cost, and risk of adverse events. Moreover, the use of a bowel preparation for colonoscopies alters the mucus layer and thus perturbs evaluation of the host-microbe interaction. Stool sampling may also be inaccurate because it reflects the end state of metabolites and proteins, failing to take into account the degradation or alteration of substrates of interest by bacterial proteases and other enzymes during passage through the colon. A novel sampling capsule, called the Recoverable Sampling System (RSS), is being developed as a complementary tool to colonoscopy. The RSS is intended to be a platform for noninvasive autonomous sampling, preservation, handling, and storage of analytes of interest found in the gastrointestinal fluids. A proprietary preservative contained within the chambers of the capsule has been developed to stabilize DNA and proteins for ex vivo microbiome and metabolomics analyses. Surrogate markers such as SPP24 and GUCA2a have been identified to correlate with gut health, intestinal permeability, and inflammation and could be locally sampled by the RSS. The potential clinical utility of an RSS device is broad and would likely be able to guide therapy by allowing for more frequent disease monitoring, aiding in disease characterization, and facilitating in the identification of novel therapeutic targets.

Rifaximin ameliorates intestinal inflammation in cirrhotic patients with hepatic encephalopathy

Rifaximin (RFX) treatment can attenuate not only hyperammonemia but also Enterococcus faecalis translocation and 10-7G values, suggesting that RFX treatment may improve intestinal inflammation and result in better overall survival.