Integrated Single-cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study.

OBJECTIVE: The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery. SUMMARY BACKGROUND DATA: SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs. METHODS: Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery. RESULTS: A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82). CONCLUSIONS: The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.

Low-dose lipopolysaccharide exposure can increase in vivo bilirubin production rates in newborn mice.

AIM: Neonatal haemolysis increases bilirubin production rates, which can then lead to severe hyperbilirubinaemia and bilirubin neurotoxicity. During haemolysis, haem is degraded by haem oxygenase (HO), which can be induced under stress conditions. It is known that neonatal sepsis is a risk factor for haemolysis and severe hyperbilirubinaemia. Here, we evaluated whether an exposure to lipopolysaccharide (LPS) to induce sepsis can upregulate HO-1, further increasing in vivo bilirubin production rates in mouse pups under haem loads. METHODS: Three-day-old pups were given LPS (1250 µg/kg) or saline (controls). Liver HO enzyme activity, HO-1 mRNA and HO-1 protein were measured post-LPS exposure. We then assessed the effects of LPS treatment on in vivo bilirubin production rates after haem loading. RESULTS: Liver HO activity significantly increased (142%) over controls 24 hours after treatment with LPS (1250 µg/kg) without mortality. Liver HO-1 mRNA was significantly upregulated 2.47-fold at 24 hours post-LPS administration, while liver HO-1 protein increased 1.29-fold 24 hours post-LPS treatment. After haem loading, pups exposed to LPS had significantly higher bilirubin production rates (1.30-fold) compared with age-matched, saline-treated controls. CONCLUSION: Low-dose LPS treatment can upregulate liver HO-1 expression and may underlie the severe hyperbilirubinaemia seen in septic infants, particularly when undergoing haemolysis.

Clinical and histopathologic characterization of SETD2-mutated colorectal cancer.

With the advent of next-generation sequencing (NGS), identifying and better understanding genetic mutations in cancer pathways has become more feasible. A mutation now commonly reported in NGS panels is the SETD2 gene (H3K36 trimethyltransferase). However, its contributions to colorectal cancer (CRC) are not well described. In this study, we describe the clinicopathologic characteristics of SETD2-mutated CRC, determine common mutation sites on the SETD2 gene, and correlate these mutations with the loss of H3K36 trimethylation and the aberrant expression of beta-catenin. By searching pathology reports at our institution which included the 161-gene NGS panel from 2019 to 2021, we identify 24 individuals with SETD2-mutated CRC. All samples were evaluated for microsatellite status, H3K36 trimethylation, and beta-catenin via immunohistochemistry. In this cohort of 24 SETD2-mutated CRC individuals (a median age of 62.4 years [interquartile range: 49.1-73.6]), 10 (41.7%) patients presented at American Joint Committee on Cancer (AJCC) tumor stage II, seven (29.2%) at stage III, six (25%) at stage IV, and one (4.2%) at stage I. Most tumors studied were adenocarcinomas with no further specification (22, 92%), and most tumors were microsatellite stable (18, 82.5%). Thirty-three mutation locations were represented by 24 patients, with one patient having six mutations in the SETD2 gene and two patients having three mutations. The dominant mutation type is missense mutations (N = 29, 87.9%), and no mutation hotspots were found. Only two samples lost trimethylation of histone H3K36, both from individuals with multiple SETD2 mutations and aberrant nuclear beta-catenin expression. SETD2-mutated CRC is similar in clinical and histologic presentation to other commonly reported CRC. SETD2 mutations were missense dominantand showed no hotspots, and multiple mutations are likely necessary for loss of H3K36 trimethylation. These results warrant further study on determining a role of SETD2-histone H3K36 pathway in CRC.

Druggable sites/pockets of the p53-DNAJA1 protein-protein interaction: In silico modeling and in vitro/in vivo validation.

Mutation of p53 is the most common genetic alteration in human cancer. The vast majority of p53 mutations found in cancer are missense mutations, with some single nucleotide point mutations leading to the accumulation of mutant p53 protein with potential gain of oncogenic function. The mechanism for stabilization and accumulation of missense mutant p53 protein in malignant cells is not fully understood. It is thought that DNAJA1 plays a crucial role as a co-chaperone protein by stabilizing mutant p53 and amplifying oncogenic potential. As such, identifying small molecule inhibitors to disrupt the protein-protein interaction between mutant p53 and DNAJA1 may lead to an effective treatment for preventing carcinogenesis. Studying protein-protein interactions and identifying potential druggable hotspots has historically been limited-protein-protein binding sites require more complex characterization than those of single proteins and the crystal structures of many proteins have not been identified. Due to these issues, identifying salient druggable targets in protein-protein interactions through bench research may take years to complete. However, in silico modeling approaches allow for rapid characterization of protein-protein interfaces and the druggable binding sites they contain. In this chapter, we first review the oncogenic potential of mutant p53 and the crucial role of DNAJA1 in stabilizing missense mutant p53. We then detail our methodology for using in silico modeling and molecular biology to identify druggable protein-protein interaction sites/pockets between mutant p53 and DNAJA1. Finally, we discuss screening for and validating the utility of a small molecule inhibitor identified through our in silico framework. Specifically, we describe GY1-22, a unique compound with activity against mutant p53 that demonstrates therapeutic potential to inhibit cancer cell growth both in vivo and in vitro.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset.

Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10-40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

Colorimetric Assay Reports on Acyl Carrier Protein Interactions.

The ability to produce new molecules of potential pharmaceutical relevance via combinatorial biosynthesis hinges on improving our understanding of acyl-carrier protein (ACP)-protein interactions. However, the weak and transient nature of these interactions makes them difficult to study using traditional spectroscopic approaches. Herein we report that converting the terminal thiol of the E. coli ACP 4'-phosphopantetheine arm into a mixed disulfide with 2-nitro-5-thiobenzoate ion (TNB-) activates this site to form a selective covalent cross-link with the active site cysteine of a cognate ketoacyl synthase (KS). The concomitant release of TNB2-, which absorbs at 412 nm, provides a visual and quantitative measure of mechanistically relevant ACP-KS interactions. The colorimetric assay can propel the engineering of biosynthetic routes to novel chemical diversity by providing a high-throughput screen for functional hybrid ACP-KS partnerships as well as the discovery of novel antimicrobial agents by enabling the rapid identification of small molecule inhibitors of ACP-KS interactions.