Heinrich Roder

Despite numerous studies focusing on the folding mechanism of globular proteins as well as ligand‐induced folding of intrinsically disordered proteins (IDPs), a unified framework for understanding both types of folding mechanisms has remained elusive. To explore the similarities and differences in the structural dynamics of spontaneous versus ligand‐dependent folding, we investigated the folding dynamics of staphylococcal nuclease (SNase) in the presence and absence of the substrate analog adenosine 3′,5′‐diphosphate (prAp). We employed equilibrium and kinetic measurements, using fluorescence and NMR spectroscopy, to study the folding of SNase coupled with the binding of prAp as a function of ligand and urea concentrations, including conditions favoring either conformational selection (CS; folding before binding) or induced fit (IF; binding before folding) scenarios. Our findings revealed that during ligand‐induced folding under IF conditions, the N‐terminal β‐barrel domain is formed first, followed by the α‐helical domain. In contrast, under CS conditions, the α‐helical domain forms before the β‐barrel domain. Additionally, the dynamics of ligand‐induced folding mirrors the sequence of events encountered along the minor of the two parallel pathways governing the spontaneous folding process. Therefore, some of the apparent mechanistic differences between spontaneous versus ligand‐induced folding can be attributed to the fact that interactions with a nucleotide ligand result in a shift in flux from the major to the minor folding pathway.

The VeriStrat® test (VS) is a blood-based assay that predicts a patient's response to therapy by analyzing eight features in a spectrum obtained from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis of human serum and plasma. In a recent analysis of the INSIGHT clinical trial (NCT03289780), it was found that the VS labels, VS Good and VS Poor, can effectively predict the responsiveness of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitor (ICI) therapy. However, while VS measures the intensities of spectral features using MALDI-TOF analysis, the specific proteoforms underlying these features have not been comprehensively identified. The objective of this study was to identify the proteoforms that are measured by VS. To resolve the features obtained from the low-resolution MALDI-TOF procedure used to acquire mass spectra for VS DeepMALDI® analysis of serum was employed. This technique allowed for the identification of finer peaks within these features. Additionally, a combination of reversed-phase fractionation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then used to identify the proteoforms associated with these peaks. The analysis revealed that the primary constituents of the spectrum measured by VS are serum amyloid A1, serum amyloid A2, serum amyloid A4, C-reactive protein, and beta-2 microglobulin. Proteoforms involved in host immunity were identified as significant components of these features. This newly acquired information improves our understanding of how VS can accurately predict patient response to therapy. It opens up additional studies that can expand our understanding even further.

Several proteins have been shown to undergo a shift in the mechanism of ligand binding-induced folding from conformational selection (CS; folding precedes binding) to induced fit (IF; binding precedes folding) with increasing ligand concentration. In previous studies of the coupled folding/binding reaction of staphylococcal nuclease (SNase) in the presence of a substrate analogue, adenosine-3',5'-diphosphate (prAp), we found that the two phosphate groups make important energetic contributions toward stabilizing its complex with the native protein as well as transient conformational states encountered at high ligand concentrations favoring IF. However, the structural contributions of each phosphate group during the reaction remain unclear. To address this question, we relied on fluorescence, nuclear magnetic resonance (NMR), absorption, and isothermal titration calorimetry to study the effects of deletion of the phosphate groups of prAp on the kinetics of ligand-induced folding, using a strategy analogous to mutational ϕ-value analysis to interpret the results. Kinetic measurements over a wide range of ligand concentrations, together with structural characterization of a transient protein-ligand encounter complex using 2D NMR, indicated that, at high ligand concentrations favoring IF, (i) the 5'-phosphate group interacts weakly with denatured SNase during early stages of the reaction, resulting in loose docking of the two domains of SNase, and (ii) the 3'-phosphate group engages in some specific contacts with the polypeptide in the transition state prior to formation of the native SNase-prAp complex.

Many important biological processes such as protein folding and ligand binding are too fast to be fully resolved using conventional stopped-flow techniques. Although advances in mixer design and detection methods have provided access to the microsecond time regime, there is room for improvement in terms of temporal resolution and sensitivity. To address this need, we developed a continuous-flow mixing instrument with a dead time of 12 to 27 µs (depending on solution viscosity) and enhanced sensitivity, sufficient for monitoring tryptophan or tyrosine fluorescence changes at fluorophore concentrations as low as 1 µM. Relying on commercially available laser microfabrication services, we obtained an integrated mixer/flow-cell assembly on a quartz chip, based on a cross-channel configuration with channel dimensions and geometry designed to minimize backpressure. By gradually increasing the width of the observation channel downstream from the mixing region, we are able to monitor a reaction progress time window ranging from ~10 µs out to ~3 ms. By combining a solid-state UV laser with a Galvano-mirror scanning strategy, we achieved highly efficient and uniform fluorescence excitation along the flow channel. Examples of applications, including refolding of acid-denatured cytochrome c triggered by a pH jump and binding of a peptide ligand to a PDZ domain, demonstrate the capability of the technique to resolve fluorescence changes down to the 10 µs time regime on modest amounts of reagents.

Accurate and precise measurement of the relative protein content of blood-based samples using mass spectrometry is challenging due to the large number of circulating proteins and the dynamic range of their abundances. Traditional spectral processing methods often struggle with accurately detecting overlapping peaks that are observed in these samples. In this work, we develop a novel spectral processing algorithm that effectively detects over 1650 peaks with over 3.5 orders of magnitude in intensity in the 3 to 30 kD m/z range. The algorithm utilizes a convolution of the peak shape to enhance peak detection, and accurate peak fitting to provide highly reproducible relative abundance estimates for both isolated peaks and overlapping peaks. We demonstrate a substantial increase in the reproducibility of the measurements of relative protein abundance when comparing this processing method to a traditional processing method for sample sets run on multiple matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) instruments. By utilizing protein set enrichment analysis, we find a sizable increase in the number of features associated with biological processes compared to previously reported results. The new processing method could be very beneficial when developing high-performance molecular diagnostic tests in disease indications.

Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI^® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat^® classification algorithm. Machine learning techniques then classified each sample as “Cancer” or “No Cancer”. Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.

Most diseases involve a complex interplay between multiple biological processes at the cellular, tissue, organ, and systemic levels. Clinical tests and biomarkers based on the measurement of a single or few analytes may not be able to capture the complexity of a patient’s disease. Novel approaches for comprehensively assessing biological processes from easily obtained samples could help in the monitoring, treatment, and understanding of many conditions. We propose a method of creating scores associated with specific biological processes from mass spectral analysis of serum samples. A score for a process of interest is created by: (i) identifying mass spectral features associated with the process using set enrichment analysis methods, and (ii) combining these features into a score using a principal component analysis-based approach. We investigate the creation of scores using cohorts of patients with non-small cell lung cancer, melanoma, and ovarian cancer. Since the circulating proteome is amenable to the study of immune responses, which play a critical role in cancer development and progression, we focus on functions related to the host response to disease. We demonstrate the feasibility of generating scores, their reproducibility, and their associations with clinical outcomes. Once the scores are constructed, only 3 µL of serum is required for the assessment of multiple biological functions from the circulating proteome. These mass spectrometry-based scores could be useful for future multivariate biomarker or test development studies for informing treatment, disease monitoring and improving understanding of the roles of various biological functions in multiple disease settings.

Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.

Background: Understanding of biological processes associated with irAEs for patients treated with immune checkpoint inhibitors (ICI) is limited. We used serum-based, proteomic scores at baseline and after treatment initiation to explore mechanisms of irAEs for patients with non-small cell lung cancer (NSCLC) treated with ICI. Methods: Under an ongoing clinical protocol, 43 patients with advanced NSCLC were consented and serum samples were prospectively collected at two timepoints: baseline and approximately 3 weeks after treatment initiation with ICI (median 22 [IQR, 21 - 26] days). Samples were analyzed, blinded to clinical data, using MALDI-ToF mass spectrometry. Protein Set Enrichment Analysis (PSEA) approach applied to mass-spectral data was used to assign biological scores characterizing activation of 10 processes of interest (e.g., Type 1 immunity, complement, interferon (IFN)-gamma). irAEs after initiation of ICI with or without chemotherapy were classified per standard definitions. Patients were classified into two groups based on irAEs of any grade: irAE positive and negative. Results: Of the 43 participants, 28 received ICI with chemotherapy and 15 received monotherapy. 18 of 43 patients (42%) were determined to have irAEs. These included the following: 9 pneumonitis, 3 thyroiditis, 3 adrenal insufficiency, 1 arthritis, 1 flare of pre-existing psoriasis, 1 mucositis, 1 colitis, 1 myocarditis, and 1 hepatitis (2 patients had both thyroiditis and adrenal insufficiency, 1 patient had both mucositis and pneumonitis). The median timeframe between treatment initiation and development of irAEs was 105 days [IQR, 42 - 169 days]. PSEA scores measured at 3 weeks after initiation of systemic therapy showed significant differences between irAE positive and negative groups in the following processes: extracellular matrix remodeling, complement activation, IFN-gamma signaling, and immune tolerance (PConclusions: Our findings demonstrate that serum-based, proteomic scores can provide insight into understanding early mechanisms for the development of irAEs in patients treated with ICI. In particular, we identified several mechanisms associated with the development of irAEs, including extracellular matrix remodeling, complement activation, IFN-gamma signaling, and immune tolerance. These associations were not present at baseline and were only observed after treatment initiation, suggesting that early changes in the blood may provide insight into prediction of irAEs. Citation Format: Andrew A. Davis, Jonghanne Park, Leeseul Kim, Gahyun Gim, Wade T. Iams, Michael S. Oh, Robert W. Lentz, Heinrich Roder, Joanna Roder, Senait Asmellash, Lelia Net, Julia Grigorieva, Nisha Mohindra, Victoria Villaflor, Young Kwang Chae. Serum proteomic scores for understanding the mechanisms of immune-related adverse events (irAEs) in non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5527.