Time-lapsed proteomics reveals a role for the novel protein, SNED1, in modulating ECM composition and protein folding

The extracellular matrix (ECM) is a complex and dynamic meshwork of proteins providing structural support to cells. It also provides biochemical signals governing cellular processes including proliferation and migration. Alterations of ECM structure and/or composition has been shown to lead to, or accompany, many pathological processes including cancer and fibrosis. To understand how the ECM contributes to diseases, we first need to obtain a comprehensive characterization of the ECM of tissues and of its changes during disease progression. Over the past decade, mass-spectrometry-based proteomics has become the state-of-the-art method to profile the protein composition of ECMs. However, existing methods do not fully capture the broad dynamic range of protein abundance in the ECM, nor do they permit to achieve the high coverage needed to gain finer biochemical information, including the presence of isoforms or post-translational modifications. In addition, broadly adopted proteomic methods relying on extended trypsin digestion do not provide structural information on ECM proteins, yet, gaining insights into ECM protein structure is critical to better understanding protein functions. Here, we present the optimization of a time-lapsed proteomic method using limited proteolysis of partially denatured samples and the sequential release of peptides to achieve superior sequence coverage as compared to standard ECM proteomic workflow. Exploiting the spatio-temporal resolution of this method, we further demonstrate how 3-dimensional time-lapsed peptide mapping can identify protein regions differentially susceptible to trypsin and can thus identify sites of post-translational modifications, including protein-protein interactions. We further illustrate how this approach can be leveraged to gain insight on the role of the novel ECM protein SNED1 in ECM homeostasis. We found that the expression of SNED1 expression by mouse embryonic fibroblasts results in the alteration of overall ECM composition and the sequence coverage of certain ECM proteins, raising the possibility that SNED1 could modify accessibility to trypsin by engaging in protein-protein interactions.

Topological data analysis identifies distinct biomarker phenotypes during the inflammatory phase of COVID-19

OBJECTIVES: The relationships between baseline clinical phenotypes and the cytokine milieu of the peak inflammatory phase of coronavirus 2019 (COVID-19) are not yet well understood. We used Topological Data Analysis (TDA), a dimensionality reduction technique to identify patterns of inflammation associated with COVID-19 severity and clinical characteristics. DESIGN: Exploratory analysis from a multi-center prospective cohort study. SETTING: Eight military hospitals across the United States between April 2020 and January 2021. PATIENTS: Adult (≥18 years of age) SARS-CoV-2 positive inpatient and outpatient participants were enrolled with plasma samples selected from the putative inflammatory phase of COVID-19, defined as 15-28 days post symptom onset. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Concentrations of 12 inflammatory protein biomarkers were measured using a broad dynamic range immunoassay. TDA identified 3 distinct inflammatory protein expression clusters. Peak severity (outpatient, hospitalized, ICU admission or death), Charlson Comorbidity Index (CCI), and body mass index (BMI) were evaluated with logistic regression for associations with each cluster. The study population (n=129, 33.3% female, median 41.3 years of age) included 77 outpatient, 31 inpatient, 16 ICU-level, and 5 fatal cases. Three distinct clusters were found that differed by peak disease severity (p <0.001), age (p <0.001), BMI (p<0.001), and CCI (p=0.001). CONCLUSIONS: Exploratory clustering methods can stratify heterogeneous patient populations and identify distinct inflammation patterns associated with comorbid disease, obesity, and severe illness due to COVID-19.

Molecular mechanisms for environmentally induced and evolutionarily rapid redistribution (plasticity) of meiotic recombination

It has long been known (circa 1917) that environmental conditions, as well as speciation, can affect dramatically the frequency distribution of Spo11/Rec12-dependent meiotic recombination. Here, by analyzing DNA sequence-dependent meiotic recombination hotspots in the fission yeast Schizosaccharomyces pombe, we reveal a molecular basis for these phenomena. The impacts of changing environmental conditions (temperature, nutrients, osmolarity) on local rates of recombination are mediated directly by DNA site-dependent hotspots (M26, CCAAT, Oligo-C). This control is exerted through environmental condition-responsive signal transduction networks (involving Atf1, Pcr1, Php2, Php3, Php5, Rst2). Strikingly, individual hotspots modulate rates of recombination over a very broad dynamic range in response to changing conditions. They can range from being quiescent to being highly proficient at promoting activity of the basal recombination machinery (Spo11/Rec12 complex). Moreover, each different class of hotspot functions as an independently controlled rheostat; a condition that increases the activity of one class can decrease the activity of another class. Together, the independent modulation of recombination rates by each different class of DNA site-dependent hotspots (of which there are many) provides a molecular mechanism for highly dynamic, large-scale changes in the global frequency distribution of meiotic recombination. Because hotspot-activating DNA sites discovered in fission yeast are conserved functionally in other species, this process can also explain the previously enigmatic, Prdm9-independent, evolutionarily rapid changes in hotspot usage between closely related species, subspecies, and isolated populations of the same species.

438. Phenotypic Differences Between Distinct Immune Biomarker Clusters During the ‘Hyperinflammatory’ Middle-Phase of COVID-19

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections peak during an inflammatory ‘middle’ phase and lead to severe illness predominately among those with certain comorbid noncommunicable diseases (NCDs). We used network machine learning to identify inflammation biomarker patterns associated with COVID-19 among those with NCDs. Methods SARS-CoV-2 RT-PCR positive subjects who had specimens available within 15-28 days post-symptom onset were selected from the DoD/USU EPICC COVID-19 cohort study. Plasma levels of 15 inflammation protein biomarkers were measured using a broad dynamic range immunoassay on samples collected from individuals with COVID-19 at 8 military hospitals across the United States. A network machine learning algorithm, topological data analysis (TDA), was performed using results from the ‘hyperinflammatory’ middle phase. Backward selection stepwise logistic regression was used to identify analytes associated with each cluster. NCDs with a significant association (0.05 significance level) across clusters using Fisher’s exact test were further evaluated comparing the NCD frequency in each cluster against all other clusters using a Kruskal-Wallis test. A sensitivity analysis excluding mild disease was also performed. Results The analysis population (n=129, 33.3% female, median 41.3 years of age) included 77 ambulatory, 31 inpatient, 16 ICU-level, and 5 fatal cases. TDA identified 5 unique clusters (Figure 1). Stepwise regression with a Bonferroni-corrected cutoff adjusted for severity identified representative analytes for each cluster (Table 1). The frequency of diabetes (p=0.01), obesity (p&lt; 0.001), and chronic pulmonary disease (p&lt; 0.001) differed among clusters. When restricting to hospitalized patients, obesity (8 of 11), chronic pulmonary disease (6 of 11), and diabetes (6 of 11) were more prevalent in cluster C than all other clusters. Cluster differences in comorbid diseases and severity by cluster. 1A: bar plot of diabetes prevalence; 1B: bar plot of chronic lung disease ; 1C: bar plot of obesity prevalence; 1D: prevalence of steroid treatment ; 1E: Topologic data analysis network with clusters labeled; 1F: Bar plot of ordinal levels of severity. Conclusion Machine learning clustering methods are promising analytical tools for identifying inflammation marker patterns associated with baseline risk factors and severe illness due to COVID-19. These approaches may offer new insights for COVID19 prognosis, therapy, and prevention. Disclosures Simon Pollett, MBBS, Astra Zeneca (Other Financial or Material Support, HJF, in support of USU IDCRP, funded under a CRADA to augment the conduct of an unrelated Phase III COVID-19 vaccine trial sponsored by AstraZeneca as part of USG response (unrelated work))

Design, development, and validation of a strand-specific RT-qPCR assay for GI and GII human Noroviruses

Human noroviruses (HuNoV) are the major cause of viral gastroenteritis worldwide. Similar to other positive-sense single-stranded RNA viruses, norovirus RNA replication requires the formation of a negative strand RNA intermediate. Methods for detecting and quantifying the viral positive or negative sense RNA in infected cells and tissues can be used as important tools in dissecting virus replication. In this study, we have established a sensitive and strand-specific Taqman-based quantitative polymerase chain reaction (qPCR) assay for both genogroups GI and GII HuNoV. This assay shows good reproducibility, has a broad dynamic range and is able to detect a diverse range of isolates. We used tagged primers containing a non-viral sequence for the reverse transcription (RT) reaction and targeted this tag in the succeeding qPCR reaction to achieve strand specificity. The specificity of the assay was confirmed by the detection of specific viral RNA strands in the presence of high levels of the opposing strands, in both RT and qPCR reactions. Finally, we further validated the assay in norovirus replicon-bearing cell lines and norovirus-infected human small intestinal organoids, in the presence or absence of small-molecule inhibitors. Overall, we have established a strand-specific qPCR assay that can be used as a reliable method to understand the molecular details of the human norovirus life cycle.

Membrane-mediated dimerization potentiates PIP5K lipid kinase activity

The phosphatidylinositol 4-phosphate 5-kinase (PIP5K) family of lipid modifying enzymes generate the majority of phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) lipids found at the plasma membrane in eukaryotic cells. PI(4,5)P2 lipids serve a critical role in regulating receptor activation, ion channel gating, endocytosis, and actin nucleation. Here we describe how PIP5K activity is regulated by cooperative binding to PI(4,5)P2 lipids and membrane-mediated dimerization of the kinase domain. In contrast to constitutively dimeric phosphatidylinositol 5-phosphate 4-kinase (PIP4K, type II PIPK), solution PIP5K exists in a weak monomer-dimer equilibrium. PIP5K monomers can associate with PI(4,5)P2 containing membranes and dimerize in a protein density dependent manner. Although dispensable for PI(4,5)P2 binding and lipid kinase activity, dimerization enhances the catalytic efficiency of PIP5K through a mechanism consistent with allosteric regulation. Additionally, dimerization amplifies stochastic variation in the kinase reaction velocity and strengthens effects such as the recently described stochastic geometry sensing. Overall, the mechanism of PIP5K membrane binding creates a broad dynamic range of lipid kinase activities that are coupled to the density of PI(4,5)P2 and membrane bound kinase.

Rapid mechanical stimulation of inner-ear hair cells by photonic pressure

Hair cells, the receptors of the inner ear, detect sounds by transducing mechanical vibrations into electrical signals. From the top surface of each hair cell protrudes a mechanical antenna, the hair bundle, which the cell uses to detect and amplify auditory stimuli, thus sharpening frequency selectivity and providing a broad dynamic range. Current methods for mechanically stimulating hair bundles are too slow to encompass the frequency range of mammalian hearing and are plagued by inconsistencies. To overcome these challenges, we have developed a method to move individual hair bundles with photonic force. This technique uses an optical fiber whose tip is tapered to a diameter of a few micrometers and endowed with a ball lens to minimize divergence of the light beam. Here we describe the fabrication, characterization, and application of this optical system and demonstrate the rapid application of photonic force to vestibular and cochlear hair cells.

External ears for non-invasive and stable monitoring of volatile organic compounds in human blood

Volatile organic compounds (VOCs) released through skin (transcutaneous gas) has been increasing in importance for the continuous and real-time assessment of diseases or metabolisms. For stable monitoring of transcutaneous gas, finding a body part with little interference on the measurement is essential. In this study, we have investigated the possibility of external ears for stable and real-time measurement of ethanol vapour by developing a monitoring system that consisted with an over-ear gas collection cell and a biochemical gas sensor (bio-sniffer). The high sensitivity with the broad dynamic range (26 ppb–554 ppm), the high selectivity to ethanol, and the capability of the continuous measurement of the monitoring system uncovered three important characteristics of external ear-derived ethanol with alcohol intake for the first time: there is little interference from sweat glands to a sensor signal at the external ear; similar temporal change in ethanol concentration to that of breath with delayed peak time (avg. 13 min); relatively high concentration of ethanol relative to other parts of a body (external ear-derived ethanol:breath ethanol = 1:590). These features indicated the suitability of external ears for non-invasive monitoring of blood VOCs.

Standardized workflow for precise mid- and high-throughput proteomics of blood biofluids

Background: Accurate discovery assay workflows are critical for identifying authentic circulating protein biomarkers in diverse blood matrices. Maximizing the commonalities in the proteomic workflows between different biofluids simplifies the approach and increases the likelihood for reproducibility. We developed a workflow that allows flexibility for high and mid–throughput analysis for three blood–based proteomes: naive plasma, plasma depleted of the 14 most abundant proteins, and dried blood. Methods: Optimal conditions for sample preparation and DIA–MS analysis were established in plasma then automated and adapted for depleted plasma and whole blood. The MS workflow was modified to facilitate sensitive high–throughput or deep profile analysis with mid–throughput analysis. Analytical performance was evaluated from 5 complete workflows repeated over 3 days as well as a linearity analysis of a 5—6–point dilution curve. Result: Using our high-throughput workflow, 74%, 93%, 87% of peptides displayed an inter-day CV<30% in plasma, depleted plasma and whole blood. While the mid-throughput workflow had 67%, 90%, 78% of peptides in plasma, depleted plasma and whole blood meeting the CV<30% standard. Lower limits of detection and quantitation were determined for proteins and peptides observed in each biofluid and workflow. Combining the analysis of both high–throughput plasma fractions exceeded the number of reliably identified proteins for individual biofluids in the mid–throughput workflows. Conclusion: The workflow established here allowed for reliable detection of proteins covering a broad dynamic range. We envisage that implementation of this standard workflow on a large scale will facilitate the translation of candidate markers into clinical use.