Intracranial pressure-flow relationships in traumatic brain injury patients expose gaps in the tenets of models and pressure-oriented management.

Background: The protocols and therapeutic guidance established for treating traumatic brain injury (TBI) in neurointensive care focus on managing cerebral blood flow (CBF) and brain tissue oxygenation based on pressure signals. The decision support process relies on assumed relationships between cerebral perfusion pressure (CPP) and blood flow, pressure-flow relationships (PFRs), and shares this framework of assumptions with mathematical intracranial hemodynamics models. These foundational assumptions are difficult to verify, and their violation can impact clinical decision-making and model validity. Methods: A hypothesis- and model-driven method for verifying and understanding the foundational intracranial hemodynamic PFRs is developed and applied to a novel multi-modality monitoring dataset. Results: Model analysis of joint observations of CPP and CBF validates the standard PFR when autoregulatory processes are impaired as well as unmodelable cases dominated by autoregulation. However, it also identifies a dynamical regime -or behavior pattern-where the PFR assumptions are wrong in a precise, data-inferable way due to negative CPP-CBF coordination over long timescales. This regime is of both clinical and research interest: its dynamics are modelable under modified assumptions while its causal direction and mechanistic pathway remain unclear. Conclusion: Motivated by the understanding of mathematical physiology, the validity of the standard PFR can be assessed a) directly by analyzing pressure reactivity and mean flow indices (PRx and Mx) or b) indirectly through the relationship between CBF and other clinical observables. This approach could potentially help to personalize TBI care by considering intracranial pressure and CPP in relation to other data, particularly CBF. The analysis suggests a threshold using clinical indices of autoregulation jointly generalizes independently set indicators to assess CA functionality. These results support the use of increasingly data-rich environments to develop more robust hybrid physiological-machine learning models.

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research.

Refined management of mechanically ventilation is an obvious target for improving patient outcomes, but is impeded by the nature of data for study and hypothesis generation. The connections between clinical outcomes and temporal development of iatrogenic injuries current lung-protective ventilator settings remain poorly understood. Analysis of lung-ventilator system (LVS) evolution at relevant timescales is frustrated by data volume and multiple sources of heterogeneity. This work motivates, presents, and validates a computational pipeline for resolving LVS systems into the joint evolution of data-conditioned model parameters and ventilator information. Applied to individuals, the workflow yields a concise low-dimensional representation of LVS behavior expressed in phenotypic breath waveforms suitable for analysis. The effectiveness of this approach is demonstrated through application to multi-day observational series of 35 patients. Individual patient analyses reveal multiple types of patient-oriented dynamics and breath behavior to expose the complexity of LVS evolution; less than 10% of phenotype changes related to ventilator settings changes. Dynamics are shown to including both stable and unstable phenotype transitions as well as both discrete and continuous changes unrelated to ventilator settings. At a cohort scale, 721 phenotypes constructed from individual data are condensed into a set of 16 groups that empirically organize around certain settings (positive end-expository pressure and ventilator mode) and structurally similar pressure-volume loop characterizations. Individual and cohort scale phenotypes, which may be refined by hypothesis-specific constructions, provide a common framework for ongoing temporal analysis and investigation of LVS dynamics.

Intracranial pressure-flow relationships in traumatic brain injury patients expose gaps in the tenets of models and pressure-oriented management.

Background: The protocols and therapeutic guidance established for treating traumatic brain injuries (TBI) in neurointensive care focus on managing cerebral blood flow (CBF) and brain tissue oxygenation based on pressure signals. The decision support process relies on assumed relationships between cerebral perfusion pressure (CPP) and blood flow, pressure-flow relationships (PFRs), and shares this framework of assumptions with mathematical intracranial hemodynamic models. These foundational assumptions are difficult to verify, and their violation can impact clinical decision-making and model validity. Method: A hypothesis- and model-driven method for verifying and understanding the foundational intracranial hemodynamic PFRs is developed and applied to a novel multi-modality monitoring dataset. Results: Model analysis of joint observations of CPP and CBF validates the standard PFR when autoregulatory processes are impaired as well as unmodelable cases dominated by autoregulation. However, it also identifies a dynamical regime -or behavior pattern- where the PFR assumptions are wrong in a precise, data-inferable way due to negative CPP-CBF coordination over long timescales. This regime is of both clinical and research interest: its dynamics are modelable under modified assumptions while its causal direction and mechanistic pathway remain unclear. Conclusions: Motivated by the understanding of mathematical physiology, the validity of the standard PFR can be assessed a) directly by analyzing pressure reactivity and mean flow indices (PRx and Mx) or b) indirectly through the relationship between CBF and other clinical observables. This approach could potentially help personalize TBI care by considering intracranial pressure and CPP in relation to other data, particularly CBF. The analysis suggests a threshold using clinical indices of autoregulation jointly generalizes independently set indicators to assess CA functionality. These results support the use of increasingly data-rich environments to develop more robust hybrid physiological-machine learning models.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.

OBJECTIVE: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation.

Objective: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83% ± 27%. Conclusion: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

Detailed insights into the formation pathway of CdS and ZnS in solution: a multi-modal in situ characterisation approach.

The high stability, high availability, and wide size-dependent bandgap energy of sulphidic semiconductor nanoparticles (NPs) render them promising for applications in optoelectronic devices and solar cells. However, the tunability of their optical properties depends on the strict control of their crystal structure and crystallisation process. Herein, we studied the structural evolution during the formation of CdS and ZnS in solution by combining in situ luminescence spectroscopy, synchrotron-based X-ray diffraction (XRD) and pair distribution function (PDF) analyses for the first time. The influence of precursor type, concentration, temperature and heating program on the product formation and on the bandgap or trap emission were investigated in detail. In summary, for CdS, single-source precursor (SSP) polyol strategies using the dichlorobis(thiourea)cadmium(II) complex and double-source precursor approaches combining Cd(CH3COO)2·2H2O and thiourea led to the straightforward product at 100 °C, while the catena((m2-acetato-O,O')-(acetate-O,O')-(m2-thiourea)-cadmium) complex was formed at 25 and 80 °C. For ZnS, the reaction between Zn(CH3COO)2·2H2O and thiourea at 100 °C led to the product formation after the crystallisation and dissolution of an unknown intermediate. At 180 °C, besides an unknown phase, the acetato-bis(thiourea)-zinc(II) complex was also detected as a reaction intermediate. The formation of such reaction intermediates, which generally remain undetected applying only ex situ characterisation approaches, reinforce the importance of in situ analysis for promoting the advance on the production of tailored semiconductor materials.

Hypothesis-driven modeling of the human lung-ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research.

Mechanical ventilation is an essential tool in the management of Acute Respiratory Distress Syndrome (ARDS), but it exposes patients to the risk of ventilator-induced lung injury (VILI). The human lung-ventilator system (LVS) involves the interaction of complex anatomy with a mechanical apparatus, which limits the ability of process-based models to provide individualized clinical support. This work proposes a hypothesis-driven strategy for LVS modeling in which robust personalization is achieved using a pre-defined parameter basis in a non-physiological model. Model inversion, here via windowed data assimilation, forges observed waveforms into interpretable parameter values that characterize the data rather than quantifying physiological processes. Accurate, model-based inference on human-ventilator data indicates model flexibility and utility over a variety of breath types, including those from dyssynchronous LVSs. Estimated parameters generate static characterizations of the data that are 50%-70% more accurate than breath-wise single-compartment model estimates. They also retain sufficient information to distinguish between the types of breath they represent. However, the fidelity and interpretability of model characterizations are tied to parameter definitions and model resolution. These additional factors must be considered in conjunction with the objectives of specific applications, such as identifying and tracking the development of human VILI.

Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.

OBJECTIVE: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team. MATERIALS AND METHODS: We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. RESULTS: The prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. DISCUSSION: Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction. CONCLUSION: We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales.

BACKGROUND: The clinical mitigation of intracranial hypertension due to traumatic brain injury requires timely knowledge of intracranial pressure to avoid secondary injury or death. Noninvasive intracranial pressure (nICP) estimation that operates sufficiently fast at multihour timescales and requires only common patient measurements is a desirable tool for clinical decision support and improving traumatic brain injury patient outcomes. However, existing model-based nICP estimation methods may be too slow or require data that are not easily obtained. OBJECTIVE: This work considers short- and real-time nICP estimation at multihour timescales based on arterial blood pressure (ABP) to better inform the ongoing development of practical models with commonly available data. METHODS: We assess and analyze the effects of two distinct pathways of model development, either by increasing physiological integration using a simple pressure estimation model, or by increasing physiological fidelity using a more complex model. Comparison of the model approaches is performed using a set of quantitative model validation criteria over hour-scale times applied to model nICP estimates in relation to observed ICP. RESULTS: The simple fully coupled estimation scheme based on windowed regression outperforms a more complex nICP model with prescribed intracranial inflow when pulsatile ABP inflow conditions are provided. We also show that the simple estimation data requirements can be reduced to 1-minute averaged ABP summary data under generic waveform representation. CONCLUSIONS: Stronger performance of the simple bidirectional model indicates that feedback between the systemic vascular network and nICP estimation scheme is crucial for modeling over long intervals. However, simple model reduction to ABP-only dependence limits its utility in cases involving other brain injuries such as ischemic stroke and subarachnoid hemorrhage. Additional methodologies and considerations needed to overcome these limitations are illustrated and discussed.

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study.

BACKGROUND: The SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality. RESEARCH QUESTIONS: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA. STUDY DESIGN AND METHODS: We conducted a prospective cohort study of a regional health system with 12 hospitals in Colorado between March 2020 and July 2020. All patients >14 years old hospitalized during the study period without a do not resuscitate order were included. Patients were stratified by the diagnosis of COVID-19. From this cohort, we developed and validated a model using stacked generalization to predict mortality using data widely available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. RESULTS: We prospectively analyzed 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) included intensive care unit (ICU)-level care, 1,480 (5.4%) included invasive mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted overall mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted overall mortality with AUROC 0.94. In the subset of patients with COVID-19, we predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. INTERPRETATION: We developed and validated an accurate, in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model, that improved upon SOFA. TAKE HOME POINTS: Study Question: Can we improve upon the SOFA score for real-time mortality prediction during the COVID-19 pandemic by leveraging electronic health record (EHR) data?Results: We rapidly developed and implemented a novel yet SOFA-anchored mortality model across 12 hospitals and conducted a prospective cohort study of 27,296 adult hospitalizations, 1,358 (5.0%) of which were positive for SARS-CoV-2. The Charlson Comorbidity Index and SOFA scores predicted all-cause mortality with AUROCs of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94.Interpretation: A novel EHR-based mortality score can be rapidly implemented to better predict patient outcomes during an evolving pandemic.

From ligand exchange to reaction intermediates: what does really happen during the synthesis of emissive complexes?

In situ monitoring of the formation of emissive complexes is essential to enable the development of rational synthesis protocols, to provide accurate control over the generation of structure-related properties (such as luminescence) and to facilitate the development of new compounds. In situ luminescence analysis of coordination sensors (ILACS) utilizes the sensitivity of the spectroscopic properties of lanthanide ions to their coordination environment to detect structural changes during crystallization processes. Here, ILACS was utilized to monitor the formation of [Eu(bipy)2(NO3)3] (bipy = 2,2'-bipyridine) during co-precipitation synthesis. Validity of the ILACS results was ensured by concomitant utilization of in situ monitoring of other reaction parameters, including in situ measurements of pH value, ionic conductivity, and infrared spectra, as well as ex situ and synchrotron-based in situ X-ray diffraction analyses. Gradual desolvation of the Eu3+ ions and attachment of ligands were detected by an exponential increase of the intensity of the 5D0 → 7FJ (J = 0-4) transitions in the emission spectrum. Additionally, the in situ emission spectra show a decrease in the crystallization rate and an increase in the induction time in response to a reduction in the concentration of the starting solutions from 12 mM until crystallization ceased at starting reactant concentrations <6 mM. An increase to a three-fold higher concentration leads to the formation of a reaction intermediate, and its stability was determined to be highly concentration-dependent. The in situ luminescence measurements also demonstrated the existence of a ligand exchange process within the [Eu(bipy)2(NO3)3] complex upon addition of a phen (phen = 1,10'-phenanthroline) solution and the generation of a new phen-containing emissive complex. In attempting to solve the structure of this new phen-containing complex, a different, but nevertheless previously unsynthesized complex, [Eu(phen)2(NO3)3]bipy, was obtained, which shows characteristic Eu3+ luminescence in the red spectral range.

Experimental increase in accommodative potential after neodymium: yttrium-aluminum-garnet laser photodisruption of paired cadaver lenses.

PURPOSE: Loss of lens elasticity is one of several proposed mechanisms responsible for the decline in accommodation with age and is the most accepted explanation for presbyopia. We wish to confirm the lens elasticity premise and attempt to experimentally reverse the age-dependent loss of accommodative potential as measured by polar strain. DESIGN: Experimental human autopsy eye study. PARTICIPANTS AND CONTROLS: Thirty-six cadaver lenses were tested to determine the age-dependent polar strain. Eleven lens pairs were then tested with one lens treated with neodymium:yttrium-aluminum-garnet (Nd:YAG) laser and the other left untreated before rotation as an age control. TESTING: Using a custom-made rotational apparatus (described by Fisher, 1971), freshly excised cadaver lenses (<48 hours postmortem) were rotated at 1000 rpm on a 9-mm diameter pedestal to simulate the physiologic pull of the zonules. Lenses were initially tested to determine the age-dependent polar strain. One lens in a pair was then treated with an Nd:YAG laser and the other left untreated before testing. Treatment consisted of 100 suprathreshold pulse placed in a central annular pattern of 2- to 4-mm diameter. Treatment energies varied from 2.5 to 7.0 mJ/pulse, depending on the relative clarity of the lenses. Polar strain was both microscopically measured and calculated from projected photographs before and after rotation of both lased and unlased lenses. Statistically significant differences were determined by paired t test. MAIN OUTCOME MEASURES: Polar strain (decrease in axial thickness with rotation) of the lens. RESULTS: An age-dependent decrease in polar strain was observed that paralleled the findings of Fisher. Both measured and projected polar strain were greater in the lased than unlased lens, and this difference was highly significant by paired t test (P = 0.001 and P = 0.004, respectively). CONCLUSIONS: Age-dependent loss of lens elasticity (polar strain) can be experimentally reversed (increased) by selective intralenticular photodisruption.

Spontaneous alpha-N-6-phosphogluconoylation of a "His tag" in Escherichia coli: the cause of extra mass of 258 or 178 Da in fusion proteins.

Several proteins expressed in Escherichia coli with the N-terminus Gly-Ser-Ser-[His]6- consisted partly (up to 20%) of material with 178 Da of excess mass, sometimes accompanied by a smaller fraction with an excess 258 Da. The preponderance of unmodified material excluded mutation, and the extra masses were attributed to posttranslational modifications. As both types of modified protein were N-terminally blocked, the alpha-amino group was modified in each case. Phosphatase treatment converted +258-Da protein into +178-Da protein. The modified His tags were isolated, and the mass of the +178-Da modification estimated as 178.06 +/- 0.02 Da by tandem mass spectrometry. As the main modification remained at +178 Da in 15N-substituted protein, it was deemed nitrogen-free and possibly carbohydrate-like. Limited periodate oxidations suggested that the +258-Da modification was acylation with a 6-phosphohexonic acid, and that the +178-Da modification resulted from its dephosphorylation. NMR spectra of cell-derived +178-Da His tag and synthetic alpha-N-d-gluconoyl-His tag were identical. Together, these results suggested that the +258-Da modification was addition of a 6-phosphogluconoyl group. A plausible mechanism was acylation by 6-phosphoglucono-1,5-lactone, produced from glucose 6-phosphate by glucose-6-phosphate dehydrogenase (EC 1.1.1.49). Supporting this, treating a His-tagged protein with excess d-glucono-1,5-lactone gave only N-terminal gluconoylation.

Identification of a firefly luciferase active site peptide using a benzophenone-based photooxidation reagent.

Firefly luciferase catalyzes the highly efficient emission of yellow-green light from substrate luciferin by a series of reactions that require MgATP and molecular oxygen. We prepared 2-(4-benzoylphenyl)thiazole-4-carboxylic acid (BPTC), a novel benzophenone-based substrate analog, intending to use it in photoaffinity labeling studies to probe the luciferase active site. Instead, we found that while BPTC was a potent photoinactivating reagent for firefly luciferase, it was not a photoaffinity labeling agent. Using proteolysis, reverse phase high-performance liquid chromatography, tandem high performance liquid chromatography-electrospray ionization mass spectrometry, and Edman sequencing, we identified a single luciferase peptide, 244HHGF247, the degradation of which was directly correlated to luciferase photoinactivation. Results of enzyme kinetics and related studies were consistent with this peptide being at or near the luciferin binding site. Further, peptide model studies and additional investigations on the nature of the photoinactivation process strongly suggested that BPTC catalyzed the formation of singlet oxygen at the active site of the enzyme. We describe here an uncommon example of active site-directed photooxidation of an enzyme by singlet oxygen.

Inactivation of firefly luciferase with N-(iodoacetyl)-N'- (5-sulfo-1-naphthyl)ethylenediamine (I-AEDANS).

N-Iodoacetyl-N'-(5-sulfo-1-naphthyl)ethylenediamine (I-AEDANS), a fluorescent reagent that selectively modifies cysteine residues, was demonstrated to irreversibly inhibit native Photinus pyralis luciferase purified from firefly lanterns. Complete inactivation of luciferase activity was accompanied by the blockage of all four cysteine thiols and the concomitant incorporation of 4 mol of N-acetyl-N'-(5-sulfo-1-naphthyl)ethylenediamine (AEDANS) per mole of enzyme. Employing proteolytic digestions of AEDANS-labeled luciferase and reverse-phase-high-performance liquid chromatography (RP-HPLC), seven tagged peptides were isolated. The AEDANS label provided a convenient spectroscopic marker for the identification of the modified peptides. The sequences of the labeled peptides were deduced from electrospray ionization mass spectrometry (ESMS) and N-terminal sequencing. The fluorescent peptides included cysteine residues and spanned sequences composed of amino acids Leu78-Lys85, Thr214-Arg218, Asp224-Arg275, and Gly388-Met396. The luciferin substrate provided substantial protection against luciferase inactivation resulting in a 60-67% decrease in the labeling of all four cysteine thiols. Thus, it does not appear that a specific cysteine mediates the loss of luciferase activity. Additional LC/ESMS studies permitted the identification of 78% of the native luciferase molecule, which, unlike the recombinant protein, was found to contain an acetylated N-terminus. The AEDANS labeling results and the identification of well-defined proteolytic fragments should facilitate future structure-function investigations of the firefly luciferases.

Modification of chloride ion transport in human erythrocyte ghost membranes by photoaffinity labeling reagents based on the structure of 5-nitro-2-(3-phenylpropylamino)-benzoic acid (NPPB).

Using human red blood cell ghost membranes, we have evaluated 5-nitro-2-[N-3-(4-azidophenyl)-propylamino]-benzoic acid and 5-nitro-2-[N-3-(4-azido-2,3,5,6-tetrafluorophenyl)-propylamino]- benzoic acid (FAzNPPB) as photoaffinity labeling agents based on the structure of the widely important Cl- channel blocker 5-nitro-2-(3-phenylpropyl-amino)-benzoic acid (NPPB). The tetrafluoro-substituted aryl azide was found to be a more effective photoinactivating agent than the corresponding protio compound. Using a tritiated version ([3H]FAzNPPB), we demonstrated that photoinactivation of Cl- flux was accompanied by photolabeling of the band 3 protein and membrane lipids. Both processes were diminished in the presence of NPPB and the related arylanthranilate flufenamic acid. Photolabeling resulted in the incorporation of 1.0 +/- 0.2 mol 3H/mol protein in the band 3 integral membrane domain, whereas the cytoplasmic domain was essentially unlabeled. By sodium dodecyl sulfate-polyacrylamide gel electrophoresis, photolabeling was found to be the result of partial labeling of at least three different regions of the membrane domain. Based on trypsin proteolysis, reverse-phase high-performance liquid chromatography and electrospray ionization mass spectrometry analysis, it is proposed that one of the sites of photolabeling is the peptide lys-phe-lys (590-592). FAzNPPB is a successful polyfluoro aryl azide photoaffinity labeling agent which may be of further use in studying the diverse effects of arylanthranilates on biological membranes.

The structural basis for the specificity of epidermal growth factor and heregulin binding.

Heregulin is a ligand for the erbB3 and erbB4 receptors, with a region of high homology to epidermal growth factor (EGF). Despite this homology, these ligands bind to their corresponding receptors with great specificity. We report here the synthesis of heregulin beta 177-241 and show that a region consisting of amino acids 177-226 is sufficient both for binding and stimulation of receptor phosphorylation. Studies of chimeric EGF/heregulin peptides revealed that amino acids 177-181 of heregulin provide the specificity for binding to the heregulin receptor. The substitution of amino acids 177-181 of heregulin for the N terminus of EGF produced a unique bifunctional agonist that binds with high affinity to both the EGF receptor and the heregulin receptor.

Use of capillary electrophoresis-electrospray ionization mass spectrometry in the analysis of synthetic peptides.

We have constructed a capillary electrophoresis (CE) system with UV detection and have successfully interfaced it to an electrospray ionization mass spectrometry (ES-MS) system. A synthesized fragment of heregulin-beta (212-226) was thought to be a single component by re-injection into an HPLC system, but results from CE-UV-ES-MS indicated that a dehydration product was present in the desired peptide sample. A synthetic heregulin-alpha (177-241) was isolated by preparative HPLC, but re-injection on an analytical system indicated a tailing peak. CE-UV-ES-MS indicated a mixture whose two major components were of the same nominal molecular mass (within experimental error), suggesting the presence of an isomer or a deamidation product. The results show that CE-UV-ES-MS can be used as an orthogonal analytical technique to solve practical problems encountered in peptide synthesis laboratories.