Pituitary Metastasis: Lung Cancer Presenting as Bitemporal Hemianopsia with Diabetes Insipidus and Anterior Pituitary Deficiency

Symptoms and signs of pituitary disease are rarely the first manifestations of malignancy originating in another organ. We present a case which exemplifies the key points that suggest a diagnosis of metastatic pituitary disease. Our patient\u27s diagnosis was adenocarcinoma of the lung with a metastasis to the intrasellar and suprasellar regions, which caused diabetes insipidus, anterior pituitary deficiency, and visual field defects. The metastasis had a dumbbell appearance and extended from the sella turcica into the suprasellar region. Diabetes insipidus was the initial clinical manifestation of lung cancer in this patient. A metastasis to the pituitary should be suspected if diabetes insipidus is the initial manifestation of an intrasellar mass

Optimization workflow for the analysis of cross-linked peptides using a Quadrupole Time-of-Flight Mass Spectrometer

Cross-linking mass spectrometry is an emerging structural biology technique. Almost exclusively, the analyzer of choice for such an experiment has been the Orbitrap. We present an optimized protocol for the use of a Synapt G2-Si for the analysis of cross-linked peptides. We first tested six different energy ramps and analyzed the fragmentation behavior of cross-linked peptides identified by xQuest. By combining the most successful energy ramps, cross-link yield can be increased by up to 40%. When compared to previously published Orbitrap data, the Synapt G2-Si also offers improved fragmentation of the β peptide. In order to improve cross-link quality control we have also developed ValidateXL, a programmatic solution that works with existing cross-linking software to improve cross-link quality control

HDX and In Silico Docking Reveal that Excipients Stabilize G-CSF via a Combination of Preferential Exclusion and Specific Hotspot Interactions

Assuring the stability of therapeutic proteins is a major challenge in the biopharmaceutical industry, and a better molecular understanding of the mechanisms through which formulations influence their stability is an ongoing priority. While the preferential exclusion effects of excipients are well known, the additional presence and impact of specific protein–excipient interactions have proven to be more elusive to identify and characterize. We have taken a combined approach of in silico molecular docking and hydrogen deuterium exchange-mass spectrometry (HDX-MS) to characterize the interactions between granulocyte colony-stimulating factor (G-CSF), and some common excipients. These interactions were related to their influence on the thermal-melting temperatures (Tm) for the nonreversible unfolding of G-CSF in liquid formulations. The residue-level interaction sites predicted in silico correlated well with those identified experimentally and highlighted the potential impact of specific excipient interactions on the Tm of G-CSF

Dynamic changes in the brain protein interaction network correlates with progression of Aβ42 pathology in Drosophila

Alzheimer’s disease (AD), the most prevalent form of dementia, is a progressive and devastating neurodegenerative condition for which there are no effective treatments. Understanding the molecular pathology of AD during disease progression may identify new ways to reduce neuronal damage. Here, we present a longitudinal study tracking dynamic proteomic alterations in the brains of an inducible Drosophila melanogaster model of AD expressing the Arctic mutant Aβ42 gene. We identified 3093 proteins from flies that were induced to express Aβ42 and age-matched healthy controls using label-free quantitative ion-mobility data independent analysis mass spectrometry. Of these, 228 proteins were significantly altered by Aβ42 accumulation and were enriched for AD-associated processes. Network analyses further revealed that these proteins have distinct hub and bottleneck properties in the brain protein interaction network, suggesting that several may have significant effects on brain function. Our unbiased analysis provides useful insights into the key processes governing the progression of amyloid toxicity and forms a basis for further functional analyses in model organisms and translation to mammalian systems

Deconvolution of ion mobility mass spectrometry arrival time distributions using a genetic algorithm approach: application to α1-antitrypsin peptide binding

Ion mobility mass spectrometry (IM-MS) is a fast and sample-efficient method for analysing the gas phase conformation of proteins and protein complexes. Subjecting proteins to increased collision energies prior to ion mobility separation can directly probe their unfolding behaviour. Recent work in the field has utilised this approach to evaluate the effect of small ligand binding upon protein stability, and to screen compounds for drug discovery. Its general applicability for high-throughput screening will, however, depend upon new analytical methods to make the approach scalable. Here we describe a fully automated program, called Benthesikyme, for summarising the ion mobility results from such experiments. The program automatically creates collision induced unfolding (CIU) fingerprints and summary plots that capture the increase in collision cross section and the increase in conformational flexibility of proteins during unfolding. We also describe a program, based on a genetic algorithm, for the deconvolution of arrival time distributions from the \{CIU\} data. This multicomponent analysis method was developed to require as little user input as possible. Aside from the IM-MS data, the only input required is an estimate of the number of conformational families to be fitted to the data. In cases where the appropriate number of conformational families is unclear, the automated procedure means it is straightforward to repeat the analysis for several values and optimize the quality of the fit. We have employed our new methodology to study the effects of peptide binding to α1-antitrypsin, an abundant human plasma protein whose misfolding exemplifies a group of conformational diseases termed the serpinopathies. Our analysis shows that interaction with the peptide stabilises the protein and reduces its conformational flexibility. The previously unresolved patterns of unfolding detected by the deconvolution algorithm will allow us to set up a fully automated screen for new ligand molecules with similar properties

The role of disulfide bond replacements in analogues of the Tarantula toxin ProTx-II and their effects on inhibition of the voltage-gated sodium ion channel Nav1.7

Spider venom toxins, such as Protoxin-II (ProTx-II), have recently received much attention as selective Nav1.7 channel blockers, with potential to be developed as leads for the treatment of chronic nocioceptive pain. ProTx-II is a 30-amino acid peptide with three disulfide bonds that has been reported to adopt a well-defined inhibitory cystine knot (ICK) scaffold structure. Potential drawbacks with such peptides include poor pharmacodynamics and potential scrambling of the disulfide bonds in vivo. In order to address these issues, in the present study we report the solid-phase synthesis of lanthionine-bridged analogues of ProTx-II, in which one of the three disulfide bridges is replaced with a thioether linkage, and evaluate the biological properties of these analogues. We have also investigated the folding and disulfide bridging patterns arising from different methods of oxidation of the linear peptide precursor. Finally, we report the X-ray crystal structure of ProTx-II to atomic resolution; to our knowledge this is the first crystal structure of an ICK spider venom peptide not bound to a substrate

Dynamic changes in the brain protein interaction network correlates with progression of A?42 pathology in Drosophila

Alzheimer’s disease (AD), the most prevalent form of dementia, is a progressive and devastating neurodegenerative condition for which there are no effective treatments. Understanding the molecular pathology of AD during disease progression may identify new ways to reduce neuronal damage. Here, we present a longitudinal study tracking dynamic proteomic alterations in the brains of an inducible Drosophila melanogaster model of AD expressing the Arctic mutant Aβ42 gene. We identified 3093 proteins from flies that were induced to express Aβ42 and age-matched healthy controls using label-free quantitative ion-mobility data independent analysis mass spectrometry. Of these, 228 proteins were significantly altered by Aβ42 accumulation and were enriched for AD-associated processes. Network analyses further revealed that these proteins have distinct hub and bottleneck properties in the brain protein interaction network, suggesting that several may have significant effects on brain function. Our unbiased analysis provides useful insights into the key processes governing the progression of amyloid toxicity and forms a basis for further functional analyses in model organisms and translation to mammalian systems

Structure of the stationary phase survival protein YuiC from B.subtilis

- Background: Stationary phase survival proteins (Sps) were found in Firmicutes as having analogous domain compositions, and in some cases genome context, as the resuscitation promoting factors of Actinobacteria, but with a different putative peptidoglycan cleaving domain. - Results: The first structure of a Firmicute Sps protein YuiC from B. subtilis, is found to be a stripped down version of the cell-wall peptidoglycan hydrolase MltA. The YuiC structures are of a domain swapped dimer, although some monomer is also found in solution. The protein crystallised in the presence of pentasaccharide shows a 1,6-anhydrodisaccharide sugar product, indicating that YuiC cleaves the sugar backbone to form an anhydro product at least on lengthy incubation during crystallisation. - Conclusions: The structural simplification of MltA in Sps proteins is analogous to that of the resuscitation promoting factor domains of Actinobacteria, which are stripped down versions of lysozyme and soluble lytic transglycosylase proteins

Deconvolution of ion mobility mass spectrometry arrival time distributions using a genetic algorithm approach: application to α₁-antitrypsin peptide binding

Ion mobility mass spectrometry (IM-MS) is a fast and sample-efficient method for analysing the gas phase conformation of proteins and protein complexes. Subjecting proteins to increased collision energies prior to ion mobility separation can directly probe their unfolding behaviour. Recent work in the field has utilised this approach to evaluate the effect of small ligand binding upon protein stability, and to screen compounds for drug discovery. Its general applicability for high-throughput screening will, however, depend upon new analytical methods to make the approach scalable. Here we describe a fully automated program, called Benthesikyme, for summarising the ion mobility results from such experiments. The program automatically creates collision induced unfolding (CIU) fingerprints and summary plots that capture the increase in collision cross section and the increase in conformational flexibility of proteins during unfolding. We also describe a program, based on a genetic algorithm, for the deconvolution of arrival time distributions from the CIU data. This multicomponent analysis method was developed to require as little user input as possible. Aside from the IM-MS data, the only input required is an estimate of the number of conformational families to be fitted to the data. In cases where the appropriate number of conformational families is unclear, the automated procedure means it is straightforward to repeat the analysis for several values and optimize the quality of the fit. We have employed our new methodology to study the effects of peptide binding to α1-antitrypsin, an abundant human plasma protein whose misfolding exemplifies a group of conformational diseases termed the serpinopathies. Our analysis shows that interaction with the peptide stabilises the protein and reduces its conformational flexibility. The previously unresolved patterns of unfolding detected by the deconvolution algorithm will allow us to set up a fully automated screen for new ligand molecules with similar properties