Piwi-piRNA complexes induce stepwise changes in nuclear architecture at target loci.

PIWI-interacting RNAs (piRNAs) are germline-specific small RNAs that form effector complexes with PIWI proteins (Piwi-piRNA complexes) and play critical roles for preserving genomic integrity by repressing transposable elements (TEs). Drosophila Piwi transcriptionally silences specific targets through heterochromatin formation and increases histone H3K9 methylation (H3K9me3) and histone H1 deposition at these loci, with nuclear RNA export factor variant Nxf2 serving as a co-factor. Using ChEP and DamID-seq, we now uncover a Piwi/Nxf2-dependent target association with nuclear lamins. Hi-C analysis of Piwi or Nxf2-depleted cells reveals decreased intra-TAD and increased inter-TAD interactions in regions harboring Piwi-piRNA target TEs. Using a forced tethering system, we analyze the functional effects of Piwi-piRNA/Nxf2-mediated recruitment of piRNA target regions to the nuclear periphery. Removal of active histone marks is followed by transcriptional silencing, chromatin conformational changes, and H3K9me3 and H1 association. Our data show that the Piwi-piRNA pathway can induce stepwise changes in nuclear architecture and chromatin state at target loci for transcriptional silencing.

MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data.

MOTIVATION: The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. RESULTS: In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. AVAILABILITY AND IMPLEMENTATION: The source code and installation instruction for MHCSeqNet2 are available at https://github.com/cmb-chula/MHCSeqNet2.

IFN-γ ELISpot-enabled machine learning for culprit drug identification in nonimmediate drug hypersensitivity.

BACKGROUND: Diagnosing drug-induced allergy, especially nonimmediate phenotypes, is challenging. Incorrect classifications have unwanted consequences. OBJECTIVE: We sought to evaluate the diagnostic utility of IFN-γ ELISpot and clinical parameters in predicting drug-induced nonimmediate hypersensitivity using machine learning. METHODS: The study recruited 393 patients. A positive patch test or drug provocation test (DPT) was used to define positive drug hypersensitivity. Various clinical factors were considered in developing random forest (RF) and logistic regression (LR) models. Performances were compared against the IFN-γ ELISpot-only model. RESULTS: Among the 102 patients who had 164 DPTs, most patients had severe cutaneous adverse reactions (35/102, 34.3%) and maculopapular exanthems (33/102, 32.4%). Common suspected drugs were antituberculosis drugs (46/164, 28.1%) and ß-lactams (42/164, 25.6%). Mean (SD) age of patients with DPT was 52.7 (20.8) years. IFN-γ ELISpot, fixed drug eruption, Naranjo categories, and nonsteroidal anti-inflammatory drugs were the most important features in all developed models. The RF and LR models had higher discriminating abilities. An IFN-γ ELISpot cutoff value of 16.0 spot-forming cells/106 PBMCs achieved 94.8% specificity and 57.1% sensitivity. Depending on clinical needs, optimal cutoff values for RF and LR models can be chosen to achieve either high specificity (0.41 for 96.1% specificity and 0.52 for 97.4% specificity, respectively) or high sensitivity (0.26 for 78.6% sensitivity and 0.37 for 71.4% sensitivity, respectively). CONCLUSIONS: IFN-γ ELISpot assay was valuable in identifying culprit drugs, whether used individually or incorporated in a prediction model. Performances of RF and LR models were comparable. Additional test datasets with DPT would be helpful to validate the model further.

Origination of LTR Retroelement-Derived NYNRIN Coincides with Therian Placental Emergence.

The emergence of the placenta is a revolutionary event in the evolution of therian mammals, to which some LTR retroelement-derived genes, such as PEG10, RTL1, and syncytin, are known to contribute. However, therian genomes contain many more LTR retroelement-derived genes that may also have contributed to placental evolution. We conducted large-scale evolutionary genomic and transcriptomic analyses to comprehensively search for LTR retroelement-derived genes whose origination coincided with therian placental emergence and that became consistently expressed in therian placentae. We identified NYNRIN as another Ty3/Gypsy LTR retroelement-derived gene likely to contribute to placental emergence in the therian stem lineage. NYNRIN knockdown inhibited the invasion of HTR8/SVneo invasive-type trophoblasts, whereas the knockdown of its nonretroelement-derived homolog KHNYN did not. Functional enrichment analyses suggested that NYNRIN modulates trophoblast invasion by regulating epithelial-mesenchymal transition and extracellular matrix remodeling and that the ubiquitin-proteasome system is responsible for the functional differences between NYNRIN and KHNYN. These findings extend our knowledge of the roles of LTR retroelement-derived genes in the evolution of therian mammals.

MHCVision: estimation of global and local false discovery rate for MHC class I peptide binding prediction.

MOTIVATION: MHC-peptide binding prediction has been widely used for understanding the immune response of individuals or populations, each carrying different MHC molecules as well as for the development of immunotherapeutics. The results from MHC-peptide binding prediction tools are mostly reported as a predicted binding affinity (IC50) and the percentile rank score, and global thresholds e.g. IC50 value < 500 nM or percentile rank < 2% are generally recommended for distinguishing binding peptides from non-binding peptides. However, it is difficult to evaluate statistically the probability of an individual peptide binding prediction to be true or false solely considering predicted scores. Therefore, statistics describing the overall global false discovery rate (FDR) and local FDR, also called posterior error probability (PEP) are required to give statistical context to the natively produced scores. RESULT: We have developed an algorithm and code implementation, called MHCVision, for estimation of FDR and PEP values for the predicted results of MHC-peptide binding prediction from the NetMHCpan tool. MHCVision performs parameter estimation using a modified expectation maximization framework for a two-component beta mixture model, representing the distribution of true and false scores of the predicted dataset. We can then estimate the PEP of an individual peptide's predicted score, and conversely the probability that it is true. We demonstrate that the use of global FDR and PEP estimation can provide a better trade-off between sensitivity and precision over using currently recommended thresholds from tools. AVAILABILITY AND IMPLEMENTATION: https://github.com/PGB-LIV/MHCVision. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework.

Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpected processing of RNAs and proteins. De novo peptide sequencing approaches address this limitation but often suffer from low accuracy and require extensive validation by experts. Here, we develop SMSNet, a deep learning-based de novo peptide sequencing framework that achieves >95% amino acid accuracy while retaining good identification coverage. Applications of SMSNet on landmark proteomics and peptidomics studies reveal over 10,000 previously uncharacterized HLA antigens and phosphopeptides, and in conjunction with database-search methods, expand the coverage of peptide identification by almost 30%. The power to accurately identify new peptides of SMSNet would make it an invaluable tool for any future proteomics and peptidomics studies, including tumor neoantigen discovery, antibody sequencing, and proteome characterization of non-model organisms.

Establishment of Human-Induced Pluripotent Stem Cell-Derived Neurons-A Promising In Vitro Model for a Molecular Study of Rabies Virus and Host Interaction.

Rabies is a deadly viral disease caused by the rabies virus (RABV), transmitted through a bite of an infected host, resulting in irreversible neurological symptoms and a 100% fatality rate in humans. Despite many aspects describing rabies neuropathogenesis, numerous hypotheses remain unanswered and concealed. Observations obtained from infected primary neurons or mouse brain samples are more relevant to human clinical rabies than permissive cell lines; however, limitations regarding the ethical issue and sample accessibility become a hurdle for discovering new insights into virus-host interplays. To better understand RABV pathogenesis in humans, we generated human-induced pluripotent stem cell (hiPSC)-derived neurons to offer the opportunity for an inimitable study of RABV infection at a molecular level in a pathologically relevant cell type. This study describes the characteristics and detailed proteomic changes of hiPSC-derived neurons in response to RABV infection using LC-MS/MS quantitative analysis. Gene ontology (GO) enrichment of differentially expressed proteins (DEPs) reveals temporal changes of proteins related to metabolic process, immune response, neurotransmitter transport/synaptic vesicle cycle, cytoskeleton organization, and cell stress response, demonstrating fundamental underlying mechanisms of neuropathogenesis in a time-course dependence. Lastly, we highlighted plausible functions of heat shock cognate protein 70 (HSC70 or HSPA8) that might play a pivotal role in regulating RABV replication and pathogenesis. Our findings acquired from this hiPSC-derived neuron platform help to define novel cellular mechanisms during RABV infection, which could be applicable to further studies to widen views of RABV-host interaction.

Epigenetic regulation of condensin-mediated genome organization during the cell cycle and upon DNA damage through histone H3 lysine 56 acetylation.

Complex genome organizations participate in various nuclear processes including transcription, DNA replication, and repair. However, the mechanisms that generate and regulate these functional genome structures remain largely unknown. Here, we describe how the Ku heterodimer complex, which functions in nonhomologous end joining, mediates clustering of long terminal repeat retrotransposons at centromeres in fission yeast. We demonstrate that the CENP-B subunit, Abp1, functions as a recruiter of the Ku complex, which in turn loads the genome-organizing machinery condensin to retrotransposons. Intriguingly, histone H3 lysine 56 (H3K56) acetylation, which functions in DNA replication and repair, interferes with Ku localization at retrotransposons without disrupting Abp1 localization and, as a consequence, dissociates condensin from retrotransposons. This dissociation releases condensin-mediated genomic associations during S phase and upon DNA damage. ATR (ATM- and Rad3-related) kinase mediates the DNA damage response of condensin-mediated genome organization. Our study describes a function of H3K56 acetylation that neutralizes condensin-mediated genome organization.

MHCSeqNet: a deep neural network model for universal MHC binding prediction.

BACKGROUND: Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells expressing unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synthetic peptide vaccine have been proven effective in both mouse models and human patients. Because only a tiny fraction of cancer-specific neoepitopes actually elicits immune response, selection of potent, immunogenic neoepitopes remains a challenging step in cancer vaccine development. A basic approach for immunogenicity prediction is based on the premise that effective neoepitope should bind with the Major Histocompatibility Complex (MHC) with high affinity. RESULTS: In this study, we developed MHCSeqNet, an open-source deep learning model, which not only outperforms state-of-the-art predictors on both MHC binding affinity and MHC ligand peptidome datasets but also exhibits promising generalization to unseen MHC class I alleles. MHCSeqNet employed neural network architectures developed for natural language processing to model amino acid sequence representations of MHC allele and epitope peptide as sentences with amino acids as individual words. This consideration allows MHCSeqNet to accept new MHC alleles as well as peptides of any length. CONCLUSIONS: The improved performance and the flexibility offered by MHCSeqNet should make it a valuable tool for screening effective neoepitopes in cancer vaccine development.

Global deceleration of gene evolution following recent genome hybridizations in fungi.

Polyploidization events such as whole-genome duplication and inter-species hybridization are major evolutionary forces that shape genomes. Although long-term effects of polyploidization have been well-characterized, early molecular evolutionary consequences of polyploidization remain largely unexplored. Here, we report the discovery of two recent and independent genome hybridizations within a single clade of a fungal genus, Trichosporon Comparative genomic analyses revealed that redundant genes are experiencing decelerations, not accelerations, of evolutionary rates. We identified a relationship between gene conversion and decelerated evolution suggesting that gene conversion may improve the genome stability of young hybrids by restricting gene functional divergences. Furthermore, we detected large-scale gene losses from transcriptional and translational machineries that indicate a global compensatory mechanism against increased gene dosages. Overall, our findings illustrate counteracting mechanisms during an early phase of post-genome hybridization and fill a critical gap in existing theories on genome evolution.

Recognition and delineation of yeast genera based on genomic data: Lessons from Trichosporonales.

Delineation and characterization of genera in Trichosporonales (Agaricomycotina, Basidiomycota) was performed using 24 haploid and 3 naturally occurring hybrid genomes, with 3 Tremellales genomes used as outgroups. Orthologous group analysis of those genomes showed presence-absence patterns of orthologs that were consistent with the genus classifications. Many shared unique orthologs were identified in the well-supported lineages (genera Apiotrichum and Trichosporon), supporting the definitions of the genera Apiotrichum and Trichosporon from a genomic perspective. Specifically, we obtained 24 and 285 genus-specific genes from eight Apiotrichum and five Trichosporon species, respectively, and propose that these genus-specific genes can be used for delineation of those genera. On the other hand, the genus Cutaneotrichosporon shared only one genus-specific gene among eight genomes, indicating that this genus definition might require re-examination based on genomic data. In addition, taxonomic revisions are presented in this study, including the proposal of two genera, Pascua and Prillingera. Because genomic data can be systematically obtained and analyzed to compare species from a comprehensive viewpoint, they can be used not only to reconstruct reliable phylogenetic trees, but also to re-examine the definitions of taxonomic classifications. To our knowledge, this is the first report to discuss the 'natural system' of genus level classification in fungi based on genomic data.

Supervised machine learning to decipher the complex associations between neuro-immune biomarkers and quality of life in schizophrenia.

Stable phase schizophrenia is characterized by altered patterning in tryptophan catabolites (TRYCATs) and memory impairments, which are associated with PHEMN (psychosis, hostility, excitation, mannerism and negative) and DAPS (depression, anxiety and physio-somatic) symptoms. This study was carried out to examine the association between TRYCAT patterning, memory impairments, psychopathological features and health-related quality of life (HR-QoL) in schizophrenia. The World Health Organization (WHO) QoL instrument-Abbreviated version (WHO-QoL-BREF), IgA/IgM responses to TRYCATs, cognitive tests, Scale for the Assessment of Negative Symptoms (SANS), Hamilton and Depression (HAMD) and Anxiety (HAMA) Rating Scales and the Fibromyalgia and Chronic Fatigue Syndrome Rating Scale (FF) were measured in 80 schizophrenia patients and 40 controls. Neural Network analysis shows that the total HR-Qol score is best predicted by (in descending order) HAMA, FF, HAMD, and psychosis. Partial least Squares (PLS) analysis shows that 56.7% of the variance in the WHO-QoL scores is explained by PHEMN / DAPS symptoms, while 64.3% of the variance in those symptoms is explained by TRYCAT patterning and episodic/semantic memory impairments. IgA responses to picolinic acid, xanthurenic acid and 3-hydroxy-kynurenine (all negatively) and anthranilic acid (positively) have highly significant indirect effects on WHO-QoL scores, which are completely mediated by cognitive impairments and PHEMN / DAPS symptoms. The results show that lowered HR-Qol in schizophrenia is strongly associated with noxious TRYCATs and that these effects are mediated by impairments in episodic / semantic memory and schizophrenia phenomenology, especially physio-somatic and anxiety symptoms. Mucosal activation of the TRYCAT pathway combined with a deficit in natural IgM isotype antibodies to TRYCATs determine cognitive impairments and DAPS/PHEMN symptoms, which together determine to a large extent lowered HR-QoL in schizophrenia.

A Trichosporonales genome tree based on 27 haploid and three evolutionarily conserved 'natural' hybrid genomes.

To construct a backbone tree consisting of basidiomycetous yeasts, draft genome sequences from 25 species of Trichosporonales (Tremellomycetes, Basidiomycota) were generated. In addition to the hybrid genomes of Trichosporon coremiiforme and Trichosporon ovoides that we described previously, we identified an interspecies hybrid genome in Cutaneotrichosporon mucoides (formerly Trichosporon mucoides). This hybrid genome had a gene retention rate of ~55%, and its closest haploid relative was Cutaneotrichosporon dermatis. After constructing the C. mucoides subgenomes, we generated a phylogenetic tree using genome data from the 27 haploid species and the subgenome data from the three hybrid genome species. It was a high-quality tree with 100% bootstrap support for all of the branches. The genome-based tree provided superior resolution compared with previous multi-gene analyses. Although our backbone tree does not include all Trichosporonales genera (e.g. Cryptotrichosporon), it will be valuable for future analyses of genome data. Interest in interspecies hybrid fungal genomes has recently increased because they may provide a basis for new technologies. The three Trichosporonales hybrid genomes described in this study are different from well-characterized hybrid genomes (e.g. those of Saccharomyces pastorianus and Saccharomyces bayanus) because these hybridization events probably occurred in the distant evolutionary past. Hence, they will be useful for studying genome stability following hybridization and speciation events. Copyright © 2017 John Wiley & Sons, Ltd.

Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer's Disease Tests: A Multivariate and Machine Learning Study.

BACKGROUND: The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer's dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. OBJECTIVES: To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. METHODS: The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). RESULTS: MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. CONCLUSIONS: The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI.

Deficit schizophrenia is a discrete diagnostic category defined by neuro-immune and neurocognitive features: results of supervised machine learning.

Deficit schizophrenia is characterized by neurocognitive impairments and changes in the patterning of IgA/IgM responses to plasma tryptophan catabolites (TRYCATs). In the current study, supervised pattern recognition methods, including logistic regression analysis (LRA), Support Vector Machine (SVM), and Soft Independent Modeling of Class Analogy (SIMCA), were used to examine whether deficit schizophrenia is a discrete diagnostic class with respect to Consortium To Establish a Registry for Alzheimer's disease (CERAD) and Cambridge Neuropsychological Test Automated Battery (CANTAB) tests and IgA/IgM responses to noxious (NOX) and generally more protective (PRO) TRYCATs. We recruited patients with (n = 40) and without (n = 40) deficit schizophrenia and healthy volunteers (n = 40). The combined use of TRYCAT and CERAD features strongly segregates deficit from nondeficit schizophrenia and healthy controls. Three out of the top five most important features in LRA, SVM and SIMCA agreed, namely two different NOX/PRO TRYCAT ratios and false memory recall. SIMCA shows that deficit schizophrenia is significantly separated from nondeficit schizophrenia and controls with as top 6 features IgA responses to picolinic acid, IgM responses to 3-OH-kynurenine and kynurenic acid, and impairments in Word List Memory and Verbal Fluency Tests and Mini-Mental State Examination. Nevertheless, nondeficit schizophrenia was not significantly separated from controls. The results show that schizophrenia is not a unitary disease with mere continuous differences in severity of illness between apparent subtypes. Deficit schizophrenia is a qualitatively distinct class defined by neuroimmune (autoimmune responses to TRYCATs) and neurocognitive (episodic and semantic memory) features coupled or not with clinical (negative) symptoms.

Probing large conformational rearrangements in wild-type and mutant spectrin using structural mass spectrometry.

Conformational changes of macromolecular complexes play key mechanistic roles in many biological processes, but large, highly flexible proteins and protein complexes usually cannot be analyzed by crystallography or NMR. Here, structures and conformational changes of the highly flexible, dynamic red cell spectrin and effects of a common mutation that disrupts red cell membranes were elucidated using chemical cross-linking coupled with mass spectrometry. Interconversion of spectrin between closed dimers, open dimers, and tetramers plays a key role in maintaining red cell shape and membrane integrity, and spectrins in other cell types serve these as well as more diverse functions. Using a minispectrin construct, experimentally verified structures of closed dimers and tetramers were determined by combining distance constraints from zero-length cross-links with molecular models and biophysical data. Subsequent biophysical and structural mass spectrometry characterization of a common hereditary elliptocytosis-related mutation of α-spectrin, L207P, showed that cell membranes were destabilized by a shift of the dimer-tetramer equilibrium toward closed dimers. The structure of αL207P mutant closed dimers provided previously unidentified mechanistic insight into how this mutation, which is located a large distance from the tetramerization site, destabilizes spectrin tetramers and cell membrane integrity.

The Physiological Molecular Shape of Spectrin: A Compact Supercoil Resembling a Chinese Finger Trap.

The primary, secondary, and tertiary structures of spectrin are reasonably well defined, but the structural basis for the known dramatic molecular shape change, whereby the molecular length can increase three-fold, is not understood. In this study, we combine previously reported biochemical and high-resolution crystallographic data with structural mass spectroscopy and electron microscopic data to derive a detailed, experimentally-supported quaternary structure of the spectrin heterotetramer. In addition to explaining spectrin's physiological resting length of ~55-65 nm, our model provides a mechanism by which spectrin is able to undergo a seamless three-fold extension while remaining a linear filament, an experimentally observed property. According to the proposed model, spectrin's quaternary structure and mechanism of extension is similar to a Chinese Finger Trap: at shorter molecular lengths spectrin is a hollow cylinder that extends by increasing the pitch of each spectrin repeat, which decreases the internal diameter. We validated our model with electron microscopy, which demonstrated that, as predicted, spectrin is hollow at its biological resting length of ~55-65 nm. The model is further supported by zero-length chemical crosslink data indicative of an approximately 90 degree bend between adjacent spectrin repeats. The domain-domain interactions in our model are entirely consistent with those present in the prototypical linear antiparallel heterotetramer as well as recently reported inter-strand chemical crosslinks. The model is consistent with all known physical properties of spectrin, and upon full extension our Chinese Finger Trap Model reduces to the ~180-200 nm molecular model currently in common use.

Probing structures of large protein complexes using zero-length cross-linking.

Structural mass spectrometry (MS) is a field with growing applicability for addressing complex biophysical questions regarding proteins and protein complexes. One of the major structural MS approaches involves the use of chemical cross-linking coupled with MS analysis (CX-MS) to identify proximal sites within macromolecules. Identified cross-linked sites can be used to probe novel protein-protein interactions or the derived distance constraints can be used to verify and refine molecular models. This review focuses on recent advances of "zero-length" cross-linking. Zero-length cross-linking reagents do not add any atoms to the cross-linked species due to the lack of a spacer arm. This provides a major advantage in the form of providing more precise distance constraints as the cross-linkable groups must be within salt bridge distances in order to react. However, identification of cross-linked peptides using these reagents presents unique challenges. We discuss recent efforts by our group to minimize these challenges by using multiple cycles of LC-MS/MS analysis and software specifically developed and optimized for identification of zero-length cross-linked peptides. Representative data utilizing our current protocol are presented and discussed.

Solution structure of the reduced form of human peroxiredoxin-6 elucidated using zero-length chemical cross-linking and homology modelling.

Peroxiredoxin-6 (PRDX6) is an unusual member of the peroxiredoxin family of antioxidant enzymes that has only one evolutionarily conserved cysteine. It reduces oxidized lipids and reactive oxygen species (ROS) by oxidation of the active-site cysteine (Cys(47)) to a sulfenic acid, but the mechanism for conversion back to a thiol is not completely understood. Moreover, it has phospholipase A2 (PLA2) activity in addition to its peroxidase activity. Interestingly, some biochemical data are inconsistent with a known high-resolution crystal structure of the catalytic intermediate of the protein, and biophysical data indicate that the protein undergoes conformational changes that affect enzyme activity. In order to further elucidate the solution structure of this important enzyme, we used chemical cross-linking coupled with high-resolution MS (CX-MS), with an emphasis on zero-length cross-links. Distance constraints from high confidence cross-links were used in homology modelling experiments to determine a solution structure of the reduced form of the protein. This structure was further evaluated using chemical cross-links produced by several homo-bifunctional amine-reactive cross-linking reagents, which helped to confirm the solution structure. The results show that several regions of the reduced version of human PRDX6 are in a substantially different conformation from that shown for the crystal structure of the peroxidase catalytic intermediate. The differences between these two structures are likely to reflect catalysis-related conformational changes. These studies also demonstrate that CX-MS using zero-length cross-linking is a powerful strategy for probing protein conformational changes that is complementary to alternative methods such as crystallographic, NMR and biophysical studies.

The common hereditary elliptocytosis-associated α-spectrin L260P mutation perturbs erythrocyte membranes by stabilizing spectrin in the closed dimer conformation.

Hereditary elliptocytosis (HE) and hereditary pyropoikilocytosis (HPP) are common disorders of erythrocyte shape primarily because of mutations in spectrin. The most common HE/HPP mutations are located distant from the critical αß-spectrin tetramerization site, yet still interfere with formation of spectrin tetramers and destabilize the membrane by unknown mechanisms. To address this question, we studied the common HE-associated mutation, αL260P, in the context of a fully functional mini-spectrin. The mutation exhibited wild-type tetramer binding in univalent binding assays, but reduced binding affinity in bivalent-binding assays. Biophysical analyses demonstrated the mutation-containing domain was only modestly structurally destabilized and helical content was not significantly changed. Gel filtration analysis of the αL260P mini-spectrin indicated more compact structures for dimers and tetramers compared with wild-type. Chemical crosslinking showed structural changes in the mutant mini-spectrin dimer were primarily restricted to the vicinity of the αL260P mutation and indicated large conformational rearrangements of this region. These data indicate the mutation increased the stability of the closed dimer state, thereby reducing tetramer assembly and resulting in membrane destabilization. These results reveal a novel mechanism of erythrocyte membrane destabilization that could contribute to development of therapeutic interventions for mutations in membrane proteins containing spectrin-type domains associated with inherited disease.