Computationally Designed Molecules Modulate ALS-Related Amyloidogenic TDP-43307-319 Aggregation.

Abnormal cytosolic aggregation of TAR DNA-binding protein of 43 kDa (TDP-43) is observed in multiple diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration, and Alzheimer's disease. Previous studies have shown that TDP-43307-319 located at the C-terminal of TDP-43 can form higher-order oligomers and fibrils. Of particular interest are the hexamers that adopt a cylindrin structure that has been strongly correlated to neurotoxicity. In this study, we use the joint pharmacophore space (JPS) model to identify and generate potential TDP-43 inhibitors. Five JPS-designed molecules are evaluated using both experimental and computational methods: ion mobility mass spectrometry, thioflavin T fluorescence assay, circular dichroism spectroscopy, atomic force microscopy, and molecular dynamics simulations. We found that all five molecules can prevent the amyloid fibril formation of TDP-43307-319, but their efficacy varies significantly. Furthermore, among the five molecules, [AC0101] is the most efficient in preventing the formation of higher-order oligomers and dissociating preformed higher-order oligomers. Molecular dynamics simulations show that [AC0101] both is the most flexible and forms the most hydrogen bonds with the TDP-43307-319 monomer. The JPS-designed molecules can insert themselves between the ß-strands in the hexameric cylindrin structure of TDP-43307-319 and can open its structure. Possible mechanisms for JPS-designed molecules to inhibit and dissociate TDP-43307-319 oligomers on an atomistic scale are proposed.

Multiscale simulations reveal TDP-43 molecular-level interactions driving condensation.

The RNA-binding protein TDP-43 is associated with mRNA processing and transport from the nucleus to the cytoplasm. TDP-43 localizes in the nucleus as well as accumulating in cytoplasmic condensates such as stress granules. Aggregation and formation of amyloid-like fibrils of cytoplasmic TDP-43 are hallmarks of numerous neurodegenerative diseases, most strikingly present in >90% of amyotrophic lateral sclerosis (ALS) patients. If excessive accumulation of cytoplasmic TDP-43 causes, or is caused by, neurodegeneration is presently not known. In this work, we use molecular dynamics simulations at multiple resolutions to explore TDP-43 self- and cross-interaction dynamics. A full-length molecular model of TDP-43, all 414 amino acids, was constructed from select structures of the protein functional domains (N-terminal domain, and two RNA recognition motifs, RRM1 and RRM2) and modeling of disordered connecting loops and the low complexity glycine-rich C-terminus domain. All-atom CHARMM36m simulations of single TDP-43 proteins served as guides to construct a coarse-grained Martini 3 model of TDP-43. The Martini model and a coarser implicit solvent C⍺ model, optimized for disordered proteins, were subsequently used to probe TDP-43 interactions; self-interactions from single-chain full-length TDP-43 simulations, cross-interactions from simulations with two proteins and simulations with assemblies of dozens to hundreds of proteins. Our findings illustrate the utility of different modeling scales for accessing TDP-43 molecular-level interactions and suggest that TDP-43 has numerous interaction preferences or patterns, exhibiting an overall strong, but dynamic, association and driving the formation of biomolecular condensates.

Computationally Designed Small Molecules Disassemble Both Soluble Oligomers and Protofibrils of Amyloid ß-Protein Responsible for Alzheimer's Disease.

Alzheimer's disease (AD) is one of the world's most pressing health crises. AD is an incurable disease affecting more than 6.5 million Americans, predominantly the elderly, and in its later stages, leads to memory loss, dementia, and death. Amyloid ß (Aß) protein aggregates have been one of the pathological hallmarks of AD since its initial characterization. The early stages of Aß accumulation and aggregation involve the formation of oligomers, which are considered neurotoxic and play a key role in further aggregation into fibrils that eventually appear in the brain as amyloid plaques. We have recently shown by combining ion mobility mass spectrometry (IM-MS) and atomic force microscopy (AFM) that Aß42 rapidly forms dodecamers (12-mers) as the terminal oligomeric state, and these dodecamers seed the early formation of Aß42 protofibrils. The link between soluble oligomers and fibril formation is one of the essential aspects for understanding the root cause of the disease state and is critical to developing therapeutic interventions. Utilizing a joint pharmacophore space (JPS) method, potential drugs have been designed specifically for amyloid-related diseases. These small molecules were generated based on crucial chemical features necessary for target selectivity. In this paper, we utilize our combined IM-MS and AFM methods to investigate the impact of three second-generation JPS small-molecule inhibitors, AC0201, AC0202, and AC0203, on dodecamer as well as fibril formation in Aß42. Our results indicate that AC0201 works well as an inhibitor and remodeler of both dodecamers and fibril formation, AC0203 behaves less efficiently, and AC0202 is ineffective.

Effect of Cosolutes on the Aggregation of a Tau Fragment: A Combined Experimental and Simulation Approach.

The intrinsically disordered protein Tau represents the main component of neurofibrillary tangles that are a hallmark of Alzheimer's disease. A small fragment of Tau, known as paired helical filament 6 (PHF6), is considered to be important for the formation of the ß-structure core of the fibrils. Here we study the aggregation of this fragment in the presence of different cosolutes, including urea, TMAO, sucrose and 2-hydroxypropyl-ß-cyclodextrin (2-HPßCD), using both experiments and molecular dynamics simulations. A novel implicit solvation approach (MIST - Model with Implicit Solvation Thermodynamics) is used, where an energetic contribution based on the concept of transfer free energies describes the effect of the cosolutes. The simulation predictions are compared to thioflavin-T and atomic force microscopy results, and the good agreement observed confirms the predictive ability of the computational approach herein proposed. Both simulations and experiments indicate that PHF6 aggregation is inhibited in the presence of urea and 2-HPßCD, while TMAO and sucrose stabilize associated conformations. The remarkable ability of HPßCD to inhibit aggregation represents an extremely promising result for future applications, especially considering the widespread use of this molecule as a drug carrier to the brain and as a solubilizer/excipient in pharmaceutical formulations.

VO Cluster-Stabilized H2O Adsorption on a TiO2 (110) Surface at Room Temperature.

We probe the adsorption of molecular H2O on a TiO2 (110)-(1 × 1) surface decorated with isolated VO clusters using ultrahigh-vacuum scanning tunneling microscopy (UHV-STM) and temperature-programmed desorption (TPD). Our STM images show that preadsorbed VO clusters on the TiO2 (110)-(1 × 1) surface induce the adsorption of H2O molecules at room temperature (RT). The adsorbed H2O molecules form strings of beads of H2O dimers bound to the 5-fold coordinated Ti atom (5c-Ti) rows and are anchored by VO. This RT adsorption is completely reversible and is unique to the VO-decorated TiO2 surface. TPD spectra reveal two new desorption states for VO stabilized H2O at 395 and 445 K, which is in sharp contrast to the desorption of water due to recombination of hydroxyl groups at 490 K from clean TiO2(110)-(1 × 1) surfaces. Density functional theory (DFT) calculations show that the binding energy of molecular H2O to the VO clusters on the TiO2 (110)-(1 × 1) surface is higher than binding to the bare surface by 0.42 eV, and the resulting H2O-VO-TiO2 (110) complex provides the anchor point for adsorption of the string of beads of H2O dimers.

Evaluation of the Clinical Utility of the Bone Metastases Ensemble Trees for Survival Decision Support Platform (BMETS-DSP): A Case-Based Pilot Assessment.

PURPOSE: The Bone Metastases Ensemble Trees for Survival Decision Support Platform (BMETS-DSP) provides patient-specific survival predictions and evidence-based recommendations to guide multidisciplinary management for symptomatic bone metastases. We assessed the clinical utility of the BMETS-DSP through a pilot prepost design in a simulated clinical environment. METHODS: Ten Radiation Oncology physicians reviewed 55 patient cases at two time points: without and then with the use of BMETS-DSP. Assessment included 12-month survival estimate, confidence in and likelihood of sharing estimates with patients, and recommendations for open surgery, systemic therapy, hospice referral, and radiotherapy (RT) regimen. Paired statistics compared pre- versus post-DSP outcomes. Reported statistical significance is P < .05. RESULTS: Pre- versus post-DSP, overestimation of true minus estimated survival time was significantly reduced (mean difference -2.1 [standard deviation 4.1] v -1 month [standard deviation 3.5]). Prediction accuracy was significantly improved at cut points of < 3 (72 v 79%), ≤ 6 (64 v 71%), and ≥ 12 months (70 v 81%). Median ratings of confidence in and likelihood of sharing prognosis significantly increased. Significantly greater concordance was seen in matching use of 1-fraction RT with the true survival < 3 months (70 v 76%) and < 10-fraction RT with the true survival < 12 months (55 v 62%) and appropriate use of open surgery (47% v 53%), without significant changes in selection of hospice referral or systemic therapy. CONCLUSION: This pilot study demonstrates that BMETS-DSP significantly improved physician survival estimation accuracy, prognostic confidence, likelihood of sharing prognosis, and use of prognosis-appropriate RT regimens in the care of symptomatic bone metastases, supporting future multi-institutional validation of the platform.

Recommendations on the use and reporting of race, ethnicity, and ancestry in genetic research: Experiences from the NHLBI TOPMed program.

How race, ethnicity, and ancestry are used in genomic research has wide-ranging implications for how research is translated into clinical care and incorporated into public understanding. Correlation between race and genetic ancestry contributes to unresolved complexity for the scientific community, as illustrated by heterogeneous definitions and applications of these variables. Here, we offer commentary and recommendations on the use of race, ethnicity, and ancestry across the arc of genetic research, including data harmonization, analysis, and reporting. While informed by our experiences as researchers affiliated with the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, these recommendations are applicable to basic and translational genomic research in diverse populations with genome-wide data. Moving forward, considerable collaborative effort will be required to ensure that race, ethnicity, and ancestry are described and used appropriately to generate scientific knowledge that yields broad and equitable benefit.

Tachykinin Neuropeptides and Amyloid ß (25-35) Assembly: Friend or Foe?

Amyloid ß (Aß) protein is responsible for Alzheimer's disease, and one of its important fragments, Aß(25-35), is found in the brain and has been shown to be neurotoxic. Tachykinin neuropeptides, including Neuromedin K (NK), Kassinin, and Substance P, have been reported to reduce Aß(25-35)'s toxicity in cells even though they share similar primary structures with Aß(25-35). Here, we seek to understand the molecular mechanisms of how these peptides interact with Aß(25-35) and to shed light on why some peptides with similar primary structures are toxic and others nontoxic. We use both experimental and computational methods, including ion mobility mass spectrometry and enhanced-sampling replica-exchange molecular dynamics simulations, to study the aggregation pathways of Aß(25-35), NK, Kassinin, Substance P, and mixtures of the latter three with Aß(25-35). NK and Substance P were observed to remove the higher-order oligomers (i.e., hexamers and dodecamers) of Aß(25-35), which are related to its toxicity, although Substance P did so more slowly. In contrast, Kassinin was found to promote the formation of these higher-order oligomers. This result conflicts with what is expected and is elaborated on in the text. We also observe that even though they have significant structural homology with Aß(25-35), NK, Kassinin, and Substance P do not form hexamers with a ß-sheet structure like Aß(25-35). The hexamer structure of Aß(25-35) has been identified as a cylindrin, and this structure has been strongly correlated to toxic species. The reasons why the three tachykinin peptides behave so differently when mixed with Aß(25-35) are discussed.

Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy.

PURPOSE: Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients. MATERIALS AND METHODS: CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared. RESULTS: CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82). CONCLUSION: Machine learning-based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.

Catalytic Cross Talk between Key Peptide Fragments That Couple Alzheimer's Disease with Amyotrophic Lateral Sclerosis.

Protein aggregation is a common feature in prominent neurodegenerative diseases, usually thought to be due to the assembly of a single peptide or protein. Recent studies have challenged this notion and suggested several proteins may be involved in promoting and amplifying disease. For example, the TDP-43 protein associated with Amyotrophic Lateral Sclerosis has been found in the brain along with Aß assemblies associated with Alzheimer's disease, and those patients that show the presence of TDP-43 are 10 times more likely to demonstrate cognitive impairment compared to TDP-43-negative Alzheimer's patients. Here we examine the interactions between the amyloidogenic core of TDP-43, TDP-43307-319, and a neurotoxic physiologically observed fragment of Aß, Aß25-35. Utilizing ion mobility mass spectrometry in concert with atomic force microscopy and molecular dynamics simulations, we investigate which oligomers are involved in seeding aggregation across these two different protein systems and gain insight into which structures initiate and result from these interactions. Studies were conducted by mixing Aß25-35 with the toxic wild type TDP-43307-319 peptide and with the nontoxic synthetic TDP-43307-319 mutant, G314V. Our findings identify a strong catalytic effect of TDP-43307-319 WT monomer in the acceleration of Aß25-35 aggregation to its toxic cylindrin and ß barrel forms. This observation is unprecedented in both its speed and specificity. Interestingly, the nontoxic G314V mutant of TDP-43307-319 and dimers or higher order oligomers of WT TDP-43307-319 do not promote aggregation of Aß25-35 but rather dissociate preformed toxic higher order oligomers of Aß25-35. Reasons for these very different behaviors are reported.

Latent Models of Molecular Dynamics Data: Automatic Order Parameter Generation for Peptide Fibrillization.

Variational autoencoders are artificial neural networks with the capability to reduce highly dimensional sets of data to smaller dimensional, latent representations. In this work, these models are applied to molecular dynamics simulations of the self-assembly of coarse-grained peptides to obtain a singled-valued order parameter for amyloid aggregation. This automatically learned order parameter is constructed by time-averaging the latent parametrizations of internal coordinate representations and compared to the nematic order parameter which is commonly used to study ordering of similar systems in literature. It is found that the latent space value provides more tailored insight into the aggregation mechanism's details, correctly identifying fibril formation in instances where the nematic order parameter fails to do so. A means is provided by which the latent space value can be analyzed so that the major contributing internal coordinates are identified, allowing for a direct interpretation of the latent space order parameter in terms of the behavior of the system. The latent model is found to be an effective and convenient way of representing the data from the dynamic ensemble and provides a means of reducing the dimensionality of a system whose scale exceeds molecular systems so-far considered with similar tools. This bypasses a need for researcher speculation on what elements of a system best contribute to summarizing major transitions and suggests latent models are effective and insightful when applied to large systems with a diversity of complex behaviors.

Terminal Capping of an Amyloidogenic Tau Fragment Modulates Its Fibrillation Propensity.

Aberrant protein folding leading to the formation of characteristic cross-ß-sheet-rich amyloid structures is well known for its association with a variety of debilitating human diseases. Often, depending upon amino acid composition, only a small segment of a large protein participates in amyloid formation and is in fact capable of self-assembling into amyloid, independent of the rest of the protein. Therefore, such peptide fragments serve as useful model systems for understanding the process of amyloid formation. An important factor that has often been overlooked while using peptides to mimic full-length protein is the charge on the termini of these peptides. Here, we show the influence of terminal charges on the aggregation of an amyloidogenic peptide from microtubule-associated protein Tau, implicated in Alzheimer's disease and tauopathies. We found that modification of terminal charges by capping the peptide at one or both of the termini drastically modulates the fibrillation of the hexapeptide sequence paired helical filament 6 (PHF6) from repeat 3 of Tau, both with and without heparin. Without heparin, the PHF6 peptide capped at both termini and PHF6 capped only at the N-terminus self-assembled to form amyloid fibrils. With heparin, all capping variants of PHF6, except for PHF6 with both termini free, formed typical amyloid fibrils. However, the rate and extent of aggregation both with and without heparin as well as the morphology of aggregates were found to be highly dependent on the terminal charges. Our molecular dynamics simulations on PHF6 capping variants corroborated our experiments and provided critical insights into the mechanism of PHF6 self-assembly. Overall, our results emphasize the importance of terminal modifications in fibrillation of small peptide fragments and provide significant insights into the aggregation of a small Tau fragment, which is considered essential for Tau filament assembly.

Consolidative Radiotherapy in Oligometastatic Lung Cancer: Patient Selection With a Prediction Nomogram.

BACKGROUND: Patients with stage IV oligometastatic (≤ 3 sites) non-small-cell lung cancer have a progression-free survival (PFS) and overall survival benefit when all sites of metastatic disease and the primary tumor are treated radically with consolidative radiotherapy (cRT). However, the optimal selection of patients most likely from cRT is yet to be defined. PATIENTS AND METHODS: Patients with metastatic non-small-cell lung cancer treated with definitive radiotherapy to all metastatic sites and primary tumor (2008-2019) were retrospectively identified. Univariable Cox proportional-hazards model was used to compare outcomes with demographic and clinical characteristics. A predictive nomogram model for selection of patients most likely to benefit from cRT was constructed. RESULTS: There were 91 patients identified with a total of 114 metastases treated. Median PFS from the start of cRT was 10.9 months (95% confidence interval [CI], 8.1-16.6), while the median survival time was 37.0 months (95% CI, 31.3-NR). On univariable modeling, patients with squamous histology (hazard ratio, 4.16; 95% CI, 1.99-8.71; P < .001) and those treated with non-stereotactic body radiotherapy hypofractionated therapy (hazard ratio, 5.43; 95% CI, 2.10-14.01; P < .001) had worse overall survival, while patients with targetable mutations (hazard ratio, 0.49; 95% CI, 0.25-0.98; P = .04) had a longer survival. Using a predictive nomogram model, patients with a solitary site of metastasis, targetable mutations, intracranial disease, and metachronous timing of oligometastases had a larger PFS benefit from cRT. CONCLUSION: cRT is associated with favorable outcomes in PFS and overall survival. These results may aid in patient counseling, selection for aggressive local therapy, and stratification in future prospective clinical trials.

Spatial Radiation Dose Influence on Xerostomia Recovery and Its Comparison to Acute Incidence in Patients With Head and Neck Cancer.

PURPOSE: Radiation-induced xerostomia is one of the most prevalent symptoms during and after head and neck cancer radiation therapy (RT). We aimed to discover the spatial radiation dose-based (voxel dose) importance pattern in the major salivary glands in relation to the recovery of xerostomia 18 months after RT, and to compare the recovery voxel dose importance pattern to the acute incidence (injury) pattern. METHODS AND MATERIALS: This study included all patients within our database with xerostomia outcomes after completion of curative intensity modulated RT. Common Terminology Criteria for Adverse Events xerostomia grade was used to define recovered versus nonrecovered group at baseline, between end of treatment and 18 months post-RT, and beyond 18 months, respectively. Ridge logistic regression was performed to predict the probability of xerostomia recovery. Voxel doses within geometrically defined parotid glands (PG) and submandibular glands (SMG), demographic characteristics, and clinical factors were included in the algorithm. We plotted the normalized learned weights on the 3-dimensional PG and SMG structures to visualize the voxel dose importance for predicting xerostomia recovery. RESULTS: A total of 146 head and neck cancer patients from 2008 to 2016 were identified. The superior region of the ipsilateral and contralateral PG was the most influencial for xerostomia recovery. The area under the receiver operating characteristic curve evaluated using 10-fold cross-validation for ridge logistic regression was 0.68 ± 0.07. Compared with injury, the recovery voxel dose importance pattern was more symmetrical and was influenced by lower dose voxels. CONCLUSIONS: The superior portion of the 2 PGs (low dose region) are the most influential on xerostomia recovery and seem to be equal in their contribution. The dissimilarity of the influence pattern between injury and recovery suggests different underlying mechanisms. The importance pattern identified by spatial radiation dose and machine learning methods can improve our understanding of normal tissue toxicities in RT. Further external validation is warranted.

Modulating ALS-Related Amyloidogenic TDP-43307-319 Oligomeric Aggregates with Computationally Derived Therapeutic Molecules.

TDP-43 aggregates are a salient feature of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and a variety of other neurodegenerative diseases, including Alzheimer's disease (AD). With an anticipated growth in the most susceptible demographic, projections predict neurodegenerative diseases will potentially affect 15 million people in the United States by 2050. Currently, there are no cures for ALS, FTD, or AD. Previous studies of the amyloidogenic core of TDP-43 have demonstrated that oligomers greater than a trimer are associated with toxicity. Utilizing a joint pharmacophore space (JPS) method, potential drugs have been designed specifically for amyloid-related diseases. These molecules were generated on the basis of key chemical features necessary for blood-brain barrier permeability, low adverse side effects, and target selectivity. Combining ion-mobility mass spectrometry and atomic force microscopy with the JPS computational method allows us to more efficiently evaluate a potential drug's efficacy in disrupting the development of putative toxic species. Our results demonstrate the dissociation of higher-order oligomers in the presence of these novel JPS-generated inhibitors into smaller oligomer species. Additionally, drugs approved by the Food and Drug Administration for the treatment of ALS were also evaluated and demonstrated to maintain higher-order oligomeric assemblies. Possible mechanisms for the observed action of the JPS molecules are discussed.

Catalytic Prion-Like Cross-Talk between a Key Alzheimer's Disease Tau-Fragment R3 and the Type 2 Diabetes Peptide IAPP.

The aberrant association of proteins/peptides is implicated in the etiology and pathogenesis of a variety of human diseases. In general, the primary protein component responsible for the formation of aggregates is different in each case and is specific to a particular disease condition. However, there are instances where multiple protein aggregates have been found to coexist in the same or different tissue(s), thereby leading to mixed pathologies and exacerbation of disease symptoms. In this context, a strong link has been established between Alzheimer's disease (AD) and type 2 diabetes (T2D). However, the underlying molecular details still remain elusive. Here, we report the direct interaction of an AD-associated amyloidogenic cytotoxic fragment of Tau (R3:306-336) with islet amyloid polypeptide (IAPP) implicated in T2D. Using ion-mobility mass spectrometry (IM-MS) in conjunction with fluorescence spectroscopy, circular dichroism, and transmission electron microscopy, we have been able to provide critical mechanistic insights into these interactions. Our IM-MS data showed the formation of hetero-oligomers of R3 and IAPP. Additionally, using IM-MS, we found that the amyloidogenic extended beta hairpin conformation of IAPP is favored much more in the R3-IAPP mixture, when compared with IAPP alone. Furthermore, we found that the oligomerization of R3 occurs much faster in the presence of IAPP. We also observed a secondary nucleation step in our kinetics data for the R3-IAPP mixture. We believe that the secondary nucleation step is demonstrative of R3 aggregation which otherwise requires the presence of anionic cofactors. Our results provide the first experimental evidence for direct molecular interaction between Tau and IAPP and highlights the repercussion of possible "prion-like" cross-talk in the proliferation of diseases that are associated with different tissues/organs.

Characterizing TDP-43307-319 Oligomeric Assembly: Mechanistic and Structural Implications Involved in the Etiology of Amyotrophic Lateral Sclerosis.

Aggregation of TAR DNA-binding protein of 43 kDa (TDP-43) is a salient feature of amyotrophic lateral sclerosis (ALS), a debilitating neurodegenerative disorder affecting over 200 000 people worldwide. The protein undergoes both functional and pathogenic aggregation; the latter is irreversible and hypothesized to produce soluble oligomers that are toxic to neurons in addition to inclusions made of stable fibrous deposits. Despite progress made toward identifying disease-related proteins, the underlying pathogenic mechanism associated with these toxic oligomers remains elusive. Utilizing a multimodal approach that combines several measurement techniques (circular dichroism (CD), thioflavin T spectroscopy (ThT), Fourier transform infrared spectroscopy (FTIR)) and high spatial resolution imaging tools (electron microscopy (EM) and atomic force microscopy (AFM)), with soft ion mobility mass spectrometry (IM-MS) and atomistic molecular dynamics (MD) simulations, we explore the oligomerization mechanisms, structures, and assembly pathways of TDP-43307-319. This fragment is both amyloidogenic and toxic and is within the glycine-rich C-terminal domain essential for both toxicity and aggregation of the full-length protein. In addition to the wild-type peptide, two ALS-related mutants (A315T and A315E) and a non-axon-toxic mutant (G314V) were investigated to determine how mutations affect the oligomerization of TDP-43307-319 and structures of toxic oligomers. The results of our study provide new insights into how ALS-related mutants, A315T and A315E, accelerate or alter the pathogenic mechanism and highlight the role of an internal glycine, G314, in maintaining efficient packing known to be critical for functional oligomer assembly. More importantly, our data demonstrate that G314 plays a vital role in TDP-43 assembly and prevents cytotoxicity via its unique aversion to oligomers larger than trimer. Our observation is consistent with previous studies showing that G314V mutation of the full-length TDP-43 induced remediation of both axonotoxicity and neuronal apoptosis. Our findings reveal a distinct aggregation mechanism for each peptide and elucidate oligomeric species and possible structures that may be involved in the pathology of ALS.

The Classifying Autoencoder: Gaining Insight into Amyloid Assembly of Peptides and Proteins.

Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure?activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifying autoencoder (CAE). This machine learning technique uses specialized architecture of artificial neural networks to provide insight into typically opaque classification processes. The method proves to be robust to noisy and limited data sets, as well as being capable of disentangling relatively complicated rules over data sets. We demonstrate its capabilities by applying the technique to an experimental database (the Waltz database) and demonstrate the CAE?s capability to provide insight into a novel descriptor, dimeric isotropic deviation?an experimental measure of the aggregation properties of the amino acids. We measure this value for all 20 of the common amino acids and find correlation between dimeric isotropic deviation and the failure to form amyloids when hydrophobic effects are not a primary driving force in amyloid formation. These applications show the value of the new method and provide a flexible and general framework to approach problems in biochemistry using artificial neural networks.

Machine Learning Methods Uncover Radiomorphologic Dose Patterns in Salivary Glands that Predict Xerostomia in Patients with Head and Neck Cancer.

PURPOSE: Patients with head-and-neck cancer (HNC) may experience xerostomia after radiation therapy (RT), which leads to compromised quality of life. The purpose of this study is to explore how the spatial pattern of radiation dose (radiomorphology) in the major salivary glands influences xerostomia in patients with HNC. METHODS AND MATERIALS: A data-driven approach using spatially explicit dosimetric predictors, voxel dose (ie, actual radiation dose in voxels in parotid glands [PG] and submandibular glands [SMG]) was used to predict whether patients would develop xerostomia 3 months after RT. Using planned radiation dose data and other nondose covariates including baseline xerostomia grade of 427 patients with HNC in our database, the machine learning methods were used to investigate the influence of dose patterns across subvolumes in PG and SMG on xerostomia. RESULTS: Of the 3 supervised learning methods studied, ridge logistic regression yielded the best predictive performance. Ridge logistic regression was also preferred to evaluate the influence pattern of highly correlated dose on xerostomia, which showed a discriminative pattern of influence of doses in the PG and SMG on xerostomia. Moreover, the superior-anterior portion of the contralateral PG and medial portion of the ipsilateral PG were determined to be the most influential regions regarding dose effect on xerostomia. The area under the receiver operating characteristic curve from a 10-fold cross-validation was 0.70 ± 0.04. CONCLUSIONS: Radiomorphology, combined with machine learning methods, is able to suggest patterns of dose in PG and SMG that are the most influential on xerostomia. The influence pattern identified by this data-driven approach and machine learning methods may help improve RT treatment planning and reduce xerostomia after treatment.