Liquid–Liquid Phase Separation in Oligomeric Peptide Solutions

Oligomeric peptides exist widely in living organisms and play a role in a broad range of biological functions. We report the first observation of liquid–liquid phase separation (LLPS) in peptide solutions, in particular, solutions of peptides consisting of noncovalent oligomers. We determined the binary phase boundary of the oligomeric peptide solution and compared the result to the well-established phase diagram of globular proteins. We also provide simple theoretical interpretations of the similarities and differences between the phase diagrams of peptides and proteins. Finally, by tuning inter-oligomer interactions using a crowding agent, we demonstrated that LLPS is a universal phenomenon that can be observed under different solution conditions for a variety of peptides

Quantitative Evaluation of Colloidal Stability of Antibody Solutions using PEG-Induced Liquid–Liquid Phase Separation

Colloidal stability of antibody solutions, i.e., the propensity of the folded protein to precipitate, is an important consideration in formulation development of therapeutic monoclonal antibodies. In a protein solution, different pathways including crystallization, colloidal aggregation, and liquid–liquid phase separation (LLPS) can lead to the formation of precipitates. The kinetics of crystallization and aggregation are often slow and vary from protein to protein. Due to the diverse mechanisms of these protein condensation processes, it is a challenge to develop a standardized test for an early evaluation of the colloidal stability of antibody solutions. LLPS would normally occur in antibody solutions at sufficiently low temperature, provided that it is not preempted by freezing of the solution. Poly­(ethylene glycol) (PEG) can be used to induce LLPS at temperatures above the freezing point. Here, we propose a colloidal stability test based on inducing LLPS in antibody solutions and measuring the antibody concentration of the dilute phase. We demonstrate experimentally that such a PEG-induced LLPS test can be used to compare colloidal stability of different antibodies in different solution conditions and can be readily applied to high-throughput screening. We have derived an equation for the effects of PEG concentration and molecular weight on the results of the LLPS test. Finally, this equation defines a binding energy in the condensed phase, which can be determined in the PEG-induced LLPS test. This binding energy is a measure of attractive interactions between antibody molecules and can be used for quantitative characterization of the colloidal stability of antibody solutions

Role of Species-Specific Primary Structure Differences in Aβ42 Assembly and Neurotoxicity

A variety of species express the amyloid β-protein (Aβ (the term “Aβ” refers both to Aβ40 and Aβ42, whereas “Aβ40” and “Aβ42” refer to each isoform specifically). Those species expressing Aβ with primary structure identical to that expressed in humans have been found to develop amyloid deposits and Alzheimer’s disease-like neuropathology. In contrast, the Aβ sequence in mice and rats contains three amino acid substitutions, Arg5Gly, His13Arg, and Tyr10Phe, which apparently prevent the development of AD-like neuropathology. Interestingly, the brush-tailed rat, Octodon degus, expresses Aβ containing only one of these substitutions, His13Arg, and <i>does</i> develop AD-like pathology. We investigate here the biophysical and biological properties of Aβ peptides from humans, mice (Mus musculus), and rats (Octodon degus). We find that each peptide displays statistical coil → β-sheet secondary structure transitions, transitory formation of hydrophobic surfaces, oligomerization, formation of annuli, protofibrils, and fibrils, and an inverse correlation between rate of aggregation and aggregate size (faster aggregation produced smaller aggregates). The rank order of assembly rate was mouse > rat > Aβ42. The rank order of neurotoxicity of assemblies formed by each peptide immediately after preparation was Aβ42 > mouse ≈ rat. These data do <i>not</i> support long-standing hypotheses that the primary factor controlling development of AD-like neuropathology in rodents is Aβ sequence. Instead, the data support a hypothesis that assembly quaternary structure <i>and</i> organismal responses to toxic peptide assemblies mediate neuropathogenetic effects. The implication of this hypothesis is that a valid understanding of disease causation within a given system (organism, tissue, etc.) requires the coevaluation of both biophysical and cell biological properties of that system

Individual Performance of Significantly Altered Serum Biomarkers.

<p>Cut-point – minimum (maximum for prolactin) value (pg/ml) for diagnosis as case at 95% specificity.</p><p>SN – sensitivity at 95% specificity.</p><p>AUC – area under ROC curve.</p

Complete List of Evaluated Biomarkers.

#<p>- our unpublished observations</p

Demographic and Clinical Characteristics of PLCO-selected Study Population.

<p>Demographic and Clinical Characteristics of PLCO-selected Study Population.</p

Biomarker levels in relation to time to diagnosis.

<p>Biomarker levels were plotted against the elapsed time interval between blood draw and cancer diagnosis and plots were evaluated by linear regression. Biomarkers demonstrating slopes differing significantly from zero are presented.</p

Prediagnostic distributions of serum biomarker levels.

<p>Levels of 67 biomarkers were evaluated in sera obtained from 135 subjects enrolled in the PLCO cancer screening trial who were subsequently diagnosed with pancreatic cancer and 540 matched controls. Circulating levels of biomarkers demonstrating significant differences between cases and healthy controls are presented. Level of significance: * - p<0.03, ** - p<0.01, *** - p<0.001, **** - p<0.0001.</p

Performance of Multimarker Combinations in PLCO Training and Validation Sets.

<p>*Case/Control set described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094928#pone.0094928-Brand1" target="_blank">[8]</a>.</p>#<p>Statistical significance of differences in SN in comparison with CA 19-9 alone, method descrived in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094928#pone.0094928-Hawass1" target="_blank">[18]</a>.</p><p>SN/SP/AUC – sensitivity/specificity/area under ROC curve.</p><p>MTD 1–12 – months to diagnosis 1–12, samples collected <12 months prior to diagnosis.</p><p>MTD 12–35 – months to diagnosis 12–35, samples collected 12 to 35 months prior to diagnosis.</p

Reproducibility of measurements of representative biomarkers in duplicate PLCO samples.

<p>Reproducibility of measurements of representative biomarkers in duplicate PLCO samples.</p