Obi, C. | Search bioRxiv

bioRxiv2026

Saponin Chemistry Controls Anionic Lipid Tolerance and Divalent Metal Ion Responses in Magnetically Alignable Bicelles

Saponin-phospholipid bicelles have emerged as promising membrane-mimetic systems for anisotropy-based NMR studies, but their utility depends on their ability to accommodate physiologically relevant lipid compositions and ionic environments. Here, we systematically investigated how saponin chemistry governs three key properties of magnetically alignable bicelles: tolerance to anionic lipid incorporation, responsiveness to lanthanide-induced alignment reorientation, and stability in the presence of divalent metal ions. Using 31P NMR spectroscopy as a sensitive probe of phase behavior and alignment, we compared bicelles formed with glycyrrhizic acid (GA), hederacoside C (HC), and crude Quillaja saponins (CQS). While all saponins effectively solubilized the anionic lipid DMPG, only HC supported magnetically aligned bicelles with high anionic lipid fractions (up to 70%), whereas GA was limited to low incorporation (10%). Both GA- and HC-based bicelles underwent lanthanide-induced alignment flipping, though with significant spectral broadening and intermediate alignment states indicative of increased heterogeneity. Divalent cation effects were strongly ion- and saponin-dependent; HC bicelles were robust to the presence of Ca2+ but were destabilized by Mg2+, while GA bicelles were disrupted by both ions at low concentrations. Together, these results demonstrate that saponin identity critically determines bicelle compatibility with charged lipids and ionic conditions, establishing design principles for tailoring saponin-based bicelles as versatile, biomimetic alignment media for membrane-protein structural studies.

biophysics↗

bioRxiv2018

Unsupervised Extraction of Epidemic Syndromes from Participatory Influenza Surveillance Self-reported Symptoms

Seasonal influenza surveillance is usually carried out by sentinel general practitioners who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This practice for surveillance is generally affected by two main issues: i) reports are usually released with a lag of about one week or more, ii) the definition of a case of influenza-like illness based on patients symptoms varies from one surveillance system to the other, i.e. from one country to the other. The availability of novel data streams for disease surveillance can alleviate these issues; in this paper, we employed data from Influenzanet, a participatory web-based surveillance project which collects symptoms directly from the general population in real time. We developed an unsupervised probabilistic framework that combines time series analysis of symptoms counts and performs an algorithmic detection of groups of symptoms, hereafter called syndromes. Symptoms counts were collected through the participatory web-based surveillance platforms of a consortium called Influenzanet which is found to correlate with Influenza-like illness incidence as detected by sentinel doctors. Our aim is to suggest how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. We evaluated the performance of our framework by showing that the temporal trends of the detected syndromes closely follow the ILI incidence as reported by the traditional surveillance, and consist of combinations of symptoms that are compatible with the ILI definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.\n\nAuthor summaryThis study suggests how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.

epidemiology↗