To ensure timely cytokinesis, the equatorial actomyosin contractile ring constricts at a relatively constant rate despite its progressively decreasing size. Thus, the per-unit-length constriction rate increases as ring perimeter decreases. To understand this acceleration, we monitored cortical surface and ring component dynamics during the first cytokinesis of the Caenorhabditis elegans embryo. We found that, per unit length, the amount of ring components (myosin, anillin) and the constriction rate increase with parallel exponential kinetics. Quantitative analysis of cortical flow indicated that the cortex within the ring is compressed along the axis perpendicular to the ring, and the per-unit-length rate of cortical compression increases during constriction in proportion to ring myosin. We propose that positive feedback between ring myosin and compression-driven flow of cortex into the ring drives an exponential increase in the per-unit-length amount of ring myosin to maintain a high ring constriction rate and support this proposal with an analytical mathematical model.

An integrated robotic laser and microscope system has been developed to automatically analyze individual sperm motility and energetics. The custom-designed optical system directs near-infrared laser light into an inverted microscope to create a single-point 3-D gradient laser trap at the focal spot of the microscope objective. A two-level computer structure is described that quantifies the sperm motility (in terms of swimming speed and swimming force) and energetics (measuring mid-piece membrane potential) using real-time tracking (done by the upper-level system) and fluorescent ratio imaging (done by the lower-level system). The communication between these two systems is achieved by a gigabit network. The custom-built image processing algorithm identifies the sperm swimming trajectory in real-time using phase contrast images, and then subsequently traps the sperm by automatically moving the microscope stage to relocate the sperm to the laser trap focal plane. Once the sperm is stably trapped (determined by the algorithm), the algorithm can also gradually reduce the laser power by rotating the polarizer in the laser path to measure the trapping power at which the sperm is capable of escaping the trap. To monitor the membrane potential of the mitochondria located in a sperm’s mid-piece, the sperm is treated with a ratiometrically-encoded fluorescent probe. The proposed algorithm can relocate the sperm to the center of the ratio imaging camera and the average ratio value can be measured in real-time. The three parameters, sperm escape power, sperm swimming speed and ratio values of the mid-piece membrane potential of individual sperm can be compared with respect to time. This two-level automatic system to study individual sperm motility and energetics has not only increased experimental throughput by an order of magnitude but also has allowed us to monitor sperm energetics prior to and after exposure to the laser trap. This system should have application in both the human fertility clinic and in animal husbandry.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by progressive cognitive decline. Over 200 pathogenic mutations in amyloid-β precursor protein (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2), have been implicated in AD. Yet, many rare and common variants have not been completely classified as protective or benign, risk-modifiers, or pathogenic, which is important for research on the disease mechanisms and discovery of treatment methods. The majority of these variants are missense mutations, and there is an active need for computational approaches to accurately predict their molecular consequences. AlphaMissense (AM) is a novel technology that uses population frequency data along with structural and sequential contexts from AlphaFold to predict the pathogenicity of missense mutations. Herein, we sought to evaluate the capabilities of AM on 114 variants of unknown significance (VUS), including 56 missense variants of PSEN1, 25 of APP, and 33 of PSEN2 by benchmarking its prediction against their respective Aβ isoform levels in vitro, respectively. We found that the AM scores correlated moderately well with the critical Aβ42/Aβ40 biomarker and Aβ40 levels in the transmembrane proteins compared to weaker correlations in traditional approaches, including Combined Annotation Dependent Depletion (CADD) v1.7, evolutionary model of variant effect (EVE), and Evolutionary Scale Modeling-1b (ESM-1B). Yet, there were non-significant correlations identified with Aβ42 levels in all models. Furthermore, we found that AM does not rely completely on structural contexts from AlphaFold2, as it accurately predicted the effects of known variants on residues with a low predicted local distance difference test (pLDDT) score. Additionally, based on the receiver operating characteristic-area under the curve analysis (ROC-AUC), we found that AM retained a high performance on 263 validated variants of these amyloidogenic genes, and performed the greatest compared to other models for the 114 VUS. We believe this is the first study to provide comprehensive characterization and validation of AM in comparison to the widely utilized pathogenicity scoring models for VUS involved in proteins implicated in AD.

Pathogenic missense mutations are commonly found in protein-coding regions of DNA, and often alter protein function. In the past decade, enormous experimental efforts have been undertaken through the development of functional assays, mutagenesis screenings, and computational prediction algorithms to characterize these variants. Indeed, most efforts have been focused towards identifying degrees of loss-of-function (LoF) effects on the three-dimensional structures of proteins, but gain-of-function (GoF) mutations remain poorly understood. Herein, we performed a case study of the PIEZO1 mechanosensitive ion channel protein whose GoF variants (n = 56) are implicated in hereditary xerocytosis (HX) and LoF variants (n = 6) in lymphatic dysplasia (LD), respectively. This study evaluated the abilities of AlphaMissense (AM) to decipher both mutation types, and benchmarked its performance against other algorithmic approaches, including Combined Annotation Dependent Depletion (CADD) v1.7, evolutionary model of variant effect (EVE), and Evolutionary Scale Modeling-1b (ESM-1B). We found that all approaches excelled in identifying LoF variants but were often ambiguous in their predictions for GoF PIEZO1 variants. ESM-1b was a notable exception that demonstrated balance sensitivity to both GoF and LoF variants as it likely identified certain sequential features not utilized in other approaches. Secondly, our findings suggest that GoF variants of HX do not significantly destabilize the PIEZO1 structure and are not generally identified by conventional signatures of damage from these algorithms. GoF variants are not synonymous with direct changes in changes in free energy upon mutations. Furthermore, we validated computational structure predictions of PIEZO1 against resolved cryo-EM structures, and provided biophysical data for variants located on unresolved residues of this ion channel. We formulated a weighted ensemble model that performed similarly to AM and outperformed all other traditional approaches evaluated in this study. Overall, this is the first study to directly evaluate the capabilities of pathogenicity prediction algorithms for deciphering GoF and LoF variants, and underscores the limitations present in current approaches.