Publications

During eukaryotic cell division, microtubules connect to chromosomes by attaching to the kinetochore via the NDC80 complex (NDC80c). The regulation of kinetochore microtubule (KMT) detachment is crucial for correcting mitotic errors. Here, we investigate the mechanism of KMT detachment by combining photoconversion measurements of KMT detachment rate, FLIM-FRET measurements of NCD80c/KMT binding, and mathematical modeling. Our results support a dual detachment mechanism in which KMTs detach from kinetochores when either 1) all NDC80c spontaneously unbind from the KMT or 2) following KMT catastrophe. We identify kinetochore components that selectively impact these two mechanisms and show that the affinity of NDC80c for KMTs is reduced at low-tension, non-bioriented kinetochores due to centromere-localized Aurora B phosphorylating the NDC80c, resulting in an elevated detachment rate for the associated KMTs. Taken together, this work leads to a biophysical model for the molecular basis of KMT detachments and their regulation during mitotic error correction.

The 3D pose estimation problem – aligning pairs of noisy 3D point clouds – is a problem with a wide variety of real- world applications. Here we focus on the use of quaternion- based neural network approaches to this problem and ap- parent anomalies that have arisen in previous efforts to re- solve them. In addressing these anomalies, we draw heav- ily from the extensive literature on closed-form methods to solve this problem. We suggest that the major concerns that have been put forward could be resolved using a sim- ple multi-valued training target derived from rigorous theo- retical properties of the rotation-to-quaternion map of Bar- Itzhack. This multi-valued training target is then demon- strated to have good performance for both simulated and ModelNet targets. We provide a comprehensive theoretical context, using the quaternion adjugate, to confirm and es- tablish the necessity of replacing single-valued quaternion functions by quaternions treated in the extended domain of multiple-charted manifolds.

BK virus (BKV; human polyomavirus 1) infections are asymptomatic in most individuals, and the virus persists throughout life without harm. However, BKV is a threat to transplant patients and those with immunosuppressive disorders. Under these circumstances, the virus can replicate robustly in proximal tubule epithelial cells (PT). Cultured renal proximal tubule epithelial cells (RPTE) are permissive to BKV and have been used extensively to characterize different aspects of BKV infection. Recently, lines of hTERT-immortalized RPTE have become available, and preliminary studies indicate they support BKV infection as well. Our results indicate that BKV infection leads to a similar response in primary and immortalized RPTE. In addition, we examined the patterns of global gene expression of primary and immortalized RPTE and compared them with uncultured PT freshly dissociated from human kidney. As expected, PT isolated from the healthy kidney express a number of differentiation-specific genes that are associated with kidney function. However, the expression of most of these genes is absent or repressed in cultured RPTE. Rather, cultured RPTE exhibit a gene expression profile indicative of a stressed or injured kidney. Inoculation of cultured RPTE with BKV results in the suppression of many genes associated with kidney stress. In summary, this study demonstrated similar global gene expression patterns and responses to BKV infection between primary and immortalized RPTE. Moreover, results from bulk transcriptome sequencing (RNA-seq) and SCT experiments revealed distinct transcriptomic signatures representing cell injury and stress in primary RPTE in contrast to the uncultured, freshly dissociated PT from human kidney.

Active colloidal systems with nonequilibrium self-organization constitute a long-standing, challenging area in material sciences and biology. To understand how hydrodynamic flow may be used to actively control self-assembly of Janus particles (JPs), we developed a model for the many-body hydrodynamics of amphiphilic JPs suspended in a viscous fluid with imposed far-field background flows [Fu et al., J. Fluid Mech. 941, A41 (2022)]. In this paper we alter the hydrophobic distribution on the JP-solvent interface to investigate the hydrodynamics that underlies the various morphologies and rheological properties of the JP assembly in the suspension. We find that JPs assemble into unilamellar, multilamellar, and striated structures. To introduce dynamics, we include a planar linear shear flow and a steady Taylor-Green mixing flow and measure the collective dynamics of JP particles in terms of their (a) free energy from the hydrophobic interactions between the JPs, (b) order parameter for the ordering of JPs in terms of alignment of their directors, and (c) strain parameter that captures the deformation in the assembly. We characterize the effective material properties of the JP structures and find that the unilamellar structure increases orientation order under shear flow, the multilamellar structure behaves as a shear thinning fluid, and the striated structure possesses a yield stress. These numerical results provide insights into dynamic control of nonequilibrium active biological systems with similar self-organization.

DNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics in humans following an acute perturbation, such as infection. We characterized the temporal trajectory of blood epigenetic remodeling in 133 participants in a prospective study of young adults before, during, and after asymptomatic and mildly symptomatic SARS-CoV-2 infection. The differential methylation caused by asymptomatic or mildly symptomatic infections was indistinguishable. While differential gene expression largely returned to baseline levels after the virus became undetectable, some differentially methylated sites persisted for months of follow-up, with a pattern resembling autoimmune or inflammatory disease. We leveraged these responses to construct methylation-based machine learning models that distinguished samples from pre-, during-, and postinfection time periods, and quantitatively predicted the time since infection. The clinical trajectory in the young adults and in a diverse cohort with more severe outcomes was predicted by the similarity of methylation before or early after SARS-CoV-2 infection to the model-defined postinfection state. Unlike the phenomenon of trained immunity, the postacute SARS-CoV-2 epigenetic landscape we identify is antiprotective.

Life is a nonequilibrium phenomenon: Metabolism provides a continuous supply of energy that drives nearly all cellular processes. However, very little is known about how much energy different cellular processes use, i.e., their energetic costs. The most direct experimental measurements of these costs involve modulating the activity of cellular processes and determining the resulting changes in energetic fluxes. In this review, we present a flux balance framework to aid in the design and interpretation of such experiments and discuss the challenges associated with measuring the relevant metabolic fluxes. We then describe selected techniques that enable measurement of these fluxes. Finally, we review prior experimental and theoretical work that has employed techniques from biochemistry and nonequilibrium physics to determine the energetic costs of cellular processes.

For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don’t rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ~200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.

Single-molecule force spectroscopy (smFS) is a powerful approach to studying molecular self-organization. However, the coupling of the molecule with the ever-present experimental device introduces artifacts, that complicate the interpretation of these experiments. Performing statistical inference to learn hidden molecular properties is challenging because these measurements produce non-Markovian time series, and even minimal models lead to intractable likelihoods. To overcome these challenges, we developed a computational framework built on novel statistical methods called simulation-based inference (SBI). SBI enabled us to directly estimate the Bayesian posterior, and extract reduced quantitative models from smFS, by encoding a mechanistic model into a simulator in combination with probabilistic deep learning. Using synthetic data, we could systematically disentangle the measurement of hidden molecular properties from experimental artifacts. The integration of physical models with machine-learning density estimation is general, transparent, easy to use, and broadly applicable to other types of biophysical experiments.

A major challenge in ART is to select high-quality oocytes and embryos. The metabolism of oocytes and embryos has long been linked to their viability, suggesting the potential utility of metabolic measurements to aid in selection. Here, we review recent work on noninvasive metabolic imaging of cumulus cells, oocytes, and embryos. We focus our discussion on fluorescence lifetime imaging microscopy (FLIM) of the autofluorescent coenzymes NAD(P)H and flavine adenine dinucleotide (FAD+), which play central roles in many metabolic pathways. FLIM measurements provide quantitative information on NAD(P)H and FAD+ concentrations and engagement with enzymes, leading to a robust means of characterizing the metabolic state of cells. We argue that FLIM is a promising approach to aid in oocyte and embryo selection.

Living systems are intrinsically nonequilibrium: They use metabolically derived chemical energy to power their emergent dynamics and self-organization. A crucial driver of these dynamics is the cellular cytoskeleton, a defining example of an active material where the energy injected by molecular motors cascades across length scales, allowing the material to break the constraints of thermodynamic equilibrium and display emergent nonequilibrium dynamics only possible due to the constant influx of energy. Notwithstanding recent experimental advances in the use of local probes to quantify entropy production and the breaking of detailed balance, little is known about the energetics of active materials or how energy propagates from the molecular to emergent length scales. Here, we use a recently developed picowatt calorimeter to experimentally measure the energetics of an active microtubule gel that displays emergent large-scale flows. We find that only approximately one-billionth of the system’s total energy consumption contributes to these emergent flows. We develop a chemical kinetics model that quantitatively captures how the system’s total thermal dissipation varies with ATP and microtubule concentrations but that breaks down at high motor concentration, signaling an interference between motors. Finally, we estimate how energy losses accumulate across scales. Taken together, these results highlight energetic efficiency as a key consideration for the engineering of active materials and are a powerful step toward developing a nonequilibrium thermodynamics of living systems.