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Biology subjects

Closser, M.

Publications and source records attributed to Closser, M..

4 recordsLinked to original sources

The biophysical mechanism of mitochondrial pearling

Mitochondrial networks exhibit remarkable dynamics that are driven in part by fission and fusion events. However, there are other reorganizations of the network that do not involve fission and fusion. One such exception is the elusive, "beads-on-a-string" morphological transition of mitochondria. During such transitions, the cylindrical tubes of the mitochondrial membrane transiently undergo shape changes to a string of "pearls" connected along thin tubes. These dynamics have been anecdotally observed in many contexts and given disparate explanations. Here we unify these observations by proposing a common underlying mechanism based on the biophysical properties of tubular fluid membranes for which it is known that, under particular regimes of tension and pressure, membranes reach an instability and undergo a shape transition to a string of connected pearls. First, we use high-speed light-sheet microscopy to show that transient, short-lived pearling events occur spontaneously in the mitochondrial network in every cell type we have examined, including primary fibroblasts, T-cells, neurons, and budding yeast. We present evidence that transient mitochondrial pearling occurs during important biological events, particularly during T cell activation, neuronal firing, and replicative senescence. Using our high-temporal resolution data, we identify two distinct categories of spontaneous pearling, i) internal pressure-driven pearling generated by ionic flux, and ii) external tension-driven pearling generated by the cytoskeleton. By applying live-cell STED and FIB-SEM imaging we document the structural reorganization of inner cristae membranes during mitochondrial pearling and the role of the MICOS complex in regulating the frequency of pearling events. We then establish numerous methods for inducing pearling, including the ability to induce these dynamics with single mitochondrion precision. These methods include ionophores, channel activators, osmotic shock, detergents, laser stimulation, membrane intercalating molecules, chemical fixation, and micro-needle force. These disparate inducers establish three main physical causes of pearling, i) ionic flux producing internal osmotic pressure, ii) membrane packing lowering bending elasticity, and iii) external mechanical force increasing membrane tension. Pearling dynamics thereby reveal a fundamental biophysical facet of mitochondrial biology. We suggest that pearling should take its place beside fission and fusion as a key process of mitochondrial dynamics, with implications for physiology, disease, and aging.

biophysics↗

Ranking Reprogramming Factors for Directed Differentiation

Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. We examine the success rate of methods and data for directed differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBSeq, AME, DREME, HOMER, KMAC, diffTF, and DeepAccess) to correctly discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that utilize gene expression, biological networks, and chromatin accessibility data to identify eight sets of known reprogramming factors and comprehensively test parameter and pre-processing of input data to optimize performance of these methods. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top 10 candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF are more likely to consistently correctly rank the significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and DeepAccess are optimal methods for transcription factor recovery and ranking which will allow for systematic prioritization of transcription factor candidates to aid in the design of novel reprogramming protocols.

systems biology↗

General and cell-type-specific aspects of the motor neuron maturation transcriptional program

Building a nervous system is a protracted process that starts with the specification of individual neuron types and ends with the formation of mature neural circuits. The molecular mechanisms that regulate the temporal progression of maturation in individual cell types remain poorly understood. In this work, we have mapped the gene expression and chromatin accessibility changes in mouse spinal motor neurons throughout their lifetimes. We found that both motor neuron gene expression and putative regulatory elements are dynamic during the first three weeks of postnatal life, when motor circuits are maturing. Genes that are up-regulated during this time contribute to adult motor neuron diversity and function. Almost all of the chromatin regions that gain accessibility during maturation are motor neuron specific, yet a majority of the transcription factor binding motifs enriched in these regions are shared with other mature neurons. Collectively, these findings suggest that a core transcriptional program operates in a context-dependent manner to access cell-type-specific cis-regulatory systems associated with maturation genes. Discovery of general principles governing neuronal maturation might inform methods for transcriptional reprogramming of neuronal age and for improved modelling of age-related neurodegenerative diseases.

developmental biology↗

High resolution discovery of chromatin interactions

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a method for the genome-wide de novo discovery of chromatin interactions. Existing computational methods typically fail to detect weak or dynamic interactions because they use a peak-calling step that ignores paired-end linkage information. We have developed a novel computational method called Chromatin Interaction Discovery (CID) to overcome this limitation with an unbiased clustering approach for interaction discovery. CID outperforms existing chromatin interaction detection methods with improved sensitivity, replicate consistency, and concordance with other chromatin interaction datasets. In addition, CID also outperforms other methods in discovering chromatin interactions from HiChIP data. We expect that the CID method will be valuable in characterizing 3D chromatin interactions and in understanding the functional consequences of disease-associated distal genetic variations.

bioinformatics↗