Dissipation and energy propagation across scales in an active cytoskeletal material.

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.

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.

Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps-including biophysically interpretable models-from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time.

Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium Escherichia coli, for ≈65% of promoters we remain ignorant of their regulation. Until we crack this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method, Reg-Seq, that links massively parallel reporter assays with mass spectrometry to produce a base pair resolution dissection of more than a E. coli promoters in 12 growth conditions. We demonstrate that the method recapitulates known regulatory information. Then, we examine regulatory architectures for more than 80 promoters which previously had no known regulatory information. In many cases, we also identify which transcription factors mediate their regulation. This method clears a path for highly multiplexed investigations of the regulatory genome of model organisms, with the potential of moving to an array of microbes of ecological and medical relevance.

Mapping DNA sequence to transcription factor binding energy in vivo.

Despite the central importance of transcriptional regulation in biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to decipher the biophysical mechanisms of transcriptional regulation in living cells and determine the energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for dissecting transcriptional regulatory sequences using in vivo methods (massively parallel reporter assays) to formulate quantitative models that map a transcription factor binding site's DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 kBT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factor's sequence specificity.

Systematic approach for dissecting the molecular mechanisms of transcriptional regulation in bacteria.

Gene regulation is one of the most ubiquitous processes in biology. However, while the catalog of bacterial genomes continues to expand rapidly, we remain ignorant about how almost all of the genes in these genomes are regulated. At present, characterizing the molecular mechanisms by which individual regulatory sequences operate requires focused efforts using low-throughput methods. Here, we take a first step toward multipromoter dissection and show how a combination of massively parallel reporter assays, mass spectrometry, and information-theoretic modeling can be used to dissect multiple bacterial promoters in a systematic way. We show this approach on both well-studied and previously uncharacterized promoters in the enteric bacterium Escherichia coli In all cases, we recover nucleotide-resolution models of promoter mechanism. For some promoters, including previously unannotated ones, the approach allowed us to further extract quantitative biophysical models describing input-output relationships. Given the generality of the approach presented here, it opens up the possibility of quantitatively dissecting the mechanisms of promoter function in E. coli and a wide range of other bacteria.

Distribution of Crenosoma vulpis and Eucoleus aerophilus in the lung of free-ranging red foxes (Vulpes vulpes).

Crenosoma vulpis and Eucoleus aerophilus are nematode parasites that can cause verminous pneumonia in wild carnivores. There is a paucity of information regarding the distribution of parasites in the lungs and the relationship between histopathological and parasitological diagnoses in naturally infected foxes. The objectives of this study were: first, to study the lobar and airway distribution of C. vulpis and E. aerophilus in wild red foxes and second, to investigate the relationship between fecal and histopathological diagnoses. Samples from 6 sites of the lung and fecal contents were obtained from 51 wild foxes in Prince Edward Island. By fecal examination, 78.4% of wild foxes tested positive for C. vulpis and 68.6% for E. aerophilus. In contrast, 66.6% and 49% of foxes had histopathological evidence of C. vulpis and E. aerophilus in the lungs, respectively. Anatomically, C. vulpis was observed in the small bronchi and bronchioles of all pulmonary lobes whereas E. aerophilus was restricted to the large bronchi and the caudal lobes. Affected airways exhibited severe epithelial glandular hyperplasia and bronchiolar mucous metaplasia. It was concluded that C. vulpis is widely distributed in airways of all pulmonary lobes, whereas E. aerophilus is mainly restricted to the bronchi of caudal lobes. Also, this study showed that histological examination of lung underestimates the infection with E. aerophilus.

A simple mathematical model to aid quantification of electrophoresis gels by image analysis.

In many scientific disciplines, measurements are taken from films that have been exposed to energetic sources. Examples include radiographs where the source is an X-ray tube, autoradiography where the source is a radioactive isotope and electrophoresis gels where the source is an enhanced chemiluminescence reaction. In these situations it is of interest to quantify the darkening of the film and compute the strength of the source which in the cases of autoradiography and electrophoresis can be used to compute unknown concentrations of biochemicals. We developed a simple mathematical model of the darkening of films in radiography, autoradiography and electrophoresis bands disclosed by enhanced chemiluminescence, and present formulae to calculate the strength of the source from measurement of film blackening by image analysis. A simple model is used in two examples to predict blackening of film exposed to electromagnetic radiation. This blackening is measured by image analysis. Results show reasonable agreement between predictions of the model and blackening of film for the examples chosen. This model is proposed as an aid to quantification of electrophoresis gels.