Predicting the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes

For many applications in microbial synthetic biology, optimizing a desired function requires careful tuning of the degree to which various genes are expressed. One challenge for predicting such effects or interpreting typical characterization experiments is that in bacteria such as E. coli, genome copy number varies widely across different phases and rates of growth, which also impacts how and when genes are expressed from different loci. While such phenomena are relatively well-understood at a mechanistic level, our quantitative understanding of such processes is essentially limited to ideal exponential growth. In contrast, common experimental phenomena such as growth on heterogeneous media, metabolic adaptation, and oxygen restriction all cause substantial deviations from ideal exponential growth, particularly as cultures approach the higher densities at which industrial biomanufacturing and even routine screening experiments are conducted. To meet the need for predicting and explaining how gene dosage impacts cellular functions outside of exponential growth, we here report a novel modeling strategy that leverages agent-based simulation and high performance computing to robustly predict the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes. We show that by feeding routine experimental data, such as optical density time series, into our heterogeneous multiphasic growth simulator, we can predict genomic DNA distributions over a range of nonexponential growth conditions. This modeling strategy provides an important advance in the ability of synthetic biologists to evaluate the role of genomic DNA content and heterogeneity in affecting the performance of existing or engineered microbial functions

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Regulation of Bacterial Gene Expression by Protease-Alleviated Spatial Sequestration (PASS)

In natural microbial systems, conditional spatial sequestration of transcription factors enables cells to respond rapidly to changes in their environment or intracellular state by releasing presynthesized regulatory proteins. Although such a mechanism may be useful for engineering synthetic biology technologies ranging from cell-based biosensors to biosynthetic platforms, to date it remains unknown how or whether such conditional spatial sequestration may be engineered. In particular, based upon seemingly contradictory reports in the literature, it is not clear whether subcellular spatial localization of a transcription factor within the cytoplasm is sufficient to preclude regulation of cognate promoters on plasmid-borne or chromosomal loci. Here, we describe a modular, orthogonal platform for investigating and implementing this mechanism using protease-alleviated spatial sequestration (PASS). In this system, expression of an exogenous protease mediates the proteolytic release of engineered transcriptional regulators from the inner face of the <i>Escherichia coli</i> cytoplasmic membrane. We demonstrate that PASS mediates robust, conditional regulation of either transcriptional repression, <i>via</i> tetR, or transcriptional activation, by the λ phage CI protein. This work provides new insights into a biologically important facet of microbial gene expression and establishes a new strategy for engineering conditional transcriptional regulation for the microbial synthetic biology toolbox

Predicting the Dynamics and Heterogeneity of Genomic DNA Content within Bacterial Populations across Variable Growth Regimes

For many applications in microbial synthetic biology, optimizing a desired function requires careful tuning of the degree to which various genes are expressed. One challenge for predicting such effects or interpreting typical characterization experiments is that in bacteria such as <i>E. coli</i>, genome copy number varies widely across different phases and rates of growth, which also impacts how and when genes are expressed from different loci. While such phenomena are relatively well-understood at a mechanistic level, our quantitative understanding of such processes is essentially limited to ideal exponential growth. In contrast, common experimental phenomena such as growth on heterogeneous media, metabolic adaptation, and oxygen restriction all cause substantial deviations from ideal exponential growth, particularly as cultures approach the higher densities at which industrial biomanufacturing and even routine screening experiments are conducted. To meet the need for predicting and explaining how gene dosage impacts cellular functions outside of exponential growth, we here report a novel modeling strategy that leverages agent-based simulation and high performance computing to robustly predict the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes. We show that by feeding routine experimental data, such as optical density time series, into our heterogeneous multiphasic growth simulator, we can predict genomic DNA distributions over a range of nonexponential growth conditions. This modeling strategy provides an important advance in the ability of synthetic biologists to evaluate the role of genomic DNA content and heterogeneity in affecting the performance of existing or engineered microbial functions

The Medicago genome provides insight into the evolution of rhizobial symbioses

Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic nitrogen fixation(1). Legumes comprise several evolutionary lineages derived from a common ancestor 60 million years ago (Myr ago). Papilionoids are the largest clade, dating nearly to the origin of legumes and containing most cultivated species(2). Medicago truncatula is a long-established model for the study of legume biology. Here we describe the draft sequence of the M. truncatula euchromatin based on a recently completed BAC assembly supplemented with Illumina shotgun sequence, together capturing similar to 94% of all M. truncatula genes. A whole-genome duplication (WGD) approximately 58 Myr ago had a major role in shaping the M. truncatula genome and thereby contributed to the evolution of endosymbiotic nitrogen fixation. Subsequent to the WGD, the M. truncatula genome experienced higher levels of rearrangement than two other sequenced legumes, Glycine max and Lotus japonicus. M. truncatula is a close relative of alfalfa (Medicago sativa), a widely cultivated crop with limited genomics tools and complex autotetraploid genetics. As such, the M. truncatula genome sequence provides significant opportunities to expand alfalfa's genomic toolbox

Clinical utility of exercise training in chronic systolic heart failure

The volume of literature attesting to the clinical benefits of exercise training in patients with stable chronic heart failure (CHF) is substantial. Training can improve symptoms and exercise capacity, as well as reducing morbidity, mortality, and rates of emergency hospitalization. These benefits are apparent in all patients with stable CHF, irrespective of age or sex, or the etiology or severity of heart failure. Training regimens for patients with stable, systolic CHF should form part of a comprehensive heart-failure support effort and are best delivered using supervised in-hospital exercise combined with some training at home or in a group setting in community centers. In this Review, the modes and intensity of exercise training, selection of patients, duration of training effects, and other clinical guidance for using this treatment option are discussed

Containment measures

OBSOLETE (project finished) - Description of containment measures during COVID'19 lockdown, in the context of SIlent Cities project. Please request access to Silent Cities if neede

Archived - General Information (DO NOT USE)

DO NOT USE - The goal of this component was to document the data collection process of the Silent Cities Dataset. This component is just left for archive