Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Nouvelles stratégies d’induction pour systèmes biologiques synthétiques

Les chercheurs en biologie de synthèse programment l’ADN pour construire des systèmes biologiques capables de répondre à certaines conditions de manière prédéfinie. Cette capacité pourrait avoir un impact sur plusieurs domaines, de la médecine à la fermentation industrielle. Le traitement de signal par des circuits biologiques synthétiques est en train d’être démontré à large échelle, mais hélas la variété des signaux d’entrée capables de contrôler ces circuits est pour l’instant limitée. Ce manque de diversité est un obstacle majeur au développement de nouvelles applications car en général chaque application requiert une réponse à des signaux de nature particulière qui lui sont spécifiques. Cette thèse cherche à apporter des solutions au manque de signaux d’entrée appropriés contrôlant les circuits biologiques en développant deux nouvelles stratégies d’induction. La première stratégie vise à étendre la diversité chimique des signaux d’entrée. A l’inverse des approches existantes, qui reposent sur la modification des systèmes de détections naturels tels que les riboswitchs ou les facteurs de transcription allostériques, j’ai cherché ici à modifier directement des molécules préalablement non-détectables afin de les rendre détectables par les systèmes de détection actuels. Pour ce faire, la transformation chimique des molécules cibles est réalisée in situ grâce à l’expression de voies métaboliques synthétiques dans la cellule. Afin de pouvoir utiliser cette stratégie de manière systématique, j’ai employé la conception assistée par ordinateur et puisé dans l’ensemble des réactions biochimiques connues afin de prédire des voies de détections pour de nouvelles molécules. J’ai ensuite implémenté in vivo plusieurs prédictions qui ont permis à E. coli de détecter de nouveaux composés. Au-delà de l’intérêt de cette méthode en biotechnologie, cela montre que le métabolisme peut jouer un rôle dans le transfert d’information, en plus de son rôle dans le transfert de matière et d’énergie, ce qui soulève la question de l’utilisation potentielle de cette stratégie de détection par la nature. Un second axe présente une façon d’épargner l’utilisation d’inducteurs chimiques pour les programmes biologiques simples, et propose d’utiliser des inducteurs biologiques à la place. Lorsqu’une seule étape d’induction ou de répression de gènes est nécessaire, comme c’est le cas en fermentation industrielle, je propose de remplacer la coûteuse étape d’induction chimique par l’infection simultanée de toutes les cellules d’une population par des particules virales capables d’injecter en temps réel l’ensemble des informations nécessaires pour déclencher l’activité biologique recherchée. A des fins de fermentation, j’ai développé des particules virales modifiées qui reprogramment dynamiquement le métabolisme d’une large population de bactérie au moment opportun et les forcent à produire des molécules à haute valeur ajoutée.Synthetic biologists program DNA with the aim of building biological systems that react under certain conditions in a predefined way. This ability could have impact in several fields, from medicine to industrial fermentation. While the scalability of synthetic biological circuits in terms of signal processing in now almost demonstrated, the variety of input signals for these circuits is limited. Because each application typically requires a circuit to react to case-specific molecules, the lack of input diversity is a major obstacle to the development of new applications. Two axis are developed over the course of this thesis to try to address input-related problems. The main axis consists in a new strategy aiming at systematically and immediately increasing the chemical diversity of inputs for synthetic circuits. Current approaches to expand the number of potential inputs focus on re-engineering sensing systems such as riboswitches or allosteric transcription factors to make them react to previously non-detectable molecules. On the contrary, here we developed a method to transform the non-detectable molecules themselves into molecules for which sensing systems already exist. These chemical transformations are realized in situ by expressing synthetic metabolic pathways in the cell. In order to systematize this strategy, we leveraged computer-aided design to predict ways of detecting new molecules by digging into all known biochemical reactions. We then implemented several predictions in vivo that successfully enabled E. coli to detect new chemicals. Aside from the interest of the method for biotechnological applications, this shows that in addition to transferring matter and energy, metabolism can also play a role in transferring information, raising the question of potential occurrences of this sensing strategy in nature. A second axis introduce a way to exempt simple programs from the need for a chemical input, and explore the use of a biological input instead. In situations where a single timely induction or repression of multiple genes is required, such as in industrial fermentation processes, we propose to replace expensive chemical induction by simultaneous infection of all the members of a growing population of cells with viral particles inputting in real-time all the necessary information for the task at hand. In the context of fermentation, we developed engineered viral particles that can dynamically reprogram the metabolism of a large population of bacteria at the optimal stage of growth and force them to produce value-added chemicals

Sensing new chemicals with bacterial transcription factors

Bacteria rely on allosteric transcription factors (aTFs) to sense a wide range of chemicals. The variety of effectors has contributed in making aTFs the most used input system in synthetic biological circuits. Considering their enabling role in biotechnology, an important question concerns the size of the chemical space that can potentially be detected by these biosensors. From digging into the ever changing repertoire of natural regulatory circuits, to advances in aTF engineering, we review here different strategies that are pushing the boundaries of this chemical space. We also review natural and synthetic cases of indirect sensing, where aTFs work in combination with metabolism to enable detection of new molecules

Expanding Biosensing Abilities through Computer-Aided Design of Metabolic Pathways

Detection of chemical signals is critical for cells in nature as well as in synthetic biology, where they serve as inputs for designer circuits. Important progress has been made in the design of signal processing circuits triggering complex biological behaviors, but the range of small molecules recognized by sensors as inputs is limited. The ability to detect new molecules will increase the number of synthetic biology applications, but direct engineering of tailor-made sensors takes time. Here we describe a way to immediately expand the range of biologically detectable molecules by systematically designing metabolic pathways that transform nondetectable molecules into molecules for which sensors already exist. We leveraged computer-aided design to predict such sensing-enabling metabolic pathways, and we built several new whole-cell biosensors for molecules such as cocaine, parathion, hippuric acid, and nitroglycerin

A Sense of Balance: Experimental Investigation and Modeling of a Malonyl-CoA Sensor in Escherichia coli.

Production of value-added chemicals in microorganisms is regarded as a viable alternative to chemical synthesis. In the past decade, several engineered pathways producing such chemicals, including plant secondary metabolites in microorganisms have been reported; upscaling their production yields, however, was often challenging. Here, we analyze a modular device designed for sensing malonyl-CoA, a common precursor for both fatty acid and flavonoid biosynthesis. The sensor can be used either for high-throughput pathway screening in synthetic biology applications or for introducing a feedback circuit to regulate production of the desired chemical. Here, we used the sensor to compare the performance of several predicted malonyl-CoA-producing pathways, and validated the utility of malonyl-CoA reductase and malonate-CoA transferase for malonyl-CoA biosynthesis. We generated a second-order dynamic linear model describing the relation of the fluorescence generated by the sensor to the biomass of the host cell representing a filter/amplifier with a gain that correlates with the level of induction. We found the time constants describing filter dynamics to be independent of the level of induction but distinctively clustered for each of the production pathways, indicating the robustness of the sensor. Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis. In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels. The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.. The authors acknowledge funding provided by ToulouseWhite Biotech (TWB) and the GIP GENOPOLE

SensiPath: computer-aided design of Sensing-enabling metabolic Pathways

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design formedical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr.SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications

Principles of Systems Biology, No. 11

This month: AI that learns patterns and facts, new protein-RNA and protein-protein relationships, engineering signaling and metabolism, and more variants of Cas9