What Can We Learn from FGF-2 Isoform-Specific Mouse Mutants? Differential Insights into FGF-2 Physiology In Vivo

Fibroblast growth factor 2 (FGF-2), ubiquitously expressed in humans and mice, is functionally involved in cell growth, migration and maturation in vitro and in vivo. Based on the same mRNA, an 18-kilo Dalton (kDa) FGF-2 isoform named FGF-2 low molecular weight (FGF-2LMW) isoform is translated in humans and rodents. Additionally, two larger isoforms weighing 21 and 22 kDa also exist, summarized as the FGF-2 high molecular weight (FGF-2HMW) isoform. Meanwhile, the human FGF-2HMW comprises a 22, 23, 24 and 34 kDa protein. Independent studies verified a specific intracellular localization, mode of action and tissue-specific spatiotemporal expression of the FGF-2 isoforms, increasing the complexity of their physiological and pathophysiological roles. In order to analyze their spectrum of effects, FGF-2LMW knock out (ko) and FGF-2HMWko mice have been generated, as well as mice specifically overexpressing either FGF-2LMW or FGF-2HMW. So far, the development and functionality of the cardiovascular system, bone formation and regeneration as well as their impact on the central nervous system including disease models of neurodegeneration, have been examined. This review provides a summary of the studies characterizing the in vivo effects modulated by the FGF-2 isoforms and, thus, offers a comprehensive overview of its actions in the aforementioned organ systems.     

Automated contact tracing: a game of big numbers in the time of COVID-19

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.     

Mathematical foundations of moral preferences

One-shot anonymous unselfishness in economic games is commonly explained by social preferences, which assume that people care about the monetary pay-offs of others. However, during the last 10 years, research has shown that different types of unselfish behaviour, including cooperation, altruism, truth-telling, altruistic punishment and trustworthiness are in fact better explained by preferences for following one''s own personal norms—internal standards about what is right or wrong in a given situation. Beyond better organizing various forms of unselfish behaviour, this moral preference hypothesis has recently also been used to increase charitable donations, simply by means of interventions that make the morality of an action salient. Here we review experimental and theoretical work dedicated to this rapidly growing field of research, and in doing so we outline mathematical foundations for moral preferences that can be used in future models to better understand selfless human actions and to adjust policies accordingly. These foundations can also be used by artificial intelligence to better navigate the complex landscape of human morality.     

Cardiac Arrhythmias and Sleep-Disordered Breathing in Patients with Heart Failure

The relationship between heart failure (HF), sleep-disordered breathing and cardiac arrhythmias is complex and poorly understood. Whereas the frequency of predominantly obstructive sleep apnea in HF patients is low and similar or moderately higher to that observed in the general population, central sleep apnea (CSA) has been observed in approximately 50% of HF patients, depending on the methods used to detect CSA and patient selection. Despite this high prevalence, it is still unclear whether CSA is merely a marker or an independent risk factor for an adverse prognosis in HF patients and whether CSA is associated with an increased risk for supraventricular as well as ventricular arrhythmias in HF patients. The current review focuses on the relationship between CSA and atrial fibrillation as the most common atrial arrhythmia in HF patients, and on the relationship between CSA and ventricular tachycardia and ventricular fibrillation as the most frequent cause of sudden cardiac death in HF patients.     

Clinical Evaluation of Extracellular ADMA Concentrations in Human Blood and Adipose Tissue

Circulating asymmetrical dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide synthesis, has been proposed as a biomarker for clinical outcome. Dimethylarginine dimethylaminohydrolase (DDAH) is the main enzyme responsible for ADMA metabolism and elimination. Adipose tissue ADMA concentrations and DDAH activity and their role in diabetes and obesity have not yet been investigated. In this study, we evaluated clinical microdialysis in combination with a sensitive analytical method (GC-MS/MS) to measure ADMA concentrations in extracellular fluid. Adipose tissue ADMA concentrations were assessed before and during an oral glucose tolerance test in lean healthy subjects and subjects with diabetes (n = 4 each), and in morbidly obese subjects before and after weight loss of 30 kg (n = 7). DDAH activity was determined in subcutaneous and visceral adipose tissue obtained during laparoscopic surgery (n = 5 paired samples). Mean interstitial ADMA concentrations did not differ between study populations (healthy 0.17 ± 0.03 μM; diabetic 0.21 ± 0.03 μM; morbidly obese 0.16 ± 0.01 and 0.17 ± 0.01 μM before and after weight loss, respectively). We did not observe any response of interstitial ADMA concentrations to the oral glucose challenge. Adipose tissue DDAH activity was negligible compared to liver tissue. Thus, adipose tissue ADMA plays a minor role in NO-dependent regulation of adipose tissue blood flow and metabolism.     

Microbial Upgrading of Acetate into Value-Added Products—Examining Microbial Diversity,Bioenergetic Constraints and Metabolic Engineering Approaches

Ecological concerns have recently led to the increasing trend to upgrade carbon contained in waste streams into valuable chemicals. One of these components is acetate. Its microbial upgrading is possible in various species, with Escherichia coli being the best-studied. Several chemicals derived from acetate have already been successfully produced in E. coli on a laboratory scale, including acetone, itaconic acid, mevalonate, and tyrosine. As acetate is a carbon source with a low energy content compared to glucose or glycerol, energy- and redox-balancing plays an important role in acetate-based growth and production. In addition to the energetic challenges, acetate has an inhibitory effect on microorganisms, reducing growth rates, and limiting product concentrations. Moreover, extensive metabolic engineering is necessary to obtain a broad range of acetate-based products. In this review, we illustrate some of the necessary energetic considerations to establish robust production processes by presenting calculations of maximum theoretical product and carbon yields. Moreover, different strategies to deal with energetic and metabolic challenges are presented. Finally, we summarize ways to alleviate acetate toxicity and give an overview of process engineering measures that enable sustainable acetate-based production of value-added chemicals.     

Chemical warfare and survival strategies in bacterial range expansions

Dispersal of species is a fundamental ecological process in the evolution and maintenance of biodiversity. Limited control over ecological parameters has hindered progress in understanding of what enables species to colonize new areas, as well as the importance of interspecies interactions. Such control is necessary to construct reliable mathematical models of ecosystems. In our work, we studied dispersal in the context of bacterial range expansions and identified the major determinants of species coexistence for a bacterial model system of three Escherichia coli strains (toxin-producing, sensitive and resistant). Genetic engineering allowed us to tune strain growth rates and to design different ecological scenarios (cyclic and hierarchical). We found that coexistence of all strains depended on three strongly interdependent factors: composition of inoculum, relative strain growth rates and effective toxin range. Robust agreement between our experiments and a thoroughly calibrated computational model enabled us to extrapolate these intricate interdependencies in terms of phenomenological biodiversity laws. Our mathematical analysis also suggested that cyclic dominance between strains is not a prerequisite for coexistence in competitive range expansions. Instead, robust three-strain coexistence required a balance between growth rates and either a reduced initial ratio of the toxin-producing strain, or a sufficiently short toxin range.     

Airflow elicits a spider's jump towards airborne prey. I. Airflow around a flying blowfly

The hunting spider Cupiennius salei uses airflow generated by flying insects for the guidance of its prey-capture jump. We investigated the velocity field of the airflow generated by a freely flying blowfly close to the flow sensors on the spider''s legs. It shows three characteristic phases (I–III). (I) When approaching, the blowfly induces an airflow signal near the spider with only little fluctuation (0.013 ± 0.006 m s⁻¹) and a strength that increases nearly exponentially with time (maximum: 0.164 ± 0.051 m s⁻¹ s.d.). The spider detects this flow while the fly is still 38.4 ± 5.6 mm away. The fluctuation of the airflow above the sensors increases linearly up to 0.037 m s⁻¹ with the fly''s altitude. Differences in the time of arrival and intensity of the fly signal at different legs probably inform the spider about the direction to the prey. (II) Phase II abruptly follows phase I with a much higher degree of fluctuation (fluctuation amplitudes: 0.114 ± 0.050 m s⁻¹). It starts when the fly is directly above the sensor and corresponds to the time-dependent flow in the wake below and behind the fly. Its onset indicates to the spider that its prey is now within reach and triggers its jump. The spider derives information on the fly''s position from the airflow characteristics, enabling it to properly time its jump. The horizontal velocity of the approaching fly is reflected by the time of arrival differences (ranging from 0.038 to 0.108 s) of the flow at different legs and the exponential velocity growth rate (16–79 s⁻¹) during phase I. (III) The air flow velocity decays again after the fly has passed the spider.