Teaching the principle of biological optimization

The lactate dehydrogenase (LDH) isoenzyme spectrum enables optimally controlling T cell glycolysis and differentiation

Isoenzyme divergence is a prevalent mechanism governing tissue-specific and developmental stage-specific metabolism in mammals. The lactate dehydrogenase (LDH) isoenzyme spectrum reflects the tissue-specific metabolic status. We found that three tetrameric isoenzymes composed of LDHA and LDHB (LDH-3/4/5) comprise the LDH spectrum in T cells. Genetically deleting LDHA or LDHB altered the isoenzyme spectrum by removing all heterotetramers and leaving T cells with LDH-1 (the homotetramer of LDHB) or LDH-5 (the homotetramer of LDHA), respectively. Accordingly, deleting LDHA suppressed glycolysis, cell proliferation, and differentiation. Unexpectedly, deleting LDHB enhanced glycolysis but suppressed T cell differentiation, indicating that an optimal zone of glycolytic activity is required to maintain cell fitness. Mechanistically, the LDH isoenzyme spectrum imposed by LDHA and LDHB is necessary to optimize glycolysis to maintain a balanced nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide hydrogen pool. Our results suggest that the LDH isoenzyme spectrum enables "Goldilocks levels" of glycolytic and redox activity to control T cell differentiation.

Maximization of growth and lipid production of a toxic isolate of Anabaena circinalis by optimization of various parameters with mathematical modeling and computational validation

Toxic cyanobacterial blooms are recurrent for few decades throughout the globe, due to climate change, atmospheric warming and various anthropogenic activities with severe impacts of potential toxins on various ecosystems finally affecting the entire environment. These cyanobacteria are merely unexplored regarding their biochemical components except toxins. Variable influences and interactions of different factors including nitrogen, carbon, and availability of light are well known to crucially regulate cyanobacterial growth and metabolism. Thus, current research work is motivated for the evaluation and optimization of the effects of the aforementioned vital factors for improvement of biomass and lipid production of a freshwater, toxic strain of Anabaena circinalis. The modelling and optimization of factors such as nitrogen, light intensity and bicarbonate concentration (source of carbon) to maximize growth and lipid production were based on 20 design point experiments by Response Surface Methodology (RSM) and optimized values were further improved and validated by Particle Swarm Optimization (PSO) algorithm. The maximum optima were obtained 1.829 g L^-1 and 39.64 % for biomass production and lipid content respectively from PSO optimization with two different sets of optimal values of factors. It shows 0.44 % and 2.77 % higher values of responses than that of RSM optimization. These asynchronous findings pioneered the enhanced lipid accumulation as well as the growth of a toxic cyanobacterium by optimizing interaction effects of culture conditions through various statistical and computational approaches.

The Quiet Embryo Hypothesis: 20 years on

This article revisits the hypothesis, proposed in 2002, that the successful development of oocytes and preimplantation mammalian embryos is associated with a metabolism which is “quiet” rather than “active”, within limits which had yet to be defined. A distinction was drawn between Functional Quietness, Loss of quietness in response to stress and Inter-individual differences in embryo metabolism and here we document applications of the hypothesis to other areas of reproductive biology. In order to encompass the requirement for “limits” and replace the simple distinction between “quiet” and “active”, evidence is presented which led to a re-working of the hypothesis by proposing the existence of an optimal range of metabolic activity, termed a “Goldilocks zone”, within which oocytes and embryos with maximum developmental potential will be located. General and specific mechanisms which may underlie the Goldilocks phenomenon are proposed and the added value that may be derived by expressing data on individual embryos as distributions rather than mean values is emphasised especially in the context of the response of early embryos to stress and to the concept of the Developmental Origins of Health and Disease. The article concludes with a cautionary note that being “quietly efficient” may not always ensure optimal embryo survival.

Going to extremes: the Goldilocks/Lagom principle and data distribution

Numerical data in biology and medicine are commonly presented as mean or median with error or confidence limits, to the exclusion of individual values. Analysis of our own and others' data indicates that this practice risks excluding 'Goldilocks' effects in which a biological variable falls within a range between 'too much' and 'too little' with a region between where its function is 'just right'; a concept captured by the Swedish term 'Lagom'. This was confirmed by a narrative search of the literature using the PubMed database, which revealed numerous relationships of biological and clinical phenomena of the Goldilocks/Lagom form including quantitative and qualitative examples from the health and social sciences. Some possible mechanisms underlying these phenomena are considered. We conclude that retrospective analysis of existing data will most likely reveal a vast number of such distributions to the benefit of medical understanding and clinical care and that a transparent approach of presenting each value within a dataset individually should be adopted to ensure a more complete evaluation of research studies in future.

Biological optimization, the Goldilocks principle, and how much is lagom in the preimplantation embryo

The quiet embryo hypothesis postulates that early embryo viability is associated with a relatively low metabolism (Leese, 2002 BioEssays 24: 845-849). This proposal is re-visited here using retrospective and prospective data on the metabolic activity and kinetics of preimplantation development alongside the concept that an optimal range of such indices and of energetic efficiency influences embryogenesis. It is concluded that these considerations may be rationalized by proposing the existence of a "Goldilocks zone," or as it is known in Sweden, of lagom-meaning "just the right amount"-within which embryos with maximum developmental potential can be categorized. Mol. Reprod. Dev. 83: 748-754, 2016 © 2016 Wiley Periodicals, Inc.

Economical Speed and Energetically Optimal Transition Speed Evaluated by Gross and Net Oxygen Cost of Transport at Different Gradients

The oxygen cost of transport per unit distance (CoT; mL·kg^-1·km^-1) shows a U-shaped curve as a function of walking speed (v), which includes a particular walking speed minimizing the CoT, so called economical speed (ES). The CoT-v relationship in running is approximately linear. These distinctive walking and running CoT-v relationships give an intersection between U-shaped and linear CoT relationships, termed the energetically optimal transition speed (EOTS). This study investigated the effects of subtracting the standing oxygen cost for calculating the CoT and its relevant effects on the ES and EOTS at the level and gradient slopes (±5%) in eleven male trained athletes. The percent effects of subtracting the standing oxygen cost (4.8 ± 0.4 mL·kg^-1·min^-1) on the CoT were significantly greater as the walking speed was slower, but it was not significant at faster running speeds over 9.4 km·h^-1. The percent effect was significantly dependent on the gradient (downhill > level > uphill, P < 0.001). The net ES (level 4.09 ± 0.31, uphill 4.22 ± 0.37, and downhill 4.16 ± 0.44 km·h^-1) was approximately 20% slower than the gross ES (level 5.15 ± 0.18, uphill 5.27 ± 0.20, and downhill 5.37 ± 0.22 km·h^-1, P < 0.001). Both net and gross ES were not significantly dependent on the gradient. In contrast, the gross EOTS was slower than the net EOTS at the level (7.49 ± 0.32 vs. 7.63 ± 0.36 km·h^-1, P = 0.003) and downhill gradients (7.78 ± 0.33 vs. 8.01 ± 0.41 km·h^-1, P < 0.001), but not at the uphill gradient (7.55 ± 0.37 vs. 7.63 ± 0.51 km·h^-1, P = 0.080). Note that those percent differences were less than 2.9%. Given these results, a subtraction of the standing oxygen cost should be carefully considered depending on the purpose of each study.