Key Environmental Innovations

This paper is based on empirical research on a taxonomy of technological environmental innovations. It draws on a databank with over 500 examples of new technologies (materials, products, processes and practices) which come with benign environmental effects. The approaches applied to interpreting the datasets are innovation life cycle analysis, and product chain analysis. Main results include the following: 1. Innovations merely aimed at eco-efficienc y do in most cases not represent significant contributions to improving the properties of the industrial metabolism. This can better be achieved by technologies that fulfill the criteria of eco-consistency (metabolic consistency), also called eco-effectiveness. 2. Ecological pressure of a technology is basically determined by its conceptual make-up and design. Most promising thus are technologies in earlier rather than later stages of their life cycle (i.e. during R&D and customisation in growing numbers), because it is during the stages before reaching the inflection point and maturity in a learning curve where technological environmental innovations can best contribute to improving ecological consistency of the industrial metabolism while at the same time delivering their maximum increase in efficiency as well.3. Moreover, environmental action needs to focus on early steps in the vertical manufacturing chain rather than on those in the end. Most of the ecological pressure of a technology is no rmally not caused end-of-chain in use or consumption, but in the more basic steps of the manufacturing chain (with the exception of products the use of which consumes energy, e.g. vehicles, appliances). There are conclusions to be drawn for refocusing attention from downstream to upstream in life cycles and product chains, and for a shift of emphasis in environmental policy from regulation to innovation. Ambitious environmental standards, though, continue to be an important regulative precondition of ecologically benign technological innovation.Technological innovation, Environmental innovation, Life cycle analysis, Sustainability strategies, Environmental policy

Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis

We unravel how functional plasticity and redundancy are essential mechanisms underlying the ability to survive of metabolic networks. We perform an exhaustive computational screening of synthetic lethal reaction pairs in Escherichia coli in a minimal medium and we find that synthetic lethal pairs divide in two different groups depending on whether the synthetic lethal interaction works as a backup or as a parallel use mechanism, the first corresponding to essential plasticity and the second to essential redundancy. In E. coli, the analysis of pathways entanglement through essential redundancy supports the view that synthetic lethality affects preferentially a single function or pathway. In contrast, essential plasticity, the dominant class, tends to be inter-pathway but strongly localized and unveils Cell Envelope Biosynthesis as an essential backup for Membrane Lipid Metabolism. When comparing E. coli and Mycoplasma pneumoniae, we find that the metabolic networks of the two organisms exhibit a large difference in the relative importance of plasticity and redundancy which is consistent with the conjecture that plasticity is a sophisticated mechanism that requires a complex organization. Finally, coessential reaction pairs are explored in different environmental conditions to uncover the interplay between the two mechanisms. We find that synthetic lethal interactions and their classification in plasticity and redundancy are basically insensitive to medium composition, and are highly conserved even when the environment is enriched with nonessential compounds or overconstrained to decrease maximum biomass formation.Comment: 22 pages, 4 figure

Integrated metabolic flux and omics analysis of leishmania major metabolism

Leishmaniasis is a virulent parasitic infection that causes a significant threat to human health worldwide. The existing drugs are becoming less effective due to the ability of Leishmania spp. to alter its metabolism to adapt to harsh environments. Understanding how this parasite manipulates its metabolism inside the host (e.g. sandfly and human) might underpin new ways to prevent the disease and develop effective treatment strategies. Despite significant advances in omics technologies, biochemistry of parasites still lacks the understanding of molecular components that determine the metabolic behavior under varying conditions. Metabolic network modeling might be of interest to identify physiologically relevant nodes in a metabolic network. The present work proposes a metabolic model iSK570 (an extension of the iAC560 model) with additional reactions for the metabolism of lipids, long chain fatty acids and carbohydrates to study the metabolic behavior of this parasite. Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm was used to verify the consistency between model flux predictions and gene expression data. Improved flux distributions were obtained, allowing a more accurate understanding of stage-specific metabolism in of promastigotes and amastigotes.This work was supported by the Initial Training Network, GlycoPar, funded by the FP7 Marie Curie Actions of the European Commission (FP7-PEOPLE-2013-ITN-608295). The authors gratefully express appreciation to SilicoLife Lda for providing required infrastructural facilities related to this work. We also thank Bruno Pereira (systems biologist at SilicoLife) and Hugo Giesteira (programmer at SilicoLife) for scientific and technical assistance during various phases of the project.info:eu-repo/semantics/publishedVersio

Comparative effects of 18 antipsychotics on metabolic function in patients with schizophrenia, predictors of metabolic dysregulation, and association with psychopathology: a systematic review and network meta-analysis.

BACKGROUND Antipsychotic treatment is associated with metabolic disturbance. However, the degree to which metabolic alterations occur in treatment with different antipsychotics is unclear. Predictors of metabolic dysregulation are poorly understood and the association between metabolic change and change in psychopathology is uncertain. We aimed to compare and rank antipsychotics on the basis of their metabolic side-effects, identify physiological and demographic predictors of antipsychotic-induced metabolic dysregulation, and investigate the relationship between change in psychotic symptoms and change in metabolic parameters with antipsychotic treatment. METHODS We searched MEDLINE, EMBASE, and PsycINFO from inception until June 30, 2019. We included blinded, randomised controlled trials comparing 18 antipsychotics and placebo in acute treatment of schizophrenia. We did frequentist random-effects network meta-analyses to investigate treatment-induced changes in body weight, BMI, total cholesterol, LDL cholesterol, HDL cholesterol, triglyceride, and glucose concentrations. We did meta-regressions to examine relationships between metabolic change and age, sex, ethnicity, baseline weight, and baseline metabolic parameter level. We examined the association between metabolic change and psychopathology change by estimating the correlation between symptom severity change and metabolic parameter change. FINDINGS Of 6532 citations, we included 100 randomised controlled trials, including 25 952 patients. Median treatment duration was 6 weeks (IQR 6-8). Mean differences for weight gain compared with placebo ranged from -0·23 kg (95% CI -0·83 to 0·36) for haloperidol to 3·01 kg (1·78 to 4·24) for clozapine; for BMI from -0·25 kg/m2 (-0·68 to 0·17) for haloperidol to 1·07 kg/m2 (0·90 to 1·25) for olanzapine; for total-cholesterol from -0·09 mmol/L (-0·24 to 0·07) for cariprazine to 0·56 mmol/L (0·26-0·86) for clozapine; for LDL cholesterol from -0·13 mmol/L (-0.21 to -0·05) for cariprazine to 0·20 mmol/L (0·14 to 0·26) for olanzapine; for HDL cholesterol from 0·05 mmol/L (0·00 to 0·10) for brexpiprazole to -0·10 mmol/L (-0·33 to 0·14) for amisulpride; for triglycerides from -0·01 mmol/L (-0·10 to 0·08) for brexpiprazole to 0·98 mmol/L (0·48 to 1·49) for clozapine; for glucose from -0·29 mmol/L (-0·55 to -0·03) for lurasidone to 1·05 mmol/L (0·41 to 1·70) for clozapine. Greater increases in glucose were predicted by higher baseline weight (p=0·0015) and male sex (p=0·0082). Non-white ethnicity was associated with greater increases in total cholesterol (p=0·040) compared with white ethnicity. Improvements in symptom severity were associated with increases in weight (r=0·36, p=0·0021), BMI (r=0·84, p<0·0001), total-cholesterol (r=0·31, p=0·047), and LDL cholesterol (r=0·42, p=0·013), and decreases in HDL cholesterol (r=-0·35, p=0·035). INTERPRETATION Marked differences exist between antipsychotics in terms of metabolic side-effects, with olanzapine and clozapine exhibiting the worst profiles and aripiprazole, brexpiprazole, cariprazine, lurasidone, and ziprasidone the most benign profiles. Increased baseline weight, male sex, and non-white ethnicity are predictors of susceptibility to antipsychotic-induced metabolic change, and improvements in psychopathology are associated with metabolic disturbance. Treatment guidelines should be updated to reflect our findings. However, the choice of antipsychotic should be made on an individual basis, considering the clinical circumstances and preferences of patients, carers, and clinicians. FUNDING UK Medical Research Council, Wellcome Trust, National Institute for Health Research Oxford Health Biomedical Research Centre

Obesity: A Biobehavioral Point of View

Excerpt: If you ask an overweight person, “Why are you fat?’, you will, almost invariably, get the answer, “Because 1 eat too much.” You will get this answer in spite of the fact that of thirteen studies, six find no significant differences in the caloric intake of obese versus nonobese subjects, five report that the obese eat significantly less than the nonobese, and only two report that they eat significantly more

Is defining life pointless? Operational definitions at the frontiers of Biology

Despite numerous and increasing attempts to define what life is, there is no consensus on necessary and sufficient conditions for life. Accordingly, some scholars have questioned the value of definitions of life and encouraged scientists and philosophers alike to discard the project. As an alternative to this pessimistic conclusion, we argue that critically rethinking the nature and uses of definitions can provide new insights into the epistemic roles of definitions of life for different research practices. This paper examines the possible contributions of definitions of life in scientific domains where such definitions are used most (e.g., Synthetic Biology, Origins of Life, Alife, and Astrobiology). Rather than as classificatory tools for demarcation of natural kinds, we highlight the pragmatic utility of what we call operational definitions that serve as theoretical and epistemic tools in scientific practice. In particular, we examine contexts where definitions integrate criteria for life into theoretical models that involve or enable observable operations. We show how these definitions of life play important roles in influencing research agendas and evaluating results, and we argue that to discard the project of defining life is neither sufficiently motivated, nor possible without dismissing important theoretical and practical research

Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome

The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.T32GM008764 - NIGMS NIH HHS; T32 GM008764 - NIGMS NIH HHS; R01 DE024468 - NIDCR NIH HHS; R01 GM121950 - NIGMS NIH HHS; DE-SC0012627 - Biological and Environmental Research; RGP0020/2016 - Human Frontier Science Program; NSFOCE-BSF 1635070 - National Science Foundation; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; R37DE016937 - NIDCR NIH HHS; R37 DE016937 - NIDCR NIH HHS; R01GM121950 - NIGMS NIH HHS; R01DE024468 - NIDCR NIH HHS; 1457695 - National Science FoundationPublished versio