Multiple Measures of Adiposity Are Associated with Mean Leukocyte Telomere Length in the Northern Finland Birth Cohort 1966

Studies of leukocyte telomere length (LTL) and adiposity have produced conflicting results, and the relationship between body mass index (BMI) and telomere length throughout life remains unclear. We therefore tested association of adult LTL measured in 5,598 participants with: i) childhood growth measures (BMI and age at adiposity rebound (AR)); ii) change in BMI from childhood to adulthood and iii) adult BMI, waist-to-hip ratio (WHR), body adiposity index (BAI). Childhood BMI at AR was positively associated with LTL at 31 years in women (P = 0.041). Adult BMI and WHR in both men (P = 0.025 and P = 0.049, respectively) and women (P = 0.029 and P = 0.008, respectively), and BAI in women (P = 0.021) were inversely associated with LTL at 31 years. An increase in standardised BMI between early childhood and adulthood was associated with shorter adult LTL in women (P = 0.008). We show that LTL is inversely associated with multiple measures of adiposity in both men and women. Additionally, BMI increase in women from childhood to adulthood is associated with shorter telomeres at age 31, potentially indicating accelerated biological ageing

Exploiting Genetic Interactions in Metabolic Engineering

By assembling parts with given properties, engineers are able to build tools and devices of increasing complexity. For example, a simple Ikea table is assembled from pieces of wood, screws and bolts withprecisely designed dimensions, diameters and thread angles. The properties of these parts do not change upon assembly, allowing for a predictable outcome: a table. This simple reductionist principle is also central to synthetic biology, the field interested in engineering living organisms. In synthetic biology, DNA sequences encoding biological parts found in the living world are copied, assembled and reintroduced in other organisms to engineer new biological functions. For example, the enzymes taking part into a metabolic pathway synthesising a plant pharmaceutical ingredientcan be expressed in the yeast Saccharomyces cerevisiae allowing for its synthesis by cultivating the modified yeast in a bioreactor.The applicability of the reductionist approach underlying traditional engineering disciplines is however limited in synthetic biology. Indeed, the properties of biological parts are often dependent on the contextof the host they are expressed in. For example, all parts expressed in a living host are synthesised from a family of building blocks (e.g. amino acids, lipids, sugars) which availability is not infinite. Cross-talkbetween parts might arise simply by competing for the same pool of resources. These interactions limit the predictability of designs and, in practice, many iterations of trial-and-error are often needed to engineera synthetic biological mechanism.In experimental genetics, the unexpected outcome arising from the combination of two genetic modifications is known as a genetic interaction. Genetic interactions can be complex and often elude our understanding, even in well studied model organisms. Yet, in rare instances, they produce a surprisingly strong phenotype hinting that they may be exploited in an engineering context.The motivation of this thesis is to turn interactions into a source of improvement for a desired biological function. Indeed, interactions between a metabolic pathway and a modification of a host gene may lead to higher product synthesis. In this thesis, I first attempt to predict interactions with machine learning. Failing to do so, I turn to a high throughput screening to identify positive interactions between a metabolic pathway and all non-essential genes in S. cerevisiae. More specifically, we use CRI-SPA, a new high throughput gene delivery method, to deliver a metabolic pathway in all the strains of either the Yeast Knock Out or the Yeast Over-expression Libraries. This systematically tests for the presence of an interaction between a gene (its absence or over-expression) and the metabolic pathway which might improve its yield. We show that this method can identify positive interactions in two case studies improving the synthesis of the plant pigment betaxanthin or that of the platform chemical cis-cis-Muconic acid.Altogether, this work shows that genetic interactions can be used to improve a desired engineered trait in synthetic biology and advocate for a shift from its original reductionist dogma

CRI-SPA mediated screening for metabolic engineering targets to improve small molecule production in <i>Saccharomyces cerevisiae</i>

Saccharomyces cerevisiae is a eukaryotic model organism and represents a commonly used cell factory chassis for the production of a wide range of small molecules, such as terpenes, non-ribosomal peptides, and polyketides. The expansive knowledge of S. cerevisiae genetics and metabolism has allowed for the development and use of genome-scale models and flux balance analysis to guide metabolic engineering efforts. However, even in a model organism such as S. cerevisiae, our understanding of gene regulation and cell metabolism remains incomplete, making it likely that potential targets for cell factory improvement are missed in such approaches. We have recently developed a mating-based method, CRI-SPA [1], which combines CRISPR-Cas9 induced gene editing with Selective Ploidy Ablation (SPA). This allows for high throughput transfer of a genetic feature of interest from a donor strain to a library of recipient strains. We have applied CRI-SPA to transfer several different biosynthetic pathways—betaxanthin (shikimate pathway-derived), bikaverin (polyketide), and aspulvinone E (non-ribosomal peptide-like)—to the genome-wide gene deletion library. All three biosynthetic pathways result in visible colour production when expressed in S. cerevisiae, allowing for visual-based screening for high and low producers. The approach generates a comprehensive dataset of the effect of each gene deletion on product formation and host fitness, which in turn can be used to devise superior metabolic engineering strategies for the production of these valuable small molecules in S. cerevisiae

Infant feeding and later obesity risk

Some 30 years ago, Günter Dörner proposed that exposure to hormones, metabolites and neurotransmitters during limited, sensitive periods of early development exert programming effects on disease risk in human adults. Early programming of long term health has since received broad scientific support and attention. For example, evidence increases for programming effects of infant feeding choices on later obesity risk. Meta-analyses of observational studies indicate that breast feeding reduces the odds ratio for obesity at school age by about 20%, relative to formula feeding, even after adjustment for biological and sociodemographic confounding variables. We hypothesized that breast feeding protects against later obesity by reducing the likelihood of high weight gain in infancy, and that this protection is caused at least partly by the lower protein supply with breast milk relative to standard infant formulae (the "Early Protein Hypothesis"). These hypotheses are tested in the European Childhood Obesity Project, a randomized double blind intervention trial in more than 1,000 infants in five European countries (Belgium, Germany, Italy, Poland, Spain). Formula fed infants were randomized to receive during the first year of life infant formulae and follow-on-formulae with higher or lower protein contents. Follow-up at 2 years of age shows that lower protein supply with formula normalizes early growth relative to a breast fed reference group and to the WHO growth reference. These results demonstrate that modification of infant feeding practice has an important potential for long-term health promotion and should prompt a review of the recommendations and policies for infant formula composition. © Springer Science + Business Media B.V. 2009.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Lower protein in infant formula is associated with lower weight up to age 2 y: A randomized clinical trial

Background: Protein intake during infancy was associated with rapid early weight gain and later obesity in observational studies. Objective: The objective was to test the hypothesis that higher protein intake in infancy leads to more rapid length and weight gain in the first 2 y of life. Design: In a multicenter European study, 1138 healthy, formula-fed infants were randomly assigned to receive cow milk-based infant and follow-on formula with lower (1.77 and 2.2 g protein/100 kcal, respectively) or higher (2.9 and 4.4 g protein/100 kcal, respectively) protein contents for the first year. For comparison, 619 exclusively breastfed children were also followed. Weight, length, weight-for-length, and BMI were determined at inclusion and at 3, 6, 12, and 24 mo of age. The primary endpoints were length and weight at 24 mo of age, expressed as length and weight-for-length z scores based on the 2006 World Health Organization growth standards. Results: Six hundred thirty-six children in the lower (n = 313) and higher (n = 323) protein formula groups and 298 children in the breastfed group were followed until 24 mo. Length was not different between randomized groups at any time. At 24 mo, the weight-forlength z score of infants in the lower protein formula group was 0.20 (0.06, 0.34) lower than that of the higher protein group and did not differ from that of the breastfed reference group. Conclusions: A higher protein content of infant formula is associated with higher weight in the first 2 y of life but has no effect on length. Lower protein intake in infancy might diminish the later risk of overweight and obesity. This trial was registered at clinicaltrials.gov as NCT00338689. © 2009 American Society for Nutrition.SCOPUS: ar.jinfo:eu-repo/semantics/publishe