Lipolysis

Between 2002 and 2006, 421 families with at least two long-lived Caucasian siblings, 1671 of their offspring and 744 of the offspring’s partners were recruited in the Leiden Longevity Study, without any selection on health. Males had to be aged 89 years or above and females 91 years or above3 (link)12 (link). For the current study (Switchbox), between March 2012 and July 2013, 135 offspring and partners from the LLS were measured at the study centre of the Leiden University Medical Centre. Inclusion criteria included being middle-aged (55–77 years) and having a stable body mass index (BMI) between 19 kg/m² and 33 kg/m². Participants were excluded if their fasting plasma glucose was above 7 mmol/l, if they had any significant chronic, renal, hepatic or endocrine disease, or if they used any medication known to influence lipolysis, thyroid function, glucose metabolism, GH/IGF-1 secretion or any other hormonal axis. Moreover, participants were excluded if they had a recent trans meridian flight, smoking addiction, use of more than 20 units of alcohol per week and extreme diet therapies. Other exclusion criteria specific for the subgroup only, were difficulties to insert and maintain an intravenous catheter, anemia (hemoglobin < 7.1 mmol/l), and blood donation within the last two months. Based on information obtained via telephone questioning, controls with a nonagenarian parent who had one or more nonagenarian siblings were also excluded. All women in this study were postmenopausal. The Switchbox protocol was approved by the Medical Ethical Committee of the Leiden University Medical Centre and was performed according to the Helsinki declaration. All participants gave written informed consent for participation.
Of the 135 subjects included, we excluded from analysis 17 subjects due to incomplete indirect calorimetry data and 6 subjects due to incomplete core body temperature data (Supplementary Fig. 1). Thus, data from 112 subjects (61 offspring, 51 partners) were available for analysis (and are referred to as full study sample). Complete series of 24 -hour blood samples were obtained for a subgroup of 38 participants (20 offspring, 18 partners).

The sera were transported on dry ice to the Department of Occupational and Environmental Medicine in Lund, Sweden, where all analysis of CB-153 and p,p'-DDE were performed. The CB-153 and p,p'-DDE were extracted from serum by solid phase extraction using on-column degradation of the lipids and analysis by gas chromatography mass spectrometry as previously described [16 (link)]. The relative standard deviations, calculated from samples analyzed in duplicate at different days, was 18% at 0.1 ng/mL (n = 990), 10 % at 0.5 ng/mL (n = 990) and 10 % at 2 ng/mL (n = 990) for CB-153 and 11 % at 1 ng/mL (n = 1058), 8 % at 3 ng/mL (n = 1058) and 7 % at 8 ng/mL (n = 1058) for p,p'-DDE. The detection limits were 0.05 ng/mL for CB-153 and 0.1 ng/mL for p,p'-DDE. The analyses of CB-153 and p,p'-DDE were part of the Round Robin inter-comparison program (Prof. Hans Drexler, Institute and Out-Patient Clinic for Occupational, Social and Environmental Medicine, University of Erlangen-Nuremberg, Germany) with analysis results within the tolerance limits. An internal control at 0.4 ng/mL for CB-153 and 0.6 ng/mL for p,p'-DDE was included in all analyzed series. The samples were analyzed in duplicate and the samples were re-analyzed a third time if the difference between the two determinations were higher than 30 %. However, at concentrations below 0.2 ng/mL an absolute deviation of 0.1 ng/mL between the duplicate samples was allowed. After the third analysis the two determinations fulfilling the above criteria was chosen. If none of the determinations fulfilled the criteria the sample were analyzed a fourth time.

DNA libraries were constructed using the TruSeq Nano DNA Library Preparation Kit-Set (#FC-121–4001, Illumina, USA) following the manufacturer’s instructions. Metagenome libraries were then sequenced on an Illumina NovaSeq 6000 platform with PE150 at LC-Bio Technology Co., Ltd. (Hangzhou, China). Sequencing adapters were removed from de-multiplexed raw sequences using cutadapt (v 1.9). Then, the low-quality reads (quality scores < 20), short reads (< 100 bp), and reads containing more than 5% “N” records were trimmed by using the sliding-window algorithm method in fqtrim (v 0.94) [34 (link)]. Quality filtered reads were first aligned to bovine genome (bosTau8 3.7, https://doi.org/10.18129/B9. bioc. BSgenome. Btaurus. UCSC. bosTau8) by using bowtie (v 2.2) to filter out host contaminations [43 (link)]. Then, the remaining reads were subjected to de novo assembly for each sample using IDBA-UD (v 1.1.1) [44 (link)] and used to assign microbial functions and taxonomy. MetaGeneMark (v 3.26) [45 (link)] was used to predict the coding regions (CDS) of the assembled contigs, and CDS sequences of all samples were clustered using CD-HIT (v 4.6.1) to obtain unigenes. DIAMOND (v 0.9.14) was used to perform a taxonomic assessment of the gut microbiota based on the RefSeq database [46 (link)]. Microbial taxa with a relative abundance > 0.01% in more than 50% of the samples were used for downstream analysis. The wilcx test was used to identify the differentially abundant species, and significances were declared at P < 0.05. An assignment of microbial functions was done using the Kyoto Encyclopedia of Genes and Genomes (KEGG). The abundance of KEGG pathways was normalized to transcripts per million (TPM) [47 (link)], and pathways with > 5 TPM in at least 50% of the samples were used for downstream analysis.
NetShift [48 (link)] was used to identify microbial species serving as “drivers” of the altered microbiomes during excessive lipolysis. Briefly, microbiomes from LNF and HNF cows were defined as the control and case, respectively. Then, the betweenness value, which quantifies the importance of each selected species was obtained by a Spearman’s rank correlation analysis in LNF and HNF cows. Next, the betweenness values of each species were input into the NetShift package to calculate neighbor shift (NESH) cores. Node sizes are proportional to their scaled NESH scores, and the node is colored red if its betweenness value increases from control to case.