Muscle Proteins

Body mass index (BMI) was calculated as body weight (kg) divided by body height squared (m²). Educational level was indicated based on the highest education qualification attained, and categorised into 3 groups: low education (never been to school, or elementary schooling only); medium education (secondary schooling or lower/intermediate vocational schooling); and high education (higher vocational schooling or university). Marital status was assessed by a closed question with the following categories: married, living with partner (HELIUS)/registered partnership (LASA), never married, divorced, widowed. Smoking status was categorized into never, past or current smoker.
Protein intake was expressed in grams per kilogram of adjusted body weight per day (g/kg adjusted BW/d). We applied adjusted body weight because in overweight persons, much ‘extra weight’ is adipose tissue, while underweight persons require extra protein to built muscle tissue. Adjusted BMI was calculated for those with a BMI >25 kg/m² (age ≤70 y) or >27 kg/m² (age >70 y) by applying the body weight corresponding to a BMI of respectively 25 or 27 kg/m². For those with a BMI <18.5 kg/m² (age ≤70 y) or <22.0 kg/m² (age >70 y) body weight corresponding to a BMI of respectively 18.5 or 22 kg/m² was applied [12 (link)]. Protein intake was classified as either normal/high (>1.0 g/kg adjusted BW/d) or low (≤1.0 g/kg adjusted BW/d) based on increasing evidence and consensus that a protein intake of >1.0 g/kg adjusted BW/d in old age has health benefits [2 (link), 6 (link), 10 (link)]. A sensitivity analysis was performed with low protein intake defined as ≤0.8 g/kg adjusted BW/d.
Baseline participant characteristics were presented as mean with standard deviation (± SD), median with interquartile range (IQR) or frequency (%). Statistical analyses were performed using SPSS Statistics version 23 (IBM Corp., Armonk, NY, USA).

We took the data published previously [14] (link), and for each protein family we repeated the analysis in that paper, i.e. we did PSI-BLAST of each key protein on chromosomes and plasmids and clustered the resulting proteins by MCL. This approach failed to produce good results because PSI-BLAST often did not converge in the searches made in chromosomes. For example, the searches for ATPases tend to put together many different ATPases of prokaryotes rendering their accurate separation difficult. We have thus used a different approach. For each protein family uncovered in our previous analysis of plasmids we did the following: (i) We carried out a multiple alignment with MUSCLE [72] (link) and built a phylogenetic tree using PHYML [73] (link). With these two pieces of evidence we removed the very few cases of extreme divergence, the proteins that were too short and the proteins that were too long (typically false positives, fusions or fissions of proteins motivated by sequencing errors or pseudogenization). (ii) We built multiple alignments with MUSCLE of the selected proteins, checked manually the alignments and trimmed them to remove poorly aligned regions at the edges, if relevant. The C-terminal regions of MOB alignments were systematically trimmed, as suggested previously [67] (link). The alignment of the T4CP family showed two conserved regions separated by a region that aligned poorly. As a result, we split this alignment in two and made separate profiles with the two conserved regions. In general the two profiles were found together but only the second was found to be present in all conjugative elements apart some of those of the Tn916 family. These latter T4CP showed poor matches to the general T4CP profiles and we built one specific profile for this family. (iii) We used HMMER 3.0 to build protein profiles from the manually curated multiple alignments.

Taurine was measured in the plasma and muscle using reverse phase high performance liquid chromatography (HPLC) as previously described [20 (link)]. In brief, plasma samples were precipitated by the addition of 20 times by weight of 5% trichloroacetic acid (TCA). Frozen muscle was crushed using a mortar and pestle under liquid nitrogen and homogenised in 100 times 5% TCA. Zebrafish tails were collected from four independent replicates (12 tails for each experimental condition). The sample for each replicate was split into two, to allow technical replication, and the mean of the technical replicates was used in the subsequent analysis. Samples were homogenised in 200 µL 5% TCA. After centrifugation, supernatants were removed and stored at −80 °C before analysis. Analytes were separated using HPLC with fluorescent detection, with pre-column derivatisation with o-phthalaldehyde (OPA) and 2-mercaptoethanol (2ME). OPA reacts rapidly with amino acids and sulfhydryl groups to yield intensely fluorescent derivatives, and 2ME, a reducing agent, prevents the OPA reagent from oxidising. An internal standard, O-phospho-DL-serine, dissolved in 5% TCA was added. Sodium borate was used to adjust the pH to 9. Samples were placed in an autosampler, which was maintained at 4 °C. Samples were mixed on a sample loop with a derivatising solution containing 20 mM OPA and 60 mM 2ME in 100 mM sodium borate, pH 10, for 30 s before injection onto the column. Separation was achieved with a C18 column (4 µm, 4.6 × 100 mm, Agilent, Santa Clara, CA, USA) using an Agilent 1260 Infinity HPLC system. Mobile phase A consisted of 50 mM potassium phosphate buffer, methanol, and tetrahydrofuran (94:3:3). Mobile phase B consisted of 90% methanol, with a gradient increase in B from 0 to 100%. Fluorescence was set at 360 nm and 455 nm for excitation and emission, respectively. The protein content of the muscle and zebrafish samples were quantified by solubilising the pellet in 0.5M sodium hydroxide, before incubation at 80 °C for 15 min. Once fully dissolved, protein concentrations of supernatants were quantified using a Bradford protein assay (Bio-Rad Australia, South Granville, NSW, Australia).