Allegro

Thirty gene sets from FactorBook were selected for motif discovery tool comparison (Fig. 2D, Table S1). These gene sets have been selected because the motif of the ChIP'ped TF was detected as top enriched motif in the top 500 peaks in FactorBook. We extracted the top 200 genes having the highest peaks in their 20 kb region around the TSS. The comparison was performed on TF and motif recovery using the parameters indicated in Table S3. The parameters were left to default and when possible, we only adjusted the parameters to allow for larger upstream regions (when possible we choose TSS+−10 kb). iRegulon was compared to eight other publicly available motif enrichment tools, namely OPOSSUM [117] (link), DIRE [80] (link), [112] (link), PASTAA [32] (link), [113] (link), PSCAN [114] (link), Clover [16] (link), AME [118] (link), Allegro [115] (link) and HOMER2 [116] (link) (in the case of Homer2, de novo and known motif discovery are performed simultaneously but we consider them as different approaches and validate them separately). We selected these tools because they mostly take as input a set of human co-expressed genes, and they all return, at least to some extent, information on which TF could be regulating the input genes. For this reason, it not feasible to compare iRegulon with classical de novo motif discovery methods (e.g., MEME-like methods) because such methods are intractable on large human gene sets (e.g., 200 genes×20 kb×10 species represents a sequence set of 40 Mb), and they result in new motifs rather than candidate TFs. We also attempted to use SMART [119] (link) but we did not succeed in running the software. For tools that require regulatory sequences as input (AME and Clover) we used the same sequences as used by iRegulon. For some tools like Clover, it is theoretically possible to use a large search space but one run on one dataset takes too long (∼17 hours), and therefore we limited the analysis to 500 bp promoter sequences. In the case of AME, we found no positive results with a large search space (data not shown), so we show the results with the default search space. For comparison, we used the number of motifs/TFs found in top 1 and within top 5 positions. The total number of detected motifs was not reported for comparison, because some tools use more stringent thresholds than others. All these tools rely on the available motif annotation to identify the candidate TF such as Jaspar (J) or Transfac (T). However, we also manually re-associated the detected motifs to candidate TFs (mainly by comparison of the detected motif with the FactorBook motif) (see column “USING SIMILARITY” in the Table S3). For Homer2, 14 motifs that are derived from ENCODE ChIP-Seq data matching the actual Factorbook ChIP-Seq data were discarded from their in-house PWM collection to avoid over-fitting (indeed, iRegulon does not include FactorBook PWMs either, nor do any of the other tools). Note that for the other large-scale analysis (e.g. full ENCODE analysis), we use a command-line version of iRegulon.

Endogenous lipids from mouse liver and heart were detected and quantified using several techniques. FC was quantified using straight-phase HPLC and ELS detection as previously described10 (link). Quantification was made against an external calibration curve. This chromatographic set-up was also used to fractionate DG. Quantification of CE, TG, SM, and phospholipids (all from the total extract) and DG (fractionated from the HPLC) was made by direct infusion (shotgun) on a QTRAP 5500 mass spectrometer (Sciex, Concord, Canada) equipped with a robotic nanoflow ion source, TriVersa NanoMate (Advion BioSciences, Ithaca, NJ)11 (link). For this analysis, total lipid extracts, stored in chloroform:methanol (2:1), were diluted with internal standard-containing chloroform/methanol (1:2) with 5mM ammonium acetate and then infused directly into the mass spectrometer. The characteristic dehydrocholesterol fragment m/z 369.3 was selected for precursor ion scanning of CE in positive ion mode12 (link). The analysis of TG and DG was performed in positive ion mode by neutral loss detection of 10 common acyl fragments formed during collision induced dissociation13 (link). The PC, LPC and SM were detected using precursor ion scanning of m/z 184.114 (link), while the PE, phosphatidylserine (PS), phosphatidylglycerol (PG) and phosphatidylinositol (PI) lipid classes were detected using neutral loss of m/z 141.0, m/z 185.0, m/z 189.0 and m/z 277.0 respectively15 (link)16 (link). For quantification, lipid class-specific internal standards were used. The internal standards were either deuterated or contained diheptadecanoyl (C17:0) fatty acids.
Ceramides (CER), dihydroceramides (DiCER), glucosylceramides (GlcCER) and lactosylceramides (LacCER) were quantified using a QTRAP 5500 mass spectrometer equipped with a Rheos Allegro quaternary ultra-performance pump (Flux Instruments, Basel, Switzerland). Before analysis the total extract was exposed to alkaline hydrolysis (0.1M potassium hydroxide in methanol) to remove phospholipids that could potentially cause ion suppression effects. After hydrolysis the samples were reconstituted in chloroform:methanol:water [3:6:2] and analyzed as previously described17 (link).
For the recovery experiments the tissue samples were spiked with non-endogenously present lipids (or endogenous lipids spiked at relatively high levels) and could therefore all be detected by lipid class specific scans using the shotgun approach. In the recovery experiment we therefore also included the PA and phosphatidylcholine plasmalogen (PC P) lipid class, which we could not measure endogenously using our current analytical platform. Due to poor ionization efficiency, FC was derivatized and analyzed as picolinyl esters according to previous publication18 (link). See Table 1 for details. With some exceptions, lipids are annotated according to Liebisch et al.19 (link).

Musical tempo was manipulated. The Mozart Sonata for Two Pianos in D major, K. 448 (Mozart, 1985 ) was selected as auditory stimulus because it was suggested by the British Epilepsy Organization to have the Mozart effect. The Sonata has three movements: Allegro (fast tempo), Andante (fairly slow tempo; at a walking pace), and Molto Allegro (fast tempo; slightly faster than Allegro). The second movement (Andante) was selected, and software was used to change the tempo of the piece to create an FT version (138 bpm) and an ST version (66 bpm).

In accordance with the PICO (Population, Intervention, Comparison, Outcome) statement, our literature search and critical assessment was based on the following research question: “In patients with epilepsy or other medically relevant conditions (P), does the exposure to the first movement “allegro con spirito” of Mozart’s sonata KV448 (I), compared with patients exposed to (i) another musical stimulus, (ii) a non-musical stimulus, or (iii) silence (C), improve their symptomatology (O)?”.

Healthy Asian Seabass (2 ± 0.5g) were obtained from a commercial hatchery (Allegro Aqua, Singapore) and fish (n=120) were divided equally into four 200 L recirculating tanks each equipped with a standard biofiltration system and aeration at the experimental marine aquarium facilities of Temasek Life Sciences Laboratory (TLL, Singapore). Fish were maintained in 30 ± 1°C UV-irradiated seawater, with a 12:12 light and dark photoperiod cycle. All fish were fed with a commercial pellet diet at 5% body weight per day. During the acclimatization period, 5% of the fish were randomly sampled and analysed for NNV and bacterial diseases common to the species. Experimental animals were ascertained to be good health and tested negative for common seabass bacterial pathogens and betanodavirus prior to start of experiments. Animal experiments for this study were approved by the Institutional Animal Care and Use Committee (IACUC) of TLL (IACUC approval number TLL(F)-22-011).