BUSCADOR RECOLECTA

Project World Ocean Circulation (WOC).-- Data acquisition: Scatterometer datasets (ASCAT-A,B,C, OSCAT and OSCAT2) and stress-equivalent ERA5 winds provided by Royal Netherlands Meteorological Institute (KNMI). Filenames: 2020010103-WOC-L4-STRESS_ERAstar_GLO_0125_1H_R20191231T18_09-v2.0-fv1.0.nc. Sensor: ASCAT-A, -B, -C onboard the EUMETSAT Metop satellite series, OSCAT onboard the Oceansat-2 and OSCAT2 onboard SCATSat-1. Spatial resolution: 0.125 degree. Spatial grid: WGS 84 / Regular longitude-latitude, The ERA* stress-equivalent wind (U10S) and stress vector product is a correction of the ECMWF ERA5 output by means of geo-located scatterometer-ERA5 differences over a few days temporal window. ERA* can correct for local, persistent NWP model output errors associated with physical processes that are absent or misrepresented by the model, e.g., strong current effects (such as WBCS, highly stationary), wind effects associated with the ocean mesoscales (SST), coastal effects (land see breezes, katabatic winds), PBL parameterization errors, and large-scale circulation effects, e.g., at the ITCZ, ESA Contract No. 4000130730/20/I-NB, L4 Erastar stress equivalent model wind u component at 10 m, erastar stress equivalent model wind v component at 10 m, erastar eastward wind stress, erastar northward wind stress, era5 stress equivalent model wind u component at 10 m, era5 stress equivalent model wind v component at 10 m, era5 eastward wind stress, era5 northward wind stress, number of scatterometer samples, land sea ice quality flag, Peer reviewed

S1 Fig. Reliability of the dose-response curve. a) Mean of eBFP2 expression as a function of IPTG. b) Mean of sfGFP expression as a function of IPTG. c) Noise of eBFP2 expression as a function of IPTG. d) Noise of sfGFP expression as a function of IPTG. Points correspond to the values of the population shown in the main figures. Error bars correspond to standard errors calculated from four different populations. Solid lines correspond to predictions with the mathematical model. S2 Fig. Numerical simulations of stochastic dynamics. a-d) Stochastic trajectories with time of eBFP2 and sfGFP for two different IPTG concentrations. In red, deterministic trajectories. The initial condition corresponds to the uninduced state in all cases. e-h) Histograms of protein expression computed from long trajectories. The Gamma distributions fitted against the experimental data (blue lines) were also represented. S3 Fig. Sensitivity analysis of the model parameters. Plots of mean and noise of expression as a function of IPTG, where solid lines correspond to the dynamics predicted with the adjusted parameter, dotted lines to the dynamics if the parameter increases 2-fold, and dashed lines to the dynamics if the parameter decreases 2-fold. S4 Fig. Stochastic gene expression described by a Gamma distribution. Histograms of experimental single-cell fluorescence for both a) eBFP2 and b) sfGFP for different induction conditions with IPTG, together with fitted Gamma distributions against the data (blue lines) and predicted Gamma distributions obtained by using the model values of mean and noise (red lines). S5 Fig. Growth curves. Three different populations (blue, red, and green) were monitored with time. Points correspond to absorbance values, while solid lines come from fitted exponential trends. S6 Fig. Relationship between cellular growth rate and volume. a) Schematics to show that as TC increases, cells grow slower and are bigger. b) Scatter plot between the cube of the forward scattering signal (proxy of cellular volume) and the growth rate for the 81 IPTG and TC conditions (colored by TC condition). An exponential trend was adjusted (solid line). S1 Appendix. Stochastic differential equations. Derivation of the mathematical expressions of noise in eBFP2 and sfGFP having followed a Langevin formalism and the mean-field approximation. S2 Appendix. Gamma distribution. Derivation of the Gamma distribution for protein expression from a general stochastic differential equation. S1 Data. Flow cytometry data. Single-cell fluorescence data of eBFP2 and sfGFP for different induction conditions with IPTG and TC after filtering events., Peer reviewed

Supplementary Table 1: Overview of the excluded patients (N=147. Supplementary Table 2: Study population demographics. Supplementary Table 3: Manual Gating strategy for the identification of lymphoid populations in blood according to the EuroFlow guidelines for analysis of blood samples stained with PIDOT. Supplementary Table 4: The clinical characteristics of the non-PID disease controls (DC) (n=116). Supplementary Table 5: Verification of the EuroFlow reference values using an independent healthy control group (N=68). Supplementary Figures Supplementary Figure 1: Box plots of serum immunoglobulin levels at time of PIDOT analysis. Supplementary Figure S2: Box plots of total memory and switched memory B-cells (% as expressed on the B-cells) measured by the PIDOT. Supplementary Figure 3: Box plots of frequency of total defective lymphoid populations (over the 22 FCM PIDOT variables). Supplementary Figure 4: Box plots of frequency of total increased cell counts (over the 22 FCM PIDOT variables). Supplementary Figure 5: Receiver Operating Characteristic (ROC) curve to assess the performance of the decision-tree algorithm in relation to the predicted probabilities for lymphoid-PID., [Introduction]: Multiparameter flow cytometry (FCM) immunophenotyping is an important tool in the diagnostic screening and classification of primary immunodeficiencies (PIDs). The EuroFlow Consortium recently developed the PID Orientation Tube (PIDOT) as a universal screening tool to identify lymphoid-PID in suspicious patients. Although PIDOT can identify different lymphoid-PIDs with high sensitivity, clinical validation in a broad spectrum of patients with suspicion of PID is missing. In this study, we investigated the diagnostic performance of PIDOT, as part of the EuroFlow diagnostic screening algorithm for lymphoid-PID, in a daily practice at a tertiary reference center for PID., [Methods]: PIDOT was tested in 887 consecutive patients suspicious of PID at the Ghent University Hospital, Belgium. Patients were classified into distinct subgroups of lymphoid-PID vs. non-PID disease controls (non-PID DCs), according to the IUIS and ESID criteria. For the clinical validation of PIDOT, comprehensive characterization of the lymphoid defects was performed, together with the identification of the most discriminative cell subsets to distinguish lymphoid-PID from non-PID DCs. Next, a decision-tree algorithm was designed to guide subsequent FCM analyses., [Results]: The mean number of lymphoid defects detected by PIDOT in blood was 2.87 times higher in lymphoid-PID patients vs. non-PID DCs (p < 0.001), resulting in an overall sensitivity and specificity of 87% and 62% to detect severe combined immunodeficiency (SCID), combined immunodeficiency with associated or syndromic features (CID), immune dysregulation disorder (ID), and common variable immunodeficiency (CVID). The most discriminative populations were total memory and switched memory B cells, total T cells, TCD4+cells, and naive TCD4+cells, together with serum immunoglobulin levels. Based on these findings, a decision-tree algorithm was designed to guide further FCM analyses, which resulted in an overall sensitivity and specificity for all lymphoid-PIDs of 86% and 82%, respectively., [Conclusion]: Altogether, our findings confirm that PIDOT is a powerful tool for the diagnostic screening of lymphoid-PID, particularly to discriminate (S)CID, ID, and CVID patients from other patients suspicious of PID. The combination of PIDOT and serum immunoglobulin levels provides an efficient guide for further immunophenotypic FCM analyses, complementary to functional and genetic assays, for accurate PID diagnostics., Peer reviewed

1 Supplementary Figures and Table: Fig. S1 Demodex is maternally inherited -Fig. S2 Functions of rapidly evolving gene families of Demodex and Acariformes -Fig. S3 Mitochondrial genome has polycistrons -Fig. S4 Distribution of the RELAX parameter K across significant selection tests - Fig. S5 Host association determines extend of AT-bias of invertebrate and vertebrate- animals -Tab. S1 Intergenic distances and introns -Fig. S6 Endopolyploidy in Demodex 2 Supplementary Items (SI): -SI 1 Benchmarking Universal Single Copy Orthologs -Fig. SI 2 Length distribution of proteins in parasitic/endosymbiotic Demodex and freeliving/plant-parasitic Tetranychus -Tab. SI 1 Probably pseudogenes of Demodex arranged according to Demodex AA length -Tab. SI 2 Orthogroups of Demodex rapidly expanding -Tab. SI 3 Minimal genome sizes and number of coding genes per taxonomic clade in the Pan-Arthropoda -Tab. SI 4 Species used in genome analysis -Tab. SI 5 Repeat content of mite genomes -Tab. SI 6 Intergenic distances and introns -Fig. SI 3 Host association determines extend of AT-bias of Acariformes -Fig. SI 4 Genome-wide codon usage bias in Acariformes -Fig. SI 5 Mutation spectrum for Demodex demonstrates an AT-mutational bias -Tab. SI 7 Orthogroups (OGs) of arthropod species -Tab. SI 8 Computational analysis of gene family evolution model parameters -Fig. SI 6 Computational Analysis of Gene Family Evolution Ihtest histogram -Fig. SI 7 Alternative IQtree topologies -Tab. SI 9 Gene families showing rapid contraction in Demodex -Tab. SI 10 Gene families showing rapid contraction in the Acariformes -Tab. SI 11 Gene families showing rapid expansion in the Acariformes -Tab. SI 12 Gene family loss in Demodex -Tab. SI 13 Gene family loss in the Acariformes -Tab. SI 14 RELAX test significant orthogroups -Tab. SI 15 Relaxed selection enrichment -Tab. SI 16 Intensified selection enrichment -Tab. SI 17 Counting of nuclei in Drosophila melanogaster -Tab. SI 18 Histidine pathway genes., Peer reviewed

Supplementary Figure 1 | Evaluation of the toxicity and knockdown efficiency for the esiRNAs used in the primary screen. (A) The relative viability of cells transfected with esiRNAs targeting cellular proteostasis components was compared to that of cells transfected with non-targeting esiRNA. (B) The expression of the targeted genes following esiRNA transfection was compared to that in cells transfected with non-targeting esiRNA. Supplementary Table 1 | Description of the genes selected for evaluation. Supplementary Table 2 | Results of the toxicity, knockdown efficiency, and SSMD for evaluated genes. Supplementary Table 3 | esiRNA information for screened genes. Supplementary Table 4 | Primer sequences for evaluation of gene knockdown using qPCR. Supplementary Table 5 | Significant hits from the primary and secondary screen RNAi screens., Peer reviewed

Figure S1: Correspondence Analysis of all the potentially deleterious SNPs, Figure S2: Unsupervised analysis of the deletion data of the genes included in iEK1011 2.0 genome-scale metabolic model, Figure S3: Principal Component Analysis of FBA flux distributions of the lineage-specific genome-scale metabolic models, Figure S4: Principal Component Analysis of the sampled solution space of each one of the lineage-specific genome-scale metabolic models, Figure S5: OPLS-DA loadings for the significantly altered subsystems between animal- and human-associated sampled models, Figure S6: Difference of exchange fluxes between sampled models and FBA flux distribution of metabolites in Middlebrock 7H9 OADC + cholesterol for each lineage’s model, Table S1: Illumina genomes information, Table S2: iEK1011 2.0 reaction information, Table S3: Removed reactions per lineage model, Table S4: OPLS-DA variable coefficients, Table S5: Statistically significant reaction fluxes between samples of animal- and human-associated models, File S1: Description of the genes removed from iEK1011 2.0 to generate lineage-specific GEMs, File S2: Lineage-specific genome scale metabolic models., Peer reviewed

Supplementary Data S1. Supplementary Raw Research Data., Peer reviewed

Bilevel problem reformulation, lycopene and naringenin biosynthetic pathway, model reduction, and impact of OptDesign parameters on biochemical production (PDF). Comparison between in silico predictions and in vivo manipulations for nine compounds; knockout and regulation candidates for succinate, lycopene, and naringenin; impact of reference flux vectors (XLSX)., Peer reviewed

Peer reviewed

Table S1. Differentially expressed genes in Vav2(Onc) and Vav2(–/–);Vav3(–/–) skin stem cells. Table S2A. Differentially expressed genes in WT TSCs vs SSCs. Table S3. Differentially expressed genes in Vav2(Onc) and WT tumor stem cells. FIGURE S1. Vav function regulates SSC numbers. FIGURE S2. The Vav-dependent skin phenotype is mostly keratinocyte-autonomous. FIGURE S3. Vav proteins regulate transcriptional programs involved in stem cell homeostasis. FIGURE S4. Vav2Onc SSC transcriptome cross-comparison. FIGURE S5. Impact of Vav2Onc catalysis-dependent signaling in tumor stem cells., Peer reviewed