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Transposon DNA sequences facilitate the tissue-specific horizontal transfer: te expression supplementary datasets

These datasets contain data on analyses of TE expression stability. Raw RNA seq counts were processed using DESeq2 R package. Low count genes and TEs were removed. Counts across samples were normalized for library sizes and log-transformed using 'regularized log' transformation. Batch normalization was performed on log-transformed data with ComBat function from sva R package.  Expression variability (EV) of TEs and genes (probes) was estimated using the previously described method [1, 2]. 1. Bashkeel, N., Perkins, T.J., Kærn, M. et al. Human gene expression variability and its dependence on methylation and aging. BMC Genomics 20, 941 (2019). https://doi.org/10.1186/s12864-019-6308-7 2. Alemu EY, Carl JW Jr, Corrada Bravo H, Hannenhalli S. Determinants of expression variability. Nucleic Acids Res. 2014;42(6):3503-3514. doi:10.1093/nar/gkt1364   PC.zip - the results of TE expression and stability in prostate cancer. <ul> <li>0.PC.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.PC.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.PC.TE.rlogcpm.mad.xls -stability measures according MAD (median absolute deviance) criteria </li> <li>0.PC.TE.rlogcpm.sd.xls - stability measures according SD criteria </li> <li>0.PC_TE_bootstrap.pdf - TE expression stability</li> <li>PC.deseq.logCPM.csv - TE log transformed expression matrix </li> <li>PC.TE_count_table.csv - TE raw count matrix </li> <li>PC_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul>   MM.zip - the results of TE expression and stability in multiple myeloma. <ul> <li>0.MM.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.MM.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.MM_TE_bootstrap.pdf - TE expression stability</li> <li>MM.deseq.logCPM.csv - TE raw count matrix </li> <li>MM.rlog.mad.xlsx- stability measures according MAD (median absolute deviance) criteria</li> <li>MM.rlog.sd.xlsx - stability measures according MAD (median absolute deviance) criteria </li> <li>MM.TE_count_table.csv - TE raw count matrix </li> <li>Myeloma_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul>

List of significantly differentially expressed genes (p.ajust < 0.01, |log2FC| > 0.3).

DEGs were obtained by pair-wise differential expression analysis of Lactobacillus johnsonii N6.2 total lipid stimulation vs. the vehicle control. Table is provided as an xlsx file.

UniProt subset about proteins and annotations generated using Shape Expressions

Subset of Uniprot obtained from Shape Expression Link to Shape expression: https://github.com/shex-consolidator/subsetting-examples/blob/master/protein/protein.shex Dumps from Uniprot downloaded on 26-June-2023 Tool employed in the creation of the subset: Pschea-rs (https://github.com/angelip2303/pschema-rs)

Selective control of transposable element expression during T cell exhaustion and anti-PD1 treatment

Supplementary data from (Bonté et al.) which contains Human and Mouse RNA-seq data including :  <ul> <li>Raw counts and TPM/FPKM expression matrices of data analyzed (genes, individual TEs, TE subfamilies) <ul> <li>raw.counts / fpkm.count / tpm.counts rds files</li> </ul> </li> <li>SQuiRE raw output files  <ul> <li>SQuiRE.results rds files</li> </ul> </li> <li>Seurat processed single cell object (with genes, TEs, Subfamilies) <ul> <li>SC.object rds files</li> </ul> </li> </ul> In details, Mouse and Human data are labelled as follows : <ul> <li>Mouse.SC : <ul> <li>Type of data : Single cell RNA-seq data of sorted CD8+ TILs (model B16 melanoma tumors )</li> <li>Label of samples : Naïve like, Early activated, EffectorMemory, Tpex, Tex</li> <li>Origin of the data : S. J. Carmona, I. Siddiqui, M. Bilous, W. Held, D. Gfeller, Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq. Oncoimmunology 9, 1737369 (2020).</li> </ul> </li> <li>Mouse.tumor.TILs : <ul> <li>Type of data : bulk RNA-seq data of sorted CD8+ TILs (model B16 melanoma tumors)</li> <li>Label of samples : Tumor Slamf6+ or Tumor Tim3+</li> <li>Origin of the data : B. C. Miller et al., Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nature Immunology 20, 326-336 (2019).</li> </ul> </li> <li>Mouse.LCMV.TILs :  <ul> <li>Type of data : bulk RNA-seq data of sorted CD8+ TILs (model LCMV clone 13)</li> <li>Label of samples : LCMV Slamf6+ or LCMV Tim3+</li> <li>Origin of the data : B. C. Miller et al., Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nature Immunology 20, 326-336 (2019).</li> </ul> </li> <li>Mouse.LCMV_Fli1.TILs : <ul> <li>Type of data : bulk RNA-seq data of sorted CD8+ TILs  (model LCMV clone 13)</li> <li>Label of samples : WT or Fli1KO</li> <li>Origin of the data : Z. Chen et al., In vivo CD8(+) T cell CRISPR screening reveals control by Fli1 in infection and cancer. Cell 184, 1262-1280 e1222 (2021).</li> </ul> </li> <li>Human.TILs : <ul> <li>Type of data : bulk RNA-seq data of sorted CD8+ TILs (NSCLC tumor tissue )</li> <li>Label of samples : Tex or Tpex</li> <li>Origin of the data : Bonté et al. (ongoing submission)</li> </ul> </li> </ul>   Type of features is labeled as follows: <ul> <li>Gene expression : genes</li> <li>Individual TE expression : TEs</li> <li>TE subfamiliy expression : SF</li> </ul> Metadata corresponding to each expression matrix can be found <a href="https://github.com/pebonte/TE_TILs">here</a>.

opencc-by-4.0Jul 2023View details →

[dataset] Behavioral expression of father-daughter bonds affects sociality and responsiveness to oxytocin and vasopressin treatments in captive juvenile female titi monkeys (Plecturocebus cupreus)

[dataset] Social bonds influence physiology and behavior, which can shape how individuals respond to physical and affective challenges. Coppery titi monkey (Plecturocebus cupreus) offspring form selective bonds with their fathers, making them ideal for investigating how father-daughter bonds influence juveniles’ responses to oxytocin (OT) and vasopressin (AVP) manipulations. We quantified expression of father-daughter bond-related behaviors in females (n = 10) and gave acute intranasal treatments of saline, low/medium/high OT, low/high AVP, or OT receptor antagonist (OTA) to subjects prior to a parent preference test. While females spent more time in proximity to their parents than strangers, we found a large degree of individual variation. Females with greater expression of bonding behaviors responded to OT treatments in a dose-dependent manner. Subjects also spent less time in proximity to strangers when treated with High OT (p = 0.003) and Low OT (p = 0.007), but more time when treated with High AVP (p = 0.007), Low AVP (p = 0.009), and OTA (p = 0.001). Findings from the present study suggest variation in expression of bond-related behaviors may alter responsiveness to OT and AVP, increasing engagement with unfamiliar social others. This enhanced sociality with strangers may promote formation of pair bonds with partners.

opencc-by-4.0May 2023View details →

Temporal changes of gene expression in health and mental disorders. Supporting Data

This dataset provides data necessary to replicate the results of the time-perturbation analysis of gene expression in health and mental disorders. The corresponding code is available in the GitHub repository <a href="https://github.com/susieavagyan/BrainExp-TemporalDynamics">https://github.com/susieavagyan/BrainExp-TemporalDynamics</a>․ The content of the data archive is following: <ul> <li>"GTEX_unzipped" folder contains  GTEx single-tissue cis-eQTL dataset (v8)</li> <li>"pheno" folder  contains sample phenotype data for datasets used in the analysis </li> <li>"scRNA-Ruzicka_data" folder  contains gene signatures for neuronally associated processes and brian cell populations from Ruzicka, W. Brad, Shahin Mohammadi, Jose Davila-Velderrain, Sivan Subburaju, Daniel Reed Tso, Makayla Hourihan, and Manolis Kellis. 2020. “Single-Cell Dissection of Schizophrenia Reveals Neurodevelopmental-Synaptic Axis and Transcriptional Resilience.” MedRxiv, January, 2020.11.06.20225342. https://doi.org/10.1101/2020.11.06.20225342 paper.</li> <li>"v5_results.SOM - Results" folder  contains the transcriptome analysis results generated with oposSOM pipeline  </li> <li>"BM.table.Rdata" file contains gene symbol ENSEMBL ID conversion table required for eQTL analysis </li> <li>"Brain_development_v3.Rdata" file contains combined raw gene expression data of datasets used in this analysis</li> <li>"som_input_v5.Rdata" file contains batch adjusted and normalized gene expression matrix that was used as an input for oposSOM pipeline.</li> </ul>

opencc-by-4.0Jul 2023View details →

Data for arricle entitled 'The grammaticalization of manner expressions into complementizers: Insights from Semitic languages', published in Linguistics

see publication

opencc-by-4.0Aug 2023View details →

Differential gene expression data from an experiment manipulating larval nutrition in female fruit flies (Drosophila melanogaster)

This file contains data on differentially expressed genes, redundant GO terms, and overlapping genes from gene expression comparisons in Tables D1 - D8 as described in, "David H. Collins, David C. Prince, Jenny L. Donelan, Tracey Chapman, and Andrew F. G. Bourke. Developmental diet alters the fecundity-longevity relationship and age-related gene expression in Drosophila melanogaster. The Journals of Gerontology: Series A. 2023."

Fig. 6 in Genome-wide characterization and expression profiling of GASA gene family in Triticum turgidum ssp. durum (desf.) husn. (Durum wheat) unveils its involvement in environmental stress responses

Fig. 6. Predicted cis-acting elements in the TdGASA genes promoter regions. The 2-kb sequences upstream of the 19 TdGASA genes were analyzed with the PlantCARE (http://bioinformatics.psb.ugent.be/webtools/plantcare/html) and New PLACE (https://www.dna.affrc.go.jp/PLACE/?action=new place) databases. The cis-acting elements were classified into three major classes: hormone-related cis-elements, development-related cis-elements, and stress-related cis-elements.

Fig. 8 in Genome-wide characterization and expression profiling of GASA gene family in Triticum turgidum ssp. durum (desf.) husn. (Durum wheat) unveils its involvement in environmental stress responses

Fig. 8. Expression of TdGASA1, TdGASA4, TdGASA14, and TdGASA19 genes confers stress tolerance to yeast cells. Wild-type transformed with empty vector (EV) or with four TdGASA genes were grown for 4 days under normal growth conditions (30 ◦ C) or under heat (37 ◦ C or 42 ◦ C), ionic (LiCl 100 mM), salt stress (NaCl 2 M), osmotic stress (Mannitol 2 M), and oxidative stress (H2O2 10 mM) in rich solid media a containing galactose as carbon source. The growth assays depicted are reflective of three independent replicates (A). (B) Cell growth (OD600) of wild-type transformed with empty vector (EV) or with four TdGASA genes under different stress conditions. Error bars represent calculated standard error of the mean (SEM) of three independent replicates. Different letters on bars represent the significant values according to Duncan's test (p <0.05).

Fig. 7 in Genome-wide characterization and expression profiling of GASA gene family in Triticum turgidum ssp. durum (desf.) husn. (Durum wheat) unveils its involvement in environmental stress responses

Fig. 7. Expression pattern of durum wheat GASA genes. (A) Heatmap of the expression pattern of TdGASA genes in roots, stems, leaves, and seeds. (B) Heatmap showing the expression pattern of TdGASA genes T. durum plants subjected to 150 mM NaCl, 15% PEG-6000, 50 μM GA3 and 50 μM ABA. The data represent means of three independent experiments. Color code is presented above the heatmap. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in Genome-wide characterization and expression profiling of GASA gene family in Triticum turgidum ssp. durum (desf.) husn. (Durum wheat) unveils its involvement in environmental stress responses

Fig. 1. Locations of the 19 TdGASA genes on durum wheat chromosomes. The scale on the left represented the length of the chromosomes. Mb = million base pair. The pairs of duplicated genes are underlined with same color. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5 in Genome-wide characterization and expression profiling of GASA gene family in Triticum turgidum ssp. durum (desf.) husn. (Durum wheat) unveils its involvement in environmental stress responses

Fig. 5. Analysis of 19 TdGASA genes structures. (A) An unrooted phylogenetic tree constructed based on TdGASA genes sequences. (B) Exon-intron structure analysis, blue boxes represent untranslated regions, yellow boxes and black lines were exon and intron positions, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Genome-wide characterization and expression profiling of GASA gene family in Triticum turgidum ssp. durum (desf.) husn. (Durum wheat) unveils its involvement in environmental stress responses

Fig. 2. Predicted three-dimensional structures of TdGASA proteins. Models were generated by using Phyr2 server. The secondary structure elements: α-helices (blue), β-sheets (yellow), and coils (cyan) are indicated for the predicted 3D structures of TdGASA proteins. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in Genome-wide characterization and expression profiling of GASA gene family in Triticum turgidum ssp. durum (desf.) husn. (Durum wheat) unveils its involvement in environmental stress responses

Fig. 4. Analysis of Triticum durum TdGASA proteins structures. (A) An unrooted phylogenetic tree generated using TdGASA protein sequences. (B) Motif identification using MEME. (C) TdGASA protein structure (D) Multiple sequence alignments of GASA domain. (E) Logo of the TdGASA conserved-domain.

Fig. 3 in Genome-wide characterization and expression profiling of GASA gene family in Triticum turgidum ssp. durum (desf.) husn. (Durum wheat) unveils its involvement in environmental stress responses

Fig. 3. Phylogenetic tree analysis of B. distachyon, O. sativa, S. bicolor, A. thaliana, T. aestivum, T. turgidum, and H. vulgare GASA proteins. Maximum likelihood method with 1000 bootstrap replicates was used to compute the distances of GASA proteins by using the MEGA11 software. The four subgroups of GASAs are presented with different colors. Black triangles denote B distachyon (Bd) proteins, yellow triangles denote the O. sativa (Os) proteins, blue triangles denote the S. bicolor (Sb), red triangles are for A. thaliana (At) proteins, black circles refer to T. turgidum (TdGASA) proteins, blue diamond's indicate T. aestivum (Ta) proteins, and yellow diamonds are for H. vulgare (Hv) proteins. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system

Fig. 1. Rapid screening of plant glycosyltransferases using the LET-based-TX-TL system. We can either use long primers which contain a promoter, a ribosome binding site, and a terminator to generate expressible linear DNAs or use short primers to amplify the targeted gene fragments and then ligate them with a promoter, a ribosome binding site, a terminator, and a backbone; then another pair of primers is used to generate expressible linear DNAs. Afterward, combine TX-TL extracts, buffers, and expressible linear DNAs to start protein expression. Then this TX-TL mixture is directly added with substrates (such as quercetin) to start glycosylation reactions. Finally, UPLC-MS is used to analyze the reaction mixture to examine whether targeted products (such as isoquercitrin) are generated.

Fig. 2 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system

Fig. 2. UPLC-MS analysis of isoquercitrin converted from quercetin by AtUGTs expressed in TX-TL. (A) A glycosylation reaction catalyzed by a UDP-glucose glycosyltransferase. (B) The chromatogram of the blank sample, which only has 50% methanol solvent. (C) The chromatogram of the negative group, which has TX-TL, quercetin, UDPglucose but no additional DNA. (D) The chromatogram of the quercetin standard. (E) The chromatogram of the isoquercitrin standard. (F–O) Chromatograms of products from the catalysis of quercetin by different AtUGTs (the final concentrations of the linear DNAs used for each AtUGTs are listed below in brackets): (F) AT1G07250 (32.25 nM), (G) AT1G07260 (29.24 nM), (H) AT2G36790 (20.64 nM), (I) AT2G15480 (29.98 nM), (J) AT2G15490 (29.50 nM), (K) AT3G16520 (25.03 nM), (L) AT3G21750 (23.7 nM), (M) AT3G46660 (24.14 nM), (N) AT4G15280 (24.47 nM), and (O) AT4G34138 (22.52 nM), see Supplementary Fig. S4 for the duplicate group. The y axis indicates ion signal abundance relative to the highest signal in each chromatogram (%). The numbers in the top right of each chromatogram (for instance 3.38e7 in [E]) indicate the ion counts represented by "100" relative abundance. The retention time of the isoquercitrin standard is 4.0 min, and the red arrow in each figure indicates the retention time of the product isoquercitrin. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system

Fig. 4. UPLC-MS analysis of isoquercitrin converted from quercetin by ArUGTs using heterologous expression. (A) The SDS-PAGE gel of heterologous expression of ArUGTs AR14572, AR11662, and AR43718 protein. The theoretical molecular weights of AR14572 protein, AR11662 protein, and AR43718 protein are 80.1 kDa, 79.8 kDa, and 80.4 kDa, respectively. (B) The chromatogram of isoquercitrin standard. (C–E) Chromatograms of products from the catalysis of quercetin by different ArUGTs using heterologous expression: (C) AR14572, (D) AR11662, and (E) AR43718, see Supplementary Fig. S6 for the duplicate group. The y axis indicates ion signal abundance relative to the highest signal in each chromatogram (%). The numbers in the top right of each chromatogram (for instance 4.56e6 in [B]) indicate the ion counts represented by "100" relative abundance. The retention time of the isoquercitrin standard is 3.93 min, and the red arrow in each figure indicates the retention time of the product isoquercitrin. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5. A in Rapid screening of glycosyltransferases in plants using a linear DNA expression template based cell-free transcription-translation system

Fig. 5. A phylogenetic tree based on protein sequences of six ArUGTs: AR06047, AR06981, AR07558, AR11662, AR14572, and AR43718. The phylogenetic tree is constructed using MEGA. Numbers at the forks are bootstrap values from 100 replicates.