Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Processed data to accompany "Clonally heritable gene expression imparts a layer of diversity within cell types"

This is the processed data underlying the paper "Clonally heritable gene expression imparts a layer of diversity within cell types" by Mold, Weissman, et al.  Data has been gone through preprocessing steps, using the Python Notebooks found at <a href="https://github.com/MartyWeissman/ClonalOmics/tree/main/Data">https://github.com/MartyWeissman/ClonalOmics/tree/main/Data</a>.   Smaller files are provided in .csv (comma-separated-value) format and larger files such as expression matrices are provided in .loom format (<a href="https://anndata.readthedocs.io/en/latest/">using the AnnData package</a>).

opencc-by-4.0Dec 2024View details →

ASPH and Hypoxia Marker Expression in Head and Neck Carcinomas: Implications for HPV-Associated Tumours

Data open: file with parametres used for the multivariate evaluation.

opencc-by-4.0Sep 2023View details →

UIBVFEDPlus-Light: Virtual facial expression dataset with lighting

This database, named UIBVFEDPlus-Light, is an extension of the previously published UIBVFED virtual facial expression dataset. It includes 100 characters, four lighting configurations and 13200 images. Images are in png format with a resolution of 1080x1920 RGB, without alpha channel and an average size of 2.0 MB. The images represent virtual characters reproducing FACS-based facial expressions. Expressions are classified based on the six universal emotions (Anger, Disgust, Fear, Joy, Sadness, and Surprise) labeled according to Faigin’s classification. The dataset aims to give researchers access to data they may use to support their research and generate new knowledge. In particular, to study the effect of lighting conditions in the fields of facial expression and emotion recognition.

opencc-by-4.0Aug 2023View details →

Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine

Supplementary Table Captions: Supplementary Table S9. Evaluation and parameter determination of host and microbiome RNA read classification using simulation datasets Supplementary Table S10. 40 pathways significantly upregulated in the cecum as compared to the transverse colon Supplementary Table S11. Host-microbiome gene co-expression network edges Supplementary Table S12. Host-host gene co-expression network edges Supplementary Table S13. Microbiome-microbiome gene co-expression network edges Supplementary Table S14. List of genes included in each gene module identified from the gene co-expression network Supplementary Table S15. Results of enrichment analysis for each gene module identified from the gene co-expression network Supplementary Table S16. The top 32 bacterial species in terms of expression abundance based on metatranscriptome profiles Supplementary Table S17. Number of microbiome RNA reads annotated by the KEGG database Supplementary Table S18. Results of enrichment analysis of gene modules for each parameter Supplementary Table S19. Evaluation of modules in each parameter of Newman algorithm Supplementary Table S20. Evaluation of modules in each parameter of Louvain algorithm Supplementary Table S21. Evaluation of modules in each parameter of Leiden algorithm Supplementary Table S22. Evaluation of modules in each parameter of WGCNA

opencc-by-4.0Dec 2023View details →

Pacing of primary murine cardiomyocytes expressing human TRPV1 using infra-red laser

<h2>Abstract</h2> The expression of human TRPV1 in cardiomyocytes allowed us to induce action potentials (APs) by pulse irradiation with infra-red (IR) diod laser. Mice cardiomyocytes were transformed by AAV-based vectors bearing construction of TRPV1 with mRuby (for expression detection). We selected cells having their own intrisic AP generation and then set them up for measuring membrane potential in a current-clamp mode. Cells were irradiated using 2 or 7 Hz laser pulses. Laser trigger pulses were recorded along with cell potential. In the dataset 05-12-22.zip, there are some records where electrical stimulation were used alone or together with IR pulses. Experimental conditions are summarized in the file experiment-descriptions.tsv. <h2>Primary cell production and transformation</h2> Experiments were carried out using C57Bl/6J mice (The Jackson Laboratory, #000664, RRID: IMSR_JAX:000664). The mixed mouse primary neonatal cardiomyocyte cell culture was obtained using a neonatal heart dissociation kit (Miltenyi Biotec, 130-098-373) according to the manufacturer’s instructions. The cells were cultured in DMEM/F12, 1:1 mixture (BioloT,  1.3.7.2.) supplemented with 10% FBS, penicillin 100 U/ml /streptomycin 100 mg/ml, and L-glutamine 0.365 g/l. The culture was seeded on 10mm coverslips coated with 10 mg/ml gelatin diluted in PBS and maintained at 37℃ in 5% CO2. For transient expression of the hTRPV1 channel, a reporter protein, and a fluorescent Ca2+ sensor GCaMP6s, we used AAV-based vectors with the encoded genes above. We used AAV-DJ serotype at a MOI of 12,000 VG/cells for cTnT_hTRPV1(sh)_P2A_mRuby based viruses, and a MOI of 2,500 VG/cells for cTnT_GCaMP6s ones. The cells were infected on the next day after plating, and the transgene expression peak was observed on the third day after the infection. <h2>Distant heating system</h2> The system was equipped with a fiber coupled laser diode (LD) 4PN-117 (SemiNex) as a powerful heating laser, providing radiation at a wavelength of 1375 nm with an average power of up to 4.3 W through a multimode fiber with a core diameter of 105 μm and 0.22NA. The LD was mounted onto a TEC-controlled plate “264 TEC HP LaserMount” (A.I.), which was operated by TEC driver TECSource 5305 (A.I.); current stabilization and control for LD were performed with LD driver LaserSource 4320 (A.I.). The laser was controlled via the TTL output from the HEKA EPC-10 amplifier. Different laser intensities, and pulse widths were used. Laser power, P, can be computed from trigger voltage, U, by an equation: P [W] = -0.28 + 1.42 * U [V] <h2>Electrophysiology of single cardiac cells </h2> Patch electrodes were pulled from hard borosilicate capillary glass (Sutter Instruments flaming/brown micropipette puller) and filled with an intracellular solution consisting of (in mM) K-gluconate, 100; KCl, 40; HEPES, 10; NaCl, 8; MgATP, 4; MgGTP, 0.3; phosphocreatine, 10 (pH 7.3 with KOH). Cells were identified visually using IR-video microscopy using a Hamamatsu ORCA-Flash4.0 V3 Digital sCMOS camera (Hamamatsu Photonics) expression of wild type or mutant TRPV1 was confirmed by the presence of red flourescent protein. Coverslips were placed in a recording chamber continuously perfused with heated Tyrode's solution. Whole-cell recordings were taken at 32°C in current-clamp mode using a HEKA EPC-10 amplifier (List Elektronik) with a sampling rate of 100 μs. Steady state current was injected to achieve a membrane potential of approximately −60 to −90 mV. For experiments using the IR laser the optic fiber was placed near the cell, and light from a green laser diode was shone onto the cell to check correct positioning.

opencc-zeroJan 2024View details →

Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics

Genomic profiling in post-mortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition is not understood. We performed deep single nuclear RNA sequencing (snRNAseq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism, which we corroborated using snATAC-seq and spatial transcriptomics. Transcriptomic changes were primarily cell type-specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain. This data is the raw differential gene expression comparing ASD versus CTL subjects for each cell cluster.

opencc-by-4.0Dec 2022View details →

Effect of shade and nitrogen content on Arabidopsis Col-0 and cytokinin mutants abcg14 and cypDM mRNA-seq gene expression processed tables.

This dataset is an add-on for Gautrat et al., containing processed files for the mRNAseq data in tab delimited txt format. Here, you can obtain the raw counts file, the normalized CPM values, and the normalized logCPM values The RNA-seq raw data supporting the conclusions of this article have been deposited in ArrayExpress (Kolesnikov et al., 2015) at EMBL-EBI (www.ebi.ac.uk/arrayexpress), under accession numbers E-MTAB-13638. All relevant custom r scripts are available at https://github.com/aromanowski/shade_N_ck

opencc-zeroFeb 2024View details →

Data from: Only rare classical MHC-I alleles are highly expressed in the European house sparrow

The exceptional polymorphism observed within genes of the major histocompatibility complex (MHC), a core component of the vertebrate immune system, has long fascinated biologists. The highly polymorphic classical MHC class-I (MHC-I) genes are maintained by pathogen-mediated balancing selection (PMBS), as shown by many sites subject to positive selection, while the more monomorphic MHC-I genes show signatures of purifying selection. In line with PMBS, at any point in time, rare classical MHC alleles are more likely than common classical MHC alleles to confer a selective advantage in host-pathogen interactions. Combining genomic and expression data from the blood of wild house sparrows Passer domesticus, we found that only rare classical MHC-I alleles were highly expressed, while common classical MHC-I alleles were lowly expressed or not expressed. Moreover, highly expressed rare classical MHC-I alleles had more positively selected sites, indicating exposure to stronger PMBS, compared with lowly expressed classical alleles. As predicted, the level of expression was unrelated to allele frequency in the monomorphic non-classical MHC-I alleles. Going beyond previous studies, we offer a fine-scale view of selection on classical MHC-I genes in a wild population by revealing differences in the strength of PMBS according to allele frequency and expression level.

opencc-zeroFeb 2024View details →

Data from: Gene expression differences between western redcedar seedlings resistant and susceptible to cedar leaf blight

Western redcedar (T. plicata) is an important Cupressaceae both at economic and cultural levels in the Pacific Northwest of North America. In adult trees, the species produces one of the most weathering-resistant heartwoods among conifers, making it one of the preferred species for outdoor applications. However, young T. plicata plants are susceptible to infection with cedar leaf blight (D. thujina), an important foliar pathogen that can be devastating in nurseries and small-spaced plantations. Despite that, variability in the resistance against D. thujina in T. plicata has been documented, and such a variability can be used to breed T. plicata for resistance against the pathogen. This investigation aimed to discern the phenotypic and gene expression differences between resistant and susceptible T. plicata seedlings to shed light on the potential constitutive resistance mechanisms against cedar leaf blight in western redcedar. The study consisted of two parts. First, the histological differences between four resistant and four susceptible families that were never infected with the pathogen were investigated. And second, the differences between one resistant and one susceptible family that were infected and not infected with the pathogen were analyzed at the chemical (C, N, mineral nutrients, lignin, fiber, starch, and terpenes) and gene expression (RNA-Seq) levels. The histological part showed that T. plicata seedlings resistant to D. thujina had constitutively thicker cuticles and lower stomata densities than susceptible plants. The chemical analyses revealed that, regardless of their infection status, resistant plants had higher foliar concentrations of sabinene and α-thujene, and higher levels of expression of transcripts that code for leucine-rich repeat receptor-like protein kinases and for bark storage proteins. In conclusion, the data collected in this study shows that constitutive differences at the phenotypic (histological and chemical) and gene expression level exist between T. plicata seedlings susceptible and resistant to D. thujina. Such differences have potential use for marker-assisted selection and breeding for resistance against cedar leaf blight in western redcedar in the future.

opencc-by-4.0Feb 2024View details →

Research Data: Facial Expression Recognition under Visual Field Restriction

This dataset contains the following files: - view_trial.xlsx: Excel spreadsheet containing data from individual trials. - view_participant.xlsx: Excel spreadsheet containing data aggregated at the participant level. - consensus.xlsx: Excel spreadsheet containing consensus data analysis. - image_id_list.txt: Text file listing the IDs of the images used in the study from The Karolinska Directed Emotional Faces (KDEF); https://kdef.se/. These files provide comprehensive data used in the research project titled "Exploring the Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency Study" conducted by M. B. Urtado, R. D. Rodrigues, and S. S. Fukusima. The dataset is intended for analysis and replication of the study's findings. Please, when using these data, we kindly request citing the following article: Urtado, M.B.; Rodrigues, R.D.; Fukusima, S.S. Visual Field Restriction in the Recognition of Basic Facial Expressions: A Combined Eye Tracking and Gaze Contingency Study. Behavioral Sciences 2024, 14, 355. <a href="https://doi.org/10.3390/bs14050355">https://doi.org/10.3390/bs14050355</a> The study was approved by the Research Ethics Committee (CEP) of the University of São Paulo (protocol code 41844720.5.0000.5407).

opencc-zeroMar 2024View details →

Data and trained models for: Human-robot facial co-expression

Large language models are enabling rapid progress in robotic verbal communication, but nonverbal communication is not keeping pace. Physical humanoid robots struggle to express and communicate using facial movement, relying primarily on voice. The challenge is twofold: First, the actuation of an expressively versatile robotic face is mechanically challenging. A second challenge is knowing what expression to generate so that they appear natural, timely, and genuine. Here we propose that both barriers can be alleviated by training a robot to anticipate future facial expressions and execute them simultaneously with a human. Whereas delayed facial mimicry looks disingenuous, facial co-expression feels more genuine since it requires correctly inferring the human's emotional state for timely execution. We find that a robot can learn to predict a forthcoming smile about 839 milliseconds before the human smiles, and using a learned inverse kinematic facial self-model, co-express the smile simultaneously with the human. We demonstrate this ability using a robot face comprising 26 degrees of freedom. We believe that the ability co-express simultaneous facial expressions could improve human-robot interaction.

opencc-by-4.0Mar 2024View details →

Cre Expression in Control Tissues of the PTF1a Pig

Immunohistochemistry (IHC) analysis of the stomach, colon, lung, muscle, and duodenum tissues from PTF1a-Cre pig reveals an absence of Cre protein expression.

FIGURE 5 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,

FIGURE 5 Mean (+SD) relative expression of gnrh2, fshb, lhb, fshr and lhr messenger (m)RNA in (a) the breeding season () 2n, and () 4n and (b) the non-breeding season in Carassius auratus red var. () 2n, and () 4n. (RCC,) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR,). T, gene detected in the testis; O, gene detected in the ovary. *, significant difference between RCC and 4nRR (P <0.05)

FIGURE 4 Deduced amino-acid sequences for the Gnrh2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,

FIGURE 4 Deduced amino-acid sequences for the Gnrh2 () and Lhr () genes in Carassius auratus red var. (RCC) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR)

FIGURE 2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,

FIGURE 2 (a) The mature eggs (scale bar = 100 μm) and (b) mature sperm (scale bar = 10 μm) of autotetraploid Carrasius auratus red var. ♀ x Megalobrama amblycephala ♂ (4nRR)

FIGURE 3 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,

FIGURE 3 Reverse-transcription (RT)-PCR analysis of the expression of (a) gnrh2, (b) fshb, (c) lhb, (d) fshr and (e) lhr messenger (m)RNA in various tissues of autotetraploid Carrasius auratus red var. ♀ x Megalobrama amblycephala ♂ (4nRR). The upper strip of each panel (a)–(e) shows the positive control of actin gene while the lower strip of each panel shows the RT-PCR amplification of the target gene

FIGURE 1 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,

FIGURE 1 The gonadal structure of Carassius auratus red var. [RCC; (a)–(c)] and autotetraploids C. auratus red var. ♀ × Megalobrama amblycephala ♂ [4nRR; (d)–(f)]: (a) ovary of 7 month-old RCC containing many phase II and a few phase III oocytes; (b) ovary of 12 month-old RCC showing many mature phase IV ova; (c) testis of 12 month-old RCC with numerous mature sperms () and a small amount of spermatocytes () in the lobules of testes; (d) ovary of 7 month-old 4nRR containing phase II and a few phase III oocytes; (e) ovary of 12 month-old 4nRR with numerous mature phase IV ova; (f) testis of 12 month-old 4nRR with numerous mature sperms () and a small amount of spermatocytes () in the lobules of testes, the scale bars: (a), (b), (d), and (e) = 100 μm; (c) and (f) = 10 μm

opencc-by-4.0Apr 2024View details →

Supplementary data of article "Domestication has altered gene expression and secondary metabolites in pea seed coat".

Table S1. Excel- GO_terms_MF_selected_WGCNA_modules. Table S2. Excel- GO_terms_MF_DEGs_UP_and_DOWN. Table S3. Excel- GO_terms_MF_DEGs_summary. Table S4. Excel- List of DEGs involved in flavonoid pathway found in WILD gene set. Table S5. Protein recoveries calculated for individual pea protein samples. Numbers 1, 2, 3 denote treatment groups corresponding to seed developmental stages (D1, D2 and mature seeds, respectively). Letters a–d denote biological replicates within the treatment groups. Table S6. Excel- Annotation of proteins differentially expressed in wild and domesticated pea seed coat samples. Table S7. Primary metabolites identified by spectral similarity library search and/or co-elution with authentic standards in pea seed coats aqua methanolic extracts. Metabolite analysis relied on GC-EI-Q-MS analysis after derivatization of the lyophilized extracts with methoxamine hydrochloride (MOA) and N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA). Table S8. Primary metabolites detected in the aq. methanolic extracts of mature Cameor seed coats demonstrating statistically significant up- and down-regulation in comparison to those of wild JI261. Table S9. Primary metabolites of mature JI92 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of wild JI261. Table S10. Primary metabolites of mature JI1794 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261. Table S11. Primary metabolites of mature JI64 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261. Table S12. Mass analyzer settings applied for QqTOF-MS experiments in analysis of seed coat (cell wall) hydrolyzates and reference authentic standards. Table S13. Cell wall-bound metabolites extracted from the seed coats of wild (JI64, JI1794, JI261) and domesticated (Cameor, JI92) peas upon alkali hydrolysis of corresponding isolated and purified cell wall material. Table S14. Excel- Coordinates of markers in S-plot obtained from OPLS-DA analysis (FIA-ESI-HRTMS, negative ionization, lock mass uncorrected). Table S15. List of identified significantly differential metabolites rising during seed coat development. Table S16. List of identified significantly differential metabolites decreasing during seed coat development (positive ionization mode). Table S17. List of identified metabolites with significantly higher content in wild compared cultivated genotypes in older developmental stages (D5-6). Table S18. Excel- Expression of genes encoding enzymes of monolignol pathway in seed coats (SC) and embryos (E) of domesticated (Cameor, JI92 and Pisum abyssinicum PI358617) and wild (JI64, JI1794, JI261) peas over five seed developmental stages (13, 17, 20, 23, 28 DAP, labelled as 1-5). PAL: phenylalanine ammonia-lyase, C4H: cinnamate-4-hydroxylase, 4CL: 4-coumaroyl: CoA ligase, HCT: hydroxycinnamoyl CoA:shikimate hydroxycinnamoyltransferase, COMT: caffeic acid O-methyltransferase, CSE: caffeoyl shikimate esterase, CAD: cinnamyl alcohol dehydrogenase, CCR: cinnamoyl CoA reductase, CCoAMT: caffeoyl CoA-3-methyltransferase, F5H: ferulate-5-hydroxylase Table S19. Studied metabolites of phenylpropanoid pathway. Table S20. Instrument settings used in the proteomics LIT-Orbitrap-MS and -MS/MS experiments. Table S21. Procedures and specific settings for data processing and post-processing of the proteomics data. Table S22. Gas chromatographic (GC) separation conditions and electron ionization-quadrupole-mass spectrometry (EI-Q-MS) settings for GC-EI-Q-MS analysis of the primary metabolites in pea seed coats. Table S23. Chromatographic conditions used for UHPLC separation of seed coat (cell wall) hydrolyzates and reference authentic standards. Table S24. Variable parameters of MS/cIMS/MS measurements. Figure S1. The dynamics of gene expression between studied developmental stages within all genotypes (a) or among genotypes in particular developmental stages (b). Figure S2. Twelve representative groups of transcription factors described within 20 gene modules of pea SC. Visualized by Cytoscape 3.9.0. Figure S3. SDS-PAGE electropherograms of the total protein fractions isolated from the seed coats of JI92 (a, c, e) and JI64 (b, d, f) seeds before and after tryptic hydrolysis. Numbers 1, 2, 3 denote seed developmental stages: DS1, DS2 and mature seeds, respectively. Letters a-d denote biological replicates. The aliquots (10 μg) of samples before hydrolysis (a, b), the incompletely digested aliquots left on filter unit after peptide elution (c, d) and aliquots of tryptic hydrolysates (corresponding to 5 μg of protein), (e, f) were loaded on gels. Inter-gel normalization relied on the total density of the Protein Ladder (PageRuler™ Prestained Protein Ladder #26616, 10–180 kDa) lane (St); the ND (non-digested) sample represents a reference protein not subjected to hydrolysis. Figure S4. The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups) (c) identified in domesticated JI92 seed coats at developmental stages D1, D2 and D6. The tryptic digests (n =&thinsp;3) obtained from seed coats were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode. Figure S5. The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups, c) identified in wild pea JI64 seed coats at D1, D2 and D6 stages. The tryptic digests (n =&thinsp;3), obtained from pea seedlings, were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode. Figure S6. Principal component analysis (PCA) with score plot representation (a) accomplished for seed coat proteins differentially expressed at developmental stages D1 and D2 and in the mature state (D6) and hierarchical clustering with a heatmap representation (b). Figure S7. Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D1. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of domesticated JI92 and wild JI64, respectively. Figure S8. Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D2. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of the domesticated JI92 and wild JI64, respectively. Figure S9. Prediction of sub-cellular localization of the proteins more expressed in the developing seeds of JI92 and JI64 with the BUSCA prediction tool. Figure S10. Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated Cameor by principal component analysis (PCA). Figure S11. Representation of the differences in the metabolic profiles of the mature seed coats obtained from the wild JI261 and domesticated Cameor by the t-test with Volcano plot representation (a) and the top 30 differentially abundant metabolites demonstrating the most pronounced differences of corresponding GC-MS signals associated with seed dormancy (b). Figure S12. Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated JI92 by principal component analysis (PCA) with score plot representation (a) and hierarchical clustering with heatmap representation (b). Figure S13. Principal component analysis (PCA) illustrating distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261. Figure S14. Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild pea genotypes, JI64 and JI261(control). Figure S15. Evaluation of the differences in the patterns of the cell wall-bound metabolites obtained from mature seed coats of wild JI261 and domesticated Cameor: principal component analysis (PCA) with score plot representation (a), hierarchical clustering with heatmap representation (b) and t-test analysis with the Volcano-plot representation (c). Figure S16. Statistical analysis (t-test with Volcano plot representation) characterizing the differences between the levels of mature seed coat cell wall-bound metabolites of Cameor compared with those of wild JI261. Figure S17. Principal component analysis (PCA) illustrates the distribution of mature seed coat metabolic profiles of domesticated JI92 and wild JI261. Figure S18. Principal component analysis (PCA) shows the distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261, control. Figure S19. Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild genotypes, JI64 and control JI261. Figure S20. Annotated cell wall-bound metabolites extracted from the seed coats of the dormant wild pea genotype JI261 and the seed coats from several pea genotypes varying in their dormancy (Cameor, JI92, JI64, and JI1794) upon alkali hydrolysis of corresponding isolated and purified cell wall material.  Figure S21. Ion mobility separation of m/z 299.0841. Figure S22. Ion mobility separation of m/z 701.1907.  Figure S23. Ion mobility separation of m/z 619.1041. Figure S24. Ion mobility separation of m/z 631.1017. Figure S25. Ion mobility separation of m/z 641.1139. Figure S26. Ion mobility separation of m/z 771.1346.  Figure S27. Reconstructed chromatograms of p-hydroxybenzoic and salicylic acids in DS5 of dormant JI64 and domesticated landraces JI92 (LC/HRTMS, negative ionization mode). Figure S28. Module-trait relationship depiction showing the correlation between expression of the gene modules and the abundance of identified metabolites of the monolignol pathway.

opencc-zeroFeb 2024View details →

Genetically identical mice express alternative reproductive tactics depending on social conditions in the field

In many species, establishing and maintaining a territory is critical to survival and reproduction, and an animal's ability to do so is strongly influenced by the presence and density of competitors. Here we manipulate social conditions to study the alternative reproductive tactics displayed by genetically identical, age-matched laboratory mice competing for territories under ecologically realistic social environmental conditions. We introduced adult males and females of the laboratory mouse strain (C57BL/6J) into a large, outdoor field enclosure containing defendable resource zones under one of two social conditions. We first created a low-density social environment, such that the number of available territories exceeded the number of males. After males established stable territories, we introduced a pulse of intruder males and observed the resulting defensive and invasive tactics employed. In response to this change in social environment, males with large territories invested more in patrolling but were less effective at excluding intruder males as compared to males with small territories. Intruding males failed to establish territories and displayed an alternative tactic featuring greater exploration as compared to genetically identical territorial males. Alternative tactics did not lead to equal reproductive success—males that acquired territories experienced greater survival and had greater access to females.