List of fold change proteomic data sets of dFCM- 39 vs. 12Day and105 vs. 12Day, cFCM- 40 vs. 12Day and115 vs. 12Day , mouse heart 90 vs. 1 day after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway Analysis (IPA Qiagen)

Mutually enriched IPA Canonical pathways that belong to sphingolipid signaling in GBMvsCTRL and MSvsCTRL contrasts.

The project contains raw and result files from a proteomic profiling of a male breast cancer (MBC) case. Label-free quantification-mass spectrometry (LFQ-MS) and bioinformatics analysis were employed to investigate the differentially expressed proteins (DEPs) among distinct tissue samples: the primary breast tumor, axillary metastatic lymph nodes and the adjacent non-tumor breast tissue. An additional proteomic comparative analysis was performed with a primary breast tumor of a female patient. A number of Ingenuity® Pathway Analysis (IPA) (QIAGEN Inc.) and functional annotation tools were used to further analyze the DEPs. Altogether, our findings revealed deregulated proteins into signaling pathways involved in the cancer development and provided a landscape of proteomic data for the MBC research.

Six female littermate piglets were used in an experiment to evaluate the mRNA expression in tissues from piglets given one or two 1 mL injections of iron dextran (200 mg Fe/mL). All piglets in the litter were administered the first 1 mL injection < 24 hours after birth. On d 7, piglets were paired by weight (mean BW = 1.72 ± 0.13 kg) and one piglet from each pair was randomly selected as control (CON) and the other received a second injection (+Fe). At weaning on d 22, each piglet was anesthetized, and samples of liver and duodenum were taken from the anesthetized piglets and preserved until mRNA extraction. Differential Gene Expression data were analyzed with a fold-change cutoff (FC) of |1.2| P < 0.05. Pathway analysis was conducted with Z-score cutoff of P < 0.05. In the duodenum 435 genes were significantly changed with a FC ≥ |1.2| P < 0.05. In the duodenum, Claudin 1 and Claudin 2 were inversely affected by +Fe. Claudin 1 (CLDN1) plays a key role in cell-to-cell adhesion in the epithelial cell sheets and was upregulated (FC = 4.48, P = 0.0423). Claudin 2 (CLDN2) is expressed in cation leaky epithelia, especially during disease or inflammation and was downregulated (FC = -1.41, P = 0.0097). In the liver, 362 genes were expressed with a FC ≥ |1.2| P < 0.05. The gene most affected by a second dose of 200 mg Fe was HAMP (Hepcidin Antimicrobial Peptide) with a FC of 40.8. HAMP is a liver-produced hormone that is the main circulating regulator of Fe absorption and distribution across tissues. It also controls the major flows of Fe into plasma by promoting endocytosis and degradation of ferroportin (SLC4A1). This leads to the retention of Fe in Fe-exporting cells and decreased flow of Fe into plasma. Metabolic pathway changes in the duodenum and liver provide evidence for the improved feed conversion and growth rates in piglets given two iron injections pre-weaning with contemporary pigs in a companion study. In the duodenum, there is a down regulation of gene clusters associated with gluconeogenesis (P < 0.05). Concurrently, there was a decrease in the mRNA expression of genes for enzymes required for urea production in the liver (P < 0.05). These observations suggest that there may be less need for gluconeogenesis, and possibly less urea production from deaminated amino acids. The genomic and pathway analyses provided empirical evidence linking gene expression with phenotypic observations of piglet health and growth improvements. Methods RNA Sequencing Samples were submitted to Zymo Research (Irvine, CA, USA) for total mRNA extraction, cDNA library preparation and RNA sequencing. Total RNA-Seq libraries were constructed from 250 ng of total RNA. To remove rRNA, a method described by Bogdanova et al., 2011 was followed. Libraries were prepared using the Zymo-Seq RiboFree Total RNA Library Prep Kit (Cat # R3000) according to the manufacturer’s instructions (Zymo-Research, 2022). The RNA- Seq libraries were sequenced on an Illumina NovaSeq to a sequencing depth of at least 30 million read pairs (150 bp paired-end sequencing) per sample. Detection of DGE and Bioinformatic Data Handling Differentially expressed genes were detected using GeneSpring software (Agilent, Santa Clara, CA) using selection criteria that accepted a DGE threshold of greater than a |1.2|-FC in expression level and statistical probability levels of P < 0.05. The filtered genes were then subjected to Ingenuity Pathway Analysis (IPA; QIAGEN Inc., Redwood City, CA; https://digitalinsights.qiagen.com/products/) to gain insights into canonical pathways, networks, and biological functions. Qiagen IPA uses algorithms, tools, and visualizations to combine the directional information from gene expression patterns (up- or down-regulation) with the expected causal effects of the genes, as reported in the published literature. A prediction for effects of a treatment on a particular biological pathway function or disease can then be made based upon the direction of change in gene expression and calculated Z-scores. Briefly, a Z-score is used to compare data that have different means and standard deviations. The Z-score is the distance of a point, such as a complete pathway, from the mean of the distribution in terms of the standard deviation (Corchete et al., 2020; Shao et al., 2020; Wieder, Lai and Ebbels, 2022).

TSPO-associated protein 1 (TSPOAP1) is a cytoplasmic protein and is closely associated with its mitochondrial transmembrane protein partner translocator protein (TSPO). To decipher the canonical signalling pathways of TSPOAP1, its role in human diseases and disorders, and relationship with TSPO; expression analyses of TSPOAP1- and TSPO-associated human genes were performed by Qiagen Ingenuity Pathway Analysis (IPA). In the expression analysis, necroptosis and sirtuin signalling pathways, mitochondrial dysfunction, and inflammasome were the top canonical pathways for both TSPOAP1 and TSPO, confirming the close relationship between these two proteins. A distribution analysis of common proteins in all the canonical pathways predicted for TSPOAP1 revealed that tumor necrosis factor receptor 1 (TNFR1), vascular cell adhesion molecule 1 (VCAM1), cyclic AMP response element-binding protein 1 (CREB1), T-cell receptor (TCR), nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain containing 3 (NLRP3), DNA-dependent protein kinase (DNA-PK or PRKDC), and mitochondrial permeability transition pore (mPTP) were the major interaction partners of TSPOAP1, highlighting the role of TSPOAP1 in inflammation, particularly neuroinflammation. An analysis of the overlap between TSPO and TSPOAP1 Homo sapiens genes and top-ranked canonical pathways indicated that TSPO and TSPOAP1 interact via voltage-dependent anion-selective channels (VDAC1/2/3). A heat map analysis indicated that TSPOAP1 has critical roles in inflammatory, neuroinflammatory, psychiatric, and metabolic diseases and disorders, and cancer. Taken together, this information improves our understanding of the mechanism of action and biological functions of TSPOAP1 as well as its relationship with TSPO; furthermore, these results could provide new directions for in-depth functional studies of TSPOAP1 aimed at unmasking its detailed functions.

Macrophages play a key role in ozone-induced lung injury by regulating both the initiation and resolution of inflammation. These distinct activities are mediated by pro-inflammatory and anti-inflammatory/pro-resolution macrophages which sequentially accumulate in injured tissues. Macrophage activation is dependent, in part, on intracellular metabolism. Herein, we used RNA-sequencing (seq) to identify signaling pathways regulating macrophage immunometabolic activity following exposure of mice to ozone (0.8 ppm, 3 hr) or air control. Analysis of lung macrophages using an Agilent Seahorse showed that inhalation of ozone increased macrophage glycolytic activity and oxidative phosphorylation at 24 and 72 hr post exposure. An increase in the percentage of macrophages in the S phase of the cell cycle was observed 24 hr post ozone. RNA-seq revealed significant enrichment of pathways involved in innate immune signaling and cytokine production among differentially expressed genes at both 24 and 72 hr after ozone, while pathways involved in cell cycle regulation were upregulated at 24 hr and intracellular metabolism at 72 hr. An interaction network analysis identified tumor suppressor 53 (TP53), E2F family of transcription factors (E2Fs), Cyclin Dependent Kinase Inhibitor 1A (CDKN1a/p21), and Cyclin D1 (CCND1) as upstream regulators of cell cycle pathways at 24 hr and TP53, nuclear receptor subfamily 4 group a member 1 (NR4A1/Nur77), and estrogen receptor alpha (ESR1/ERα) as central upstream regulators of mitochondrial respiration pathways at 72 hr. These results highlight the complex interaction between cell cycle, intracellular metabolism, and macrophage activation which may be important in the initiation and resolution of inflammation following ozone exposure. Methods Total RNA was extracted as described above from 3 mice/treatment group. In a pilot study, we found that 3 mice were sufficient to identify a significant difference in Ptgs2 gene expression by qPCR at α = 0.05 and power = 80%. RNA integrity numbers (RINs) were confirmed to be ≥ 8.8 using a 2100 Bioanalyzer Instrument (Agilent, Santa Clara, CA). cDNA libraries were prepared using mouse TruSeq® Stranded Total RNA Library Prep kit (illumina, San Diego, CA) and quantified using a KAPA Library Quantification kit (Roche, Pleasanton, CA). cDNA libraries were sequenced (75 bp single-ended, ~35-44M reads per sample) on an Illumina NextSeq instrument. Raw reads in FastQ files were trimmed using Trimmomatic-0.39 (Bolger et al. 2014) and quality control of trimmed files performed using FastQC. Salmon was used to align reads in mapping-based mode with selective alignment against a decoy-aware transcriptome generated from mouse transcriptome GENCODE Release M23 (GRCm38.p6). Estimated counts per transcript were generated using the gcBias flag and normalized to transcript length to correct for potential changes in gene length across samples from differential isoform usage (Love et al. 2016; Patro et al. 2017). Transcript level quantitation data were aggregated to the gene-level using tximport (Soneson et al. 2015). Differential gene expression analysis was performed with air exposed mice as controls using DESeq2 with corrections for differences in library size (Love et al. 2014) in R version 4.0.3. Significantly enriched canonical pathways and upstream regulators were identified with Ingenuity IPA Version 65367011 (QIAGEN Inc, https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/) using a right-tailed Fisher’s Exact Test (Krämer et al. 2014). A less stringent criteria (fold change > 1.3 and experimental false discovery rate [padj] < 0.05) was used to augment the number of genes included in the pathway analysis (Bennett et al. 2024). Data were deposited NCBI’s Gene Expression Omnibus (Edgar et al. 2002) and are accessible through GEO Series accession number GSE237594 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237594).

Summary:The datasets described here were gathered while investigating the molecular processes by which some human ductal carcinoma in situ (DCIS) lesions advance to the more aggressive form while others remain indolent.Data access:All RNA sequencing data have been deposited in the Gene Expression Omnibus with accession https://identifiers.org/geo:GSE143790 All Chip-Exo data have been deposited in the Gene Expression Omnibus with accession https://identifiers.org/geo:GSE143313 RPPA data is included together with this data record, in the file Supplementary Figure 3-RPPA.xlsx. The Raw RPPA and ANOVA tabs are the results, while the other tabs are IPA analysis performed by the authors. Permission to use figures and data generated using QIAGEN Ingenuity Pathway Analysis (IPA) is given in the file QIAGEN Ingenuity Product Support Permission letter for Dr. Behbod.pdf.The specific data underlying each figure and supplementary figure in the manuscript are provided as part of this data record, and are as follows:Figure 1-BCL9-STAT3 interaction.xlsxFigure 2-ChIP Exo.xlsxFigure 3-ChIP.xlsxFigure 4-MMP16 and avb3 MIND xenografts.xlsxFigure 5-MMP16 avb3 TMA analysis.xlsxFigure 6-Carnosic data.xlsxSupplementary Figure 3-RPPA.xlsxSupplementary Figure 4-STAT3 Reporter.xlsxSupplementary Figure 5-ChIP Exo Motifs.xlsxSupplementary Figure 6-integrin data.xlsxSupplementary Figure 7-MMP data.xlsxStudy approval and patient consent: Patients gave written informed consent for participation in the University of Kansas Medical Center Institutional Review Board–approved study allowing collection of additional biopsies and or surgical tissue for research. Animal experiments were conducted following protocols approved by the University of Kansas School of Medicine Animal Care and Use and Human Subjects Committee. Study aims and methodology: The aim of the related study was to determine the molecular processes underlying progression to invasion in DCIS using PDX DCIS MIND animal models. Using a novel intraductal model they identify downregulation of specific STAT3 targets to promote progression and use a purified component from rosemary extract to show that treatment in vivo decreases DCIS progression in patient derived DCIS and cell line models.

A significant area of rare diseases research is the investigation of druggable protein encoding genes that contribute to pathogenesis. Niemann Pick Type C (NPC) disease can be considered as a challenging one, among all rare diseases, because it has been associated with a poor prognosis and an unclear molecular pathogenesis. In recent years, Proteomics analysis has become a functional and useful technology for profiling protein expression and find possible drugs targets. In the present study, hepatocytes derived from wild type and Npc1 deficient mice were analyzed by mass spectrometry-based proteomics, followed by pathway analysis and statistical interpretation, performed with the QIAGEN Ingenuity Pathway Analysis (IPA) software. Applications for protein function was built using IPA and gene ontology analysis. We identified and reliably quantified a total of 3833 proteins, among them 416 presented a significant p-value (<0.05) being classified 149 as upregulated (log2 fold change >1) and 6 as downregulated (Log2 fold change <-1). Our analysis revealed that the most significant changed proteins are related to liver damage, lipid metabolism and inflammatory response. In addition, in the group of up/downregulated proteins, 47 proteins were identified as lysosomal proteins and 22 as mitochondrial proteins. Importantly, we found that of those proteins CTSB, LIPA, DPP7, GLMP and DECR1 are related to liver fibrosis, liver damage and steatosis.

Ordered cellular architecture and high concentrations of stable crystallins are required for the lens to maintain transparency. Here we investigate the molecular mechanism of cataractogenesis of the CRYGC c.119-123dupGCGGC (p.Cys42AlafsX63) (CRYGC5bpd) mutation. Lenses were extracted from wild-type and transgenic mice carrying the CRYGC5bpdup minigene and RNA was isolated and converted into cDNA. Expression of genes in the unfolded protein response (UPR) pathways was estimated by qRT-PCR and RNA seq and pathway analysis was carried out using the Qiagen IPA website. P3W Transgenic mice exhibited phenotypic diversity with a dimorphic population of severe and clear lenses. PCA of RNA seq data showed separate clustering of wild-type, clear CRYGC5bpd, and severe CRYGC5bpd lenses. Transgenic mice showed differential upregulation in Master regulator Grp78 (Hspa5) and downstream targets in the PERK-dependent UPR pathway including Atf4 and Chop (Ddit3), but not GADD34. Thus, high levels of CRYGC5bpd transgene expression in severely affected lenses induce UPRer and UPRmt stress responses primarily through the PERK-dependent and Atf4/Atf5/Ddit3 pathways respectively, inducing autophagy and apoptosis and thence congenital nuclear cataracts. This effect is correlated to CRYGC5bpd transgene expression, offering insight into cataract pathogenic pathways and variation in cataract severity in humans. Methods RNA isolation, cDNA synthesis, and qRT-PCR Total RNA was isolated using an RNA isolation kit (The RNeasy Plus Mini Kit; Qiagen, Valencia, CA) and quantified using a spectrophotometer (Nanodrop 2000C; ThermoFisher). A first-strand cDNA was synthesized from approximately 0.5mg of total RNA by cDNA synthesis kit (Super III first-strand synthesis for RT PCR kit; Invitrogen) according to the manufacturer's protocol. qRT-PCR was performed using Applied Biosystems ViiA7 Real-Time PCR system with the following amplification conditions: an initial incubation of the samples at 50°C for 2min and denaturation at 95°C 15min followed by 40 cycles of denaturation, annealing, and extension at 95°C 15sec, 60°C 30sec, and 72°C 30sec. Gapdh was used as an endogenous control for normalizing the target mRNA. The relative expression of each target gene was calculated using the 2^(∆∆Ct) method. The primers were standardized, and efficiencies were tested before performing qRT-PCR. RNA-Seq About 200ng of RNA samples were used for RNA seq. analysis. Total RNA samples were purified from the lenses of 3-week-old wild-type and KO mice (n = 3) using a Qiagen kit. After passing quality control criteria, RNA-sequencing was carried out using an Illumina NovaSeq platform utilizing a paired-end 150 bp sequencing strategy (Novogene Corporation Inc., Sacramento, CA, USA). Analysis was performed using Partek Flow (https://partekflow.cit.nih.gov/flow) on the NIH Biowulf supercomputing cluster.

Additional file 2. Table S1. List of somatic mutations identified in each patient. Table S2. List, information and literature supply for genes mutated or copy number altered described in the main text as belonging to DDR or MErT/EMT categories. Table S3. List of somatic copy number aberrations identified by EXCAVATOR2. Table S4. List of gains with at least two copies and losses with homozygous deletions. Table S5. List of recurrent amplified genes on chromosome 1. Table S6. List of recurrently amplified genes of chromosome 1, belonging to the two significant biological functions identified by Ingenuity Pathway Analysis (IPA®, Qiagen; Bioinformatics, Redwood City, CA, USA; http://www.qiagen.com/ingenuity ). For the entire name of the genes, reported as gene ID, see Table S5. Table S7. List of recurrent deleted genes on chromosome 6.

Up-regulated and down-regulated molecules are shown with up and down arrows, respectively.

Alveolar macrophages (AM) are unique lung resident myeloid cells and often the first cell type to contact airborne pathogens. The contribution of human AMs (HAM) to pulmonary diseases remains poorly understood due to the difficulty in accessing them from human donors and their rapid phenotypic change during in vitro culture. There remains an unmet need for cost effective methods for generating human cells with a HAM phenotype, particularly important for translational studies and clinical benefits to humans, including analyses of host cell responses to vaccine candidates. We developed cell culture conditions that mimicthe lung alveolar environment in humans using lung lipids, that is, Infasurf (calfactant, natural bovine surfactant) and lung-associated cytokines (granulocyte macrophage colony–stimulating factor, transforming growth factor-β, and interleukin 10) that facilitate the conversion of blood-obtained monocytes to an AM-like (AML) phenotype and function in tissue culture. Similar to HAM, AML cells are particularly susceptible to both Mycobacterium tuberculosis and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. This study reveals the importance of alveolar space components in the development and maintenance of HAM phenotype and function and provides a readily accessible model to study HAM in infectious and inflammatory disease processes,as well as therapies and vaccines. Freshy isolated human alveolar macrophages (HAM, n=2) and 2h adherent monocyte derived macrophages (MDM, n=3) and alveolar macrophage-like (AML, n=3) cells were washed once with 1% Dulbecco's phosphate-buffered saline (DPBS) (Gibco), lysed in TRIzol (Invitrogen) and RNA was isolated using Direct-zol RNA Miniprep kit, R2052 (Zymo Research) as per the manufacturer’s instructions. Isolated RNA was quantified using the Qubit 4 Fluorimeter (Invitrogen). RNA quality was assessed with the 4200 TapeStation System (Agilent). Samples with an RNA integrity number (RIN) higher than 7 were used for RNA-seq. RNA-seq libraries were prepared from 300ng of total RNA using the mRNA-seq library preparation and 50bp single read sequencing with approximately 25-30M reads per sample in the NEB Next RNA Ultra Kit (Qiagen) with poly (A) enrichment. RNA sequencing was carried out using the HiSeq 3000 platform (Illumina).Raw sequencing reads were preprocessed using Trim Galore! to trim the adapters and low-quality sequences. Trimmed reads were mapped to the human hg38 reference genome using HISAT2. Read counts for each sample were obtained using feature Counts. Differential gene expression analysis was carried out using DESeq2. The Benjamini-Hochberg procedure was used to control the false discovery rate (FDR), and the shrunken log2 fold changes (LFCs) were calculated using the adaptive shrinkage (ash) estimator with an Empirical Bayes approach. Genes with FDR-adjusted p-value <0.05 and LFC of more than 1 or less than -1 were considered to be differentially expressed. Gene Set Enrichment Analysis (GSEA) was carried out to identify over-represented gene ontology (GO) terms and biological pathways. Pathway and network analyses were performed using Ingenuity Pathway Analysis (IPA) (Qiagen). Functional protein association networks were analyzed using StringApp and network visualization was generated using Cytoscape version 3.8.2. Heatmaps of specific genes were generated using the pheatmap package in R.

LC-MS/MS raw dataSpectrum matching and protein identification and validation were performed with MSFragger, and quantification of protein intensities with matching between runs was performed with IonQuant as components of the FragPipe analysis pipeline using the default settings of each module. The protein database used for the search was the Mus musculus reviewed sequence database downloaded from UniProt on June 1, 2023. The results were subsequently processed to filter out common contaminants, decoy hits from the reverse database, and protein groups identified by a single peptide. The data were filtered as follows: (a) binary expression of a protein (i.e., protein exclusively identified in either scLRP1+/+ or scLRP1-/-) was only considered relevant if all scLRP1+/+ samples or all scLRP1-/- samples expressed the protein. The missing values were imputed with the minimum intensity value for each specific data set; (b) for samples expressed in both scLRP1+/+ and scLRP1-/- tissue, the filtering process required 2 or more proteins to be detected in both scLRP1+/+ and scLRP1-/- samples. False discovery analysis was performed using the Benjamini, Krieger, and Yekutieli method using GraphPad Prism 10.0 software. Causal analysis of proteomic data was performed in IPA upstream analysis software (QIAGEN). For IPA, the binary values were imputed using local minimum intensities. Enrichment analyses for gene ontology (biological process) were performed using clusterProfiler 4.2.2 R package on R 4.1.0. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD038236. Global quantification of protein expression. Sciatic nerves from scLRP1+/+ and scLRP1-/- mice were rinsed in PBS to remove the blood and frozen with liquid nitrogen in cryogenic storage tubes (#5016-0001, Thermo Fisher Scientific). Fractured tissue was transferred to a 1 mL milliTUBE containing an AFA fiber (#520135, Covaris catalog) in 200 μL of 50 mM HEPES pH 8.5, 150 mM NaCl, and 2% Triton X-114 and sonicated with a M220 Focused-Ultrasonicator (#500295, Covaris catalog). Sonication parameters were temperature 15 °C, peak power 75 W, duty factor 26, cycles/burst = 1,000, and duration 600 seconds. Extracted proteins were clarified of insoluble material by centrifugation at 15,000 x g for 20 minutes at 4 °C. Protein concentrations were determined with the Micro BCA colorimetric assay (Pierce, Thermo Fisher Scientific) with the addition of SDS to a final concentration of 1% in the assay solvent to prevent detergent clouding. Aliquots containing approximately 5 μg of protein were processed using the SP3 protocol as described (89) with some modifications. Briefly, the sample aliquots were brought to 50 μL volume, and disulfide bonds were reduced and alkylated simultaneously with 10 mM TCEP, 40 mM 2-chloroacetamide in 50 mM HEPES pH 8.5, and 1% sodium deoxycholate at 70 °C for 10 minutes, then cooled on ice. Proteins were precipitated and captured following the addition of 10 μL of a washed 10 μg/μL suspension of SpeedBeads (Cytiva) and 400 μL of ethanol. After shaking for 10 minutes at room temperature, the beads were magnetically captured and washed 3 times with 200 μL of 80% ethanol in water. Proteins were digested on the beads in 50 μL of 50 mM HEPES pH 8.5, 1% sodium deoxycholate, and 10 ng/μL trypsin (Promega) overnight at room temperature with shaking sufficient to maintain the beads in suspension. The digest was diluted 10-fold with 80% acetonitrile and 1% formic acid, then separated from the beads magnetically, and the resulting peptides were captured on 2 mm discs of Empore Cation (CDS Analytical) fitted into 1,000 μL pipette tips (Sartorious catalog 791000). Detergents and other contaminants were removed by washing the tips serially with 1) ethyl acetate; 2) 80% acetonitrile, 1% formic acid; and 3) 10% acetonitrile, 0.2% formic acid. Peptides were eluted directly into injection vials with freshly prepared 80% acetonitrile and 5% ammonium hydroxide and immediately dried down in a centrifugal vacuum evaporator. One-fifth of the recovered peptides from each sample were subsequently analyzed by liquid chromatography-tandem mass spectrometry. In-house capillary columns were constructed from 360 μm OD and 100 μm internal diameter × 30 cm fused silica tubing (Molex) with laser-pulled tips (Sutter Instruments) and were packed with Reprosil-PUR 3 μm C18-AQ (Dr. Maisch GmBH). Solvents A and B consisted of 0.1% formic acid in water and 80% acetonitrile with 0.1% formic acid, respectively. A 180-minute linear gradient from 2% to 35% solvent B was used for chromatographic separation. Peptides were analyzed with an Orbitrap Elite (Thermo Fisher Scientific) mass spectrometer using nano-electrospray ionization with an applied voltage of 1,800 V. MS1 spectra were acquired at a resolution of 120,000, and the 15 most abundant precursor ions were selected for fragmentation by higher energy collision dissociation. MS2 spectra were acquired at a resolution of 15,000. Dynamic exclusion parameters were a list size of 500, a mass window of ±7 ppm, and a duration of 1 minute. Automatic gain control settings were MS1 target 1 × 106, maximum inject time 100 ms; MS2 target 4 × 104, maximum inject time 100 ms. Principal components of the 8 samples (2 groups: 4 scLRP1+/+, 4 scLRP1-/-) were analyzed. The centroid of each group, generated by the K-nearest neighbor (KNN) algorithm, was used to define each cluster. All samples from each group were restricted to the same cluster with no overlap.RNA-Seq dataL4 and L5 DRGs from the left and right sides of each mouse were acutely isolated, pooled, and snap-frozen in liquid nitrogen (n=3 per genotype). RNA was extracted with the AllPrep DNA/RNA Micro Kit (Qiagen, Inc.). RNA quality was assessed on an Agilent 2100 Bioanalyzer. Samples with RNA integrity numbers ≥8 were used for RNA sequencing (RNA-seq). RNA-seq was conducted at the NYU Genomic Core. All samples were processed in the same time period, following the same protocol to limit batch effects and other confounders using the Illumina RNA-seq platform. The cDNA library was prepared via the standard Illumina protocol. Three samples were pooled into each lane and sequenced by 75-bp paired-end sequencing on an Illumina HiSeq 2500 using standard protocols. A Phi-X positive control provided by Illumina was spiked into all lanes at a concentration of 0.3% to monitor sequencing quality. The sample error rate was < 2% and the distribution of reads per sample in a lane was within a reasonable tolerance. Data generated during sequencing runs were transferred to the high-performance computing (HPC) cluster, with individual base calls transferred for downstream analysis. The target for average reads per sample was approximately 25 million. The QC pipeline included: 1) quality check of the raw sequencing data using FastQC (v 0.11.9) and MultiQC (v 1.9); 2) mapping the sequencing reads to the human genome (build 102) using HISAT2 (v 2.2.1), followed by SAMtools (v 1.12) to convert BAM (Binary Alignment Map) into SAM (Sequence Alignment Map) files; 3) assembly of RNA-seq reads into transcripts using StringTie (v 2.1.4); and 4) calculation of expression levels from read counts, producing a gene count matrix. To improve the rigor of gene expression analysis, a minimum read depth of 10 gene counts in at least 90% of samples was required for retention in the matrix. The Ensembl identifiers (ID) of the gene counts were annotated to Entrez IDs using the EnrichmentBrowser (v.2.18.2) package in R. The Entrez IDs were annotated to gene symbols using Homo sapiens (v. 1.3.1). Gene expression was compared using DESeq2 (v. 1.32.0), which assumes a negative binomial distribution using the mean and variance estimated from gene counts and p-values calculated using the Wald test. GSEA was performed using GSEA 3.0 (http://www.broadinstitute.org/gsea/). Adjusted p < 0.05 and a false discovery rate (FDR) q < 0.25 were considered significant. DEGs were selected when presented with an adjusted p-value < 0.05 and a log2FC (fold change) > 1 for upregulated genes and < -1 for downregulated genes. The Database for Annotation, Visualization, and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/, version 6.8) was used to perform GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Set enrichment analysis was used for the pathway by selecting non-significant differentially expressed genes specified as the “background universe” and accounting for multiple testing using a false discovery rate of q < 0.1. Differentially altered pathways were evaluated by using the enrich plot package in R for visualization of functional enrichment (i.e., dot plot).

IPA results and Sphingolipid signaling pathway DEGs with potential targeting differentially expressed serum exosomal miRNAs.

An excel file with the top six networks from frameshift and non-frameshift INDELs exclusively from the layer or broiler line. QIAGEN’s Ingenuity® Pathway Analysis (IPA®) software ( http://www.ingenuity.com/ ) with default parameters were used to find metabolic pathways of genes with relevant biological functions. (XLSX 10 kb)

Although the corpus luteum (CL) contains high concentrations of lipid in the form of steroid hormone precursors and prostaglandins, little is known about the abundance or function of other luteal lipid mediators. To address this, 79 lipid mediators were measured in bovine CL, using ultra performance liquid chromatography-tandem mass spectrometry. CL from estrous cycle days 4, 11, and 18 were compared and, separately, CL from days 18 of the estrous cycle and pregnancy were compared. Twenty-three lipids increased as the estrous cycle progressed (P < 0.05), with nine increasing between days 4 and 11 and fourteen increasing between days 4 and 18. Overall, this indicated a general upregulation of lipid mediator synthesis as the estrous cycle progressed, including increases in oxylipins and endocannabinoids. Only 15-KETE was less abundant in the CL of early pregnancy (P < 0.05), with a tendency (P < 0.10) for four others to be less abundant. Notably, 15-KETE also increased between estrous cycle days 4 and 18. Ingenuity Pathway Analysis (IPA, Qiagen) indicated that functions associated with differentially abundant lipids during the estrous cycle included leukocyte activation, cell migration, and cell proliferation. To investigate changes in CL during maternal recognition of pregnancy, this lipid dataset was integrated with a published dataset from mRNA profiling during maternal recognition of pregnancy. This analysis indicated that lipids and mRNA that changed during maternal recognition of pregnancy may regulate some of the same functions, including immune cell chemotaxis and cell-cell communication. To assess effects of these lipid mediators, luteal cells were cultured with 5-KETE or 15-KETE. One ng/mL 5-KETE reduced luteal progesterone on day 1 of culture, only in the absence of luteinizing hormone (LH), while 1 ng/mL 15-KETE induced progesterone only in the presence of LH (10 ng/mL). On day 7 of culture, 0.1 ng/mL 15-KETE reduced prostaglandin (PG)F2A-induced inhibition of LH-stimulated progesterone production, while 1 ng/mL 15-KETE did not have this effect. Overall, these data suggest a role for lipid mediators during luteal development and early pregnancy, as regulators of steroidogenesis, immune cell activation and function, intracellular signaling, and cell survival and death.

The project contains raw and result files from a comparative proteomic analysis of malignant [primary breast tumor (PT) and axillary metastatic lymph nodes (LN)] and non-tumor [contralateral (NCT) and adjacent breast (ANT)] tissues of patients diagnosed with invasive ductal carcinoma. A label-free mass spectrometry was conducted using nano-liquid chromatography coupled to electrospray ionization–mass spectrometry (LC-ESI-MS/MS) followed by functional annotation to reveal differentially expressed proteins and their predicted impacts on pathways and cellular functions in breast cancer. A total of 462 proteins was observed as differentially expressed (DEPs) among the groups of samples analyzed. Ingenuity Pathway Analysis software version 2.3 (QIAGEN Inc.) was employed to identify the most relevant signaling and metabolic pathways, diseases, biological functions and interaction networks affected by the deregulated proteins. Upstream regulator and biomarker analyses were also performed by IPA’s tools. Altogether, our findings revealed differential proteomic profiles that affected the associated and interconnected cancer signaling processes.

Five oppositely-regulated differentially expressed genes between GBMvsCTRL and MSvsCTRL contrasts.

Eukaryotic organelle genomes are generally of conserved size and gene content within phylogenetic groups. However, significant variation in genome structure may occur. Here, we report that the Stylonematophyceae red algae contain multipartite circular mitochondrial genomes (i.e., minicircles) which encode one or two genes bounded by a specific cassette and a conserved constant region. These minicircles are visualized using Fluorescence Microscope and Scanning Electron Microscope, proving the circularity. Mitochondrial gene sets are reduced in these highly divergent mitogenomes. Newly generated chromosome-level nuclear genome assembly of Rhodosorus marinus reveals that most mitochondrial ribosomal subunit genes are transferred to the nuclear genome. Hetero-concatemers that resulted from recombination between minicircles and unique gene inventory that is responsible for mitochondrial genome stability may explain how the transition from typical mitochondrial genome to minicircles occurs. Our results offer inspiration on minicircular organelle genome formation and highlight an extreme case of mitochondrial gene inventory reduction. Methods Sample preparation Culture strains of Tsunamia transpacifica JAW4874, Rufusia pilicola O7031, Stylonema alsidii JAW4424, Chroodactylon ornatum JAW4256, Chroothece mobilis SAG104.79, Rhodosorus marinus CCMP1338, and Bangiopsis subsimplex UTEX LB2854 were obtained from J.A. West (School of Biosciences 2, University of Melbourne, Parkville, Victoria 3010, Australia), F.D. Ott (905 NE Hilltop Drive, Topeka, Kansas 66617, USA), The Culture Collection of Algae at Göttingen University, Germany (SAG), The National Center for Marine Algae and Microbiota (NCMA), and the Culture Collection of Algae at The University of Texas at Austin, USA (UTEX), respectively. DY-V medium (added sea salt to 5 ppt) was used for culturing Rufusia pilicola. Chroothece mobilis and Chroodactylon ornatum were cultured in L1+DY-V (1:1 ratio) medium. The other samples were cultured in L1 medium. Culture flasks were kept under a white LED lamp (7.76 µmol photon m-2 s-1) at 20°C in a 12:12 light-dark cycle. DNA and RNA extraction Samples were either collected from 10 µm membrane filters or by centrifugation (30 min, 7830 rpm). Harvested cells were frozen in liquid nitrogen before grinding. Genomic DNAs for Illumina short-read sequencing were extracted using the Exgene Plant SV Kit (General Biosystems, Seoul, Korea) and cleaned up using DNeasy® PowerClean® Pro Cleanup Kit (QIAGEN, Hilden, Germany). For long-read sequencing, genomic DNA was extracted using the manual CTAB protocol with a customized lysis buffer 1. Harvested samples were placed in a 2 ml tube with bullet and frozen in liquid nitrogen. Then samples were machine ground. After grinding, samples were resuspended by adding 600 µl of CTAB isolation buffer (1% 2-mercaptoethanol added right before usage) and incubated at 65°C for 20 min. When samples were completely thawed, bullets were removed from the tubes and 6 µl of RNase A was added. After incubation, we centrifuged tubes at 14,000 rpm for 20 min. While not disturbing the pellets, samples were placed into another 2 ml tube and mixed with one volume of phenol:chloroform:isoamyl alcohol (25:24:1, v/v) before centrifugation at 14,000 rpm for 20 min. The aqueous phase was then mixed with one volume of chloroform in a new 2 ml tube and centrifuged at 14,000 rpm for 15 min. After centrifugation, one volume of 100% isopropanol was added and incubated at -20°C for 30 min. Samples were then centrifuged at 14,000 rpm for 20 min. Precipitated DNA was washed with 70% ethanol and centrifuged again to remove ethanol. Finally, DNA was air-dried and dissolved in 50 µl AE buffer from Exgene Plant SV Kit. Total RNA of R. marinus was extracted using RNeasy® Plant Mini Kit (QIAGEN, Hilden, Germany). Whole genome sequencing and genome assembly Library preparation and whole genome sequencing for both short-read and long-read sequencing were carried out by DNA Link Inc. (Seoul, Korea). For short-read sequencing, libraries were prepared using the Truseq Nano DNA Prep Kit (550 bp Protocol) and sequencing was done with the Illumina HiSeq2500 platform according to the protocol using 100 bp paired-end reagents. Long-read sequencing was carried out with Oxford Nanopore platform (ONT GridION) for R. marinus (6 kb size selection) and the Pacific Biosciences (PacBio) High-Fidelity (HiFi) sequencing platform for C. ornatum (no size selection). RNA-seq for R. marinus was done with the Illumina NovaSeq600 platform. The raw data from short-read sequencing were assembled using SPAdes 3.14.1 2 with ‘—careful’ pipeline option and those from long-read sequencing were assembled using NextDenovo 2.5.0 (https://github.com/Nextomics/NextDenovo) for nuclear genome of R. marinus. Assembled NextDenovo contigs were polished 3 times with Pilon 1.22 3 using short-read mapping data generated by bowtie2 2.3.5.1 4. For mitogenome assemblies using long-read data, reads that have BLAST hits to mitochondrial CDS were used. The program miniasm 0.3 (r179) 5 was used to identify the R. marinus mitogenome and IPA 1.3.1 (https://github.com/PacificBiosciences/pbipa) was used for C. ornatum. In addition, reads that had BLAST hits to the NCR were used to search for “empty” minicircle reads that do not contain a CDS, however, no contigs were assembled, meaning the collected reads are just fragments of CDS-containing reads. Because minicircles share long conserved regions that short-reads cannot discriminate, we used long-read data and NextPolish 1.4.0 (https://github.com/Nextomics/NextPolish) to polish the miniasm-derived contigs. We did not perform polishing on IPA contigs, because HiFi sequencing generates extremely accurate reads. The remaining SNPs and ambiguities were manually corrected using mapping data of long-reads containing CDS. For C. ornatum, each sequence from step 10 (10-assemble/p_ctg.fasta) was considered as a minicircle sequence, because the following step of the IPA assembler (polish and purge dups) did not function correctly. For the short-read data, sorted and verified mitochondrial genes (see below) were used as seeds for NOVOplasty 4.2 6. Using Geneious (Biomatters, Auckland, New Zealand), generated NOVOplasty contigs were then de novo assembled. Assembled contig that codes any of mitochondrial genes was considered as part of mitochondrial genome. Those contigs were polished (-SNP & Indel) with Pilon 1.22 3, using short-read mapping data generated by bowtie2 2.3.5.1 4. Trinity 2.11.0 7 was used to assemble RNA sequencing data. Sorting and verifying mitochondrial contigs BLAST 2.2.31+ 8 was used to search for mitochondrial genes. Because mitochondrial gene sequences of the Stylonematophyceae were absent in the National Center for Biotechnology Information (NCBI) database, mitochondrial protein sequences from several red algae species were searched against assembled SPAdes contigs with e-value 1e-05 using tBLASTn. All the matched sequences were translated (Genetic code 4 9) and aligned against NCBI protein database (nr). Sequences that have eukaryotic taxa in the top 100 matches were considered as candidate genes. Those that only had prokaryotic taxa in the top 100 matches with significantly low identity or query coverage were also selected as possible candidates. To exclude bacterial contigs from possible mitochondrial contigs, genomic features such as GC content, read coverage, and tBLASTn result (top match and identity) of the contig were used as criteria for selection. CDSs of each contig were compared against NCBI protein database (nr) using default parameters. These candidate contigs were verified manually using phylogenetic analysis. Using translated CDS in candidate contigs as queries, protein sequences from nr database were searched by MMSeqs2 10 (Version: 330ea3684fd3f985d0127ffe8ca5b3f13053c619) with maximum sensitivity and e-value 1e-05. Nuclear gene prediction RNA-seq reads were mapped against the assembled nuclear genome of R. marinus using hisat2 (2.2.1) 11 and STAR 2.7.7a 12 (--outFilterScoreMinOverLread 0.45 --outFilterMatchNminOverLread 0.45). Mapping information was used as training set of ab initio gene models, performed using BRAKER 2.1.5 13. Completeness was measured using BUSCO 3.0.2 with the ‘eukaryote_odb9’ database 14, following Cho, et al. (2023). RAD52 was not found in the C. crispus proteome and contaminant assemblies were found in the transcriptome assembly of C. ornatum. Therefore, we chose to generate a transcriptome assembly and perform gene modeling using the available RNA-seq data (see Supplementary Table S1). We used Trinity 2.11.0 7 to obtain the transcriptome assembly. cd-hit 4.8.1 16, 17 was used to cluster sequences with similarity over 95% and predicted proteins were generated using Transdecoder 5.5.0 (https://github.com/TransDecoder/TransDecoder). BUSCO 14 values were: C. crispus, C:97.4% [S:27.4%, D:70.0%], F:2.6%, M:-0.0%, n:303; and C. ornatum, C:96.7% [S:20.5%, D:76.2%], F:1.3%, M:2.0%, n:303. Consequently, we predicted several novel genes that are not present in existing red algal data. Comparative analysis of CDSs The mitochondrial genomes of 23 red algae representing Cyanidiophyceae, Compsopogonophyceae, Porphyridiophyceae, Rhodellophyceae, Bangiophyceae, and Florideophyceae were downloaded from NCBI nucleotide database (nt) and used for the comparison (Supplementary Table S1). Translated sequences of 11 CDSs (atp6, atp9, cob, cox1, cox2, cox3, nad1, nad2, nad4, nad5-f and nad5-s) were aligned by MAFFT 7.310 18 and concatenated. From the concatenated alignment, amino acid similarity were calculated by Geneious 10.2.3 using blosum62 matrix 19 with threshold 1 as well as nucleotide identity. Maximum-likelihood phylogenetic tree was built using IQ-TREE 1.6.8 20. Optimal evolutionary models were automatically chosen after model selection 21. Pairwise dN/dS calculation was

LimmaVoom mRNA differential expression analysis results.