This research was supported by grants from NIH K01HL133520 (PS) and K12HD055882 (PS). The authors thank Dr. Joanna Floros for the assistance with ozone exposure experiments.

All methods described here have been approved by the Institutional Animal Care and Use Committee (IACUC) of Penn State University.
1. Assessment of the Estrous Cycle Stage
<ol>
	<li>Properly restrain a female C57BL/6 mouse (8&#8211;9 weeks old) using the one-handed mouse restraint technique described in Machholz et al.10.</li>
	<li>Fill the sterile plastic pipette with 10 &#956;L of ultra-pure water.</li>
	<li>Introduce the tip of plastic pipette into the vagina.</li>
	<li>Gently flush the liquid 4&#8211;5 times to collect the sample.</li>
	<li>Place the final flush containing vaginal fluid on a glass slide.</li>
	<li>Observe the unstained vaginal flush under a light microscope with a 20x objective. 
	NOTE: Animals that do not show regular cycles due to pseudopregnancy or other causes need to be excluded from the experiment. It is recommended to perform daily vaginal secretions for at least three consecutive cycles to confirm cyclicity.</li>
</ol>
2. Exposure to Ozone
<ol>
	<li>Place a maximum of 4 mice in two 1.2 L glass containers with wire mesh lids, and water ad libitum.</li>
	<li>Put one glass container in the ozone chamber and the other in the filtered air exposure chamber.</li>
	<li>Adjust the ozone concentration to 2 ppm and monitor ozone levels regularly. 
	NOTE: The ozone apparatus delivers a regulated airflow (&#62;30 air changes/h) with controlled temperature (25 &#176;C) and relative humidity (50%). The system generates ozone by an electrical discharge ozonizer, which is monitored and controlled by an ultraviolet ozone analyzer and mass flow controllers, as described previously11.</li>
	<li>Remove glass containers after 3 h of ozone/filtered air exposure. Return animals to cage with bedding, food, and water ad libitum.</li>
</ol>
3. Lung Collection
<ol>
	<li>4 h after exposure, anesthetize animals with an intraperitoneal injection of a ketamine/xylazine cocktail (90 mg/kg ketamine, 10 mg/kg xylazine). 
	NOTE: To confirm a proper level of anesthesia, check the mouse for a pedal reflex (firm toe pinch) and adjust the anesthetic as needed.</li>
	<li>Dampen the mouse skin with 70% ethanol.</li>
	<li>Make a 2 cm midline incision using an operating scissor and surgical tweezers to expose the vena cava.</li>
	<li>Sacrifice mice by transection of the vena cava and aorta. If needed, insert a 21 G gauge needle into the vena cava above the renal veins to collect blood prior to exsanguination. Alternatively, collect blood via heart puncture following standard protocols.</li>
	<li>Use a surgical scissor to cut open the abdominal cavity and remove skin/upper muscle, moving upwards toward the ribs.</li>
	<li>Use a surgical scissor to puncture the diaphragm. 
	NOTE: Lungs will collapse away from the diaphragm.</li>
	<li>Cut away the ribcage using a surgical scissor to expose the heart and lungs.</li>
	<li>Using forceps, take a 1.5 mL RNase-free microcentrifuge tube and submerge it in liquid nitrogen to fill the tube. 
	NOTE: Use goggles and protective gloves to handle liquid nitrogen.</li>
	<li>Remove the lungs, place them into the RNase-free microcentrifuge 1.5 mL tubes filled with liquid nitrogen to snap-freeze the tissue, and wait a few seconds until the liquid evaporates.</li>
	<li>Close the tube lid and store the tissue at -80 &#176;C until use.</li>
</ol>
4. RNA Preparation
<ol>
	<li>Pulverize whole lungs using a stainless-steel tissue pulverizer. 
	NOTE: The tissue pulverizer needs to be placed in liquid nitrogen prior to use. Clean the pulverizer after each use with RNAse solution.</li>
	<li>Split the pulverized lungs and place into two 1.5 mL tubes (half lung each).</li>
	<li>Add 500 &#181;L of guanidinium thiocyanate per sample tube and mix.&#160; Homogenize each sample using 18 G, 21 G, and 23 G needles, respectively. 
	NOTE: Samples can be spiked with 5.6&#8201; x&#8201; 108&#160;copies of a small RNA spike-in control (from a different species) before proceeding with the extraction.</li>
	<li>Add 500 &#181;L of ethanol to each sample and vortex for 15 s.</li>
	<li>Load the mixture in a spin column in a collection tube and centrifuge at 12,000 x g for 1 min. Discard the flow-through.</li>
	<li>For DNase I treatment (in-column);
	<ol>
		<li>Add 400 &#181;L of RNA Wash Buffer and centrifuge at 12,000 x g for 1 min.</li>
		<li>In an RNase-free tube, add 5 &#181;L of DNase I and 75 &#181;L of 1x DNA digestion buffer and mix. Add the mix directly to the column matrix.</li>
		<li>Incubate at room temperature for 15 min.</li>
	</ol>
	</li>
	<li>Add 400 &#181;L of RNA prewash solution to the column and centrifuge at 12,000 x g for 1 min. Discard the flow-through and repeat this step.</li>
	<li>Add 700 &#181;L of RNA wash buffer to the column and centrifuge at 12,000 x g for 1 min. Discard the flow-through.</li>
	<li>Centrifuge at 12,000 x g for 2 min to remove remaining buffer. Transfer the column into an RNase-free tube.</li>
	<li>To elute RNA, add 35 &#181;L of DNase/RNase-free water directly to the column matrix and centrifuge at 12,000 x g for 1.5 min.</li>
	<li>Measure total RNA concentration (260 nm) and purity using a spectrophotometer. Follow instructions to perform RNA quantification in a 1.5 &#181;L sample aliquot. Blank the instrument with DNase/RNase-free water used for elution. 
	NOTE: A 260/280 ratio of ~2.0 is generally accepted as &#8220;pure&#8221; for RNA. Typical RNA concentration usually ranges between 750 and 2,500 ng/&#181;L.</li>
	<li>Store at -80 &#176;C.</li>
</ol>
5. miRNA Profiling
<ol>
	<li>To retro-transcribe small RNAs, use 200 ng of total RNA.
	<ol>
		<li>Prepare the reverse-transcription reaction mix on ice (total volume per reaction is 20 &#181;L). For each reaction, add 4 &#181;L of 5x buffer, 2 &#181;L of 10x nucleotides mix, 2 &#181;L of reverse transcriptase, and 2 &#181;L of RNase-free water. Mix all the components and aliquot in 600 &#181;L RNAse-free plastic tubes (10 &#181;L of mix per reaction). 
		NOTE: The reverse-transcription master mix contains all components required for first-strand cDNA synthesis except template RNA. 
		NOTE: Calculate excess volume (10%) when preparing the master mix.</li>
		<li>Add the template RNA (200 ng in 10 &#181;L) to each tube containing reverse-transcription master mix. Mix, centrifuge for 15 s at 1,000 x g, and store them on ice until placing in thermocycler or dry block.</li>
		<li>Incubate for 60 min at 37 &#176;C.</li>
		<li>Incubate for 5 min at 95 &#176;C and place the tubes on ice.</li>
		<li>Dilute the cDNA by adding 200 &#181;L of RNase-free water to each 20 &#181;L reverse-transcription reaction.</li>
	</ol>
	</li>
	<li>Perform real-time PCR using the Mouse Inflammatory Response and Autoimmunity miRNA PCR Array.
	<ol>
		<li>Prepare a reaction mix (total volume 1100 &#181;L): For each reaction, add 550 &#181;L of 2x PCR master mix, 110 &#181;L of 10x universal primer mix, 340 &#181;L of RNase-free water, and 340 &#181;L of template cDNA (diluted reaction from step 5.1.5).</li>
		<li>Add 10 &#181;L of reaction mix to each well of the pre-loaded miRNA PCR Array using a multichannel pipettor.</li>
		<li>Seal the miRNA PCR Array plate with optical adhesive film.</li>
		<li>Centrifuge the plate for 1 min at 1,000 x g at room temperature to remove bubbles.</li>
		<li>Program the real-time cycler, PCR initial activation step for 15 min at 95 &#176;C, 3-step cycling containing denaturation for 15 s at 94 &#176;C, annealing for 30 s at 55 &#176;C, and extension for 30 s at 70 &#176;C for 40 cycles number. 
		NOTE: Follow manufacturer&#8217;s cycling conditions instructions to set up the real-time cycler. Perform the dissociation curve step built into the real-time cycler software.</li>
		<li>Perform data analysis.</li>
	</ol>
	</li>
</ol>
6. Data Analysis
<ol>
	<li>Extract Ct values from the real time PCR software for each sample into an analysis software. 
	NOTE: A Ct value of 34 is considered as cutoff. If samples contain spike-in control (e.g., cel-mir-39), normalize Ct values to the spike in control for each sample. Threshold values may need to be manually set. Baseline values are automatically set.</li>
	<li>Normalize Ct values to the average Ct of six miRNA housekeeping controls: SNORD61, SNORD68, SNORD72, SNORD95, SNORD96A, RNU6-2, using the following equation: 
	&#916;Ct = (Ct_Target &#8722; Ct_housekeeping)</li>
	<li>For fold change calculations, calculate &#916;&#916;Ct values using a specific sample as the control, using the relative expression equation12: 
	miRNA relative expression: 
	2-&#8710;&#8710;Ct, where - &#8710;&#8710;Ct = -[&#8710;Ct test - &#8710;Ct control] 
	NOTE: A fold change of 200 is considered as cutoff.</li>
	<li>Export fold change expression values to perform statistical analysis on R using the limma package in Bioconductor8.</li>
	<li>Correct for multiple comparisons using the Benjamini-Hochberg method13. 
	NOTE: &#160;A copy of the R script is available at: <a href="http://psilveyra.github.io/silveyralab/">http://psilveyra.github.io/silveyralab/</a>. Datasets and analyzed data are also available at the Gene Expression Omnibus under number GSE111667,&#160;https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111667.</li>
</ol>
7. Data Analysis: Functional Analysis Software
<ol>
	<li>Arrange the dataset. Include miRNAs with their respective expression log ratios and p-values. See Table 1 for specific dataset formatting.</li>
	<li>Open functional analysis software (version 01-10).</li>
	<li>Upload the dataset using the following format: File format: &#8220;Flexible Format&#8221;, Contains Column Header: &#8220;Yes&#8221;, Select Identifier type: &#8220;miRBase (mature)&#8221;, Array platform used for experiments: Choose the array platform. 
	NOTE: Accepted files formats are .txt (tab delimited text files), .xls (excel files), and .diff (cuffdiff files).</li>
	<li>Select &#8220;Infer Observations&#8221; and verify that the experimental group labeling is correct.</li>
	<li>Go to &#8220;Dataset Summary&#8221; to revise the total amount of mapped and unmapped miRNAs.</li>
	<li>Click on the &#8220;new&#8221; button at the top left of program. Select &#8220;New MicroRNA Target Filter&#8221; and upload a microRNA dataset.
	<ol>
		<li>Set Source to: TarBase, Ingenuity Experts Findings, miRecords.</li>
		<li>Set Confidence to: Experimentally observed or high (predicted).</li>
		<li>Select &#8220;Add Columns&#8221; to include a variety of biological information about the targets such as species, diseases, tissues, pathways and more.</li>
		<li>To focus on targets that have changed expression in the experiment, select &#8220;Add/ replace mRNA dataset&#8221;. Use &#8220;Expression Pairing&#8221; to locate microRNAs with the same or different expression levels. 
		NOTE: The filter analysis will provide microRNA names and symbols, mRNA targets, the source that describes the target relationship, and the confidence level of the predicted relationship (Figure 2).</li>
	</ol>
	</li>
	<li>Click &#8220;Add to My Pathway&#8221; to send the filtered dataset to a pathway canvas and explore more biological relationships.</li>
	<li>Use Path Designer to create a publication-quality model of microRNA effects.</li>
	<li>An alternate option is to create a core analysis.
	<ol>
		<li>For core analysis type, select &#8220;Expression Analysis&#8221;.</li>
		<li>For measurement type, select &#8220;Expr Log Ratio&#8221;.</li>
		<li>Analysis Filter Summary: consider only molecules and/or relationships where: (species = mouse) AND (confidence = experimentally observed) AND (data sources = &#8220;Ingenuity Expert Findings&#8221;, &#8220;Ingenuity ExpertAssist Findings&#8221;, &#8220;miRecords&#8221;, &#8220;TarBase&#8221;, OR &#8220;TargetScan Human&#8221;).</li>
		<li>Select a cutoff of p-value = 0.05.</li>
		<li>Run the analysis. 
		NOTE: The report will include: canonical pathways, upstream regulators analysis, diseases and functions, regulator effects, networks, molecules and more.</li>
	</ol>
	</li>
</ol>

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The authors declare that they have no competing interests.

MicroRNA profiling is an advantageous technique for both disease diagnosis and mechanistic research. In this manuscript, we defined a protocol to evaluate the expression of miRNAs that are predicted to regulate inflammatory genes in the lungs of female mice exposed to ozone in different estrous cycle stages. Methods for the determination of the estrous cycle, such as the visual detection method, have been described16. However, these rely on one-time measurements, and therefore are unreliable. To accurately identify all estrous cycle stages in females that cycle regularly, the method described here is recommended. In addition, this simple protocol can also be used to indirectly estimate daily hormonal fluctuations in mice. To avoid activation of unwanted inflammatory responses due to vaginal irritation, sampling needs to be performed just once daily. Because of variability in the cycle length and housing influences, it is important to perform the protocol for two to three complete cycles before using animals in an experiment considering the cycle stage.
For the successful extraction of RNA from lung tissue, an accurate procedure is critical. This protocol describes a one-day method to isolate RNA from lung tissue that yields high-quality RNA. Modifications to the manufacturer&#39;s protocol were required to efficiently extract RNA from lungs. We added an additional centrifugation step after addition of the wash buffer to remove as much buffer as possible. We also eluted RNA with 35 &#181;L of DNase/RNase-free water, centrifuging the column for 1.5 min to ensure high concentration levels. Spectrofluorometer results confirmed the effectiveness of our RNA extraction protocol. The ratio of absorbance at 260 and 280 nm (A260/280 ratio) is frequently used to assess the purity of RNA preparations. The maximum absorbance for nucleic acids is 260 and 280 nm, respectively. It is accepted as &#34;pure&#34; for RNA if the ratio is about 2.017. Likewise, for the A260/A230 contamination absorbance ratio, the values for purity are in the range of 2.0&#8211;2.218. In this study, the average A260/A280 and A260/A230 ratios observed were 2.016 &#177; 0.002 and 2.179 &#177; 0.018, respectively (Table 1). Therefore, our RNA extraction protocol was successful. Another advantage of the protocol used is the addition of the DNAse treatment. This is important to avoid genomic-DNA contamination19. A limitation of this RNA isolation protocol is the use of purification columns to discard waste through precipitation using alcohol because some lung debris may obstruct the membrane either partially or completely, resulting in low yields. Also, if the homogenization step is not carefully performed, large quantities of lung RNA can be easily lost or degraded. If low RNA yields are obtained, RNA can be re-purified and eluted in a smaller volume. Alternatively, RNA can be precipitated overnight following published protocols20.
Microarray technologies applied to miRNA profiling are promising tools in many research fields. In our study, we used PCR arrays, which provide the advantage of higher detection threshold, and normalization strategies for detection of differentially expressed miRNAs vs. other technologies such as probe-based miRNA arrays21. A limitation of this protocol is that it requires a minimum amount of starting RNA material, and availability of specific sets of primers for the miRNAs of interest, as opposed to other available techniques such as RNAseq. Another advantage of PCR-based arrays is the option of using non-miRNA reference genes for qPCR normalization (such as small nucleolar RNAs or SNORDs) to calculate differential expression of miRNAs. Finally, using PCR arrays provides several options for data analysis, ranging from online tools provided by the manufacturers, to conventional methods to detect differential expression though Real-Time PCR. Statistical analysis with limma is convenient for both microarrays and PCR-based arrays and uses the empirical Bayes moderated f-statisitcs22. Here, we show that both p-values and q-values (adjusted for multiple testing) can be obtained with the command toptable adjusting the false discovery rate threshold and identifying differentially expressed miRNAs.
Functional analysis software is a web-based application for data analysis in pathway context. The software gives researchers powerful search abilities that can help to frame data sets or specific targets in context, within a bigger picture of biological significance. Although the software environment is flexible to different types of analysis (i.e., metabolomics, SNPs, proteomics, microRNA, toxicology, etc.), our goal here is to highlight aspects of miRNA analysis. After uploading a list of 14 miRNAs with significant expression log-ratios and p-values, all were mapped by the software. We performed the miRNA target filter and core analysis, which includes the enrichment pathway analysis. However, such analyses consider genes for which the 14 miRNAs are predicted to target and not the miRNAs themselves. The results section lists outputs such as: canonical pathways, diseases and function, genes targeted by differentially expressed miRNAs, physiological system development, regulators, and networks (Table 4). The pathway visualization is shown under the network tab, where miRNAs and molecules are shown as clickable nodes that are linked with information associated to the gene of interest (Figure 3). An advantage of the functional analysis software is the high-quality miRNA-related findings, including both experimentally validated and predicted interactions. The functional analysis software databases include: experimentally validated microRNA-mRNA interactions databases23,24, predicted microRNA-mRNA interaction database with low-confidence interactions excluded (e.g., Target Scan)25, experimentally validated human, rat, and mouse microRNA-mRNA interactions databases (e.g., miRecords)26, and literature findings (e.g., microRNA-related findings manually curated from published literature by scientific experts). Other studies comparing the effectiveness and usability of bioinformatics tools to analyze pathways associated with miRNA expression confirm the effectiveness of this software27. Overall, computational methods are cost-effective, less time-consuming, and can be easily validated by molecular methods. With the constant growth and accumulation of biomedical data, bioinformatics methods will become increasingly powerful in the discovery of miRNA-mediated mechanisms of biological and disease processes.

microRNAs (miRNAs) are short (19 to 25 nucleotides), naturally occurring, non-coding RNA molecules.Sequences of miRNAs are evolutionary conserved across species, suggesting the importance of miRNAs in regulating physiological functions1. microRNA expression profiling has been proven to be helpful for identifying miRNAs that are important in the regulation of a variety of processes, including the immune response, cell differentiation, developmental processes, and apoptosis2. More recently, miRNAs have been recognized for their potential use in disease diagnostics and therapeutics. For researchers studying mechanisms of gene regulation, measuring miRNA expression can enlighten systems-level models of regulatory processes, especially when miRNA information is merged with mRNA profiling and other genome-scale data3. On the other hand, miRNAs have also been shown to be more stable than mRNAs in a range of specimen types and are also measurable with greater sensitivity than proteins4. This has led to considerable interest in the development of miRNAs as biomarkers for diverse molecular diagnostic applications, including lung diseases.
In the lung, miRNAs play important roles in developmental processes and the maintenance of homeostasis. Moreover, their abnormal expression has been associated with the development and progression of various pulmonary diseases5. Inflammatory lung disease induced by air pollution has demonstrated greater severity and poorer prognosis in females, indicating that hormones and the estrous cycle can regulate lung innate immunity and miRNA expression in response to environmental challenges6. In this protocol, we use ozone exposure, which is a major component of air pollution, to induce a form of lung inflammation in female mice that occurs in the absence of adaptive immunity. By using ozone, we are inducing the development of airway hyperresponsiveness that is associated with airway epithelial cell damage and an increase in neutrophils and inflammatory mediators in proximal airways7. Currently, there are not well-described protocols to characterize and analyze miRNAs across the estrous cycle in ozone-exposed mice. 
Below, we describe a simple method to identify estrous cycle stages and miRNA expression in lung tissue of female mice exposed to ozone. We also address effective bioinformatics approaches to miRNA discovery and target identification, with an emphasis on computational biology. We analyze the microarray data using limma, an R/Bioconductor software that provides an integrated solution for analyzing data from gene expression experiments8. Analysis of PCR array data from limma has an advantage in terms of power over t-test based procedures when using small number of arrays/samples to compare expression. To comprehend the biological context of miRNA expression results, we then used the functional analysis software. In order to understand the mechanisms regulating transcriptional changes and to predict likely outcomes, the software combines miRNA-expression datasets and knowledge from the literature9. This is an advantage when compared with software that just look for statistical enrichment in overlapping to sets of miRNAs.

<table><tbody><tr><td>C57BL/6J mice</td><td>The Jackson Laboratory</td><td>000664</td><td>8 weeks old</td></tr><tr><td>UltraPure Water</td><td>Thermo Fisher Scientific</td><td>10813012</td><td></td></tr><tr><td>Sterile plastic pipette</td><td>Fisher Scientific</td><td>13-711-25</td><td>Capacity: 1.7 mL</td></tr><tr><td>Frosted Microscope Slides</td><td>Thermo Fisher Scientific</td><td>2951TS</td><td></td></tr><tr><td>Light microscope</td><td>Microscope World</td><td>MW3-H5</td><td>10x and 20x objective</td></tr><tr><td>Ketathesia- Ketamine HCl Injection USP</td><td>Henry Schein Animal Health</td><td>55853</td><td>90 mg/kg. Controlled drug.</td></tr><tr><td>Xylazine Sterile Solution</td><td>Lloyd Laboratories</td><td>139-236</td><td>10 mg/kg. Controlled Drug.</td></tr><tr><td>Ethanol</td><td>Fisher Scientific</td><td>BP2818100</td><td>Dilute to 70% ethanol with water.</td></tr><tr><td>21 G gauge needle</td><td>BD Biosciences</td><td>305165</td><td></td></tr><tr><td>Syringe</td><td>Fisher Scientific</td><td>329654</td><td>1 mL</td></tr><tr><td>Operating Scissors</td><td>World Precision Instruments</td><td>501221, 504613</td><td>14 cm, Sharp/Blunt, Curved and 9 cm, Straight, Fine Sharp Tip</td></tr><tr><td>Tweezer Kit</td><td>World Precision Instruments</td><td>504616</td><td></td></tr><tr><td>Spectrum&amp;nbsp;Bessman Tissue Pulverizers</td><td>Fisher Scientific</td><td>08-418-1</td><td>Capacity: 10 to 50 mg</td></tr><tr><td>RNase-free Microfuge Tubes</td><td>Thermo Fisher Scientific</td><td>AM12400</td><td>1.5 mL</td></tr><tr><td>TRIzol Reagent</td><td>Thermo Fisher Scientific</td><td>15596026</td><td></td></tr><tr><td>Direct-zol RNA MiniPrep Plus</td><td>Zymo Research</td><td>R2071</td><td></td></tr><tr><td>NanoDrop</td><td>Thermo Fisher Scientific</td><td>ND-ONE-W</td><td></td></tr><tr><td>miScript II RT kit</td><td>Qiagen</td><td>218161</td><td></td></tr><tr><td>Mouse Inflammatory Response &amp;amp; Autoimmunity miRNA PCR Array</td><td>Qiagen</td><td>MIMM-105Z</td><td></td></tr><tr><td>Thin-walled, DNase-free, RNase-free PCR tubes</td><td>Thermo Fisher Scientific</td><td>AM12225</td><td>for 20 &amp;mu;L reactions</td></tr><tr><td>miRNeasy Serum/Plasma Spike-in Control</td><td>Qiagen</td><td>219610</td><td></td></tr><tr><td>Microsoft Excel</td><td>Microsoft Corporation</td><td></td><td>https://office.microsoft.com/excel/</td></tr><tr><td>Ingenuity Pathway Analysis</td><td>Qiagen</td><td></td><td>https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/</td></tr><tr><td>R Software</td><td>The R Foundation</td><td></td><td>https://www.r-project.org/</td></tr><tr><td>Thermal cycler or chilling/heating block</td><td>General Lab Supplier</td><td></td><td></td></tr><tr><td>Microcentrifuge</td><td>General Lab Supplier</td><td></td><td></td></tr><tr><td>Real-time PCR cycler</td><td>General Lab Supplier</td><td></td><td></td></tr><tr><td>Multichannel pipettor</td><td>General Lab Supplier</td><td></td><td></td></tr><tr><td>RNA wash buffer</td><td>Zymo Research</td><td>R1003-3-48</td><td>48 mL</td></tr><tr><td>DNA digestion buffer</td><td>Zymo Research</td><td>E1010-1-4</td><td>4 mL</td></tr><tr><td>RNA pre-wash buffer</td><td>Zymo Research</td><td>R1020-2-25</td><td>25 mL</td></tr><tr><td>Ultraviolet ozone analyzer</td><td>Teledyne API</td><td>Model T400</td><td>http://www.teledyne-api.com/products/oxygen-compound-instruments/t400</td></tr><tr><td>Mass flow controllers</td><td>Sierra Instruments Inc</td><td>Flobox 951/954</td><td>http://www.sierrainstruments.com/products/954p.html</td></tr></tbody></table>

lung microrna profiling across the estrous cycle in ozone-exposed mice

MicroRNA (miRNA) profiling has become of interest to researchers working in various research areas of biology and medicine. Current studies show a promising future of using miRNAs in the diagnosis and care of lung diseases. Here, we define a protocol for miRNA profiling to measure the relative abundance of a group of miRNAs predicted to regulate inflammatory genes in the lung tissue from of an ozone-induced airway inflammation mouse model. Because it has been shown that circulating sex hormone levels can affect the regulation of lung innate immunity in females, the purpose of this method is to describe an inflammatory miRNA profiling protocol in female mice, taking into consideration the estrous cycle stage of each animal at the time of ozone exposure. We also address applicable bioinformatics approaches to miRNA discovery and target identification methods using limma, an R/Bioconductor software, and functional analysis software to understand the biological context and pathways associated with differential miRNA expression.

The different cell types observed in smears are used to identify the mouse estrous cycle stage (Figure 1). These are identified by cell morphology. During proestrus, cells are almost exclusively clusters of round-shaped, well-formed nucleated epithelial cells (Figure 1A). When the mouse is in the estrus stage, cells are cornified squamous epithelial cells, present in densely packed clusters (Figure 1B). During metestrus, cornified epithelial cells and polymorphonuclear leukocytes are seen (Figure 1C). In diestrus, leukocytes (small cells) are generally more prevalent (Figure 1D).
We extracted RNA from four mouse lungs following the protocol previously described. The nucleic acid concentrations (ng/ &#181;L) ranged between 1197.9 and 2178.1 with an average of 1583.1 &#177; 215 (Table 1). The average A260/A280 ratio fluctuated from 2.010 to 2.020 with an average of 2.016 &#177; 0.002. On the other hand, the observed A260/A230 ratios oscillated between 2.139 and 2.223 with an average of 2.179 &#177; 0.018.
Table 2 shows differential expression results obtained with limma on R. We calculated top differentially expressed miRNAs between mice exposed to ozone or filtered air in proestrus (using the command toptable)14. The first column gives the value of the log2-fold fold change in miRNA expression between ozone and filtered air exposed animals. The column t represents the moderate t-statistic calculated for each miRNA in the comparison. The columns p.value and adj.p.value represent associated p-values for each comparison before and after multiple testing adjustment, respectively. Adjustment for multiple comparisons was done with the Benjamini and Hochberg&#39;s method to control the false discovery rate15. Column B represents the log-odds that the miRNA is differentially expressed8.
We performed the miRNA target filter and core analysis that includes the enrichment pathway analysis. After uploading a list of 14 miRNAswith the significant expression log ratio and p-value, all of them were mapped by the miRNA target filter (Table 3). The results were filtered and sorted to get to certain pathways, in this case the &#34;Cellular Immune Response&#34;. The core analysis provided information about canonical pathways, diseases and function, regulators, and networks (Table 4). The functional analysis software produced a network analysis that shows the relationship between the miRNAs of interest and other molecules (Figure 3).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58664/58664fig1.jpg" /> 
Figure 1: Identification of estrous cycle stages. (A) Proestrus (predominantly nucleated epithelial cells); (B) estrus (predominantly anucleated cornified cells); (C) metestrus (all three types of cells); and (D) diestrus 2 (majority of leukocytes). Scale bar = 100 &#181;m. Magnification = 20x. <a href="https://www.jove.com/files/ftp_upload/58664/58664fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58664/58664fig2.jpg" /> 
Figure 2: Functional analysis software representative results: miRNA target filter. Comprehensive profile of miRNAs at different stages of the estrous cycle. After performing miRNA filter, the software delivers detailed listings of genes and compounds implicated in diseases and other phenotypes, which can be filter and sort to get to certain pathways, in this case &#34;Cellular Immune Response&#34;. <a href="https://www.jove.com/files/ftp_upload/58664/58664fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58664/58664fig3.jpg" /> 
Figure 3: Functional analysis software representative results: networks. Comparison of networks affected by filtered air or ozone exposure in females at different stages of the estrous cycle. Diagram of biological networks associated with miRNAs in the lungs of female mice exposed to filtered air vs. ozone in proestrus (A) or non-proestrus stages (B). This figure has been modified from Fuentes et al.6. <a href="https://www.jove.com/files/ftp_upload/58664/58664fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>ID</td>
			<td>Nucleic Acid (ng/&#181;L)</td>
			<td>A260/A280</td>
			<td>A260/A230</td>
		</tr>
		<tr>
			<td>Sample 1</td>
			<td>1197.930</td>
			<td>2.015</td>
			<td>2.192</td>
		</tr>
		<tr>
			<td>Sample 2</td>
			<td>1355.703</td>
			<td>2.018</td>
			<td>2.223</td>
		</tr>
		<tr>
			<td>Sample 3</td>
			<td>2178.104</td>
			<td>2.020</td>
			<td>2.163</td>
		</tr>
		<tr>
			<td>Sample 4</td>
			<td>1600.837</td>
			<td>2.010</td>
			<td>2.139</td>
		</tr>
		<tr>
			<td>Average</td>
			<td>1583.144 &#177; 215</td>
			<td>2.016 &#177; 0.002</td>
			<td>2.179 &#177; 0.018</td>
		</tr>
	</tbody>
</table>
Table 1: Example of RNA concentrations and absorbance ratios at 260, 230, and 280 nm from purified lung tissue samples from four mice. Concentrations were measured with a spectrophotometer.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>logFC</td>
			<td>t</td>
			<td>P.Value</td>
			<td>adj.P.Val</td>
			<td>B</td>
		</tr>
		<tr>
			<td>mmu-miR-694</td>
			<td>1.492</td>
			<td>4.071</td>
			<td>0.000153</td>
			<td>0.009514</td>
			<td>0.759</td>
		</tr>
		<tr>
			<td>mmu-miR-9-5p</td>
			<td>0.836</td>
			<td>3.916</td>
			<td>0.000254</td>
			<td>0.009514</td>
			<td>0.289</td>
		</tr>
		<tr>
			<td>mmu-miR-221-3p</td>
			<td>0.385</td>
			<td>3.106</td>
			<td>0.003014</td>
			<td>0.075361</td>
			<td>-1.982</td>
		</tr>
		<tr>
			<td>mmu-miR-181d-5p</td>
			<td>0.597</td>
			<td>2.891</td>
			<td>0.005516</td>
			<td>0.103424</td>
			<td>-2.526</td>
		</tr>
		<tr>
			<td>mmu-miR-98-5p</td>
			<td>0.558</td>
			<td>2.699</td>
			<td>0.009243</td>
			<td>0.138649</td>
			<td>-2.987</td>
		</tr>
		<tr>
			<td>mmu-miR-712-5p</td>
			<td>0.667</td>
			<td>2.563</td>
			<td>0.013169</td>
			<td>0.164609</td>
			<td>-3.299</td>
		</tr>
		<tr>
			<td>mmu-miR-106a-5p</td>
			<td>-0.528</td>
			<td>-2.412</td>
			<td>0.019278</td>
			<td>0.206547</td>
			<td>-3.632</td>
		</tr>
	</tbody>
</table>
Table 2: Limma analysis results for differentially expressed miRNAs in females exposed to ozone vs. filtered air in the proestrus stage.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>miRNAs ID</td>
			<td>Observation 1</td>
			<td>Observation 1</td>
			<td>Observation 2</td>
			<td>Observation 2</td>
		</tr>
		<tr>
			<td></td>
			<td>Expr Log Ratio </td>
			<td>P Value</td>
			<td>Expr Log Ratio </td>
			<td>P Value</td>
		</tr>
		<tr>
			<td>mmu-miR-694</td>
			<td>1.492</td>
			<td>0.000153</td>
			<td>0.543319208</td>
			<td>0.0021385</td>
		</tr>
		<tr>
			<td>mmu-miR-9-5p</td>
			<td>0.836</td>
			<td>0.000254</td>
			<td>0.677595421</td>
			<td>0.004997439</td>
		</tr>
		<tr>
			<td>mmu-miR-221-3p</td>
			<td>0.385</td>
			<td>0.003014</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>mmu-miR-181d-5p</td>
			<td>0.597</td>
			<td>0.005516</td>
			<td>0.342276659</td>
			<td>0.106467657</td>
		</tr>
		<tr>
			<td>mmu-miR-98-5p</td>
			<td>0.558</td>
			<td>0.009243</td>
			<td>0.455392799</td>
			<td>0.034724699</td>
		</tr>
		<tr>
			<td>mmu-miR-712-5p</td>
			<td>0.667</td>
			<td>0.013169</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>mmu-miR-106a-5p</td>
			<td>-0.528</td>
			<td>0.019278</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 3: Example format for multi-observation upload of datasets to functional analysis software. Multiple experimental differential expressions can be grouped into a single spreadsheet and uploaded, and as many observations as needed can be added. Columns: 1) miRNAs ID; 2) Observation 1: Expr Log Ratio; 3) Observation 1: P Value; 4) Observation 2: Expr Log Ratio; 5) Observation 2: P Value.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="3">Non-proestrus</td>
			<td colspan="3">Proestrus</td>
		</tr>
		<tr>
			<td colspan="6">A. Genes targeted by differentially expressed miRNAs</td>
		</tr>
		<tr>
			<td>CAMK2N1</td>
			<td>PAFAH1B2</td>
			<td>SEC23A</td>
			<td>ABCB9</td>
			<td>FGD4</td>
			<td>SLC25A27</td>
		</tr>
		<tr>
			<td>CARS</td>
			<td>PDE4B</td>
			<td>SNX5</td>
			<td>APOO</td>
			<td>FRMD4B</td>
			<td>SLC38A1</td>
		</tr>
		<tr>
			<td>CYP24A1</td>
			<td>PDE7A</td>
			<td>SYT4</td>
			<td>ARHGEF38</td>
			<td>GPR137B</td>
			<td>TMCC1</td>
		</tr>
		<tr>
			<td>DBF4</td>
			<td>PGM3</td>
			<td>THAP12</td>
			<td>AVEN</td>
			<td>HMBS</td>
			<td>TRIM71</td>
		</tr>
		<tr>
			<td>HMGN2</td>
			<td>PNP</td>
			<td>TMED7</td>
			<td>CASP3</td>
			<td>METAP1</td>
			<td>XKR8</td>
		</tr>
		<tr>
			<td>KMT5A</td>
			<td>REV1</td>
			<td>TNFAIP2</td>
			<td>CCNJ</td>
			<td>MITF</td>
			<td>ZCCHC11</td>
		</tr>
		<tr>
			<td>LRRC17</td>
			<td>RPS19BP1</td>
			<td>TNFRSF10C</td>
			<td>CMTR2</td>
			<td>MYC</td>
			<td>ZIM3</td>
		</tr>
		<tr>
			<td>MDH2</td>
			<td>RRAD</td>
			<td>TP53</td>
			<td>CNMD</td>
			<td>RGMB</td>
			<td>ZNF181</td>
		</tr>
		<tr>
			<td>MIS18A</td>
			<td>SEC62</td>
			<td>UBE2V2</td>
			<td>DSCR8</td>
			<td>SLC14A1</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>ZNF420</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="6">B. Differences in top diseases and biofunctions</td>
		</tr>
		<tr>
			<td colspan="2">Diseases and Disorders</td>
			<td>P Value</td>
			<td colspan="2">Diseases and Disorders</td>
			<td>P Value</td>
		</tr>
		<tr>
			<td colspan="2">Inflammatory disease</td>
			<td>3.84E-02 - 3.84E-05</td>
			<td colspan="2">Organismal injury and abnormalities</td>
			<td>4.96E-02 - 2.77E-14</td>
		</tr>
		<tr>
			<td colspan="2">Inflammatory response</td>
			<td>3.84E-02 - 3.84E-05</td>
			<td colspan="2">Reproductive system disease</td>
			<td>2.15E-02 - 2.77E-14</td>
		</tr>
		<tr>
			<td colspan="2">Organismal injury and abnormalities</td>
			<td>4.17E-02 - 4.17E-05</td>
			<td colspan="2">Cancer</td>
			<td>4.96E-02 - 1.27E-10</td>
		</tr>
		<tr>
			<td colspan="6">C.&#160; Top molecular and cellular functions</td>
		</tr>
		<tr>
			<td colspan="2">Molecular and Cellular Functions</td>
			<td>P Value</td>
			<td colspan="2">Molecular and Cellular Functions</td>
			<td>P Value</td>
		</tr>
		<tr>
			<td colspan="2">Cellular development&#160;</td>
			<td>2.05E-02 - 5.26E-07</td>
			<td colspan="2">Cellular movement</td>
			<td>3.77E-02 - 4.47E-07</td>
		</tr>
		<tr>
			<td colspan="2">Cellular compromise</td>
			<td>3.75E-04 - 3.75E-04</td>
			<td colspan="2">Cellular death and survival</td>
			<td>4.91E-02 - 5.61E-06&#160;</td>
		</tr>
		<tr>
			<td colspan="2">Cell cycle</td>
			<td>2.62E-03 - 2.62E-03</td>
			<td colspan="2">Cellular development</td>
			<td>4.97E-02 - 1.38E-06</td>
		</tr>
		<tr>
			<td colspan="6">D.&#160; Top physiological system development and function</td>
		</tr>
		<tr>
			<td colspan="2">Development and Function</td>
			<td>P Value</td>
			<td colspan="2">Development and Function</td>
			<td>P Value</td>
		</tr>
		<tr>
			<td colspan="2">Organismal development&#160;</td>
			<td>4.17E-02 - 1.31E-03</td>
			<td colspan="2">Embryonic development&#160;</td>
			<td>3.30E-02 - 2.12E-05</td>
		</tr>
		<tr>
			<td colspan="2">Embryonic development&#160;</td>
			<td>1.29E-02 - 1.29E-02</td>
			<td colspan="2">Connective tissue development and function</td>
			<td>1.79E-02 - 6.10E-05</td>
		</tr>
		<tr>
			<td colspan="2">Connective tissue development and function&#160;</td>
			<td>1.93E-02 - 1.93E-02</td>
			<td colspan="2">Tissue morphology</td>
			<td>7.88E-05 - 7.88E-05</td>
		</tr>
		<tr>
			<td colspan="6">E. Top associated network functions</td>
		</tr>
		<tr>
			<td colspan="2">Associated Network Functions</td>
			<td>Score</td>
			<td colspan="2">Associated Network Functions</td>
			<td>Score</td>
		</tr>
		<tr>
			<td colspan="2" rowspan="2">Cellular development, inflammatory disease, inflammatory response</td>
			<td></td>
			<td colspan="2" rowspan="2">Organismal injury and abnormalities, reproductive system disease, cancer</td>
			<td></td>
		</tr>
		<tr>
			<td>6</td>
			<td>19</td>
		</tr>
	</tbody>
</table>
Table 4: Functional analysis software summary of females exposed to ozone in the non-proestrus vs. proestrus stages. The functional analysis software allows the analysis of top canonical pathways, upstream regulators, diseases &#38; functions, top functions, regulator effect networks and more. This table has been modified from Fuentes et al.6.

Watch this Scientific Journal Video about Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice at JoVE.com

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Here we describe a method to assess lung expression of miRNAs that are predicted to regulate inflammatory genes using mice exposed to ozone or filtered air at different stages of the estrous cycle.

MicroRNA (miRNA) profiling has become of interest to researchers working in various research areas of biology and medicine. Current studies show a ...

lung-microrna-profiling-across-the-estrous-cycle-in-ozone-exposed-mice

Universidad San Sebastian

Research

JoVE Journal

Immunology and Infection

6.1K Views.  Pennsylvania State University College of Medicine. Here we describe a method to assess lung expression of miRNAs that are predicted to regulate inflammatory genes using mice exposed to ozone or filtered air at different stages of the estrous cycle.

Video: Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

An In Vivo Estrogen Deficiency Mouse Model for Screening Exogenous Estrogen Treatments of Cardiovascular Dysfunction After Menopause

Postmenopausal women are at greater risk of developing cardiovascular diseases than premenopausal women. Female mice ovariectomized (OVX) at weaning display increased atherosclerotic lesions in the aorta compared with female mice with intact ovarian function. However, laboratory models involving estrogen-deficient mice with atherosclerosis-prone status are lacking. This deficit is crucial because clinical estrogen deficiency in menopausal women may aggravate the incidence of pre-existing or ongoing lipid disruption and atherosclerosis. In this study, we establish an in vivo estrogen-deficient mouse model by bilateral ovariectomy via a double dorsal-lateral incision in apolipoprotein E (apoE)-/- mice. We then compare the effects of 17&#946;-estradiol and pseudoprotodioscin (PPD) (a phytoestrogen) perorally administered via hazelnut spread. We find that although PPD exerts some effect on reducing final body weight and plasma TG in OVX apoE-/- mice, it has anti-atherosclerotic and cardiac-protective capacities comparable with its 17&#946;-estradiol counterpart. PPD is a phytoestrogen that has been reported to exert anti-tumor properties. Thus, the proposed method is applicable for screening phytoestrogens via peroral administration to substitute for traditional hormone replacement therapy in postmenopausal women, which has been reported to have potentially deleterious tumorigenetic capacity. Peroral administration via hazelnut spread is noninvasive, rendering it widely applicable to many patients. This article contains step-by-step demonstrations of bilateral ovariectomy via the double dorsal-lateral incision in apoE-/- mice and peroral 17&#946;-estradiol or phytoestrogen hormone replacement via hazelnut spread. Plasma lipid and cardiovascular function analyses using echocardiography follow.

Clinically, estrogen deficiency in menopausal women may aggravate the incidence of lipid disruption and atherosclerosis. We established an in vivo estrogen deficiency model by bilateral ovariectomy via a double dorsal-lateral incision in apoE-/- mice. The mouse model is applicable for screening exogenous estrogen treatments of cardiovascular dysfunction after menopause.

Postmenopausal women are at greater risk of developing cardiovascular diseases than premenopausal women. Female mice ovariectomized (OVX) at weaning ...

Biochemistry

A Method to Study the Impact of Chemically-induced Ovarian Failure on Exercise Capacity and Cardiac Adaptation in Mice

The risk of cardiovascular disease (CVD) increases in post-menopausal women, yet, the role of exercise, as a preventative measure for CVD risk in post-menopausal women has not been adequately studied. Accordingly, we investigated the impact of voluntary cage-wheel exercise and forced treadmill exercise on cardiac adaptation in menopausal mice. The most commonly used inducible model for mimicking menopause in women is the ovariectomized (OVX) rodent. However, the OVX model has a few dissimilarities from menopause in humans. In this study, we administered 4-vinylcyclohexene diepoxide (VCD) to female mice, which accelerates ovarian failure as an alternative menopause model to study the impact of exercise in menopausal mice. VCD selectively accelerates the loss of primary and primordial follicles resulting in an endocrine state that closely mimics the natural progression from pre- to peri- to post-menopause in humans. To determine the impact of exercise on exercise capacity and cardiac adaptation in VCD-treated female mice, two methods were used. First, we exposed a group of VCD-treated and untreated mice to a voluntary cage wheel. Second, we used forced treadmill exercise to determine exercise capacity in a separate group VCD-treated and untreated mice measured as a tolerance to exercise intensity and endurance.

Two exercise paradigms were tested on a newly developed chemically induced menopausal mouse model to examine the impact of menopause on exercise capacity and cardiac adaption to exercise.

The risk of cardiovascular disease (CVD) increases in post-menopausal women, yet, the role of exercise, as a preventative measure for CVD risk in ...

Medicine

Assessing Olfactory Impairment in Mice Due to Inhaled Environmental Pollutants

Studying the Effects of Inhaled Environmental Pollutants on Olfactory Function in Mice

Olfactory impairment is a significant public health problem and independently predicts the risk of neurodegenerative diseases. Inhaled environmental pollutants exposure may impair olfaction; thereby, there is an urgent need for methods to evaluate the effects of inhaled environmental pollutants exposure on olfaction. Mice are ideal models for olfactory experiments because of their highly developed olfactory system and behavioral characteristics. To assess the effects of inhaled environmental pollutants exposure on olfactory function in mice, a detailed buried food test and social odor discrimination experiment is provided, including the experiment preparation, the selection and construction of experimental facilities, the testing process, and indexes of time. Meanwhile, timekeeping equipment, operational details, and the experimental environment are discussed to ensure the success of the assay. Zinc sulfate is used as the treatment to demonstrate the feasibility of the experimental approach. The protocol provides a simple and clear operational process for assessing the effects of inhaled environmental pollutants on olfactory function in mice.

Author Spotlight: Assessing the Olfactory Effects of Airborne Pollutants &#8212; Buried Food and Social Odor Tests

This paper provides a detailed description of the buried food test and social odor discrimination experiment to assess the effects of inhaled environmental pollutants exposure on olfactory function in mice.

Olfactory impairment is a significant public health problem and independently predicts the risk of neurodegenerative diseases. Inhaled environmental ...

Environment

Defining the Program of Maternal mRNA Translation during In vitro Maturation using a Single Oocyte Reporter Assay

Events associated with oocyte nuclear maturation have been well described. However, much less is known about the molecular pathways and processes that take place in the cytoplasm in preparation for fertilization and acquisition of totipotency. During oocyte maturation, changes in gene expression depend exclusively on the translation and degradation of maternal messenger RNAs (mRNAs) rather than on transcription. Execution of the translational program, therefore, plays a key role in establishing oocyte developmental competence to sustain embryo development. This paper is part of a focus on defining the program of maternal mRNA translation that takes place during meiotic maturation and at the oocyte-to-zygote transition. In this method paper, a strategy is presented to study the regulation of translation of target mRNAs during in vitro oocyte maturation. Here, a Ypet reporter is fused to the 3&#39; untranslated region (UTR) of the gene of interest and then micro-injected into oocytes together with polyadenylated mRNA encoding for mCherry to control for injected volume. By using time-lapse microscopy to measure reporter accumulation, translation rates are calculated at different transitions during oocyte meiotic maturation. Here, the protocols for oocyte isolation and injection, time-lapse recording, and data analysis have been described, using the Ypet/interleukin-7 (IL-7)-3&#39; UTR reporter as an example.

This protocol describes a reporter assay to study the regulation of mRNA translation in single oocytes during in vitro maturation.

Events associated with oocyte nuclear maturation have been well described. However, much less is known about the molecular pathways and processes that ...

Developmental Biology

Microdissection and Dissociation of the Murine Oviduct: Individual Segment Identification and Single Cell Isolation

Mouse model systems are unmatched for the analysis of disease processes because of their genetic manipulability and the low cost of experimental treatments. However, because of their small body size, some structures, such as the oviduct with a diameter of 200-400 &#956;m, have proven to be relatively difficult to study except by immunohistochemistry. Recently, immunohistochemical studies have uncovered more complex differences in oviduct segments than were previously recognized; thus, the oviduct is divided into four functional segments with different ratios of seven distinct epithelial cell types. The different embryological origins and ratios of the epithelial cell types likely make the four functional regions differentially susceptible to disease. For example, precursor lesions to serous intraepithelial carcinomas arise from the infundibulum in mouse models and from the corresponding fimbrial region in the human fallopian tube. The protocol described here details a method for microdissection to subdivide the oviduct in such a way to yield a sufficient amount and purity of RNA necessary for downstream analysis such as reverse transcription-quantitative PCR (RT-qPCR) and RNA sequencing (RNAseq). Also described is a mostly non-enzymatic tissue dissociation method appropriate for flow cytometry or single cell RNAseq analysis of fully differentiated oviductal cells. The methods described will facilitate further research utilizing the murine oviduct in the field of reproduction, fertility, cancer, and immunology.

A method for microdissection of the mouse oviduct that allows collection of the individual segments while maintaining RNA integrity is presented. In addition, non-enzymatic oviductal cell dissociation procedure is described. The methods are appropriate for subsequent gene and protein analysis of the functionally different oviductal segments and dissociated oviductal cells.

Mouse model systems are unmatched for the analysis of disease processes because of their genetic manipulability and the low cost of experimental ...

Generation of a Chronic Obstructive Pulmonary Disease Model in Mice by Repeated Ozone Exposure

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airflow limitation and lung parenchymal destruction. It has a very high incidence in aging populations. The current conventional therapies for COPD focus mainly on symptom-modifying drugs; thus, the development of new therapies is urgently needed. Qualified animal models of COPD could help to characterize the underlying mechanisms and can be used for new drug screening. Current COPD models, such as lipopolysaccharide (LPS) or the porcine pancreatic elastase (PPE)-induced emphysema model, generate COPD-like lesions in the lungs and airways but do not otherwise resemble the pathogenesis of human COPD. A cigarette smoke (CS)-induced model remains one of the most popular because it not only simulates COPD-like lesions in the respiratory system, but it is also based on one of the main hazardous materials that causes COPD in humans. However, the time-consuming and labor-intensive aspects of the CS-induced model dramatically limit its application in new drug screening. In this study, we successfully generated a new COPD model by exposing mice to high levels of ozone. This model demonstrated the following: 1) decreased forced expiratory volume 25, 50, and 75/forced vital capacity (FEV25/FVC, FEV50/FVC, and FEV75/FVC), indicating the deterioration of lung function; 2) enlarged lung alveoli, with lung parenchymal destruction; 3) reduced fatigue time and distance; and 4) increased inflammation. Taken together, these data demonstrate that the ozone exposure (OE) model is a reliable animal model that is similar to humans because ozone overexposure is one of the etiological factors of COPD. Additionally, it only took 6 - 8 weeks, based on our previous work, to create an OE model, whereas it requires 3 - 12 months to induce the cigarette smoke model, indicating that the OE model might be a good choice for COPD research.

This study describes the successful generation of a new chronic obstructive pulmonary disease (COPD) animal model by repeatedly exposing mice to high concentrations of ozone.

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airflow limitation and lung parenchymal destruction. It has a very high ...

In Vivo Assessment of Alveolar Macrophage Efferocytosis Following Ozone Exposure

Ozone (O3) is a criteria air pollutant that exacerbates and increases the incidence of chronic pulmonary diseases. O3 exposure is known to induce pulmonary inflammation, but little is known regarding how exposure alters processes important to the resolution of inflammation. Efferocytosis is a resolution process, whereby macrophages phagocytize apoptotic cells. The purpose of this protocol is to measure alveolar macrophage efferocytosis following O3-induced lung injury and inflammation. Several methods have been described for measuring efferocytosis; however, most require ex vivo manipulations. Described in detail here is a protocol to measure in vivo alveolar macrophage efferocytosis 24 h after O3 exposure, which avoids ex vivo manipulation of macrophages and serves as a simple technique that can be used to accurately represent perturbations in this resolution process. The protocol is a technically non-intensive and relatively inexpensive method that involves whole-body O3 inhalation followed by oropharyngeal aspiration of apoptotic cells (i.e., Jurkat T cells) while under general anesthesia. Alveolar macrophage efferocytosis is then measured by light microscopy evaluation of macrophages collected from bronchoalveolar (BAL) lavage. Efferocytosis is finally measured by calculating an efferocytic index. Collectively, the outlined methods quantify efferocytic activity in the lung in vivo while also serving to analyze the negative health effects of O3 or other inhaled insults.

This manuscript describes a protocol for determining whether exposure to ozone, a criteria air pollutant, impairs alveolar macrophage efferocytosis in vivo. This protocol utilizes commonly used reagents and techniques and can be adapted to multiple models of pulmonary injury to determine effects on alveolar macrophage efferocytosis.

Ozone (O3) is a criteria air pollutant that exacerbates and increases the incidence of chronic pulmonary diseases. O3 exposure is known to induce ...

Performing Vaginal Lavage, Crystal Violet Staining, and Vaginal Cytological Evaluation for Mouse Estrous Cycle Staging Identification

A rapid means of assessing reproductive status in rodents is useful not only in the study of reproductive dysfunction but is also required for the production of new mouse models of disease and investigations into the hormonal regulation of tissue degeneration (or regeneration) following pathological challenge. The murine reproductive (or estrous) cycle is divided into 4 stages: proestrus, estrus, metestrus, and diestrus. Defined fluctuations in circulating levels of the ovarian steroids 17-&beta;-estradiol and progesterone, the gonadotropins luteinizing and follicle stimulating hormones, and the luteotropic hormone prolactin signal transition through these reproductive stages. Changes in cell typology within the murine vaginal canal reflect these underlying endocrine events. Daily assessment of the relative ratio of nucleated epithelial cells, cornified squamous epithelial cells, and leukocytes present in vaginal smears can be used to identify murine estrous stages. The degree of invasiveness, however, employed in collecting these samples can alter reproductive status and elicit an inflammatory response that can confound cytological assessment of smears. Here, we describe a simple, non-invasive protocol that can be used to determine the stage of the estrous cycle of a female mouse without altering her reproductive cycle. We detail how to differentiate between the four stages of the estrous cycle by collection and analysis of predominant cell typology in vaginal smears and we show how these changes can be interpreted with respect to endocrine status.

Here, we describe how to identify the stage of the murine reproductive (proestrus, estrus, metestrus, or diestrus) by simple, non-invasive collection and cytological assessment of vaginal smear samples. We further describe how vaginal cytology reflects circulating hormonal levels underlying transition through the murine reproductive cycle.

A rapid means of assessing reproductive status in rodents is useful not only in the study of reproductive dysfunction but is also required for the ...

Biology

Cell-Specific Paired Interrogation of the Mouse Ovarian Epigenome and Transcriptome

Assessing cell-type-specific epigenomic and transcriptomic changes are key to understanding ovarian aging. To this end, the optimization of the translating ribosome affinity purification (TRAP) method and the isolation of nuclei tagged in specific cell types (INTACT) method was performed for the subsequent paired interrogation of the cell-specific ovarian transcriptome and epigenome using a novel transgenic NuTRAP mouse model. The expression of the NuTRAP allele is under the control of a floxed STOP cassette and can be targeted to specific ovarian cell types using promoter-specific Cre lines. Since recent studies have implicated ovarian stromal cells in driving premature aging phenotypes, the NuTRAP expression system was targeted to stromal cells using a Cyp17a1-Cre driver. The induction of the NuTRAP construct was specific to ovarian stromal fibroblasts, and sufficient DNA and RNA for sequencing studies were obtained from a single ovary. The NuTRAP model and methods presented here can be used to study any ovarian cell type with an available Cre line.

In this protocol, the translating ribosome affinity purification (TRAP) method and the isolation of nuclei tagged in specific cell types (INTACT) method were optimized for the paired interrogation of the cell-specific ovarian transcriptome and epigenome using the NuTRAP mouse model crossed to a Cyp17a1-Cre mouse line.

Assessing cell-type-specific epigenomic and transcriptomic changes are key to understanding ovarian aging. To this end, the optimization of the ...

Developing an Ovariectomized Mouse Model with Elevated FSH Level

Exploring Independent Effects of Follicle-Stimulating Hormone In Vivo in a Mouse Model

During the transition from a reproductive to a nonreproductive phase (menopause), many women experience significant physiological and pathological changes, including decreased bone mass, increased blood lipids, and increased visceral adiposity. Levels of follicle-stimulating hormone (FSH) rise during the menopausal transition. Many studies have shown that FSH in various extragonadal tissues and organs is associated with the pathogenesis of multiple diseases. Thus, building an animal model that can help study the independent effects of FSH in vivo is particularly important. In this study, C57BL/6 female mice were ovariectomized and supplemented with estradiol valerate (OVX + E2) to eliminate the effect of the hypothalamic-pituitary-gonadal axis. The OVX + E2 mice received solvent (N.S.) or different doses of recombinant FSH via intraperitoneal injection to create a mouse model (OVF) characterized by relatively stable estrogen and rising FSH levels. Thus, we successfully generated an experimental mouse model to mimic the early stage of menopause transition, characterized by elevated serum FSH levels. The OVF model has the advantages of being stable, low cost, and easy to operate, which is suitable for studies to explore the extragonadal actions of FSH. Here, we describe detailed protocols for the mouse OVF model.

Author Spotlight: Investigating the Relationship Between FSH and Pathophysiological Changes in Perimenopausal Women - Insights from a Mouse Model 

Follicle-stimulating hormone (FSH) in various extragonadal tissues and organs is associated with the pathogenesis of multiple diseases. The ovariectomized and FSH-treated mouse model (OVF) can be used to explore the extragonadal actions of FSH.