Name: a detailed protocol for characterizing the murine c1498 cell line and its associated leukemia mouse model
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Description: Watch this Scientific Journal Video about A Detailed Protocol for Characterizing the Murine C1498 Cell Line and its Associated Leukemia Mouse Model at JoVE.com

The authors would like to acknowledge the "Ligue Nationale contre le Cancer" (Comit&#233; du Septentrion), the SIRIC ONCOLille (Grant INCa-DGOS-INSERM 6041) and the Institut pour la Recherche sur le Cancer de Lille (IRCL) for supporting this work. They would like to thank Delphine Taillieu and the animal facility staff for housing the mice and maintaining their welfare. We also thank Rapha&#235;lle Caillerez and Nathalie Jouy for their respective help in microscopy and flow cytometry.

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The authors declare that they have no competing financial interests.

In previous studies, the C1498 cell line was described as an inducer of acute granulocytic5, myelomonocytic6 or NKT7 cell leukemia. However, demonstrative data in the literature were either absent or incomplete. The protocol presented here uses different techniques, such as flow cytometry, immunofluorescence, MGG staining and cytochemical assays, to characterize cultured C1498 cells and to determine the nature of the leukemia that is induced in mice after they are injected.
When we phenotyped in vitro cultured C1498 cells after immunostaining and flow cytometry analyses were performed, we observed some limitations because these cells expressed few cell surface hematopoietic markers that have been previously described in the literature6,7. In agreement with our results, Labelle et al. did not observe the cell surface expression of mature TCR on C1498 cells using flow cytometry staining. However, they considered them to be a NKT cell line after they detected CD3&#949; and TCRV&#946;8.2 mRNAs7. We also observed the intracellular expression of TCRV&#946; chains and CD3&#949; molecules in most of the cells (&#62; 70%), but their hematopoietic lineages could not be determined because there was also concomitant intracellular expression of the Mac-3 molecule.
Myeloperoxidase, MGG staining and assessments to analyze functional esterases using cytochemistry demonstrated that the C1498 cell line had a myeloid origin and was composed of monoblasts and myeloblasts. These results were concordant with the percentage of Mac-3+ cells that were obtained in flow cytometry staining analysis. Although not quantitative, these steps represent key experiments to be performed. Indeed, they remain, so far, the best existing methods for characterizing the lineage and differentiation stage of hematopoietic cells that express no or few specific phenotypic markers.
Flow cytometry staining was helpful for demonstrating the development of acute leukemia in congenic mice after C1498 cells were intravenously injected. The CD45.2+ C1498 cells that infiltrated into the peripheral blood and various organs were isolated, and their frequencies were determined. Quantification was also performed to analyze inherent medullary and splenic cells after immunophenotyping. Limitations were encountered when the C1498 cell phenotype was examined in organs as they expressed few hematopoietic markers (only a few of them were B220+). To define the nature of the observed acute leukemia, May-Gr&#252;nwald Giemsa staining and an analysis of the activities of monocytic and granulocytic esterases were performed using bone marrow. The results showed that C1498 cells preserved their myeloblastic and monoblastic morphology and function, revealing the onset of myelomonocytic leukemia.
In consideration for the critical steps described in this protocol, particular attention should be given to pH when performing cytochemical reactions and MGG staining because errors in pH can lead to incorrect interpretations of results. For instance, &#945;-naphthyl butyrate esterase activity is specific to monocytic cells only at a pH of 6.0 because granulocytes and lymphocytes can also stain positive for this test at higher pH values. Fixating the cells is not recommended before performing MGG staining, and we showed that only CAF fixation provided satisfactory results when performing esterases cytochemical reactions using C1498 cells. To preserve the expression of the CD115 molecule and its detection by flow cytometry, all of the samples (e.g., blood, bone marrow, and spleen cells) should be kept on ice during the procedure. If no staining is observed in flow cytometry or/and immunofluorescence experiments, the reference of the antibodies, their storage recommendations and their dilutions should be checked. The references specified in the materials/equipment table have been selected for flow cytometry or immunofluorescence applications. The primary/secondary antibodies or their conjugated fluorophores might have lost their activity due to inappropriate storage (e.g., exposure to light or heat), improper dilution, extensive freezing/thawing or the use of contaminated buffers. Run positive controls to ensure that they are working properly. Use mouse bone marrow or spleen-derived cells that are known to express the proteins of interest. To avoid high background and non-specific staining, make sure that the cells are washed properly and kept at high humidity (for immunofluorescence) and that the antibodies are diluted as instructed. Use the same concentration and dilution for the isotype control antibody and the primary antibody to accurately determine the background level in the sample. For esterase cytochemistry experiments, the reagents can be tested by using positive and negative control slides containing purified mouse splenic granulocytic (Ly6G+) and monocytic (CD115+) cells.
The procedure described in this study showed that many of the leukemic features observed in mice after the injection of C1498 cells shared common hallmarks with human acute myelomonocytic leukemia11,12. The invaded leukemic cells resulted in a reduction of mature and immature (progenitors and precursors) medullary hematopoietic cells. C1498 cells are present at high frequency (&#62; 20%) in the peripheral blood, as are monocytic cells. Hepatomegaly and splenomegaly were observed to result from the infiltration of leukemic cells, and significant increases in B lymphocytes and myeloid cells were also observed to accompany splenomegaly. Thrombopenia was also observed when blood platelets numbers were estimated using a hematology analyzer.
It was shown, using in vitro experiments, that C1498 cells inhibit normal murine hematopoiesis by secreting soluble factors13. In several tumor mouse models, immature myeloid cells (including monocytic and granulocytic cells) have also been shown to migrate from the bone marrow to the spleen, where they inhibit anti-tumor specific T cell activation and proliferation14. Thus, the reduction in hematopoietic cells that was observed in the bone marrow could have resulted from either a deficiency in hematopoiesis and/or from their emigration. This latter mechanism could explain the presence of monocytosis in the peripheral blood or the observation of enlarged myeloid fractions in the spleen. It is also conceivable that these cells could have been derived from improved splenic hematopoiesis. Indeed, under steady-state conditions, some subsets of splenic B cells were identified as precursors of mature B lymphocytes15. Moreover, under inflammatory conditions, medullary stem and progenitors cells have been shown to relocate to the spleen to induce the production of mature monocytes16. This protocol does not allow us to draw conclusions regarding the mechanisms that are involved in the development of leukemia, and additional functional as well as molecular assays should be employed to do so. However, these data include detailed information about the clinical features of acute myelomonocytic leukemia and will help researchers to evaluate and understand the effects of new therapeutic agents.

Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of hematopoietic myeloid cells that are blocked at different stages of maturation. This dysregulation can affect the granulocytic, monocytic, erythrocytic or megaryocytic differentiation pathways1. AML cells accumulate in the bone marrow, leading to impaired hematopoiesis, which results in thrombopenia, lymphopenia and anemia. The leukemic cells also invade the blood and non-lymphoid organs.
The C1498 mouse model has been used for decades as a model for acute leukemia since cancer cells were isolated from a leukemic 10-month-old C57BL/6 (H-2b) female mouse in 1941. The literature describes the invasion into the blood, hematopoietic organs (e.g., the spleen and lymph nodes) and non-hematopoietic organs (e.g., the liver, lungs, ovaries, and kidneys) by highly proliferative C1498 cells after they were injected via an intravenous, subcutaneous or intra-peritoneal route into susceptible mice2-4. However, this mouse model was reported to induce either granulocytic2,5 or myelomonocytic6 leukemia. More recently, a study published in 2002 described this type of cancer as murine NKT cell leukemia7. Thus, the literature differs concerning the nature of this C1498 cell line and the associated leukemia it induces in mice. These discrepancies are mainly due to a lack of detailed and updated published information about the cells and the leukemic disease in general because many studies were performed in the 1950 - 70&#39;s.
Here we provide a detailed protocol to describe how to characterize C1498 cells and analyze the nature of the leukemic disease that is induced by their intravenous injection into mice. The first section of this protocol is dedicated to a description of C1498 cells that have been cultured in vitro. Fluorescent antibodies directed against surface and intracellular hematopoietic markers were used to determine their phenotype using flow cytometry. The presence of myeloperoxidase was assessed using immunofluorescence microscopy, their hematopoietic lineage and differentiation stage were evaluated using cytochemistry to assess the activity of esterases, and May-Gr&#252;nwald Giemsa staining was performed. The C1498 cells were then injected into mice, and the acute leukemia disease that was induced is described in the second section of this manuscript. The same techniques were used to determine the frequencies and the phenotypes of leukemic and inherent cells in the bone marrow, peripheral blood, spleen and non-hematopoietic organs (the liver and lungs).
This protocol is highly reproducible, and the data presented here will help researchers to assess the effects of new therapeutic strategies. This leukemia mouse model has already been used to test immunotherapy approaches and different cancer chemotherapeutic drugs8,9. Their efficacy was evaluated by determining the evolution of tumor burden and survival rates. This protocol can be used to provide additional information about the distribution and subsistence of leukemic and other hematopoietic cell populations during treatment.

<table><tbody><tr><td>C1498 cell line</td><td>ATCC</td><td>TIB 49</td><td></td></tr><tr><td>C57BL/6J-Ly5.1</td><td>Charles River</td><td>B6.SJL-Ptprc a Pep3 b/BoyCrl</td><td></td></tr><tr><td>&lt;strong&gt;Cells culture reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2-Mercaptoethanol, Gibco</td><td>ThermoFisher Scientific</td><td>21985</td><td></td></tr><tr><td>Fetal Bovine Serum (FBS)</td><td>ThermoFisher Scientific</td><td>10270</td><td></td></tr><tr><td>HEPES, Gibco (1 M)</td><td>ThermoFisher Scientific</td><td>15630</td><td></td></tr><tr><td>Non-Essential Amino Acids Solution, Gibco&amp;nbsp;&amp;nbsp;</td><td>ThermoFisher Scientific</td><td>11140</td><td></td></tr><tr><td>Penicillin-Streptomycin, Gibco&amp;nbsp;</td><td>ThermoFisher Scientific</td><td>15140</td><td></td></tr><tr><td>RPMI 1640 Medium (Gibco, GlutaMAX Supplement)</td><td>ThermoFisher Scientific</td><td>61870</td><td></td></tr><tr><td>Sodium Pyruvate, Gibco (100 mM)</td><td>ThermoFisher Scientific</td><td>11360</td><td></td></tr><tr><td>&lt;strong&gt;Flow cytometry staining reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>anti-mouse B220 APC (1)</td><td>ebiosciences</td><td>17-0452</td><td>clone RA3-6B2, final dilution 1/100</td></tr><tr><td>anti-mouse B220 biotin (2)</td><td>ebiosciences</td><td>13-0452</td><td>clone RA3-6B2, 1/400</td></tr><tr><td>anti-mouse CD3 eFluor450 (1)</td><td>ebiosciences</td><td>48-0032</td><td>clone 17A2, 1/100</td></tr><tr><td>anti-mouse CD3 PE (2)</td><td>BD biosciences</td><td>555275</td><td>clone 17A2, 1/100</td></tr><tr><td>anti-mouse CD3 PE-Cy5 (3)</td><td>ebiosciences</td><td>15-0031</td><td>clone 145-2C11, 1/100</td></tr><tr><td>anti-mouse CD4 APC (1)</td><td>ebiosciences</td><td>17-0041</td><td>clone GK1.5, 1/500</td></tr><tr><td>anti-mouse CD4 PE (2)</td><td>ebiosciences</td><td>12-0041</td><td>clone GK1.5, 1/200</td></tr><tr><td>anti-mouse CD8 biotin (1)</td><td>ebiosciences</td><td>13-0081</td><td>clone 53-6.7, 1/100</td></tr><tr><td>anti-mouse CD8 eFluor450 (2)</td><td>ebiosciences</td><td>48-0081</td><td>clone 53-6.7, 1/500</td></tr><tr><td>anti-mouse CD11a biotin</td><td>ebiosciences</td><td>13-0111</td><td>clone M17/4, 1/100</td></tr><tr><td>anti-mouse CD11b PE&amp;nbsp;</td><td>ebiosciences</td><td>12-0112</td><td>clone M1/70, 1/200</td></tr><tr><td>anti-mouse CD16/32 biotin</td><td>ebiosciences</td><td>13-0161</td><td>clone 93, 1/400</td></tr><tr><td>purified anti-mouse CD16/32 (FcR blocking)&amp;nbsp;</td><td>BD biosciences</td><td>553141</td><td>clone 2.4G2</td></tr><tr><td>anti-mouse CD18 biotin (1)</td><td>ebiosciences</td><td>13-0181</td><td>clone M18/2, 1/100</td></tr><tr><td>anti-mouse CD18 FITC (2)</td><td>ebiosciences</td><td>11-0181</td><td>clone M18/2, 1/50</td></tr><tr><td>anti-mouse CD19 PE</td><td>ebiosciences</td><td>12-0193</td><td>clone 1D3, 1/200</td></tr><tr><td>anti-mouse CD21/35 PE</td><td>ebiosciences</td><td>12-0211</td><td>clone 8D9, 1/50</td></tr><tr><td>anti-mouse CD31 PE</td><td>ebiosciences</td><td>12-0311</td><td>clone 390, 1/100</td></tr><tr><td>anti-mouse CD34 eFluor660</td><td>ebiosciences</td><td>50-0341</td><td>clone RAM34, 1/20</td></tr><tr><td>anti-mouse CD44 PE</td><td>BD biosciences</td><td>553134</td><td>clone IM7, 1/50</td></tr><tr><td>anti-mouse CD45.2 FITC&amp;nbsp;</td><td>ebiosciences</td><td>11-0454</td><td>clone 104, 1/100</td></tr><tr><td>anti-mouse CD49b/Pan NK PE</td><td>&amp;nbsp;BD biosciences</td><td>553858</td><td>clone DX5, 1/50</td></tr><tr><td>anti-mouse CD80 biotin</td><td>ebiosciences</td><td>13-0801</td><td>clone 16-10A1, 1/200</td></tr><tr><td>anti-mouse CD86 biotin</td><td>ebiosciences</td><td>13-0862</td><td>clone GL1, 1/200</td></tr><tr><td>anti-mouse CD107b (Mac-3) PE</td><td>ebiosciences</td><td>12-5989</td><td>clone M3/84, 1/40</td></tr><tr><td>anti-mouse CD115 PE</td><td>ebiosciences</td><td>12-1152</td><td>clone AFS98, 1/100</td></tr><tr><td>anti-mouse CD117 eFluor450</td><td>ebiosciences</td><td>48-1171</td><td>clone 2B8, 1/100</td></tr><tr><td>anti-mouse CD127 PE</td><td>ebiosciences</td><td>12-1271</td><td>clone A7R34, 1/100</td></tr><tr><td>anti-mouse CD150 APC</td><td>ebiosciences</td><td>17-1501</td><td>clone 9D1, 1/20</td></tr><tr><td>anti-mouse CD274 (PD-L1) biotin</td><td>ebiosciences</td><td>13-5982</td><td>clone MIH5, 1/200</td></tr><tr><td>anti-mouse Ly-6A/E (Sca-1) PE</td><td>ebiosciences</td><td>12-5981</td><td>clone D7, 1/100</td></tr><tr><td>anti-mouse Ly-6C APC</td><td>ebiosciences</td><td>17-5932</td><td>clone HK1.4, 1/200</td></tr><tr><td>anti-mouse Ly-6G&amp;nbsp; (Gr-1) biotin (1)</td><td>ebiosciences</td><td>13-5931</td><td>clone RB6-8C5, 1/400</td></tr><tr><td>anti-mouse Ly-6G FITC (2)</td><td>ebiosciences</td><td>11-9668</td><td>clone 1A8, 1/50</td></tr><tr><td>anti-mouse MHC class I (H-2Db) biotin</td><td>ebiosciences</td><td>13-5999</td><td>clone 28-14-8, 1/50</td></tr><tr><td>anti-mouse MHC class II (I-A/I-E) PE-Cy5</td><td>ebiosciences</td><td>15-5321</td><td>clone M5/114.15.2, 1/1000</td></tr><tr><td>anti-mouse NK1.1 PE</td><td>BD biosciences</td><td>557391</td><td>clone PK136, 1/50</td></tr><tr><td>anti-mouse TCRVb FITC</td><td>BD biosciences</td><td>553170</td><td>clone H57-597, 1/50</td></tr><tr><td>streptavidin PE-Cy5</td><td>ebiosciences</td><td>15-4317</td><td>&amp;nbsp;&amp;nbsp; 1/200</td></tr><tr><td>Immunofluorescence staining reagents</td><td></td><td></td><td></td></tr><tr><td>Anti-mouse myeloperoxidase (heavy chain antibody)&amp;nbsp;</td><td>Santa Cruz Biotechnology</td><td>sc-16129</td><td>1/10 (20 &amp;micro;g/ml)</td></tr><tr><td>&amp;nbsp;anti-goat IgG (Texas Red coupled antibody)</td><td>Jackson Immunoresearch</td><td>705-076-147</td><td>1/250</td></tr><tr><td>Normal donkey serum</td><td>Jackson Immunoresearch</td><td>017-000-001</td><td></td></tr><tr><td>Hoechst solution</td><td>BD Biosciences</td><td>561908</td><td>1/1000</td></tr><tr><td>Mounting medium 1 (Fluoromount)&amp;nbsp;</td><td>Sigma-Aldrich</td><td>F4680</td><td></td></tr><tr><td>Acetone</td><td>VWR&amp;nbsp;</td><td>20066</td><td></td></tr><tr><td>Methanol</td><td>Merck Millipore</td><td>106009</td><td></td></tr><tr><td>&lt;strong&gt;Esterase cytochemical staining reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Naphtol AS-D chloroacetate solution</td><td>Sigma-Aldrich</td><td>911</td><td></td></tr><tr><td>Dye solution (Fast Red Violet LB Base solution)</td><td>Sigma-Aldrich</td><td>912</td><td></td></tr><tr><td>pH6.3 buffer concentrate (TRIZMAL)</td><td>Sigma-Aldrich</td><td>913</td><td></td></tr><tr><td>Sodium nitrite solution</td><td>Sigma-Aldrich</td><td>914</td><td></td></tr><tr><td>Citrate solution</td><td>Sigma-Aldrich</td><td>915</td><td></td></tr><tr><td>Hematoxylin solution</td><td>Sigma-Aldrich</td><td>GHS3</td><td></td></tr><tr><td>Acetone</td><td>VWR&amp;nbsp;</td><td>20066</td><td></td></tr><tr><td>Formaldehyde solution, 37%</td><td>Sigma-Aldrich</td><td>F1635</td><td></td></tr><tr><td>&amp;alpha;-naphthyl butyrate solution</td><td>Sigma-Aldrich</td><td>1801</td><td></td></tr><tr><td>Phosphate buffer solution</td><td>Sigma-Aldrich</td><td>1805</td><td></td></tr><tr><td>Sodium nitrite Tablet</td><td>Sigma-Aldrich</td><td>1809</td><td></td></tr><tr><td>Pararosaniline solution</td><td>Sigma-Aldrich</td><td>1804</td><td></td></tr><tr><td>Methylene blue solution</td><td>Sigma-Aldrich</td><td>1808</td><td>1/10</td></tr><tr><td>mounting medium 2 (Clearmount)</td><td>Invitrogen</td><td>00-8010</td><td></td></tr><tr><td>&lt;strong&gt;May-Gr&amp;uuml;nwald Giemsa staining reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>May-Gr&amp;uuml;nwald solution</td><td>RAL Diagnostics</td><td>320070</td><td></td></tr><tr><td>Giemsa R solution&amp;nbsp;</td><td>RAL Diagnostics</td><td>320310</td><td>&amp;nbsp;&amp;nbsp; 1/20</td></tr><tr><td>pH 6.8 buffer solution</td><td>RAL Diagnostics</td><td>330368</td><td></td></tr><tr><td>&lt;strong&gt;Others materials, reagents and equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Ketamine 1000 (100 mg/ml)</td><td>VIRBAC</td><td>3597132111010</td><td></td></tr><tr><td>Xylazine SEDAXYLAN (20 mg/ml)</td><td>CEVA</td><td></td><td></td></tr><tr><td>Bovin albumin serum (BSA) powder</td><td>ThermoFisher Scientific</td><td>BP671</td><td></td></tr><tr><td>PBS solution (1x concentrate)</td><td>ThermoFisher Scientific</td><td>14190</td><td></td></tr><tr><td>Paraformaldehyde (PFA)&amp;nbsp;</td><td>Sigma-Aldrich</td><td>158127</td><td></td></tr><tr><td>Saponin&amp;nbsp;</td><td>Sigma-Aldrich</td><td>47036</td><td></td></tr><tr><td>Ultra Pure 0.5 M EDTA solution, pH 8.0</td><td>ThermoFisher Scientific</td><td>15575</td><td></td></tr><tr><td>Separating solution (Pancoll Mouse)</td><td>PANBIOTECH</td><td>P04-64100</td><td></td></tr><tr><td>Lysis buffer (Red Blood Cells Lysing Buffer) (10x)</td><td>BD Biosciences</td><td>555899</td><td>1/10</td></tr><tr><td>NaOH 10 N</td><td>ThermoFisher Scientific</td><td>SS267</td><td></td></tr><tr><td>Trypan blue solution (0.4 %)</td><td>ThermoFisher Scientific</td><td>15250-061</td><td>1/2</td></tr><tr><td>50 ml tube (Falcon)</td><td>Fisher Scientific</td><td>14-432-22</td><td></td></tr><tr><td>70 &amp;micro;m Cell Strainer (Falcon)&amp;nbsp;</td><td>Corning Life Sciences</td><td>352350</td><td></td></tr><tr><td>&amp;nbsp;Chamber &amp;amp; filter card (EZ Cytofunnel Shandon)</td><td>Thermo Scientific</td><td>A78710003</td><td></td></tr><tr><td>Microscope Cover Glasses, 24 x 24mm</td><td>Knittel Glass</td><td>VD1 2424 Y100</td><td></td></tr><tr><td>Slides (Starfrost - ground edges 90)</td><td>Knittel Glass</td><td>VS1137# 077FKB</td><td></td></tr><tr><td>EDTA Tube</td><td>Greiner Bio-One</td><td>454034</td><td></td></tr><tr><td>Pasteur Pipette 150 mm (capillary tube)</td><td>Fisherbrand</td><td>1154-6963</td><td></td></tr><tr><td>26 G needle</td><td>Terumo</td><td>NN-2613R</td><td></td></tr><tr><td>&amp;nbsp;insulin syringe and needle 29 G</td><td>Terumo</td><td>BS05M2913</td><td></td></tr><tr><td>30 G needle&amp;nbsp;</td><td>Becton &amp;amp; Dickinson</td><td>304000</td><td></td></tr><tr><td>Flow cytometry tubes (blue)</td><td>Beckman Coulter</td><td>2523749</td><td></td></tr><tr><td>Water-repellent pen (Dakopen)</td><td>Dako</td><td>S200230</td><td></td></tr><tr><td>Sharp sterile scissors&amp;nbsp;</td><td>Nessi-care</td><td>SCI-01</td><td></td></tr><tr><td>Sterile forceps</td><td>Dominique Dutscher</td><td>956506</td><td></td></tr><tr><td>Thoma cell counting chamber</td><td>VWR</td><td>631-0397</td><td></td></tr><tr><td>Petri Dishes (Fisherbrand Plastic)</td><td>ThermoFisher Scientific</td><td>S33580A</td><td></td></tr><tr><td>Microcentrifuge tube (1.5 ml)</td><td>ThermoFisher Scientific</td><td>05-408-129</td><td></td></tr><tr><td>Cylindrical Restrainer 15 - 30 gm</td><td>Stoelting</td><td>51338</td><td></td></tr><tr><td>Shandon Cytospin 3 Cytocentrifuge</td><td>ThermoFisher Scientific</td><td></td><td></td></tr><tr><td>10 ml syringe</td><td>Terumo</td><td>SS-10L</td><td></td></tr><tr><td>1 ml syringe</td><td>Terumo</td><td>SS-01T</td><td></td></tr><tr><td>Ethanol</td><td>Merck</td><td>1.08543</td><td></td></tr><tr><td>Sterile gauze sponges</td><td>URGO</td><td>501580</td><td></td></tr></tbody></table>

a detailed protocol for characterizing the murine c1498 cell line and its associated leukemia mouse model

The intravenous injection of C1498 cells into syngeneic or congenic mice has been performed since 1941. These injections result in the development of acute leukemia. However, the nature of this disease has not been well documented in the literature. Here, we provide a technical protocol for characterizing C1498 cells in vitro and for determining the nature of the induced leukemia in vivo. The first part of this procedure is focused on determining the hematopoietic lineage and the stage of differentiation of cultured C1498 cells. To achieve this, multi-parametric flow cytometric staining is used to detect hematopoietic cell markers. Immunofluorescence microscopy, cytochemistry and a May-Gr&#252;nwald Giemsa staining are then performed to assess the expression of myeloperoxidase, the activity of esterases and cellular morphology, respectively. The second part of this protocol is dedicated to describing the leukemia disease that is induced in vivo. The latter can be achieved by determining the frequencies of leukemic and inherent cells in the blood, hematopoietic organs (e.g., bone marrow and spleen) and non-lymphoid tissues (e.g., the liver and lungs) using specific staining and flow cytometry analyses. The nature of the leukemia is then confirmed using May-Gr&#252;nwald Giemsa staining and staining for specific esterases in the bone marrow. Here, we present the results that were obtained using this protocol in age-matched C1498- and PBS-injected mice.

To characterize the C1498 mouse model, we proceeded with two major steps. First, the C1498 cells were characterized to determine their hematopoietic lineage and maturation stage in vitro (Figure 1). These cells were then injected into congenic mice, and the nature of the induced leukemic disease was assessed to determine different features: leukemic cell infiltration, their phenotype, a quantification of the hematopoietic cells (mature and progenitors/precursors) in bone marrow, the frequencies of C1498 cells and mature hematopoietic cells in the blood and an evaluation of organ swelling (in the spleen, liver, and lungs) and cellular composition.
To characterize the C1498 cell phenotypes in vitro, the cells were labeled with antibodies directed against molecules that are expressed by hematopoietic precursors and mature cells (Table 1), and the results were analyzed using flow cytometry. The C1498 cells were positive for cell surface expression of Mac-1 (CD11b/CD18) (&#8764; 7%), B220 (&#62; 25%), and they displayed intracellular expression of CD3&#949;, T-Cell Receptor (TCR) V&#946; chains and Mac-3 (Figures 2A and B). The cells were negative for the cell surface markers Ly6G, Ly6C, CD115, CD21/CD35, CD19, CD3, CD4, CD8, NK1.1, and pan-NK molecules and for the intracellular expression of CD4 and CD8 (data not shown). They were then examined for markers of hematopoietic stem cells and progenitors (Table 1). They were also negative for cell surface expression of CD117, CD34, Sca-1, CD150 and CD16/32 (data not shown). These leukemic cells were then tested to determine the expression of adhesion, antigen presentation and co-stimulatory molecules. The cells expressed the surface markers LFA-1 (CD11a/CD18), CD44, CD31 (PECAM-1), and H-2Db and were negative for MHC class II, CD80, CD86 and CD274 (data not shown). C1498 cells therefore expressed both myeloid (Mac-1, Mac-3) and lymphoid markers (B220, CD3, TCR).
To better characterize their hematopoietic lineage, myeloperoxidase expression was assessed using immunofluorescence microscopy. All of the cells were positive for the myeloperoxidase, which verified their myeloid origin (Figure 3A). A majority of the cells also stained positive for &#945;-naphthyl butyrate esterases (Figure 3B, left panel), and some of them stained for the naphthol AS-D chloroacetate esterases (black arrows) (Figure 3B, right panel). The results indicate that the cells contained mixtures of monocytic and granulocytic cells. After May-Gr&#252;nwald Giemsa staining was performed, the C1498 cells were observed to display a blast-like morphology with a high nucleo-cytoplasmic ratio, 3 to 5 nucleoli in the nucleus, a perinuclear halo, numerous vacuoles and a basophilic cytoplasm (Figure 3C). Thus, the C1498 cell line is composed of monoblasts and myeloblasts.
The C1498 cells (CD45.2+) were then intravenously injected into CD45.1+ mice. The mice succumbed 17 to 19 days after the cells were injected. These mice were sacrificed so that their leukemia type could be analyzed before they died from the disease. The control mice, which were injected with PBS, were analyzed at the same time points for comparison. The C1498 cell-injected mice displayed massive infiltration of C1498 cells into their bone marrow, as demonstrated by the blast-like appearance of the cells after May-Gr&#252;nwald Giemsa staining was performed (Figure 4A). They also preserved their monocytic and granulocytic phenotypes (Figure 4B and C), demonstrating an accumulation of monoblastic and myeloblastic cells that is characteristic of acute myelomonocytic leukemia.
To determine whether medullary hematopoietic cells numbers were lower following leukemic cells invasion, CD45.2+ C1498 cells, B lymphocytic, monocytic and granulocytic populations (including progenitors, precursors and mature cells), were quantified using immunofluorescent staining and multi-parametric flow cytometry analysis. Leukemic cells represented 16 to 36% of the hematopoietic cells (data not shown). The other cell types were all present in significantly lower numbers in the C1498-injected mice than in the PBS-injected mice (by 5-fold on average for B cell subsets, 4-fold on average for granulocytic cells and 3-fold on average for monocytic subsets) (Figure 5A to C).
An investigation of the frequencies of mononuclear cells in leukemic and control mouse blood samples showed that they contained a comparable percentage of lymphocytes (Figure 6A) but a higher frequency of monocytic and leukemic cells. These characteristics are representative of acute myelomonocytic leukemia11 (Figure 6B).
Among the other features of acute myelomonocytic leukemia12, the C1498-injected mice presented with swollen livers (hepatomegaly), lungs and spleens (splenomegaly) (Figure 7A). Various frequencies of CD45.2+ C1498 cells were detected in these organs using immunofluorescent staining and flow cytometry analysis (Figure 7B). As splenomegaly can result from high numbers of infiltrated monocytes, we also estimated the proportions of splenic populations. The numbers of cells in the B lymphocytic, monocytic and granulocytic cell fractions were significantly larger, by an average of 2-fold, 2.5-fold and 3-fold, respectively, in leukemic spleens than in control spleens (Figure 7C).
<img alt="Figure 1" src="/files/ftp_upload/54270/54270fig1.jpg" /> 
Figure 1. Schematic Representation of the Protocol Set Up for Characterizing in vitro Cultured C1498 Cell Lines and in vivo Descriptions of Acute Leukemia. The hematopoietic lineage and the differentiation stage of tissue-cultured C1498 cells were first determined. C1498 cells were then injected into congenic mice to induce the development of acute leukemia. The isolation of bone marrow, peripheral blood, spleen, liver and lung tissues was performed to determine the frequencies, phenotypes and morphological changes after the C1498 cells infiltration. IV: Intravenous MGG: May-Gr&#252;nwald Giemsa. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54270/54270fig2.jpg" /> 
Figure 2. Phenotypic Analysis of C1498 Cells after in vitro Culture. Representative flow cytometry dot plots and histograms of cell surface (A) and intracellular (B) C1498-expressed molecules that were associated with hematopoietic mature cell differentiation are shown. C1498 cells were harvested from cultures, washed and labeled using fluorescent antibodies that were specific for the cell surface CD11b, CD18 and B220 markers or their isotype controls. For intracellular staining, the cells were fixed, permeabilized and labeled using antibodies directed against Mac-3, CD3&#949;, and a common epitope of the TCR (T-Cell Receptor) V&#946; chain or their isotype controls. Analyses were performed using gating with live cells. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54270/54270fig3.jpg" /> 
Figure 3. Functional and Morphological Characterization of Cultured C1498 Cells. C1498 cells were harvested from cultures and centrifuged on slides for microscopy. (A) Staining for myeloperoxidase expression was performed using immunofluorescence. (B) Cytochemical reactions were used to analyze the &#945;-naphthyl butyrate esterase (NBE) and naphthol AS-D chloroacetate esterase (CAE) activities in C1498 cells. Cells were considered to be positive for each label when brown and red-purple, large cytoplasmic granules, respectively, were observed. (C) May-Gr&#252;nwald Giemsa (MGG) staining of C1498 cells. For each staining experiment, the microscopy objective magnification is indicated. Each image is representative of three separate experiments. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54270/54270fig4.jpg" /> 
Figure 4. Bone Marrow Morphologies in PBS- and C1498-injected Mice. Bone marrow cells were isolated from PBS- and C1498 cell-injected mice and centrifuged onto slides for microscopy. (A) May-Gr&#252;nwald Giemsa (MGG) staining. (B) &#945;-naphthyl butyrate esterase (NBE) and (C) naphthol AS-D chloroacetate esterase (CAE) functions were evaluated using cytochemistry. In panel A, the band (immature) or segmented (mature) neutrophils are less visible in the bone marrow of the C1498-injected mice than the PBS-injected mice. Panel B and C indicate that there was an accumulation of monocytic and granulocytic cells in the leukemic bone marrow compared to the numbers observed in the control bone marrow. All microscopic analyses were performed using a 100X magnification objective. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/54270/54270fig5.jpg" /> 
Figure 5. Quantitative Analysis of Medullary Populations in PBS- and C1498-injected Mice. Bone marrow cells were isolated from PBS- and C1498 cell-injected mice and estimated after cell counting was performed. The frequencies of the different cell populations were determined after immunostaining and live cell gated flow cytometry analysis. (A) The B cell subsets included CD19+B220+cells in stages from pro-B cells to mature B lymphocytes (B) granulocytic cells in the CD3- and CD11b+Ly6G+ lineages, which included precursors and immature and mature granulocytes. (C) The monocytic subsets were defined as CD3-CD115+ and included cells in the progenitor to mature monocyte stages. n = 7 mice/group, and the data are presented as histograms showing the means &#177; SEM. ***, p&#60; 0.0001 and **, p&#60; 0.01, unpaired Student&#39;s t-test comparing PBS- and C1498-injected mice. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/54270/54270fig6.jpg" /> 
Figure 6. Blood Analysis of Mononuclear Cell Subsets in PBS- and C1498-injected mice. Representative flow cytometry dot plots of (A) T and B lymphocyte percentages, which were respectively defined as CD3+ and B220+ cells in PBS- and C1498 cell-injected mice. (B) Monocytic cell frequencies in C1498 leukemic and control (PBS) mice were determined by analyzing CD115+Ly6C- and CD115+Ly6Chigh cells. The analysis was performed by gating live cells. To compare leukemic and control mice, CD45.2+ C1498 cells were excluded. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/54270/54270fig7.jpg" /> 
Figure 7. Estimation of Splenic Populations in Leukemic and Control Mice. (A) Representative photographs of liver, lung and spleen swelling in leukemic mice compared to control mice. Spleens were collected and weighed, and splenocytes were counted following tissue disruption. (B) Histogram representing leukemic cell frequencies in different organs after immunostaining was performed for CD45.2+ cells and the results were analyzed using flow cytometry. (C) Estimations of splenic B, granulocytic and monocytic cell numbers after immunostaining and flow cytometry analysis gating were performed to identify live CD19+B220+, CD3-CD11b+Ly6G+, CD3-CD11b+Ly6C- and CD3-CD11b+Ly6Chigh cells. The scale bars shown for the lungs, spleens and livers indicate 1 cm. n = 5 - 8 mice/group, and the data are represented in histograms as the means&#160;&#177; SEM. *, p&#60; 0.05; **, p= 0.0033, unpaired Student&#39;s t-test comparing PBS- and C1498-injected mice. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Cell Type</td>
			<td>Membrane or Intracellular Molecules&#160;</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Precursors and Mature cells</td>
			<td></td>
		</tr>
		<tr>
			<td>NK cells</td>
			<td>NK1.1+, pan-NK+</td>
		</tr>
		<tr>
			<td>NKT cells</td>
			<td>NK1.1+, pan-NK+, TCR Vbeta+(8.2), CD3+</td>
		</tr>
		<tr>
			<td>T lymphocytes</td>
			<td>TCR Vbeta+, CD3+, CD4+, CD8+</td>
		</tr>
		<tr>
			<td>B cells precursors and B lymphocytes</td>
			<td>B220+, CD19+, CD21/35+&#160;</td>
		</tr>
		<tr>
			<td>granulocytic precursors and granulocytes</td>
			<td>Ly6G+, Mac-1+, CD11b+</td>
		</tr>
		<tr>
			<td>monocytic precursors and monocytes/macrophages</td>
			<td>CD11b+, Mac-1+, Mac-3+, CD21/35+, CD115+, Ly6Chi</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Progenitors</td>
			<td></td>
		</tr>
		<tr>
			<td>multipotent progenitors</td>
			<td>CD117+ Sca-1+ CD34+ (Lin- CD150-)</td>
		</tr>
		<tr>
			<td>lymphoid-primed multipotent progenitors&#160;</td>
			<td>CD117hi Sca-1hi CD127+ (Lin- )</td>
		</tr>
		<tr>
			<td>common lymphoid progenitors&#160;</td>
			<td>CD117lo Sca-1lo CD127+ (Lin- )</td>
		</tr>
		<tr>
			<td>common myeloid progenitors&#160;</td>
			<td>CD16/32lo CD117+ CD34int (Lin- Sca-1-)</td>
		</tr>
		<tr>
			<td>granulocyte-macrophage progenitors&#160;</td>
			<td>CD16/32hi CD117+ CD34hi (Lin- Sca-1-)</td>
		</tr>
		<tr>
			<td>megakaryocyte-erythroid progenitors&#160;</td>
			<td>CD16/32lo CD117+ CD34lo (Lin- Sca-1-)</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Hematopoietic stem cells&#160;</td>
			<td>CD117+ Sca-1+ CD150+ (Lin- CD34-)</td>
		</tr>
	</tbody>
</table>
Table 1. Markers of Hematopoietic Cell Lineages and Differentiation. 
CD: cluster of differentiation; Lin: markers of mature cells; lo: low expression; hi: high expression; int: intermediate expression; NK: natural killer cells; TCR: T-cell receptor.

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A Detailed Protocol for Characterizing the Murine C1498 Cell Line and its Associated Leukemia Mouse Model

This manuscript provides a technical procedure that can be used to characterize C1498 cell cultures in vitro and the acute leukemia induced in mice after their injection. Phenotypic and functional analyses are performed using flow cytometry, immunofluorescence microscopy, cytochemistry and May-Gr&#252;nwald Giemsa staining.

The intravenous injection of C1498 cells into syngeneic or congenic mice has been performed since 1941. These injections result in the development of ...

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Universidad de Cartagena UDEC

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Cancer Research

20.0K Views.  INSERM, UMR-S-1172, Centre de Recherche Jean-Pierre Aubert (JPARC). This manuscript provides a technical procedure that can be used to characterize C1498 cell cultures in vitro and the acute leukemia induced in mice after their injection. Phenotypic and functional analyses are performed using flow cytometry, immunofluorescence microscopy, cytochemistry and May-Grünwald Giemsa staining.

Video: A Detailed Protocol for Characterizing the Murine C1498 Cell Line and its Associated Leukemia Mouse Model

Balmer Series

The Bohr Model
Niels Bohr proposed a model for the hydrogen atom in 1913 that described discrete energy states are associated with a fixed electron orbit around the nucleus. Importantly, an atom cannot discharge energy while its electrons are in stationary states. An electron can only emit energy by changing energy states. To change energy states, an electron must move from one orbit to another by either absorbing or emitting energy. This change can only occur if the absorbed or emitted energy equals the difference between the two states. Electrons cannot exist between orbits.
The quantum number, n, is used to label the different energy states. The lowest energy state is the ground state, which is n equal to one. The excited states are labeled n equal to 2, 3, 4, and so on. When an electron at the ground state absorbs a photon whose energy equals the difference between the ground and second state, the electron becomes excited and transitions from the ground state to the n= 2 excited state. If the energy of the photon equals the difference between the ground and third state, the electron moves to the n=3 state.
According to Bohr’s model, the potential energy of an electron in the nth level can be calculated using the following equation:
<img src="https://jove.unicartagenaproxy.elogim.com/CDNSource/lm/labs/54/54_Concepts_1.jpg" alt="Bohr model energy equation \(E_n=-\frac{Rhc}{n^2}\), quantum mechanics concept." data-altupdatedat="1747908711000">
where En is the potential energy, R is the Rydberg constant (1.0974 × 107 m-1), h is Planck's constant (6.62607004 × 10-34 m2·kg/s), and c is the speed of light (~ 3 × 108 m/s). Electrons can also spontaneously return to the ground state or any other lower excited state. When this happens, the excess energy is released in the form of an emitted photon. The energy of the photon is equal to the energy difference between the higher and lower energy states. That energy corresponds to wavelengths of light. Since each type of atom has different energy levels, the light emitted from each transition varies for each atom. For a sample of mixed molecules, the emitted light contains a range of wavelengths in what is called a continuous spectrum. For a sample containing atoms of a single element, the emitted light contains only certain wavelengths, which can be viewed as discrete lines once separated by a prism.
<h4>The Hydrogen Atom</h4>
Looking specifically at the hydrogen atom, the excitation of its electrons requires the absorption of sufficient energy to split the bond in the diatomic molecule H2. Since more energy is used to split the molecule than needed, the electrons in the hydrogen atom absorb the excess energy and are excited to a higher energy level. When the electrons spontaneously return to a lower energy level, light is emitted, which corresponds to the energy difference between the excited level and lower level.
When discussing the emission of energy, the higher energy level is considered the initial level, or ni, while the lower level is considered the final level, or nf. The wavelengths of emitted light ultimately depend on the energy difference between the two levels.
In a pure sample of hydrogen gas, the emission spectrum appears as distinct lines of discrete wavelengths that are specific to the element hydrogen. Some of these lines are in the visible range of the electromagnetic spectrum, while some are in the ultraviolet or infrared range.
<h4>The Balmer Series</h4>
The series of visible lines in the hydrogen atom spectrum are named the Balmer series. This series of spectral emission lines occur when the electron transitions from a high-energy level to the lower energy level of n=2. Johann Balmer observed these spectral lines at 410.2 nm, 434.1 nm, 486.1 nm, and 656.3 nm, which correspond to transitions from the n=6, n=5, n=4, and n=3 energy levels to the n=2 level, respectively.
Balmer was able to relate these wavelengths of emitted light using the Balmer formula.
<img src="https://jove.unicartagenaproxy.elogim.com/CDNSource/lm/labs/54/54_Concepts_2.jpg" alt="Wavelength formula λ=C(m²/(m²−n²)); physics equation; diagram for wave analysis." data-altupdatedat="1747908711000">
Here, λ is the observed wavelength, C is a constant (364.50682 nm), n is the lower energy level with a value of 2, and m is the higher energy level, which has a value greater than 3. This observation was then refined by Johannes Rydberg, where R is the Rydberg constant.
<img src="https://jove.unicartagenaproxy.elogim.com/CDNSource/lm/labs/54/54_Concepts_3.jpg" alt="Rydberg formula for spectral lines, equation, physics, spectroscopy calculation." data-altupdatedat="1747908711000">
Remember that this equation describes emitted light, so the higher energy level is considered the initial level, or ni, while the lower level is considered the final level, or nf. In the case of the Balmer series, nf is equal to 2. This equation was combined with the Bohr model to calculate the energy needed to move an electron between its initial and final energy levels, ΔE.
<img src="https://jove.unicartagenaproxy.elogim.com/CDNSource/lm/labs/54/54_Concepts_4.jpg" alt="Energy change formula, ΔE=Rhc(1/nf²-1/ni²), spectral transition equation, quantum physics." data-altupdatedat="1747908711000">
Later, other spectral series for the hydrogen atom were discovered. For example, the Lyman series contains emission lines with energies in the ultraviolet region.
<h4>References</h4>
<ol>
	<li style="font-size:17px">Kotz, J.C., Treichel Jr, P.M., Townsend, J.R. (2012). Chemistry and Chemical Reactivity. Belmont, CA: Brooks/Cole, Cengage Learning.</li>
	<li style="font-size:17px">Silderberg, M.S. (2009). Chemistry: The Molecular Nature of Matter and Change. Boston, MA: McGraw Hill.</li>
</ol>

The Hydrogen Atom: Bohr Model and Balmer Series - Concept

Seria Balmer

The Bohr Model
Niels Bohr proposed a model for the hydrogen atom in 1913 that described discrete energy states are associated with a fixed electron orbit ...

Education

JoVE Lab Manual

Chemistry

Genetics of Organisms

<h4>Mendelian Genetics</h4>
Evolution is caused by changes in the genetic composition of populations. In the field of population genetics, scientists model this process as changes in the frequency of alleles at individual genetic loci. This simple representation of how evolution occurs dates to Gregor Mendel&#8217;s analysis of trait inheritance patterns in pea plants, first presented in 1865. Mendel determined, using rigorous collection of data, that noticeable traits are controlled by two alleles of each gene (he called them factors), one from each parent. In addition, he concluded that some alleles are dominant to others (deemed recessive). Interestingly, Mendel&#8217;s data was not appreciated until 1900, probably in part because his approach to the study of inheritance was so divergent from his predecessors. Even after his brilliant experiments were rediscovered, it took 30 additional years for Mendelian inheritance to become integrated with evolutionary theory and to gain universal acceptance.
Why was such a seemingly clear and straightforward process dormant for so long and then the center of controversy for decades? One key to the controversy is that we rarely see the clear patterns of inheritance described by Mendel. Animal and plant breeders most often see evidence for what was described as blending inheritance, or offspring whose phenotypes are intermediate between those of their parents, rather than the discrete segregation of traits into well-defined categories, like smooth versus wrinkled peas or red versus white flowers. Mendelian inheritance thus did not align well with practical experience and it took decades to reconcile the two. Additionally, there are now known to be modes of inheritance that, at first glance, appear to violate Mendel&#8217;s laws. Deeper investigations into genetics using molecular biology have effectively characterized these novel modes of inheritance. The new information does not invalidate Mendel&#8217;s findings, however, it simply enhances his model.
<h4>Phenomena that Violate Mendelian Rules</h4>
There are three main phenomena: epistasis, pleiotropy, and sex-linkage that appear to violate the basic dominance and recessivity rules of Mendelian inheritance. Epistasis occurs when two or more genes or alleles interact to affect a phenotype. A function of one gene product may be required to allow another gene to be expressed or to function normally. On the other hand, pleiotropy occurs when one gene controls the expression of multiple phenotypes in an individual. For example, a large proportion of cats with white fur and blue eyes are deaf 1. Pleiotropy is often antagonistic, meaning that the same gene causes beneficial changes in one facet of an individual&#8217;s phenotype while at the same time causing damaging changes in some other aspect of an individual&#8217;s phenotype. Antagonistic pleiotropy is seen as a tradeoff or constraint on evolution, as proposed by George C. Williams. For example, the age specific decline in performance, or senescence, is thought to be a pleiotropic trait controlled by the same genes that increase early-life fecundity2,3.
<h4>Sex Linkage</h4>
Sex linkage is another phenomenon discovered as a counter-example to basic Mendelian inheritance. In many organisms, pairs of sex chromosomes determine gender. In humans and many mammals, XX individuals are female while XY individuals are male. Male offspring inherit an X from their mother and a Y from their father; females inherit an X from their mother and an X from their father. In most species, the ratio of males to females at birth under &#8220;normal&#8221; environmental conditions is 1:1. Y-chromosomes undergo little recombination with X chromosomes during meiosis, due to the fact that, over evolutionary time, they have lost a substantial number of genes. Interestingly, there are only 16 genes shared by the human X and Y chromosomes4.
For X-linked traits (traits encoded by genes on the X chromosome) females can be homozygous (having two copies of the same allele) or heterozygous (having two different alleles). If the trait is recessive, females will only show the phenotype for the trait if they are homozygous. Heterozygous females &#8220;carry&#8221; the recessive allele, but do not express the phenotype of that allele. Statistically speaking, half of her sons will inherit this recessive allele and all will express the phenotype because males have only one X chromosome.
A commonly used example of an X-linked phenotype is hemophilia, a disease that prevents the synthesis of blood clotting proteins. If a female is heterozygous (is a carrier, genotype XH Xh) mates with a normal male (genotype XH Y), 50% of the sons will be affected. If the female was instead homozygous for the trait (genotype Xh Xh ), none of her daughters would be hemophiliacs but 100% of her sons would be. The differential phenotypic ratios seen in sons and daughters appears to violate Mendel&#8217;s laws, yet Mendel&#8217;s first law still applies; each offspring has a 50% chance of inheriting an X-chromosome carrying the hemophilia allele from a heterozygous mother5.
Like humans, the fruit fly Drosophila melanogaster has an XY-sex determination system. D. melanogaster make excellent laboratory organisms because they are easy to keep, breed, and manipulate. Wild-type flies have what is considered normal body morphology and red eyes. Many lines are readily available for purchase, including some with mutations resulting in flies without eyes, with eyes of varying color, or missing wings. These organisms can be used to study or demonstrate both Mendelian and non-Mendelian inheritance patterns. In fact, 1933 Nobel Prize winner, Thomas Hunt Morgan, first noticed that some genes are sex-linked using this system6.
<h4>References</h4>
<ol class="references">
	<li>Strain, George M., &#39; Cat Breeds With Congenital Deafness,&#39; [Online]. Available: https://www.lsu.edu/deafness/catbreeds.htm. [Accessed 29 August 2018].</li>
	<li>W. GC., &#39;Pleiotropy, natural selection, and the evolution of senescence.,&#39; Evolution, vol. 11, p. 398&#8211;411, 1957.</li>
	<li>T. &#38; S. P. S. Flatt, &#39;Integrating Evolutionary and Molecular Genetics of Aging,&#39; Biochimica et Biophysica Acta, vol. 1790, no. 10, p. 951&#8211;962, 2009.</li>
	<li>D. Bachtrog, &#39;Y chromosome evolution: emerging insights into processes of Y chromosome degeneration,&#39; Nature Reviews. Genetics, Vols. 14(2), 113&#8211;124, 2013.</li>
	<li>US National Library of Medicine, &#39;Hemophilia,&#39; Genetics Home Reference, [Online]. Available: https://ghr.nlm.nih.gov/condition/hemophilia. [Accessed 31 August 2018].</li>
	<li>&#39;The Nobel Prize in Physiology or Medicine 1933,&#39; [Online]. Available: https://www.nobelprize.org/prizes/medicine/1933/summary/. [Accessed 31 August 2018].</li>
</ol>

Genetics of Organisms: REF and WEF Fly Genetics - Concept

Genetyka organizmów

Mendelian Genetics
Evolution is caused by changes in the genetic composition of populations. In the field of population genetics, scientists model this ...

Biology

Genetics

Animal Behavior

<h4>Drosophila as a Model Organism</h4>
The common fruit fly, Drosophila melanogaster, is a widely used model organism in biology, though it may be more commonly recognized as a fruit-loving pest. There are multiple reasons that make D. melanogaster an excellent experimental organism: their roughly two-week generation time allows studying multiple generations, they are easily maintained in very small tubes, and their sexual dimorphism enables investigators to easily distinguish between males that have black coloration towards the posterior and females that have banded abdomens.
Thomas Hunt Morgan and his students were among the first to make use of Drosophila, in the early 20th century. In 1933, Morgan received a Nobel Prize for his discovery of the role of chromosomes in heredity, which was founded on his observations of mutant Drosophila. Morgan continued his studies by generating new mutants with radiation. The presence of multiple mutants with discrete effects on the appearance of the fly enabled him to look at patterns of multifactor inheritance. A key aspect of his early work was the discovery of apparent violations of Mendel&#8217;s second law, which states that genes segregate independently of one another. Morgan&#8217;s observations indicated that genes will not segregate independently if they are joined together on the same chromosome. Furthermore, it became possible to determine gene locations on a chromosome relative to each other through analyses of how often they recombined thus triggering gene mapping studies to identify the location of a gene and distances between genes. Consequently, associates of Morgan&#8217;s lab received two additional Nobel Prizes for other studies of Drosophila genetics. Morgan&#8217;s success and the accumulating information about Drosophila inspired others to adopt them as experimental organisms, which added to our knowledge of them and progressively increased their value as a model organism. Other scientists have developed many mutations. Over 27,000 unique Drosophila lines are now maintained as stock cultures at the Drosophila stock centers and are readily available to laboratories that use them as tools for studying diverse aspects of biology, including behavior1.
<h4>Drosophila Ethology</h4>
Animal behavior, or ethology, is the study of how animals interact with each other and their environments. Although it is a relatively young biological field, ethology has important implications since an organism&#8217;s survival depends on how it behaves with regard to possible mates, food sources, and natural obstacles. Studying these organisms helps scientists to understand the contexts of these behaviors and the underlying mechanisms as to how they have evolved. Thus, a goal of the study of animal behavior is to find a genetic basis for the behaviors exhibited by individuals or groups of individuals. For example, kin selection, first proposed by William Hamilton, suggests that individuals will behave altruistically when making a sacrifice for a family member rather than a stranger2. This is because related individuals share more genes than unrelated ones, and the future reproduction by their relatives contributes in a way to their own fitness, or reproductive success. Hence, observing how organisms like Drosophila interact with their environment can allow us to understand how these behaviors are hard-wired. Additionally, these behaviors provide insight into how these organisms have adapted to cope with their environment and survive.
Directional behaviors are commonly studied to understand the basis of how and why an organism moves in its environment. Kinesis and taxis are two forms of directional behaviors. Kinesis involves stimulus-induced movements in random directions, such as the random movement of flies when chased away. On the other hand, taxis involves stimulus-induced movement towards a specific direction, such as moths flying towards a lightbulb. Taxis behaviors are positive if the animal moves towards the stimulus and negative if the animal moves away from the stimulus. Furthermore, taxis behaviors are named based on the specific stimulus that induces them. For example, geotaxis is a response to gravity, where positive geotaxis means that the organism moves with gravity. Phototaxis is movement in response to light, such as positive phototaxis of moths towards a lightbulb. Similarly, chemotaxis is attraction to or avoidance of an airborne chemical cue, such as the negative chemotaxis of many predators, including humans, in response to the smell of skunk spray. Furthermore, there are many other types of taxes that are studied by scientists, including aerotaxis, barotaxis, hydrotaxis, and magnetotaxis, that are movements in response to oxygen, pressure, water, and magnetic fields, respectively.
<h4>Study of Drosophila taxis behavior</h4>
A common and simple method to study Drosophila taxis behavior is to use a choice chamber, which allows the observation of a directional response of the fly to a particular stimulus, such as gravity, light, or a chemical. Drosophila are inserted in the center of the choice chamber and then allowed to wander freely about the chamber, which is designed so that alternative stimuli can be presented at opposing sides. After a period of wandering, the number of flies is counted on either side of the choice chamber. The number of flies on each side of the chamber is then analyzed with the Chi Square test to gauge whether or not there is a preference for a stimulus.
Comparison of directional behavior of mutant and wild-type Drosophila allows researchers to determine the role of the mutated gene in that specific behavior. Moreover, scientists are also trying to understand how animals decide what to do in the presence of combinations of stimuli, including conflicting stimuli3. Together, these studies can help predict how organisms will react to certain stimuli or changes in environmental factors.
<h4>References</h4>
<ol class="references">
	<li>Cook KR, Parks AL, Jacobus LM, Kaufman TC, Matthews K. New research resources at the Bloomington Drosophila Stock Center. Fly. 2010, Vol. 4, 1: 88-91.</li>
	<li>WD, Hamilton. Altruism and related phenomena, mainly in social insects. Ann Rev Ecol Systematics. 1972, Vol. 3, 193-202.</li>
	<li>Gepner R, Skanata MM, Bernat NM, Kaplow M, Gershow M. Computations underlying Drosophila photo-taxis, odor-taxis, and multi-sensory integration. eLife. 2015, Vol. 4, e06229.</li>
</ol>

Animal Behavior: Studying Taxis Behavior of Drosophila - Concept

Zachowanie zwierząt

Drosophila as a Model Organism
The common fruit fly, Drosophila melanogaster, is a widely used model organism in biology, though it may be more commonly ...

Ecology

Hardy-Weinberg and Genetic Drift

Evolutionary change is interesting and important to study, but changes in populations occur over long periods of time and in huge physical spaces and are therefore very difficult to measure. In general, studying phenomena like this requires the use of mathematical models which are built using parameters that can be conveniently measured. These models are then used to make predictions about how changes to the system might affect the outcome. 
For example, changes to the frequency of genetic alleles at individual loci in species are often observed over long time periods, but are not generally observable over short time periods. The use of computer models allows researchers to predict changes within a species&#8217; gene pool using observations that have already been gathered, and allow for simulations of a potentially unlimited number of generations of the population. This would certainly not be possible within a human lifetime.
<h4>The Hardy-Weinberg Equilibrium Principle</h4>
One equation used to model populations is the Hardy-Weinberg equilibrium equation. It was formulated independently in 1908 by both G. H. Hardy and Wilhelm Weinberg1,2. The simple equation describes the expected allele frequency of a population that is not evolving. Because most real-life populations are evolving in response to the forces of natural selection, this formula acts as a useful null hypothesis. It is used to test whether or not different types of selection are, or are not, occurring. If measured allele frequencies differ from those that were predicted using the Hardy-Weinberg equation, then the gene in question is undergoing evolutionary change.
The Hardy-Weinberg equation can be represented as p2 + 2pq + q2 = 1 where p represents the frequency of the dominant allele and q represents the frequency of the recessive allele for the gene in question. Each expression in the equation represents the predicted frequency of one of the three possible diploid genotypes. Homozygous dominant frequency is represented by p2, homozygous recessive by q2, and heterozygous by 2pq. If these frequencies are summed, it adds up to 1, which makes sense since the full population had been divided into the three available categories.
It is also possible to describe the population, or gene pool, in terms of the two alleles, without regard to how they are packaged into diploid individuals. This equation is represented as p + q = 1, that is, the frequencies of the dominant and recessive alleles must add up to 1 within the population. Again, this makes sense, since the model includes just two allele possibilities. Notice that the frequency of genotypes in the population is just the quadratic expansion of the frequency of alleles in the population because (p + q)2 = p2 + 2pq + q2.
The Hardy-Weinberg equation, like most other models, requires a series of assumptions. Usefully, if the real-life data differs from the predicted data, it is possible to hypothesize which of the starting assumptions was false. The assumptions of the Hardy-Weinberg equilibrium equations are: 1) the population is very large, 2) the population is closed, meaning that there are no individuals immigrating into or emigrating out of the population, 3) there are no mutations occurring on the gene in question, 4) individuals within the population are mating at random&#8212;individuals are not choosing their mates, 5) natural selection is not occurring. Again, keep in mind that the Hardy-Weinberg equilibrium equation is a null hypothesis. Frequently, real populations violate one or more of these assumptions. The equation is used to determine if and how evolution is happening.
<h4>Genetic Drift</h4>
In a relatively small population, a condition that violates the first Hardy-Weinberg assumption, it is possible for allele frequencies to have resulted from chance. This phenomenon is referred to as genetic drift. One version of this is referred to as the founder effect. If a small number of individuals move to an isolated location and start a new population, the specific genetics of those particular individuals will shape the future generations. This new small gene pool may have the same allele frequency as the original, but it is also possible, even likely, that it does not. Say the original population included 50% dominant alleles and 50% recessive. In the extreme, if all of the individuals in a migrating founder group are homozygous recessive, then the dominant allele has been lost completely and the recessive is now at 100%. A similar phenomenon, called the bottleneck effect, can occur if a population sustains a large drop in numbers due to a natural disaster, human intervention, or disease.
Importantly, genetic drift represents a type of evolution that is not necessarily adaptive. It does not specifically select for traits that are fit for the environment. Nonetheless, it is an important evolutionary force in shaping populations with a small number of individuals and will cause deviations from Hardy-Weinberg predictions. In populations that are larger, deviations from the predictions are more likely to mean that the population in question is undergoing evolution through natural selection. Under these circumstances, further consideration of the mechanisms causing this evolution can be explored.
Modern applications of the Hardy-Weinberg model include analysis of human and animal populations in terms of how their immune systems relate to their susceptibility to infectious disease3,4. Many research groups are cataloging genes which encode specific immune system molecules, such as CCR5 and the major histocompatibility complex (MHC, known in humans as the human leukocyte antigen (HLA), and correlating this information to epidemiological studies regarding disease and its progression3,5-6. From comparing these two types of data sets, patterns are emerging showing that some individuals are genetically resistant to particular infections while others are more likely to succumb to disease4. Hardy and Weinberg&#8217;s model has been essential to the analysis of this data and has contributed to our understanding of how infections have shaped evolution.
<h4>References</h4>
<ol class="references">
	<li>Hardy, G. H. (Jul 1908). 'Mendelian Proportions in a Mixed Population' (PDF). Science. 28 (706): 49&#8211;50. doi:10.1126/science.28.706.49. ISSN 0036-8075. PMID 17779291.</li>
	<li>Weinberg, W. (1908). '&#220;ber den Nachweis der Vererbung beim Menschen'. Jahreshefte des Vereins f&#252;r vaterl&#228;ndische Naturkunde in W&#252;rttemberg. 64: 368&#8211;382.</li>
	<li>Eguchi S, Matsuura M. Testing the Hardy-Weinberg equilibrium in the HLA system. Biometrics. 1990 Jun;46(2):415-26. PubMed PMID: 2364131.</li>
	<li>Phillips KP, Cable J, Mohammed RS, et al. Immunogenetic novelty confers a selective advantage in host-pathogen coevolution. Proc Natl Acad Sci U S A. 2018;115(7):1552-1557.</li>
	<li>Lim JK, Glass WG, McDermott DH, Murphy PM CCR5: no longer a 'good for nothing' gene--chemokine control of West Nile virus infection. Trends Immunol. 2006 Jul; 27(7):308-12.</li>
	<li><a target="__blank" href="https://hla-net.eu/tools/frequency-estimation/">https://hla-net.eu/tools/frequency-estimation/</a></li>
	<li>Santiago Rodriguez, Tom R. Gaunt and Ian N. M. Day. Hardy-Weinberg Equilibrium Testing of Biological Ascertainment for Mendelian Randomization Studies. American Journal of Epidemiology Advance Access published on January 6, 2009, DOI 10.1093/aje/kwn359. http://www.oege.org/software/hwe-mr-calc.html</li>
</ol>
<h4>Further Reading</h4>
Andrews, C. (2010) The Hardy-Weinberg Principle. Nature Education Knowledge 3(10):65 https://www.nature.com/scitable/knowledge/library/the-hardy-weinberg-principle-13235724

Hardy-Weinberg Equilibrium Principle and Genetic Drift - Concept

Hardy-Weinberg i dryf genetyczny

Evolutionary change is interesting and important to study, but changes in populations occur over long periods of time and in huge physical spaces and are ...

Evolution

Evolutionary Relationships

Humans have been attempting to properly classify living things since Aristotle made the first attempt during the 4th century BC. Aristotle&#8217;s system was improved upon during the Renaissance and then, subsequently, by Carolus Linnaeus in the mid 1700&#8217;s. These more formal classification and organization systems grouped species by their physical similarity to one another. For example, all vertebrates have a backbone, but invertebrates do not. Traits like the backbone are called synapomorphies, which are traits that are shared by a group of organisms, presumably because they were derived from a common ancestor. As we will explore, this method has been shown to have limitations and has more recently been amended to include genetic analysis. Still, scientists construct trees called dendrograms to create a visual representation of how species are related to one another and share common ancestors. These dendrograms can aid in our understanding of the evolutionary processes that drive these relationships. Genetic comparisons have added an important tool guiding the analysis of evolutionary relationships.
<h4>Types of Dendrogram</h4>
A type of dendrogram, called a cladogram, depicts the hypothetical genealogical relationships between species with the tips (or leaves) of the tree representing a species and the branches showing how species are related to each other. A slightly more complicated type of tree, called a phylogram, differs from a cladogram in that the branches leading to the species are of different lengths. The length of a branch in this type of tree represents the degree of change between species: the longer the branch, the more time since the species have diverged from a common ancestor. In both types of tree, the common ancestor of a group of species is indicated by a node, which is the point where a series of branches meet. Species that are more closely related to each other (most recently shared a common ancestor) will be located closest to the node. The two species that share a node are called a sister group1.
<h4>Understanding Evolutionary Relationships using Genetic Data</h4>
Historically, cladograms were constructed by comparing the morphology (physical structure) of organisms. This method is still practiced but the techniques have been modernized to include comparison of DNA (deoxyribonucleic acid) sequences between species. Using DNA for building trees has several advantages over relying solely on morphology, including being able to calculate an estimate of how long ago different species shared a common ancestor1. However, using DNA is not always feasible, especially when trees include extinct organisms. DNA is best found in soft tissues, which are not preserved during the fossilization process, and therefore it is uncommon for a DNA sample of an extinct species to be available.
DNA is passed on from parents to their offspring in hereditary units called genes. The nucleotide (A, G, C, and T) sequence of genes found in different species are frequently quite similar, likely due to their having come from a common ancestor. This fact allows researchers to align sequences from different species with one another to build the trees described above. Species with more similarity between their nucleotide sequences will be placed next to each other in a tree, and species with less sequence similarity will be placed further apart from each other.
Bioinformatics are the tools used by biologists to analyze large datasets using a combination of computer science, mathematical modeling, and statistics. One such tool is called BLAST (Basic Local Alignment Search Tool), which can be used to quickly search the entire genome of any species that is available in the NCBI (National Center for Biotechnology Information) database2. The NCBI database combines several different databases that hold different types of DNA sequence information. The process of a BLAST search includes complex computer algorithms, but basically, BLAST aligns sequences of each nucleotide base from a submitted DNA sequence (known as the query sequence) with sequences in the data base that most closely match it. The DNA sequences that are found will be listed in order of similarity to the sequence in question, and will therefore be from species closely related to the species containing the query gene. This comparison may or may not depict the actual evolutionary relationship between species because genes evolve at different rates. Additionally, genomes sometimes contain more than one instance of a similar sequence.
Comparison of DNA sequences of genes is valuable beyond consideration of evolutionary relationships. Frequently, genes are identified in model organisms, such as the fruit fly, Drosophila melanogaster, or the mouse3. Integral to studying a gene, the function of its product is commonly identified and analyzed. If a researcher is interested in studying that function in a different organism (humans for example), BLAST or other bioinformatic tools can be used to find candidate genes based on their similarities to the genes of known function from model organisms.
Human genes can also be used as the starting point to find homologs in model organisms. In fact, human disease research depends heavily on this. Once a human gene of interest is identified, mice can be genetically manipulated to have the homologous gene disrupted, or &#8220;knocked out,&#8221; creating a model of the human disease that can be studied in order to understand and treat the disease. There are many of these mouse strains currently available. For example, there is a mouse model for human Cystic Fibrosis (CF) called the Cftr knockout mouse and another modeling atherosclerosis, called the Apoe knockout3. 
 
<h4>References:</h4>
<ol class="references">
	<li>Annenberg Learner. Online Textbook: Unit 3 Evolution and Phylogenetics. Rediscovering Biology. [Online] 2017. [Cited: August 23, 2018.] https://www.learner.org/courses/biology/textbook/compev/compev_3.html.</li>
	<li>National Center for Biotechnology Information, U.S. National Library of Medicine. BLAST: Basic Local Alignment Search Tool. [Online] https://blast.ncbi.nlm.nih.gov/Blast.cgi.</li>
	<li>National Human Genome Research Institute. Background on Mouse as a Model Organism. National Human Genome Research Institute. [Online] December 2002. [Cited: August 23, 2018.] <a href="https://www.genome.gov/10005834/background-on-mouse-as-a-model-organism/" target="__blank">https://www.genome.gov/10005834/background-on-mouse-as-a-model-organism/</a>.</li>
</ol>

Evolutionary Relationships: Using BLAST to Test Evolutionary Hypotheses - Concept

Związki ewolucyjne

Humans have been attempting to properly classify living things since Aristotle made the first attempt during the 4th century BC. Aristotle&#8217;s system ...

Animal Diversity

Kingdom Animalia is composed of a range of organisms united by a set of common characteristics. Barring a few exceptions, animals are multicellular eukaryotes that move, consume organic matter, and reproduce sexually. Although these attributes are shared, species within this kingdom are also extremely diverse. This diversity is due to adaptation of each species to a different niche. The niche of a species includes the area, function, and interrelationship of that species with other biotic and abiotic factors in its environment. Niche specialization through evolutionary adaptation allows species to survive and reproduce effectively in their environment and reduces competition among species within the same habitat1-2.
<h4>Classification</h4>
Diversity in Kingdom Animalia has led to the classification of 36 distinct phyla, based on their evolutionary lineage. Seven of these phyla will be discussed, including Porifera, Cnidaria, Platyhelminthes, Annelida, Mollusca, Arthropoda, and Chordata. The first, phylum Porifera, is the earliest, simplest, most ancestral phylum. It includes multicellular, asymmetrical filter feeding sponges that lack distinct tissue layers. Interestingly, sponges can regrow after being broken apart to the level of a single cell. Next, increasing in complexity, the phylum Cnidaria includes jellyfish and coral. Members of this phylum are radially symmetric and diploblastic (possessing two tissue layers). These tissue layers are called the endoderm, which makes up the inner layer, and the ectoderm, which forms the outer layer. Organisms in phylum Cnidaria contain a simple gut with a single opening through which food enters and waste is excreted. Both Porifera and Cnidaria include organisms that are largely sedentary as adults. However, the remaining phyla have evolved adaptations that allow movement and more sophisticated behavior.
Following Cnidaria, there are two phyla of worms, termed Platyhelminthes and Annelida. Worms from both of these phyla breathe by exchanging gases across their skin. As a result, most worms have a large surface area to volume ratio and live in moist or aquatic environments. The skin of these organisms must remain moist to facilitate effective diffusion of gases. Another common characteristic of worms is their bilateral symmetry. This means that an organism can be symmetrically divided in half along a plane. Bilateral symmetry is seen not only in worms, but also in every phyla from this evolutionary point forward. Such symmetry is important to allow directed movement and to concentrate sensory organs near the anterior tip, or head, where they interact with their surroundings. Worms and later phyla also contain an additional tissue layer called the mesoderm that lies between the ectoderm and endoderm. As a result, these organisms are considered &#8220;triploblastic.&#8221;
Of the two phyla of worms, Platyhelminthes is the more ancient and simple. These include flatworms and trematodes, which may be free-living or parasitic. Like Cnidarians, they possess a simple gut with a single opening. Phylum Annelida is more complex, including earthworms, leaches, and polychaeta. These organisms contain a body cavity, called the coelom, between the gut and body wall. They are also distinct due to the segmentation of their bodies. Division of an organism into segments was an important evolutionary step, allowing adaptation of each segment to specialized forms and functions. Major evolutionary advances in this phyla include the development of anterior segments with appendages specialized for grasping and processing food as well as posterior segments with appendages specialized for locomotion.
The next phylum, Mollusca, contains highly morphologically diverse organisms, including octopuses, snails, slugs, and clams. There are few defining features for this group. However, all mollusks possess a mantle, which acts as a protective cover for the respiratory organs, digestive tract, and reproductive structures. Next, the most diverse group of organisms is phylum Arthropoda. This phyla alone makes up approximately three quarters of all living and extinct species known today, including crustaceans, insects, and arachnids. All members of this phylum have a hard, outer coating made of chitin, called an exoskeleton. This exoskeleton is periodically shed for a new, larger one to take its place. Like Annelida, arthropods are segmented. However, instead of multiple repeating sections, arthropods are broken up into three distinct segments called the head, thorax, and abdomen. Most have specialized sensing structures attached to the head called antennae.
Lastly, phylum Chordata includes all animals with a backbone and a few without. Two defining features that unite this group are the presence of a nerve chord and a notochord. The nerve cord is a major part of the chordate nervous system and the notochord is a cartilaginous structure that runs between the nerve cord and the digestive tract. Phylum Chordata includes tunicates (which lose their nerve cord and notochord after metamorphosis from the larval to adult life stage), fish, amphibians, birds, reptiles, and mammals (including humans)3.
<h4>Form and Function</h4>
The vast diversity in body structure, habitats, and behavior observed across all 36 phyla within Kingdom Animalia is truly astounding. To understand these differences better, it is important to consider the connection between form and function. &#8220;Form&#8221; simply means the shape, size, and substance of a structure or organism while &#8220;function&#8221; stands for the way in which that structure is used. Accordingly, the function of a structure can be predicted based on the form observed, or vice versa. For example, a bird specialized to crack hard nuts would be predicted to have a sturdy, sharp beak while a bird that eats fruit may have a smaller, more rounded beak.
Importantly, the form of an organism is constrained by its physiological requirements. For instance, all animals must consume and digest organic matter and exchange gas with their environment. As a result, any adaptations to an animal&#8217;s form must continue to facilitate these processes efficiently. For example, in smaller animals, diffusion of gases across the skin is possible and often useful. However, as the size of an animal increases, its surface area to volume ratio decreases until diffusion across the skin can no longer provide sufficient gas exchange. Such larger animals have developed complex systems to deal with gas exchange, such as the lungs of a whale or the gills of a fish. As a result, the morphology of gas exchange systems depends on both the size of an organism and its environment.
Similarly, it generally follows with other types of adaptations that form follows function, constrained only by the physiological requirement of the organism. However, there are cases in which form does not follow function. Vestigial structures are those that served a purpose in an organism&#8217;s ancestor but are no longer useful in their current form. Such structures have little to no impact on the fitness of an individual, and are thus not evolutionarily selected for or against within the population. As a result, some structures persist with limited functional capacity. For example, the human appendix, a small pocket at the end of the large intestine, is an evolutionary remnant from our ancestors. While this structure has been hypothesized to store beneficial gut bacteria, removal of the appendix is possible with no deleterious effects.
<h4>Convergent Evolution</h4>
Among adaptations seen in Kingdom Animalia, many have developed multiple times throughout evolutionary history. Consider the wings of a bat and those of a bird as an example. These structures are not a result of evolutionary relatedness, but have instead arisen from convergent evolution4. Convergent evolution occurs when two organisms independently evolve similar traits due to similar environmental pressures or niches. The wings of bats, birds, and insets have all developed independently and are said to be &#8220;convergent traits.&#8221; Clearly, such traits are quite beneficial for the niches in which they arise.
<h4>References</h4>
<ol class="references">
	<li>Hollins, J., et al. (2018). 'A physiological perspective on fisheries-induced evolution.' Evol Appl 11(5): 561-576.</li>
	<li>Leong, M., et al. (2017). 'The Habitats Humans Provide: Factors affecting the diversity and composition of arthropods in houses.' Sci Rep 7(1): 15347.</li>
	<li>Wray, G. A. (2015). 'Molecular clocks and the early evolution of metazoan nervous systems.' Philos Trans R Soc Lond B Biol Sci 370(1684).</li>
	<li>Foll, M., et al. (2014). 'Widespread signals of convergent adaptation to high altitude in Asia and America.' Am J Hum Genet 95(4): 394-407.</li>
</ol>

Animal Diversity: Form and Function of Crickets and Crayfish - Concept

Różnorodność zwierząt

Kingdom Animalia is composed of a range of organisms united by a set of common characteristics. Barring a few exceptions, animals are multicellular ...

Microbial and Fungal Diversity

Bacteria and fungi are two highly diverse groups of organisms that can have significant beneficial or detrimental impacts on human health. For this reason, it is important to understand and distinguish between individual species of these groups. As you will recall, biological taxonomists group organisms based on their phylogenetic relatedness. The three domains of life, Bacteria, Archaea, and Eukaryota segregate life into three distinct groupings. While all bacteria fall within the domain Bacteria, fungi are found in the domain Eukaryota (Kingdom Fungi). In addition to Archaea, domain Bacteria exclusively includes single-celled prokaryotes, meaning their cells lack both a nuclear membrane and membrane-bound organelles. In contrast, fungi of domain Eukaryota have membrane-bound organelles and include both unicellular and multicellular organisms.
<h4>Bacteria</h4>
Bacteria are found nearly everywhere on Earth and are highly adapted to the environments in which they grow. As a result, this domain includes a staggering amount of diversity. The impact of these organisms on human health is equally diverse. For example, species like Streptococcus thermophilus can be used to produce fermented foods like yogurt, while spores of the bacteria Bacillus anthracis are responsible for the potentially deadly disease anthrax.
To aid in the identification of such organism, microbiologist have developed a myriad of tests to characterize both individual cells and entire colonies of bacteria1. One of the most common methods for identifying bacteria is by Gram staining. This test uses colored dyes to determine the composition of the bacteria&#8217;s cell wall. Gram-positive bacteria, which bear a thick cell wall made up of a protein called peptidoglycan, retain a dye called crystal violet and thus appear purple. In contrast, gram-negative bacteria have a thinner cell wall without peptidoglycan and do not retain the crystal violet. Instead, these cells appear pink due to a counter stain called safranin. The use of Gram staining often serves as a preliminary step in the identification of a bacterial species. Further classification requires the identification of additional characteristics. This can include the shape of individual bacteria, which may be either cocci (round), spirillum (spiral), or bacilli (rod-shaped). Additionally, the color, shape, sheen, texture, and even smell of bacterial colonies can be used to distinguish between species.
<h4>Fungi</h4>
Fungi of domain Eukaryota are also highly ecologically diverse and can range in size from microscopic yeast to the largest organism on earth, the honey fungus (Armillaria ostoyae). All fungi are heterotrophic, meaning that they must acquire their food from the environment. These organisms also exhibit diversity in their specific activities, niches, and potential effects on human health. For example, some fungi are consumed or used to produce food by humans while others can cause severe or even deadly infections2.
Within kingdom Fungi, two distinct taxonomic divisions exist based on the reproductive strategy of the species. First, Ascomycota, known as the sac fungi, have a reproductive structure called an ascus. Together, many asci form the ascocarp, or &#8220;fruiting body&#8221; of the fungi. These fungi can reproduce sexually or asexually by budding. Ascomycota consist of some economically important species like the yeast used in brewing beer and baking bread, and some species popular in cuisine, such as morels and truffles. The second group, Basidiomycota, includes organisms commonly known as &#8220;club fungi&#8221; due to the presence of a large club-shaped reproductive structure called the basidium. In contrast to Ascomycota, most Basidomucota reproduce exclusively by sexual reproduction. This occurs when two haploid mycelia join to form a basidiocarp. On a button mushroom, this is the cap structure. On the underside of the basidiocarp, reproductive structures called gills lined with structures called basidia produce haploid nuclei that eventually form basidiospores. Basidiospores are then dispersed by the wind. This taxonomic division includes many commonly eaten button mushrooms, as well as wild puff mushrooms.
<h4>Importance of Microbiology</h4>
Overall, it is clear that the diverse functions and properties of microorganisms make their identification extremely important for human health and well-being. While a vast number of species of fungi and bacteria have been identified to date, it is estimated that millions more have yet to be identified and classified. In addition, human disciplines that explore Fungi and Bacteria are as diverse as the organisms themselves and will undoubtedly continue to grow. The relevance of microorganisms to the medical fields is more prevalent now then ever, as multi-antibiotic resistant species of infectious bacteria threaten human health. Understanding the structure and biological characteristics of infectious and non-infectious species provides key insights necessary for the creation of new antibiotic treatments and preventative strategies3-4.
Microbiology is also critical to the food industry. Numerous products, including yogurt, cheese, kombucha, bread, and alcohol are made using specific categories or species of microbes. At the same time, food scientists focus on engineering chemical compounds and preservatives that will reduce the growth of unwanted microbes on food. In all these processes, an understanding of how microbes survive, reproduce, and affect their environment is necessary.
<h4>References </h4>
<ol class="references">
	<li>Hiroshi Asakura, Holger Brueggemann, Sou-ichi Makino, and Yoshiko Sugita-Konishi, &#8220;Molecular Approaches for the Classification of Microbial Pathogens of Public Health Significance,&#8221; BioMed Research International, vol. 2014, Article ID 725801, 2 pages, 2014. https://doi.org/10.1155/2014/725801.</li>
	<li>Treseder, K. K. and J. T. Lennon (2015). 'Fungal traits that drive ecosystem dynamics on land.' Microbiol Mol Biol Rev 79(2): 243-262.</li>
	<li>Arvanitis, M. and E. Mylonakis (2015). 'Fungal-bacterial interactions and their relevance in health.' Cell Microbiol 17(10): 1442-1446.</li>
	<li>Aarestrup, F. M., et al. (2008). 'Resistance in bacteria of the food chain: epidemiology and control strategies.' Expert Rev Anti Infect Ther 6(5): 733-750.</li>
</ol>

Microbial Colony and Fungal Diversity - Concept

Różnorodność mikrobiologiczna i grzybicza

Bacteria and fungi are two highly diverse groups of organisms that can have significant beneficial or detrimental impacts on human health. For this ...

Cell Division

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Preparation of Onion Root Tips and Solutions
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">For the cell cycle experiment using root tips, first, leave an onion suspended over a beaker of water to grow roots for several days. 
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Then, clean the onion roots of any dirt or debris. 
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Next, in a 1.5 mL tube, dissolve 10 mcg of nocodazole per 1 mL of dimethyl sulfoxide solution, making 1 tube per student. 
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Finally, set out all of the other tools, materials, and reagents from the list of materials onto each student&#39;s lab bench. 
</div></li></ul></li></ol>

Cell Division: Observing Cell Cycle in a Root Tip - Prep

Podział komórek

Preparation of Onion Root Tips and Solutions
ExpandFor the cell cycle experiment using root tips, first, leave an onion suspended over a beaker of water ...

Cellular Processes

Lab Manual Sub Articles

Bacterial Transformation

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Bacterial Transformation
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">First, ensure that each student or student group has a filled ice bucket.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Then, set up a tube rack for each work station.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Into the tube racks, place two empty, sterile 1.5 mL tubes, and then two one mL aliquots of SOC media.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Then, place the plasmid tube into the ice bucket and add two tubes of competent E. coli cells, allowing them to thaw if frozen.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Set out two LB agar plates per group and two LB agar ampicillin plates per group.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Ensure the students have the appropriate pipettes.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">And finally, set a water bath to 42 &#176;C and a temperature-controlled shaker to 37 &#176;C.
</div></li></ul></li></ol>

Bacterial Transformation using Plasmids - Prep

Transformacja bakteryjna

Bacterial Transformation
ExpandFirst, ensure that each student or student group has a filled ice bucket.
Then, set up a tube rack for each work station.
...

Cell Structure

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Visualizing Onion and Cheek Cells
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">Immediately before the experiment, wash and peel onion bulbs for the class.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Remove the entire brown outer skin and cut the onion in half with a knife. Pull apart the layers of the onion. The thin, nearly transparent film layers within the onion will be used by the students.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Place the onion film into a Petri dish and then place this into the refrigerator until the students arrive.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Bring out the microscopes and set them on the lab bench.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Set the microscopes to the lowest magnification and plug them into an electrical outlet.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Clean the lenses with lens paper or laboratory wipes and test the knobs to make sure the lenses focus properly.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Make sure that the stage can be moved.
</div></li></ul></li></ol>

Cell Structure: Visualizing Onion and Human Cells - Prep

Struktura komórki

Visualizing Onion and Cheek Cells
ExpandImmediately before the experiment, wash and peel onion bulbs for the class.
Remove the entire brown outer skin and ...