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1.4.7.11: Why It Matters- Prokaryotes - Biology

1.4.7.11: Why It Matters- Prokaryotes - Biology


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Why learn about different kinds of prokaryotes and bacteria?

In August 2016, a woman in Nevada died from an incurable bacterial infection. She was infected with a strain of Klebsiella that was resistant to all twenty-six of the antibiotics at the hospital. Doctors suspect she picked up the bacteria in India, where this kind of resistance is particular prevalent.[1] How do you think this resistance came about? Aren’t antibiotics supposed to kill all bacteria?

However, not all prokaryotes are dangerous—in fact, some strains are essential for our bodies to function correctly. For example, each person has a normal microbial flora (also known as a gut microbiota) in our stomachs. In other words, we have approximately 100 trillion bacteria living in our stomachs. These bacteria help us with digestion, synthesizing vitamins, and producing hormones.

In this module we will focus on a limited number of both the beneficial and the harmful prokaryotes around us.



First genome sequencing and comparative analyses of Corynebacterium pseudotuberculosis strains from Mexico

Corynebacterium pseudotuberculosis is a pathogenic bacterium which has been rapidly spreading all over the world, causing economic losses in the agricultural sector and sporadically infecting humans. Six C. pseudotuberculosis strains were isolated from goats, sheep, and horses with distinct abscess locations. For the first time, Mexican genomes of this bacterium were sequenced and studied in silico. All strains were sequenced using Ion Personal Genome Machine sequencer, assembled using Newbler and SPAdes software. The automatic genome annotation was done using the software RAST and in-house scripts for transference, followed by manual curation using Artemis software and BLAST against NCBI and UniProt databases. The six genomes are publicly available in NCBI database. The analysis of nucleotide sequence similarity and the generated phylogenetic tree led to the observation that the Mexican strains are more similar between strains from the same host, but the genetic structure is probably more influenced by transportation of animals between farms than host preference. Also, a putative drug target was predicted and in silico analysis of 46 strains showed two gene clusters capable of differentiating the biovars equi and ovis: Restriction Modification system and CRISPR-Cas cluster.


Week 10: June 14-17

Learning Targets

  • Identify the structures of the digestive system and explain what they do.
  • Differentiate between ingestion, digestion, absorption, and elimination.
  • Differentiate between physical and chemical digestion.
  • Differentiate between the body&rsquos three lines of defenses against infectious diseases.
  • Explain how vaccines work.
  • Compare and contrast active vs passive immunity.
  • Identify how habits and choices can affect a person&rsquos health.

Monday, 6/14: Structure & Movement Assessment

Tuesday, 6/15: The Digestive System

Wednesday, 6/16: Defenses Against Disease

Thursday, 6/17: Staying Healthy

Week 9: June 7-11

Learning Targets

  • Name and describe the three types of blood vessels.
  • Differentiate between different types of circulation.
  • Name and describe some of the common circulatory system diseases.
  • Identify ways to keep my circulatory system healthy.
  • Identify the function on blood in or body.
  • Name the four components of blood.
  • Differentiate between different blood types.
  • Describe the functions of the muscular system.
  • Differentiate between different types of muscle tissue.
  • Differentiate between voluntary and involuntary muscles.
  • Describe the functions of the skeletal system.
  • Differentiate between the axial and appendicular skeleton.
  • Differentiate between the different parts of a bone.
  • Name and describe the different classifications of bones.
  • Name and describe the different types of joints.
  • Differentiate between tendons and ligaments.
  • Explain how muscles work in pairs.

Monday, 6/7: Blood Vessels & Circulatory System Health

Tuesday, 6/8: Blood, Respiration & Circulation assessment

Wednesday, 6/9: The Muscular System

Thursday, 6/10: The Skeletal System

Friday, 6/11: Types of Bones & Joints

Learning Targets :

  • Identify the functions of the respiratory system.
  • Name the structures of the respiratory system and explain what they do.
  • Describe the breathing process and the relationship between volume and pressure in this process.
  • Identify other body systems that interact with the respiratory system to maintain homeostasis in the body.
  • Name some of the most common respiratory illnesses and list their causes and symptoms.
  • Identify ways to keep my respiratory system healthy.
  • Conduct research on a respiratory illness of my choice.
  • Describe the functions of the circulatory system.
  • Name the structures that make up the heart.
  • Explain the pathway of blood through the heart.
  • Describe the functions of blood.
  • Identify the different parts of blood and explain their function.
  • Explain how a blood clot forms.
  • Differentiate between different blood types.

Tuesday, 6/1: The Respiratory System & Breathing

Wednesday, 6/2: Respiratory System Disorders

Thursday, 6/3: The Circulatory System- Part 1

Week 7: May 24-28

Learning Targets:

  • Differentiate between asexual and sexual reproduction.
  • Name and describe the phases of meiosis.
  • Differentiate between mitosis and meiosis.
  • Explain how sexual reproduction results in offspring with a variety of traits.
  • Name and differentiate between the different levels of biological organization.

Monday, 5/24: Cell Cycle and Mitosis Presentation Project

Tuesday, 5/25: Sexual Reproduction and Meiosis

Wednesday, 5/26: Comparing and Contrasting Mitosis and Meiosis

Thursday, 5/27: Cell Cycle and Reproduction Final

Friday, 5/28: The Human Body

Week 6: May 17-21

Learning Targets

  • Describe how cells obtains energy.
  • Explain how some cells are able to make food molecules.
  • Compare and contrast photosynthesis and cellular respiration.
  • Differentiate between reactants and products in a chemical equation.
  • Name and describe the phases of the cell cycle.
  • Identify the 4 stages of mitosis and explain what happens in each.

Monday, 5/17: Cells and Energy

Tuesday, 5/18 : Cell Processes Test

Wednesday, 5/19: The Cell Cycle and Cell Division

Thursday, 5/20: Cell Cycle & Mitosis Presentation Project

Friday, 5/21 : Cell Cycle & Mitosis Presentation Project

Week 5: May 10-14

Learning Targets:

  • Name the types of molecules that make up the cell membrane.
  • Describe the functions of the cell membrane.
  • Define matter and permeability.
  • Explain how cells move materials through diffusion and osmosis.
  • Differentiate between solutes, solvents, and solutions.
  • Differentiate between isotonic, hypertonic, and isotonic solutions.
  • Name and describe the three types of variables in a science experiment.
  • Form a hypothesis and justify my prediction using science concepts accurately.
  • Use science concepts accurately and appropriately to explain what happened, why it happened, and what it proves in an investigation.
  • Differentiate between diffusion and facilitated diffusion.
  • Define active transport.
  • Compare and contrast passive transport and active transport.
  • Define endocytosis and exocytosis.
  • Explain why it is necessary for cells to remain small

Monday, 5/10: Cell Membrane & Diffusion

Tuesday, 5/11: The Gummy Bear Investigation

Wednesday, 5/12: The Naked Egg Investigation/CER

Thursday, 5/13: The Naked Egg Investigation/CER

Friday, 5/14: Other Types of Transport

Week 4: May 3-7

  • Safely and appropriately operate a compound light microscope.
  • Effectively use a virtual compound light microscope.
  • Effectively bring into focus cells and tissues.
  • Make a wet-mount slide.
  • Create quality biological drawings.

Monday, 5/3: Cohort A: Observing Cells: Part 1, Cohort B & Remote: Virtual Microscope Lab

Tuesday, 5/4: Cohort B: Observing Cells: Part 1, Cohort A & Remote: Virtual Microscope Lab

Wednesday, 5/5: Cohort A: Observing Cells: Part 2, Cohort B & Remote: Virtual Microscope Lab

Thursday, 5/6: Cohort A: Observing Cells: Part 2, Cohort B & Remote: Virtual Microscope Lab

Friday, 5/7: Complete Observing Cells Lab analysis questions and Virtual Microscope Lab

Week 3: April 26-30

  • Provide a description of the functions of cell organelles
  • Create accurate analogies for cells and their organelles
  • Identify the structures of a compound light microscope

Monday, 4/26: Create an analogy for a cell

Tuesday, 4/27: Who Should Be President of the Cell? Project

Wednesday, 4/28 : Review for cells test, work time

Thursday, 4/29: Cell Substances & Structures test, Introduction to Microscopes

Friday, 4/30: Microscope assignment

Week 2: April 19-23

  • Name the major organelles in an animal cell and describe their functions.
  • Create a model to illustrate the structures inside of a cell.
  • Explain how an egg can be used as an analogy for a cell.

Monday, 4/19 : Cell Model Projects

Tuesday, 4/20: Cell Model Projects

Wednesday, 4/21 : Cohort A-Lab: Why Do Eggs Have Shells?, Cohort B- Cell Model Project (*Remote students have choice to join either Cohort)

Thursday, 4/22: Cohort B-Lab: Why Do Eggs Have Shells ?, Cohort A- Cell Model Project

Friday, 4/23: Choice of Cell Model Project or cell enrichment activities


Discussion

Our analysis supports early indirect approximations to the number of gene conversions events, strongly rejecting the one prior NGS-based estimate, which suggested equal numbers of CO and gene conversion events (9). This rejection is rendered yet more robust by our conservative assumption that 10- to 500-kb events are COs, not gene conversions. The cause of these midsized blocks is, however, yet to be fully resolved. A few similarly sized recombination events were observed previously (9) and assumed to reflect an interference-free mode of crossing-over. Consistent with this, we find no evidence for interference for small COs (SI Appendix, Fig. S13). The same is true for the shared COs, which are almost only present in <100-kb spans. Similarly, we find no evidence for distorted G and C content, consistent with an absence of gene conversion. In principle, however, tracts a little over 10 kb may reflect gene conversions created by helicase-mediated resolution of double Holliday junctions (14, 24) (rather than through the SDSA mechanism), tract lengths for which are unknown or could be mitotic conversion events (13, 25). However, mitotic conversion rates are typically 10 4 –10 5 lower than meiotic conversion (25), making the latter unlikely. Unfortunately, with segregation distortion common across the chromosomes, we cannot perform a segregation analysis and so cannot definitively conclude that these are CO events.

The commonality of gene conversion events has implications for population genetic inferences. Regular gene conversion events are likely to reduce the structure of linkage disequilibrium (26) and will have a strong effect on the distribution of nucleotide polymorphisms. Adding gene conversion to genetic models will make them more appropriate for the inference of population history from linkage disequilibrium (26). CO rate inference from linkage disequilibrium data are robust to moderate gene conversion rates (treating it as genotyping errors) and would have little or no problem were the recent (9) lower end estimate correct. With our new more extreme estimates, caution is advisable in application of such methods.

Why So Much Gene Conversion?

The abundant gene conversions in Arabidopsis suggest that plants are more like mammals (7) than yeast, the latter having relatively common crossing-over compared with gene conversion (6). This difference between taxa we suggest may reflect differences in repeat content, as repetitive sequences are a source of genomic instability during meiosis (27), owing to nonallelic homologous recombination (28). Compared with COs, non-CO [e.g., via SDSA (2)], which yields the most gene conversions, poses the least genomic threat among mechanisms that repair DSBs (29). Analysis of repeat poor genomes of multicellular species (e.g., Oikopleura) will be informative.

Mechanistically, the above suggestion may require that organisms scan the local sequence environment to determine how to resolve a DSB. In a related vein, Dooner (30) has suggested that the local diversity levels may mediate the choice between gene conversion and crossing-over following a DSB. However, we find no significant differences in the distribution of SNPs and indels around gene conversions and COs (t test, P = 0.42). Although this fails to support Dooner’s conjecture, our evidence is not decisive as we consider only small indels (≤3 bp), whereas Dooner’s hypothesis concerns larger indels in addition.

Pericentromeric Recombination May Explain Prior Unusual Observations.

The apparently high-frequency pericentromeric recombination events may explain some prior data. First, our data could explain why the CO frequencies were seen, in lower resolution maps, to increase adjacent to the centromeres (8). When examining the distribution of all COs (Fig. 1B), more frequent recombination can be identified between two arms of chromosome 1, due to the denser distribution of small COs around the centromere. Given that many of these COs are double COs, they are unable to cause a recombination between the two arms (or part of the arms). When excluding those COs, however, the potential frequency can still be as high as about 1/4 on this chromosome, suggesting a partly free exchange between two arms.

Second, the finding of abundant recombination, including crossing-over, near centromeres helps resolve a prior paradoxical result. In many taxa, there is a positive correlation between intrapopulation diversity and genomically local recombination rates (3). In A. thaliana (16, 17) and the outbred A. lyrata (31), there is, unusually, high sequence diversity near centromeres. This has been considered contrary to classical theory as centromeres were assumed to have low CO rates, and thus prone to weak Hill–Robertson interference reducing diversity (31). Reduction of such interference under high CO rates may not, however, be the full explanation. A. thaliana is a near obligate selfer, and as such CO should have relatively little effect on Hill–Robertson-mediated diversity. Moreover, that we observe that the breakpoints of both COs and gene conversion events are often located in regions with high diversity (SI Appendix, Fig. S6) suggests instead that either (i) there is a preference for DSBs to occur in domains of high polymorphism or (ii) DSBs promote polymorphism. The latter may be mediated by a coupling between DSB repair and the mutation process (32) or reflect the activity of biased gene conversion, which can increase load at gene conversion hot spots even if inbreeding levels are very high (33). Biased gene conversion is supported by SNP analysis (see above) and from the finding that in the 100-bp sequences around the tracts of gene conversion, the gene conversion content (0.368) in shared loci, with two or more gene conversions among different individuals, is higher than that at unshared (0.345) or randomly sampled loci (0.348 P = 14 × 10 −10 ). Recent evidence (22) supports a higher mutation rate in proximity to centromeres.


Genome sequencing information

Genome project history

The present project is a collaboration between the National Autonomous University of Mexico (UNAM), Mexico City, Mexico, and the Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil. The six C. pseudotuberculosis strains were isolated by UNAM researchers. Sequencing was performed at the National Reference Laboratory for Aquatic Animal Diseases (AQUACEN), and the two processes of assembly and annotation were performed at the Laboratory of Cellular and Molecular Genetics (LGCM), both laboratories located at UFMG. All genomes are complete and available at the National Center for Biotechnology Information (NCBI). This information is shown in Table  2 and conforms with MIGS recommendations [41]. As mentioned above, the present study presents the first sequencing of C. pseudotuberculosis, and the first isolation of the biovar equi, from Mexico. This data can provide new insights into the diagnosis and treatment of diseases caused by this organism.

Table 2

MIGS IDPropertyTerm
MIGS 31Finishing qualityFinished
MIGS-28Libraries usedFragments
MIGS 29Sequencing platformsIon Torrent PGM
MIGS 31.2Fold coverage115× (MEX1) 129× (MEX9) 99× (MEX25) 135× (MEX29) 81× (MEX30) 123× (MEX31).
MIGS 30AssemblersNewbler, SPAdes.
MIGS 32Gene calling methodRAST
Locus TagCpMEX1_ (MEX1) CpMEX9_ (MEX9) AN397_ (MEX25) CpMEX29_ (MEX29) CpMEX30_ (MEX30) CpMEX31_ (MEX31)
Genbank ID <"type":"entrez-nucleotide","attrs":<"text":"CP017711","term_id":"1137409946","term_text":"CP017711">> CP017711 (MEX1) <"type":"entrez-nucleotide","attrs":<"text":"CP014543","term_id":"1032992363","term_text":"CP014543">> CP014543(MEX9) <"type":"entrez-nucleotide","attrs":<"text":"CP013697","term_id":"969860777","term_text":"CP013697">> CP013697 (MEX25) <"type":"entrez-nucleotide","attrs":<"text":"CP016826","term_id":"1098508570","term_text":"CP016826">> CP016826 (MEX29) <"type":"entrez-nucleotide","attrs":<"text":"CP017291","term_id":"1125824259","term_text":"CP017291">> CP017291 (MEX30) <"type":"entrez-nucleotide","attrs":<"text":"CP017292","term_id":"1125828921","term_text":"CP017292">> CP017292 (MEX31)
GenBank Date of Release2017/01/30 (MEX1) 2016/05/27 (MEX9) 2015/12/23 (MEX25) 2016/11/03 (MEX29) 2016/12/27 (MEX30) 2016/12/27 (MEX1)
GOLD ID- (MEX1) Go0366057 (MEX9) Go0139540 (MEX25) Go0364114 (MEX29) Go0364489 (MEX30) Go0364678 (MEX31)
BIOPROJECTPRJNA348354 (MEX1) PRJNA312392 (MEX9) PRJNA294672 (MEX25) PRJNA335634 (MEX29) PRJNA343017 (MEX30) PRJNA341961 (MEX31)
MIGS 13Source Material IdentifierBHI broth
Project relevanceAnimal Pathogen, Medical

Growth conditions and genomic DNA preparation

The samples used in the present study are in the sample collection of LGCM. All six strains were grown in a brain-heart-infusion media (BHI-HiMedia Laboratories Pvt. Ltd., India) with 1.5% of bacteriological agar and supplemented with 0.5% of Tween 80, at 37 ଌ for 72 h under rotation. Genomic DNA was extracted following the protocol of Pacheco et al. [36].

Genome sequencing and assembly

The first step in sequencing each genome was the library construction, following manufacturer’s recommendations (IonXpress™ Plus gDNA Fragment Library Preparation). This was performed in three steps: (i) DNA fragmentation using the Ion Shear™ Plus Reagents Kit, (ii) addition of adapters using Ion Xpress™ Barcode Adapters and (iii) library amplification using the Ion PGM™ Template OT2 200 kit (all kits from Thermo Fisher Scientific, USA). The resulting library was put on the semiconductor chip Ion 318 Chip Kit v2 (Thermo Fisher Scientific) and then into the sequencer Ion Personal Genome Machine™ (Thermo Fisher Scientific). The number of reads and the mean read length of MEX1, MEX9, MEX25, MEX29, MEX30 and MEX31 strains are respectively: 1,100,551 and 244 1,496,261 and 201 1,117,243 and 206 1,371,907 and 230 1,127,325 and 186 and, 1,262,316 and 230.

The assembly process was managed using SIMBA software [42]. The quality assessment of the reads was performed using FastQC software [43]. The assemblies were performed using SPAdes version 3.6 [44] on MEX1 and MEX31 and, Newbler version 2.9 (Roche, USA) on MEX9, MEX25, MEX29, and MEX30. This produced the following contigs: 6 on MEX1, 7 on MEX9, 7 on MEX25, 9 on MEX29, 33 on MEX30 and 13 on MEX31. The N50 s were: 543,202 on MEX1, 372,309 on MEX9, 543,326 on MEX25, 367,275 on MEX29, 103,276 on MEX30 and 535,978 on MEX31. The QUAST software [45] was used to evaluate the quality of the assemblies for all strains. The scaffolds were constructed using CONTIGuator software version 2.0 [46] with C. pseudotuberculosis strain 29,156 ( <"type":"entrez-nucleotide","attrs":<"text":"CP010795","term_id":"1802440752","term_text":"CP010795">> CP010795.1) as a reference to MEX9, MEX25 and MEX29, C. pseudotuberculosis strain MEX9 as a reference to MEX1, C. pseudotuberculosis strain 316 ( <"type":"entrez-nucleotide","attrs":<"text":"CP003077","term_id":"1784315086","term_text":"CP003077">> CP003077.1) as a reference to MEX30 and C. pseudotuberculosis strain E19 ( <"type":"entrez-nucleotide","attrs":<"text":"CP012136","term_id":"902819033","term_text":"CP012136">> CP012136.1) as a reference to MEX31. Gap closure was performed using CLC Genomics Workbench 7 (Qiagen, USA). This process resulted in six complete genome sequences.

Genome annotation

Genome annotation was performed in two steps: automatic annotation and manual curation. The RAST [47] and tRNAscan-SE [48] software were used in the automated annotation. An in-house script was also employed to transfer the annotation from a reference genome. The Artemis software version 16.0.0 [49], the UniProt [50] and the National Center for Biotechnology Information (NCBI) databases [51] were used in the manual curation. Putative frameshifts were analyzed using CLC Genomics Workbench 7 (Qiagen, USA) and fixed whenever possible.


3 RESULTS

3.1 Flower visitors

Stachyurus praecox had both diurnal and nocturnal flower visitors. Hymenopteran and dipteran insects were the main visitors during the day (Figures 1b–e and 2, Table S1). Small bees were the most frequent flower visitors during the day at both sites across the study years (except for a female in 2019 and a male in 2018 at Takedao Figure 2 and Table S1). Small bees, hoverflies, bee flies (Bombylius major) and other flies (mainly Bibionidae and Empididae) visited the flowers for both pollen and nectar. Visitations by eusocial bees such as Bombus and Apis were infrequent. Apis were infrequent at both sites, and only one visit by A. cerana japonica was observed in 2017 at Takedao. Visitations by the bee flies and diurnal butterflies and moths were observed only at Ashiu. Settling moths (Noctuidae, Geometridae and Drepanidae) visited the flowers for nectar and were exclusive nocturnal visitors at both sites across the years (Figures 1f–h and 2, Table S1). Settling moths carried pollen grains of S. praecox on their probosces (Figure 1h), whereas the small bees and hoverflies carried S. praecox pollen on various parts of their bodies, including the head, thorax and legs (Figure 1b,e).

The number of diurnal flower visitors was higher than the number of nocturnal flower visitors (Figure S1). The number of visits per observation was influenced by the observation period (GLM, t = −2.933, p = .009), but not by the plant sex (t = 1.351, p = .192).

Pollen grains of S. praecox were found on the bodies of the flower visitors collected from both male and female flowers at both study sites (Table 1, Table S1). The proportion of insects with pollen grains of S. praecox was not significantly different between the diurnal and nocturnal visitors that visited the female flowers (Table 1 diurnal, 78.3%, n = 23 nocturnal, 59.0%, n = 39 Fisher's exact test, p = .1673). Pollen grains of S. praecox were found in 23 (59.0%) of the 39 nocturnal settling moth specimens collected on female flowers six moths carried 1–10 pollen grains, three carried 11–100 grains and 14 carried >100 grains (Table 1). Pollen grains of S. praecox were found on 11 of the 14 small bee specimens that were collected on female flowers five small bees carried 11–100 grains and six small bees carried >100 grains (Table 1).

Plant sex Period Visitor functional group Number of flower visitors
Stachyurus pollen grains a a The number of insects with 0, 1–10, 11–100 and > 100 pollen grains of Stachyurus praecox.
Total
0 1–10 11–100 >100
Female Day Bee flies 0 0 1 0 1
Hoverflies 0 0 0 1 1
Other flies 2 0 2 3 7
Small bees 3 0 5 6 14
Day subtotal 5 0 8 10 23
Night Nocturnal settling moths 16 6 3 14 39
Night subtotal 16 6 3 14 39
Male Day Bee flies 0 0 3 1 4
Hoverflies 1 0 1 5 7
Other flies 0 0 0 5 5
Eusocial bees 0 0 0 3 3
Small bees 0 1 0 16 17
Diurnal butterflies and moths 0 0 1 0 1
Day subtotal 1 1 5 30 37
Night Nocturnal settling moths 4 1 7 11 23
Night subtotal 4 1 7 11 23
  • Note: Data across the sites and years were pooled. Note that this table does not include the data of all flower visitors, as all individuals could not be collected.
  • a The number of insects with 0, 1–10, 11–100 and > 100 pollen grains of Stachyurus praecox.

3.2 Pollination experiments

The results of the pollination experiments showed that S. praecox was pollinated by both diurnal and nocturnal pollinators (Figure 3). However, the number of seeds produced in the nocturnal pollination experiment was significantly lower than that in the diurnal pollination experiment (GLM, t = −3.828, p = .001, adjusted p = .002), whereas the number of seeds produced in the diurnal pollination experiment was not significantly different from that in the cross-pollination experiment (GLM, t = −0.525, p = .603, adjusted p = .603). The natural pollination experiment produced significantly fewer seeds than the diurnal pollination experiment (t = −2.395, p = .022, adjusted p = .027) and cross-pollination (t = −3.407, p = .002, adjusted p = .003).


1.4.7.11: Why It Matters- Prokaryotes - Biology

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Covalent bonds are formed between nonmetals by the sharing of valence electrons. But why do nonmetals prefer to share electrons rather than transfer them like in ionic bonds? Nonmetals have high ionization energies, which makes it difficult to transfer valence electrons from one atom to another.

Consider a molecule of ammonia. The nitrogen atom requires three more electrons to reach an octet, and the hydrogen atom needs an electron to reach a duet.

Therefore, the nitrogen atom bonds with three hydrogen atoms such that both nitrogen and hydrogen reach stable electron configurations. Since each of these bonds shares one pair of electrons, it is called a single bond.

The shared pair of electrons in the covalent bond is called a bonding pair. The valence electrons not participating in bonding are called the lone pair or nonbonding electrons.

With 6 valence electrons, oxygen atoms need two more electrons to reach an octet. Therefore, two oxygen atoms share two-electron pairs forming a double bond. Nitrogen, on the other hand, shares the three unpaired electrons in its diatomic form, creating a triple bond.

Single and multiple bonds differ in terms of bond length and strength. Triple bonds are shorter than double bonds, which are shorter than single bonds. The bond strength increases with bond multiplicity. This is why it is hard to break the triple bond in nitrogen, making it relatively unreactive.

The Lewis model helps to predict the structure and stability of molecules. According to the Lewis model, H2O is a stable molecule because both oxygen and hydrogen have achieved stable electron configurations.

If the oxygen shares electrons with only one hydrogen atom, the resulting OH molecule is not stable since oxygen has only 7 valence electrons and cannot reach the octet. However, if an extra electron is added to the oxygen to complete the octet, the resulting hydroxide ion becomes stable with a negative charge.

Covalent bonds are directional because the shared electrons link two specific pairs of atoms. In contrast, ionic bonds are nondirectional and hold several ions in the lattice. Thus, in a covalent compound, the individual molecules are considered to be fundamental units.

9.6: Covalent Bonding and Lewis Structures

Compared to ionic bonds, which results from the transfer of electrons between metallic and nonmetallic atoms, covalent bonds result from the mutual attraction of atoms for a &ldquoshared&rdquo pair of electrons.

Covalent bonds are formed between two atoms when both have similar tendencies to attract electrons to themselves (i.e., when both atoms have identical or fairly similar ionization energies and electron affinities).

Physical Properties of Covalent Compounds

Compounds that contain covalent bonds exhibit different physical properties than ionic compounds. Because the attraction between molecules, which are electrically neutral, is weaker than that between electrically charged ions, covalent compounds generally have much lower melting and boiling points than ionic compounds. In fact, many covalent compounds are liquids or gases at room temperature, and, in their solid states, they are typically much softer than ionic solids. Furthermore, whereas ionic compounds are good conductors of electricity when dissolved in water, most covalent compounds are insoluble in water since they are electrically neutral, they are poor conductors of electricity in any state.

Formation of Covalent Bonds

Nonmetal atoms frequently form covalent bonds with other nonmetal atoms. For example, the hydrogen molecule, H2, contains a covalent bond between its two hydrogen atoms. Two separate hydrogen atoms with particular potential energy approach each other, their valence orbitals (1s) begin to overlap. The single electrons on each hydrogen atom then interact with both atomic nuclei, occupying the space around both atoms. The strong attraction of each shared electron to both nuclei stabilizes the system, and the potential energy decreases as the bond distance decreases. If the atoms continue to approach each other, the positive charges in the two nuclei begin to repel each other, and the potential energy increases. The bond length is determined by the distance at which the lowest potential energy is achieved.

It is essential to remember that energy must be added to break chemical bonds (an endothermic process), whereas forming chemical bonds releases energy (an exothermic process). In the case of H2, the covalent bond is very strong a large amount of energy, 436 kJ, must be added to break the bonds in one mole of hydrogen molecules and cause the atoms to separate:

Conversely, the same amount of energy is released when one mole of H2 molecules forms from two moles of H atoms:

Lewis Structures

Lewis symbols can be used to indicate the formation of covalent bonds, which are shown in Lewis structures, drawings that describe the bonding in molecules and polyatomic ions. For example, when two chlorine atoms form a chlorine molecule, they share one pair of electrons:

The Lewis structure indicates that each Cl atom has three pairs of electrons that are not used in bonding (called lone pairs) and one shared pair of electrons (written between the atoms). A dash (or line) is sometimes used to indicate a shared pair of electrons: Cl&mdashCl.

  • A single shared pair of electrons is called a single bond. Each Cl atom interacts with eight valence electrons: the six in the lone pairs and the two in the single bond.
  • However, a pair of atoms may need to share more than one pair of electrons in order to achieve the requisite octet. A double bond forms when two pairs of electrons are shared between a pair of atoms, as between the carbon and oxygen atoms in CH2O (formaldehyde) and between the two carbon atoms in C2H4 (ethylene).
  • A triple bond forms when three electron pairs are shared by a pair of atoms, as in carbon monoxide (CO) and the cyanide ion (CN&ndash).

The periodic table can be used to predict the number of valence electrons in an atom and the number of bonds that will be formed to reach an octet. Group 18 elements such as Argon and Helium have filled electron configuration and thus rarely participate in chemical bonding. However, atoms from group 17, such as bromine or iodine, need only one electron to reach octet. Hence atoms belonging to group 17 can form a single covalent bond. The atoms of group 16 need 2 electrons to reach an octet hence they can form two covalent bonds. Similarly, carbon that belongs to group 14, needs 4 more electrons to reach an octet thus carbon can form four covalent bonds.


Dna Extraction Lab Report

DNA EXTRACTION
Aim : To extract the DNA from an egg yolk using various enzymes and to compare with other groups the most effective way to extract DNA.

Hypothesis :
To be able to observe white springy substances after mixing with enzyme and alcohol.

Apparatus : -Test tube, spatula, glass rod, dropper, beaker, test tube rack, skewer. Materials : - 1 egg, meat tenderizer, salt, water , soap, isopropyl alcohol 91%, pineapple juice.

Variables :
Manipulated Variable : Responding Variable : Constant Variable : The different type of enzyme used. Identify the white springy substance as the DNA. The amount of egg yolk in test tube, the drops of enzymes.

Procedure : 1. The egg is cracked open and gently separate the yolk from the white into a small bowl. 2. Then add a spoonful of salt and a few drops of water to the yolk. 3. Use a glass rod and mix the mixture. 4. Take a spoonful of dish soap and mix into the mixture for 5 minutes. 5. The 3 test tubes will be then filled with small amount of yolk mixture using a dropper. 6. A pinch of meat tenderizer will be added into each test tube. 7. The test tube is then added with isopropyl alcohol carefully by using a dropper. 8. Observe and record the results, repeat step 1-6 with another 3 test tube by using pineapple juice as the enzyme.

Figure 1.1 : The arrow shows the white springy
substance as DNA. Visibility is low.

Figure 1.2 : The arrow shows a visible image of
DNA floats on the surface of alcohol.

Observation :
After adding 3 drops of isopropyl alcohol and meat tenderizer in the egg yolk solution, after a few minutes you are able to observe a little DNA white springy substance. Once DNA is visible it rose into the clear alcohol that also floats on water. On the test 3 and test 4 , we added 5 drops of isopropyl alcohol into both 3 small test tubes and to investigate the differences of enzyme affecting the visibility of DNA.

Figure 1.3 : Shows the white springy
substance from meat tenderizer as an

Figure 1.4 : Shows the white springy
substance from pineapple as an enzyme.

As you can see from the observation above, 3 drops of isopropyl alcohol did not show much of a difference into extracting a large scale of DNA compared to dropping of 5 drops of isopropyl alcohol. In our group we did 2 test on how enzyme affects the DNA. The figure 1.3 and figure 1.4 shows how clearly meat tenderizer with isopropyl alcohol performs a better enzyme compared to pineapple juice. It breaks down the nucleus to release DNA better than pineapple juice. To examine this it is just by looking at the image the amount of white springy

Results :
Test Test 1 (Meat Tenderizer,3 drops isopropyl) Test 2 (Pineapple Juice,3 drops isopropyl) Test 3 (Meat Tenderizer,5 drops isopropyl) Test 4 (Pineapple Juice,5 drops isopropyl) Is the DNA visible ? Yes, but a little only. No. Yes, its very visible. Yes, but a little only.

On the first test we actually did 3 drops of isopropyl alcohol, we then see the result it was difficult to distinguish between the egg yolk and the DNA. On third test, we added 5 drops of isopropyl alcohol and the results came in few minutes, we are then able to see the DNA clearly as white stringy substance appears. On the second test we tried a different type of enzyme, pineapple juice. We started off with 3 drops of pineapple juice, few minutes later there is no difference to the egg yolk, then on test 2 we did on the 5 drops of pineapple juice and there are DNA but it is very hard to notice the white stringy substance of DNA.

Discussion :
Source of Enzyme Meat Tenderizer Results Meat tenderizer acts as a catalyst to break down, it breaks the open the cell nucleus to release the DNA. The juice of the pineapple contains Bromelain , a substance which able to break down protein into amino acid.

Test
#1
(90ml
water,
acetone,
1
strawberry) None


Basically the distinction boils down to this (as it pertains to the Bible*): Hermeneutics is the field of study concerned with how we interpret the Bible. Exegesis is the actual interpretation of the Bible by drawing the meaning out of the Biblical text.

The distinction is not quite as simple as "theory vs. application," though, since hermeneutics is not just concerned with the philosophy of exegesis, and exegesis is not merely the application of hermeneutical theory -- even if we restrict our comparison to Biblical hermeneutics and Biblical exegesis. Here are a couple of examples to illustrate this:

Hermeneutics also studies the role of eisegesis in interpretation, which is by definition not part of exegesis.

Hermeneutics considers the role of church doctrine and theology in interpretation -- both of which are (often) irrelevant to exegesis.

(Ray explained the challenges with seeing exegesis as "applied hermeneutics" in this meta post.)

So we are sort of comparing apples to. ontology here. In a sense there is no overlap The focus of exegesis is the text. The focus of hermeneutics is stuff like exegesis. why do we do it? how do we do it? how should we do it? As far as sequence, I suppose it could be argued that since exegesis is "critical" in nature, it implies some scientific method, which implies some prior hermeneutic. That is as far as I think we could go in relating the two sequentially, though.**

**Given the scope of this site, I am assuming the question is specifically about the distinction between Biblical hermeneutics and Biblical exegesis.*

***Gordon Fee and Dougless Stewart, in How to Read the Bible for All Its Worth) say that exegesis is Step 1 and hermeneutics is Step 2 to emphasize that what we think about the text should be based on what the text actually says. (But they essentially had to redefined their terms in order to make this point.)*

After a chat discussion, this is my understanding:

Hermeneutics is the theories and methods for studying text. Exegesis is the interpretation of text.

The difference is in theory verses practice.

For example, hermeneutics has techniques available, such as contextual analysis, or lexical-syntatical analysis. Hermeneutics is the theory behind translating text.

By comparison, exegesis is the application of interpreting and translating text. There are no "exegesis techniques" (that would be hermeneutics). Instead, there are commentaries regarding the text, which are entire books of exegesis.

Example qeustions here on the site (the examples better suited for meta):

"What does 'water' mean in 5 Timothy 127:33?" - Exegesis
"What are the steps of Specific Infallibility Analysis?" - Hermeneutics
NOTE: both of those questions are totally made up, as should be obvious

1/4 of the total I've actually technically asked the vast majority of the actual hermeneutics questions around here and everybody else is asking for exegesis? Does this mean we basically have Programmers and SO rolled into one here without even including Christianity.SE? These sound like things for our meta :) &ndash Caleb Oct 6 '11 at 21:04

I completely agree with Richard's great answer, but would boil it down to this:

  • Exegesis: interpretation (the process)
  • Hermeneutics: rules of interpretation (the principles which should guide the process)

Description of the selection methods in the different selection environments [ (1) 44 ) (2) 45 ) (3) 48 ) (4) 49 ) (5) 23 ) (6) 15 ) (7) 34 ) (8) 35 ) (9) 38 ) (10) 13 ) (11) 14 ) (12) 29 ) (13) 28 )].

Expression of life history traits for every selection regime in every assay environment (mean and SE). Every selection regime consists of three lines. Ori: Origin of the lines, Env: assay environment, Sel: selection regime (abbreviations of the selection regimes: see methods), Bas: Basel, Gro: Groningen, Irv: Irvine, Lon: London. Asterisks indicate probability values from the analysis of variance for the effect of the selection regime (analysis A). *P < 0.05 **P < 0.005 ***P < 0.00059 (this reduced significance level leads to a questionwise error rate α of 0.05).

Appendix 1 Description of the assay methods in the different assay environments.

Appendix 2 Description of the selection methods in the different selection environments

Appendix 3 Expression of life history traits for every selection regime in every assay environment (mean and standard error). Every selection regime consists of three lines. Ori: Origin of the lines, Env: Assay environment, Sel: Selection regime (abbreviations of the selection regimes: see methods), Bas: Basel, Gro: Groningen, Irv: Irvine, Lon: London. Asterisks indicate probability values from the analysis of variance for the effect of the selection regime (analysis A). *P <0.05 **P < 0.005 ***P < 0.00059 (this reduced significance level leads to a questionwise error rate a of 0.05).

Filename Description
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Watch the video: Celletyper (July 2022).


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