Part 1:

Below are basic arguments in English. Choose one argument and translate the argument into the symbolism of predicate logic. Use one of the proof techniques from Chapter 8 to demonstrate the validity of the argument.

1. Every fetus has an immortal soul. A thing has an immortal soul only if it has a right to life. Hence, every fetus has a right to life. (Fx = x is a fetus, Sx = x has an immortal soul, Rx = x has a right to life).

2. Some wars are just. No war of aggression is just. Therefore, there are wars that are not wars of aggression. (Wx = x is a war, Jx = x is just, Ax = x is a war of aggression).

3. At least one instance of intentional killing is not wrong. But every murder is wrong. Hence, some instances of intentional killing are not murder. (Kx = x is an instance of intentional killing, Wx = x is wrong, Mx = x is murder)

4. Only things that have human bodies are human. No soul has a human body. Only souls survive the death of the body. Therefore, no humans survive the death of the body. (Bx = x has a human body, Hx = x is human, Sx = x is a soul, Dx = x survives the death of the body)

Part 2:

Now, construct an alternate proof. In other words, if the proof was done using RAA, now use CP; if you used CP, now use RAA. Consider the following questions, as well, in your journal response: • Will a direct proof work for any of these? • Can the proof be performed more efficiently by using different equivalence rules?

One major challenge in cancer research is developing robust pre-clinical models for new therapies, ones that will accurately reflect a human response to a novel compound. All too often, a potential treatment that initially looked promising in cells or animal models will not have the same effects in a human cancer patient. Given the enormous costs of clinical trials, researchers need pre-clinical models that accurately reflect human disease genetics and reliably predict which drugs have the most potential to succeed in patients. In Cell Stem Cell this week, a team led by Zuzana Tothova, a postdoctoral scholar at the Broad Institute of MIT and Harvard and instructor in medicine at Dana-Farber Cancer Institute (DFCI), and Broad institute member Ben Ebert, also a professor of medicine at Harvard Medical School and chair of medical oncology at DFCI, describe a new approach that has the potential to make this leap. Using multiplex CRISPR-Cas9 editing of human hematopoietic, or blood-forming, stem cells followed by transplantation in mice, the team designed customized mouse models for the progression of leukemia. In a number of different experiments, the animal models successfully reflected human responses to a therapeutic agent commonly used to treat blood cancers. "With our models, we can really test -- in a very controlled fashion, in the right setting, and using the right cells -- the genetic predictors of response to specific agents," said Tothova. Learning from human genetics The research team started by examining large-scale sequencing data from Ebert's lab and The Cancer Genome Atlas to determine which combinations of mutations occur most commonly in myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), blood cancers in which the bone marrow fails to produce healthy blood cells. The researchers landed on nine genes that are recurrently mutated in MDS and AML. "We use human genetics to teach us which combinations of mutations lead to cancer," explained Ebert. "If we have sequencing data from enough tumors, we can identify the genes that are mutated recurrently and which combinations of mutations co-occur more commonly than expected by chance." Currently, many cancer models (such as cell lines) do not reflect the cancer genetics that a particular investigator would like to study, which often leaves both researchers and patients at a disadvantage. One strategy is to transplant an actual human cancer sample into a mouse, but the cancer tissue often doesn't engraft well, and researchers are only able to test against the specific combination of mutations accumulated in a given cancer sample in the first place. To study these specific MDS-driving mutations in combination, the team developed a pipeline to insert them into new lab models. "Say we're trying to develop a new drug against a particular combination of mutations, which we know about through the cancer sequencing efforts," said Tothova. "You might not have any sample available to study with that particular combination of mutations. We wanted to be able to engineer the right lesions in human cells, let them expand in mice, and generate an accurate genetic model of disease for testing new therapies. This has been a longstanding goal for cancer researchers, and for the pharmaceutical industry, for a very long time." Customizing cancer mutations with CRISPR To create models with the right mutations, Tothova and her team established a customizable system to introduce the cancer-driving mutations into human hematopoietic stem cells, where MDS and AML originate. The researchers already had extensive experience working with hematopoietic stem cells and progenitor cells, largely from umbilical cord blood or adult bone marrow, and in 2014, they published a Nature Biotechnology paper in which they described using the CRISPR-Cas9 system to create similar models of mouse cancers. This time, the team was aiming to model MDS in human cells, a much more challenging goal. The researchers took primary cells from healthy donors and used CRISPR-Cas9 to engineer them with a number of different mutation combinations, rather than a single alteration, in order to reflect the complexity of tumor mutations seen in patients. The combinations of mutations that the cells tolerated -- those that successfully altered the genes without killing the cells -- and that led to expansion over time were also the ones seen in human tumor samples. "Nobody so far has done this kind of multiplex CRISPR engineering in the actual hematopoietic stem cell compartment, adding specific mutations in combination to generate disease models," said Tothova. From there, the team injected the stem cells into the mice's circulatory systems, where a portion incorporated themselves into the bone marrow. The team monitored their progression, extracting and sequencing the human cells five months later to determine which engineered cells successfully propagated and which mutations became the most common over time in these pre-malignant and early malignant states. Testing therapeutic agents The mainstay of treatment for MDS patients are hypomethylating agents called azacitidine and decitabine. Based on previous studies, the team identified specific genetic mutations that could be used to predict cancer cells' response to these compounds in humans. (For example, mutations in a gene called TET2 predict treatment success for MDS patients, while mutations in the ASXL1 gene predict resistance in the tumors.) When the researchers treated the mice with azacitidine, they found that the response in the engineered cells matched what was expected from the human data: TET2-mutated cells responded to the drug, while ASXL1-mutated cells were resistant to the therapy. The team also discovered that mutations in a cohesin gene, SMC3, increased sensitivity to the drug -- data that could be important to clinicians and patients whose tumors share those mutations. "We are able to recapitulate findings previously seen in human clinical trials, which makes us feel more confident in the power of these models," said Tothova. "The data that comes from patients reflects the most important experiment we are trying to understand." She is currently working with clinical collaborators at DFCI to extend some of these findings into clinical trials. The team believes their approach to create this type of leukemia progression model for therapeutic testing can be applied to other types of cancer as well, as long as sequencing data is available to choose appropriate mutations and progenitor cells can be acquired from the desired tissue. "People in the field are hungry for these kinds of models," said Ebert. "We are modeling the disease in the right cellular context with a genetic complexity that reflects what we see in patients. This hasn't been done before, and it could become a really beneficial tool.

This study demonstrated which of the following?

A heptapeptide is shown to have the amino acid composition: phenylalanine glutamate methionine valine lysine threonine and histidine.

I   treatment of the heptapeptide with cyanogen bromide yields a tetra and a tri-peptide

II treatment of the tetrapeptide with dansyl chloride yields the dansyl derivative of histidine upon acid hydrolysis

III treatment of the tetrapeptide with trypsin yields 2 dipeptides

IV treatment of the tripeptide with carboxy peptidase yields primarily valine

V a single round of Edman degradation of the tripeptide yields the phenylthiohydantoin (PTH) derivative of threonine

VI treatment of the original heptapeptide with lithium borohydride followed by acid hydrolysis yields the alcohol derivative of valine.

the sequence of the heptapeptide is

1. Below is the sequence for the SARS-CoV-2 spike protein that encodes a 1757 amino acid protein capable of binding surface receptors on some cell types. The nucleotide sequence is from base pair 21,563 to 25,384 in the viral genome (numbers in left-most column). Upon binding, the virus is uptaken into the cell, the coat is shed, and viral RNA released into the cytoplasm. When this occurs, host cell ribosomes begin transcription and translation of the RNA, including this protein. In order to study its binding capabilities to host cell receptors, you want to clone the gene into a plasmid designed for protein expression and purification. Design forward and reverse primers that will successfully bind the targeted gene sequence and amplify the gene. The primers should be 12-15 nucleotides long. The sequence has been reverse transcribed (RNA to DNA) and presented in the 5’ to 3’ format below. Bold or underline the nucleotides you will use to design your primers. Label the 5’ and 3’ ends of the primers.

21563-atgtttgt ttttcttgtt ttattgccac tagtctctag

    21601 tcagtgtgtt aatcttacaa ccagaactca attaccccct gcatacacta attctttcac

    21661 acgtggtgtt tattaccctg acaaagtttt cagatcctca gttttacatt caactcagga

    21721 cttgttctta cctttctttt ccaatgttac ttggttccat gctatacatg tctctgggac

    21781 caatggtact aagaggtttg ataaccctgt cctaccattt aatgatggtg tttattttgc

    21841 ttccactgag aagtctaaca taataagagg ctggattttt ggtactactt tagattcgaa

    21901 gacccagtcc ctacttattg ttaataacgc tactaatgtt gttattaaag tctgtgaatt

    21961 tcaattttgt aatgatccat ttttgggtgt ttattaccac aaaaacaaca aaagttggat

    22021 ggaaagtgag ttcagagttt attctagtgc gaataattgc acttttgaat atgtctctca

    22081 gccttttctt atggaccttg aaggaaaaca gggtaatttc aaaaatctta gggaatttgt

    22141 gtttaagaat attgatggtt attttaaaat atattctaag cacacgccta ttaatttagt

    22201 gcgtgatctc cctcagggtt tttcggcttt agaaccattg gtagatttgc caataggtat

    22261 taacatcact aggtttcaaa ctttacttgc tttacataga agttatttga ctcctggtga

    22321 ttcttcttca ggttggacag ctggtgctgc agcttattat gtgggttatc ttcaacctag

    22381 gacttttcta ttaaaatata atgaaaatgg aaccattaca gatgctgtag actgtgcact

    22441 tgaccctctc tcagaaacaa agtgtacgtt gaaatccttc actgtagaaa aaggaatcta

    22501 tcaaacttct aactttagag tccaaccaac agaatctatt gttagatttc ctaatattac

    22561 aaacttgtgc ccttttggtg aagtttttaa cgccaccaga tttgcatctg tttatgcttg

    22621 gaacaggaag agaatcagca actgtgttgc tgattattct gtcctatata attccgcatc

    22681 attttccact tttaagtgtt atggagtgtc tcctactaaa ttaaatgatc tctgctttac

    22741 taatgtctat gcagattcat ttgtaattag aggtgatgaa gtcagacaaa tcgctccagg

    22801 gcaaactgga aagattgctg attataatta taaattacca gatgatttta caggctgcgt

    22861 tatagcttgg aattctaaca atcttgattc taaggttggt ggtaattata attacctgta

    22921 tagattgttt aggaagtcta atctcaaacc ttttgagaga gatatttcaa ctgaaatcta

    22981 tcaggccggt agcacacctt gtaatggtgt tgaaggtttt aattgttact ttcctttaca

    23041 atcatatggt ttccaaccca ctaatggtgt tggttaccaa ccatacagag tagtagtact

    23101 ttcttttgaa cttctacatg caccagcaac tgtttgtgga cctaaaaagt ctactaattt

    23161 ggttaaaaac aaatgtgtca atttcaactt caatggttta acaggcacag gtgttcttac

    23221 tgagtctaac aaaaagtttc tgcctttcca acaatttggc agagacattg ctgacactac

    23281 tgatgctgtc cgtgatccac agacacttga gattcttgac attacaccat gttcttttgg

    23341 tggtgtcagt gttataacac caggaacaaa tacttctaac caggttgctg ttctttatca

    23401 ggatgttaac tgcacagaag tccctgttgc tattcatgca gatcaactta ctcctacttg

    23461 gcgtgtttat tctacaggtt ctaatgtttt tcaaacacgt gcaggctgtt taataggggc

    23521 tgaacatgtc aacaactcat atgagtgtga catacccatt ggtgcaggta tatgcgctag

    23581 ttatcagact cagactaatt ctcctcggcg ggcacgtagt gtagctagtc aatccatcat

    23641 tgcctacact atgtcacttg gtgcagaaaa ttcagttgct tactctaata actctattgc

    23701 catacccaca aattttacta ttagtgttac cacagaaatt ctaccagtgt ctatgaccaa

    23761 gacatcagta gattgtacaa tgtacatttg tggtgattca actgaatgca gcaatctttt

    23821 gttgcaatat ggcagttttt gtacacaatt aaaccgtgct ttaactggaa tagctgttga

    23881 acaagacaaa aacacccaag aagtttttgc acaagtcaaa caaatttaca aaacaccacc

    23941 aattaaagat tttggtggtt ttaatttttc acaaatatta ccagatccat caaaaccaag

    24001 caagaggtca tttattgaag atctactttt caacaaagtg acacttgcag atgctggctt

    24061 catcaaacaa tatggtgatt gccttggtga tattgctgct agagacctca tttgtgcaca

    24121 aaagtttaac ggccttactg ttttgccacc tttgctcaca gatgaaatga ttgctcaata

    24181 cacttctgca ctgttagcgg gtacaatcac ttctggttgg acctttggtg caggtgctgc

    24241 attacaaata ccatttgcta tgcaaatggc ttataggttt aatggtattg gagttacaca

    24301 gaatgttctc tatgagaacc aaaaattgat tgccaaccaa tttaatagtg ctattggcaa

    24361 aattcaagac tcactttctt ccacagcaag tgcacttgga aaacttcaag atgtggtcaa

    24421 ccaaaatgca caagctttaa acacgcttgt taaacaactt agctccaatt ttggtgcaat

    24481 ttcaagtgtt ttaaatgata tcctttcacg tcttgacaaa gttgaggctg aagtgcaaat

    24541 tgataggttg atcacaggca gacttcaaag tttgcagaca tatgtgactc aacaattaat

    24601 tagagctgca gaaatcagag cttctgctaa tcttgctgct actaaaatgt cagagtgtgt

    24661 acttggacaa tcaaaaagag ttgatttttg tggaaagggc tatcatctta tgtccttccc

    24721 tcagtcagca cctcatggtg tagtcttctt gcatgtgact tatgtccctg cacaagaaaa

    24781 gaacttcaca actgctcctg ccatttgtca tgatggaaaa gcacactttc ctcgtgaagg

    24841 tgtctttgtt tcaaatggca cacactggtt tgtaacacaa aggaattttt atgaaccaca

    24901 aatcattact acagacaaca catttgtgtc tggtaactgt gatgttgtaa taggaattgt

    24961 caacaacaca gtttatgatc ctttgcaacc tgaattagac tcattcaagg aggagttaga

    25021 taaatatttt aagaatcata catcaccaga tgttgattta ggtgacatct ctggcattaa

    25081 tgcttcagtt gtaaacattc aaaaagaaat tgaccgcctc aatgaggttg ccaagaattt

    25141 aaatgaatct ctcatcgatc tccaagaact tggaaagtat gagcagtata taaaatggcc

    25201 atggtacatt tggctaggtt ttatagctgg cttgattgcc atagtaatgg tgacaattat

    25261 gctttgctgt atgaccagtt gctgtagttg tctcaagggc tgttgttctt gtggatcctg

    25321 ctgcaaattt gatgaagacg actctgagcc agtgctcaaa ggagtcaaat tacattacac

    25381 ataa

1.)  Part of the amino acid sequence of the A chain of insulin is "glutamine-cysteine-cysteine-alanine". Which of the following DNA strands could encode this peptide?

A. 5'-CCCCCGCAGAAG-3'

B. 5'-GGCATCGTGGAG-3'

C. 5'-CTGCCCCGACAC-3'

D. 5'-CAGTGCTGTGCC-3'

E. 5'-GTCACGACACGG-3'

Explain how you determined the answer.

2.) Arrange the following genetic terms in order of complexity from largest to smallest.

1. chromosome

2. nucleotide

3. genome

4. double helix

3.) Using the following template strand of DNA, what would be the translation? 5’ TACCGTACT 3’

4.)  Using the following coding strand of DNA, what would be the translation? 5’ ACGTATGCT 3’

(1) In an experiment, you purify a cysteine-charged tRNA and chemically alter the amino acid attached to it by converting it to alanine. You place this charged tRNA in a cell-free protein-synthesizing system with all of the other charged tRNAs, mRNA and other substances needed for the synthesis of proteins. Why might this have an effect on the final protein produced (be specific)?

(2) _____What would happen to a “housekeeping” receptor that is endocytosed from the plasma membrane? It would be brought to ((please explain why))

A. the early endosome, then late endosome, then moved back to plasma membrane.

B. the early endosome and immediately recycled back to the surface.

C. the early endosome, then late endosome, then lysosome to be degraded.

D. the lysosome directly to be degraded.

(3) _____Synthesis of which protein would be completed in the cytosol? (please explain why)

A. Transported in to nucleus, mitochondria, chloroplast or peroxisome

B. Secreted

C. Integral membrane

D. Endomembrane resident

The trp operon: Bacterial cells can take up the amino acid tryptophan from their surroundings, or, if the external supply is insufficient, they can synthesize tryptophan from small molecules in the cell.  

When external supplies of tryptophan are plentiful, the cells suppress transcription of the trp operon, which encodes the tryptophan biosynthetic enzymes. When external supplies of tryptophan are not plentiful, the cells express the trp operon.

The trp operon repressor protein inhibits transcription of the genes in the trp operon. Upon binding tryptophan, the tryptophan repressor binds to a site in the promoter of the operon and represses transcription.

A. (2 pts) Why is tryptophan-dependent binding to the operon a useful property for the tryptophan repressor?

B. (4 pts) How would regulation of transcription of tryptophan biosynthetic enzymes be affected in cells that express a mutant form of the tryptophan repressor that (i) cannot bind to the DNA or (ii) bind to DNA even when no tryptophan is bound to it? Also address how tryptophan synthesis would be affected and how that would affect the cell. Please bullet point your response (i) and (ii) for each mutant for ease of grading.

Serotonin is synthesized from the amino acid tryptophan (normal cells can synthesize serotonin when they are given tryptophan). You have identified two populations of mutant cells that cannot synthesize serotonin. Each population has a mutation that has caused one gene to stop functioning. Population 1 cannot synthesize serotonin at all, even if you provide it additional nutrients. Population 2 can synthesize serotonin, but only if you give it a nutrient called 5-HTP.

a. Based on this information, draw a biochemical pathway starting with tryptophan and ending with serotonin. How does 5-HTP fit into this pathway? Be sure to indicate the location of any enzymes.

b. Describe the mutation you expect to see in each population based on your biochemical pathway