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Why Hfr cells are called high frequency recombination cells?

Why Hfr cells are called high frequency recombination cells?



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I understand that Hfr cells are formed when fragment of bacterial plasmid integrates itself in another bacteria's (host) genome through homologous recombination but my question is why do we call these cells as "high frequency recombination cells". Where is the term high frequency coming from?


According to Luca Cavalli-Sforza, the scientist who coined the acronym Hfr:

Recombination as observed at the beginning had a very low frequency. It stopped being rare when I found a mutant strain which I called Hfr for “high frequency of recombination.” I found it accidentally in 1949 while I was selecting mutations resistant to nitrogen mustard and radiation. The first two resistant mutants, which had undergone a rather heavy treatment in the process of selecting for resistance to nitrogen mustard, proved to be exceptional in their mating behavior. One was Hfr and it showed immediately its remarkable mating ability, which was higher than that of normal crosses by a factor of 1000 or more. I repeated the experiment two more times before believing it. The other mutation, as I later proved, was an F- (self-sterile) mutation of an F+ (fertile) strain. (emphases mine)

Source: Forty Years Ago in GENETICS: The Unorthodox Mating Behavior of Bacteria


3 Modes of Genetic Transfer in Bacterial Cells |Biology

Three modes of genetic transfer in bacterial Cells are : (a) Transformation, (b) Transduction, (c) Conjugation.

Bacteria divide very rapidly. The doubling time is also called generation time and it may be as low as 20 minutes. Bacteria mainly reproduce by asexual reproduction but do not exhibit true sexual reproduction as they do not produce diploid phase. Thus, meiosis is lacking. However, bacteria exchange genetic material between two cells.

Modes of genetic transfer in bacteria:

Three modes of genetic transfer between bacterial cells are:

(a) Transformation:

The phenomenon by which DNA isolated from one type of cell, when introduced into another type is able to bestow some of its properties into the latter, is referred to as transformation. It was confirmed by Griffith with his experiments on bacteria Streptococcus pneumonia.

The transfer of genetic material from one bacterium to another through bacteriophage is called transduction.

The unidirectional transfer of DNA from one cell to another through a cytoplasmic bridge is called conjugation. The process is equivalent to sexual mating in eukaryotes. Two bacterial haploid cells of different strains come close to each other.

They recognise each other by complementary macromolecules borne on their surface. Donor or male cell passes part or whole of the chromosome into recipient or female cell. The ability of transferring the genetic material from male is controlled by sex or fertility factor (F gene) present in a plasmid.

Thus, genes can be transferred from donor to recipient cell on a molecule of DNA which acts as sex factor called F gene. This sex gene can reside in a bacterial chromosome or it may exist as an autonomous unit in cytoplasm.

Male bacterium with thorn-like protuberances called as sex pili come in contact with female bacterium which lacks pili and donate its DNA. F factor (a plasmid) carries genes for producing pili and other functions required to transfer DNA. At times F factor integrates into bacterial chromosome.

Such bacteria can transfer their genetic material into female cell with high frequency (Hfr) in a particular sequence. They are called as Hfr -strains. Conjugation was first demonstrated by Lederberg and Tatum in E. coli. The frequency of recombination was very low in Lederberg’s experiments.

The Hfr cell acts as the male bacterium and when mixed with the female (F—) cell forms a conjugation bridge. The F factor containing DNA breaks at a particular point and starts inserting the DNA into the female and the sequence of chromosomal gene transfer is always in the same order (A, B, C and D genes).

The F factor is transferred last. The conjugation bridge usually breaks before the entire chromosome is transferred. Only the genes A and B have been transferred in the example given. These A and/or B genes can recombine with the corresponding genes in the F— chromosome.

Thus, if B’ in the F— cell is a mutated form of B, theft the B’ in the F— chromosome can become B as a result of recombination after conjugation. Thus, genetic markers can be transferred from a host to a suitable recipient lacking such markers.

The order in which such markers are transferred to the recipient would follow the order in which they are present in the donor. Thus, conjugation experiments are useful in constructing the gene maps (order of arrangement of genes in the chromosome) of organisms.

Hayes (1952) found a strain of E. coli in which the frequency of recombination was as high as 100 to 1000 times as reported by Lederberg. The strain was called high frequency recombinant (Hfr) strain.


BIOL230_Test2

Enzymatic reaction
Substrate (S) + Enzyme (E) &rarr Enzyme/Substrate Complex (ES) &rarr E + Product1 + Product2
(Note that the enzyme used in the reaction is also one of the end products. Enzymes can be reused about 1 million times.)

-Oxidation - when a chemical loses H+ and/or e-.
-Reduction - when a chemical gains H+ and/or e-.
-When an oxidation and reduction occurs energy is released which is then stored.

  • Starting material : 1 molecule of Glucose + 2 ATP's
  • Products : 2 molecules of Pyruvic Acid + 10 ATP's
  • Overall respiration glycolosis produces 8 ATP's
  • Starting material : 1 molecule of Glucose + 2 ATP's
  • Products : 4 ATP's
  • Overall fermentation glycolosis produces 2 ATP's
  • 4 ATP's are produced directly from Glycolysis
  • 2 sets of (2 H+ and 2 e-) are sent to ETS for a total of 6 ATP's
  • 2 ATP's are used to start process
  • 4 + 6 - 2 = 8 ATP's
  • 4 ATP's are produced directly from
  • 2 ATP's are used to start process
  • 4 - 2 = 2 ATP's

Each of the 2 Pyruvic Acids from Glycolosis put into the Kreb's cycle yields 5 sets of (2 H+ and 2 e-) that to go to ETS to give 3 ATP's each for a total of 30 ATP's

2 pyruvic acids x 5 sets (2 H+ and 2 e-) x 3 ATP's produced from ETS = 30 ATP's

  • Contains the hereditary information for all living cells.
  • Contains the code for all cell proteins.
  • RNA is single stranded, not double stranded.
  • RNA has the nitrogenous base of Uracil instead of Thymine (still pairs with ADE).
  • RNA uses the sugar Ribose in its sugar-phosphate backbone instead of deoxyribose.
  • Most bacteria must cut the circular DNA to make it linear.
  • An enzyme cuts the DNA at a specific starting point.
  • 2 cut ends of the DNA attaches to 2 sites on the cell membrane.
  • The 2 DNA strands unwind.
  • As the strands unwind the hydrogen bonds between base pairs break exposing the nitrogenous bases.
  • New DNA nucleotides attach to original exposed nucleotides and rebuild 2 new complementary strands.
  • New sugar/phosphate backbones are formed on new complementary strands.
  • 2 new strands rewind.
  • 2 new strands separate from cell membrane.
  • 2 new strands reform circular structure.
  • 30s and 50s ribosomal subunits bind to m-RNA at "start" codon (AUG).
  • Initial "charged" t-RNA with complementary anticodon (UAC) lines up with the start codon of mRNA.
  • A codon is a sequence of 3 bases on mRNA that code for a specific amino acid.
  • An anticodon is a sequence of 3 bases on tRNA that complementarily pairs with the codon on mRNA.
  • A "charged" tRNA refers to a tRNA that is carrying an amino acid.
  • An "uncharged" tRNA refers to a tRNA that is not carrying an amino acid. It has left the amino acid at the ribosome to build the protein.
  • 1. A "charged" tRNA complementary to the 2nd codon binds to the 2nd codon of mRNA.
  • 2. The previous amino acid leaves the 1st tRNA and forms peptide bond with 2nd amino acid on 2nd t-RNA.
  • 3. The ribosome shifts down the mRNA strand so that the next codon is in place to receive the appropriate tRNA along with it's amino acid.
  • 4. Then the first 3 steps are repeated until the protein coded for is completed.
  • Ribosome shifts to a nonsense codon.
  • Protein is released from the final t-RNA.
  • The 50s and 30s ribosomal subunits come off of m-RNA.
  • No, all amino acids can have more than 1 codon that codes for it.
  • Except the "start" codon AUG which only codes for the amino acid Methionine, and the codon UGG which only codes for Tryptophan.
  • 1 or more DNA bases are swapped w/ another base that may code for different amino acid.
  • Not usually harmful, unless code changes to stop codon, or changes code for a needed protein.
  • Sickle cell anemia and Tay-Sachs disease.
  • 1 base is removed from the sequence resulting in a reading frame shift. The frame shift starts at the deletion site and continues on down the DNA strand.
  • Usually fatal to cell.

Addition or Deletions of entire codons are usually not harmful.

  • Steps 1&2 - donor bacteria dies and degrades leaving fragments of DNA.
  • Step 3 - DNA fragments from dead donor penetrates a living competent recipient bacterium.
  • Step 4&5 - Donor DNA fragment is exchanged for a piece of the recipient bacterium's DNA.
  • Streptococcus pneumoniae - leading cause of pneumonia in humans.
  • 2 strains
  • Smooth strain - will make capsules (harmful)
  • Rough strain - will not make capsules (not harmful)
  • Everyone has the rough strain naturally.
  • If the smooth strain is introduced the "smooth" genes will pass to the rough strains through transformation.
  • Step 1 - Temperate phage adsorbs to bacterium and injects its genome.
  • Step 2 - Phage genome inserts into bacteria nucleoid to become a prophage.
  • Step 3 - Occasionally during spontaneous induction, a small piece of donor bacterial DNA is picked up in place of some of the phage DNA as part of the phage genome, and some of the phage DNA is left in the bacterial nucleoid. (A mistake in spontaneous induction.)
  • Step 4 - As the phage replicates, the piece of bacterial DNA replicates as part of the phage genome. Every phage now carries that bacterial DNA.
  • Step 5 - Defective phage adsorbs to recipient bacterium and injects genome.
  • Step 6 - Phage genome, carrying donor bacterium DNA, inserts into recipient bacterium's nucleoid.
  • Streptococcus pyogenes
  • 2 strains
  • Toxigenic strain - produces Erythrogenic toxin and causes Scarlet Fever
  • Non-toxigenic strain - does not produce Erythrogenic toxin, causes Strep Throat
  • The use of antibiotics early in the course of infection of the non-toxigenic strain reduces the progression toward Scarlet Fever because antibiotics kill the bacteria non-toxigenic strain before transduction changes them to the toxigenic strain.
  • F+ Conjugation
  • Hfr Conjugation (high frequency)
  • R-Plasmid Conjugation (Resistance-Plasmid)
  • Donor bacterium is F+ male (contains genetic code for f-pilis production)
  • Recipient bacterium is F- female (does not contain genetic code for f-pilis production).
  • Donor bacterium creates a F+ plasmid.
  • Plasmid is a double stranded DNA fragment that codes for f-pilis production.
  • Donor conjugates with recipient with sex pilis and transfers 1 strand of the F+ plasmid.
  • Both donor and recipient makes the complementary copy of its plasmid strand.
  • Both bacteria are now F+ males and can make a sex pilis.
  • There is no transfer of chromosomal DNA.
  • F+ male donor inserts F+ plasmid into nucleoid to become a Hfr male and conjugates with a F- female via a sex pilis.
  • One donor DNA strand at end opposite inserted F+ plasmid begins to enter recipient bacterium.
  • The cells break apart easily so usually only a portion of the donor DNA strand is transferred.
  • Remaining DNA strand in donor makes a complementary copy and remains and Hfr male.
  • Fragment of transferred DNA strand to recipient makes a complementary copy.
  • Donor DNA fragment is exchanged for a portion of the recipient bacterium's DNA.
  • Recipient bacterium usually remains a F- female (transfer of chromosomal DNA but not plasmid).
  • R-Plasmid = resistance plasmid.
  • Male donor bacterium with R-Plasmid (multiple antibiotic resistant and male) conjugates with bacterium without R-plasmid (antibiotic sensitive and female) with sex pilis.
  • One strand of the R-plasmid is transferred to recipient bacterium.
  • Both bacterium make a complementary copy of the R-plasmid.
  • Both bacterium have R-plasmids and are multiple antibiotic resistant and male.
  • Anatomical Barriers
  • Reflexes and Secretions
  • Non-Specific Body Chemical
  • Normal resident microorganisms (bacteria)
  • Inflammation
  • The skin is a physical barrier that very few infections can get through.
  • 3 Exceptions : Staphylococcus aureus , Tularemia (rabbit fever), and Dermatophyte molds (tinea, ringworm)

Hair also serves as a physical barrier.

eye brows, eye lashes, nose hair, pubic hair, ear hair

What are the reflex and/or secretion defense associated with the following:

1. eyes
2. nose
3. mouth
4. throat
5. stomach
6. intestines
7. urinary tract
8. vaginal tract
9. ear canal


Why Hfr cells are called high frequency recombination cells? - Biology

Viruses are special living organisms, so special that there was a time when they were not recognized as living beings. Viruses even have no cell structure, no meiosis and mitosis. Their genetic material is DNA or RNA, which is viral genome. This lecture note provides a summary of genetic mechanisms which operate in viruses and some of the practical implications which arise from these.

It is possible to separate molecular analysis of virus genomes into two types of approach:

  • Physical analysis of structure and nucleotide sequence, essentially performed in vitro.
  • Biological analysis of the structure-function relationships of intact virus genomes and individual genetic elements usually involving analysis of the virus phenotype in vivo.

Conventional genetic analysis of animal viruses is based on the isolation and analysis of mutants, using plaque-purification techniques. For viruses which do not form plaques, little genetic analysis was possible before the development of molecular genetics, but certain tricks make it possible to use these genetic techniques for such viruses for example,

  • Focal immunoassays
  • Biochemical analysis
  • Physical analysis
  • Molecular biology
  • Formation of transformed 'foci' of cells.
  • Recombination Maps
  • Reassortment Maps/Groups
  • Physical Maps
  • Restriction Maps
  • Transcription Maps
  • Translation Maps

'Strain', 'type', 'variant', 'mutant' and even 'isolate' are all terms used interchangably to differentiate them from original 'parental', 'wild-type' or 'street' viruses. According to the sources of mutagenesis there are two distinguishable kinds of mutations:

  • Spontaneous mutations, which occur naturally in environment
  • Induced mutations, which are produced by researchers with mutagens.

Both above mentioned kinds of mutations may have different types of mutations as follows:

  • Plaque morphology
  • Host range
  • Temperature-sensitive (t.s.)
  • Cold-sensitive
  • Nonsense
  • Deletions
  • Biochemical markers
  • Revertants

Genetic Interactions Between Viruses:

  • Allelic (intragenic) complementation: different mutants have complementing defects in the same protein.
  • Non-allelic (intergenic) complementation: mutants with defects in different genes.

  • Heterozygosis
  • interference
  • Suppression
  • Phenotypic mixing
  • Pseudotyping

Some comparison of genetic mechanisms and its applications between different living organisms has been written in MITOPENCOURSEWARE ( PDF).

Lecture 19. Bacterial plasmids and conjugation

Bacteria are typical representatives of prokaryotes. Prokaryotes have cell structure and their DNA is concentrated in a so-called nucleoid region. However, different from eucaryotes, bacteria have neither meiosis nor mitosis.

All the properties of the cell, including its virulence, pathogenicity and antibiotic resistance, are determined by it genome. This is encoded by the specific sequences of nucleotide bases in the DNA. Recall that the bacterial DNA is a single, circular ds-DNA, which does NOT have a nuclear membrane and has NO histones. E.coli is an exception since its DNA is an irregular coiled bundle lying freely in the cytoplasm.

In addition to the main ds-DNA, some bacterial cells may also carry one or more extra-chromosomal elements these appear circular and are termed PLAMIDS.

Plasmids replicate INDEPENDENTLY of the main chromosome in the cell. They carry supplemental genetic information coding for certain properties such as antibiotic resistance or the ability to survive harsh environments.

A third source of genetic information in the bacterial cells is provided by BACTERIOPHAGES. These are also termed bacterial viruses. They consist of a viral genome enclosed by a protein coat. They need a bacterial host in order to multiply, and when they do so, they cause death of the bacterial cell. In some instances, though, they can undergo LYSOGENY. This is controlled, long term replication within the bacterial cell without causing the bacterial cell’s lysis. In this way, it may become a temporary part of the cell and change the bacterial cell’s properties.

Comparison between the 3 types of genetic information sources in the bacterial cell
Genetic element TYPE Configuration Size in kb
Chromosome DNA ds circular 2.0 to 4.0 x10
Plasmids DNA ds circular 9 to 60
Bacteriophages RNA/DNA ss/ds Linear/circular 3 to 40

There are three principal mechanisms of genetic recombination (genetic material transfer into a HOST organism) in bacteria occurring in nature and also in lab conditions: conjugation, transformation and transduction.

CONJUGATION This is the most important mechanism for widespread transfer of genetic information between bacteria. It is the direct transfer of bacterial DNA between organisms and requires CELL-TO-CELL CONTACT.

Most conjugation is PLASMID- MEDIATED.

PLASMIDS are small, circular, ds-DNA molecules, measuring about 2-10 kilobases. They all have the ability to replicate in bacteria. Multiple copies of plasmids can be found in a host cell however, not all plasmids can transfer themselves.

A CONJUGATIVE plasmid has the codes for the genes allowing transfer between cells.

A NONCONJUGATIVE plasmid requires the help of a conjugative plasmid to transmit itself to another bacteria.

NARROW-HOST RANGE PLASMIDS exist only within a single species.

BROAD-HOST RANGE PLASMIDS exist in different genera of organisms.

  1. 1. An ORI or origin of replication that allows autonomous replication.
  2. 2. One or more genes that confer specific antibiotic resistance.
  3. 3. Sites for restriction endonucleases which are used for inserting specific DNA fragments.
  4. 4. Code for virulence factors such as toxins or adhesions

Plasmids capable of mediating conjugation carry genes coding for the production of a PILUS, a protein appendage which is found on the surface of the donor cells. Bacteria which carry these plasmids are called F(+) cells, making them the donor cells. The tip of the pilus attaches to the surface of the recipient cell, and DNA transfer occurs. It is unclear whether the DNA is transferred through the pilus, or if the pilus serves as the mechanism for holding the two cells together. Either way, this is the reason why it has also been termed BACTERIAL SEX.

Once the recipient cell acquires the F plasmid, it changes from a F(-) to a F (+) cell, thus allowing it to become a donor cell now.

In a very small proportion of cells, the F plasmid is incorporated into the bacterial chromosome. Once inserted, the entire bacterial chromosome acts like a F plasmid. These are then called high frequency recombination strains (Hfr strains). This leads to 2 processes:

  1. 1. The F plasmid and the entire bacterial DNA undergoes conjugation with an F(-) cell.
  2. 2. The F plasmid is excised along with a portion of the bacterial DNA hence the F plasmid is termed F prime (F’).

For instance, the F-lac plasmid is an F’ plasmid carrying the genes for E.coli lactose operon. When this plasmid is transferred to non-lactose formers, they are converted to lactose fermenters.

Note that the F plasmid is confined to the Escherichia genus and other closely related enteric bacteria.

In order to perform tests for dominance or for complementation in bacteria, using conjugation, please see ( PDF)*.

Lecture 20. Bacterial transformation

Transformation is a process in which genetic material taken in from the environment is added to a part of the bacterial DNA. The DNA may also replace an existing gene or part of it from the genome of the bacteria, thus resulting in loss of the activity of that gene.

For the first time transformation was demonstrated in 1928 by Frederick Griffith, an English bacteriologist searching for a vaccine against bacterial pneumonia. Griffith discovered that a non-virulent strain of Streptococcus pneumoniae could be transformed into a virulent one by exposure to strains of virulent S. pneumoniae that had been killed with heat. In 1944 it was demonstrated that the transforming factor was genetic, when Oswald Avery, Colin MacLeod, and Maclyn McCarty showed gene transfer in S. pneumoniae. Avery, Macleod and McCarty called the uptake and incorporation of DNA by bacteria "transformation."

Bacterial transformation may be referred to as a stable genetic change brought about by taking up naked DNA (DNA without associated cells or proteins), and competence refers to the state of being able to take up exogenous DNA from the environment. Two different forms of competence should be distinguished: natural and artificial.

Natural competence Some bacteria (around 1% of all species) are naturally capable of taking up DNA under laboratory conditions many more may be able to take it up in their natural environments. Such species carry sets of genes specifying machinery for bringing DNA across the cell's membrane or membranes.[2]

Natural competence Artificial competence is not encoded in the cell's genes. Instead it is induced by laboratory procedures in which cells are passively made permeable to DNA, using conditions that do not normally occur in nature.[3]

Electroporation is another way to make holes in bacterial (and other) cells, by briefly shocking them with an electric field of 10-20kV/cm. Plasmid DNA can enter the cell through these holes. This method is amenable to use with large plasmid DNA. [4] Natural membrane-repair mechanisms will rapidly close these holes after the shock.

Plasmid transformation In order to persist and be stably maintained in the cell, a plasmid DNA molecule must contain an origin of replication, which allows it to be replicated in the cell independently of the chromosome. Because transformation usually produces a mixture of rare transformed cells and abundant non-transformed cells, a method is needed to identify the cells that have acquired the plasmid. Plasmids used in transformation experiments will usually also contain a gene giving resistance to an antibiotic that the intended recipient strain of bacteria is sensitive to. Cells able to grow on media containing this antibiotic will have been transformed by the plasmid, as cells lacking the plasmid will be unable to grow.

Another marker, used for identifying E. coli cells that have acquired recombinant plasmids, is the lacZ gene, which codes for β-galactosidase. Because β-galactosidase is a homo-tetramer, with each monomer made up of one lacZ-α and one lacZ-ω protein, if only one of the two requisite proteins is expressed in the resulting cell, no functional enzyme will be formed. Thus, if a strain of E. coli without lacZ-α in its genome is transformed, using a plasmid containing the missing gene fragment, transformed cells will produce β-galactosidase, while untransformed cells will not, as they are only able to produce the omega half of the monomer. In this type of transformation, the polylinker region of the plasmid lies in the lacZ-α gene fragment, meaning that successfully produced recombinant plasmids will have the desired gene inserted somewhere within lacZ-α. When this disrupted gene fragment is expressed by E. coli, no usable lacZ-α protein is produced, and therefore no usable β-galactosidase is formed. When grown on media containing the colorless, modified galactose sugar X-gal, colonies that are able to metabolize the substrate (and that have therefore been transformed, but not by recombinant plasmids) will appear blue in color colonies that are not able to metabolize the substrate (and that have therefore been transformed by recombinant plasmids) will appear white.

Transformation in plant and animal Nowadays transformation is a rutin technique used in many labs of molecular biology for transfering DNA into plant and animal cells. A number of transformation mechanisms are available for plant cells:

    mediated transformation is the easiest and most simple plant transformation. These bacteria can infect plant tissue and insert its DNA into cells of some plant species. : Coat small gold or tungsten particles with DNA and shoot them into young plant cells or plant embryos. Some genetic material will stay in the cells and transform them. : make transient holes in cell membranes using electric shock this allows DNA to enter as described above for Bacteria. (transduction): Package the desired genetic material into a suitable plant virus and allow this modified virus to infect the plant. If the genetic material is DNA, it can recombine with the chromosomes to produce transformant cells.

In principles animal transformation mechanisms are similar. Introduction of DNA into animal cells is usually called transfection.

21. Bacterial transduction

As abovementioned, transduction is one of three mechanisms of genetic recombination in bacteria occuring in nature and also in lab conditions. In other words, transduction is a process, in which nucleic acid from one organism can be transfered into another living organim by means of a intermediate virus.

In other words transduction is the phage-mediated transfer of bacterial DNA.

Phages are bacterial viruses with a DNA molecule enclosed in a protein shell. The structure of the protein shell imposes a limitation on the amount of DNA that can be enclosed by the virus, usually about 50 kilobases.

Most phages carry ds-DNA coiled up inside a protein coat. There may be species of phages which have a ss-DNA or ss-RNA as well.

There are two kinds of transductions:

When phages enter a bacterial cell and replicate, each phage head usually has a copy of the replicated phage genome. Sometimes, an empty phage head is produced. Bacterial DNA may be mistakenly packaged into that empty phage head, then transferred to another bacterial cell. Thus, the DNA that enters the second bacterial cell is a short segment of the chromosome of the first bacterial cell. As with transformation, recombination must occur for transduction to be successful.

It is termed generalized since the phage will pick up any portion of the bacterial chromosome at random. (It is not specific for a particular chromosomal segment.)

The genes can be transferred only within a certain species because phages usually attack a limited range of bacteria.

Phages can also pick up and transfer plasmid DNA. For example, the penicillinase gene of Staphlococci is located in a plasmid, and the phages can pick up that plasmid and through transduction, pass it on to another cell, thus transferring the penicillanase gene to other staphylococci species.

Bacteriophages that lyse the host cell are known as virulent phages, and cause a lytic cycle of infection. In contrast, phages that can infect a bacterial cell without causing its death are called temperate phages.

The bacterial cell which survives after phage infection is called a lysogen, and the integrated temperate phage inside of it is termed a prophage.

The phage DNA is then inserted into the bacterial cell DNA and is also replicated as part of the host cell chromosome.

When a bacterial cell is infected by one phage, no other phage can attack the bacteria, because once the phage is integrated into the bacterial cell DNA, it imparts immunity against superinfection by other phages.

The lysogen phase is stable but not permanent. If the prophage is excised during the process of replication, the bacterial cell may eventually be lysed. This can be replicated in the lab by exposure of bacteria to UV light. (Eg., hospital sterilization of rooms with UV.)

In contrast to generalized transduction, temperate phages normally have a specific insertion site on the chromosome and can pick up only a short length of DNA containing a few genes on either side of this site. Thus, it is termed specialized or restrictive transduction.

Defective phages can occur. As it attaches to the bacterial DNA chromosome and picks up DNA adjacent to the phage integration site, the amount of DNA may be too much thus the phage will lack a few phage genes. When that defective phage is transduced to a second bacterial cell, the phage can still integrate into its phage specific site, but it cannot replicate normally nor can it lyse the bacterial cell. The final product is the integration of the 1st bacterial cell’s DNA into the 2nd bacteria’s DNA.

How does transduction influence the virulence of the bacteria for humans? Since the prophage carries genes, its presence in a bacterial cell may cause the expression of certain proteins. This is called lysogenic conversion. For example, the diphtheria toxin of Corynebacterium diptheriae will only be produced if the Beta Phage infects the bacteria.

Another common use for the phages are the lambda phages of E.coli. They have a genome of 50 kilobases, but once the nonessential genes are removed, they are a reduced to 30 kilobases, making room for the insertion of foreign DNA. The phage then infects E.coli, which in turn replicates along with the phage (phage-synthesizing factory) then the E.coli cell is lyzed, and the phages infect other cells.

E.coli is the ideal host since it replicates every 20 minutes. Also, E.coli has all the distinct phases in the bacterial growth curve, so its growth can be monitored in the lab.

Transduction can be used for gene manipulation as well as genetic analysis as shown experimentally in the lecture from MITOPENCOURSEWARE ( PDF).

Lecture 22. Bacterial transposition

Transposition is a movement of so-called transposons, which are sequences of DNA that can move around to different positions within the genome of a single cell. In the process, they can cause mutations and change the amount of DNA in the genome. Transposons were also once called "jumping genes", and are examples of mobile genetic elements. They were discovered by Barbara McClintock early in her career[1], and for which she was awarded a Nobel prize in 1983. There are a variety of mobile genetic elements, and they can be grouped based on their mechanism of transposition. Class I mobile genetic elements, or retrotransposons, move in the genome by being transcribed to RNA and then back to DNA by reverse transcriptase, while class II mobile genetic elements move directly from one position to another within the genome using a transposase to "cut and paste" them within the genome. Transposons are very useful to researchers as a means to alter DNA inside of a living organism. Transposons make up a large fraction of genome sizes which is evident through the C-values of eukaryotic species.

Transposons are classified into two classes based on their mechanism of transposition: Retrotranspositions and DNA transposons.

Retrotransposons work by copying themselves and pasting copies back into the genome in multiple places. Initially retrotransposons copy themselves to RNA (transcription) but, in addition to being transcribed, the RNA is copied into DNA by a reverse transcriptase (often coded by the transposon itself) and inserted back into the genome.

Retrotransposons behave very similarly to retroviruses such as HIV, giving a clue to the possible evolutionary origins of such viruses.

There are three main classes of retrotransposons:

  • Viral: encode reverse transcriptase (to reverse transcribe RNA into DNA), have long terminal repeats (LTRs), similar to retroviruses. : encode reverse transcriptase, lack LTRs, transcribed by RNA polymerase II.
  • Nonviral superfamily: do not code for reverse transcriptase, transcribed by RNA polymerase III.

Retroviruses as transposable elements

Retroviruses were first identified 80 years ago as agents involved in the onset of cancer. More recently the AIDS epidemic has been shown to be due to the HIV retrovirus. In the early 1970s it was discovered that retroviruses had the ability to replicate their RNA genomes via conversion into DNA which became stably integrated in the DNA of the host cell. It is only comparatively recently that retroviruses have been recognized as particularly specialized forms of eukaryotic transposons. In effect they are transposons which move via RNA intermediates that usually can leave the host cells and infect other cells. The integrated DNA form (provirus) of the retrovirus bears a marked similarity to a transposon.

The transposition cycle of retroviruses has other similarities to prokaryotic transposons, and this suggests a distant familial relationship between these two types of transposon. Crucial intermediates in retrovirus transposition are extrachromosomal DNA molecules. These are generated by copying the RNA of the virus particle into DNA by a retrovirus-encoded polymerase called reverse transcriptase. The extra chromosomal linear DNA is the direct precursor of the integrated element and the insertion mechanism bears a strong similarity to "cut and paste" transposition.

The major difference of class II transposons from retrotransposons is that their transposition mechanism does not involve an RNA intermediate. Class II transposons usually move by a mechanism analogous to cut and paste, rather than copy and paste, using the transposase enzyme. Different types of transposase work in different ways. Some can bind to any part of the DNA molecule, and the target site can therefore be anywhere, while others bind to specific sequences. Transposase makes a staggered cut at the target site producing sticky ends, cuts out the transposon and ligates it into the target site. A DNA polymerase fills in the resulting gaps from the sticky ends and DNA ligase closes the sugar-phosphate backbone. This results in target site duplication and the insertion sites of DNA transposons may be identified by short direct repeats (a staggered cut in the target DNA filled by DNA polymerase) followed by inverted repeats (which are important for the transposon excision by transposase). The duplications at the target site can result in gene duplication, and this is supposed to play an important role in evolution[2]:284 .

Not all DNA transposons transpose through the cut and paste mechanism. In some cases a replicative transposition is observed in which a transposon replicates itself to a new target site.

The transposons which only move by cut and paste may duplicate themselves if the transposition happens during S phase of the cell cycle when the "donor" site has already been replicated, but the "target" site has not.

Both classes of transposon may lose their ability to synthesise reverse transcriptase or transposase through mutation yet continue to jump through the genome because other transposons are still producing the necessary enzyme.

Transposons causing diseases Transposons are mutagens. They can damage the genome of their host cell in different ways:

  • A transposon or a retroposon that inserts itself into a functional gene will most likely disable that gene.
  • After a transposon leaves a gene, the resulting gap will probably not be repaired correctly.
  • Multiple copies of the same sequence, such as Alu sequences, can hinder precise chromosomal pairing during mitosis and meiosis, resulting in unequal crossovers, one of the main reasons for chromosome duplication.

Diseases that are often caused by transposons include hemophilia A and B, severe combined immunodeficiency, porphyria, predisposition to cancer, and Duchenne muscular dystrophy.

Additionally, many transposons contain promoters which drive transcription of their own transposase. These promoters can cause aberrant expression of linked genes, causing disease or mutantphenotypes.

Evolution of transposons The evolution of transposons and their effect on genome evolution is currently a dynamic field of study.

Transposons are found in all major branches of life. They may or may not have originated in the last universal common ancestor, or arisen independently multiple times, or perhaps arisen once and then spread to other kingdoms by horizontal gene transfer[7]. While transposons may confer some benefits on their hosts, they are generally considered to be selfish DNAparasites that live within the genome of cellular organisms. In this way, they are similar to viruses. Viruses and transposons also share features in their genome structure and biochemical abilities, leading to speculation that they share a common ancestor.

Since excessive transposon activity can destroy a genome, many organisms seem to have developed mechanisms to reduce transposition to a manageable level. Bacteria may undergo high rates of gene deletion as part of a mechanism to remove transposons and viruses from their genomes while eukaryoticorganisms may have developed the RNA interference (RNAi) mechanism as a way of reducing transposon activity. In the nematode Caenorhabditis elegans, some genes required for RNAi also reduce transposon activity.

Transposons may have been co-opted by the vertebrate immune system as a means of producing antibody diversity. The V(D)J recombination system operates by a mechanism similar to that of transposons.

Evidence exists that transposable elements may act as mutators in bacteria.

Applications The first transposon was discovered in the plant maize (Zea mays, corn species), and is named dissociator (Ds). Likewise, the first transposon to be molecularly isolated was from a plant (Snapdragon). Appropriately, transposons have been an especially useful tool in plant molecular biology. Researchers use transposons as a means of mutagenesis. In this context, a transposon jumps into a gene and produces a mutation. The presence of the transposon provides a straightforward means of identifying the mutant allele, relative to chemical mutagenesis methods.

Sometimes the insertion of a transposon into a gene can disrupt that gene's function in a reversible manner transposase-mediated excision of the transposon restores gene function. This produces plants in which neighboring cells have different genotypes. This feature allows researchers to distinguish between genes that must be present inside of a cell in order to function (cell-autonomous) and genes that produce observable effects in cells other than those where the gene is expressed.

Transposons are also a widely used tool for mutagenesis of most experimentally tractable organisms.

Lecture 23. Recombinant DNA in bacteria

Recombinant DNA is the general name for taking a piece of one DNA, and combining it with another strand of DNA. Recombinant DNA is also sometimes referred to as "chimera." By combining two or more different strands of DNA, scientists are able to create a new strand of DNA. The most common recombinant process involves combining the DNA of two different organisms.

There are three different methods by which Recombinant DNA is made. They are Transformation, Phage Introduction and Non-Bacterial Transformation. See Bacterial transformation in the lecture note 20.Non-Bacterial Transformation is a process very similar to Transformation, which was described above. The only difference between the two is that non-bacterial does not use bacteria such as E. coli for the host.

In microinjection, the DNA is injected directly into the nucleus of the cell being transformed. In biolistics, the host cells are bombarded with high velocity microprojectiles such as particles of gold or tungsten that have been coated with DNA.

Phage introduction is the process of transfection, which is equivalent to transformation, except that a phage is used instead of bacteria. In vitro packagings of a vector are used. This uses lambda or MI3 phages to produce phage plaques which contain recombinants. The recombinants that are created can be identified by differences in the recombinants and non-recombinants using various selection methods.

Recombinant DNA works when the host cell expresses protein from the recombinant genes.

A significant amount of recombinant protein will not be produced by the host unless expression factors are added. Protein expression depends upon the gene being surrounded by a collection of signals which provide instructions for the transcription and translation of the gene by the cell. These signals include the promoter, the ribosome binding site, and the terminator. Expression vectors, in which the foreign DNA is inserted, contain these signals. Signals are species specific. In the case of E. Coli, these signals must be E. coli signals as E. Coli is unlikely to understand the signals of human promoters and terminators. Problems are encountered if the gene contains introns or contains signals which act as terminators to a bacterial host. This results in premature termination, and the recombinant protein may not be processed correctly, be folded correctly, or may even be degraded. Production of recombinant proteins in eukaryotic systems generally takes place in yeast and filamentous fungi. The use of animal cells is difficult because many need a solid support surface, unlike bacteria, and have complex growth needs. However, some proteins are too complex to be produced in bacterium, so eukaryotic cells must be used. Recombinant DNA has been gaining in importance over the last few years, and recombinant DNA will only become more important in the 21st century as genetic diseases become more prevalent and agricultural area is reduced. Below are some of the areas where Recombinant DNA will have an impact.

  • Better Crops (drought & heat resistance)
  • Recombinant Vaccines (i.e., Hepatitis B)
  • Prevention and cure of sickle cell anemia
  • Prevention and cure of cystic fibrosis
  • Production of clotting factors
  • Production of insulin
  • Production of recombinant pharmaceuticals
  • Plants that produce their own insecticides
  • Germ line and somatic gene therapy

Now click over to (PDF) for seeing how to do experiments on Gene cloning and Cloning by complementation in bacteria.

Lecture 24. Gene regulation in prokaryotes

In prokaryotes gene regulation is necessary because for maximum efficiency a cell needs to be able to

  1. 1. control the quantities of gene products produced.
    • Some are needed in large quantities.
      • ribosomal proteins
    • Some are needed in only small quantities.
      • many enzymes
  2. 2. respond to the environment by turning on (or off) specific genes or groups of genes.
    • the Lac operon, heat shock genes
  3. 3. turn genes on and off in the correct temporal pattern.
    • phage or viral infection, development

There are three main levels of gene regulation

  1. 1. Control of RNA abundance (transcriptional regulation)
    • initiation, elongation, stability
  2. 2. Control of protein synthesis (translational regulation)
    • ribosome binding, rate of translation, termination
  3. 3. Control of protein activity
    • stability, modification, allosteric effects
  • constituitive, inducible or repressible
  • positive, negative, both positive and negative
  • and with gene sets there is also coordinate and temporal regulation
  • some examples of these in regulatory circuits are the lac operon, the trp operon and the lysogenic and lytic genes of lambda
    • the lac operon is an inducible system that is under both negative and positive regulation
    • the trp operon is a repressible system with two types of negative control
    • the lytic genes of lambda show how genes can be temporaly regulated

    To understand more detail on gene regulation click over to Negative control (PDF), Positive control (PDF) and Regulatory circuits (PDF).


    2 MATERIALS AND METHODS

    2.1 Study site and sampling

    To evaluate the reservoir of Plasmodium spp. in Bongo District (BD), located in the Upper East Region (UER) of Ghana, an age-stratified cross-sectional study was carried out at the end of the dry season in June 2012. BD is characterized by marked seasonal transmission, and malaria represents a major public health concern for the district. Details on the study design, study population, and data collection procedures have been described previously (Ruybal-Pesantez et al., 2017 ). Briefly, sampling was carried out across two broad catchment areas (Vea/Gowrie and Soe) in BD that were selected because they were considered to be similar in population size, age structure, and ethnic composition. It was however hypothesized that they may differ with respects to malaria transmission intensity and seasonality as Vea/Gowrie is proximal to the Vea dam/irrigation area, while Soe is not near any large bodies of water, although smaller dams for irrigation are scattered throughout the area. The catchment areas were further divided into smaller villages: Vea, Gowrie, Soe Sanabisi, and Soe Boko, with participants enrolled from sections within these villages (Vea: Gonga and Nayire Gowrie: Nayire Kura and Tingre Soe Sanabisi: Tindingo and Akulgoo and Soe Boko: Tamolinga and Mission Area). For these analyses, participants with microscopically confirmed P. falciparum infections were included (N = 267). Methods related to the microscopy, msp2 PCR, and the microsatellite PCR are described in detail in (Ruybal- Pesantez et al., 2017 ). Microsatellite data are available for 200 P. falciparum samples, and var DBLα tags were sequenced for 209 P. falciparum samples.

    2.2 Diversity of var DBLα types and microsatellite alleles

    We analyzed the var antigenic diversity within 209 asymptomatic individuals in Ghana with P. falciparum infections. For 163 of these individuals, we also sequenced 12 microsatellite loci as described in detail in (Ruybal-Pesantez et al., 2017 ). The total number of observed peaks (alleles) at each locus was used to estimate MOI. Single-clone infections were defined as those with at most one microsatellite allele at every microsatellite locus. For multiple-clone infections, the dominant peak (allele) at each of the 12 microsatellite loci was determined for each isolate. The dominant peaks made up what we term the “dominant haplotype” or “dominant infection” for each isolate. Isolates that had an MOI of 1 or 2, determined by the maximum number of peaks observed at a given microsatellite loci, made up the so-called “high confidence infections”, and this represents a standard method used in the field (Schultz et al., 2010 ). Due to high levels of MOI, we were able to determine high confidence infections for only 59 of the 163 isolates. Considerable sequence diversity was observed within types, both within and between isolates. Because this represents a mixture of natural sequence diversity and sequencing errors, and because these two sources cannot be distinguished using our methods, we ignored within-type sequence diversity in this study.

    2.3 Var sequencing methods and type assignment

    DBLα, the only domain found in nearly all var genes, is a molecular marker of var gene diversity (Kraemer & Smith, 2003 Lavstsen, Salanti, Jensen, Arnot, & Theander, 2003 Smith, Subramanian, Gamain, Baruch, & Miller, 2000 Taylor, Kyes, Harris, Kriek, & Newbold, 2000 ). With average read lengths of 400 bp or greater using 454 sequencing, we sequenced the entire length of the PCR amplicon without the need for assembly (Day et al., 2017 Rask, Petersen, Chen, Day, & Pedersen, 2016 ).

    We assigned DBLα sequences to var DBLα sequence “types” in a manner consistent with the 96% nucleotide identity definition commonly used (Barry et al., 2007 ). More specifically, DBLα types are defined here using a clustering algorithm applied to the raw sequence data, such that each DBLα sequence type cluster corresponds roughly to sequences with a >97% amino acid sequence identity. This threshold is consistent with the majority of prior work defining distinct types within DBLα tag sequences because it ensures that each distinct sequence type very likely represents a naturally occurring distinct variant (i.e, and is not merely the result of sequencing errors).

    Most analyses were run using Mathematica v8 scripts unless otherwise noted. We translated DNA sequences to AA sequences using the software program EMBOSS Transeq (Goujon et al., 2010 Rice, Longden, & Bleasby, 2000 ). We excluded from the analysis sequences that had an unexpected reading frame, apparent frameshift substitutions, or stop codons.

    Three genomic isolates were used as positive controls for our sequencing and analysis methods: 3D7, Dd2, and HB3. These samples differ from our field isolates in several respects: The number of var genes in each genome is known the MOI is exactly 1 the complete set of DBLα sequences is known with high precision so it is possible to identify multiple var sequences of the same type within these genomes.

    2.4 Measuring relatedness

    Relatedness is measured as the number of DBLα types or microsatellite alleles shared between two isolates divided by the average number of DBLα types or microsatellite alleles in an isolate, for that pair. For DBLα types, this is equivalent to pairwise type sharing (PTS) as defined by Barry et al. ( 2007 ).

    2.5 Var repertoire overlap indices

    One major aspect of population structure we examined is the overlap among pairs of isolates. We compared the overlap among observed isolates to the overlap among randomized isolates using two different indices: pairwise type sharing (PTS) and the Jaccard similarity (JS) index. If isolate A has a repertoire of nA types and isolate B has a repertoire of nB types, and the two isolates share a total of nAB types, and the union of the two sets is UAB, we define PTSAB = 2nAB/(nA + nB) and JSAB = nAB/UAB. The PTS similarity index, which was designed for comparing var repertoires, was defined here exactly as in Barry et al. ( 2007 ).

    We address whether the observed overlap differs from random by randomizing the var DBLα sequence types in isolates to make a null distribution. We take into consideration several aspects of the observed data to construct a null hypothesis. First, in the field isolates, we can never observe multiple copies of the same DBLα type because we do not determine genomic location and any within-type sequence variation cannot be distinguished from sequencing errors. In our null distribution of randomized isolates, we maintain the binary nature of the type-isolate matrix so that there are no repeated DBLα types in any randomized isolates. We also preserve the total number of isolates and DBLα types, the number of DBLα types sampled per isolate, and the observed frequency distribution of DBLα types within the dataset. We preserve these aspects of the observed data by maintaining row and column totals of the matrix of DBLα types in isolates while randomizing the 0/1 entries of the matrix. Finally, we also preserve the connectedness of the original matrix during the randomization. We then asked whether the observed distribution of overlap indices between isolates differed from the distributions of overlap indices for these randomizations. We use an efficient switch algorithm to build our null distribution, called the Curveball algorithm (Strona, Nappo, Boccacci, Fattorini, & San-Miguel-ayanz, 2014 ). The method was implemented with a program written for assessing modularity and stability of ecological networks (Grilli, Rogers, & Allesina, 2016 ). We sampled every 100 swap units, which is four times the recommended minimum in Strona et al. ( 2014 ) (each swap unit is equal to the number of rows or columns, whichever is lower, and it only counts the number of actual swaps in the matrix as opposed to all the proposed swaps).

    2.6 Linkage coefficient and modularity of linkage networks

    Another aspect of structure predicted by strain theory is that of linkage between var genes. This linkage emerges from selection against recombinants and is maintained dynamically. We used the linkage disequilibrium coefficient, D, to measure correlations between DBLα types with respect to their presence in isolates. We created networks of DBLα types that had D values above a given threshold of D > 0.02 and analyzed the structure of these networks. We carried out linkage analysis using the entire dataset and then repeated the analysis using just singly infected isolates. For the microsatellite alleles linkage network, we also used a threshold of D > 0.02.

    For the DBLα type linkage network, we only included DBLα types that occur more than once in the dataset as these are the only ones that can have significant linkage relationships. We only considered positive linkage disequilibrium coefficients because negative linkages can result from alleles sharing a locus, and the genomic location of DBLα types is not determined in this study. We considered D values statistically significant when they exceeded the threshold described in Hedrick, Jain, and Holden ( 1978 ).

    To identify sets of var genes that tend to co-occur together, we conducted modularity analysis of var linkage networks. For comparison, the modularity of microsatellite linkage networks was also performed. We used the software MODULAR (Marquitti, Guimarães, Pires, & Bittencourt, 2013 ), which defines the optimal number of modules within a network and specifies their members, the overall modularity of the network, and the significance of the modularity relative to a null model that preserves the original degree distribution. While the method was designed for ecological systems, it is based on general network theory and the definition of modularity as “subsets of tightly connected elements”. The following parameter settings were used to identify modules in both the var and microsatellite linkage networks. All linkage disequilibrium coefficients greater than 0.02 (D > 0.02) were used to construct a unipartite network, expressed as a binary matrix. We specified that 1,000 randomized matrices should be used to build the null model for determining the significance of the modularity, and we used spectral partitioning as the optimization method. In the case of the DBLα type linkage network, we confirmed significance (p < .01) using a more conservative null model than the ones generated by MODULAR. This conservative null was based on 100 randomizations that maintained the row and column totals of the original matrix as described below.

    For the DBLα type linkage network, we used the 29 single infection isolates to determine linkage disequilibrium coefficients. For the microsatellite allele linkage network, we used the 55 complete high confidence infections to determine linkage disequilibrium coefficients. To address whether forces related to transmission or demography, that would shape diversity at microsatellite and var loci similarly, might be responsible for structure observed in the var linkage network, we created Figure 8 using the 45 isolates for which we have both complete microsatellite high confidence infections and DBLα type data (regardless of whether they are single infections by the strict microsatellite criteria).

    2.7 HB recombination network

    An important alternative neutral explanation for the existence of the var linkage modules is the so-called var recombination hierarchy, which describes how recombination occurs preferentially among certain groups of var genes. We sought to test whether these var recombination constraints could explain the linkage modules by first building a var recombination network and then testing whether the linkage modules clustered within this network. The recombination network was constructed by identifying homology blocks (HBs), which are conserved units of var recombination that are present in all the DBLα tags. HBs were identified using the VARDOM web server (Rask, Hansen, Theander, Pedersen, & Lavstsen, 2010 ), with a gathering cutoff of 9.97 to define a match. We then connected DBLα types with an edge when they shared a homology block, as this can be considered direct evidence of a historical recombination event between these two types. We did not consider the three HBs that are >50% frequent in the DBLα tag (HB 5, 14, and 36).

    2.8 Randomizations for the assessment of population structure

    We asked whether the number of shared DBLα types and shared microsatellite alleles between areas A and B is more or less than what we would expect randomly given the number of types, and their distributions, in each of the areas. To address this question, we randomized the catchment area location of each of the isolates, so that the number of isolates in each area was conserved, and then we assessed the number of shared DBLα types or microsatellite alleles in the two areas. We assessed the number of shared DBLα types or microsatellite alleles each time we performed the randomization and built a distribution from 10,000 randomizations, which we used to calculate a one-tailed p-value for the observed number of shared types or microsatellite alleles. For the microsatellite analysis, the sample included all alleles from the dominant infections of the 163 isolates for which we could calculate a dominant microsatellite haplotype and for which we had var (and location) data. For the var analysis, the sample consisted of all unique DBLα types for which we had sampling location data.

    2.9 Population genetics variables

    While the concept of expected heterozygosity and homozygosity does not clearly pertain to the largely nonallelic var gene family, we defined an analogous statistic that is appropriate for var genes—var expected heterozygosity (Hv)—and we used it here to address questions about var gene diversity at different hierarchical levels of the population. Pairwise type sharing (PTS) between isolates is a concept that was introduced by Barry et al. ( 2007 ) in part because it adapts useful concepts from population genetics: expected heterozygosity (H) and expected homozygosity (1 − H), to the case of var genes. PTS can be considered roughly equivalent to expected homozygosity. Here, we extended the analogy further for the purpose of randomizations and the construction of FST-like statistics, and we introduced the terms “var expected heterozygosity” (Hv), “var expected homozygosity” (1 − Hv), and varFST (FSTv) for clarity.

    Strictly for the purpose of creating metrics to gauge var diversity, we imagined that all DBLα types are alleles at a single locus. While this assumption is excessively simplistic, given the ectopic nature of their recombination system, it is not altogether biologically inappropriate. In this sense, we considered a parasite to be a

    60N individual with respect to var genes. The standard method for assessing expected heterozygosity and related statistics is to randomly sample haploid organisms (“gametes”) from the population. In our case, we sampled single var genes from the population to represent the gametes. We then combined two of these single var gene gametes to create diploid parasites, each containing two var genes. We did this in order to describe patterns in the observed diversity that differs between the two catchment areas, or other subsets of our isolate sample. Differences between the DBLα type diversity sampled from different locations were analyzed at a range of resolutions: at the low resolution of the two catchment areas down to the eight sections, and we also considered the DBLα types within versus between distinct isolates located in the same section.

    To ask whether expected var homozygosity within the Vea/Gowrie or Soe catchment areas is greater than expected, we randomized the existing var gene variation into 2N isolates (in order to avoid resampling from the limited set of genes). We reshuffled the genes 10,000 times to create a distribution. We tested whether the microsatellites reflected geospatial structure between the two catchment areas by considering the number of shared microsatellite alleles between the areas and asking whether this number deviated significantly from the random expectation. We also asked whether the catchment areas had a lower number of shared microsatellite alleles than expected at random, using the randomization procedure described above.


    Ovarian cancer: Status of homologous recombination pathway as a predictor of drug response

    Epithelial ovarian cancer (EOC), particularly high-grade serous subtype, is associated with germline mutations in BRCA1/BRCA2 genes in up to 20% of the patients. BRCA1/BRCA2 proteins are important components of the homologous recombination (HR) pathway, a vital DNA repair process that protects the genome from double-strand DNA damage. Recent studies revealed frequent somatic mutations of BRCA1/BRCA2 and hypermethylation of the promoter of BRCA1 in EOC, in addition to germline mutations. Comparison of DNA copy number changes in tumors with or without BRCA1/BRCA2 alterations, lead to the identification of several signatures that detect HR pathway defects, here named “HRness”. These signatures predict platinum-sensitivity and survival in EOC, as it was previously shown for germline mutations of BRCA1/BRCA2. They are currently investigated in clinical trials as potential predictive biomarker for response to poly(ADP- ribose) polymerase inhibitors.


    Catching electrons in the act: Science on the attosecond scale

    Long-wavelength laser light approaches an atom (left). The laser pulse ionizes the atom by boosting one of its electrons (center), but before it can escape, the light’s electric field reverses and forces the electron to recombine with the atom (right). The electron’s extra aquired energy is released as an attosecond burst of high-frequency x-rays (relative length of the pulse exaggerated for clarity).

    (PhysOrg.com) -- Understanding how to create artificial photosynthesis, or tough, flexible high-temperature superconductors, or better solar cells, or a myriad other advances, will only be possible when we have the ability to image electrons by freezing time within a few quintillionths of a second. A leader in attosecond science tells how it's done.

    When lasers that could emit ultrashort pulses of light became available in the 1980s, Steve Leone recalls, they ushered in a new field of “femtochemistry.” A femtosecond is a quadrillionth of a second, 10 15 second.

    “From then until now, people have mostly studied relative atomic motions or the electronic transitions governed by these motions,” says Leone, a member of Berkeley Lab’s Chemical Sciences Division, a professor of chemistry and physics at UC Berkeley, and Director of the Chemical Dynamics Beamline at the Advanced Light Source. “These include vibrations, rotations, and the like - motions measured in timescales of femtoseconds.”

    The fastest motion known between atomic nuclei is about eight femtoseconds, the vibrational period between the two hydrogen atoms in a hydrogen molecule. Electrons bind, release, and move among the atoms in a molecule or crystal, but almost all of the atom’s mass is in its nucleus, which drags its electrons around with it. So a lot of chemistry can be done by watching atoms move, even when their electrons can’t be seen directly.

    But for Leone, femtoseconds don’t do the job. He wants to see electrons moving for themselves.

    “Electrons are lighter and quicker and move in a much, much shorter time than nuclei,” Leone says. “Electron dynamics and electron correlation are the problems to be solved if we want to really understand and eventually control chemical processes and complex materials, such as high-speed electronics. To get at the electron dynamics directly, we need to work on the attosecond timescale.”

    These thoughts motivated the start of the attosecond science program at Berkeley in 2004, a collaborative effort led by Leone and his colleague in UC Berkeley’s Chemistry Department, Daniel Neumark, Director of Berkeley Lab’s Chemical Sciences Division.

    While there are more femtoseconds in a single second than there are seconds in 32 million years, attoseconds are a thousand times shorter yet - slices of time so fine that, while they can be counted and measured, they can hardly be imagined. In the time it takes a hydrogen molecule to make a single, vibratory bounce, its two electrons whiz around the molecule 300 times.

    Catching these electrons in the act requires subfemtosecond laser pulses, from hundreds down to just a few attoseconds. How is it possible to create pulses of light so short? The secret lies in the intimate relationships between photons and electrons. Photons give electrons energy under certain conditions, the electrons can give back that energy and more.

    Steve Leone defines “attosecond” in Berkeley Lab’s Video Glossary

    Surfing light to make faster light

    Imagine a red or near-infrared laser pulse (its long wavelengths are called “optical” wavelengths) the waves of the pulse’s electromagnetic field rise and fall like surf, and as it drives through a medium such as pressurized neon gas, the rising wave lifts electrons out of their orbits around atoms and accelerates them toward freedom.

    The intensity of the driver pulse isn’t always sufficient to permanently ionize the gas, however. Often, before the electrons can escape, the wave crests and reverses the electrons are drawn back and reaccelerate into the atoms, carrying the extra energy they gained from the electromagnetic field.

    Upon recombination, the atom emits a burst of higher-frequency (ultraviolet or x-ray) light measured in attoseconds. As electrons continue to recombine, the process repeats with each half wave of the optical cycle, making a sequence of bright, high-frequency attosecond flashes, perfectly synchronized with the optical driver’s wave frequency, carrying them along in the same direction.

    This three-step process - electron acceleration away from the atom, acceleration back to the atom, and recombination that emits an attosecond flash - is called high harmonic generation.

    “High harmonic generation is one basic and most common way of creating an attosecond laser pulse,” says Leone. “If the pulse from the drive laser is long enough, it’s relatively easy to create trains of attosecond pulses, one after the other. What’s hard is to make an individual attosecond pulse. Only four or five groups in the world have done it.”

    Leone’s group, in partnership with Neumark’s, is one of those, and they have achieved individual attosecond pulses in a new way. The most frequently used method for making attosecond pulses depends on filters that select the highest-frequency slice of the harmonic pulse, which rides along with the most energetic half-cycle wave in the carrier pulse envelope. But Leone and Neumark, with their students and postdocs, used a method called ionization gating.

    Ionization gating begins with a much more intense optical pulse - one so intense that the front of the pulse envelope knocks electrons right off the atoms in the gas, forming a dense plasma through which the pulse must plow. Not all the gas atoms are ionized, however recombination of energized electrons and atoms still creates attosecond pulses of x-rays, but there’s a switch-like termination of the pulse train.

    The “switch” is a process called phase match gating. The attosecond pulses, produced on the leading edge of the optical pulse, are tunable in x-ray energy by adjusting the phase of the half cycles within the driver pulse envelope. The attosecond pulses need not be locked to the strongest half-cycle in the optical pulse envelope.

    Once the train has been terminated, the individual attosecond pulses, along with the original laser beam, continue on to a separate interaction region of the laser set-up, where experiments can be carried out.

    A red laser produces attosecond pulses of x-rays by harmonic generation. Optical light and x-rays travel together into the interaction region, then are separated by a mirror and recorded by detectors. Photoelectrons generated by the attosecond pulses in the interaction region are analyzed by a time-of-flight detector (circle). The streaked photoelectron spectrum, shown versus the laser pulse delay time, reveals the duration of the x-ray pulse to be about 430 attoseconds.

    Watching what happens in an attosecond

    Once the attosecond pulses have entered the interaction region, Leone uses techniques called “carrier-envelope phase scanning” and “optical streaking” to observe what happens. With these he can identify and characterize individual attosecond pulses.

    Carrier-envelope phase scanning: When an electron is boosted away from an atom during ionization, the photon that does the boosting consists of an electric field varying in one direction and then another. These fields can both add to and subtract from the electron’s momentum, so depending on when it is born, the photoelectron experiences a different force - sometimes stronger, sometimes weaker. Scanning the gas in the interaction region (whatever gas is the subject of the experiment) allows the timing of the electrons produced by the attosecond bursts to be identified by their extra momentum.

    Optical streaking: A subsequent streak spectrogram compares the energy of the attosecond pulse to the energy of the photoelectron, as the two change over time. The streak spectrogram confirms the existence of individual attosecond pulses, measures their length, and determines when a secondary electron is produced.

    “We have measured 450 attosecond pulses,” says Leone. “The time is limited by the particular optics used to reflect the x-rays. The method can actually produce much shorter pulses, tunable to various frequencies.”

    Someday the method will deliver stability, reliability, and ease of use in the production of attosecond pulses, although at present Leone’s experimental laser system in Building 2 is still finicky. Leone compares it to a “TV set that’s a little temperamental it works, but you have to have the touch.”

    Even with this oversensitive instrument, Leone’s group has done unique scientific experiments on gas-phase samples. Using attosecond x-rays as pump pulses to ionize a thin gas of sulfur hexafluoride (SF6), they follow up with precisely timed probe pulses from the longer-wavelength, femtosecond laser beam, using ionization spectroscopy to see what happens as the molecules “evolve” - that is, as the sulfur compounds fall apart in an ordered routine dictated by nature. Knowing how nature does it could lead to control of such complex processes.

    Practically next door to Leone’s lab in Building 2, Robert Kaindl of the Materials Sciences Division also uses an attosecond laser, disentangling electronic correlations in nanoparticles and complex materials. Meanwhile, Leone and Neumark pursue solid state physics at another lab on the UC Berkeley campus, studying such phenomena as excitons (the bound states of negatively charged electrons with positively charged holes). This work is vital to developing better solar cells, since excitons are important precursors to the separation of charges in semiconductors for converting sunlight to electrical current.

    The science is so promising that other attosecond laser systems are already in the works. In January the W. M. Keck Foundation awarded $1 million to Leone and Neumark to upgrade a campus laboratory for attosecond science. Shortly thereafter the Department of Defense awarded Leone a National Security Science and Engineering Faculty Fellowship amounting to $850,000 per year over five years. “The Keck grant will go for equipment and the DOD award will fund operations, supplementing the generous support from the Department of Energy,” he says.

    The ultrafast light sources of the future

    Already on the horizon are powerful light sources able to generate ultrabright attosecond pulses of x-rays up to a million times a second, using some combination of linear accelerators, advanced electron injectors, and free electron lasers (FELs).

    When he was with Berkeley Lab’s Accelerator and Fusion Research Division, Sasha Zholents (who is now at Argonne’s Advanced Photon Source) devised a laser pulse-slicing scheme using wiggler magnets, now used by the Advanced Light Source to produce femtosecond x-ray beams. He also pioneered a similar concept to produce attosecond pulses. The Zholents schemes “were the precursors to attosecond hopes for FELs,” Leone says.

    High intensity, FEL-produced attosecond pulses, which may be made by such machines as Berkeley Lab’s proposed Next Generation Light Source, will be crucial to using attosecond pulses for both pump pulses and (until now impractical) probe pulses as well. Only in this way will it become possible to create and control the states of matter and chemical processes that theorists can visualize and model with computers.

    “There are challenging problems with all the many techniques involved with attosecond science,” Leone says. “The strength of my own contributions lies in thinking about new ways to make the measurements and the science to be done with these short pulses.”

    * “Time-resolved spectroscopy of attosecond quantum dynamics,” by Thomas Pfeifer, Mark J. Abel, Phillip M. Nagel, Aurélie Jullien, Zhi-Heng Loh, M. Justine Bell, Daniel M. Neumark, and Stephen R. Leone, appears in Chemical Physics Letters and is available online to subscribers.
    * “Isolated attosecond pulses from ionization gating of high-harmonic emission,” by Mark J. Abel, Thomas Pfeifer, Phillip M. Nagel, Willem Boutu, M. Justine Bell, Colby P. Steiner, Daniel M. Neumark, and Stephen R. Leone, appears in Chemical Physics and is available online to subscribers.


    Lecture 2 - introduction to genetics

    Genetics is the study of inheritance and the manipulation of genetic information (usually DNA or RNA). It can help us:

     Detect and treat diseases  Exploit organisms for humankinds benefit

    Genetic approaches before DNA technology development was classical genetics which involved:

     Random mutagenesis  Selection  Reassortment of organisms characteristics by genetic crosses (recombination)

    Transformation is the editing of dna and putting it back into the cell. Using in vitro techniques allows us to resynthesis dna sequences with a high level of precision and introduce the resulting constructs into organism under study.

    In vitro is test tube experiments. In vivo is within the organism.

    A new in vivo genetic engineering process has been used to correct defects leading to genetic diseases in embryonic cells – CRISPR.

     Double stranded  Cytosine and guanine  Adenine and thymine  Deoxyribose

     Single stranded  Cytosine and guanine  Adenine and uracil  Ribose

    A-T has 3 hydrogen bonds and C-G has 2 hydrogen bonds. Purines are adenine and guanine and these are larger than pyrimidines (cytosine and thymine).

    In the 2 strands of dna, the strands are polarised due to the 3’ and 5’ ends.

    Wildtype is an unmodified natural isolate of a species. We compare everything to this organism. It comes straight from the environment.

    Mutant is an organism that’s different from the wildtype as a result of a specific change in its dna sequence.

    Mutation is a specific change in the dna sequence of an organism that’s different from the dna sequence in the wildtype.

    Allele is one copy of a given gene, whether it’s a wildtype of a mutant.

    Phenotype is an identifiable or observable trait that can be altered by a mutation. Genotype is the defined nucleotide sequence of an organism, usually expressed in terms of alleles of its genes. Recessive wont be shown unless it’s a homozygous recessive.

    Bacteria are good as model organisms as they are:

     Relatively simple  Easy to manipulate  Well known  Regenerate fast by binary fission

     Haploid cells – they only have one copy of each gene so mutated cells are easily identified and the phenotype will be expressed immediately.

    Higher organisms are usually diploid (2 or more copies of one gene) and will be more prone to mutations. Due to mutations normally being recessive, an observable difference will not be there.

    Vertical gene transfer is when heritable properties of organisms are passed onto their progeny. Changes to these properties can occur randomly and when the change is beneficial, it leads to natural selection. This applied to high organisms but bacteria was supposed to have adapted to their environment by direct changes which would also be passed on.

    Luria and delbruck (1941) showed that the above principles applies to bacteria too. They showed the resistance developed due to dna changes in low frequencies – mutations.

    Fred Griffith shows the heritable properties of bacteria could be transferred from 1 bacteria to another

     Non-pathogenic - pathogenic  This is known as genetic transformation

    The transforming principle for the above process was discovered to be dna. Lederberg and Tatum showed when 2 strains of E.coli was mixed with different characteristics, a progeny with characteristics from both parents was formed. This is known as conjugation.

    This is the transfer of genes from one bacterial cell (donor) and another (recipient) by direct cell-cell contact.

    Conjugation is normally controlled by a plasmid. A plasmid is like auxiliary chromosomes but much smaller – 0.1-1% of the size of a main chromosome. They are usually expendable.

    In E.coli, the conjugative plasmid encodes a sex pilus that establishes the initial link between the cells. The pilus contracts and pulls the cells together establishing surface-surface contact. In most cases, it’s the plasmid dna that’s transferred from the donor to the recipient.

    Non-conjugative plasmids can be transferred during conjugation if they have specific mobilisation gene – these genes are removed from plasmid cloning vectors to reduce the likelihood of recombinant plasmids spreading within natural populations.

    Donor cell attaches to a recipient cell with its pilus. The pilus draws the cells together and the cells contact one another. One strand of plasmid dna transfers to the recipient. The recipient synthesised a complementary strand to become an F+ cell the donor synthesises a complementary strand, restoring its complete plasmid.

    Only exceptionally are host chromosomal genes transferred by conjugation as in the case of so called High frequency (Hfr) recombination strains.

    F plasmid integrates into chromosome by recombination. Cells join via a conjugation pilus. Portion of F plasmid partially moves into recipient cell trailing a strand of donors dna. Conjugation ends with pieces of F plasmid and donor dna in recipient cells cells synthesise complementary dna strands. Donor dna and recipient dna recombine making a recombinant F- cell.

    Transduction is gene transfer controlled by a bacterial virus (phage). Phages can transfer genes between bacterium because they make mistakes when packing dna into their phage particles. The phage particles become filled with host chromosomal dna or a mixture of host and phage dna – these are aka transducing particles.

    Homologous recombination is when dna in the host cell and the dna injected into a new host cell can integrate and nucleotide sequences are exchanged between the 2 similar dna molecules. This can happen in E.coli bacteriophage P1.

    The phage infects a bacterial cell by pumping the dna into the cell. This causes another phage to form in the cell. The phage infection causes zones to lysis and the phage produces more progeny which are released.

    Phage infection

    Phage infect its dna. The enzymes which comes from the phage degrade the host dna. The cells synthesises new phages that incorporate phage dna and some host dna. Transducing phage injects donor dna. Donor dna is incorporated into recipients chromosome by recombination.


    Histogenesis and premalignant lesion

    The histogenesis of FL is closely linked to key events of normal B-cell development and differentiation. During early B-cell development in the bone marrow, progenitor B cells undergo V(D)J recombination processes to assemble immunoglobulin heavy (IGH) and light chain V region genes that encode the variable parts of antibody molecules. 3 This involves DNA double-strand breaks at specific recombination signal sequences (RSSs), located at the ends of the rearranging V, D, and J genes. First, in a pro-B cell, an IGHD gene is joined to an IGHJ gene, and in the next step, an IGHV gene is recombined to the DHJH joint. 3 B-cell precursors expressing a heavy-chain gene as pre-B-cell receptor are pre-B cells. At the recombination sites, further diversity is generated by exonucleolytic removal of several bases and by addition of non–germ line–encoded bases, the N-nucleotides. 3 Once a functional heavy chain is expressed, light-chain gene rearrangements are performed to generate a mature B-cell receptor (BCR).

    In rare instances, mistakes happen during V(D)J recombination, and when the ends of the rearranging genes in 1 of the immunoglobulin loci are erroneously joined to DNA breaks in another chromosome, a reciprocal chromosomal translocation occurs. 4 Such a translocation is likely the first event in the pathogenesis of FL. About 90% of FLs show a t(1418)(q32p21) in which the B-cell lymphoma 2 (BCL2) gene is joined to an IGHJ gene or a DHJH joint in the IGH locus. 5,6 Specific characteristics of the translocations, in particular the presence of N-nucleotides at the joining sites of the 2 chromosomes and the location of the breakpoints in the IGH locus close to the RSS site of an IGHD or IGHJ gene, strongly argue for misguided V(D)J recombination at the pro-B-cell stage as the mechanism. 4 As a consequence, the BCL2 gene is brought under control of the enhancers of the IGH locus, causing constitutive expression of the antiapoptotic BCL2 protein. 4

    BCL2 is not a strong proto-oncogene, and as naive B cells physiologically express BCL2, it seems that the presence of a t(1418) IGH-BCL2 translocation does not cause a major disturbance of mature naive B-cell physiology. It unfolds its pathogenetic function when a naive B cell is driven into a T-cell–dependent immune response and becomes a GC B cell. In the dark zone of the GC, antigen-activated B cells undergo massive clonal expansion and activate the process of somatic hypermutation (SHM) to modify their IGV region genes. 3,7 As somatic mutations are largely random, only few cells will acquire affinity-increasing mutations and are positively selected by T follicular helper (TFH) cells in the light zone. 8 Most acquire disadvantageous mutations and are destined to die by apoptosis. 8,9 The apoptosis proneness of GC B cells is also a tolerance mechanism to prevent survival of autoreactive B cells. 10,11 Apparently, apoptosis is the default pathway for GC B cells, and an important factor for this intrinsic program is that they downregulate the antiapoptotic factor BCL2 (Figure 1). 9,12 Only in positively selected light-zone GC B cells that undergo differentiation toward memory B cells or plasma cells is BCL2 expression reinduced. 12,13 If a B cell with an IGH-BCL2 translocation is driven into a GC reaction, the normal apoptosis and selection process is disturbed, and such B cells have a survival advantage. 14 This then increases the risk for acquisition of further genetic lesions and ultimately may lead to the development of a FL in some such cells. As will be discussed in more detail in the following sections, these additional genetic lesions and pathogenetic events cause differentiation arrest at the stage of a GC B cell.

    B cells with IGH-BCL2 translocations are also detectable in healthy human adults. The frequency of such cells is ∼1 per 10 5 peripheral blood B cells, with wide variation between individuals. 15,16 The frequencies tend to rise with increasing age. 17 Initially, it was thought that these BCL2 translocation-positive circulating cells are polyclonal naive B cells, stemming from numerous independent translocation events, and accumulating in this mature B-cell compartment. However, later elegant studies revealed that t(1418)-carrying cells in healthy adults are mostly present among immunoglobulin M–positive (IgM + )IgD + CD27 + memory B cells with somatically mutated IGV genes, and that the pool of such cells is mostly dominated by 1 or a few clones. 18,19 These clones often persist, and, in a few instances, it was shown that circulating clones present in peripheral blood several years before diagnosis of a FL already carried a number of genetic lesions in addition to the BCL2 translocation. 20,21 Combined studies with human B cells and a murine model indicate that B cells constantly overexpressing BCL2 undergo several GC passages until they finally give rise to a FL (Figure 2). 22

    Scenario for FL pathogenesis. During early B-cell development, in rare instances, pro-B cells acquire a t(1418) IGH-BCL2 translocation as a mistake of IGH recombination. This leads to constitutive expression of BCL2. t(14:18)-bearing B cells can develop further into mature naive B cells and may enter a GC reaction upon antigenic stimulation. In the GC, BCL2 is normally downregulated to promote apoptosis proneness of GC B cells. However, t(1418)-carrying GC B cells have a survival advantage, and may clonally expand and become memory B cells. t(1418)-positive B cells in the peripheral blood are mainly found among IgM memory B cells. Such cells may undergo repeated GC reactions and thereby acquire further genetic lesions. In some reactive lymph nodes, GCs dominated by monoclonal BCL2 + GC B cells can be found. These are called FLIS and the B-cell clones can be considered as premalignant as they often carry besides the t(1418) further genetic lesions. From such structures, FLs can develop after additional gene mutations occurred. Furthermore, mutations promoting N-glycosylation of amino acids in the variable regions of the BCR have been detected in FLIS, 28,61 so that chronic antigenic stimulation as an additional pathogenetic factor occurring through BCR stimulation by lectins on stromal cells can occur already in FLIS (and perhaps even earlier, as the mutations causing N-glycosylation may well occur in early GC passages, when SHM is highly active). Nearly half of FL cases express IgG, so that at some stage during FL pathogenesis, a considerable fraction of cases have undergone class-switch recombination (CSR).

    Scenario for FL pathogenesis. During early B-cell development, in rare instances, pro-B cells acquire a t(1418) IGH-BCL2 translocation as a mistake of IGH recombination. This leads to constitutive expression of BCL2. t(14:18)-bearing B cells can develop further into mature naive B cells and may enter a GC reaction upon antigenic stimulation. In the GC, BCL2 is normally downregulated to promote apoptosis proneness of GC B cells. However, t(1418)-carrying GC B cells have a survival advantage, and may clonally expand and become memory B cells. t(1418)-positive B cells in the peripheral blood are mainly found among IgM memory B cells. Such cells may undergo repeated GC reactions and thereby acquire further genetic lesions. In some reactive lymph nodes, GCs dominated by monoclonal BCL2 + GC B cells can be found. These are called FLIS and the B-cell clones can be considered as premalignant as they often carry besides the t(1418) further genetic lesions. From such structures, FLs can develop after additional gene mutations occurred. Furthermore, mutations promoting N-glycosylation of amino acids in the variable regions of the BCR have been detected in FLIS, 28,61 so that chronic antigenic stimulation as an additional pathogenetic factor occurring through BCR stimulation by lectins on stromal cells can occur already in FLIS (and perhaps even earlier, as the mutations causing N-glycosylation may well occur in early GC passages, when SHM is highly active). Nearly half of FL cases express IgG, so that at some stage during FL pathogenesis, a considerable fraction of cases have undergone class-switch recombination (CSR).

    B cells carrying IGH-BCL2 translocations can also be been detected in the GC of ∼2% to 3% of reactive lymph nodes from healthy adults. 23-25 Often, their numbers are rather small and they are scattered among normal GC B cells. 26 In other instances, typically, several GCs are dominated by BCL2-expressing GC B cells with the t(1418) translocation. This has been designated as FL in situ (FLIS). 24,27 The cells express typical GC B-cell markers, such as CD10, are monoclonal, and show active hypermutation. 24,28 However, their proliferation rate is lower than that of normal GCs, with an increased fraction of light-zone B cells. 24 Progression of FLIS to FL is seen only in rare instances, but FLIS nevertheless seems to represent precursor lesions of FL and premalignant B-cell clonal expansions because, in a few instances, a clonal relationship between an FLIS and a subsequent FL was demonstrated, and FLIS frequently already carries genomic imbalances as further genetic lesions in addition to the t(1418). 29,30


    HDL METABOLISM AND ATHEROSCLEROSIS

    HDL and Reverse Cholesterol Transport

    Apolipoprotein A-I (apoA-I) is the major protein on HDL and provides both structure and function. Lipid-poor apoA-I and mature HDL both contribute to removing cholesterol from macrophages and prevent foam cell formation (Figure 2). Although cholesterol flux from macrophages to HDL (or apoA-I) alleviates cholesterol-accumulation in lesions, the net flux of cholesterol from the lesion has little to no effect on systemic cholesterol levels. Nevertheless, macrophage cholesterol efflux to HDL reduces inflammation and the atherosclerotic burden, and is the first step in reverse cholesterol transport (RCT) (Figure 10) (685-687). This pathway was first described in 1966 (688). The rate at which cholesterol flows through the RCT pathway is of greater importance than steady state levels of HDL-cholesterol (HDL-C). Interestingly, cholesterol movement from macrophages to HDL occurs through at least 4 routes (70). First, lipid-poor apoA-I stimulates the efflux of phospholipid and free cholesterol through interaction with ATP-binding cassette transporter A1 (ABCA1) (Figure 2), which generates pre-beta HDL and nascent discoidal particles (689). The more lipidated the apoA-1 becomes, the discoidal HDL particles transition into a spherical structure and lose their ability to interact with ABCA1 and stimulate cholesterol efflux through ABCA1. Both discoidal HDL particles and mature spherical HDL particles can also promote free cholesterol efflux from another transporter, ATP-binding cassette transport G1 (ABCG1), which is thought to reside on sub-cellular organelles as opposed to the plasma membrane (Figure 2) (690,691). This transporter is a critical regulator of intracellular cholesterol trafficking cellular cholesterol availability, and cholesterol export (690,692). HDL’s primary receptor for cholesteryl ester (CE) uptake, scavenger receptor BI (SR-BI), is also a bidirectional free cholesterol transporter in that it facilitates the efflux and influx of free cholesterol between cells and mature HDL (693-695) (Figure 2). The net direction of cholesterol flux is determined by the cholesterol concentration gradient (plasma membrane and HDL ratio of free cholesterol to phospholipid) (696) as well as by the phospholipid subspecies (697,698). Finally, cholesterol can simply move from the plasma membrane to HDL through passive aqueous diffusion, which is a major route of cholesterol efflux from macrophages (Figure 2) (70,687,695). On HDL free cholesterol is solubilized in the phospholipid surface layer and is rapidly esterified by lecithin:cholesterol acyltransferase (LCAT) (Figure 6), and the hydrophobic CE is then mobilized to HDL’s core (699,700).

    Figure 10.

    Beneficial Functions of HDL. HDL mediates a number of atheroprotective processes. HDL is critical in reverse cholesterol transport where it mediates the first step of removing cholesterol from the periphery and macrophage foam cells for clearance by the liver. HDL can directly mediate the last step in reverse cholesterol transport by delivering cholesterol to the liver via interaction with SR-BI. HDL reduces LDL oxidation and cell oxidative status by removing lipid hydroperoxides from LDL and cells. HDL also prevents LDL oxidation via its anti-oxidant enzymes (PON1, LCAT, and Lp-PLA2) and by the reduction of lipid hydroperoxides by apoA-I. HDL maintains the endothelial cell barrier by stimulating vasorelaxation resulting from enhanced nitric oxide production from HDL induced signaling via a number of endothelial cell receptors (SR-BI, S1P, ABCG1). HDL prevents thrombus formation by inhibiting coagulation factors and by stimulating efflux of cholesterol from platelets via SR-BI to reduce platelet aggregation. HDL prevents endothelial cell and macrophage apoptosis by signaling pathways which modulate expression of the pro-apoptotic protein, Bid, and the anti-apoptotic factor, Bcl-xl. HDL also reduces apoptosis susceptibility by alleviating endoplasmic reticulum stress by removing excess free cholesterol and lipid hydroperoxides from cells. HDL limits atherosclerotic lesion inflammation by inhibiting endothelial cell activation resulting in less monocyte recruitment. HDL also reduces lesion inflammation by promoting the macrophage anti-inflammatory M2 phenotype via ABCA1/ JAK2 signaling to enhance anti-inflammatory cytokine production (IL-10, TGF-β). HDL inhibits conversion to the macrophage inflammatory M1 phenotype by preventing antigen-specific activation of T helper 1 (Th-1) cell to produce interferon gamma. HDL contains an array of proteins and bioactive lipids that regulate HDL function. In addition, HDL controls a number of atheroprotective processes by modulating gene expression by transferring microRNAs to recipient cells.

    Spherical mature HDL then transports CE to peripheral cells and tissues, and back to the liver as part of the RCT pathway (Figure 10). HDL delivers CE to the liver through 2 primary routes. HDL delivers CE to the liver through binding to SR-BI (Figure 6), which drives selective uptake of core lipids (694). Another major route of cholesterol delivery to the liver is mediated through LDL and the LDL receptor (LDLR) (Figure 6) (701). In the circulation, HDL exchanges CE for TG from VLDL and LDL through cholesteryl ester transfer protein (CETP) activity (Figure 6), and this action is responsible for directing CE through the LDL receptor pathway (702). Besides these major routes holoparticle uptake of HDL may also contribute to delivery of HDL-CE to the liver. Hepatocytes, and many other cell types in other tissues, likely participate in HDL retro-endocytosis where apoA-I or HDL particles are taken up by endocytosis and resecreted without degradation in late endosomes and lysosomes (703,704). SR-BI and CD36 may participate in this process as well as other potential HDL receptors (705-707) . For example, the F0F1 ATPase and P2Y13 receptor have been reported to facilitate the uptake of the entire HDL particle (703,704,708,709). The liver then excretes both cholesterol and bile acids-derived from cholesterol into the bile which are removed from the body in feces, thus completing RCT from peripheral macrophages to bile through HDL and the liver (710). Recent evidence suggests there is also likely an HDL-independent pathway for systemic cholesterol removal through transintestinal cholesterol excretion (TICE) (711). Historically, HDL’s anti-atherogenic properties were largely attributed to HDL’s role in RCT and removing excess cholesterol from macrophages and peripheral tissues however, continually emerging alternative HDL functions likely significantly contribute to HDL’s protection against CVD.

    HDL Levels and Risk of CVD

    Historically, HDL-C was synonymous with the term HDL however, the amount of cholesterol in the HDL pool (HDL-C) and the number and quality of HDL particles (HDL-P) are independent concepts that are important to consider in the context of HDL function. Several decades of high-quality epidemiological studies have clearly shown that HDL-C levels are inversely correlated to CVD risk and events, independent of race, gender, and ethnicity (712). In well-controlled studies assessing CVD risk using multivariate approaches to adjust for covariates, both apoA-I and HDL-C are strong independent predictors of CVD risk (474). Nonetheless, HDL-C levels are also inversely correlated to insulin resistance, obesity, and triglycerides. As such, HDL-C’s causality in protection from CVD is difficult to define and is somewhat controversial, mainly due to epidemiological discrepancies between the dose-response of HDL-C levels to CVD outcomes. It is possible that HDL-C levels may simply be a biomarker for CVD and not play a causal role in atherosclerosis however, an increasing number of functional studies clearly support HDL’s functional relevance in biochemical mechanisms of atherosclerosis. In any case, epidemiological studies over the past 50 years have provided many insights into HDL-C and CVD risk. The first evidence came from the Framingham Heart Study in 1966 demonstrating a link between HDL-C and ASCVD (713). In 1975, HDL-C levels were found to be inversely associated with CVD in a Norwegian trial (Tromso Heart Study) (714). In subsequent years, the Honolulu Heart Study (1976) (715) and Framingham Heart Study (1977) (559) both reported that many CVD patients had low HDL-C levels. Over the years, low HDL-C levels have consistently been reported to be associated with increased risk of ASCVD and events (716-718). By the late 1980s and early 1990s, the relationship between HDL-C and CVD was generally accepted, as studies during this period established that low HDL-C levels were associated with CVD risk independent of other risk factors even in patients with normal total cholesterol levels (719-721).

    Clinical Outcomes Trials

    Prior to the statin-era, results from randomized controlled clinical trials suggested that increasing HDL-C levels 1 mg/dL or 1% reduces mortality from CVD by 3-4% (722,723). In the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS), treatment of men and women with average TC and LDL-C levels and below-average HDL-C levels with lovastatin (20-40 mg) reduced LDL-C by 25% and raised HDL-C 6%, resulting in a 37% reduction in the risk for the first major acute coronary event (724). These results showed that statin therapy was effective in reducing risk for CVE in subjects with low-HDL-C. The extent to which the benefit came from HDL-C raising is unclear. Studies completed in subjects on statins have yielded inconsistent results with regard to the importance of raising HDL-C partly due to evidence suggesting that statin (fluvastatin) use in low HDL-C subjects decreased coronary artery disease (CAD) with little to no increase in HDL-C levels (725-727). In the fluvastatin regression study, low HDL-C subjects on placebo showed increased disease (angiographic) progression compared to subjects with high HDL-C levels (727). Collectively, evidence from these and a large number of epidemiological studies overwhelmingly support a clear inverse association between HDL-C levels and CVD risk. This is demonstrated clinically as raising HDL levels through injections of reconstituted HDL (rHDL) resulted in atherosclerotic plaque regression, as determined by intravascular ultrasound (728). A number of animal studies clearly support the HDL-C hypothesis. For example, raising HDL in mice and rabbits consistently blocks atherogenesis( 729-731). However, raising HDL-C levels by mono- or combined therapy to reduce risk and events has proven challenging. Two major clinical outcomes trials of raising HDL with niacin failed to show a benefit. In subjects with CAD with LDL-C levels well controlled with a statin, the addition of extended release niacin in AIM-HIGH (732) and extended release niacin plus laropiprant (prostaglandin D2 receptor blocker to inhibit flushing) in HPS-2THRIVE (733) failed to reduce cardiovascular outcomes. However, structural limitations of the two Niacin trials design complicated their interpretation (734). In addition, major cardiovascular outcomes trials of 3 CETP inhibitors torcetrapib (735), dalcetrapib (736), evacetrapib have now failed to show a benefit in reducing cardiovascular events. More recently, the CETP inhibitor (anacetrapib) was tested in the REVEAL trial, which was a positive outcomes trial (737). However, the benefit of anacetrapib in reducing CVE seems to be largely explained by lowering of non-HDL, rather than increases in HDL-C (738). More recently, two recombinant apoA-I products MDCO-216 and CER001 showed no benefit in imaging studies (739,740). Collectively, the failure of these clinical studies has raised doubts about the HDL hypothesis. Indeed, raising HDL-C is presently not a primary target for therapeutic intervention. Nevertheless, HDL infusion in humans has been reported to improve endothelial function, which should contribute to inhibiting atherogenesis (741). At this time, HDL particle infusion therapies have not been proven to be an effective approach to reduce cardiovascular events (742) however, clinical trials with reconstituted HDL are still ongoing. Furthermore, recent studies indicate that HDL particle number and cholesterol efflux capacity are better indicators of CHD risk than HDL-C levels (743,744). The therapeutic targeting of HDL non-cholesterol cargo, quality, and function are emerging and gaining support, as HDL have many other biological properties that likely contribute to prevention of atherosclerosis and CVD (745). In addition, quantifying HDL function, including cholesterol efflux capacity, will provide a better risk index than steady-state HDL-C levels (746).

    Particle Number and Cholesterol Efflux

    A major blow to HDL causality in atherosclerosis comes from genetic studies. Mendelian disorders resulting in very low HDL-C levels have yielded conflicting data, as mutations to critical lipoprotein genes (e.g. apoA-I) were found to be associated with protection from atherosclerosis in one study (747) and increased risk in another study (748). The ApoA-I Milano mutation is associated with low levels of HDL-C and reduced risk of CVD (747). Infusion of recombinant apoA-I Milano was reported to induce regression of atherosclerosis (749), but there has not been clear progress in developing it as an approach to therapy since the initial regression study was published. The evidence that some genetic causes of low HDL-C are associated with increased risk for premature atherosclerosis, whereas others are not, supports the notion that HDL function may be more important than HDL-C levels. Nonetheless, Mendelian disorders of low HDL-C levels are rare, and thus the sample sizes in these studies are limited and it is difficult to draw accurate conclusions. To address this issue, genome-wide association studies were completed to attempt to resolve if HDL-C is a risk index or causal factor. These studies are limited in that many variants that raise or lower HDL-C levels also affect other lipoproteins, namely LDL-C levels. For example, variants in CETP raise HDL-C levels and reduce LDL-C levels, which complicates risk prediction based on HDL-C levels (750). Nevertheless, studies have found that variance solely associated with HDL-C levels is not linked to cardiovascular events. For example, single nucleotide polymorphisms (SNPs) in endothelial lipase (LIPG), which raises HDL-C levels, are not associated with decreased CVD(751).

    As failed clinical trials aimed at raising HDL-C levels and genetic studies do not uniformly support causality for HDL-C in CVD, HDL functional tests in future prospective studies will likely provide more resolution to HDL’s causal role in CVD. Cholesterol efflux capacity, a marker of HDL function, has been reported to be inversely associated with CVD risk independent of HDL-C levels (744,746). This was first demonstrated in a cross-sectional study using radio-tracing of cholesterol efflux (746). A subsequent study also found an inverse association between HDL efflux capacity and atherosclerosis, but reported a positive link to cardiovascular events (752). In a third study assessing HDL cholesterol efflux in a US cohort using a fluorescence method, efflux was again linked to decreased risk of CVD (743). Recently, HDL cholesterol efflux capacity was found to be inversely associated with CVD risk and events in a large nested case-control prospective study (n=3,494 subjects) from the EPIC-Norfolk Study (744,753). These associations were independent of many other co-founding factors, including HDL-C, T2DM, obesity, LDL-C, and age amongst others (744).

    In addition to HDL cholesterol efflux and functional indices as risk predictors, HDL particle number (HDL-P) has also been reported to provide biomarker potential. HDL-P numbers can be quantified using nuclear magnetic resonance (754) or calibrated ion mobility assays (755). HDL-P was found to be inversely associated with carotid intima medial thickness (cIMT) and coronary heart disease (CHD) independent of LDL particle numbers and HDL-C levels in the large multi-ethnic study of atherosclerosis (MESA) (756). Importantly, HDL-P remains inversely associated to CHD after adjusting for triglycerides and apolipoprotein B (apoB), thus suggesting that HDL-P is far superior to HDL-C levels as a biomarker of ASCVD and events (757,758). Furthermore, neither HDL-C levels nor HDL-P levels correlate to cholesterol efflux from macrophages therefore, the rate of cholesterol efflux is still critical to understanding RCT and HDL function. Likewise, HDL quality is more important than apoA-I levels, which also do not correlate with HDL function, e.g. RCT (759). Serum samples with identical apoA-I and HDL-C levels were found to have differing cholesterol acceptance capacities, mostly due to pre-beta HDL levels, which contributed to altered ABCA1-mediated cholesterol efflux (759). These studies strongly suggest that HDL function (cholesterol efflux capacity), as opposed to HDL-C, HDL-P, and apoA-I levels, provide a more important risk assessment and better predictor of future events as well as a more reasoned therapeutic target for reducing CVD risk and events. However, clinical assays for apoA-I and HDL-P are widely available and well-established, whereas assays for cholesterol efflux capacity have not been standardized and remain a research tool at present.

    HDL Composition and Analysis

    Historically, HDL have been isolated by density-gradient ultracentrifugation (DGUC) based on isopycnic equilibrium, and HDL have been defined by their density 1.063-1.21 g/mL since the 1950s (760,761). Based on mass, HDL can also be separated from other lipoproteins by size-exclusion chromatography (fast protein liquid chromatography, FPLC), and HDL’s molecular weight ranges from 175,000 - 360,000 Da (762). In addition to DGUC and FPLC, affinity chromatography can also be used to purify HDL from plasma using antibodies against apoA-I (763) or apoA-II, as HDL heterogeneity includes particles containing apoA-I:apoA-II (75%) or apoA-I only (25%) (763,764). Furthermore, asymmetric flow field-flow fractionation is now being used to isolate and characterize HDL (765). HDL can also be separated by non-denaturing gradient gel electrophoresis, e.g. polyacrylamide gel electrophoresis. Large HDL (HDL2, 8.8-12.9 nm in diameter) and small HDL (HDL3, 7.2-8.8 nm) are both α migrating particles (high negative charge), whereas pre-β HDL (5.4-7 nm) are β migrating particles for which they are defined. To quantify pre-β HDL particles, 2-D gel electrophoresis is often used to separate pre-β from mature HDL (766). HDL-P numbers can be quantified by either nuclear magnetic resonance spectroscopy or calibrated ion mobility assays. HDL can also be quantified and qualified by other methods, including vertical rotor ultracentrifugation, and transmission electron microscopy.

    HDL are very dynamic and should be acknowledged as a heterogeneous pool of sub-classes with differing sizes, shapes, densities, protein compositions, and lipid diversity. Lipid-free apoA-I is secreted from the liver and small intestine as an amphipathic helix, and it quickly becomes lipidated by ABCA1 to form pre-β HDL, which then becomes discoidal after accepting phospholipid and free cholesterol from hepatocytes and peripheral cells. Upon further lipidation and cholesterol accumulation and esterification, nascent spherical HDL forms that range 7-12 nm in diameter. Mature HDL contains 3-4 apoA-I molecules of which 1 remains on the particle and the other apoA-I are free to (dis)associate (exchange) on and off the particle with other HDL. This is predominantly associated with rearrangement of HDL’s aqueous phase and surface area (767). As such, HDL are in a constant state of remodeling and interconversion. Each spherical HDL particle has approximately 50-130 phospholipids, 10-50 free cholesterol molecules, 30-90 CE molecules, and 10-20 triglyceride (TG) molecules (536). Phosphatidylcholine makes up the largest amount of lipid on HDL (approximately 90%) however, over 200 species of lipids have been reported, including sphingolipids, acylglycerols, isoprenoids, glycerophospholipids, and vitamins (768,769). The HDL proteome has been extensively studied and there is a general consensus of approximately 80 proteins (770,771). In addition to apoA-I and apoA-II, HDL transports over a dozen other apolipoproteins, as well as many enzymes and other factors. HDL have also been found to transport small RNAs, namely microRNAs (miRNA), which were found to be altered in hypercholesterolemia and atherosclerosis (772,773). Most interestingly, HDL have been demonstrated to transport a wide-variety of exogenous non-host small RNAs, including rRNA and tRNA fragments derived from bacterial and fungal species present in the microbiome and environment (774).The size of HDL is determined by the amount of CE and triglyceride (TG) in the hydrophobic core, and HDL is generally separated into 5 sub-classes based on size. Distinct HDL sub-species have been associated with CVD risk, and the sub-species have differential biological functions, e.g. large HDL are less anti-inflammatory (775-777). Many of the cargo or components of HDL are enriched in the small HDL sub-class which provides many of the alternative functions to the total HDL pool (778,779). The concentration of all HDL particles in plasma is approximately 20 umol/L however, small HDL particles are the most abundant sub-class at approximately 10 umol/L. HDL are heterogeneous particles that transport a wide-variety of proteins, lipids, and nucleic acids, which confer many of HDL’s biological properties and beneficial functions in health and dysfunction in specific diseases.

    HDL Cell Signaling

    Many of HDL’s cellular functions – cell survival, proliferation, vasodilation -- are mediated by HDL-induced cell signaling cascades (780). As such, HDL can be characterized as hormone-like agonists. Although substantial work still remains in identifying HDL binding proteins and receptors on the cell surface, HDL have been found to activate many signaling cascades through various receptors. The most studied example of this is HDL’s ability to bind to the plasma membrane and through cell signaling mobilize cholesterol from intracellular stores in organelles to the plasma membrane for efflux. This has been attributed to HDL-induced activation of protein kinase C (PKC) (781). Specifically, apoA-I binds to ABCA1 and activates phosphatidylcholine lipases, which activate PKC leading to the movement of cellular cholesterol from intracellular stores to the plasma membrane for efflux, as well as PKC-mediated phosphorylation of ABCA1, which increases the transporter’s stability and efflux activity (782-784). This is a prime example of HDL-induced cell signaling that contributes to HDL cholesterol efflux capacity, which reduces the cholesterol burden for macrophages in the lesion, prevents foam cell formation, and antagonizes atherogenesis. Other HDL-induced signaling pathways that result in increased cholesterol and lipid efflux include protein kinase A (PKA) (785,786), cell division control protein 42 (Cdc42) (787), and Janus kinases-2 (JAK2) (788,789) cascades. HDL (i.e. apoA-I)-induced cell signaling through ABCA1 also suppresses macrophage M1 phenotype activation and pro-inflammatory cytokine production (Figure 10), and promotes M2 phenotype anti-inflammatory cytokine secretion (e.g. interleukin 10 (IL-10)) through JAK2 signaling and activation of signal transducer and activator of transcription 3 (STAT3) (75). In addition, the apoA-I:ABCA1:JAK2 axis was reported to suppress inflammation in endothelial cells through cyclooxygenase-2 (COX-2) activation leading to increased prostaglandins (PGI2), which also suppresses atherogenesis (790). HDL have also been reported to induce cell signaling through SR-BI. HDL binding to SR-BI’s extracellular loop was reported to trigger activation of SR-BI’s cytoplasmic C terminal domain leading to the phosphorylation of protein kinase Src and activation of both liver kinase B1 (LKB1) and calmodulin-dependent protein kinase (CAMK) (791,792). This results in cell signaling through downstream kinases – AMP-activated protein kinases (AMPK) (792), protein kinase Akt (791), and mitogen-activated protein kinase (MAPK)(791) – which ultimately regulates angiogenesis (ubiquitin ligase Siah (Siah1/2) and hypoxia-inducible factor 1α (HIF1α) (793)), insulin sensitivity (glucose transporter 4 (Glut4)(794)), re-vascularization (Rac1(795)), and vasodilation (COX(796), endothelial nitric oxide synthase (eNOS)(797,798)). Interestingly, macrophage SR-BI has recently been shown to mediate efferocytosis (phagocytosis of dead cells) in the setting of atherosclerosis via a Src/Akt/Rac1 signaling pathway, reducing necrosis in lesions (185). All of these downstream effects contribute to HDL function, and to a lesser degree atherogenesis.

    The most robust HDL signaling activation is mediated by bioactive lipids on HDL, namely the lysosphingolipid sphingosine-1-phosphate (S1P). A majority of S1P in circulation is associated with HDL, and HDL-S1P activates the G-coupled S1P receptors (S1P1-5) on the surface of many vascular cell types, including macrophages, endothelial cells, and smooth muscle cells. Activation of S1P1 and S1P2 receptors turns on a host of signaling cascades and factors that directly contribute to the many anti-atherogenic properties of HDL, including increasing endothelial barrier function (799) and angiogenesis (800,801) while decreasing inflammation (802) and apoptosis (803). HDL were also found to inhibit smooth muscle migration through S1P signaling, a key factor in restenosis and plaque development (804). All of these are critical processes to atherogenesis. In support of these studies, subjects with CAD were found to have decreased HDL-S1P levels (805). The key terminal effector factors in these G-protein receptor signaling cascades are focal adhesion kinase (FAK), nuclear factor κ beta (NF-㮫), nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, eNOS, STAT3, and B-cell lymphoma-extra-large (Bcl-xl) (780). This HDL-S1P signaling pathway has also been linked to vasorelaxation (806) and cytoprotection (e.g. cardiomyocytes) (807). In addition to these direct pathways, HDL also likely activates cell signaling indirectly through ATP (β-ATPase/P2Y12/13)(808) or toll-like receptors (809). Collectively, HDL induced cell signaling in vascular and inflammatory cells underlies HDL’s anti-atherogenic properties in health, and deficits in HDL signaling likely link HDL dysfunction in metabolic diseases to increased risk of atherosclerosis.

    Anti-Inflammatory HDL

    Outside reverse cholesterol transport, HDL’s anti-inflammatory properties have been the most extensively studied HDL function and likely play a large role in HDL’s anti-atherogenicity (Figure 10). HDL’s anti-inflammatory properties are conferred by numerous mechanisms in many types of cells. In addition to providing the vascular barrier, endothelial cells control vascular inflammation through expressing adhesion molecules that aid in monocyte adhesion and ultimate migration into the atherosclerotic lesion. Moreover, activated endothelial cells secrete cytokines and recruit monocytes through chemokine release. The induction of adhesion molecules, cytokines, and chemokines in activated endothelial cells is largely due to NF-㮫 transcriptional activation. In humans, injection of apoA-I resulted in decreased adhesion molecule expression in atherosclerotic plaques (810). One mechanism by which HDL suppresses endothelial cell and monocyte activation is through inhibiting NF-kB activity by attenuating IkB kinase activity (811). Nonetheless, HDL decreases adhesion molecule expression through multiple mechanisms. Cells pre-treated with HDL or apoA-I are protected from TNFα or oxidized LDL (oxLDL)-induced adhesion molecule expression. In addition, HDL binding to SR-BI may also contribute to inhibition of adhesion molecule expression, as SR-BI-mediated Akt activation promoted heme oxygenase-1 expression. In addition, up-regulation of 3-beta-hydroxysteroid-delta 24 (DHCR24) by HDL binding to SR-BI was reported to underlie HDL’s ability to suppress adhesion molecules (812). Furthermore, HDL suppression of intracellular adhesion molecule-1 (ICAM-1) in endothelial cells was found to be mediated, in part, through the transfer of miR-223 to recipient cells (773). Recent studies also suggest that TGFβ and AMPK also contribute to HDL’s suppression of adhesion molecule expression (813).

    In addition to HDL’s profound effects on vascular endothelium, HDL suppresses myelopoiesis, monocyte recruitment, macrophage activation, proliferation, and emigration from atherosclerotic lesions. Similar to its impact on endothelial cells, HDL also suppresses adhesion molecule expression in monocytes, which inhibits monocyte adhesion and migration to atherosclerotic lesions (814). HDL and apoA-I were demonstrated to suppress CD11b expression on human monocytes through both ABCA1-dependent and independent mechanisms (814). HDL inhibition of monocyte activation, which includes suppression of cytokines and adhesion molecules, is mediated through both peroxisome proliferator-activated receptor gamma (PPARγ) and NF-kB transcription factors (815). Suppression of chemokine and cytokines in myeloid cells inhibits infiltration and migration of circulating monocytes, and thus antagonizes atherosclerosis. HDL have also been reported to mediate macrophage reprogramming through the transcription factor ATF3 that reduces Toll-like receptor signaling (816). Importantly, much of HDL’s (and apoA-I’s) inhibition of macrophage activation is mediated through altering cholesterol levels in plasma membrane lipid rafts through cholesterol efflux mediated by ABCG1/SR-BI and ABCA1 however, apoA-I induced signaling through ABCA1 and the JAK/STAT pathway independent of cholesterol efflux may also contribute to HDL’s effect, as described above (75,817) (814,818). HDL have also been demonstrated to promote macrophage emigration through removing excess cholesterol and induction of signaling pathways (208). In addition to HDL’s impact on monocytes and macrophages, HDL also strongly suppresses neutrophil activation and vascular smooth muscle cell secretion of monocyte chemoattractant protein-1 (MCP1) (819).

    In addition to HDL’s roles in innate immunity, recent evidence suggests that HDL play multiple roles in adaptive immunity (820). Mice lacking apoA-I develop autoimmunity when challenged with a high cholesterol (diet and background, Ldlr -/- ), which includes T cell activation and production of autoantibodies (821,822). This phenotype was rescued by apoA-I injections. HDL have also been reported to repress both antigen-presenting cell (APC) activation of T cells and T cell activation of monocytes, thus preventing the secretion of proinflammatory cytokines and chemokines (Figure 10) (823,824). ApoA-I also prevents the phenotypic switching of T-regs into pro-inflammatory follicular helper T cells during atheroprogression (92). Moreover, cholesterol efflux to HDL and apoA-I have been reported to suppress myelopoiesis and proliferation of myelopoietic stem and progenitor cells, as loss of function for both Abca1 and Abcg1 in mice resulted in increased myelopoiesis (820). Injection of apoA-I was also found to rescue this phenotype (825). In addition, HDL and cholesterol efflux were reported to suppress megakaryocyte progenitor proliferation, platelet levels, and thrombocytosis (826). Collectively, HDL and apoA-I inhibit circulating levels of hematopoietic progenitor cells, monocytes, neutrophils, and platelets all of which contribute to HDL’s capacity to limit inflammation and atherosclerosis.

    Antithrombotic HDL

    Another anti-atherogenic function of HDL is the capacity to directly and indirectly inhibit platelet activation, aggregation, and thrombus formation (Figure 10). HDL-C levels were found to be inversely associated with thrombus formation in humans (827). HDL is required to remove excess cholesterol from the plasma membrane of platelets for proper function, and platelets isolated from mice lacking SR-BI to mediate cholesterol efflux to HDL were found to be more susceptible to activation (828,829). Both HDL and cyclodextrin-mediated cholesterol efflux were found to inhibit platelet aggregation (828). However, HDL-induced cell signaling through binding to glycoprotein IIb/IIIa on the surface of platelets was reported to activate phospholipase C (PLC) and PKC, thus leading to flux through the Na+/H+ antiport system (717). This pathway can result in alkalization of the cytoplasm and calcium release, which can reduce platelet activation (830). Furthermore, HDL dose-dependently inhibits stimulated platelet activation, which leads to reduced platelet aggregation, granule secretion and fibrinogen binding. In rats, apoA-I injections inhibited thrombus formation and reduced thrombus mass (831). HDL’s anti-thrombotic effects are also mediated, in part, through HDL’s ability to inhibit tissue factor and factors X, Va, and VIIIa (Figure 10) (832). HDL also prevents thrombus formation through cell signaling and nitric oxide (NO) production in endothelial cells (828), and suppression of tissue factor and platelet-activating factor expression in endothelial cells (833,834). HDL also reduces erythrocyte influence on thrombus formation (835). Collectively, HDL has multiple biological mechanisms that inhibit thrombus formation, and thus, contribute to HDL’s anti-atherogenic properties.

    Pro-Vasodilatory HDL

    The endothelium significantly contributes to vascular tone, and HDL confer protection against endothelial cell activation, apoptosis, and loss of barrier function, which is critical to atherogenesis. HDL have been reported to induce endothelium-dependent vasodilation in aortic rings (806), and individuals with low HDL have reduced endothelium-dependent vasorelaxation (Figure 10) (741). HDL’s benefit to endothelial cells is largely mediated by cell signaling through phosphatidylinositol 3-kinase (PI3K) and Akt and is induced by bioactive lipids and associated proteins on HDL, including lysosulfatide, S1P, and sphingosylphosphorylcholine (SPC) (791,798,806). A key outcome of HDL-induced cell signaling is the production of NO (Figure 10) through both signaling induced phosphorylation of eNOS and increased eNOS expression (791,836). HDL can trigger eNOS-phosphorylation through SR-BI, S1P receptor (S1P1-5), and ABCG1-mediated cholesterol efflux (806,837). HDL-induced NO underlies many of HDL’s beneficial properties to endothelial cells, including HDL-induced vasodilation, tightening of cell-to-cell junctions and increased barrier function, differentiation of endothelial progenitor cells, cell survival and proliferation, cell migration, inhibition of apoptosis, and suppression of adhesion molecule expression. In addition, HDL also has NO-independent properties on endothelial cells, including induced proliferation, increased barrier function, suppressed inflammation and decreased apoptosis (838). These studies clearly define a beneficial role for HDL in vascular integrity, which underlies HDL protection against atherosclerosis.

    Anti-Apoptotic HDL

    HDL have multiple anti-apoptotic properties that enhance cell survival (Figure 10). By various metrics, HDL support mitochondrial function and prevent the release of apoptotic signals, including cytochrome C (205,839). Moreover, HDL drives the expression of Bcl-xl, which is a strong anti-apoptotic factor and suppresses Bid, which is a pro-apoptic protein (839,840). HDL mediates these gene expression changes through cell signaling and NO production through activation of surface receptors by HDL-associated proteins and bioactive lipids, including apolipoprotein J (apoJ) and S1P (803,840). In addition, there are likely alternative anti-apoptotic mechanisms resulting from HDL-induced signaling. Nonetheless, HDL has been demonstrated to suppress apoptosis in endothelial cells (Figure 10) activated with tumor necrosis factor (TNFα) and oxLDL (839,841,842). HDL proteins (apolipoprotein M, apoM) and apoM-binding lipids (S1P) contribute to HDL’s ability to increase tight junctions and endothelial cell survival (843). Mice deficient in apoM have reduced S1P levels and loss of endothelium barrier function (843). HDL’s ability to support the endothelium barrier function is a key feature of its anti-atherosclerosis properties and represents a classic example of HDL’s control of cellular gene expression and phenotype that are beneficial to vascular health. However, HDL also have many capacities in the extracellular space (e.g. plasma) that protect against atherosclerosis.

    Anti-Oxidative HDL

    A key factor in monocyte activation and chemotaxis in the vascular wall is the accumulation of oxLDL, which is more pro-inflammatory and pro-atherogenic than unmodified LDL. LDL can become oxidized by a variety of endogenous mechanisms (844). In the vascular wall, LDL can be modified (oxidized) by many cell types, including vascular smooth muscle cells, endothelial cells, and macrophages (776). Remarkably, HDL prevents the oxidation of LDL (Figure 10) and recent evidence suggests that this may occur through 4 distinct proteins circulating on HDL – apoA-I (845,846), LCAT (847), lipoprotein-associated phospholipase A2 (Lp-PLA2)(848,849), and paraoxonase 1 (PON1) (430,846). First, HDL can simply soak up oxidized lipids or oxidizing factors from cells preventing their association with LDL and their modification of LDL lipids and proteins. In addition, HDL removes lipid hydroperoxides from LDL particles (846). Specifically, small apoAI containing HDL particles are the most efficient at accepting lipid hydroperoxides, which are reduced to their inactive lipid hydroxides via oxidation of the methionine residues in apoA-I (850). Compared to apoA-II the methionine residues in apoA-I are more conformationally conducive to reducing lipid hydroperoxides (851,852). In addition, HDL with low surface free cholesterol and sphingomyelin are more efficient at accepting lipid hydroperoxides (745,853). The capacity of HDL to prevent oxidation via this mechanism is also maintained by the selective removal of HDL lipid hydroperoxides and hydroxides by hepatocyte SR-BI (854). In addition, ApoA-I methionine sulfoxide is reduced to methionine by methionine sulfoxide reductases.(850). LCAT circulates on HDL and has also been reported to block LDL oxidation, as LCAT over-expression in mice reduced LDL oxidation as determined by reduced LDL autoantibodies (855). Lp-PLA2 appears to be pro-atherogenic on LDL and anti-atherogenic on HDL (856). Its activity on HDL likely contributes to HDL’s anti-oxidative capacity, as inhibition of HDL-associated Lp-PLA2 attenuated HDL’s ability to block LDL oxidation (848). The strongest anti-oxidative HDL protein is likely PON1. Over-expression of PON1 in mice confers enhanced HDL anti-oxidative capacity, and PON1 itself prevents LDL oxidation in vitro (432). Most importantly, HDL isolated from mice lacking PON1 have reduced ability to prevent LDL oxidation. HDL’s anti-oxidative capacity likely plays a large role in preventing inflammation and atherogenesis, and like many of the other alternative functions, confer HDL’s beneficial role in health.

    HDL Intercellular Communication

    HDL also likely participate in intercellular communication through the transfer of nucleic acids between tissues. Recently, HDL have been reported to transport miRNA (Figure 10), which are small non-coding RNAs that suppress gene expression through binding to complimentary target sites in the 3’ untranslated region of mRNAs, and thus inhibit translation and induce mRNA degradation (772). Most interestingly, the HDL-miRNA profile is significantly altered in hypercholesterolemia and atherosclerosis (772). miRNAs have been reported to be exported from macrophages to HDL, and HDL has been demonstrated to transfer specific miRNAs to recipient hepatoma cells (Huh7) and endothelial cells, likely through HDL’s receptor SR-BI (773). In endothelial cells, HDL was found to deliver miR-223 to recipient cells, where it directly targeted intracellular adhesion molecule-1 (ICAM-1) expression (Figure 10), and thus inhibited neutrophil adhesion to the cells (773). miR-223 is not transcribed or processed in endothelial cells and HDL delivery of mature miR-223 to endothelium likely confers, in part, HDL’s anti-inflammatory capacity associated with adhesion molecule suppression. Future studies are needed to determine the physiological relevance and functional impact of HDL-miRNAs in humans and animal models in the context of atherosclerosis and other inflammatory diseases.

    Anti-Infectious HDL

    HDL also contributes to innate immunity by modulating immune cell function. However, this hypothesis has not been extensively studied in the context of atherosclerosis. HDL are anti-infectious, anti-parasitic, and anti-viral. HDL have the unique capacity to prevent endotoxic shock and readily binds to lipopolysaccharides (LPS) and contributes to removing LPS through biliary excretion thus aiding innate immunity (857-859). Amongst the many proteins that circulate on HDL, apolipoprotein L1 (apo-L1) (also known as trypanosome lytic factor) is present in specific sub-classes of HDL (860,861). This factor kills Trypanosome brucei and Trypanosome brucei rhdesiense, parasites that cause sleeping sickness, through creating ionic pores in endosomes (860-862). Although promising, future studies are required to define how HDL regulation of innate immunity contributes to the inhibition of atherogenesis.

    HDL Dysfunction

    HDL confer many anti-atherogenic properties that are lost in atherosclerosis and other inflammatory and metabolic diseases. These include 9 key processes –


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