Antibody class switching is a biological mechanism that changes a B cell's production of antibodies from one class to another, such as from IgM to IgG. It involves changing the constant region of the antibody heavy chain through DNA recombination, while retaining the same variable region and antigen specificity. Double-stranded breaks are generated in switch regions of DNA upstream of constant region genes. DNA is cleaved at two switch regions and the intervening DNA is deleted, allowing substitution of a different constant region exon and changing the class of antibody produced. The free DNA ends are joined by non-homologous end joining to link the variable region to the new constant region gene. This allows a B cell to produce different antibody classes while recognizing
CLONAL SELECTION THEORY IS AN SCIENTIFIC THEORY IN IMMUNOLOGY THAT EXPALINS THE FUNCTION OF CELLS OF THE IMMUNE SYSTEM IN RESPONSE TO SPECIFIC ANTIGEN INVADING THE BODY.
ANTIGEN, HAPTEN, ALL TYPES OF ANTIGENS, IMMUNOGEN , ATTRIBUTES OF ANTIGENICITY, DETERMINANTS OF ANTIGENICITY,
IMMUNOLOGY KUBY, MEDICAL MICROBIOLOGY & IMMUNOLOGY OF PANIKER , LIPPINCOTT'S IMMUNOLOGY, OTHER SOURCES.
The ppt covers the following topic-
1.Introduction about antibody.
2. Types of antibody.
3.Genetic basis of antibody diversity.
4. Antibody diversity.
5.Light chain gene segment.
6. Mechanism of variable region DNA rearrangment.
7. Heavy chain gene segment.
8.Alternate splicing.
Antibodies are immune system-related proteins called immunoglobulins. Each antibody consists of four polypeptides– two heavy chains and two light chains joined to form a "Y" shaped molecule. ... This variable region, composed of 110-130 amino acids, give the antibody its specificity for binding antigen.
One of the important parts in the study of Immunology.I prepared it for the sake of a seminar series competition conducted in my university. Now I thought of sharing it with others.
CLONAL SELECTION THEORY IS AN SCIENTIFIC THEORY IN IMMUNOLOGY THAT EXPALINS THE FUNCTION OF CELLS OF THE IMMUNE SYSTEM IN RESPONSE TO SPECIFIC ANTIGEN INVADING THE BODY.
ANTIGEN, HAPTEN, ALL TYPES OF ANTIGENS, IMMUNOGEN , ATTRIBUTES OF ANTIGENICITY, DETERMINANTS OF ANTIGENICITY,
IMMUNOLOGY KUBY, MEDICAL MICROBIOLOGY & IMMUNOLOGY OF PANIKER , LIPPINCOTT'S IMMUNOLOGY, OTHER SOURCES.
The ppt covers the following topic-
1.Introduction about antibody.
2. Types of antibody.
3.Genetic basis of antibody diversity.
4. Antibody diversity.
5.Light chain gene segment.
6. Mechanism of variable region DNA rearrangment.
7. Heavy chain gene segment.
8.Alternate splicing.
Antibodies are immune system-related proteins called immunoglobulins. Each antibody consists of four polypeptides– two heavy chains and two light chains joined to form a "Y" shaped molecule. ... This variable region, composed of 110-130 amino acids, give the antibody its specificity for binding antigen.
One of the important parts in the study of Immunology.I prepared it for the sake of a seminar series competition conducted in my university. Now I thought of sharing it with others.
From studies and predictions such as Dreyer and Bennett's, it shows that the light chains and heavy chains are encoded by separate multigene families on different chromosomes. They are referred to as gene segments and are separated by non-coding regions. The rearrangement and organization of these gene segments during the maturation of B cells produce functional proteins. The entire process of rearrangement and organization of these gene segments is the vital source where our body immune system gets its capabilities to recognize and respond to variety of antigens.
This presentation aims to describe the variability present in antibodies.what the Ig superfamily have in common and the various functions it performs.Role of different enzymes imparting diversity to the variable region has been covered.
Structure and function of immunoglobulins(antibodies) Likhith KLIKHITHK1
Immunoglobulins (Ig) or antibodies are glycoproteins that are produced by plasma cells. B cells are instructed by specific immunogens.For, example, bacterial proteins, to differentiate into plasma cells, which are protein-making cells that participate in humoral immune responses against bacteria, viruses, fungi, parasites, cellular antigens, chemicals, and synthetic substances.
The immunogen or antigen reacts with a B-cell receptor (BCR) on the cell surface of B lymphocytes, and a signal is produced that directs the activation of transcription factors to stimulate the synthesis of antibodies, which are highly specific for the immunogen that stimulated the B cell. Furthermore, one clone of B cell makes an immunoglobulin (specificity). Besides, the immune system remembers the antigens that caused a previous reaction (memory) due to the development of memory B cells. These are intermediate, differentiated B cells with the capability to quickly become plasma cells. Circulating antibodies recognize antigen in tissue fluids and serum. This activity describes the physiology and pathophysiology of immunoglobulins
V(D)J rearrangements and Antigen Antibody interactionsTathagat Sah
This presentation discusses the concept of the generation of antibody diversity through immunoglobulin(Ig) gene rearrangements. The mechanism of V-J rearrangements (light chain) and V-D-J rearrangements (heavy chain) is elaborated in detail along with the Ig constant region rearrangements, called class-switching. The factors dictating antibody diversity and antigen-antibody interactions have also been discussed briefly.
Generation of Antibody Diversity- Quick revision from Kuby through presentationSharmistaChaitali
Immunology, Kuby's fifth edition notes for strong background in the topic, General introduction, Types of Antibody and Structure, Experiments, Mechanisms
General structure of Antibody and its functions pptRenukaR17
This presentation explains the general structure of immunoglobulins, action of papain, pepsin and mercaptoethanol on the structure of Igs and its functions.
02.11.09(a): Joining Variable & Constant Region GenesOpen.Michigan
Slideshow is from the University of Michigan Medical
School's M1 Immunology sequence
View additional course materials on Open.Michigan:
openmi.ch/med-M1Immunology
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
2. What Is Ab Class Switching?
• a biological mechanism that changes a B cell's production
of immunoglobulin (antibodies) from one class to another,
such as from the isotype IgM to the isotype IgG.
• the constant-region portion of the antibody heavy chain is
changed, but the variable region of the heavy chain stays the
same.
• the variable region does not change, class switching does not
affect antigen specificity. Instead, the antibody
retains affinity for the same antigens, but can interact with
different effector molecules.
CHARLIE 2
6. Ab Class Switch in B-Cell by Cytokine
mediated Response.
CHARLIE 6
(from CD4 T cells)
7. Processing of Genes Re-arranged.
• Double-stranded breaks are generated in DNA at conserved nucleotide motifs,
called switch (S) regions, which are upstream from gene segments that encode the
constant regions of antibody heavy chains; these occur adjacent to all heavy chain
constant region genes with the exception of the δ-chain.
• DNA is nicked and broken at two selected S-regions by the activity of a series
of enzymes, including Activation-Induced (Cytidine) Deaminase (AID), uracil DNA
glycosylase and apyrimidic/apurinic (AP)-endonucleases. The intervening DNA
between the S-regions is subsequently deleted from the chromosome, removing
unwanted μ or δ heavy chain constant region exons and allowing substitution of a
γ, α or ε constant region gene segment.
• The free ends of the DNA are rejoined by a process called non-homologous end
joining (NHEJ) to link the variable domain exon to the desired downstream
constant domain exon of the antibody heavy chain. In the absence of non-
homologous end joining, free ends of DNA may be rejoined by an alternative
pathway biased toward microhomology joins. With the exception of the μ and δ
genes, only one antibody class is expressed by a B cell at any point in time.
CHARLIE 7
Immunoglobulin class switching, also known as isotype switching, isotypic commutation or class-switch recombination (CSR), is a biological mechanism that changes a B cell's production of immunoglobulin (antibodies) from one class to another, such as from the isotype IgM to the isotype IgG. During this process, the constant-region portion of the antibody heavy chain is changed, but the variable region of the heavy chain stays the same (the terms "variable" and "constant" refer to changes or lack thereof between antibodies that target different epitopes). Since the variable region does not change, class switching does not affect antigen specificity. Instead, the antibody retains affinity for the same antigens, but can interact with differenteffector molecules.
Joining can occur anywhere within the large, repetitive
S-regions. There is no obvious conserved sequence motif.
Unlike V(D)J recombination, this gene splicing occurs
between, rather than within, coding sequences.
Like V(D)J recombination, targeting of elements for
rearrangement is correlated with prior “sterile transcription”
Recombination is targeted by DNA "accessibility" that is controlled by sterile transcription starting at the I-region.
I region transcription is regulated by short and long range cytokines provided mainly by T cells.
The specificity of the cytokines determines the H-chain class to be used, and hence, the effector function.
Like V(D)J recombination, class switch recombination is regulated by
1) expression of a recombination machine
2) targeted “accessibility” mediated by nearby enhancers and promoters
