Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_2796_Библиотеки_им_академика_М_И_Перельмана
.pdf
236 ◾ Sys te ms Med icine
Age of Onset
gY
Circuit Motifs in the Periodic Table
Number of cells
ø = Cell death
T=Autoimmune T cell
S = Stem cell
h = Secreted molecule
s = Signal molecule
https://t.me/medicina_free
Permanent
11
>10
Front-line
years
Turnover
Secretory
years-months
Barrier
months-days
10
11
SAD
ø
s
10
10
age
x
9
10
8
10
7
10
X= Damage
SD
ø ø
D = Differentiated cell
A =Transit amplifying cell
FIGURE 9.17 Each class of diseases in the periodic table corresponds to a specic cell circuit motif.
D
h
T
s
Dh
SAD
ø
FIGURE 9.18 Age of onset shows patterns in the periodic table of diseases. Disease age of onset
from youngest to oldest ranks as immune hypersensitivity, autoimmune, toxic adenoma, progressive brotic and degenerative diseases.
Very
Old
Old
Youn
Old
oung
Total Lifetime Risk by Disease Class
https://t.me/medicina_free
Very common
40%
Periodic Table of Diseases ◾ 237
Universal
>90%
Very common
50%
Common
5%,
Rare
1%,
Very
common
10%
FIGURE 9.19 Disease prevalence shows patterns in the periodic table of diseases. Shown are typi-
cal values, some diseases are exceptions.
Current therapy approaches
Permanent
Front-line
years
Secretory
years-months
Barrier
months-days
Surgery, Chemothrapy,
Biologics, CAR-T cells
Immune checkpoint
inhibition
No Cure
Replacement
FIGURE 9.20 Current treatments for each class of diseases.
In this book we described several potential treatment strategies for the future
(Figure 9.21). Age-related diseases can be treated by senolytics or other approaches that
slow the core drivers of aging (Zhang et al. 2022). is is the Geroscience hypothesis, that
many age-related diseases can be treated at once by targeting their main risk factor, aging.
Organ
Hormone
Supplement
Surgical
Removal
Cytokine
inhibitors
238 ◾ Sys te ms Me dicine
https://t.me/medicina_free
Future therapy discussed in this book
Permanent
Senolytics
FIGURE 9.21 Possible future treatments for each class of diseases discussed in this book.
Front-line
years
Senolytics,
disruption of
Secretory
years-months
Modulate
autoimmune
surveillance
Barrier
months-days
Microenvironment
circuit disruption
Progressive brotic diseases may be treatable by disrupting the brosis circuit, such as
inhibition of the myobroblast autocrine loop of Chapter 5. is is an example of therapy
that aims to modulate ecosystems of interacting cell populations by taking advantage of
their most sensitive interactions. An adaptation of this strategy may target the cell populations that make up the cancer microenvironment.
e mutant surveillance theory for autoimmune diseases suggests that the autoimmune
surveillance T-cells may have special “license to kill” properties that could make them
potential targets for therapy. erapeutic approaches for a given disease could be adapted
for diseases that are adjacent on the table.
PREDICTED DISEASES IN THE TABLE
We can continue with the periodic table metaphor and look for unknown diseases which
it predicts.
If we see a secretory cell type in the 1–10 g range, we can predict a T-cell-based autoimmune disease. One place to look is in classes of immune cells that secrete alarm signals
(Figure 9.22).
For example, plasmacytoid dendritic cells – pDC cells for short – are the main source of
the alarm signal interferon-1, which causes cells to raise their defenses against pathogens.
e pDC cells sense pathogens using an innate immune receptor (TLR7), which controls
their growth and interferon secretion, in a “secrete-and-grow” circuit (Figure 9.22).
threat
https://t.me/medicina_free
Periodic Table of Diseases ◾ 239
pDC
kill
Surveillance
against
hypersecreting
mutant cells
cells
Pathogen
pDC
Pathogen
T
007
Transition to
autoimmune
IFN1
sense
disease
alarm
IFN1
B cells
anti-IFN1
antibodies
FIGURE 9.22 A predicted autoimmune disease may explain anti-interferon autoantibodies. A cir-
cuit in which a signal aects both growth and function of an immune cell is fragile to mutants that
hyper-sense the signal. ese mutant cells can expand and raise a perpetual alarm. An example is
pDC cells that secrete the alarm signal interferon. e autoimmune surveillance theory suggests
that T cells can remove hyper-secreting mutant pDC cells by sensing an interferon antigen, with
the potential to set o adiseasein which B cells produceanti-interferon autoantibodies.
e pDC cell type is in the 1g range, predicting an autoimmune disease analogous to
type-1 diabetes. e surveillance T-cells should target interferon-1 peptides, and in the predicted disease should activate B cells to make antibodies against interferon-1 (Figure 9.22,
bottom panel).
Indeed, about 1% of humanity has anti-interferon-1 antibodies. ese individuals have
defects in their response to infection, such as an increased risk for severe COVID-19
(Manry et al. 2022).
ere may thus be a yet unnamed T-cell-based autoimmune disease against pDC, as
a fragility of surveillance against hyper-secreting pDC clones that would put the body in
perpetual alarm (Figure 9.23).
We may predict analogous autoimmune diseases for other types of immune cells. ese
diseases may explain the prevalence of anti-cytokine antibodies which are currently a mystery.
Another class of predicted diseases concerns toxic adenomas. If we see a secretory cell
type with about 10
8
adenoma, a mutant-expansion disease.
cells (0.1 g) or fewer, we can guess it might show a hyper-secreting
240 ◾ Sys te ms Me dicine
https://t.me/medicina_free
Permanent
11
>10
11
10
10
10
9
10
Number of cells
8
10
Predicted Diseases
Tu rnover
Years
Front-line
Pancreas
Alpha cells
Predicted toxic
adenoma
Years-months
Secretory
Plasma pDCs
Predicted
autoimmune
disease
Months-days
Barrier
7
10
FIGURE 9.23 Examples of predicted diseases in the periodic table of diseases.
As an example, pancreatic alpha cells are in this range. Alpha cells are in the islets
together with beta cells. ey secrete glucagon, the counter-hormone to insulin, which
stimulates the liver to produce glucose out of amino acids, raising blood glucose levels.
e periodic table predicts a mutant-expansion disease of alpha cells at old age, causing excess glucagon, perhaps with a prevalence of around 0.1%. e symptoms should be
similar to type-2 diabetes: excess glucose. Such expansions might contribute to diabetes in
a small fraction of cases.
A brief technical note: the predicted toxic adenomas are distinct from ultra-rare cancers called neuroendocrine tumors (in this case, glucagonomas). Neuroendocrine cancer is
caused by oncogenic mutations that make the cells grow uncontrollably, whereas toxic adenomas remain under control of their size-control circuit. e toxic adenoma cells behave
like normal endocrine cells responding to high signal, due to the “delusion” caused by their
hyper-sensing mutation.
Periodic Table of Diseases ◾ 241
https://t.me/medicina_free
Another example is a cell-type in the kidneys that secretes renin, a hormone that
8
raises blood pressure. ere are about 10
such renin-secreting cells in the kidneys,
called juxtaglomerular cells. e predicted toxic adenomas should cause hypertension
– chronically high blood pressure. ey might explain a small part of this prevalent
condition.
is discussion raises the possibility of cell-type-specic diseases that are under the
radar. ese diseases go undetected because their symptoms are subclinical in most conditions. Or perhaps they go undetected because their symptoms are too similar to a common
condition like diabetes, hypertension or obesity. e latter possibility suggests ways to nd
new causes for common pathologies, operative in a small number of cases. Such cases may
be unresponsive to the standard treatments for these conditions.
e book is almost at its end! To celebrate, let’s take a nice deep sigh of relief. I hope that
you enjoyed our journey. We stated basic physiological laws and used them to dene circuit
motifs. ese circuits carry out essential functions like organ size control. ey also provide
robustness to variations in physiological parameters. However, they have fragilities which are
the basis for diseases. e circuits dene disease classes that can be organized in a periodic
table. I wonder what additional patterns and unifying principles await to be discovered.
EXERCISES
Exercise 9.1: Comorbidity
Suppose that an individual is susceptible to two age-related diseases In the model of
X
Chapter 8, the diseases have thresholds
a. Is there a predicted order in which the diseases occur in a given individual?
b. Design a computational experiment to discern such ordering in a medical dataset
containing the age of onset of diseases in a large number of individuals.
c. What might be confounding factors in such an attempt to discern ordering?
Exercise 9.2: Disease networks
Diseases can be arranged in networks, where two diseases are linked if they share genetic
risk factors or phenotypic features (Barabási, Gulbahce and Loscalzo 2010). Compare this
approach to the periodic table. What are its main merits?
Exercise 9.3: Female-male prevalence
Most autoimmune diseases in the secretory cell column are more common in females than
males (McCombe and Greer 2014). Most toxic adenomas in the same column are also more
prevalent in females. Form a hypothesis for how the autoimmune surveillance mechanism
of Chapter 4 may contribute to the origin of a higher female prevalence. Note the fact that
many of the endocrine glands involved undergo enhanced cell division during pregnancy
and female reproductive cycles.
and Xd2.
d1
242 ◾ Syste ms Med icine
https://t.me/medicina_free
Exercise 9.4: Biphasic mechanism
We discussed how plasma dendritic cells (pDCs) may harbor a predicted autoimmune disease. ese cells show an exhaustion mechanism in which they down-regulate themselves
if their receptor is activated for long times (Macal et al. 2018). Discuss this in analogy to
biphasic mechanisms like glucotoxicity of Chapter 2.
Exercise 9.5: Rare genetic disorders
Read about cell-type-specic diseases caused by mutations in the fertilized egg, such as
cystic brosis and Duchenne muscular dystrophy. How would you add them to the table?
Do they tend to conform to the disease class of their cell type, or to dierent classes?
Exercise 9.6: Atherosclerosis master exercise
Read about the mechanisms of atherosclerosis. In atherosclerosis, macrophages and other
white blood cells accumulate at a point of inammation in the artery wall. ey recruit
smooth muscle cells to proliferate and secrete bers in a way analogous to myobroblasts.
e macrophages ingest cholesterol-carrying particles (LDL) which get oxidized by the
inammatory environment, and these oxidized LDL particles further stimulate inammation, macrophage recruitment and macrophage senescence. e upshot is a brous and
fatty plaque that can grow and eventually block the artery.
a. Write a cell circuit analogous to the brosis circuit of Chapter 5.
b. Explain why LDL levels in the blood are a risk factor for atherosclerosis, and derive a
mathematical expression for this eect.
c. Explain how senescent cells with their inammatory factors could push blood vessels
across a threshold for atherosclerosis.
d. Explain the exponential incidence of vascular disease with age along the lines of the
disease-threshold model of Chapter 8.
REFERENCES
Barabási, A. L., N. Gulbahce, and J. Loscalzo. 2010. “Network Medicine: A Network-Based Approach
to Human Disease.” Nature Reviews Genetics 12 (1): 56–68. doi: 10.1038/nrg2918.
Cagan, A., A. Baez-Ortega, N. Brzozowska, F. Abascal, T.H. Coorens, M.A. Sanders, A.R. Lawson,
L.M. Harvey, S. Bhosle, D. Jones, and R.E. Alcantara. 2022. “Somatic Mutation Rates Scale with
Lifespan Across Mammals.” Nature 604 (7906): 517–24. doi: 10.1038/s41586-022-04618-z.
Domínguez-Hüttinger, E., P. Christodoulides, K. Miyauchi, A.D. Irvine, M. Okada-Hatakeyama,
M. Kubo, R.J. Tanaka. 2017. “Mathematical Modeling of Atopic Dermatitis Reveals ‘DoubleSwitch’ Mechanisms Underlying 4 Common Disease Phenotypes.” Journal of Allergy and
Clinical Immunology 139(6): 1861–1872.e7. doi: 10.1016/j.jaci.2016.10.026.
Lako, G., and M. Johnson. 2008. Metaphors We Live By. University of Chicago Press. https://books.
google.co.il/books?id=r6nOYYtxzUoC.
Lei Dai, Kirill S Korolev, Je Gore. 2015. “Relation between stability and resilience determines the
performance of early warning signals under dierent environmental drivers.” 112(32):10056-
61. doi: 10.1073/pnas.1418415112. PMID: 26216946; PMCID: PMC4538670.
Periodic Table of Diseases ◾ 243
https://t.me/medicina_free
Macal, M., Y. Jo, S. Dallari, A.Y. Chang, J. Dai, S. Swaminathan, E.J. Wehrens, P. Fitzgerald-Bocarsly,
and E.I. Zúñiga. 2018. “Self-Renewal and Toll-Like Receptor Signaling Sustain Exhausted
Plasmacytoid Dendritic Cells during Chronic Viral Infection.” Immunity. 48 (4): 730–44. doi:
10.1016/J.IMMUNI.2018.03.020.
Manry, J. et al. 2022. “e Risk of COVID-19 Death Is much greater and Age Dependent with Type
I IFN Autoantibodies.” Proceedings of the National Academy of Sciences 119 (21): e2200413119.
doi: 10.1073/PNAS.2200413119/SUPPL_FILE/PNAS.2200413119.SD01.DOCX.
McCombe, P. A. and J. M. Greer. 2014. “Sexual Dimorphism in the Immune System.” In e
Autoimmune Diseases, 319–28. Academic Press. doi: 10.1016/B978-0-12-812102-3.00024-5.
McCombe, Pamela A., and Judith M. Greer. 2020. “Chapter 24 - Sexual Dimorphism in the Immune
System.” In e Autoimmune Diseases (Sixth Edition), edited by Noel R. Rose and Ian R.
Mackay, 419–28. Academic Press. https://doi.org/10.1016/B978-0-12-812102-3.00024-5.
Sender, R. and R. Milo. 2021 “e Distribution of Cellular Turnover in the Human Body.” Nature
Medicine 27 (1): 45–8. doi: 10.1038/s41591-020-01182-9.
Stearns, S.C. and R. Medzhitov. 2015. Evolutionary Medicine – Paperback. Oxford University Press.
Accessed June 26, 2023. https://global.oup.com/ukhe/product/evolutionary-medicine-9781605
352602
Tomasetti, C. and B. Vogelstein. 2015. “Response.” Science 347 (6223): 729–31. doi: 10.1126/SCIENCE.
AAA6592.
Tomasetti, C., L. Li, and B. Vogelstein. 2017. “Stem Cell Divisions, Somatic Mutations, Cancer
Etiology, and Cancer Prevention.” Science 355 (6331): 1330–34. doi: 10.1126/SCIENCE.
AAF9011/SUPPL_FILE/TOMASETTI.SM.PDF.
Zhang, L., L.E. Pitcher, M.J. Yousefzadeh, L.J. Niedernhofer, P.D. Robbins, and Y. Zhu. 2022.
“Cellular Senescence: A Key erapeutic Target in Aging and Diseases.” e Journal of Clinical
Investigation 132 (15): e158450. doi: 10.1172/JCI158450.
Epilogue
https://t.me/medicina_free
Simplicity in Systems Medicine
are when you rst glimpse simplicity in a com-
M
plicated system. Simplicity is beautiful. It also provides ways to understand and
eventually modulate nature. Finding simplicity is the bread and butter of physics, but no
one guarantees simplicity in biology – cells and organs are far more complex than atoms.
at’s why this book originates in a sense of wonder. e wonder comes because human
biology evolved to function, not to be comprehensible to scientists. ere is no a priori
reason why our immensely complicated physiology and its diseases would be understandable. But still, simplifying principles can be found that make aspects of systems medicine
understandable to us.
is epilogue is about simplicity in systems medicine. We will review simplicity in structure and timescales, in the ability to form simplied models, and in the ultimate causes of
diseases (Alon, 2003).
SIMPLICITY IN STRUCTURE
One level of simplicity is found in the structure of physiological circuits. ere is a huge
number of possible ways that cells can regulate each other; the number of possible cell circuits is therefore very large. e surprise is that organs show only a few types of recurring
interaction patterns. We call these recurring patterns circuit motifs, because they appear
again and again in dierent organs.
Each circuit motif carries out essential functions, and these functions are universal they apply to every organ. e rst function is organ size control. Without size control, cell
numbers would grow or shrink exponentially. e secrete-and-grow circuit motif provides
size control by binding together cell growth and cell function in the same signal. is
signal, in turn, is regulated by the integrated organ functional output. As a result, the cell
population is locked into a size that provides proper function.
Another such circuit motif is the stem-cell feedback motif. Feedback from dierentiated
cells enhances the rate of dierentiation, to keep both stem cells and dierentiated cells in
homeostatic balance.
244
Epilogue ◾ 245
https://t.me/medicina_free
ese motifs not only determine organ size they also provide a second crucial function:
robustness to variations in physiological parameters. Physiological parameters change
from person to person and over time. is includes secretion rates, molecular half-lives,
blood volume, and sensitivity of tissues to hormones.
e size control motifs compensate for these variations by changing the organ functional mass. is is the principle of dynamic compensation: aer a transient period of
days to months in which organ size changes, the size reaches a new steady state in which
the system behaves as if the parameters had never changed; not only is the steady-state
function preserved, but even the temporal response curves to input stimuli are fully
compensated.
Basic circuit motifs are sometimes wired together to produce larger circuits (Adler and
Medzhitov 2022). For example, two secrete-and-grow motifs are stacked on top of each
other in the HPA axis and the thyroid axis. ese motifs are wired together in a way that
preserves their original functions of dynamic compensation and size control. e larger
circuits also have new features, including oscillatory responses on the timescales of months.
SIMPLICITY IN MODELS
In addition to the structural simplicity of a small number of circuit motifs, there is a second level of simplicity. is is the ability to treat circuits with minimal mathematical models that capture the essence of their behavior.
is minimal description is surprising because it contrasts with the complex biochemical mechanisms by which cells carry out their functions. ese biological particulars are
astoundingly rich, but the dynamics can be described by mathematical models that do not
require exhaustive knowledge of the molecular details. e models require only information on whether X activates or inhibits Y, and at what threshold.
In these models, graphical tools like phase portraits and rate plots provide a back of the
envelope sketch of the behavior and its dependence on parameters. Even if we do not know
the precise form of the interactions, we can deduce the existence of bifurcations in which
the system changes behavior all at once. Indeed, we saw that many diseases correspond
to such bifurcations – diabetes to the crossing of a glucotoxicity threshold, brosis to the
crossing of a separatrix in the broblast-macrophage phase plane, and age-related diseases
to threshold crossing triggered by accumulation of senescent cells.
ere is a universal principle that helps such modeling: separation of timescales. Circuit
motifs combine slow cell growth on the scale of days to months with much faster molecular signals that work on the scale of minutes to hours. erefore, one can safely assume
that cell numbers remain constant over the time it takes molecular concentrations to reach
their steady states.
With this assumption, the models become far simpler. e number of parameters is
reduced, because many molecular rates can be lumped together into a single systemslevel parameter, oen the ratio of molecular production and removal rates. As a result,
the essence can usually be captured with models that have only one or two dynamical
variables. e qualitative behavior becomes clear in one dimensional rate plots and twodimensional phase portraits.
Соседние файлы в папке Библиотека им академика М.И. Перельмана