Presenter: Paul Cohen, MD, PhD, Albert Resnick, M.D. Associate Professor, Rockefeller University Abstract White and brown adipocytes not only play a central role in energy storage and combustion but are also dynamic secretory cells that secrete signaling molecules linking levels of energy stores to vital physiological systems. Disruption of the signaling properties of adipocytes, as occurs in obesity, contributes to insulin resistance, type 2 diabetes, and other metabolic disorders. Fat cells have been estimated to secrete over 1,000 polypeptides and microproteins and an even larger number of small molecule metabolites. The great majority of the adipocyte secretome has not been defined or characterized. A major obstacle has been the lack of suitable technologies to quantitatively identify circulating proteins and metabolites, determine their cellular origin, and elucidate their function. Building on key innovations in chemical biology and mass spectrometry, our team is generating an encyclopedia of the white and brown adipocyte secretome in mouse models and humans. Our work has the potential to identify new secreted mediators with roles in obesity, type 2 diabetes, and metabolic diseases, provide a crucial resource for researchers and clinicians, and lead to new biomarkers and therapies. The top 3 key questions that this resource can answer: 1. What techniques can be used to characterize the secretome of a cell type in vitro and in vivo? 2. What is the full complement of proteins and metabolites secreted by different kinds of adipocytes? 3. How should one prioritize uncharacterized secreted mediators for functional study? Resource link: https://secrepedia.org/ Upcoming webinars schedule: https://dknet.org/about/webinar

1. An Encyclopedia of the Adipose Secretome
to Identify Mediators of Health and Disease
Paul Cohen, MD, PhD
The Rockefeller University
dkNet Webinar
February 9, 2024

2. Mechanisms linking obesity and disease
Obesity medical complications
Pulmonary Disease
Stroke
Liver Disease
Coronary
Heart Disease
Gall Bladder Disease
Gynecologic
Abnormalities
Osteoarthritis
Skin
Gout
Cancer
Breast
Uterus
Cervix
Colon
Esophagus
Pancreas
Kidney
Prostate
Diabetes
Dyslipidemia
Hypertension
COVID19
How does this occur?

3. Obesity, white fat distribution, and metabolic health
Metabolically Unhealthy Metabolically Healthy
Visceral, Apple Subcutaneous, Pear
Insulin sensitive, less T2D
Less hypertension
More favorable lipids
Decreased cardiovascular disease
Decreased cancer
Decreased mortality
Insulin resistance, more T2D
More hypertension
Less favorable lipids
Increased cardiovascular disease
Increased cancer
Increased mortality

4. Brown Fat is Associated with Reduced T2DM and
Improved Cardiovascular Health
Becher et al., Nature Medicine, 2021

5. White and brown fat
Cinti, Comp Physiol, 2018
White Adipose Tissue: Energy storage Brown Adipose Tissue: Energy Dissipation

6. Adipose tissue as an endocrine organ

7. Endocrine Properties of Adipose Tissue
• Fat cells are predicted to secrete > 1,000 unique polypeptides and
likely an even greater number of small molecule metabolites and
peptides
• The vast majority of these molecules along with their molecular
targets and biological functions have not been characterized
• A major limitation has been the lack of suitable technologies
• Doing so has the potential to (a) explain the links between obesity
and disease, (b) explain the protective effects of brown fat, and (c)
lead to new therapeutic targets

8. An Encyclopedia of the Adipose Tissue Secretome to Identify
Mediators of Health and Disease
• Secretome of in vitro adipocytes
• Secretome of murine adipose tissue
• Secretome of murine adipose tissue in response to physiological
stress and in pathological states
• Adipose tissue secretome in humans
• Characterize the function of novel secreted factors

9. RC2 Collaborative Team
Paul Cohen
Rockefeller
Adipose tissue biology,
translational studies in
humans
Brian Chait
Rockefeller
Mass spectrometry,
proteomics
David Fenyo
NYU
Computational biology,
proteomics
Alan Saghatelian
Salk
Natural small molecule
metabolites and
peptides
Alice Ting
Stanford
Chemical biology,
technology
development

10. Discovering and characterizing the adipose secretome
Chan Hee Choi
MD-PhD Student
Kaja Plucinska
Postdoctoral Fellow
Corey Model
Research Assistant
Samir Zaman
Medical Student
Will Barr
Research Assistant
Ruijie Xiang
Research Assistant

11. Novel Methods
• Bio-orthogonal non-canonical amino acid tagging (BONCAT)
• Proximity labeling (TURBO-ID)
• Natural small molecule metabolites
• Small peptides
• Next generation mass spectrometry

12. Challenge in Typical Proteomic Analysis
Schiess et al. (2009) Mol Oncol

13. Dieterich et al. (2006), Proc Natl Acad Sci USA; Eichelbaum et al. (2012) Nat Biotechnol; Ali Khan et al. (2018), Mol Cell Prot
Metabolic Labeling of Proteome Using BONCAT
(Bio-Orthogonal Non-Canonical Amino Acid Tagging)
Nascent
Protein
Translation
Met-tRNA
Synthetase
ATP PPi
Met-tRNA
ATP PPi
Translation
AHA-tRNA
Azide-Labeled
Nascent Protein

14. Profiling the Secretome of Primary Adipocytes
Choi et al., eLife, 2022

15. Cell Type Selective Enrichment of the Secretome
Choi et al., eLife, 2022

16. BONCAT profiling of nascent serum proteome in vivo

17. • BONCAT allows profiling of adipocyte secretome in the presence of 10% FBS
• > 600 proteins were detected in adipocyte conditioned media, including many proteins
not predicted to be secreted.
• 348 proteins are differentially secreted across different types of adipocytes and
demonstrate functional enrichment
• In vivo BONCAT enables detection of serum proteins in the µg/mL range, including
various adipokines
• BONCAT 2.0 detects >1,000 proteins in adipocyte conditioned media, including some in
ng/ml range
Summary of secretome profiling studies

18. Strategy for prioritizing adipose-derived circulating factors

19. Lrg1 is expressed in major adipose tissues and liver

20. Lrg1 is expressed by mature adipocytes

21. LRG1 is secreted by mature adipocytes

22. LRG1 is regulated by obesity

23. LRG1 is an obesity-regulated adipokine

24. LRG1 overexpression in WAT using AAV8 transduction

25. Chronic LRG1 overexpression promotes insulin sensitization
No difference in body weight observed

26. LRG1-overexpressing Leprdb/db mice show increased weight gain

27. LRG1 overexpression in Leprdb/db delays diabetic phenotype

28. LRG1-overexpressing Leprdb/db have increased white fat mass

29. Using CRISPR-Cas9 system to generate KO mice

30. LRG1-KO males demonstrate higher fasting glucose

31. Deciphering the mechanism of LRG1 action

32. LRG1 suppresses crown-like structure (CLS) formation in WAT

33. Adipose tissue inflammation and insulin resistance
LRG1

34. LRG1 suppresses eWAT inflammation

35. LRG1 decreases eWAT macrophage accumulation

36. Suppression of inflammation by LRG1 is systemic

37. Deciphering the mechanism of LRG1 action
?

38. Leucine-rich repeat (LRR) domains specialize in
protein-protein interactions
De Wit et al. (2011) Annu Rev Cell Dev Biol Wang et al. (2013) Nature
Predicted Structure of LRG1

39. LRG1 binds cytochrome c (Cyt c), a marker of cell death

40. Obesity leads to increased extracellular Cyt c

41. Deciphering the mechanism of LRG1 action

42. Cyt c activates TLR4 signaling in macrophages

43. LRG1 mitigates pro-inflammatory activity of Cyt c

44. Proposed mechanism of LRG1 action

45. LRG1 is most effective during times of adipocyte turnover
Strissel et al. (2007) Diabetes

46. • LRG1 binds Cyt c and dampens Cyt c’s pro-inflammatory effect on macrophages
• Extracellular Cyt c could serve as a biomarker and trigger for obesity-related
inflammation
• LRG1 in circulation may act as a buffer against deleterious effects of Cyt c
release
Summary of mechanistic studies on LRG1

47. LRG1 as an insulin-sensitizer and suppressor of inflammation

48. LRG1 as an insulin-sensitizer and suppressor of inflammation

49. LRG1 as an insulin-sensitizer and suppressor of inflammation
Biomarker of MHO?

50. Profiling Secretome in Vivo
1. Difficulty modeling various (patho)physiologic conditions in vitro
e.g., obesity, fasted/fed, cold exposure
2. Cellular heterogeneity & artificial nature of in vitro-differentiated primary
adipocytes
3. Identifying factors that circulate at physiologically meaningful levels

51. Mahdavi et al. (2016). J Am Chem Soc
Cell-specific Labeling of Proteome Using L274G Mutant MetRS
and Azidonorleucine (ANL)
ATP PPi
Nascent
Protein
Translation
MetRS
Met-tRNA
ATP PPi
Translation
ANL-tRNA
Azide-Labeled
Nascent Protein
L274G MetRS

52. Genetic Scheme for Adipose-Specific Labeling of Proteome
Alvarez-Castelao et al. (2017). Nat Biotechnol
Can couple with physiological perturbation:
Fed/Fasted
Chow/HFD
RT/Cold
Sedentary/Exercise

53. 7 12
0.1% Met Diet 30°C
19
30°C or 8°C
ANL 400mM gavage
harvest
Day 0
Ucp1/MetRS mice
n=16
BONCAT in vivo: labeling endocrine factors from thermogenic fat
In collaboration with Brian Chait, David Fenyo, Alan Saghatelian, Alice Ting

54. Approaches to identify brown fat derived circulating mediators
Cell-type selective labeling
BONCAT
AQ/MetRS
Ucp1/MetRS
AQ/TurboID
Ucp1/TurboID
ZP3/TurboID
KDEL
Turbo-ID
20 young and healthy
New Yorkers
Cold Vest Study: acute cold
Plasma:
SOMAscan
Olink
Lipidomics
Metabolomics
Human studies
30°C vs 8°C
PRDM16 KO/ CTRL mice
Bulk serum/plasma
Serum/plasma:
Proteomics
Peptides/SMORFs
Lipidomics
Tissue:
RNASeq
Lipidomics
MIWI Study: chronic cold
Minnesotan winter swimmers

55. Cold vest study design
Temp
[°C]
Time [min]
Shivering Threshold [ST]
ST + 2°C
∼24°C
COOLING PROTOCOL
0 60 120 180
BodPod
BMI
WHR A B
VEST
ON
VEST
OFF
Inclusion:
• Men and women
• 18 – 28 years of age
• 18.5 <BMI< 25
Exclusion:
• Diagnosis of T1D, T2D, thyroid
disease, cancer, scleroderma
• Any prescribed medication
• Any vaccination in the last 2 weeks
• Consumption of nicotine, elicit drugs
in last 6 months
• Consumption of THC/CBD in the last
30 days

56. Cold vest study data collection
1. Plasma samples will be analyzed using:
• SOMAscan
• O-link
• Metabolomics
2. Identify factors commonly regulated by acute activation of BAT
3. Intersect data with BAT transcriptomes to ID potential brown fat biomarkers
4. Create a short list of blood borne ‘BATokine’ candidates
5. Functionally assess top candidates in mice and cell culture models
Blood BATokine screen
Biomarker ID
In collaboration with Robert Gerszten, Alan Saghatelian

57. The MIWI Study: Minneapolis Ice Water Immersion Cohort
A group of a few hundred ice water dippers
Cedar Lake and Lake Harriet, Minnesota
IRB Number: KPL1029
Eligibility:
• 18+ years of age
• Liability waiver for Cedar Lake and Lake Harriet
• Practicing ice water immersion at least 2 times a week for a month
Aims:
• To identify blood molecules linked with chronic exposure to cold
• To confirm potential BAT biomarkers in large cohort of individuals
In collaboration with Betsy Seaquist, UMN

58. Secrepedia.org

59. Experimental Data View

60. Experimental Data View

61. Secretion Predictor Tool

62. Gene Search Across Experiments (in development)

63. Ongoing goals of our collaborative project
• First encyclopedia of adipose secretome in mouse in normal
physiology and in response to physiological stress and
pathological states
• Functional characterization of novel secreted molecules with roles
in obesity, T2DM, metabolic diseases
• Adipose tissue secretome in humans spanning from insulin
sensitive to T2DM and in response to cold exposure
• Development of novel molecular, chemical genetic, and mass spec
technologies to label and detect secreted proteins and identify their
targets

64. Anticipated impact
• Technical advances will be of broad use across many fields
• Characterization of secreted molecules could provide new insights
into the pathophysiology of obesity, type 2 diabetes, and other
metabolic diseases
• Data sets generated as a byproduct of these studies will provide a
rich resource for obesity and type 2 diabetes researchers studying
cellular crosstalk
• Can be readily applied to any cell type or tissue, providing
opportunity to build whole animal interactome

65. Adipokines as therapeutic targets of obesity-related complications

66. Acknowledgements
Cohen Lab
Alp Doymaz
Nicolas Gomez Banoy
Colleen Hadley
Zahraa Hotait
Xiaojing Huang
Nakul Karandikar
Mascha Koenen
Zeran Lin
Yue Liu
Ksusha Morozova
Luke Olsen
Giulia Pagano
Kaja Plucinska
Ruijie Xiang
Raquelle Yu
Lishu Yue
Alumni
Sarah Ackerman
Will Barr
Tobias Becher
Jingyi Chi
Chan Hee Choi
Mahmoud Eljalby
Audrey Crane
Blair Jia
Aarthi Maganti
Olivia Maguire
Francois Marchildon
Sarah Marx
Corey Model
Lily Nguyen
Sean O’Connor
Sri Palanisamy
Saba Tegegne
Lauren Turner
Jack Volpert
Samir Zaman
Support
ADA Pathway Program
HFSP
Leducq Foundation
Mallinckrodt Foundation
Mark Foundation
NIH/NIDDK
Robertson Therapeutic Development Fund
Sinsheimer Foundation
Susan G. Komen
Collaborators on work shown
Tom Carroll (RU)
Brian Chait (RU)
Aaron Cypess (NIDDK)
David Fenyo (NYU)
Robert Gerszten (BIDMC)
Ken Loh (Yale)
Ji-Dung Luo (RU)
Alan Saghatelian (Salk)
Betsy Seaquist (UMN)
Alice Ting (Stanford)
Katya Vinogradova (RU)