Cindy Yang
· ProfessorVerifiedCornell University · Pharmacology and Chemical Biology
Active 1994–2025
About
Dr. Cindy Yang is a professor leading the Yang Lab at Weill Cornell Medicine. Her research builds upon the work of the late Dr. Anthony Sauve, focusing on investigating novel NAD⁺ compounds, their metabolic pathways, and the potential therapeutic benefits of altering NAD⁺ concentrations in cell and animal models. NAD⁺ is a vital coenzyme involved in energy metabolism, DNA repair, and cellular signaling. Her lab has developed synthetic methodologies for various novel NAD⁺ precursors, including dihydronicotinamide riboside (NRH), and has pioneered advanced techniques to study NAD⁺ metabolism, such as isotope syntheses for key precursors like nicotinamide, nicotinamide riboside (NR), and nicotinamide mononucleotide (NMN). These tools enable comprehensive investigations of NAD⁺ metabolism both in vitro and in vivo. The lab aims to manipulate NAD⁺ concentrations in pathological conditions such as aging and metabolic diseases, with the goal of discovering new treatments to alleviate age-related and obesity-induced morbidities.
Research topics
- Biology
- Cell biology
- Genetics
- Computational biology
- Chemistry
- Cancer research
- Biochemistry
Selected publications
2025-11-24
articleOpen access<p>Supplementary Figures S1-S7 includes Supplementary Figure S1-S7 and the figure legend for each figure. Supplementary Fig. S1 shows that the polyamine-hypusine circuit is activated in many human cancers including MYC-driven lymphoma. Supplementary Fig. S1 is related to Fig. 1. Supplementary Fig. S2 shows that inhibition of DHPS enzyme activity, or silencing eIF5A or DHPS, suppresses the growth of mouse MYC-driven lymphoma. Supplementary Fig. S2 is related to Fig. 2. Supplementary Fig. S3 shows that hypusinated eIF5A (eIF5AHyp) contributes to the tumorigenic potential and maintenance of MYC-driven lymphoma. Supplementary Fig. S3 is related to Fig. 3. Supplementary Fig. S4 shows the effects of eIF5A or DHPS depletion on the transcriptional landscape of MYC-driven lymphoma. Supplementary Fig. S4 is related to Fig. 4. Supplementary Fig. S5 shows that depletion of eIF5A or DHPS impairs the translation efficiency of subsets of mRNA in MYC-driven lymphoma. Supplementary Fig. S5 is related to Fig. 5. Supplementary Fig. S6 shows the validation of select eIF5AHyp translation targets identified by the multi-omics analyses, and that the translation of key regulatory cell cycle factors is controlled by hypusinated eIF5A. Supplementary Fig. S6 is related to Fig. 6. Supplementary Fig. S7 shows that hypusinated eIF5A is essential for the development of MYC-driven lymphoma. Supplementary Fig. S7 is related to Fig. 7.</p>
SSRN Electronic Journal · 2025-01-01
preprintOpen access2025-11-24
articleOpen access<p>Supplementary Tables S1, S2, S5-S13 includes Supplementary Tables S1, S2, S5-S13. Supplementary Table S1 provides a summary of BL and DHL patient demographics for the immunohistochemistry study presented in Fig. 1, E and F. Supplementary Table S2 lists the MYC-dysregulated genes whose expression is significantly altered following depletion of eIF5A or DHPS, related to Fig. 4F. Supplementary Table S5 shows DHPS GISTIC count and survival of select TCGA PanCancer datasets, related to Fig. 7J. Supplementary Table S6 shows the genetic mouse models used in this study, related to Methods. Supplementary Table S7 shows the mouse and human cell lines used in this study, related to Methods. Supplementary Table S8 lists the antibodies used in this study, related to Methods. Supplementary Table S9 lists reagents used in this study, related to Methods. Supplementary Table S10 summarizes the plasmids used in this study, related to Methods. Supplementary Table S11 lists the sequences of the oligonucleotides used in this study, related to Methods. Supplementary Table S12 lists accession numbers and publicly deposited data from this study, related to Methods. Supplementary Table S13 lists software and algorithms used in this study, related to Methods.</p>
Journal of Orthopaedic Translation · 2025-07-01 · 3 citations
articleOpen accessAlzheimer's disease (AD) is marked by amyloid β (Aβ) accumulation, neuroinflammation, and cognitive decline. While neuroinflammation is a key feature of AD, the potential involvement of bone marrow-derived cells in its pathology remains unclear. This study aimed to investigate the role of bone marrow-derived myeloid cells in driving neuroinflammation in AD. We developed a transgenic mouse model (FAD4T) by overexpressing human APPSwe/Ind and PSEN1 M146L/L286V on a C57BL/6J background. FAD 4T mice were characterized for hallmark AD features, including amyloid deposition, glial activation, and cognitive deficits. Additionally, single-cell transcriptomic analysis was performed to profile bone marrow and brain myeloid cells. Bone marrow transplantation experiments were conducted to assess the contribution of bone marrow-derived macrophages to neuroinflammation in AD. FAD 4T mice exhibited hallmark AD phenotypes such as amyloid deposition, glial activation, and cognitive impairment, alongside osteoporosis-like changes. Single-cell transcriptomic analysis identified a significant increase in bone marrow-derived macrophages in the brains of FAD 4T mice. These cells showed upregulation of AD-related genes, including Cst7 and Ctsd , suggesting their active role in neuroinflammation. Bone marrow transplantation experiments further confirmed that bone marrow-derived macrophages contributed to the inflammatory processes in the AD brain. Our findings demonstrate that bone marrow-derived myeloid cells infiltrate the brain and might play a critical role in driving neuroinflammation in AD. Targeting these cells may represent a novel therapeutic strategy for mitigating inflammation and disease progression in AD. Our findings suggest that bone marrow-derived inflammation play a critical role in AD-associated inflammation, offering potential targets for therapeutic intervention such as Cst7 and Ctsd in bone marrow-derived myeloid cells.
Lymphoma accelerates T cell and tissue aging
Cancer Cell · 2025-08-21 · 7 citations
articleOpen accessThe combined effects of aging and cancer on immune cells were investigated in young versus aged mice harboring B cell lymphoma, and in T cells from young and aged B cell lymphoma patients. These analyses revealed that lymphoma alone is sufficient to trigger transcriptional, epigenetic, and phenotypic alterations in young T cells that manifest in aged T cells. In contrast, aged T cells are largely resistant to lymphoma-induced changes. Pathway analyses revealed open chromatin regions and genes controlling iron homeostasis are induced by both lymphoma and aging, and lymphoma-experienced and aged T cells have increased iron pools and are resistant to ferroptosis. Furthermore, both aged and lymphoma-experienced T cells have defects in proteostasis. B cell lymphoma also accelerates aging of other tissues, as evidenced by elevated expression of Cdkn2a and Tnfa . Finally, some lymphoma-induced aging phenotypes are reversible whereas others are fixed, indicating opportunities for improving some cancer-associated aging comorbidities. • Aging confers resistance to B cell lymphoma-induced changes manifest in young T cells • Lymphoma accelerates aging of young T cells and tissues • Lymphoma drives transcriptional and epigenetic hallmarks present in aged T cells • Lymphoma induces age-related inflammation and alters protein and iron homeostasis Hesterberg et al. show that lymphoma provokes marked and rapid changes in young T cells that recapitulate the transcriptional, epigenetic, and senescent features manifest in aged T cells, including aging-associated alterations in inflammation, proteostasis, and iron homeostasis that also occur in other tissues of lymphoma-bearing individuals.
2024-09-16
supplementary-materialsOpen access<p>Supplementary Tables S1, S2, S5-S13 includes Supplementary Tables S1, S2, S5-S13. Supplementary Table S1 provides a summary of BL and DHL patient demographics for the immunohistochemistry study presented in Fig. 1, E and F. Supplementary Table S2 lists the MYC-dysregulated genes whose expression is significantly altered following depletion of eIF5A or DHPS, related to Fig. 4F. Supplementary Table S5 shows DHPS GISTIC count and survival of select TCGA PanCancer datasets, related to Fig. 7J. Supplementary Table S6 shows the genetic mouse models used in this study, related to Methods. Supplementary Table S7 shows the mouse and human cell lines used in this study, related to Methods. Supplementary Table S8 lists the antibodies used in this study, related to Methods. Supplementary Table S9 lists reagents used in this study, related to Methods. Supplementary Table S10 summarizes the plasmids used in this study, related to Methods. Supplementary Table S11 lists the sequences of the oligonucleotides used in this study, related to Methods. Supplementary Table S12 lists accession numbers and publicly deposited data from this study, related to Methods. Supplementary Table S13 lists software and algorithms used in this study, related to Methods.</p>
2024-09-16
supplementary-materialsOpen access<p>Supplementary Table S3 lists 665 shared differentially translated transcripts (DTT) identified following depletion of eIF5A or DHPS, related to Fig. 5C.</p>
2024-09-16
supplementary-materialsOpen access<p>Supplementary Table S4 lists oncogenes and tumor suppressor genes analyzed for changes in translation efficiency (TE) following depletion of eIF5A or DHPS, related to Fig. 5E.</p>
2024-09-16
supplementary-materialsOpen access<p>Supplementary Table S3 lists 665 shared differentially translated transcripts (DTT) identified following depletion of eIF5A or DHPS, related to Fig. 5C.</p>
2024-09-16
supplementary-materialsOpen access<p>Supplementary Table S4 lists oncogenes and tumor suppressor genes analyzed for changes in translation efficiency (TE) following depletion of eIF5A or DHPS, related to Fig. 5E.</p>
Frequent coauthors
- 161 shared
John L. Cleveland
- 84 shared
Bo Xu
Tianjin Medical University Cancer Institute and Hospital
- 77 shared
William Roush
Scripps Research Institute
- 64 shared
Anders Berglund
Moffitt Cancer Center
- 58 shared
Mohammad Fallahi
Tarbiat Modares University
- 57 shared
John M. Koomen
Molecular Oncology (United States)
- 54 shared
Robert J. Rounbehler
Clinical Insights
- 53 shared
Joanne R. Doherty
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