138 results on '"Vahedi, G"'
Search Results
2. Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes
- Author
-
Fasolino M, Schwartz GW, Patil AR, Mongia A, Golson ML, Wang YJ, Morgan A, Liu C, Schug J, Liu J, Wu M, Traum D, Kondo A, May CL, Goldman N, Wang W, Feldman M, Moore JH, Japp AS, Betts MR, HPAP Consortium, Faryabi RB, Naji A, Kaestner KH, and Vahedi G
- Subjects
General Economics, Econometrics and Finance - Published
- 2022
- Full Text
- View/download PDF
3. Exploiting Chromatin Biology to Understand Immunology
- Author
-
Johnson, J.L., primary and Vahedi, G., additional
- Published
- 2016
- Full Text
- View/download PDF
4. Assessment of network module identification across complex diseases
- Author
-
Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Marbach, D, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, W, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Choobdar S., Ahsen M. E., Crawford J., Tomasoni M., Fang T., Lamparter D., Lin J., Hescott B., Hu X., Mercer J., Natoli T., Narayan R., Aicheler F., Amoroso N., Arenas A., Azhagesan K., Baker A., Banf M., Batzoglou S., Baudot A., Bellotti R., Bergmann S., Boroevich K. A., Brun C., Cai S., Caldera M., Calderone A., Cesareni G., Chen W., Chichester C., Cowen L., Cui H., Dao P., De Domenico M., Dhroso A., Didier G., Divine M., del Sol A., Feng X., Flores-Canales J. C., Fortunato S., Gitter A., Gorska A., Guan Y., Guenoche A., Gomez S., Hamza H., Hartmann A., He S., Heijs A., Heinrich J., Hu Y., Huang X., Hughitt V. K., Jeon M., Jeub L., Johnson N. T., Joo K., Joung I. S., Jung S., Kalko S. G., Kamola P. J., Kang J., Kaveelerdpotjana B., Kim M., Kim Y. -A., Kohlbacher O., Korkin D., Krzysztof K., Kunji K., Kutalik Z., Lage K., Lang-Brown S., Le T. D., Lee J., Lee S., Li D., Li J., Liu L., Loizou A., Luo Z., Lysenko A., Ma T., Mall R., Marbach D., Mattia T., Medvedovic M., Menche J., Micarelli E., Monaco A., Muller F., Narykov O., Norman T., Park S., Perfetto L., Perrin D., Pirro S., Przytycka T. M., Qian X., Raman K., Ramazzotti D., Ramsahai E., Ravindran B., Rennert P., Saez-Rodriguez J., Scharfe C., Sharan R., Shi N., Shin W., Shu H., Sinha H., Slonim D. K., Spinelli L., Srinivasan S., Subramanian A., Suver C., Szklarczyk D., Tangaro S., Thiagarajan S., Tichit L., Tiede T., Tripathi B., Tsherniak A., Tsunoda T., Turei D., Ullah E., Vahedi G., Valdeolivas A., Vivek J., von Mering C., Waagmeester A., Wang B., Wang Y., Weir B. A., White S., Winkler S., Xu K., Xu T., Yan C., Yang L., Yu K., Yu X., Zaffaroni G., Zaslavskiy M., Zeng T., Zhang J. D., Zhang L., Zhang W., Zhang X., Zhang J., Zhou X., Zhou J., Zhu H., Zhu J., Zuccon G., Stolovitzky G., Cowen L. J., Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Marbach, D, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, W, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Choobdar S., Ahsen M. E., Crawford J., Tomasoni M., Fang T., Lamparter D., Lin J., Hescott B., Hu X., Mercer J., Natoli T., Narayan R., Aicheler F., Amoroso N., Arenas A., Azhagesan K., Baker A., Banf M., Batzoglou S., Baudot A., Bellotti R., Bergmann S., Boroevich K. A., Brun C., Cai S., Caldera M., Calderone A., Cesareni G., Chen W., Chichester C., Cowen L., Cui H., Dao P., De Domenico M., Dhroso A., Didier G., Divine M., del Sol A., Feng X., Flores-Canales J. C., Fortunato S., Gitter A., Gorska A., Guan Y., Guenoche A., Gomez S., Hamza H., Hartmann A., He S., Heijs A., Heinrich J., Hu Y., Huang X., Hughitt V. K., Jeon M., Jeub L., Johnson N. T., Joo K., Joung I. S., Jung S., Kalko S. G., Kamola P. J., Kang J., Kaveelerdpotjana B., Kim M., Kim Y. -A., Kohlbacher O., Korkin D., Krzysztof K., Kunji K., Kutalik Z., Lage K., Lang-Brown S., Le T. D., Lee J., Lee S., Li D., Li J., Liu L., Loizou A., Luo Z., Lysenko A., Ma T., Mall R., Marbach D., Mattia T., Medvedovic M., Menche J., Micarelli E., Monaco A., Muller F., Narykov O., Norman T., Park S., Perfetto L., Perrin D., Pirro S., Przytycka T. M., Qian X., Raman K., Ramazzotti D., Ramsahai E., Ravindran B., Rennert P., Saez-Rodriguez J., Scharfe C., Sharan R., Shi N., Shin W., Shu H., Sinha H., Slonim D. K., Spinelli L., Srinivasan S., Subramanian A., Suver C., Szklarczyk D., Tangaro S., Thiagarajan S., Tichit L., Tiede T., Tripathi B., Tsherniak A., Tsunoda T., Turei D., Ullah E., Vahedi G., Valdeolivas A., Vivek J., von Mering C., Waagmeester A., Wang B., Wang Y., Weir B. A., White S., Winkler S., Xu K., Xu T., Yan C., Yang L., Yu K., Yu X., Zaffaroni G., Zaslavskiy M., Zeng T., Zhang J. D., Zhang L., Zhang W., Zhang X., Zhang J., Zhou X., Zhou J., Zhu H., Zhu J., Zuccon G., Stolovitzky G., and Cowen L. J.
- Abstract
Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.
- Published
- 2019
5. BACH2 represses effector differentiation and cytokine expression to stabilize Treg-mediated immune homeostasis: 217
- Author
-
Roychoudhuri, R., Hirahara, K., Mousavi, K., Clever, D., Bonelli, M., Sciume, G., Zare, H., Vahedi, G., Klebanoff, C., Sartorelli, V., Kanno, Y., Gattinoni, L., Nakamura, A., Muto, A., O’Shea, J., and Restifo, N.
- Published
- 2013
- Full Text
- View/download PDF
6. Zymosan attenuates melanoma growth progression, increases splenocyte proliferation and induces TLR2 and TNF-α expression in mice
- Author
-
Taghavi, M, Mortaz, E, Khosravi, A, Vahedi, G, Folkerts, G, Varahram, M, Kazempour-Dizaji, M, Garssen, J, Adcock, IM, and Wellcome Trust
- Subjects
CANCER-TREATMENT ,Science & Technology ,RECEPTOR ,IMMUNE-RESPONSES ,Immunology ,TLR-2 ,Zymosan ,TLR-4 ,1103 Clinical Sciences ,CYTOKINE PRODUCTION ,INNATE ,PARTICULATE ,ACTIVATION ,TNF-α ,BETA-GLUCANS ,CELLS ,1115 Pharmacology And Pharmaceutical Sciences ,Life Sciences & Biomedicine ,SKIN ,TNF-alpha - Abstract
Background : Melanoma is one of the most common types of skin malignancies. Since current therapies are suboptimal, considerable interest has focused on novel natural - based treatments. Toll - like receptors (TLRs) play an important role in evoking innate immunity agains t cancer cells. Zymosan, a known TLR - 2 agonist, is a glucan derived from yeast cell walls with promising immunomodulatory effects. The aim of this study was to evaluate whether Saccharomyces cerevisiae - derived z ymosan - modulated skin melanoma progression by regulation of TLR - 2 expression in peritoneal macrophages and serum TNF - level. Methods: Male C57BL/6 mice were divided into four groups: i) zymosan - treated (Z), ii) Melanoma - bearing mice (M), iii) Melanoma - bearing mice treated with zymosan (ZM) and iv) a healthy control group (negative control). 15 days after melanoma induction, mice were injected i.p. with zymosan (10 g) daily for 4 consecutive days. Mice were CO 2 - euthanized and serum TNF - α level, TLR - 2 expression in peritoneal macrophages and tumor gro wth measured. Splenocytes were treated ex - vivo with zymosan to determine viability and proliferation. Results: Tumor weight significantly decreased following therapeutic dosing with zymosan ( P
- Published
- 2018
7. Chronic mucocutaneous candidiasis, a case study and literature review
- Author
-
Khosravi, A.R., primary, Mansouri, P., additional, Saffarian, Z., additional, Vahedi, G., additional, and Nikaein, D., additional
- Published
- 2018
- Full Text
- View/download PDF
8. Chapter Fifteen - Exploiting Chromatin Biology to Understand Immunology
- Author
-
Johnson, J.L. and Vahedi, G.
- Published
- 2016
- Full Text
- View/download PDF
9. Abstract P2-04-07: Triple negative breast cancer-specific chromatin conformation links Notch signal to tumor-specific transcriptional program
- Author
-
Petrovic, J, primary, Zhou, Y, additional, Georgakilas, G, additional, Vahedi, G, additional, Pear, WS, additional, and Faryabi, RB, additional
- Published
- 2018
- Full Text
- View/download PDF
10. Presence and distribution of yeasts in the reproductive tract in healthy female horses
- Author
-
Azarvandi, A., primary, Khosravi, A. R., additional, Shokri, H., additional, Talebkhan Garoussi, M., additional, Gharahgouzlou, F., additional, Vahedi, G., additional, and Sharifzadeh, A., additional
- Published
- 2017
- Full Text
- View/download PDF
11. Physiotherapy with and without Superficial Dry Needling Affects Pain and Muscle Strength in Patients with Patellofemoral Pain Syndrome
- Author
-
Miri Abyaneh, H., primary, Mosallanezhad, Z., additional, Mohammadalizade, H., additional, Bakhshi, E., additional, Vahedi, G., additional, and Nourbakhsh, Mr., additional
- Published
- 2016
- Full Text
- View/download PDF
12. Bio ceramic Zirconia/Hydroxyapatite nano composite extracted from bovine bone
- Author
-
Mohamaddoost, F., Mohd Yusoff, Hamdan, Matori, Khamirul Amin, Ostovan, F., Vahedi, G. R., Mohamaddoost, F., Mohd Yusoff, Hamdan, Matori, Khamirul Amin, Ostovan, F., and Vahedi, G. R.
- Abstract
These days bone and joint problem is one of the serious health issues in the whole world, millions of people are suffered from it and number is increasing with an alarming rate. Annually, there are more than million surgeries getting done in the world just because of injuries to human hard tissue system. Recently in medical applications, synthetic Hydroxyapatite (HA) has been widely used as an important material because of excellent properties such as bio affinity and high osteogenic potential. HA, particles prevent the growth of cancer cells. Recently, natural hydroxyapatite bio ceramics are extracted by normal calcinations of some bio wastes. Biologically derived natural materials such as bovine bones, fish bones, oyster shells, corals and egg shells, they have converted into useful biomaterials. Moreover, extraction of HA from bio-waste is simple, economically and environmentally preferable. The mechanical Properties of HA is low in comparison with cortical bone. As a result, incorporation of resistant oxide phase has been resistant to optimize biocompatibility and improve mechanical properties of HA. Zirconia (ZrO2), is one of the best materials which can increase the HA properties. ZrO2 is a well known material which has high mechanical properties and greater strength, low toxicity and lower magnetic susceptibility in comparison with Ti and Titanium's alloys. In the present work, HA/ZrO2 bio ceramic were fabricated in various sintering conditions and nano particle size is achieved by milling technique. HA was derived from natural sources that chosen bovine bone. Effects of ZrO2 on the composites were investigated. Adding the additive resulted in the values of higher density. Density of the sintered samples was determined by using the Archimedes method and distilled water was used as the fluid medium. The phase formation of the sintered samples was analyzed by X-ray diffraction technique (XRD). The micro structural investigation of the samples was performed usin
- Published
- 2014
13. 217
- Author
-
Roychoudhuri, R., primary, Hirahara, K., additional, Mousavi, K., additional, Clever, D., additional, Bonelli, M., additional, Sciume, G., additional, Zare, H., additional, Vahedi, G., additional, Klebanoff, C., additional, Sartorelli, V., additional, Kanno, Y., additional, Gattinoni, L., additional, Nakamura, A., additional, Muto, A., additional, O’Shea, J., additional, and Restifo, N., additional
- Published
- 2013
- Full Text
- View/download PDF
14. Inference of Boolean networks under constraint on bidirectional gene relationships
- Author
-
Vahedi, G., primary, Dougherty, E.R., additional, and Ivanov, I.V., additional
- Published
- 2009
- Full Text
- View/download PDF
15. Optimal Intervention Strategies for Cyclic Therapeutic Methods
- Author
-
Vahedi, G., primary, Faryabi, B., additional, Chamberland, J.-F., additional, Datta, A., additional, and Dougherty, E.R., additional
- Published
- 2009
- Full Text
- View/download PDF
16. Mean first-passage time control policy versus reinforcement-learning control policy in gene regulatory networks.
- Author
-
Vahedi, G., Faryabi, B., Chamberland, J.-F., Datta, A., and Dougherty, E.R.
- Published
- 2008
- Full Text
- View/download PDF
17. Optimal intervention in semi-Markov-based asynchronous genetic regulatory networks.
- Author
-
Faryabi, B., Chamberland, J.-F., Vahedi, G., Datta, A., and Dougherty, E.R.
- Published
- 2008
- Full Text
- View/download PDF
18. Constrained reduction mapping for a class of network models of genomic regulation.
- Author
-
Ivanov, I., Vahedi, G., and Dougherty, E.
- Published
- 2007
- Full Text
- View/download PDF
19. 217: BACH2 represses effector differentiation and cytokine expression to stabilize Treg-mediated immune homeostasis
- Author
-
Roychoudhuri, R., Hirahara, K., Mousavi, K., Clever, D., Bonelli, M., Sciume, G., Zare, H., Vahedi, G., Klebanoff, C., Sartorelli, V., Kanno, Y., Gattinoni, L., Nakamura, A., Muto, A., O’Shea, J., and Restifo, N.
- Published
- 2013
- Full Text
- View/download PDF
20. Optimal Intervention in Asynchronous Genetic Regulatory Networks.
- Author
-
Faryabi, B., Chamberland, J.-F., Vahedi, G., Datta, A., and Dougherty, E.R.
- Abstract
There is an ongoing effort to design optimal intervention strategies for discrete state-space synchronous genetic regulatory networks in the context of probabilistic Boolean networks; however, to date, there has been no corresponding effort for asynchronous networks. This paper addresses this issue by postulating two asynchronous extensions of probabilistic Boolean networks and developing control policies for both. The first extension introduces deterministic gene-level asynchronism into the constituent Boolean networks of the probabilistic Boolean network, thereby providing the ability to cope with temporal context sensitivity. The second extension introduces asynchronism at the level of the gene activity profiles. Whereas control policies for both standard probabilistic Boolean networks and the first proposed extension are characterized within the framework of Markov decision processes, asynchronism at the profile level results in control being treated in the framework of semi-Markov decision processes. The advantage of the second model is the ability to obtain the necessary timing information from sequences of gene-activity profile measurements. Results from the theory of stochastic control are leveraged to determine optimal intervention strategies for each class of proposed asynchronous regulatory networks, the objective being to reduce the time duration that the system spends in undesirable states. [ABSTRACT FROM PUBLISHER]
- Published
- 2008
- Full Text
- View/download PDF
21. Fungicidal effect of Origanum vulgare essential oil against Candida glabrata and its cytotoxicity against macrophages
- Author
-
Vahedi, G., Khosravi, A. R., Shokri, H., Moosavi, Z., Delirezh, N., Aghil Sharifzadeh, Barin, A., Shahrokh, S., and Balal, A.
- Subjects
Medicine (General) ,R5-920 ,Oropharyngeal candidiasis ,Candida glabrata ,Therapeutics. Pharmacology ,RM1-950 ,Antifungal ,Origanum vulgare ,Essential oil - Abstract
Introduction: Candida glabrata is a yeast fungus regularly isolated from patients with impaired immunity who receive a routine antifungal therapy. Drug-resistant strains of C. glabrata have been emerged in recent years. The aim of this study was to examine the therapeutic efficacy Origanum vulgare essential oil (OVEO) against drug-resistant strains of C. glabrata and its cytotoxic effect on macrophages.Methods: Specimens were collected from mucosal surfaces of the oral cavity of medically approved oropharyngeal candidiasis (OPC) in HIV-positive patients and volunteered healthy individuals using sterile swabs or mouthwashes. In vitro antifungal susceptibility testing was done using microdilution and disc diffusion methods. Chemical composition of OVEO was determined using gas chromatography mass spectrometry. The cytotoxic effect of essential oil on macrophages was examined using tetrazolium dye (MTT).Results: Minimum inhibitory concentration (MIC) range of OVEO in healthy individuals and OPC patients was 150-200 and 150-250 μg/mL, respectively. OVEO efficiently inhibited growth of resistant isolates. In isolates obtained from HIV patients, both MIC50 and MIC90 of OVEO were 200 μg/mL while in healthy individuals were 150 and 200 μg/mL, respectively. Moreover, OVEO induced significant reduction in proliferation of murine RAW264.7 and peritoneal macrophages in concentrations higher than 100 and 300 μg/mL, respectively. Main constituents of OVEO were thymol (27.3%), γ-terpinene (20.7%) and carvacrol (16.1%).Conclusion: OVEO could be used as a fungicidal agent against fungal infections caused by azole-resistant C. glabrata. A combination therapy along with standard antifungals is suggested to avoid its cytotoxic effects.
22. In vitro antifungal activity of aqueous-ethanolic extract of Allium jesdianum against fluconazole-susceptible and -resistant human vaginal Candida glabrata isolate
- Author
-
Shahrokh, S., Vahedi, G., Khosravi, A. -R, Mohammadreza Mahzounieh, Ebrahimi, A., Sharifzadeh, A., and Balal, A.
23. The effect of Aloe vera extract on humoral and cellular immune response in rabbit
- Author
-
Vahedi, G., Mehdi Taghavi, Maleki, A. K., and Habibian, R.
- Subjects
Aloe vera, cellular and humoral immune, immunization, rabbits - Abstract
Some plant polysaccharides are well known to possess immunostimulatory effects. Aloe vera possesses confirmed curative or healing actions. The aim of this study was to evaluate the effect of the administration of A. vera plant extract on cellular and humoral immune response in rabbits. 20 healthy male New Zealand white rabbits were randomly divided into five treatment groups: Groups consisted of: 1) control group (normal saline); 2) A. vera control; 3) vaccine control; 4) 50 mg A. vera extract + vaccine; 5) 150 mg A. vera extract + vaccine. The used vaccine was for myxomatosis. Blood samples were obtained at four time points: days 0, 7, 14 and 21 of the study. CD4+ and CD8+ lymphocytes frequency and serum immunoglobulin concentrations were evaluated. According to the results, oral administration of A. vera affected the composition of lymphocyte subsets and serum immunoglobulins positively. These findings demonstrated that A. vera may stimulate both cellular and humoral immune responses after immunization.Key words: Aloe vera, cellular and humoral immune, immunization, rabbits.
24. Critical Boolean networks require minimal intervention rate.
- Author
-
Vahedi, G., Shmulevich, I., Wenbin Liu, and Dougherty, E.
- Published
- 2009
- Full Text
- View/download PDF
25. Open-Loop Feedback Intervention: Control of gene regulatory networks with unmeasurable context.
- Author
-
Faryabi, B., Vahedi, G., Chamberland, J.-F., Datta, A., and Dougherty, E.R.
- Published
- 2009
- Full Text
- View/download PDF
26. Modeling cyclic therapy in gene regulatory networks.
- Author
-
Vahedi, G., Faryabi, B., Chamberland, J.-F., Datta, A., and Dougherty, E.
- Published
- 2008
- Full Text
- View/download PDF
27. Constrained intervention in a cancerous mammalian cell cycle network.
- Author
-
Faryabi, B., Vahedi, G., Chamberland, J.-F., Datta, A., and Dougherty, E.R.
- Published
- 2008
- Full Text
- View/download PDF
28. Bidirectional Relationships and Attractor Structure of Boolean Networks.
- Author
-
Vahedi, G., Ivanov, I., and Dougherty, E.R.
- Published
- 2007
- Full Text
- View/download PDF
29. Assessment of network module identification across complex diseases
- Author
-
Choobdar, Sarvenaz, Ahsen, Mehmet E., Natoli, Ted, Lysenko, Artem, Ma, Tianle, Mall, Raghvendra, Marbach, Daniel, Mattia, Tomasoni, Medvedovic, Mario, Menche, Jörg, Mercer, Johnathan, Micarelli, Elisa, Monaco, Alfonso, Narayan, Rajiv, Müller, Felix, Narykov, Oleksandr, Norman, Thea, Park, Sungjoon, Perfetto, Livia, Perrin, Dimitri, Pirrò, Stefano, Przytycka, Teresa M., DREAM Module Identification Challenge Consortium, Qian, Xiaoning, Raman, Karthik, Ramazzotti, Daniele, Ramsahai, Emilie, Ravindran, Balaraman, Rennert, Philip, Sáez Rodríguez, Julio, Schärfe, Charlotta, Sharan, Roded, Shi, Ning, Subramanian, Aravind, Shin, Wonho, Shu, Hai, Sinha, Himanshu, Slonim, Donna K., Spinelli, Lionel, Srinivasan, Suhas, Suver, Christine, Szklarczyk, Damian, Tangaro, Sabina, Zhang, Jitao D., Thiagarajan, Suresh, Tichit, Laurent, Tiede, Thorsten, Tripathi, Beethika, Tsherniak, Aviad, Tsunoda, Tatsuhiko, Türei, Dénes, Ullah, Ehsan, Vahedi, Golnaz, Valdeolivas, Alberto, Stolovitzky, Gustavo, Vivek, Jayaswal, von Mering, Christian, Waagmeester, Andra, Wang, Bo, Wang, Yijie, Weir, Barbara A., White, Shana, Winkler, Sebastian, Xu, Ke, Xu, Taosheng, Kutalik, Zoltán, Yan, Chunhua, Yang, Liuqing, Yu, Kaixian, Yu, Xiangtian, Zaffaroni, Gaia, Zaslavskiy, Mikhail, Zeng, Tao, Zhang, Lu, Zhang, Weijia, Lage, Kasper, Zhang, Lixia, Zhang, Xinyu, Zhang, Junpeng, Zhou, Xin, Zhou, Jiarui, Zhu, Hongtu, Zhu, Junjie, Zuccon, Guido, Crawford, Jake, Cowen, Lenore J., Bergmann, Sven, Aicheler, Fabian, Amoroso, Nicola, Arenas, Alex, Azhagesan, Karthik, Baker, Aaron, Banf, Michael, Batzoglou, Serafim, Tomasoni, Mattia, Baudot, Anaïs, Bellotti, Roberto, Boroevich, Keith A., Brun, Christine, Cai, Stanley, Caldera, Michael, Calderone, Alberto, Cesareni, Gianni, Chen, Weiqi, Fang, Tao, Chichester, Christine, Cowen, Lenore, Cui, Hongzhu, Dao, Phuong, De Domenico, Manlio, Dhroso, Andi, Didier, Gilles, Divine, Mathew, Lamparter, David, Del Sol, Antonio, Feng, Xuyang, Flores-Canales, Jose C., Fortunato, Santo, Gitter, Anthony, Gorska, Anna, Guan, Yuanfang, Guénoche, Alain, Gómez, Sergio, Lin, Junyuan, Hamza, Hatem, Hartmann, András, He, Shan, Heijs, Anton, Heinrich, Julian, Hescott, Benjamin, Hu, Xiaozhe, Hu, Ying, Huang, Xiaoqing, Hughitt, V. Keith, Jeon, Minji, Jeub, Lucas, Johnson, Nathan T., Joo, Keehyoung, Joung, InSuk, Jung, Sascha, Kalko, Susana G., Kamola, Piotr J., Kang, Jaewoo, Kaveelerdpotjana, Benjapun, Kim, Minjun, Kim, Yoo-Ah, Kohlbacher, Oliver, Korkin, Dmitry, Krzysztof, Kiryluk, Kunji, Khalid, Kutalik, Zoltàn, Lang-Brown, Sean, Le, Thuc Duy, Lee, Jooyoung, Lee, Sunwon, Lee, Juyong, Li, Dong, Li, Jiuyong, Liu, Lin, Loizou, Antonis, Luo, Zhenhua, Choobdar, Sarvenaz, Ahsen, Mehmet E., Crawford, Jake, Tomasoni, Mattia, Le, Thuc Duy, Li, Jiuyong, Liu, Lin, Zhang, W, Marbach, D, The DREAM Module Identification Challenge Consortium, Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Spinelli, Lionel, Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Marseille medical genetics - Centre de génétique médicale de Marseille (MMG), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Theories and Approaches of Genomic Complexity (TAGC), DREAM Module Identification Challenge Consortium, Aicheler, F., Amoroso, N., Arenas, A., Azhagesan, K., Baker, A., Banf, M., Batzoglou, S., Baudot, A., Bellotti, R., Bergmann, S., Boroevich, K.A., Brun, C., Cai, S., Caldera, M., Calderone, A., Cesareni, G., Chen, W., Chichester, C., Choobdar, S., Cowen, L., Crawford, J., Cui, H., Dao, P., De Domenico, M., Dhroso, A., Didier, G., Divine, M., Del Sol, A., Fang, T., Feng, X., Flores-Canales, J.C., Fortunato, S., Gitter, A., Gorska, A., Guan, Y., Guénoche, A., Gómez, S., Hamza, H., Hartmann, A., He, S., Heijs, A., Heinrich, J., Hescott, B., Hu, X., Hu, Y., Huang, X., Hughitt, V.K., Jeon, M., Jeub, L., Johnson, N.T., Joo, K., Joung, I., Jung, S., Kalko, S.G., Kamola, P.J., Kang, J., Kaveelerdpotjana, B., Kim, M., Kim, Y.A., Kohlbacher, O., Korkin, D., Krzysztof, K., Kunji, K., Kutalik, Z., Lage, K., Lamparter, D., Lang-Brown, S., Le, T.D., Lee, J., Lee, S., Li, D., Li, J., Lin, J., Liu, L., Loizou, A., Luo, Z., Lysenko, A., Ma, T., Mall, R., Marbach, D., Mattia, T., Medvedovic, M., Menche, J., Mercer, J., Micarelli, E., Monaco, A., Müller, F., Narayan, R., Narykov, O., Natoli, T., Norman, T., Park, S., Perfetto, L., Perrin, D., Pirrò, S., Przytycka, T.M., Qian, X., Raman, K., Ramazzotti, D., Ramsahai, E., Ravindran, B., Rennert, P., Saez-Rodriguez, J., Schärfe, C., Sharan, R., Shi, N., Shin, W., Shu, H., Sinha, H., Slonim, D.K., Spinelli, L., Srinivasan, S., Subramanian, A., Suver, C., Szklarczyk, D., Tangaro, S., Thiagarajan, S., Tichit, L., Tiede, T., Tripathi, B., Tsherniak, A., Tsunoda, T., Türei, D., Ullah, E., Vahedi, G., Valdeolivas, A., Vivek, J., von Mering, C., Waagmeester, A., Wang, B., Wang, Y., Weir, B.A., White, S., Winkler, S., Xu, K., Xu, T., Yan, C., Yang, L., Yu, K., Yu, X., Zaffaroni, G., Zaslavskiy, M., Zeng, T., Zhang, J.D., Zhang, L., Zhang, W., Zhang, X., Zhang, J., Zhou, X., Zhou, J., Zhu, H., Zhu, J., and Zuccon, G.
- Subjects
Identification methods ,Cellular signalling networks ,Computer science ,Population genetics ,[SDV]Life Sciences [q-bio] ,Quantitative Trait Loci ,Gene regulatory network ,DREAM challenge ,network ,modules ,predictions ,Genome-wide association study ,Computational biology ,Biochemistry ,Models, Biological ,Polymorphism, Single Nucleotide ,Gene regulatory networks ,Functional clustering ,03 medical and health sciences ,Human disease ,Humans ,Disease ,ddc:610 ,Protein Interaction Maps ,Molecular Biology ,ComputingMilieux_MISCELLANEOUS ,030304 developmental biology ,0303 health sciences ,Network module ,[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,Network topology ,Gene Expression Profiling ,Computational Biology ,Cell Biology ,[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,Gene expression profiling ,[SDV] Life Sciences [q-bio] ,Molecular network ,Phenotype ,Protein network ,Network Module Identification ,Analysis ,Algorithms ,Biotechnology ,Genome-Wide Association Study - Abstract
Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology., In this DREAM challenge, 75 methods for the identification of disease-relevant modules from molecular networks are compared and validated with GWAS data. The authors provide practical guidelines for users and establish benchmarks for network analysis.
- Published
- 2019
- Full Text
- View/download PDF
30. TET2 regulates early and late transitions in exhausted CD8 + T cell differentiation and limits CAR T cell function.
- Author
-
Dimitri AJ, Baxter AE, Chen GM, Hopkins CR, Rouin GT, Huang H, Kong W, Holliday CH, Wiebking V, Bartoszek R, Drury S, Dalton K, Koucky OM, Chen Z, Giles JR, Dils AT, Jung IY, O'Connor R, Collins S, Everett JK, Amses K, Sherrill-Mix S, Chandra A, Goldman N, Vahedi G, Jadlowsky JK, Young RM, Melenhorst JJ, Maude SL, Levine BL, Frey NV, Berger SL, Grupp SA, Porter DL, Herbst F, Porteus MH, Carty SA, Bushman FD, Weber EW, Wherry EJ, Jordan MS, and Fraietta JA
- Subjects
- Animals, Mice, Humans, Lymphocytic choriomeningitis virus immunology, Lymphocytic Choriomeningitis immunology, Lymphocytic Choriomeningitis virology, Immunotherapy, Adoptive methods, Cell Differentiation, CD8-Positive T-Lymphocytes immunology, CD8-Positive T-Lymphocytes metabolism, Proto-Oncogene Proteins metabolism, Proto-Oncogene Proteins genetics, Dioxygenases, DNA-Binding Proteins genetics, DNA-Binding Proteins metabolism, Receptors, Chimeric Antigen metabolism, Receptors, Chimeric Antigen immunology, Receptors, Chimeric Antigen genetics
- Abstract
CD8
+ T cell exhaustion hampers control of cancer and chronic infections and limits chimeric antigen receptor (CAR) T cell efficacy. Targeting TET2 in CAR T cells provides therapeutic benefit; however, TET2's role in exhausted T cell (TEX ) development is unclear. In chronic lymphocytic choriomeningitis virus (LCMV) infection, TET2 drove conversion from stem cell-like TEX progenitors toward terminally differentiated and effector (TEFF )-like TEX . TET2 also enforced a terminally differentiated state in the early bifurcation between TEFF and TEX , indicating broad roles for TET2 in acquisition of effector biology. To exploit the therapeutic potential of TET2, we developed clinically actionable TET2- targeted CAR T cells by disrupting TET2 via knock-in of a safety switch alongside CAR knock-in at the TRAC locus. TET2 -targeted CAR T cells exhibited restrained terminal exhaustion in vitro and enhanced antitumor responses in vivo. Thus, TET2 regulates fate transitions in TEX differentiation and can be targeted with a safety mechanism in CAR T cells for improved tumor control.- Published
- 2024
- Full Text
- View/download PDF
31. Wriggly woes: Helminths stirring up T cell trouble.
- Author
-
Pondevida CM, Jay A, and Vahedi G
- Subjects
- Animals, Humans, Helminthiasis immunology, Helminthiasis parasitology, Host-Parasite Interactions immunology, Mice, Helminths immunology, CD4-Positive T-Lymphocytes immunology
- Abstract
Understanding determinants of immune response variation is central to developing treatment options. Even et al. show that naive CD4
+ T cell transcriptional heterogeneity is altered by helminth infection leading to impaired immune responses independent of commensals., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)- Published
- 2024
- Full Text
- View/download PDF
32. Untangling the genetics of beta cell dysfunction and death in type 1 diabetes.
- Author
-
Robertson CC, Elgamal RM, Henry-Kanarek BA, Arvan P, Chen S, Dhawan S, Eizirik DL, Kaddis JS, Vahedi G, Parker SCJ, Gaulton KJ, and Soleimanpour SA
- Subjects
- Humans, Genetic Predisposition to Disease, Animals, Cell Death genetics, Genome-Wide Association Study, Diabetes Mellitus, Type 1 genetics, Diabetes Mellitus, Type 1 metabolism, Insulin-Secreting Cells metabolism
- Abstract
Background: Type 1 diabetes (T1D) is a complex multi-system disease which arises from both environmental and genetic factors, resulting in the destruction of insulin-producing pancreatic beta cells. Over the past two decades, human genetic studies have provided new insight into the etiology of T1D, including an appreciation for the role of beta cells in their own demise., Scope of Review: Here, we outline models supported by human genetic data for the role of beta cell dysfunction and death in T1D. We highlight the importance of strong evidence linking T1D genetic associations to bona fide candidate genes for mechanistic and therapeutic consideration. To guide rigorous interpretation of genetic associations, we describe molecular profiling approaches, genomic resources, and disease models that may be used to construct variant-to-gene links and to investigate candidate genes and their role in T1D., Major Conclusions: We profile advances in understanding the genetic causes of beta cell dysfunction and death at individual T1D risk loci. We discuss how genetic risk prediction models can be used to address disease heterogeneity. Further, we present areas where investment will be critical for the future use of genetics to address open questions in the development of new treatment and prevention strategies for T1D., Competing Interests: Declaration of competing interest KJG has done consulting for Genentech, received honoraria from Pfizer, and holds stock in Neurocrine biosciences. S.C. is the co-founders of OncoBeat, LLC. S.A.S has received grant funding from Ono Pharmaceutical Co., Ltd. and is a consultant for Novo Nordisk. DLE is a member of the Scientific Advisory Board of InSphero AG., (Copyright © 2024 The Authors. Published by Elsevier GmbH.. All rights reserved.)
- Published
- 2024
- Full Text
- View/download PDF
33. Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.
- Author
-
Patil AR, Schug J, Liu C, Lahori D, Descamps HC, Naji A, Kaestner KH, Faryabi RB, and Vahedi G
- Subjects
- Humans, Autoantibodies immunology, Gene Expression Profiling methods, Male, Female, Insulin-Secreting Cells metabolism, Adult, Diabetes Mellitus, Type 1 genetics, Diabetes Mellitus, Type 1 immunology, Diabetes Mellitus, Type 1 pathology, Machine Learning, Single-Cell Analysis methods, Islets of Langerhans metabolism, Islets of Langerhans immunology, Transcriptome genetics, Disease Progression
- Abstract
Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
- Published
- 2024
- Full Text
- View/download PDF
34. AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.
- Author
-
Mongia A, Zohora FT, Burget NG, Zhou Y, Saunders DC, Wang YJ, Brissova M, Powers AC, Kaestner KH, Vahedi G, Naji A, Schwartz GW, and Faryabi RB
- Subjects
- Humans, Islets of Langerhans metabolism, Islets of Langerhans cytology, Single-Cell Analysis methods, Neural Networks, Computer, CD8-Positive T-Lymphocytes metabolism, Image Cytometry methods, Proteomics methods, Diabetes Mellitus, Type 1 pathology, Diabetes Mellitus, Type 1 metabolism, Algorithms, Pancreas cytology, Pancreas metabolism
- Abstract
Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8
+ T cells infiltration in islets during type 1 diabetes progression., (© 2024. The Author(s).)- Published
- 2024
- Full Text
- View/download PDF
35. Intrinsically disordered domain of transcription factor TCF-1 is required for T cell developmental fidelity.
- Author
-
Goldman N, Chandra A, Johnson I, Sullivan MA, Patil AR, Vanderbeck A, Jay A, Zhou Y, Ferrari EK, Mayne L, Aguilan J, Xue HH, Faryabi RB, John Wherry E, Sidoli S, Maillard I, and Vahedi G
- Subjects
- Cell Differentiation genetics, Cell Lineage genetics, T Cell Transcription Factor 1 genetics, Chromatin metabolism, Transcription Factors metabolism, T-Lymphocytes metabolism
- Abstract
In development, pioneer transcription factors access silent chromatin to reveal lineage-specific gene programs. The structured DNA-binding domains of pioneer factors have been well characterized, but whether and how intrinsically disordered regions affect chromatin and control cell fate is unclear. Here, we report that deletion of an intrinsically disordered region of the pioneer factor TCF-1 (termed L1) leads to an early developmental block in T cells. The few T cells that develop from progenitors expressing TCF-1 lacking L1 exhibit lineage infidelity distinct from the lineage diversion of TCF-1-deficient cells. Mechanistically, L1 is required for activation of T cell genes and repression of GATA2-driven genes, normally reserved to the mast cell and dendritic cell lineages. Underlying this lineage diversion, L1 mediates binding of TCF-1 to its earliest target genes, which are subject to repression as T cells develop. These data suggest that the intrinsically disordered N terminus of TCF-1 maintains T cell lineage fidelity., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)
- Published
- 2023
- Full Text
- View/download PDF
36. High-throughput Oligopaint screen identifies druggable 3D genome regulators.
- Author
-
Park DS, Nguyen SC, Isenhart R, Shah PP, Kim W, Barnett RJ, Chandra A, Luppino JM, Harke J, Wai M, Walsh PJ, Abdill RJ, Yang R, Lan Y, Yoon S, Yunker R, Kanemaki MT, Vahedi G, Phillips-Cremins JE, Jain R, and Joyce EF
- Subjects
- Humans, DNA analysis, DNA metabolism, Interphase, Reproducibility of Results, RNA analysis, RNA metabolism, Signal Transduction drug effects, Cohesins, Chromatin drug effects, Chromatin genetics, Chromatin metabolism, Chromosome Positioning drug effects, Chromosomes, Human drug effects, Chromosomes, Human genetics, Chromosomes, Human metabolism, Genome, Human drug effects, Genome, Human genetics, Glycogen Synthase Kinases antagonists & inhibitors, Glycogen Synthase Kinases deficiency, Glycogen Synthase Kinases genetics, High-Throughput Screening Assays methods, Single-Cell Analysis methods
- Abstract
The human genome functions as a three-dimensional chromatin polymer, driven by a complex collection of chromosome interactions
1-3 . Although the molecular rules governing these interactions are being quickly elucidated, relatively few proteins regulating this process have been identified. Here, to address this gap, we developed high-throughput DNA or RNA labelling with optimized Oligopaints (HiDRO)-an automated imaging pipeline that enables the quantitative measurement of chromatin interactions in single cells across thousands of samples. By screening the human druggable genome, we identified more than 300 factors that influence genome folding during interphase. Among these, 43 genes were validated as either increasing or decreasing interactions between topologically associating domains. Our findings show that genetic or chemical inhibition of the ubiquitous kinase GSK3A leads to increased long-range chromatin looping interactions in a genome-wide and cohesin-dependent manner. These results demonstrate the importance of GSK3A signalling in nuclear architecture and the use of HiDRO for identifying mechanisms of spatial genome organization., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)- Published
- 2023
- Full Text
- View/download PDF
37. Quantitative control of Ets1 dosage by a multi-enhancer hub promotes Th1 cell differentiation and protects from allergic inflammation.
- Author
-
Chandra A, Yoon S, Michieletto MF, Goldman N, Ferrari EK, Abedi M, Johnson I, Fasolino M, Pham K, Joannas L, Kee BL, Henao-Mejia J, and Vahedi G
- Subjects
- Humans, Mice, Animals, Cell Differentiation genetics, Hematopoiesis, Inflammation genetics, Regulatory Sequences, Nucleic Acid, Enhancer Elements, Genetic genetics, T-Lymphocytes, Hypersensitivity genetics
- Abstract
Multi-enhancer hubs are spatial clusters of enhancers present across numerous developmental programs. Here, we studied the functional relevance of these three-dimensional structures in T cell biology. Mathematical modeling identified a highly connected multi-enhancer hub at the Ets1 locus, comprising a noncoding regulatory element that was a hotspot for sequence variation associated with allergic disease in humans. Deletion of this regulatory element in mice revealed that the multi-enhancer connectivity was dispensable for T cell development but required for CD4
+ T helper 1 (Th1) differentiation. These mice were protected from Th1-mediated colitis but exhibited overt allergic responses. Mechanistically, the multi-enhancer hub controlled the dosage of Ets1 that was required for CTCF recruitment and assembly of Th1-specific genome topology. Our findings establish a paradigm wherein multi-enhancer hubs control cellular competence to respond to an inductive cue through quantitative control of gene dosage and provide insight into how sequence variation within noncoding elements at the Ets1 locus predisposes individuals to allergic responses., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 Elsevier Inc. All rights reserved.)- Published
- 2023
- Full Text
- View/download PDF
38. Dynamic enhancer interactome promotes senescence and aging.
- Author
-
Wang L, Donahue G, Zhang C, Havas A, Lei X, Xu C, Wang W, Vahedi G, Adams PD, and Berger SL
- Abstract
Gene expression programs are regulated by enhancers which act in a context-specific manner, and can reside at great distances from their target genes. Extensive three-dimensional (3D) genome reorganization occurs in senescence, but how enhancer interactomes are reconfigured during this process is just beginning to be understood. Here we generated high-resolution contact maps of active enhancers and their target genes, assessed chromatin accessibility, and established one-dimensional maps of various histone modifications and transcription factors to comprehensively understand the regulation of enhancer configuration during senescence. Hyper-connected enhancer communities/cliques formed around genes that are highly expressed and within essential gene pathways in each cell state. In addition, motif analysis indicates the involvement of specific transcription factors in hyper-connected regulatory elements in each condition; importantly, MafK, a bZIP family transcription factor, was upregulated in senescence, and reduced expression of MafK ameliorated the senescence phenotypes. Because the accumulation of senescent cells is a key feature of aging, we further investigated enhancer connectomes in the liver of young and aged mice. Hyper-connected enhancer communities were identified during aging, which regulate essential genes that maintain cell differentiation and homeostasis. These findings reveal that hyper-connected enhancer communities correlate with high gene expression in senescence and aging and provide potential hotspots for therapeutic intervention in aging and age-associated diseases.
- Published
- 2023
- Full Text
- View/download PDF
39. Single-cell expression profiling of islets generated by the Human Pancreas Analysis Program.
- Author
-
Patil AR, Schug J, Naji A, Kaestner KH, Faryabi RB, and Vahedi G
- Subjects
- Humans, Pancreas metabolism, Islets of Langerhans metabolism
- Published
- 2023
- Full Text
- View/download PDF
40. Systems Approaches for Studying Immunity.
- Author
-
Vahedi G and Oltz EM
- Subjects
- Systems Biology, Immunity
- Published
- 2023
- Full Text
- View/download PDF
41. AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.
- Author
-
Mongia A, Saunders DC, Wang YJ, Brissova M, Powers AC, Kaestner KH, Vahedi G, Naji A, Schwartz GW, and Faryabi RB
- Abstract
Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8
+ T cells infiltration in islets during type 1 diabetes progression., Competing Interests: Competing Interests The authors declare no competing interests.- Published
- 2023
- Full Text
- View/download PDF
42. Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program.
- Author
-
Patil AR, Schug J, Naji A, Kaestner KH, Faryabi RB, and Vahedi G
- Abstract
Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. Therefore, efforts have been made by various teams including the Human Pancreas Analysis Program (HPAP) to collect pancreatic tissues from deceased organ donors with different clinical phenotypes. HPAP is designed to define the molecular pathogenesis of islet dysfunction by generating detailed datasets of functional, cellular, and molecular information in pancreatic tissues of clinically well-defined organ donors with Type 1 and Type 2 diabetes. Moreover, data generated by HPAP continously become available through a centralized database, PANC-DB, thus enabling the diabetes research community to access these multi-dimensional data prepublication. Here, we present the computational workflow for single-cell RNA-seq data analysis of 258,379 high-quality cells from the pancreatic islets of 67 human donors generated by HPAP, the largest existing scRNA-seq dataset of human pancreatic tissues. We report various computational steps including preprocessing, doublet removal, clustering and cell type annotation across single-cell RNA-seq data from islets of four distintct classes of organ donors, i.e. non-diabetic control, autoantibody positive but normoglycemic, Type 1 diabetic, and Type 2 diabetic individuals. Moreover, we present an interactive tool, called CellxGene developed by the Chan Zuckerberg initiative, to navigate these high-dimensional datasets. Our data and interactive tools provide a reliable reference for singlecell pancreatic islet biology studies, especially diabetes-related conditions.
- Published
- 2023
- Full Text
- View/download PDF
43. Multiscale 3D genome organization underlies ILC2 ontogenesis and allergic airway inflammation.
- Author
-
Michieletto MF, Tello-Cajiao JJ, Mowel WK, Chandra A, Yoon S, Joannas L, Clark ML, Jimenez MT, Wright JM, Lundgren P, Williams A, Thaiss CA, Vahedi G, and Henao-Mejia J
- Subjects
- Humans, Inflammation genetics, Inflammation metabolism, Cell Lineage, Promoter Regions, Genetic, Lymphocytes, Immunity, Innate
- Abstract
Innate lymphoid cells (ILCs) are well-characterized immune cells that play key roles in host defense and tissue homeostasis. Yet, how the three-dimensional (3D) genome organization underlies the development and functions of ILCs is unknown. Herein, we carried out an integrative analysis of the 3D genome structure, chromatin accessibility and gene expression in mature ILCs. Our results revealed that the local 3D configuration of the genome is rewired specifically at loci associated with ILC biology to promote their development and functional differentiation. Importantly, we demonstrated that the ontogenesis of ILC2s and the progression of allergic airway inflammation are determined by a unique local 3D configuration of the region containing the ILC-lineage-defining factor Id2, which is characterized by multiple interactions between the Id2 promoter and distal regulatory elements bound by the transcription factors GATA-3 and RORα, unveiling the mechanism whereby the Id2 expression is specifically controlled in group 2 ILCs., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)
- Published
- 2023
- Full Text
- View/download PDF
44. TCF-1 promotes chromatin interactions across topologically associating domains in T cell progenitors.
- Author
-
Wang W, Chandra A, Goldman N, Yoon S, Ferrari EK, Nguyen SC, Joyce EF, and Vahedi G
- Subjects
- CCCTC-Binding Factor genetics, CCCTC-Binding Factor metabolism, Cell Cycle Proteins metabolism, Gene Expression Regulation, T-Lymphocytes metabolism, Chromatin, Enhancer Elements, Genetic genetics
- Abstract
The high mobility group (HMG) transcription factor TCF-1 is essential for early T cell development. Although in vitro biochemical assays suggest that HMG proteins can serve as architectural elements in the assembly of higher-order nuclear organization, the contribution of TCF-1 on the control of three-dimensional (3D) genome structures during T cell development remains unknown. Here, we investigated the role of TCF-1 in 3D genome reconfiguration. Using gain- and loss-of-function experiments, we discovered that the co-occupancy of TCF-1 and the architectural protein CTCF altered the structure of topologically associating domains in T cell progenitors, leading to interactions between previously insulated regulatory elements and target genes at late stages of T cell development. The TCF-1-dependent gain in long-range interactions was linked to deposition of active enhancer mark H3K27ac and recruitment of the cohesin-loading factor NIPBL at active enhancers. These data indicate that TCF-1 has a role in controlling global genome organization during T cell development., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)
- Published
- 2022
- Full Text
- View/download PDF
45. α Cell dysfunction in islets from nondiabetic, glutamic acid decarboxylase autoantibody-positive individuals.
- Author
-
Doliba NM, Rozo AV, Roman J, Qin W, Traum D, Gao L, Liu J, Manduchi E, Liu C, Golson ML, Vahedi G, Naji A, Matschinsky FM, Atkinson MA, Powers AC, Brissova M, Kaestner KH, and Stoffers DA
- Subjects
- Autoantibodies, Glucagon, Glucose, Humans, Diabetes Mellitus, Type 1, Glutamate Decarboxylase
- Abstract
BACKGROUNDMultiple islet autoantibodies (AAbs) predict the development of type 1 diabetes (T1D) and hyperglycemia within 10 years. By contrast, T1D develops in only approximately 15% of individuals who are positive for single AAbs (generally against glutamic acid decarboxylase [GADA]); hence, the single GADA+ state may represent an early stage of T1D.METHODSHere, we functionally, histologically, and molecularly phenotyped human islets from nondiabetic GADA+ and T1D donors.RESULTSSimilar to the few remaining β cells in the T1D islets, GADA+ donor islets demonstrated a preserved insulin secretory response. By contrast, α cell glucagon secretion was dysregulated in both GADA+ and T1D islets, with impaired glucose suppression of glucagon secretion. Single-cell RNA-Seq of GADA+ α cells revealed distinct abnormalities in glycolysis and oxidative phosphorylation pathways and a marked downregulation of cAMP-dependent protein kinase inhibitor β (PKIB), providing a molecular basis for the loss of glucose suppression and the increased effect of 3-isobutyl-1-methylxanthine (IBMX) observed in GADA+ donor islets.CONCLUSIONWe found that α cell dysfunction was present during the early stages of islet autoimmunity at a time when β cell mass was still normal, raising important questions about the role of early α cell dysfunction in the progression of T1D.FUNDINGThis work was supported by grants from the NIH (3UC4DK112217-01S1, U01DK123594-02, UC4DK112217, UC4DK112232, U01DK123716, and P30 DK019525) and the Vanderbilt Diabetes Research and Training Center (DK20593).
- Published
- 2022
- Full Text
- View/download PDF
46. Topologically associating domains are disrupted by evolutionary genome rearrangements forming species-specific enhancer connections in mice and humans.
- Author
-
Gilbertson SE, Walter HC, Gardner K, Wren SN, Vahedi G, and Weinmann AS
- Subjects
- Animals, Enhancer Elements, Genetic genetics, Evolution, Molecular, Gene Rearrangement genetics, Genomics, Humans, Mice, Chromatin, Genome, Human
- Abstract
Distinguishing between conserved and divergent regulatory mechanisms is essential for translating preclinical research from mice to humans, yet there is a lack of information about how evolutionary genome rearrangements affect the regulation of the immune response, a rapidly evolving system. The current model is topologically associating domains (TADs) are conserved between species, buffering evolutionary rearrangements and conserving long-range interactions within a TAD. However, we find that TADs frequently span evolutionary translocation and inversion breakpoints near genes with species-specific expression in immune cells, creating unique enhancer-promoter interactions exclusive to the mouse or human genomes. This includes TADs encompassing immune-related transcription factors, cytokines, and receptors. For example, we uncover an evolutionary rearrangement that created a shared LPS-inducible regulatory module between OASL and P2RX7 in human macrophages that is absent in mice. Therefore, evolutionary genome rearrangements disrupt TAD boundaries, enabling sequence-conserved enhancer elements from divergent genomic locations between species to create unique regulatory modules., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2022
- Full Text
- View/download PDF
47. Under hypoxic conditions, MSCs affect the expression and methylation level of survival-related genes in ALL independent of apoptosis pathways in vitro.
- Author
-
Marofi F, Shomali N, Younus LA, Hassanzadeh A, Vahedi G, Kuznetsova MY, Solali S, Gharibi T, Hosseini A, Mohammed RN, Mohammadi H, Tamjidifar R, Firouzi-Amandi A, and Farshdousti Hagh M
- Subjects
- Apoptosis genetics, Bone Marrow Cells metabolism, Cell Hypoxia genetics, Humans, Hypoxia metabolism, Methionine Adenosyltransferase, Methylation, RNA, Messenger metabolism, Sirolimus, Mesenchymal Stem Cells metabolism, Precursor Cell Lymphoblastic Leukemia-Lymphoma metabolism
- Abstract
Mesenchymal stem cells (MSCs) are one of the most prominent cells in the bone marrow. MSCs can affect acute lymphocytic leukemia (ALL) cells under hypoxic conditions. With this aim, we used MOLT-4 cells as simulators of ALL cells cocultured with bone marrow mesenchymal stem cells (BMMSCs) under hypoxic conditions in vitro. Then, mRNA and protein expression of the MAT2A, PDK1, and HK2 genes were evaluated by real-time PCR and Western blot which was also followed by apoptosis measurement by a flow-cytometric method. Next, the methylation status of the target genes was investigated by MS-qPCR. Additionally, candidate gene expressions were examined after treatment with rapamycin using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. We found that the mRNA expression of the candidate genes was augmented under the hypoxic condition in which MAT2A was upregulated in cocultured cells compared to MOLT-4, while HK2 and PDK1 were downregulated. Moreover, we found an association between gene expression and promoter methylation levels of target genes. Besides, expressions of the candidate genes were decreased, while their methylation levels were promoted following treatment with rapamycin. Our results suggest an important role for the BMMSC in regulating the methylation of genes involved in cell survival in hypoxia conditions; however, we found no evidence to prove the MSCs' effect on directing malignant lymphoblastic cells to apoptosis., (© 2021 International Union of Biochemistry and Molecular Biology, Inc.)
- Published
- 2022
- Full Text
- View/download PDF
48. Stripenn detects architectural stripes from chromatin conformation data using computer vision.
- Author
-
Yoon S, Chandra A, and Vahedi G
- Subjects
- Algorithms, Computers, Genome, Chromatin genetics, Image Processing, Computer-Assisted
- Abstract
Architectural stripes tend to form at genomic regions harboring genes with salient roles in cell identity and function. Therefore, the accurate identification and quantification of these features are essential for understanding lineage-specific gene regulation. Here, we present Stripenn, an algorithm rooted in computer vision to systematically detect and quantitate architectural stripes from chromatin conformation measurements using various technologies. We demonstrate that Stripenn outperforms existing methods and highlight its biological applications in the context of B and T lymphocytes. By comparing stripes across distinct cell types and different species, we find that these chromatin features are highly conserved and form at genes with prominent roles in cell-type-specific processes. In summary, Stripenn is a computational method that borrows concepts from widely used image processing techniques to demarcate and quantify architectural stripes., (© 2022. The Author(s).)
- Published
- 2022
- Full Text
- View/download PDF
49. Transcription factors combine to paint the methylation landscape.
- Author
-
Goldman N, Chandra A, and Vahedi G
- Subjects
- CpG Islands, Epigenesis, Genetic, Humans, Paint, DNA Methylation, Transcription Factors
- Abstract
There is paucity of information about DNA methylation dynamics in immune cells. Roy et al. mapped the DNA methylation status of several thousand differentially methylated CpGs in human immune cells. They reported that the extent of cell type-specific hypermethylation is intriguingly most prevalent in adaptive immune cells rather than innate cells., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2021 Elsevier Ltd. All rights reserved.)
- Published
- 2021
- Full Text
- View/download PDF
50. Publisher Correction: BRD4 orchestrates genome folding to promote neural crest differentiation.
- Author
-
Linares-Saldana R, Kim W, Bolar NA, Zhang H, Koch-Bojalad BA, Yoon S, Shah PP, Karnay A, Park DS, Luppino JM, Nguyen SC, Padmanabhan A, Smith CL, Poleshko A, Wang Q, Li L, Srivastava D, Vahedi G, Eom GH, Blobel GA, Joyce EF, and Jain R
- Published
- 2021
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.