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  1. Article ; Online: Jennifer A. Wargo, Nadim J. Ajami, and Carrie R. Daniel-MacDougall.

    Wargo, Jennifer A / Ajami, Nadim J / Daniel-MacDougall, Carrie R

    Cell reports. Medicine

    2024  Volume 5, Issue 4, Page(s) 101509

    Abstract: Dr. Jennifer A. Wargo, Dr. Nadim J. Ajami, and Dr. Carrie R. Daniel-MacDougall describe their academic and clinical work on the role of the microbiome to determine response to immunotherapies and discuss current challenges and potential needs to ... ...

    Abstract Dr. Jennifer A. Wargo, Dr. Nadim J. Ajami, and Dr. Carrie R. Daniel-MacDougall describe their academic and clinical work on the role of the microbiome to determine response to immunotherapies and discuss current challenges and potential needs to integrate their findings into clinical practice.
    Language English
    Publishing date 2024-04-01
    Publishing country United States
    Document type Journal Article
    ISSN 2666-3791
    ISSN (online) 2666-3791
    DOI 10.1016/j.xcrm.2024.101509
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Using gut microorganisms to treat cancer.

    Seo, Y David / Ajami, Nadim / Wargo, Jennifer A

    Nature medicine

    2023  Volume 29, Issue 8, Page(s) 1910–1911

    MeSH term(s) Humans ; Gastrointestinal Tract ; Neoplasms/therapy
    Language English
    Publishing date 2023-07-18
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-023-02460-y
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    Zs.A 4212: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 39: Show issues

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  3. Article ; Online: The Microbiome: the Link to Colorectal Cancer and Research Opportunities.

    Cass, Samuel H / Ajami, Nadim J / White, Michael G

    Current treatment options in oncology

    2022  Volume 23, Issue 5, Page(s) 631–644

    Abstract: Opinion statement: In recent years, we have seen an increase in the study and interest of the role of the microbiome in the development of malignancies, their progression, and evasion of therapies. This has been particularly fruitful in the case of ... ...

    Abstract Opinion statement: In recent years, we have seen an increase in the study and interest of the role of the microbiome in the development of malignancies, their progression, and evasion of therapies. This has been particularly fruitful in the case of colorectal cancer; multiple investigators have described correlative observations as well as hypotheses strengthened in preclinical studies that have begun to elucidate the critical role the gut and tumoral microbiome plays in carcinogenesis. Furthermore, these landmark studies lay the groundwork in describing the microbiome's role in carcinogenesis and provide a rich field of future study. Here, we review contemporary understandings of these observations and proposed mechanisms behind them.
    MeSH term(s) Carcinogenesis ; Colorectal Neoplasms/etiology ; Gastrointestinal Microbiome ; Humans ; Microbiota
    Language English
    Publishing date 2022-03-07
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2057351-0
    ISSN 1534-6277 ; 1527-2729
    ISSN (online) 1534-6277
    ISSN 1527-2729
    DOI 10.1007/s11864-022-00960-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 5523: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: Correction to: The Microbiome: the Link to Colorectal Cancer and Research Opportunities.

    Cass, Samuel H / Ajami, Nadim J / White, Michael G

    Current treatment options in oncology

    2022  Volume 23, Issue 5, Page(s) 774

    Language English
    Publishing date 2022-04-05
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 2057351-0
    ISSN 1534-6277 ; 1527-2729
    ISSN (online) 1534-6277
    ISSN 1527-2729
    DOI 10.1007/s11864-022-00980-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 5523: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: AI finds microbial signatures in tumours and blood across cancer types.

    Ajami, Nadim J / Wargo, Jennifer A

    Nature

    2020  Volume 579, Issue 7800, Page(s) 502–503

    MeSH term(s) Computational Biology ; Genomics ; Humans ; Microbiota ; Neoplasms
    Language English
    Publishing date 2020-03-11
    Publishing country England
    Document type News ; Comment
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/d41586-020-00637-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 26: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 9: Show issues
    Zs.MO 244: Show issues

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  6. Article: TARO: tree-aggregated factor regression for microbiome data integration.

    Mishra, Aditya K / Mahmud, Iqbal / Lorenzi, Philip L / Jenq, Robert R / Wargo, Jennifer A / Ajami, Nadim J / Peterson, Christine B

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Motivation: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data ... ...

    Abstract Motivation: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns.
    Results: We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the phylogenetic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances.
    Availability and implementation: The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package .
    Language English
    Publishing date 2023-10-19
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.10.17.562792
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Dissecting the Impact of the Gut Microbiome on Cancer Immunotherapy.

    Jain, Rakesh / Hadjigeorgiou, Andreas / Harkos, Constantinos / Mishra, Aditya / Morad, Golnaz / Johnson, Sarah / Ajami, Nadim / Wargo, Jennifer / Munn, Lance / Stylianopoulos, Triantafyllos

    Research square

    2023  

    Abstract: The gut microbiome has emerged as a key regulator of response to cancer immunotherapy. However, there is a gap in our understanding of the underlying mechanisms by which the microbiome influences immunotherapy. To this end, we developed a mathematical ... ...

    Abstract The gut microbiome has emerged as a key regulator of response to cancer immunotherapy. However, there is a gap in our understanding of the underlying mechanisms by which the microbiome influences immunotherapy. To this end, we developed a mathematical model based on i) gut microbiome data derived from preclinical studies on melanomas after fecal microbiota transplant, ii) mechanistic modeling of antitumor immune response, and iii) robust association analysis of murine and human microbiome profiles with model-predicted immune profiles. Using our model, we could distill the complexity of these murine and human studies on microbiome modulation in terms of just two model parameters: the activation and killing rate constants of immune cells. We further investigated associations between specific bacterial taxonomies and antitumor immunity and immunotherapy efficacy. This model can guide the design of studies to refine and validate mechanistic links between the microbiome and immune system.
    Language English
    Publishing date 2023-11-30
    Publishing country United States
    Document type Preprint
    DOI 10.21203/rs.3.rs-3647386/v1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch.

    Nolte Fong, Joy V / Miketinas, Derek / Moore, Linda W / Nguyen, Duc T / Graviss, Edward A / Ajami, Nadim / Patterson, Mindy A

    Nutrients

    2022  Volume 14, Issue 2

    Abstract: Individual glycemic responses following dietary intake result from complex physiological processes, and can be influenced by physical properties of foods, such as increased resistant starch (RS) from starch retrogradation. Predictive equations are needed ...

    Abstract Individual glycemic responses following dietary intake result from complex physiological processes, and can be influenced by physical properties of foods, such as increased resistant starch (RS) from starch retrogradation. Predictive equations are needed to provide personalized dietary recommendations to reduce chronic disease development. Therefore, a precision nutrition model predicting the postprandial glucose response (PPGR) in overweight women following the consumption of potatoes was formulated. Thirty overweight women participated in this randomized crossover trial. Participants consumed 250 g of hot (9.2 g RS) or cold (13.7 g RS) potatoes on two separate occasions. Baseline characteristics included demographics, 10-day dietary records, body composition, and the relative abundance (RA) and α-diversity of gut microbiota. Elastic net regression using 5-fold cross-validation predicted PPGR after potato intake. Most participants (70%) had a favorable PPGR to the cold potato. The model explained 32.2% of the variance in PPGR with the equation: 547.65 × (0 [if cold, high-RS potato], ×1, if hot, low-RS potato]) + (BMI [kg/m
    MeSH term(s) Adult ; Area Under Curve ; Blood Glucose/metabolism ; Body Mass Index ; Cross-Over Studies ; Diet ; Faecalibacterium ; Female ; Gastrointestinal Microbiome ; Glycemic Index ; Humans ; Models, Biological ; Nutritional Status ; Obesity/blood ; Obesity/microbiology ; Overweight/blood ; Overweight/microbiology ; Postprandial Period ; Resistant Starch/pharmacology ; Solanum tuberosum/chemistry ; Vegetables/chemistry ; Young Adult
    Chemical Substances Blood Glucose ; Resistant Starch
    Language English
    Publishing date 2022-01-09
    Publishing country Switzerland
    Document type Clinical Trial ; Journal Article ; Observational Study ; Validation Study
    ZDB-ID 2518386-2
    ISSN 2072-6643 ; 2072-6643
    ISSN (online) 2072-6643
    ISSN 2072-6643
    DOI 10.3390/nu14020268
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch

    Nolte Fong, Joy V. / Miketinas, Derek / Moore, Linda W. / Nguyen, Duc T. / Graviss, Edward A. / Ajami, Nadim / Patterson, Mindy A.

    Nutrients. 2022 Jan. 09, v. 14, no. 2

    2022  

    Abstract: Individual glycemic responses following dietary intake result from complex physiological processes, and can be influenced by physical properties of foods, such as increased resistant starch (RS) from starch retrogradation. Predictive equations are needed ...

    Abstract Individual glycemic responses following dietary intake result from complex physiological processes, and can be influenced by physical properties of foods, such as increased resistant starch (RS) from starch retrogradation. Predictive equations are needed to provide personalized dietary recommendations to reduce chronic disease development. Therefore, a precision nutrition model predicting the postprandial glucose response (PPGR) in overweight women following the consumption of potatoes was formulated. Thirty overweight women participated in this randomized crossover trial. Participants consumed 250 g of hot (9.2 g RS) or cold (13.7 g RS) potatoes on two separate occasions. Baseline characteristics included demographics, 10-day dietary records, body composition, and the relative abundance (RA) and α-diversity of gut microbiota. Elastic net regression using 5-fold cross-validation predicted PPGR after potato intake. Most participants (70%) had a favorable PPGR to the cold potato. The model explained 32.2% of the variance in PPGR with the equation: 547.65 × (0 [if cold, high-RS potato], ×1, if hot, low-RS potato]) + (BMI [kg/m²] × 40.66)—(insoluble fiber [g] × 49.35) + (Bacteroides [RA] × 8.69)—(Faecalibacterium [RA] × 73.49)—(Parabacteroides [RA] × 42.08) + (α-diversity × 110.87) + 292.52. This model improves the understanding of baseline characteristics that explain interpersonal variation in PPGR following potato intake and offers a tool to optimize dietary recommendations for a commonly consumed food.
    Keywords Bacteroides ; body composition ; chronic diseases ; cold ; cross-over studies ; demographic statistics ; equations ; food intake ; glucose ; glycemic control ; insoluble fiber ; intestinal microorganisms ; models ; overweight ; potatoes ; resistant starch ; retrogradation ; variance
    Language English
    Dates of publication 2022-0109
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2518386-2
    ISSN 2072-6643
    ISSN 2072-6643
    DOI 10.3390/nu14020268
    Database NAL-Catalogue (AGRICOLA)

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  10. Article: Evidence of recombination in coronaviruses implicating pangolin origins of nCoV-2019.

    Wong, Matthew C / Javornik Cregeen, Sara J / Ajami, Nadim J / Petrosino, Joseph F

    bioRxiv : the preprint server for biology

    2020  

    Abstract: A novel coronavirus (nCoV-2019) was the cause of an outbreak of respiratory illness detected in Wuhan, Hubei Province, China in December of 2019. Genomic analyses of nCoV-2019 determined a 96% resemblance with a coronavirus isolated from a bat in 2013 ( ... ...

    Abstract A novel coronavirus (nCoV-2019) was the cause of an outbreak of respiratory illness detected in Wuhan, Hubei Province, China in December of 2019. Genomic analyses of nCoV-2019 determined a 96% resemblance with a coronavirus isolated from a bat in 2013 (RaTG13); however, the receptor binding motif (RBM) of these two genomes share low sequence similarity. This divergence suggests a possible alternative source for the RBM coding sequence in nCoV-2019. We identified high sequence similarity in the RBM between nCoV-2019 and a coronavirus genome reconstructed from a viral metagenomic dataset from pangolins possibly indicating a more complex origin for nCoV-2019.
    Keywords covid19
    Language English
    Publishing date 2020-02-13
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2020.02.07.939207
    Database MEDical Literature Analysis and Retrieval System OnLINE

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