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Article: Repurposing Didanosine as a Potential Treatment for COVID-19 Using Single-Cell RNA Sequencing Data.

Alakwaa, Fadhl M

mSystems

2020 Volume 5, Issue 2

Abstract: As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test ... ...

Abstract	As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test potential drugs. In an urgent response to this pandemic, I developed a bioinformatics pipeline to identify compounds and drug candidates to potentially treat COVID-19. This pipeline is based on publicly available single-cell RNA sequencing (scRNA-seq) data and the drug perturbation database "Library of Integrated Network-Based Cellular Signatures" (LINCS). I developed a ranking score system that prioritizes these drugs or small molecules. The four drugs with the highest total score are didanosine, benzyl-quinazolin-4-yl-amine, camptothecin, and RO-90-7501. In conclusion, I have demonstrated the utility of bioinformatics for identifying drugs than can be repurposed for potentially treating COVID-19 patients.
Keywords	covid19
Language	English
Publishing date	2020-04-14
Publishing country	United States
Document type	Journal Article
ISSN	2379-5077
ISSN	2379-5077
DOI	10.1128/mSystems.00297-20
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Repurposing Didanosine as a Potential Treatment for COVID-19 Using Single-Cell RNA Sequencing Data

Fadhl M. Alakwaa

mSystems, Vol 5, Iss 2, p e00297-

2020 Volume 20

Abstract	As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test potential drugs. In an urgent response to this pandemic, I developed a bioinformatics pipeline to identify compounds and drug candidates to potentially treat COVID-19. This pipeline is based on publicly available single-cell RNA sequencing (scRNA-seq) data and the drug perturbation database “Library of Integrated Network-Based Cellular Signatures” (LINCS).As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test potential drugs. In an urgent response to this pandemic, I developed a bioinformatics pipeline to identify compounds and drug candidates to potentially treat COVID-19. This pipeline is based on publicly available single-cell RNA sequencing (scRNA-seq) data and the drug perturbation database “Library of Integrated Network-Based Cellular Signatures” (LINCS). I developed a ranking score system that prioritizes these drugs or small molecules. The four drugs with the highest total score are didanosine, benzyl-quinazolin-4-yl-amine, camptothecin, and RO-90-7501. In conclusion, I have demonstrated the utility of bioinformatics for identifying drugs than can be repurposed for potentially treating COVID-19 patients.
Keywords	covid-19 ; drug ; repurposing ; Microbiology ; QR1-502 ; covid19
Subject code	610
Language	English
Publishing date	2020-04-01T00:00:00Z
Publisher	American Society for Microbiology
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Repurposing didanosine as a potential treatment for covid-19 using single-cell RNA sequencing data

Alakwaa, Fadhl M.

mSystems

Abstract	As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test potential drugs. In an urgent response to this pandemic, I developed a bioinformatics pipeline to identify compounds and drug candidates to potentially treat COVID-19. This pipeline is based on publicly available single-cell RNA sequencing (scRNA-seq) data and the drug perturbation database “Library of Integrated Network-Based Cellular Signatures” (LINCS). I developed a ranking score system that prioritizes these drugs or small molecules. The four drugs with the highest total score are didanosine, benzylquinazolin-4-yl-amine, camptothecin, and RO-90-7501. In conclusion, I have demonstrated the utility of bioinformatics for identifying drugs than can be repurposed for potentially treating COVID-19 patients.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #67500
Database	COVID19

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Article ; Online: Repurposing Didanosine as a Potential Treatment for COVID-19 Using Single-Cell RNA Sequencing Data

Alakwaa, Fadhl M.

mSystems

2020 Volume 5, Issue 2

Abstract: ABSTRACT As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to ... ...

Abstract	ABSTRACT As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test potential drugs. In an urgent response to this pandemic, I developed a bioinformatics pipeline to identify compounds and drug candidates to potentially treat COVID-19. This pipeline is based on publicly available single-cell RNA sequencing (scRNA-seq) data and the drug perturbation database “Library of Integrated Network-Based Cellular Signatures” (LINCS). I developed a ranking score system that prioritizes these drugs or small molecules. The four drugs with the highest total score are didanosine, benzyl-quinazolin-4-yl-amine, camptothecin, and RO-90-7501. In conclusion, I have demonstrated the utility of bioinformatics for identifying drugs than can be repurposed for potentially treating COVID-19 patients.
Keywords	covid19
Language	English
Publisher	American Society for Microbiology
Publishing country	us
Document type	Article ; Online
ISSN	2379-5077
DOI	10.1128/msystems.00297-20
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Correction to "Bioinformatics Analysis of Metabolomics Data Unveils Association of Metabolic Signatures with Methylation in Breast Cancer".

Alakwaa, Fadhl M / Garmire, Lana X / Savelieff, Masha G

Journal of proteome research

2021 Volume 20, Issue 3, Page(s) 1817

Language	English
Publishing date	2021-02-19
Publishing country	United States
Document type	Published Erratum
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.1c00032
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Article ; Online: Bioinformatics Analysis of Metabolomics Data Unveils Association of Metabolic Signatures with Methylation in Breast Cancer.

Alakwaa, Fadhl M / Savelieff, Masha G

Journal of proteome research

2020 Volume 19, Issue 7, Page(s) 2879–2889

Abstract: Breast cancer (BC) contributes the highest global cancer mortality in women. BC tumors are highly heterogeneous, so subtyping by cell-surface markers is inadequate. Omics-driven tumor stratification is urgently needed to better understand BC and tailor ... ...

Abstract	Breast cancer (BC) contributes the highest global cancer mortality in women. BC tumors are highly heterogeneous, so subtyping by cell-surface markers is inadequate. Omics-driven tumor stratification is urgently needed to better understand BC and tailor therapies for personalized medicine. We used unsupervised
MeSH term(s)	Breast Neoplasms/genetics ; Computational Biology ; Female ; Humans ; Metabolome ; Metabolomics ; Methylation
Language	English
Publishing date	2020-01-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.9b00755
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Article ; Online: Association of mental health-related patient reported outcomes with blood pressure in adults and children with primary proteinuric glomerulopathies.

Schuchman, Matthew / Brady, Tammy M / Glenn, Dorey A / Tuttle, Katherine R / Cara-Fuentes, Gabriel / Levy, Rebecca V / Gonzalez-Vicente, Agustin / Alakwaa, Fadhl M / Srivastava, Tarak / Sethna, Christine B

Journal of nephrology

2024

Abstract: Introduction: The prevalence of mental health disorders including anxiety and depression is increasing and is linked to hypertension in healthy individuals. However, the relationship of psychosocial patient-reported outcomes on blood pressure (BP) in ... ...

Abstract	Introduction: The prevalence of mental health disorders including anxiety and depression is increasing and is linked to hypertension in healthy individuals. However, the relationship of psychosocial patient-reported outcomes on blood pressure (BP) in primary proteinuric glomerulopathies is not well characterized. This study explored longitudinal relationships between psychosocial patient-reported outcomes and BP status among individuals with proteinuric glomerulopathies. Methods: An observational cohort study was performed using data from 745 adults and children enrolled in the Nephrotic Syndrome Study Network (NEPTUNE). General Estimating Equations for linear regression and binary logistic analysis for odds ratios were performed to analyze relationships between the exposures, longitudinal Patient-Reported Outcome Measurement Information System (PROMIS) measures and BP and hypertension status as outcomes. Results: In adults, more anxiety was longitudinally associated with higher systolic and hypertensive BP. In children, fatigue was longitudinally associated with increased odds of hypertensive BP regardless of the PROMIS report method. More stress, anxiety, and depression were longitudinally associated with higher systolic BP index, higher diastolic BP index, and increased odds of hypertensive BP index in children with parent-proxy patient-reported outcomes. Discussion/conclusion: Chronically poor psychosocial patient-reported outcomes may be significantly associated with higher BP and hypertension in adults and children with primary proteinuric glomerulopathies. This interaction appears strong in children but should be interpreted with caution, as multiple confounders related to glomerular disease may influence both mental health and BP independently. That said, access to mental health resources may help control BP, and proper disease and BP management may improve overall mental health.
Language	English
Publishing date	2024-03-21
Publishing country	Italy
Document type	Journal Article
ZDB-ID	1093991-x
ISSN	1724-6059 ; 1120-3625 ; 1121-8428
ISSN (online)	1724-6059
ISSN	1120-3625 ; 1121-8428
DOI	10.1007/s40620-024-01919-6
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Article: Severe preeclampsia is not associated with significant DNA methylation changes but cell proportion changes in the cord blood - caution on the importance of confounding adjustment.

Liu, Wenting / Yang, Xiaotong / Mao, Zhixin / Du, Yuheng / Lassiter, Cameron / AlAkwaa, Fadhl M / Benny, Paula A / Garmire, Lana X

medRxiv : the preprint server for health sciences

2023

Abstract: Epigenome-wide DNA methylation analysis (EWAS) is an important approach to identify biomarkers for early disease detection and prognosis prediction, yet its results could be confounded by other factors such as cell-type heterogeneity and patient ... ...

Abstract	Epigenome-wide DNA methylation analysis (EWAS) is an important approach to identify biomarkers for early disease detection and prognosis prediction, yet its results could be confounded by other factors such as cell-type heterogeneity and patient characteristics. In this study, we address the importance of confounding adjustment by examining DNA methylation patterns in cord blood exposed to severe preeclampsia (PE), a prevalent and potentially fatal pregnancy complication. Without such adjustment, a misleading global hypomethylation pattern is obtained. However, after adjusting cell type proportions and patient clinical characteristics, most of the so-called significant CpG methylation changes associated with severe PE disappear. Rather, the major effect of PE on cord blood is through the proportion changes in different cell types. These results are validated using a previously published cord blood DNA methylation dataset, where global hypomethylation pattern was also wrongfully obtained without confounding adjustment. Additionally, several cell types significantly change as gestation progress (eg. granulocyte, nRBC, CD4T, and B cells), further confirming the importance of cell type adjustment in EWAS study of cord blood tissues. Our study urges the community to perform confounding adjustments in EWAS studies, based on cell type heterogeneity and other patient characteristics.
Language	English
Publishing date	2023-08-31
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.08.31.23294898
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Article ; Online: Interactive Effects of Empagliflozin and Hyperglycemia on Urinary Amino Acids in Individuals With Type 1 Diabetes.

Kugathasan, Luxcia / Sridhar, Vikas S / Lovblom, Leif Erik / Matta, Shane / Saliba, Afaf / Debnath, Subrata / AlAkwaa, Fadhl M / Nair, Viji / Bjornstad, Petter / Kretzler, Matthias / Perkins, Bruce A / Sharma, Kumar / Cherney, David Z I

Diabetes

2023 Volume 73, Issue 3, Page(s) 401–411

MeSH term(s)	Young Adult ; Humans ; Diabetes Mellitus, Type 1/drug therapy ; Sodium-Glucose Transporter 2 ; Leucine ; Isoleucine ; Amino Acids/metabolism ; Hyperglycemia/drug therapy ; Valine ; RNA, Transfer ; Benzhydryl Compounds ; Glucosides
Chemical Substances	empagliflozin (HDC1R2M35U) ; Sodium-Glucose Transporter 2 ; Leucine (GMW67QNF9C) ; Isoleucine (04Y7590D77) ; Amino Acids ; Valine (HG18B9YRS7) ; RNA, Transfer (9014-25-9) ; Benzhydryl Compounds ; Glucosides
Language	English
Publishing date	2023-12-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	80085-5
ISSN	1939-327X ; 0012-1797
ISSN (online)	1939-327X
ISSN	0012-1797
DOI	10.2337/db23-0694
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Article: Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data

Alakwaa, Fadhl M / Chaudhary, Kumardeep / Garmire, Lana X

Journal of proteome research. 2018 Jan. 05, v. 17, no. 1

2018

Abstract: Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it ... ...

Abstract	Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER−) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER– patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.
Keywords	ABC transporters ; absorption ; breast neoplasms ; digestion ; discriminant analysis ; estrogen receptors ; gene expression ; metabolomics ; microarray technology ; models ; patients ; prediction ; proteome ; support vector machines ; tissues
Language	English
Dates of publication	2018-0105
Size	p. 337-347.
Publishing place	American Chemical Society
Document type	Article
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.7b00595
Database	NAL-Catalogue (AGRICOLA)

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