Publications
1.
Alsaafeen, Besan H.; Ali, Bassam R.; Elkord, Eyad
Resistance mechanisms to immune checkpoint inhibitors: updated insights Journal Article
In: Molecular Cancer, vol. 24, no. 1, 2025, ISSN: 1476-4598, (Publisher Copyright: © The Author(s) 2025.).
@article{60ace5f9c89f4a5ca1a6ddeaea8d8a7b,
title = {Resistance mechanisms to immune checkpoint inhibitors: updated insights},
author = {Besan H. Alsaafeen and Bassam R. Ali and Eyad Elkord},
doi = {10.1186/s12943-024-02212-7},
issn = {1476-4598},
year = {2025},
date = {2025-12-01},
journal = {Molecular Cancer},
volume = {24},
number = {1},
publisher = {BioMed Central},
abstract = {The last decade has witnessed unprecedented succusses with the use of immune checkpoint inhibitors in treating cancer. Nevertheless, the proportion of patients who respond favorably to the treatment remained rather modest, partially due to treatment resistance. This has fueled a wave of research into potential mechanisms of resistance to immune checkpoint inhibitors which can be classified into primary resistance or acquired resistance after an initial response. In the current review, we summarize what is known so far about the mechanisms of resistance in terms of being tumor-intrinsic or tumor-extrinsic taking into account the multimodal crosstalk between the tumor, immune system compartment and other host-related factors.},
note = {Publisher Copyright: © The Author(s) 2025.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The last decade has witnessed unprecedented succusses with the use of immune checkpoint inhibitors in treating cancer. Nevertheless, the proportion of patients who respond favorably to the treatment remained rather modest, partially due to treatment resistance. This has fueled a wave of research into potential mechanisms of resistance to immune checkpoint inhibitors which can be classified into primary resistance or acquired resistance after an initial response. In the current review, we summarize what is known so far about the mechanisms of resistance in terms of being tumor-intrinsic or tumor-extrinsic taking into account the multimodal crosstalk between the tumor, immune system compartment and other host-related factors.
2.
Statsenko, Yauhen; Liaonchyk, Katsiaryna; Morozova, Darya; Voitetskii, Roman; Pazniak, Mikalai; Likhorad, Elena; Pazniak, Aleh; Beliakouski, Pavel; Abelskyi, Dmitriy; Smetanina, Darya; Simiyu, Gillian Lylian; Gorkom, Klaus N. -V.; Mahmoud, Tahra A. Al; Aldhaheri, Huda; Rokers, Bas; Ljubisavljevic, Milos
Precision medicine in modelling effectiveness of corneal cross-linking for keratoconus Journal Article
In: Heliyon, pp. e43050, 2025, ISSN: 2405-8440.
@article{STATSENKO2025e43050,
title = {Precision medicine in modelling effectiveness of corneal cross-linking for keratoconus},
author = {Yauhen Statsenko and Katsiaryna Liaonchyk and Darya Morozova and Roman Voitetskii and Mikalai Pazniak and Elena Likhorad and Aleh Pazniak and Pavel Beliakouski and Dmitriy Abelskyi and Darya Smetanina and Gillian Lylian Simiyu and Klaus N. -V. Gorkom and Tahra A. Al Mahmoud and Huda Aldhaheri and Bas Rokers and Milos Ljubisavljevic},
url = {https://www.sciencedirect.com/science/article/pii/S2405844025014318},
doi = {https://doi.org/10.1016/j.heliyon.2025.e43050},
issn = {2405-8440},
year = {2025},
date = {2025-01-01},
journal = {Heliyon},
pages = {e43050},
abstract = {Background: Corneal collagen cross-linking (CXL) is the treatment that halts progression of keratoconus (KC). However, CXL effectiveness varies among patients. Aim: The aim of the study is to detect a combination of preoperative diagnostic findings that would adequately reflect CXL outcomes. Methods: We reviewed medical charts of 107 KC patients (131 eyes), excluding cases of re-CXL and corneal diseases other than KC. The study dataset consisted of 796 pairs of observations before and after the treatment. After correlation feature selection, we created models of maximal corneal power (Kmax). Results: CXL is less effective at advanced stages of KC: pre- and postoperative keratometry readings are strongly correlated (r=0.92, p<0.005 for Kmax). A linear model of postoperative change in Kmax outperforms the second-degree equation. However, their accuracy differs insubstantially. The quadratic function depicts a sustainable Kmax reduction for 2 years, a subsequent 4-month-long plateau, and a rise in the maximal corneal power afterwards. The optimal model of Kmax is XGB (2.26±3.18% MAE/ROV). Conclusions: The findings advocate for a personalised approach to candidate selection for CXL. Personal risk assessment requires thoroughly inspecting the individual with pachymetry, visiometry, refractometry and topography tests.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Corneal collagen cross-linking (CXL) is the treatment that halts progression of keratoconus (KC). However, CXL effectiveness varies among patients. Aim: The aim of the study is to detect a combination of preoperative diagnostic findings that would adequately reflect CXL outcomes. Methods: We reviewed medical charts of 107 KC patients (131 eyes), excluding cases of re-CXL and corneal diseases other than KC. The study dataset consisted of 796 pairs of observations before and after the treatment. After correlation feature selection, we created models of maximal corneal power (Kmax). Results: CXL is less effective at advanced stages of KC: pre- and postoperative keratometry readings are strongly correlated (r=0.92, p<0.005 for Kmax). A linear model of postoperative change in Kmax outperforms the second-degree equation. However, their accuracy differs insubstantially. The quadratic function depicts a sustainable Kmax reduction for 2 years, a subsequent 4-month-long plateau, and a rise in the maximal corneal power afterwards. The optimal model of Kmax is XGB (2.26±3.18% MAE/ROV). Conclusions: The findings advocate for a personalised approach to candidate selection for CXL. Personal risk assessment requires thoroughly inspecting the individual with pachymetry, visiometry, refractometry and topography tests.
3.
Alsaafeen, Besan H.; Ali, Bassam R.; Elkord, Eyad
Combinational therapeutic strategies to overcome resistance to immune checkpoint inhibitors Journal Article
In: Frontiers in Immunology, vol. Volume 16 - 2025, 2025, ISSN: 1664-3224.
@article{10.3389/fimmu.2025.1546717,
title = {Combinational therapeutic strategies to overcome resistance to immune checkpoint inhibitors},
author = {Besan H. Alsaafeen and Bassam R. Ali and Eyad Elkord},
url = {https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1546717},
doi = {10.3389/fimmu.2025.1546717},
issn = {1664-3224},
year = {2025},
date = {2025-01-01},
journal = {Frontiers in Immunology},
volume = {Volume 16 - 2025},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
4.
Alawathugoda, Thilina T.; Sheikh, Muhammad Abid; Challagandla, Anil Kumar; Dheen, S. Thameem; Emerald, Bright Starling; Ansari, Suraiya Anjum
In: Journal of Biological Chemistry, vol. 301, no. 2, pp. 108173, 2025, ISSN: 0021-9258.
@article{ALAWATHUGODA2025108173,
title = {Maternal obesity alters histone modifications mediated by the interaction between EZH2 and AMPK, impairing neural differentiation in the developing embryonic brain cortex},
author = {Thilina T. Alawathugoda and Muhammad Abid Sheikh and Anil Kumar Challagandla and S. Thameem Dheen and Bright Starling Emerald and Suraiya Anjum Ansari},
url = {https://www.sciencedirect.com/science/article/pii/S0021925825000201},
doi = {https://doi.org/10.1016/j.jbc.2025.108173},
issn = {0021-9258},
year = {2025},
date = {2025-01-01},
journal = {Journal of Biological Chemistry},
volume = {301},
number = {2},
pages = {108173},
abstract = {Neurodevelopmental disorders have complex origins that manifest early during embryonic growth and are associated with intricate gene regulation dynamics. A perturbed metabolic environment such as hyperglycemia or dyslipidemia, particularly due to maternal obesity, poses a threat to the optimal development of the embryonic central nervous system. Accumulating evidence suggests that these metabolic irregularities during pregnancy may alter neurogenesis pathways, thereby predisposing the developing fetus to neurodevelopmental disorders. One primary mechanism through which such disruptions may occur involves changes in histone modifications resulting from fluctuations in the expression of histone-modifying enzymes or the availability of their substrates. Herein, we have used a rat model of maternal obesity induced by a high-fat diet before and during gestation to investigate the cellular and molecular repercussions of maternal obesity on embryonic cortical neurogenesis. Maternal obesity impairs neurogenesis by reducing cell proliferation, increasing neuronal marker expression, and shifting development toward astrogliogenesis. Differentially expressed genes revealed disruptions in key developmental signaling pathways and reduced AKT phosphorylation, particularly at E14.5. These changes were associated with epigenetic alterations, mainly the differential expression and phosphorylation of EZH2 and subsequent changes in global histone modifications. Chromatin immunoprecipitation sequencing revealed reduced H3K27me3 at genes upregulated due to maternal obesity, which could have resulted from reduced expression and increased phosphorylation of EZH2 at Thr311. Interestingly, EZH2 also showed increased O-GlcNAcylation in high-fat diet embryos along with increased association with AMPK-Thr172 in accordance with previous studies showing that Ampk catalyzes EZH2-Thr311p. These results suggest that an epigenetic gene regulatory mechanism mediated by Ampk and Ezh2 interactions resulted in reduced H3K27me3 and derepression of key developmental genes, which could have led to cell fate changes observed in the developing embryo brain cortex due to maternal obesity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neurodevelopmental disorders have complex origins that manifest early during embryonic growth and are associated with intricate gene regulation dynamics. A perturbed metabolic environment such as hyperglycemia or dyslipidemia, particularly due to maternal obesity, poses a threat to the optimal development of the embryonic central nervous system. Accumulating evidence suggests that these metabolic irregularities during pregnancy may alter neurogenesis pathways, thereby predisposing the developing fetus to neurodevelopmental disorders. One primary mechanism through which such disruptions may occur involves changes in histone modifications resulting from fluctuations in the expression of histone-modifying enzymes or the availability of their substrates. Herein, we have used a rat model of maternal obesity induced by a high-fat diet before and during gestation to investigate the cellular and molecular repercussions of maternal obesity on embryonic cortical neurogenesis. Maternal obesity impairs neurogenesis by reducing cell proliferation, increasing neuronal marker expression, and shifting development toward astrogliogenesis. Differentially expressed genes revealed disruptions in key developmental signaling pathways and reduced AKT phosphorylation, particularly at E14.5. These changes were associated with epigenetic alterations, mainly the differential expression and phosphorylation of EZH2 and subsequent changes in global histone modifications. Chromatin immunoprecipitation sequencing revealed reduced H3K27me3 at genes upregulated due to maternal obesity, which could have resulted from reduced expression and increased phosphorylation of EZH2 at Thr311. Interestingly, EZH2 also showed increased O-GlcNAcylation in high-fat diet embryos along with increased association with AMPK-Thr172 in accordance with previous studies showing that Ampk catalyzes EZH2-Thr311p. These results suggest that an epigenetic gene regulatory mechanism mediated by Ampk and Ezh2 interactions resulted in reduced H3K27me3 and derepression of key developmental genes, which could have led to cell fate changes observed in the developing embryo brain cortex due to maternal obesity.
5.
Rustamov, Jaloliddin; Rustamov, Zahiriddin; Mohamad, Mohd Saberi; Zaki, Nazar; Tenaiji, Amal Al; Harbi, Mariam Al; Jasmi, Fatma Al
An expert rule-based approach for identifying infantile-onset Pompe disease patients using retrospective electronic health records Journal Article
In: Scientific reports, vol. 14, no. 1, 2024, ISSN: 2045-2322, (Publisher Copyright: © The Author(s) 2024.).
@article{09e254e683cd4971ba4b1af893f94a86,
title = {An expert rule-based approach for identifying infantile-onset Pompe disease patients using retrospective electronic health records},
author = {Jaloliddin Rustamov and Zahiriddin Rustamov and Mohd Saberi Mohamad and Nazar Zaki and Amal Al Tenaiji and Mariam Al Harbi and Fatma Al Jasmi},
doi = {10.1038/s41598-024-72259-5},
issn = {2045-2322},
year = {2024},
date = {2024-12-01},
journal = {Scientific reports},
volume = {14},
number = {1},
publisher = {Nature Research},
abstract = {Pompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due to an enzyme deficiency, particularly harming the heart and muscles. Infantile-onset Pompe disease (IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the UAE to develop an expert rule-based screening approach operationalized through a dashboard. The study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were formulated to identify potential high-risk IOPD patients based on their age, particular symptoms, and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and specificity. The proposed approach accurately identified five true positives, one false negative, and four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA depletion syndrome 12-A (SLC25A4 gene), Immunodeficiency-71 (ARPC1B mutation), Niemann–Pick disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis. The proposed approach of integrating expert rules with a dashboard facilitated efficient data visualization and automated patient screening, which aids in the early detection of Pompe disease. Future studies are encouraged to investigate the application of machine learning methodologies to enhance further the precision and efficiency of identifying patients with IOPD.},
note = {Publisher Copyright: © The Author(s) 2024.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due to an enzyme deficiency, particularly harming the heart and muscles. Infantile-onset Pompe disease (IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the UAE to develop an expert rule-based screening approach operationalized through a dashboard. The study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were formulated to identify potential high-risk IOPD patients based on their age, particular symptoms, and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and specificity. The proposed approach accurately identified five true positives, one false negative, and four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA depletion syndrome 12-A (SLC25A4 gene), Immunodeficiency-71 (ARPC1B mutation), Niemann–Pick disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis. The proposed approach of integrating expert rules with a dashboard facilitated efficient data visualization and automated patient screening, which aids in the early detection of Pompe disease. Future studies are encouraged to investigate the application of machine learning methodologies to enhance further the precision and efficiency of identifying patients with IOPD.
6.
Idriss, Ienas; Ali, Abdelmoneim H.; Alam, Aftab; Fernandez-Cabezudo, Maria; Ayyash, Mutamed; al-Ramadi, Basel K.
Differential in vitro cytotoxic effects and metabolomic insights into raw and powdered Manuka honey through UPLC-Q-TOF-MS Journal Article
In: Scientific reports, vol. 14, no. 1, 2024, ISSN: 2045-2322, (Publisher Copyright: © The Author(s) 2024.).
@article{8ac5f299f42549c2b15e39f79070e873,
title = {Differential in vitro cytotoxic effects and metabolomic insights into raw and powdered Manuka honey through UPLC-Q-TOF-MS},
author = {Ienas Idriss and Abdelmoneim H. Ali and Aftab Alam and Maria Fernandez-Cabezudo and Mutamed Ayyash and Basel K. al-Ramadi},
doi = {10.1038/s41598-024-68387-7},
issn = {2045-2322},
year = {2024},
date = {2024-12-01},
journal = {Scientific reports},
volume = {14},
number = {1},
publisher = {Nature Research},
abstract = {Manuka honey (MH) has garnered much attention due to its remarkable antimicrobial, anticancer, immunomodulatory and wound-healing properties. This study compared the antiproliferative effects of raw and powdered MH (pMH) on various human and murine cancer cell lines. A detailed metabolomics analysis was also carried out using untargeted ultrahigh-performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry (UPLC-Q-TOF-MS) to compare the constituents in raw MH and pMH. The results of the viability studies showed that both raw MH and pMH caused a dose-dependent inhibition of tumor cell growth at concentrations of > 1% w/v (equivalent to ~ 10 mg/ml). A differential susceptibility to MH was observed among the cell lines with the human MDA-MB-231 and A549 cells and murine B16.F10 cells being relatively resistant to MH while the murine MC38 colorectal adeno-carcinoma cells showing the most sensitivity. The effect of raw MH and pMH on cell viability was validated using 2 indepndent assays. Metabolomics analysis detected 2440 compounds, out of which 833 were successfully identified. Among these, 90 phytochemical compounds, predominantly comprising terpenoids, flavonoids, coumarins and derivatives, and phenylpropanoic acids, and 79 lipids were identifiable. Significant differences in 5 metabolite classes, including flavonoids, phenols, terpenoids, carbohydrates, and organic acids were observed between the raw and pMH. Moreover, several altered metabolic pathways were identified in pMH compared to raw MH, such as energy metabolism, amino acid metabolism, and various other pathways that collectively influence biological functions associated with cellular growth, signaling, and stress response.},
note = {Publisher Copyright: © The Author(s) 2024.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Manuka honey (MH) has garnered much attention due to its remarkable antimicrobial, anticancer, immunomodulatory and wound-healing properties. This study compared the antiproliferative effects of raw and powdered MH (pMH) on various human and murine cancer cell lines. A detailed metabolomics analysis was also carried out using untargeted ultrahigh-performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry (UPLC-Q-TOF-MS) to compare the constituents in raw MH and pMH. The results of the viability studies showed that both raw MH and pMH caused a dose-dependent inhibition of tumor cell growth at concentrations of > 1% w/v (equivalent to ~ 10 mg/ml). A differential susceptibility to MH was observed among the cell lines with the human MDA-MB-231 and A549 cells and murine B16.F10 cells being relatively resistant to MH while the murine MC38 colorectal adeno-carcinoma cells showing the most sensitivity. The effect of raw MH and pMH on cell viability was validated using 2 indepndent assays. Metabolomics analysis detected 2440 compounds, out of which 833 were successfully identified. Among these, 90 phytochemical compounds, predominantly comprising terpenoids, flavonoids, coumarins and derivatives, and phenylpropanoic acids, and 79 lipids were identifiable. Significant differences in 5 metabolite classes, including flavonoids, phenols, terpenoids, carbohydrates, and organic acids were observed between the raw and pMH. Moreover, several altered metabolic pathways were identified in pMH compared to raw MH, such as energy metabolism, amino acid metabolism, and various other pathways that collectively influence biological functions associated with cellular growth, signaling, and stress response.
7.
Khader, Thanumol Abdul; Ahmad, Waqar; Akhlaq, Shaima; Panicker, Neena Gopinathan; Gull, Bushra; Baby, Jasmin; Rizvi, Tahir A.; Mustafa, Farah
Transactivation of the novel 5’ cis-acting element of mouse mammary tumor virus (MMTV) by human retroviral transactivators Tat and Tax Journal Article
In: Communications Biology, vol. 7, no. 1, 2024, ISSN: 2399-3642, (Publisher Copyright: © The Author(s) 2024.).
@article{bd544bb8c36b46bca6f2fb4c7c916e9d,
title = {Transactivation of the novel 5’ cis-acting element of mouse mammary tumor virus (MMTV) by human retroviral transactivators Tat and Tax},
author = {Thanumol Abdul Khader and Waqar Ahmad and Shaima Akhlaq and Neena Gopinathan Panicker and Bushra Gull and Jasmin Baby and Tahir A. Rizvi and Farah Mustafa},
doi = {10.1038/s42003-024-07139-9},
issn = {2399-3642},
year = {2024},
date = {2024-12-01},
journal = {Communications Biology},
volume = {7},
number = {1},
publisher = {Springer Nature},
abstract = {The mouse mammary tumor virus (MMTV) encodes a 5’ element crucial for transcription of its genome along with the Rem/Rem-responsive element (RmRE) responsible for nuclear export of this unspliced RNA. Whether the 5’ element is Rem-responsive or has any functional interaction with host/viral factors to facilitate MMTV gene expression was tested in this study. Our results reveal that the 5’ element is non-responsive to Rem, but can be transactivated by both HIV Tat and HTLV-1 Tax activators. Reciprocally, MMTV could transactivate not only HIV TAR (similar to HTLV Tax), but also its 5’ element. Furthermore, we reveal involvement of pTEFb, a general elongation factor associated with transactivation by Tat/Tax. This makes MMTV the first betaretrovirus to encode both Rem/RRE and Tat/TAR-Tax/TRE-like transcription regulatory systems. This study should enhance not only our understanding of retrovirus replication and virally-induced cancers/immunodeficiency syndromes, but also development of improved retroviral vectors for human gene therapy. (Figure presented.)},
note = {Publisher Copyright: © The Author(s) 2024.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The mouse mammary tumor virus (MMTV) encodes a 5’ element crucial for transcription of its genome along with the Rem/Rem-responsive element (RmRE) responsible for nuclear export of this unspliced RNA. Whether the 5’ element is Rem-responsive or has any functional interaction with host/viral factors to facilitate MMTV gene expression was tested in this study. Our results reveal that the 5’ element is non-responsive to Rem, but can be transactivated by both HIV Tat and HTLV-1 Tax activators. Reciprocally, MMTV could transactivate not only HIV TAR (similar to HTLV Tax), but also its 5’ element. Furthermore, we reveal involvement of pTEFb, a general elongation factor associated with transactivation by Tat/Tax. This makes MMTV the first betaretrovirus to encode both Rem/RRE and Tat/TAR-Tax/TRE-like transcription regulatory systems. This study should enhance not only our understanding of retrovirus replication and virally-induced cancers/immunodeficiency syndromes, but also development of improved retroviral vectors for human gene therapy. (Figure presented.)
8.
Gariballa, Nesrin; Mohamed, Feda; Badawi, Sally; Ali, Bassam R.
In: Journal of Biomedical Science, vol. 31, no. 1, 2024, ISSN: 1021-7770, (Publisher Copyright: © The Author(s) 2024.).
@article{c8e35c62ab0f4b04aa44de8162742f05,
title = {The double whammy of ER-retention and dominant-negative effects in numerous autosomal dominant diseases: significance in disease mechanisms and therapy},
author = {Nesrin Gariballa and Feda Mohamed and Sally Badawi and Bassam R. Ali},
doi = {10.1186/s12929-024-01054-1},
issn = {1021-7770},
year = {2024},
date = {2024-12-01},
journal = {Journal of Biomedical Science},
volume = {31},
number = {1},
publisher = {BioMed Central},
abstract = {The endoplasmic reticulum (ER) employs stringent quality control mechanisms to ensure the integrity of protein folding, allowing only properly folded, processed and assembled proteins to exit the ER and reach their functional destinations. Mutant proteins unable to attain their correct tertiary conformation or form complexes with their partners are retained in the ER and subsequently degraded through ER-associated protein degradation (ERAD) and associated mechanisms. ER retention contributes to a spectrum of monogenic diseases with diverse modes of inheritance and molecular mechanisms. In autosomal dominant diseases, when mutant proteins get retained in the ER, they can interact with their wild-type counterparts. This interaction may lead to the formation of mixed dimers or aberrant complexes, disrupting their normal trafficking and function in a dominant-negative manner. The combination of ER retention and dominant-negative effects has been frequently documented to cause a significant loss of functional proteins, thereby exacerbating disease severity. This review aims to examine existing literature and provide insights into the impact of dominant-negative effects exerted by mutant proteins retained in the ER in a range of autosomal dominant diseases including skeletal and connective tissue disorders, vascular disorders, neurological disorders, eye disorders and serpinopathies. Most crucially, we aim to emphasize the importance of this area of research, offering substantial potential for understanding the factors influencing phenotypic variability associated with genetic variants. Furthermore, we highlight current and prospective therapeutic approaches targeted at ameliorating the effects of mutations exhibiting dominant-negative effects. These approaches encompass experimental studies exploring treatments and their translation into clinical practice.},
note = {Publisher Copyright: © The Author(s) 2024.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The endoplasmic reticulum (ER) employs stringent quality control mechanisms to ensure the integrity of protein folding, allowing only properly folded, processed and assembled proteins to exit the ER and reach their functional destinations. Mutant proteins unable to attain their correct tertiary conformation or form complexes with their partners are retained in the ER and subsequently degraded through ER-associated protein degradation (ERAD) and associated mechanisms. ER retention contributes to a spectrum of monogenic diseases with diverse modes of inheritance and molecular mechanisms. In autosomal dominant diseases, when mutant proteins get retained in the ER, they can interact with their wild-type counterparts. This interaction may lead to the formation of mixed dimers or aberrant complexes, disrupting their normal trafficking and function in a dominant-negative manner. The combination of ER retention and dominant-negative effects has been frequently documented to cause a significant loss of functional proteins, thereby exacerbating disease severity. This review aims to examine existing literature and provide insights into the impact of dominant-negative effects exerted by mutant proteins retained in the ER in a range of autosomal dominant diseases including skeletal and connective tissue disorders, vascular disorders, neurological disorders, eye disorders and serpinopathies. Most crucially, we aim to emphasize the importance of this area of research, offering substantial potential for understanding the factors influencing phenotypic variability associated with genetic variants. Furthermore, we highlight current and prospective therapeutic approaches targeted at ameliorating the effects of mutations exhibiting dominant-negative effects. These approaches encompass experimental studies exploring treatments and their translation into clinical practice.
9.
Shurrab, Saeed; Guerra-Manzanares, Alejandro; Magid, Amani; Piechowski-Jozwiak, Bartlomiej; Atashzar, S. Farokh; Shamout, Farah E.
Multimodal Machine Learning for Stroke Prognosis and Diagnosis: A Systematic Review Journal Article
In: IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 11, pp. 6958-6973, 2024, ISSN: 2168-2208.
@article{10643640,
title = {Multimodal Machine Learning for Stroke Prognosis and Diagnosis: A Systematic Review},
author = {Saeed Shurrab and Alejandro Guerra-Manzanares and Amani Magid and Bartlomiej Piechowski-Jozwiak and S. Farokh Atashzar and Farah E. Shamout},
doi = {10.1109/JBHI.2024.3448238},
issn = {2168-2208},
year = {2024},
date = {2024-11-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {28},
number = {11},
pages = {6958-6973},
abstract = {Stroke is a life-threatening medical condition that could lead to mortality or significant sensorimotor deficits. Various machine learning techniques have been successfully used to detect and predict stroke-related outcomes. Considering the diversity in the type of clinical modalities involved during management of patients with stroke, such as medical images, bio-signals, and clinical data, multimodal machine learning has become increasingly popular. Thus, we conducted a systematic literature review to understand the current status of state-of-the-art multimodal machine learning methods for stroke prognosis and diagnosis. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during literature search and selection, our results show that the most dominant techniques are related to the fusion paradigm, specifically early, joint and late fusion. We discuss opportunities to leverage other multimodal learning paradigms, such as multimodal translation and alignment, which are generally less explored. We also discuss the scale of datasets and types of modalities used to develop existing models, highlighting opportunities for the creation of more diverse multimodal datasets. Finally, we present ongoing challenges and provide a set of recommendations to drive the next generation of multimodal learning methods for improved prognosis and diagnosis of patients with stroke.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stroke is a life-threatening medical condition that could lead to mortality or significant sensorimotor deficits. Various machine learning techniques have been successfully used to detect and predict stroke-related outcomes. Considering the diversity in the type of clinical modalities involved during management of patients with stroke, such as medical images, bio-signals, and clinical data, multimodal machine learning has become increasingly popular. Thus, we conducted a systematic literature review to understand the current status of state-of-the-art multimodal machine learning methods for stroke prognosis and diagnosis. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during literature search and selection, our results show that the most dominant techniques are related to the fusion paradigm, specifically early, joint and late fusion. We discuss opportunities to leverage other multimodal learning paradigms, such as multimodal translation and alignment, which are generally less explored. We also discuss the scale of datasets and types of modalities used to develop existing models, highlighting opportunities for the creation of more diverse multimodal datasets. Finally, we present ongoing challenges and provide a set of recommendations to drive the next generation of multimodal learning methods for improved prognosis and diagnosis of patients with stroke.
10.
Nasarudin, Nurul Athirah; Jasmi, Fatma Al; Sinnott, Richard O.; Zaki, Nazar; Ashwal, Hany Al; Mohamed, Elfadil A.; Mohamad, Mohd Saberi
A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction Journal Article
In: Artificial Intelligence Review, vol. 57, no. 9, 2024, ISSN: 0269-2821, (Publisher Copyright: © The Author(s) 2024.).
@article{765a2f6f47d1462982893c9fd3cfa1a2,
title = {A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction},
author = {Nurul Athirah Nasarudin and Fatma Al Jasmi and Richard O. Sinnott and Nazar Zaki and Hany Al Ashwal and Elfadil A. Mohamed and Mohd Saberi Mohamad},
doi = {10.1007/s10462-024-10876-2},
issn = {0269-2821},
year = {2024},
date = {2024-09-01},
journal = {Artificial Intelligence Review},
volume = {57},
number = {9},
publisher = {Springer Netherlands},
abstract = {A fundamental obstacle to healthcare transformation continues to be the acquisition of knowledge and insightful data from complex, high dimensional, and heterogeneous biological data. As technology has improved, a wide variety of data sources, including omics data, imaging data, and health records, have been available for use in healthcare research contexts. Electronic health records (EHRs), which are digitalized versions of medical records, have given researchers a significant chance to create computational methods for analyzing healthcare data. EHR systems typically keep track of all the data relating to a patient’s medical history, including clinical notes, demographic background, and diagnosis details. EHR data can offer valuable insights and support doctors in making better decisions related to disease and diagnostic forecasts. As a result, several academics use deep learning to forecast diseases and track health trajectories in EHR. Recent advances in deep learning technology have produced innovative and practical paradigms for building end-to-end learning models. However, scholars have limited access to online HER databases, and there is an inherent need to address this issue. This research examines deep learning models, their architectures, and readily accessible EHR online databases. The goal of this paper is to examine how various architectures, models, and databases differ in terms of features and usability. It is anticipated that the outcomes of this review will lead to the development of more robust deep learning models that facilitate medical decision-making processes based on EHR data and inform efforts to support the selection of architectures, models, and databases for specific research purposes.},
note = {Publisher Copyright: © The Author(s) 2024.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A fundamental obstacle to healthcare transformation continues to be the acquisition of knowledge and insightful data from complex, high dimensional, and heterogeneous biological data. As technology has improved, a wide variety of data sources, including omics data, imaging data, and health records, have been available for use in healthcare research contexts. Electronic health records (EHRs), which are digitalized versions of medical records, have given researchers a significant chance to create computational methods for analyzing healthcare data. EHR systems typically keep track of all the data relating to a patient’s medical history, including clinical notes, demographic background, and diagnosis details. EHR data can offer valuable insights and support doctors in making better decisions related to disease and diagnostic forecasts. As a result, several academics use deep learning to forecast diseases and track health trajectories in EHR. Recent advances in deep learning technology have produced innovative and practical paradigms for building end-to-end learning models. However, scholars have limited access to online HER databases, and there is an inherent need to address this issue. This research examines deep learning models, their architectures, and readily accessible EHR online databases. The goal of this paper is to examine how various architectures, models, and databases differ in terms of features and usability. It is anticipated that the outcomes of this review will lead to the development of more robust deep learning models that facilitate medical decision-making processes based on EHR data and inform efforts to support the selection of architectures, models, and databases for specific research purposes.
11.
Kumar, Kukkala Kiran; Aburawi, Elhadi Husein; Ljubisavljevic, Milos; Leow, Melvin Khee Shing; Feng, Xu; Ansari, Suraiya Anjum; Emerald, Bright Starling
Exploring histone deacetylases in type 2 diabetes mellitus: pathophysiological insights and therapeutic avenues Journal Article
In: Clinical Epigenetics, vol. 16, no. 1, pp. 78, 2024, ISSN: 1868-7083.
@article{Kumar2024,
title = {Exploring histone deacetylases in type 2 diabetes mellitus: pathophysiological insights and therapeutic avenues},
author = {Kukkala Kiran Kumar and Elhadi Husein Aburawi and Milos Ljubisavljevic and Melvin Khee Shing Leow and Xu Feng and Suraiya Anjum Ansari and Bright Starling Emerald},
url = {https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-024-01692-0},
doi = {10.1186/s13148-024-01692-0},
issn = {1868-7083},
year = {2024},
date = {2024-06-01},
journal = {Clinical Epigenetics},
volume = {16},
number = {1},
pages = {78},
abstract = {Diabetes mellitus is a chronic disease that impairs metabolism, and its prevalence has reached an epidemic proportion globally. Most people affected are with type 2 diabetes mellitus (T2DM), which is caused by a decline in the numbers or functioning of pancreatic endocrine islet cells, specifically the $β$ -cells that release insulin in sufficient quantity to overcome any insulin resistance of the metabolic tissues. Genetic and epigenetic factors have been implicated as the main contributors to the T2DM. Epigenetic modifiers, histone deacetylases (HDACs), are enzymes that remove acetyl groups from histones and play an important role in a variety of molecular processes, including pancreatic cell destiny, insulin release, insulin production, insulin signalling, and glucose metabolism. HDACs also govern other regulatory processes related to diabetes, such as oxidative stress, inflammation, apoptosis, and fibrosis, revealed by network and functional analysis. This review explains the current understanding of the function of HDACs in diabetic pathophysiology, the inhibitory role of various HDAC inhibitors (HDACi), and their functional importance as biomarkers and possible therapeutic targets for T2DM. While their role in T2DM is still emerging, a better understanding of the role of HDACi may be relevant in improving insulin sensitivity, protecting $β$ -cells and reducing T2DM-associated complications, among others.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Diabetes mellitus is a chronic disease that impairs metabolism, and its prevalence has reached an epidemic proportion globally. Most people affected are with type 2 diabetes mellitus (T2DM), which is caused by a decline in the numbers or functioning of pancreatic endocrine islet cells, specifically the $β$ -cells that release insulin in sufficient quantity to overcome any insulin resistance of the metabolic tissues. Genetic and epigenetic factors have been implicated as the main contributors to the T2DM. Epigenetic modifiers, histone deacetylases (HDACs), are enzymes that remove acetyl groups from histones and play an important role in a variety of molecular processes, including pancreatic cell destiny, insulin release, insulin production, insulin signalling, and glucose metabolism. HDACs also govern other regulatory processes related to diabetes, such as oxidative stress, inflammation, apoptosis, and fibrosis, revealed by network and functional analysis. This review explains the current understanding of the function of HDACs in diabetic pathophysiology, the inhibitory role of various HDAC inhibitors (HDACi), and their functional importance as biomarkers and possible therapeutic targets for T2DM. While their role in T2DM is still emerging, a better understanding of the role of HDACi may be relevant in improving insulin sensitivity, protecting $β$ -cells and reducing T2DM-associated complications, among others.
12.
Kaimala, Suneesh; Lootah, Shareena Saeed; Mehra, Neha; Kumar, Challagandla Anil; Marzooqi, Saeeda Al; Sampath, Prabha; Ansari, Suraiya Anjum; Emerald, Bright Starling
The Long Non-Coding RNA Obesity-Related (Obr) Contributes To Lipid Metabolism Through Epigenetic Regulation Journal Article
In: Advanced Science, vol. n/a, no. n/a, pp. 2401939, 2024.
@article{https://doi.org/10.1002/advs.202401939,
title = {The Long Non-Coding RNA Obesity-Related (Obr) Contributes To Lipid Metabolism Through Epigenetic Regulation},
author = {Suneesh Kaimala and Shareena Saeed Lootah and Neha Mehra and Challagandla Anil Kumar and Saeeda Al Marzooqi and Prabha Sampath and Suraiya Anjum Ansari and Bright Starling Emerald},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/advs.202401939},
doi = {https://doi.org/10.1002/advs.202401939},
year = {2024},
date = {2024-05-05},
journal = {Advanced Science},
volume = {n/a},
number = {n/a},
pages = {2401939},
abstract = {Abstract Obesity is a multifactorial disease that is part of today's epidemic and also increases the risk of other metabolic diseases. Long noncoding RNAs (lncRNAs) provide one tier of regulatory mechanisms to maintain metabolic homeostasis. Although lncRNAs are a significant constituent of the mammalian genome, studies aimed at their metabolic significance, including obesity, are only beginning to be addressed. Here, a developmentally regulated lncRNA, termed as obesity related (Obr), whose expression in metabolically relevant tissues such as skeletal muscle, liver, and pancreas is altered in diet-induced obesity, is identified. The Clone 9 cell line and high-fat diet-induced obese Wistar rats are used as a model system to verify the function of Obr. By using stable expression and antisense oligonucleotide-mediated downregulation of the expression of Obr followed by different molecular biology experiments, its role in lipid metabolism is verified. It is shown that Obr associates with the cAMP response element-binding protein (Creb) and activates different transcription factors involved in lipid metabolism. Its association with the Creb histone acetyltransferase complex, which includes the cAMP response element-binding protein (CBP) and p300, positively regulates the transcription of genes involved in lipid metabolism. In addition, Obr is regulated by Pparγ in response to lipid accumulation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Obesity is a multifactorial disease that is part of today's epidemic and also increases the risk of other metabolic diseases. Long noncoding RNAs (lncRNAs) provide one tier of regulatory mechanisms to maintain metabolic homeostasis. Although lncRNAs are a significant constituent of the mammalian genome, studies aimed at their metabolic significance, including obesity, are only beginning to be addressed. Here, a developmentally regulated lncRNA, termed as obesity related (Obr), whose expression in metabolically relevant tissues such as skeletal muscle, liver, and pancreas is altered in diet-induced obesity, is identified. The Clone 9 cell line and high-fat diet-induced obese Wistar rats are used as a model system to verify the function of Obr. By using stable expression and antisense oligonucleotide-mediated downregulation of the expression of Obr followed by different molecular biology experiments, its role in lipid metabolism is verified. It is shown that Obr associates with the cAMP response element-binding protein (Creb) and activates different transcription factors involved in lipid metabolism. Its association with the Creb histone acetyltransferase complex, which includes the cAMP response element-binding protein (CBP) and p300, positively regulates the transcription of genes involved in lipid metabolism. In addition, Obr is regulated by Pparγ in response to lipid accumulation.
13.
Ghebrehiwet, Isaias; Zaki, Nazar; Damseh, Rafat; Mohamad, Mohd Saberi
Revolutionizing personalized medicine with generative AI: a systematic review Journal Article
In: Artificial Intelligence Review, vol. 57, no. 5, 2024, ISSN: 0269-2821, (Publisher Copyright: © The Author(s) 2024.).
@article{68f8864a72554cc790686f8a21f9f025,
title = {Revolutionizing personalized medicine with generative AI: a systematic review},
author = {Isaias Ghebrehiwet and Nazar Zaki and Rafat Damseh and Mohd Saberi Mohamad},
doi = {10.1007/s10462-024-10768-5},
issn = {0269-2821},
year = {2024},
date = {2024-05-01},
journal = {Artificial Intelligence Review},
volume = {57},
number = {5},
publisher = {Springer Netherlands},
abstract = {Background: Precision medicine, targeting treatments to individual genetic and clinical profiles, faces challenges in data collection, costs, and privacy. Generative AI offers a promising solution by creating realistic, privacy-preserving patient data, potentially revolutionizing patient-centric healthcare. Objective: This review examines the role of deep generative models (DGMs) in clinical informatics, medical imaging, bioinformatics, and early diagnostics, showcasing their impact on precision medicine. Methods: Adhering to PRISMA guidelines, the review analyzes studies from databases such as Scopus and PubMed, focusing on AI's impact in precision medicine and DGMs' applications in synthetic data generation. Results: DGMs, particularly Generative Adversarial Networks (GANs), have improved synthetic data generation, enhancing accuracy and privacy. However, limitations exist, especially in the accuracy of foundation models like Large Language Models (LLMs) in digital diagnostics. Conclusion: Overcoming data scarcity and ensuring realistic, privacy-safe synthetic data generation are crucial for advancing personalized medicine. Further development of LLMs is essential for improving diagnostic precision. The application of generative AI in personalized medicine is emerging, highlighting the need for more interdisciplinary research to advance this field.},
note = {Publisher Copyright: © The Author(s) 2024.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Precision medicine, targeting treatments to individual genetic and clinical profiles, faces challenges in data collection, costs, and privacy. Generative AI offers a promising solution by creating realistic, privacy-preserving patient data, potentially revolutionizing patient-centric healthcare. Objective: This review examines the role of deep generative models (DGMs) in clinical informatics, medical imaging, bioinformatics, and early diagnostics, showcasing their impact on precision medicine. Methods: Adhering to PRISMA guidelines, the review analyzes studies from databases such as Scopus and PubMed, focusing on AI's impact in precision medicine and DGMs' applications in synthetic data generation. Results: DGMs, particularly Generative Adversarial Networks (GANs), have improved synthetic data generation, enhancing accuracy and privacy. However, limitations exist, especially in the accuracy of foundation models like Large Language Models (LLMs) in digital diagnostics. Conclusion: Overcoming data scarcity and ensuring realistic, privacy-safe synthetic data generation are crucial for advancing personalized medicine. Further development of LLMs is essential for improving diagnostic precision. The application of generative AI in personalized medicine is emerging, highlighting the need for more interdisciplinary research to advance this field.
14.
Alhammadi, Muna A; Bajbouj, Khuloud; Talaat, Iman M; Hamoudi, Rifat
The role of RNA-modifying proteins in renal cell carcinoma Journal Article
In: Cell death & disease, vol. 15, no. 3, pp. 227, 2024, ISSN: 2041-4889.
@article{PMID:38503745,
title = {The role of RNA-modifying proteins in renal cell carcinoma},
author = {Muna A Alhammadi and Khuloud Bajbouj and Iman M Talaat and Rifat Hamoudi},
url = {https://europepmc.org/articles/PMC10951318},
doi = {10.1038/s41419-024-06479-y},
issn = {2041-4889},
year = {2024},
date = {2024-03-01},
journal = {Cell death & disease},
volume = {15},
number = {3},
pages = {227},
abstract = {Gene expression is one of the most critical cellular processes. It is controlled by complex mechanisms at the genomic, epigenomic, transcriptomic, and proteomic levels. Any aberration in these mechanisms can lead to dysregulated gene expression. One recently discovered process that controls gene expression includes chemical modifications of RNA molecules by RNA-modifying proteins, a field known as epitranscriptomics. Epitranscriptomics can regulate mRNA splicing, nuclear export, stabilization, translation, or induce degradation of target RNA molecules. Dysregulation in RNA-modifying proteins has been found to contribute to many pathological conditions, such as cancer, diabetes, obesity, cardiovascular diseases, and neurological diseases, among others. This article reviews the role of epitranscriptomics in the pathogenesis and progression of renal cell carcinoma. It summarizes the molecular function of RNA-modifying proteins in the pathogenesis of renal cell carcinoma.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gene expression is one of the most critical cellular processes. It is controlled by complex mechanisms at the genomic, epigenomic, transcriptomic, and proteomic levels. Any aberration in these mechanisms can lead to dysregulated gene expression. One recently discovered process that controls gene expression includes chemical modifications of RNA molecules by RNA-modifying proteins, a field known as epitranscriptomics. Epitranscriptomics can regulate mRNA splicing, nuclear export, stabilization, translation, or induce degradation of target RNA molecules. Dysregulation in RNA-modifying proteins has been found to contribute to many pathological conditions, such as cancer, diabetes, obesity, cardiovascular diseases, and neurological diseases, among others. This article reviews the role of epitranscriptomics in the pathogenesis and progression of renal cell carcinoma. It summarizes the molecular function of RNA-modifying proteins in the pathogenesis of renal cell carcinoma.
15.
Khasawneh, Lubna Q.; Alsafar, Habiba; Alblooshi, Hiba; Allam, Mushal; Patrinos, George P.; Ali, Bassam R.
The diversity and clinical implications of genetic variants influencing clopidogrel bioactivation and response in the Emirati population Journal Article
In: Human Genomics, vol. 18, no. 1, pp. 2, 2024, ISSN: 1479-7364.
@article{Khasawneh2024,
title = {The diversity and clinical implications of genetic variants influencing clopidogrel bioactivation and response in the Emirati population},
author = {Lubna Q. Khasawneh and Habiba Alsafar and Hiba Alblooshi and Mushal Allam and George P. Patrinos and Bassam R. Ali},
url = {https://doi.org/10.1186/s40246-023-00568-3},
doi = {10.1186/s40246-023-00568-3},
issn = {1479-7364},
year = {2024},
date = {2024-01-03},
journal = {Human Genomics},
volume = {18},
number = {1},
pages = {2},
abstract = {Clopidogrel is a widely prescribed prodrug that requires activation via specific pharmacogenes to exert its anti-platelet function. Genetic variations in the genes encoding its transporter, metabolizing enzymes, and target receptor lead to variability in its activation and platelet inhibition and, consequently, its efficacy. This variability increases the risk of secondary cardiovascular events, and therefore, some variations have been utilized as genetic biomarkers when prescribing clopidogrel.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Clopidogrel is a widely prescribed prodrug that requires activation via specific pharmacogenes to exert its anti-platelet function. Genetic variations in the genes encoding its transporter, metabolizing enzymes, and target receptor lead to variability in its activation and platelet inhibition and, consequently, its efficacy. This variability increases the risk of secondary cardiovascular events, and therefore, some variations have been utilized as genetic biomarkers when prescribing clopidogrel.
16.
Choon, Yee Wen; Choon, Yee Fan; Nasarudin, Nurul Athirah; Jasmi, Fatma Al; Remli, Muhamad Akmal; Alkayali, Mohammed Hassan; Mohamad, Mohd Saberi
Artificial intelligence and database for NGS-based diagnosis in rare disease Journal Article
In: Frontiers in Genetics, vol. Volume 14 - 2023, 2024, ISSN: 1664-8021.
@article{10.3389/fgene.2023.1258083,
title = {Artificial intelligence and database for NGS-based diagnosis in rare disease},
author = {Yee Wen Choon and Yee Fan Choon and Nurul Athirah Nasarudin and Fatma Al Jasmi and Muhamad Akmal Remli and Mohammed Hassan Alkayali and Mohd Saberi Mohamad},
url = {https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1258083},
doi = {10.3389/fgene.2023.1258083},
issn = {1664-8021},
year = {2024},
date = {2024-01-01},
journal = {Frontiers in Genetics},
volume = {Volume 14 - 2023},
abstract = {Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<p>Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases.</p>
17.
Alhosani, Faisal; Ilce, Burcu Yener; Alhamidi, Reem Sami; Bhamidimarri, Poorna Manasa; Hamad, Alaa Mohamed; Alkhayyal, Noura; Künstner, Axel; Khandanpour, Cyrus; Busch, Hauke; Al-Ramadi, Basel; Sayed, Kadria; AlFazari, Ali; Bendardaf, Riyad; Hamoudi, Rifat
In: International Journal of Molecular Sciences, vol. 25, no. 19, 2024, ISSN: 1422-0067.
@article{ijms251910367,
title = {Transcriptome Profiling Associated with CARD11 Overexpression in Colorectal Cancer Implicates a Potential Role for Tumor Immune Microenvironment and Cancer Pathways Modulation via NF-κB},
author = {Faisal Alhosani and Burcu Yener Ilce and Reem Sami Alhamidi and Poorna Manasa Bhamidimarri and Alaa Mohamed Hamad and Noura Alkhayyal and Axel Künstner and Cyrus Khandanpour and Hauke Busch and Basel Al-Ramadi and Kadria Sayed and Ali AlFazari and Riyad Bendardaf and Rifat Hamoudi},
url = {https://www.mdpi.com/1422-0067/25/19/10367},
doi = {10.3390/ijms251910367},
issn = {1422-0067},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Molecular Sciences},
volume = {25},
number = {19},
abstract = {The immune system plays a critical role in inflammation by initiating responses to infections or tissue damage. The nuclear factor-κB (NF-κB) pathway plays a key role in inflammation and innate immunity, as well as other cellular activities. Dysregulation of this well-choreographed pathway has been implicated in various diseases, including cancer. CARD11 is a key molecule in the BCL10-MALT1 complex, which is involved in transducing the signal downstream of the NF-κB pathway. This study aims to elucidate how CARD11 overexpression exacerbates the prognosis of colorectal cancer (CRC). To identify the cellular pathways influenced by CARD11, transcriptomic analysis in both CRC cell lines and patients was carried out on CARD11– overexpressed HCT-116 and HT-29 CRC cell lines alongside empty vector-transfected cell lines. Furthermore, a comparison of transcriptomic data from adenoma and carcinoma CRC patients with low- (CARD11–) and high-(CARD11+) CARD11 expression was carried out. Whole transcriptomics and bioinformatics analysis results indicate that CARD11 appears to play a key role in CRC progression. Absolute GSEA (absGSEA) on HCT-116 transcriptomics data revealed that CARD11 overexpression promotes cell growth and tissue remodeling and enhances immune response. Key genes co-expressed with CARD11, such as EP300, KDM5A, HIF1A, NFKBIZ, and DUSP1, were identified as mediators of these processes. In the HT-29 cell line, CARD11 overexpression activated pathways involved in chemotaxis and extracellular matrix (ECM) organization, marked by IL1RN, MDK, SPP1, and chemokines like CXCL1, CXCL3, and CCL22, which were shown to contribute to the more invasive stage of CRC. In patient samples, adenoma patients exhibited increased expression of genes associated with the tumor immune microenvironment, such as IL6ST, collagen family members, and CRC transition markers, such as GLI3 and PIEZO2, in CARD11+ adenoma patients. Carcinoma patients showed a dramatic increase in the expression of MAPK8IP2 in CARD11+ carcinoma patients alongside other cancer-related genes, including EMB, EPHB6, and CPEB4.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The immune system plays a critical role in inflammation by initiating responses to infections or tissue damage. The nuclear factor-κB (NF-κB) pathway plays a key role in inflammation and innate immunity, as well as other cellular activities. Dysregulation of this well-choreographed pathway has been implicated in various diseases, including cancer. CARD11 is a key molecule in the BCL10-MALT1 complex, which is involved in transducing the signal downstream of the NF-κB pathway. This study aims to elucidate how CARD11 overexpression exacerbates the prognosis of colorectal cancer (CRC). To identify the cellular pathways influenced by CARD11, transcriptomic analysis in both CRC cell lines and patients was carried out on CARD11– overexpressed HCT-116 and HT-29 CRC cell lines alongside empty vector-transfected cell lines. Furthermore, a comparison of transcriptomic data from adenoma and carcinoma CRC patients with low- (CARD11–) and high-(CARD11+) CARD11 expression was carried out. Whole transcriptomics and bioinformatics analysis results indicate that CARD11 appears to play a key role in CRC progression. Absolute GSEA (absGSEA) on HCT-116 transcriptomics data revealed that CARD11 overexpression promotes cell growth and tissue remodeling and enhances immune response. Key genes co-expressed with CARD11, such as EP300, KDM5A, HIF1A, NFKBIZ, and DUSP1, were identified as mediators of these processes. In the HT-29 cell line, CARD11 overexpression activated pathways involved in chemotaxis and extracellular matrix (ECM) organization, marked by IL1RN, MDK, SPP1, and chemokines like CXCL1, CXCL3, and CCL22, which were shown to contribute to the more invasive stage of CRC. In patient samples, adenoma patients exhibited increased expression of genes associated with the tumor immune microenvironment, such as IL6ST, collagen family members, and CRC transition markers, such as GLI3 and PIEZO2, in CARD11+ adenoma patients. Carcinoma patients showed a dramatic increase in the expression of MAPK8IP2 in CARD11+ carcinoma patients alongside other cancer-related genes, including EMB, EPHB6, and CPEB4.
18.
Carreras, Joaquim; Hamoudi, Rifat
In: BioMedInformatics, vol. 4, no. 2, pp. 1480–1505, 2024, ISSN: 2673-7426.
@article{biomedinformatics4020081,
title = {Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma},
author = {Joaquim Carreras and Rifat Hamoudi},
url = {https://www.mdpi.com/2673-7426/4/2/81},
doi = {10.3390/biomedinformatics4020081},
issn = {2673-7426},
year = {2024},
date = {2024-01-01},
journal = {BioMedInformatics},
volume = {4},
number = {2},
pages = {1480–1505},
abstract = {Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene expression data of 414 patients from the Lymphoma/Leukemia Molecular Profiling Project (GSE10846), and immunohistochemistry in 10 reactive tonsils and 30 DLBCL cases. Results: First, an unsupervised anomaly detection analysis pinpointed outliers (anomalies) in the series, and 12 genes were identified: DPM2, TRAPPC1, HYAL2, TRIM35, NUDT18, TMEM219, CHCHD10, IGFBP7, LAMTOR2, ZNF688, UBL7, and RELB, which belonged to the apoptosis, MAPK, MTOR, and NF-kB pathways. Second, these 12 genes were used to predict overall survival using machine learning, artificial neural networks, and conventional statistics. In a multivariate Cox regression analysis, high expressions of HYAL2 and UBL7 were correlated with poor overall survival, whereas TRAPPC1, IGFBP7, and RELB were correlated with good overall survival (p < 0.01). As a single marker and only in RCHOP-like treated cases, the prognostic value of RELB was confirmed using GSEA analysis and Kaplan–Meier with log-rank test and validated in the TCGA and GSE57611 datasets. Anomaly detection analysis was successfully tested in the GSE31312 and GSE117556 datasets. Using immunohistochemistry, RELB was positive in B-lymphocytes and macrophage/dendritic-like cells, and correlation with HLA DP-DR, SIRPA, CD85A (LILRB3), PD-L1, MARCO, and TOX was explored. Conclusions: Anomaly detection and other bioinformatic techniques successfully predicted the prognosis of DLBCL, and high RELB was associated with a favorable prognosis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene expression data of 414 patients from the Lymphoma/Leukemia Molecular Profiling Project (GSE10846), and immunohistochemistry in 10 reactive tonsils and 30 DLBCL cases. Results: First, an unsupervised anomaly detection analysis pinpointed outliers (anomalies) in the series, and 12 genes were identified: DPM2, TRAPPC1, HYAL2, TRIM35, NUDT18, TMEM219, CHCHD10, IGFBP7, LAMTOR2, ZNF688, UBL7, and RELB, which belonged to the apoptosis, MAPK, MTOR, and NF-kB pathways. Second, these 12 genes were used to predict overall survival using machine learning, artificial neural networks, and conventional statistics. In a multivariate Cox regression analysis, high expressions of HYAL2 and UBL7 were correlated with poor overall survival, whereas TRAPPC1, IGFBP7, and RELB were correlated with good overall survival (p < 0.01). As a single marker and only in RCHOP-like treated cases, the prognostic value of RELB was confirmed using GSEA analysis and Kaplan–Meier with log-rank test and validated in the TCGA and GSE57611 datasets. Anomaly detection analysis was successfully tested in the GSE31312 and GSE117556 datasets. Using immunohistochemistry, RELB was positive in B-lymphocytes and macrophage/dendritic-like cells, and correlation with HLA DP-DR, SIRPA, CD85A (LILRB3), PD-L1, MARCO, and TOX was explored. Conclusions: Anomaly detection and other bioinformatic techniques successfully predicted the prognosis of DLBCL, and high RELB was associated with a favorable prognosis.
19.
Khalil, Bariaa; Sharif-Askari, Narjes Saheb; Hafezi, Shirin; Sharif-Askari, Fatemeh Saheb; Anouti, Fatme Al; Hamid, Qutayba; Halwani, Rabih
Vitamin D regulates COVID-19 associated severity by suppressing the NLRP3 inflammasome pathway Journal Article
In: PLOS ONE, vol. 19, no. 5, pp. 1-19, 2024.
@article{10.1371/journal.pone.0302818,
title = {Vitamin D regulates COVID-19 associated severity by suppressing the NLRP3 inflammasome pathway},
author = {Bariaa Khalil and Narjes Saheb Sharif-Askari and Shirin Hafezi and Fatemeh Saheb Sharif-Askari and Fatme Al Anouti and Qutayba Hamid and Rabih Halwani},
url = {https://doi.org/10.1371/journal.pone.0302818},
doi = {10.1371/journal.pone.0302818},
year = {2024},
date = {2024-01-01},
journal = {PLOS ONE},
volume = {19},
number = {5},
pages = {1-19},
publisher = {Public Library of Science},
abstract = {Background The role of vitamin D3 (VitD3) in modulating innate and adaptive immunity has been reported in different disease contexts. Since the start of the coronavirus disease-2019 (COVID-19) pandemic, the role of VitD3 has been highlighted in many correlational and observational studies. However, the exact mechanisms of action are not well identified. One of the mechanisms via which VitD3 modulates innate immunity is by regulating the NLRP3-inflammasome pathway, being a main underlying cause of SARS-CoV-2-induced hyperinflammation. Aims and main methods Blood specimens of severe COVID-19 patients with or without VitD3 treatment were collected during their stay in the intensive care unit and patients were followed up for 29 days. qPCR, western blot, and ELISA were done to investigate the mechanism of action of VitD3 on the NLRP3 inflammasome activation. Key findings We here report the ability of VitD3 to downregulate the NLRP3-inflammsome pathway in severe COVID-19 patients. Lower inflammasome pathway activation was observed with significantly lower gene and protein expression of NLRP3, cleaved caspase-1, ASC and IL-1β among severe COVID-19 patients treated with VitD3. The reduction of the inflammasome pathway was associated with a reduction in disease severity markers and enhancement of type I IFN pathway. Significance Our data reveals an important anti-inflammatory effect of VitD3 during SARS-CoV-2 infection. Further investigations are warranted to better characterize the ability of VitD3 to control disease pathogenesis and prevent progression to severe states. This will allow for a more efficient use of a low cost and accessible treatment like VitD3.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background The role of vitamin D3 (VitD3) in modulating innate and adaptive immunity has been reported in different disease contexts. Since the start of the coronavirus disease-2019 (COVID-19) pandemic, the role of VitD3 has been highlighted in many correlational and observational studies. However, the exact mechanisms of action are not well identified. One of the mechanisms via which VitD3 modulates innate immunity is by regulating the NLRP3-inflammasome pathway, being a main underlying cause of SARS-CoV-2-induced hyperinflammation. Aims and main methods Blood specimens of severe COVID-19 patients with or without VitD3 treatment were collected during their stay in the intensive care unit and patients were followed up for 29 days. qPCR, western blot, and ELISA were done to investigate the mechanism of action of VitD3 on the NLRP3 inflammasome activation. Key findings We here report the ability of VitD3 to downregulate the NLRP3-inflammsome pathway in severe COVID-19 patients. Lower inflammasome pathway activation was observed with significantly lower gene and protein expression of NLRP3, cleaved caspase-1, ASC and IL-1β among severe COVID-19 patients treated with VitD3. The reduction of the inflammasome pathway was associated with a reduction in disease severity markers and enhancement of type I IFN pathway. Significance Our data reveals an important anti-inflammatory effect of VitD3 during SARS-CoV-2 infection. Further investigations are warranted to better characterize the ability of VitD3 to control disease pathogenesis and prevent progression to severe states. This will allow for a more efficient use of a low cost and accessible treatment like VitD3.
20.
Statsenko, Yauhen; Smetanina, Darya; Simiyu, Gillian Lylian; Belghali, Maroua; Ghenimi, Nadirah; Mannaerts, Guido Hein Huib; Almaramah, Leena; Alhashmi, Maryam; Mohammad, Nazia Chun; Hamed, Rahaf Al; Alblooshi, Sara F.; Talbi, Khawla; Albreiki, Maitha; Alkaabi, Fatima; Ponomareva, Anna; Ljubisavljevic, Milos
In: Healthcare, vol. 12, no. 18, 2024, ISSN: 2227-9032.
@article{healthcare12181830,
title = {Race, Ethnicity, and Geography as Determinants of Excessive Weight and Low Physical Activity in Pediatric Population: Protocol for Systematic Review and Meta-Analysis},
author = {Yauhen Statsenko and Darya Smetanina and Gillian Lylian Simiyu and Maroua Belghali and Nadirah Ghenimi and Guido Hein Huib Mannaerts and Leena Almaramah and Maryam Alhashmi and Nazia Chun Mohammad and Rahaf Al Hamed and Sara F. Alblooshi and Khawla Talbi and Maitha Albreiki and Fatima Alkaabi and Anna Ponomareva and Milos Ljubisavljevic},
url = {https://www.mdpi.com/2227-9032/12/18/1830},
doi = {10.3390/healthcare12181830},
issn = {2227-9032},
year = {2024},
date = {2024-01-01},
journal = {Healthcare},
volume = {12},
number = {18},
abstract = {The rationale for the current study is the sparsity of data on the combined effect of the environmental and individual risks of obesity and sedentary lifestyle in children of different races/ethnicities from different regions. An effective weight management strategy is hard to design due to insufficient evidence. This work was initiated to study race, ethnicity, and geography as determinants of excessive weight and low physical activity in the pediatric population. To achieve this aim, we systematically review publications on daily length of physical activity of light, moderate, and vigorous intensity, as well as sedentary time and BMI and its dynamics in children of different races/ethnicities and geographies. The extracted data are stratified into six major geographic regions and six races/ethnicities. Then, a random-effects meta-analysis is used to calculate the pooled mean of each outcome measure. A ridge regression is constructed to explore age-related change in BMI. A Kruskal–Wallis H test is applied to compare the pooled duration of physical activity and sedentary time in the subgroups. Finally, we calculate paired correlation coefficients between BMI and physical activity/inactivity for each group. The findings can be further used in public health surveillance to clarify the epidemiology of obesity, to guide priority setting and planning, and to develop and evaluate public health policy and strategy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The rationale for the current study is the sparsity of data on the combined effect of the environmental and individual risks of obesity and sedentary lifestyle in children of different races/ethnicities from different regions. An effective weight management strategy is hard to design due to insufficient evidence. This work was initiated to study race, ethnicity, and geography as determinants of excessive weight and low physical activity in the pediatric population. To achieve this aim, we systematically review publications on daily length of physical activity of light, moderate, and vigorous intensity, as well as sedentary time and BMI and its dynamics in children of different races/ethnicities and geographies. The extracted data are stratified into six major geographic regions and six races/ethnicities. Then, a random-effects meta-analysis is used to calculate the pooled mean of each outcome measure. A ridge regression is constructed to explore age-related change in BMI. A Kruskal–Wallis H test is applied to compare the pooled duration of physical activity and sedentary time in the subgroups. Finally, we calculate paired correlation coefficients between BMI and physical activity/inactivity for each group. The findings can be further used in public health surveillance to clarify the epidemiology of obesity, to guide priority setting and planning, and to develop and evaluate public health policy and strategy.