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Article ; Online: Different Low-complexity Regions of SFPQ Play Distinct Roles in the Formation of Biomolecular Condensates.

Marshall, Andrew C / Cummins, Jerry / Kobelke, Simon / Zhu, Tianyi / Widagdo, Jocelyn / Anggono, Victor / Hyman, Anthony / Fox, Archa H / Bond, Charles S / Lee, Mihwa

Journal of molecular biology

2023 Volume 435, Issue 24, Page(s) 168364

Abstract: Demixing of proteins and nucleic acids into condensed liquid phases is rapidly emerging as a ubiquitous mechanism underlying the complex spatiotemporal organisation of molecules within the cell. Long disordered regions of low sequence complexity (LCRs) ... ...

Abstract	Demixing of proteins and nucleic acids into condensed liquid phases is rapidly emerging as a ubiquitous mechanism underlying the complex spatiotemporal organisation of molecules within the cell. Long disordered regions of low sequence complexity (LCRs) are a common feature of proteins that form liquid-like microscopic biomolecular condensates. In particular, RNA-binding proteins with prion-like regions have emerged as key drivers of liquid demixing to form condensates such as nucleoli, paraspeckles and stress granules. Splicing factor proline- and glutamine-rich (SFPQ) is an RNA- and DNA-binding protein essential for DNA repair and paraspeckle formation. SFPQ contains two LCRs of different length and composition. Here, we show that the shorter C-terminal LCR of SFPQ is the main region responsible for the condensation of SFPQ in vitro and in the cell nucleus. In contrast, we find that the longer N-terminal prion-like LCR of SFPQ attenuates condensation of the full-length protein, suggesting a more regulatory role in preventing aberrant condensate formation in the cell. The compositions of these respective LCRs are discussed with reference to current literature. Our data add nuance to the emerging understanding of biomolecular condensation, by providing the first example of a common multifunctional nucleic acid-binding protein with an extensive prion-like region that serves to regulate rather than drive condensate formation.
MeSH term(s)	Biomolecular Condensates ; RNA-Binding Proteins/metabolism ; DNA-Binding Proteins/genetics ; DNA-Binding Proteins/metabolism ; RNA ; Prions/genetics ; Prions/metabolism
Chemical Substances	RNA-Binding Proteins ; DNA-Binding Proteins ; RNA (63231-63-0) ; Prions
Language	English
Publishing date	2023-11-10
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2023.168364
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables

Xiaoran Li / Peilin Ge / Jocelyn Zhu / Haifang Li / James Graham / Adam Singer / Paul S. Richman / Tim Q. Duong

PeerJ, Vol 8, p e

2020 Volume 10337

Abstract: Background This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. Methods This retrospective study consisted of 5, ... ...

Abstract	Background This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. Methods This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). Results The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760–0.785]) and 0.844 (95% CI [0.839–0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726–0.729]) and 0.848 (95% CI [0.847–0.849]), respectively. Conclusions Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.
Keywords	Machine learning ; Coronavirus ; Pneumonia ; SARS-CoV-2 ; Prediction model ; Medicine ; R ; Biology (General) ; QH301-705.5
Subject code	310
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	PeerJ Inc.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients.

Zhu, Jocelyn S / Ge, Peilin / Jiang, Chunguo / Zhang, Yong / Li, Xiaoran / Zhao, Zirun / Zhang, Liming / Duong, Tim Q

Journal of the American College of Emergency Physicians open

2020 Volume 1, Issue 6, Page(s) 1364–1373

Abstract: Objective: The large number of clinical variables associated with coronavirus disease 2019 (COVID-19) infection makes it challenging for frontline physicians to effectively triage COVID-19 patients during the pandemic. This study aimed to develop an ... ...

Abstract	Objective: The large number of clinical variables associated with coronavirus disease 2019 (COVID-19) infection makes it challenging for frontline physicians to effectively triage COVID-19 patients during the pandemic. This study aimed to develop an efficient deep-learning artificial intelligence algorithm to identify top clinical variable predictors and derive a risk stratification score system to help clinicians triage COVID-19 patients. Methods: This retrospective study consisted of 181 hospitalized patients with confirmed COVID-19 infection from January 29, 2020 to March 21, 2020 from a major hospital in Wuhan, China. The primary outcome was mortality. Demographics, comorbidities, vital signs, symptoms, and laboratory tests were collected at initial presentation, totaling 78 clinical variables. A deep-learning algorithm and a risk stratification score system were developed to predict mortality. Data were split into 85% training and 15% testing. Prediction performance was compared with those using COVID-19 severity score, CURB-65 score, and pneumonia severity index (PSI). Results: Of the 181 COVID-19 patients, 39 expired and 142 survived. Five top predictors of mortality were D-dimer, O Conclusions: Deep-learning prediction model and the resultant risk stratification score may prove useful in clinical decisionmaking under time-sensitive and resource-constrained environment.
Keywords	covid19
Language	English
Publishing date	2020-08-25
Publishing country	United States
Document type	Journal Article
ISSN	2688-1152
ISSN (online)	2688-1152
DOI	10.1002/emp2.12205
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables.

Li, Xiaoran / Ge, Peilin / Zhu, Jocelyn / Li, Haifang / Graham, James / Singer, Adam / Richman, Paul S / Duong, Tim Q

PeerJ

2020 Volume 8, Page(s) e10337

Abstract: Background: This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients.: Methods: This retrospective study consisted ...

Abstract	Background: This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. Methods: This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). Results: The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760-0.785]) and 0.844 (95% CI [0.839-0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726-0.729]) and 0.848 (95% CI [0.847-0.849]), respectively. Conclusions: Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.
Keywords	covid19
Language	English
Publishing date	2020-11-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	2703241-3
ISSN	2167-8359
ISSN	2167-8359
DOI	10.7717/peerj.10337
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A polygenic two-hit hypothesis for prostate cancer.

Journal of the National Cancer Institute

2023 Volume 115, Issue 4, Page(s) 468–472

Abstract: Prostate cancer is one of the most heritable cancers. Hundreds of germline polymorphisms have been linked to prostate cancer diagnosis and prognosis. Polygenic risk scores can predict genetic risk of a prostate cancer diagnosis. Although these scores ... ...

Abstract	Prostate cancer is one of the most heritable cancers. Hundreds of germline polymorphisms have been linked to prostate cancer diagnosis and prognosis. Polygenic risk scores can predict genetic risk of a prostate cancer diagnosis. Although these scores inform the probability of developing a tumor, it remains unknown how germline risk influences the tumor molecular evolution. We cultivated a cohort of 1250 localized European-descent patients with germline and somatic DNA profiling. Men of European descent with higher genetic risk were diagnosed earlier and had less genomic instability and fewer driver genes mutated. Higher genetic risk was associated with better outcome. These data imply a polygenic "two-hit" model where germline risk reduces the number of somatic alterations required for tumorigenesis. These findings support further clinical studies of polygenic risk scores as inexpensive and minimally invasive adjuncts to standard risk stratification. Further studies are required to interrogate generalizability to more ancestrally and clinically diverse populations.
MeSH term(s)	Male ; Humans ; Prostatic Neoplasms/genetics ; Prostatic Neoplasms/pathology ; Risk Factors ; Prognosis ; Genetic Predisposition to Disease
Language	English
Publishing date	2023-01-05
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2992-0
ISSN	1460-2105 ; 0027-8874 ; 0198-0157
ISSN (online)	1460-2105
ISSN	0027-8874 ; 0198-0157
DOI	10.1093/jnci/djad001
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Article ; Online: A Mineral-Doped Micromodel Platform Demonstrates Fungal Bridging of Carbon Hot Spots and Hyphal Transport of Mineral-Derived Nutrients.

Bhattacharjee, Arunima / Qafoku, Odeta / Richardson, Jocelyn A / Anderson, Lindsey N / Schwarz, Kaitlyn / Bramer, Lisa M / Lomas, Gerard X / Orton, Daniel J / Zhu, Zihua / Engelhard, Mark H / Bowden, Mark E / Nelson, William C / Jumpponen, Ari / Jansson, Janet K / Hofmockel, Kirsten S / Anderton, Christopher R

mSystems

2022 Volume 7, Issue 6, Page(s) e0091322

Abstract: Soil fungi facilitate the translocation of inorganic nutrients from soil minerals to other microorganisms and plants. This ability is particularly advantageous in impoverished soils because fungal mycelial networks can bridge otherwise spatially ... ...

Abstract	Soil fungi facilitate the translocation of inorganic nutrients from soil minerals to other microorganisms and plants. This ability is particularly advantageous in impoverished soils because fungal mycelial networks can bridge otherwise spatially disconnected and inaccessible nutrient hot spots. However, the molecular mechanisms underlying fungal mineral weathering and transport through soil remains poorly understood primarily due to the lack of a platform for spatially resolved analysis of biotic-driven mineral weathering. Here, we addressed this knowledge gap by demonstrating a mineral-doped soil micromodel platform where mineral weathering mechanisms can be studied. We directly visualize acquisition and transport of inorganic nutrients from minerals through fungal hyphae in the micromodel using a multimodal imaging approach. We found that Fusarium sp. strain DS 682, a representative of common saprotrophic soil fungus, exhibited a mechanosensory response (thigmotropism) around obstacles and through pore spaces (~12 μm) in the presence of minerals. The fungus incorporated and translocated potassium (K) from K-rich mineral interfaces, as evidenced by visualization of mineral-derived nutrient transport and unique K chemical moieties following fungus-induced mineral weathering. Specific membrane transport proteins were expressed in the fungus in the presence of minerals, including those involved in oxidative phosphorylation pathways and the transmembrane transport of small-molecular-weight organic acids. This study establishes the significance of a spatial visualization platform for investigating microbial induced mineral weathering at microbially relevant scales. Moreover, we demonstrate the importance of fungal biology and nutrient translocation in maintaining fungal growth under water and carbon limitations in a reduced-complexity soil-like microenvironment.
MeSH term(s)	Hyphae/chemistry ; Mycorrhizae/chemistry ; Minerals/analysis ; Potassium/analysis ; Soil/chemistry
Chemical Substances	Minerals ; Potassium (RWP5GA015D) ; Soil
Language	English
Publishing date	2022-11-17
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ISSN	2379-5077
ISSN (online)	2379-5077
DOI	10.1128/msystems.00913-22
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables

Li, Xiaoran Ge Peilin Zhu Jocelyn Li Haifang Graham James Singer Adam Richman Paul S. / Duong, Tim Q.

PeerJ

Abstract: Background This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients Methods This retrospective study consisted of 5, ... ...

Abstract	Background This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients Methods This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020 Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality Prediction performance used the receiver operating characteristic area under the curve (AUC) Results The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation Age and troponin were unique top predictors for mortality but not ICU admission The deep-learning model predicted ICU admission and mortality with an AUC of 0 780 (95% CI [0 760–0 785]) and 0 844 (95% CI [0 839–0 848]), respectively The corresponding risk scores yielded an AUC of 0 728 (95% CI [0 726–0 729]) and 0 848 (95% CI [0 847–0 849]), respectively Conclusions Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #914775
Database	COVID19

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Article ; Online: Deep‐learning artificial intelligence analysis of clinical variables predicts mortality in COVID‐19 patients

Zhu, Jocelyn S / Ge, Peilin / Jiang, Chunguo / Zhang, Yong / Li, Xiaoran / Zhao, Zirun / Zhang, Liming / Duong, Tim Q.

Journal of the American College of Emergency Physicians Open ; ISSN 2688-1152 2688-1152

2020

Keywords	covid19
Language	English
Publisher	Wiley
Publishing country	us
Document type	Article ; Online
DOI	10.1002/emp2.12205
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A database for ITS2 sequences from nematodes.

Workentine, Matthew L / Chen, Rebecca / Zhu, Shawna / Gavriliuc, Stefan / Shaw, Nicolette / Rijke, Jill de / Redman, Elizabeth M / Avramenko, Russell W / Wit, Janneke / Poissant, Jocelyn / Gilleard, John S

BMC genetics

2020 Volume 21, Issue 1, Page(s) 74

Abstract: Background: Marker gene surveys have a wide variety of applications in species identification, population genetics, and molecular epidemiology. As these methods expand to new types of organisms and additional markers beyond 16S and 18S rRNA genes, ... ...

Abstract	Background: Marker gene surveys have a wide variety of applications in species identification, population genetics, and molecular epidemiology. As these methods expand to new types of organisms and additional markers beyond 16S and 18S rRNA genes, comprehensive databases are a critical requirement for proper analysis of these data. Results: Here we present an ITS2 rDNA database for marker gene surveys of both free-living and parasitic nematode populations and the software used to build the database. This is currently the most complete and up-to-date ITS2 database for nematodes and is able to reproduce previous analysis that used a smaller database. Conclusions: This database is an important resource for researchers working on nematodes and also provides a tool to create ITS2 databases for any given taxonomy.
MeSH term(s)	Animals ; Computational Biology ; DNA, Ribosomal Spacer/genetics ; Databases, Genetic ; Genetic Markers ; Nematoda/genetics ; Software
Chemical Substances	DNA, Ribosomal Spacer ; Genetic Markers
Language	English
Publishing date	2020-07-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1471-2156
ISSN (online)	1471-2156
DOI	10.1186/s12863-020-00880-0
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Article ; Online: Birth outcomes in relation to neighborhood food access and individual food insecurity during pregnancy in the Environmental Influences on Child Health Outcomes (ECHO)-wide cohort study.

Aris, Izzuddin M / Lin, Pi-I D / Wu, Allison J / Dabelea, Dana / Lester, Barry M / Wright, Rosalind J / Karagas, Margaret R / Kerver, Jean M / Dunlop, Anne L / Joseph, Christine Lm / Camargo, Carlos A / Ganiban, Jody M / Schmidt, Rebecca J / Strakovsky, Rita S / McEvoy, Cindy T / Hipwell, Alison E / O'Shea, Thomas Michael / McCormack, Lacey A / Maldonado, Luis E /

The American journal of clinical nutrition

2024

Abstract: Background: Limited access to healthy foods, resulting from residence in neighborhoods with low-food access or from household food insecurity, is a public health concern. Contributions of these measures during pregnancy to birth outcomes remain ... ...

Abstract	Background: Limited access to healthy foods, resulting from residence in neighborhoods with low-food access or from household food insecurity, is a public health concern. Contributions of these measures during pregnancy to birth outcomes remain understudied. Objectives: We examined associations between neighborhood food access and individual food insecurity during pregnancy with birth outcomes. Methods: We used data from 53 cohorts participating in the nationwide Environmental Influences on Child Health Outcomes-Wide Cohort Study. Participant inclusion required a geocoded residential address or response to a food insecurity question during pregnancy and information on birth outcomes. Exposures include low-income-low-food-access (LILA, where the nearest supermarket is >0.5 miles for urban or >10 miles for rural areas) or low-income-low-vehicle-access (LILV, where few households have a vehicle and >0.5 miles from the nearest supermarket) neighborhoods and individual food insecurity. Mixed-effects models estimated associations with birth outcomes, adjusting for socioeconomic and pregnancy characteristics. Results: Among 22,206 pregnant participants (mean age 30.4 y) with neighborhood food access data, 24.1% resided in LILA neighborhoods and 13.6% in LILV neighborhoods. Of 1630 pregnant participants with individual-level food insecurity data (mean age 29.7 y), 8.0% experienced food insecurity. Residence in LILA (compared with non-LILA) neighborhoods was associated with lower birth weight [β -44.3 g; 95% confidence interval (CI): -62.9, -25.6], lower birth weight-for-gestational-age z-score (-0.09 SD units; -0.12, -0.05), higher odds of small-for-gestational-age [odds ratio (OR) 1.15; 95% CI: 1.00, 1.33], and lower odds of large-for-gestational-age (0.85; 95% CI: 0.77, 0.94). Similar findings were observed for residence in LILV neighborhoods. No associations of individual food insecurity with birth outcomes were observed. Conclusions: Residence in LILA or LILV neighborhoods during pregnancy is associated with adverse birth outcomes. These findings highlight the need for future studies examining whether investing in neighborhood resources to improve food access during pregnancy would promote equitable birth outcomes.
Language	English
Publishing date	2024-03-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	280048-2
ISSN	1938-3207 ; 0002-9165
ISSN (online)	1938-3207
ISSN	0002-9165
DOI	10.1016/j.ajcnut.2024.02.022
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