Biology subjects

Saber, N.

Publications and source records attributed to Saber, N..

5 recordsLinked to original sources

bioRxiv2024

Root Remodeling Mechanisms and Salt Tolerance Trade-Offs: The Roles of HKT1, TMAC2, and TIP2;2 in Arabidopsis

Plant responses to salt stress involve complex processes integrating short- and long-term adaptations, including changes in ion transport, systemic signaling, root architecture, and biomass distribution. A key adaptive mechanism involves the regulation of sodium (Na+) and potassium (K+) ion transport via Class 1 HKT1 transporters, which reduce Na+ accumulation in shoots, thereby enhancing salinity tolerance but at the expense of lateral root development. In this study, we identified differential roles of TMAC2 in modulating ABA accumulation and lateral root development under salt stress in two distinct Arabidopsis genotypes, Col-0 and C24. Overexpression of TMAC2 in the Col-0 background increased ABA accumulation, resulting in reduced lateral root development, suggesting a positive feedback loop involving HKT1, TMAC2, and ABA signaling. In contrast, TMAC2 overexpression in C24 reduced ABA accumulation in lines overexpressing HKT1, indicating genotype-specific differences in the TMAC2-HKT1 interaction. Additionally, we observed that the co-expression of TMAC2 and HKT1 in Col-0 induced ABI4 and ABI5 transcription factors, which are known to mediate salt sensitivity. These findings reveal a regulatory network where TMAC2 and HKT1 modulate salt stress responses through genotype-dependent feedback mechanisms. Our results highlight the complexity of root remodeling under salt stress and the crucial role of genetic background in shaping these adaptive responses.

plant biology↗

bioRxiv2024

Genome assembly of a diversity panel of Chenopodium quinoa

Quinoa (Chenopodium quinoa) is an important crop for the future challenges of food and nutrient security. Deep characterization of quinoa diversity is needed to support the agronomic improvement and adaptation of quinoa as its worldwide cultivation expands. In this study, we report the construction of chromosome-scale genome assemblies of eight quinoa accessions covering the range of phenotypic and genetic diversity of both lowland and highland quinoas. The assemblies were produced from a combination of PacBio HiFi reads and Bionano Saphyr optical maps, with total assembly sizes averaging 1.28 Gb with a mean N50 of 71.1 Mb. Between 43,733 and 48,564 gene models were predicted for the eight new quinoa genomes, and on average, 66% of each quinoa genome was classified as repetitive sequences. Alignment between the eight genome assemblies allowed the identification of structural rearrangements including inversions, translocations, and duplications. These eight novel quinoa genome assemblies provide a resource for association genetics, comparative genomics, and pan-genome analyses for the discovery of genetic components and variations underlying agriculturally important traits.

genomics↗

bioRxiv2023

Human A2-CAR T cells reject HLA-A2+ human islets transplanted into mice without inducing graft versus host disease

BackgroundType 1 diabetes (T1D) is an autoimmune disease characterised by T cell mediated destruction of pancreatic beta-cells. Islet transplantation is an effective therapy, but its success is limited by islet quality and availability along with the need for immunosuppression. New approaches include use of stem cell-derived insulin-producing cells and immunomodulatory therapies, but a limitation is the paucity of reproducible animal models in which interactions between human immune cells and insulin-producing cells can be studied without the complication of xenogeneic graft-versus-host disease (xGVHD). MethodsWe expressed an HLA-A2-specific chimeric antigen receptor (A2-CAR) in human CD4+ and CD8+ T cells and tested their ability to reject HLA-A2+ islets transplanted under the kidney capsule or anterior chamber of the eye of immunodeficient mice. T cell engraftment, islet function and xGVHD were assessed longitudinally. ResultsThe speed and consistency of A2-CAR T cells-mediated islet rejection varied depending on the number of A2-CAR T cells and the absence/presence of co-injected peripheral blood mononuclear cells (PBMCs). When <3 million A2-CAR T cells were injected, co-injection of PBMCs accelerated islet rejection but also induced xGVHD. In the absence of PBMCs, injection of 3 million A2-CAR T cells caused synchronous rejection of A2+ human islets within 1 week and without xGVHD for 12 weeks. ConclusionsInjection of A2-CAR T cells can be used to study rejection of human insulin-producing cells without the complication of xGVHD. The rapidity and synchrony of rejection will facilitate in vivo screening of new therapies designed to improve the success of isletreplacement therapies.

immunology↗

bioRxiv2020

Genetic mapping of the early responses to salt stress in Arabidopsis thaliana

Salt stress decreases plant growth prior to significant ion accumulation in the shoot. However, the processes underlying this rapid reduction in growth are still unknown. To understand the changes in salt stress responses through time and at multiple physiological levels, examining different plant processes within a single setup is required. Recent advances in phenotyping has allowed the image-based estimation of plant growth, morphology, colour and photosynthetic activity. In this study, we examined the salt stress-induced responses of 191 Arabidopsis accessions from one hour to seven days after treatment using high-throughput phenotyping. Multivariate analyses and machine learning algorithms identified that quantum yield measured in the light-adapted state (Fv'/Fm') greatly affected growth maintenance in the early phase of salt stress, while maximum quantum yield (QY max) was crucial at a later stage. In addition, our genome-wide association study (GWAS) identified 770 loci that were specific to salt stress, in which two loci associated with QY max and Fv'/Fm' were selected for validation using T-DNA insertion lines. We characterised an unknown protein kinase found in the QY max locus, which reduced photosynthetic efficiency and growth maintenance under salt stress. Understanding the molecular context of the identified candidate genes will provide valuable insights into the early plant responses to salt stress. Furthermore, our work incorporates high-throughput phenotyping, multivariate analyses and GWAS, uncovering details of temporal stress responses, while identifying associations across different traits and time points, which likely constitute the genetic components of salinity tolerance.

plant biology↗

bioRxiv2018

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

ObjectiveTo validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.\n\nSettingThis cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.\n\nParticipantsFor retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included.\n\nResultsAt severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P<001), SOFA (0.74; P<.001), and SIRS (0.62; P<.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.\n\nConclusionsThe MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.\n\nKEY POINTSO_ST_ABSQuestionC_ST_ABSIs a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?\n\nFindingsIn a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset. In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.\n\nMeaningThe accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes.\n\nSTRENGTHS AND LIMITATIONS OF THIS STUDYO_LIA retrospective study of machine learning severe sepsis prediction from a dataset with 510,497 patient encounters demonstrates high accuracy up to 48 hours prior to onset.\nC_LIO_LIA multicenter clinical study of real-world data using this machine learning algorithm for severe sepsis alerts achieved reductions of in-hospital mortality, length of stay, and 30-day readmissions.\nC_LIO_LIThe required presence of an ICD-9 code to classify a patient as severely septic in our retrospective analysis potentially limits our ability to accurately classify all patients.\nC_LIO_LIOnly adults in US hospitals were included in this study.\nC_LIO_LIFor the real-world section of the study, we cannot eliminate the possibility that implementation of a sepsis algorithm raised general awareness of sepsis within a hospital, which may lead to higher recognition of septic patients, independent of algorithm performance.\nC_LI

bioinformatics↗