IKZF3/Aiolos H195Y mutation identified in a mouse model of B cell leukemia results in altered DNA binding and altered STAT5-dependent gene expression

Precursor B cell acute lymphoblastic leukemia (Pre-B-ALL) arises from developing B cells and frequently involves mutations in genes encoding transcription factors. In this study, we investigated the function of mutations in the transcription factor IKZF3 (Aiolos), R137* and H195Y, discovered in a mouse model of pre-B-ALL. R137* IKZF3 mutation resulted in a truncated protein, while electrophoretic mobility shift assay showed that H195Y IKZF3 mutation resulted in a protein with altered DNA binding. 38B9 pre-B cell lines were generated expressing WT and H195Y IKZF3 proteins. Anti-IKZF3 ChIP-seq showed that H195Y IKZF3 interacted with a larger number of sites that were different than WT IKZF3. Treatment with interleukin-7 induced changes in gene expression in 38B9 cells expressing WT IKZF3, but did not induce any changes in gene expression in cells expressing H195Y IKZF3. Anti-STAT5 ChIP-seq showed that expression of H195Y IKZF3 resulted in redistribution of STAT5 binding sites in the genome. H195Y IKZF3 binding sites overlapped with a subset of STAT5 binding sites, including in the promoter of the Cish gene. These findings suggest that H195Y mutation of IKZF3 results in altered DNA binding specificity and altered binding of STAT5 to target genes.

N-Acetylcysteine Alters Disease Progression and Increases Janus Kinase Mutation Frequency in a Mouse Model of Precursor B-Cell Acute Lymphoblastic Leukemia

B-cell acute lymphoblastic leukemia (B-ALL) is the most prevalent type of cancer in young children and is associated with high levels of reactive oxygen species (ROS). The antioxidant N-acetylcysteine (NAC) was tested for its ability to alter disease progression in a mouse model of B-ALL. Mb1-CreΔPB mice have deletions in genes encoding PU.1 and Spi-B in B cells and develop B-ALL at 100% incidence. Treatment of Mb1-CreΔPB mice with NAC in drinking water significantly reduced the frequency of CD19+ pre-B-ALL cells infiltrating the thymus at 11 weeks of age. However, treatment with NAC did not reduce leukemia progression or increase survival by a median 16 weeks of age. NAC significantly altered gene expression in leukemias in treated mice. Mice treated with NAC had increased frequencies of activating mutations in genes encoding Janus kinases 1 and 3. In particular, frequencies of Jak3 R653H mutations were increased in mice treated with NAC compared with control drinking water. NAC opposed oxidization of PTEN protein ROS in cultured leukemia cells. These results show that NAC alters leukemia progression in this mouse model, ultimately selecting for leukemias with high Jak3 R653H mutation frequencies. SIGNIFICANCE STATEMENT: In a mouse model of precursor B-cell acute lymphoblastic leukemia associated with high levels of reactive oxygen species, treatment with N-acetylcysteine did not delay disease progression but instead selected for leukemic clones with activating R653H mutations in Janus kinase 3.

Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning

BACKGROUND

Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (characteristics of the soil and the plant), mapping, and applying inputs according to the plants' needs. This differentiated management is precision coffee growing and it stands out for its increased yield and sustainability.

RESULTS

This research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data were obtained in a field of 54.6 ha during two consecutive seasons, applying varied fertilization rates in accordance with the recommendations of soil attribute maps. Leaf analysis maps also were monitored with the aim of establishing a correlation between input parameters and yield prediction. The machine-learning models obtained from these data predicted coffee yield efficiently. The best model demonstrated predictive fit results with a Pearson correlation of 0.86. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models.

CONCLUSION

These findings have important implications for optimizing coffee management and cultivation, providing valuable insights for producers and researchers interested in maximizing yield using precision agriculture. © 2024 Society of Chemical Industry.

Selection of forage grasses for cultivation under water-limited conditions using Manhattan distance and TOPSIS

Extreme weather events, such as severe droughts, pose a threat to the sustainability of beef cattle by limiting the growth and development of forage plants and reducing the available pasture for animals. Thus, the search for forage species that are more tolerant and adapted to soil water deficit conditions is an important strategy to improve food supply. In this study, we propose utilizing the mathematical concept of the Manhattan distance to assess the variations in the morphological variables of tropical forage grasses under water-limited conditions. This study aimed to select genotypes of tropical forage grasses under different water stress levels (moderate or severe) at this distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Nine varieties from five species were examined. Forage grasses were grown in 12-L pots under three soil irrigation regimes [100% pot capacity-PC (well-irrigated control), 60% PC (moderate drought stress), and 25% PC (severe drought stress)] with four replicates. Drought stress treatments were applied for 25 days during the forage grass tillering and stalk elongation phases. After exposure to drought stress, the growth and morphological traits of forage plants were evaluated. The results show that the use of the Manhattan distance combined with TOPSIS helps in the genotypic selection of more stable tropical forage grass varieties when comparing plants exposed to moderate and severe drought conditions in relation to the nonstressful environment (control). The 'ADR 300', 'Pojuca', 'Marandu', and 'Xaraés' varieties show greater stability when grown in a greenhouse and subjected to water stress environments. The selected forage varieties can be used as parents in plant breeding programs, allowing us to obtain new drought-resistant genotypes.

Selection of Soybean Genotypes under Drought and Saline Stress Conditions Using Manhattan Distance and TOPSIS

The search for soybean genotypes more adapted to abiotic stress conditions is essential to boost the development and yield of the crop in Brazil and worldwide. In this research, we propose a new approach using the concept of distance (or similarity) in a vector space that can quantify changes in the morphological traits of soybean seedlings exposed to stressful environments. Thus, this study was conducted to select soybean genotypes exposed to stressful environments (saline or drought) using similarity based on Manhattan distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. TOPSIS is a multi-criteria decision method for selecting the best alternative using the concept of distance. The use of TOPSIS is essential because the genotypes are not absolutely similar in both treatments. That is, just the distance measure is not enough to select the best genotype simultaneously in the two stress environments. Drought and saline stresses were induced by exposing seeds of 70 soybean genotypes to -0.20 MPa iso-osmotic solutions with polyethylene glycol-PEG 6000 (119.6 g L^-1) or NaCl (2.36 g L^-1) for 14 days at 25 °C. The germination rate, seedling length, and seedling dry matter were measured. We showed here how the genotypic stability of soybean plants could be quantified by TOPSIS when comparing drought and salinity conditions to a non-stressful environment (control) and how this method can be employed under different conditions. Based on the TOPSIS method, we can select the best soybean genotypes for environments with multiple abiotic stresses. Among the 70 tested soybean genotypes, RK 6813 RR, ST 777 IPRO, RK 7214 IPRO, TMG 2165 IPRO, 5G 830 RR, 98R35 IPRO, 98R31 IPRO, RK 8317 IPRO, CG 7464 RR, and LG 60177 IPRO are the 10 most stable genotypes under drought and saline stress conditions. Owing to high stability and gains with selection verified for these genotypes under salinity and drought conditions, they can be used as genitors in breeding programs to obtain offspring with higher resistance to antibiotic stresses.

Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection

BACKGROUND AND OBJECTIVE

Premature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long time heart monitoring. For this purpose, monitoring through Holter devices is often used and computational tools can provide essential assistance to specialists. This paper presents a new premature ventricular contraction recognition method based on a simplified set of features, extracted from geometric figures constructed over QRS complexes (Q, R and S waves).

METHODS

Initially, a preprocessing stage based on wavelet denoising electrocardiogram signal scaling is applied. Then, the signal is segmented taking into account the ventricular depolarization timing and a new set of geometrical features are extracted. In order to validate this approach, simulations encompassing eight different classifiers are presented. To select the best classifiers, a new methodology is proposed based on the Analytic Hierarchy Process.

RESULTS

The best results, achieved with an Artificial Immune System, were 98.4%, 91.1% and 98.7% for accuracy, sensitivity and specificity, respectively. When artificial examples were generated to balance the dataset, the recognition performance increased to 99.0%, 98.5% and 99.5%, employing the Support Vector Machine classifier.

CONCLUSIONS

The proposed approach is compared with some of latest references and results indicate its effectiveness as a method for recognizing premature ventricular contraction. Besides, the overall system presents low computation load.

Perception of a nursing team in the implantation of a reception with risk classification sector for pregnant women

Abstract Objectives: to analyze the perception of a nursing team in the implantation of a Reception with Risk Classification (RRC) sector for pregnant women. Methods: a descriptive cross-sectional study with a qualitative approach performed in a private hospital and linked to the Public Health System in Feira de Santana city in Bahia State in 2016. 10 nursing team professionals participated in the study that provided direct care for the pregnant women who were in labor and in puerperium. A semi-structured questionnaire was applied with questions identifying and characterizing their sociodemographic profile and an interview addressing questions about RRC sector, the advantages of implanting RRC sector to care for the pregnant women in labor and the possible changes in the implantation in the professionals’ routine. Results: the interviewees recognize that the RSRC is a way to administrate in the health services, reorganizing the work process, ensuring the quality of care, so its implementation is useful to differentiate care for pregnant women, with humanization and sensitivity, and create a bond among the professionals and the health users. Conclusions: the implementation of the RRC sector establishes improvement that ensures a relationship of trust among the health users and the professionals and the effectiveness of care for pregnancy emergencies and urgencies.