Multiplicative Interaction Model Research Articles

Genetic stability analysis of early maturing pigeonpea genotypes using AMMI and WAASB models

In the present study, 27 early maturing pigeonpea genotypes were evaluated over multi-locations for yielding potential and analyzed using analysis of variance (ANOVA), additive main effect and multiplicative interaction (AMMI) model and Weighted Average of Absolute Scores (WAASB) models. ANOVA displayed significant variation among genotypes, environments and genotype-environment interaction. AMMI models further explained the main and interaction effects with PC1 and PC2 covering 81.8% and 18.2% variance, respectively. AMMI biplots and WAASB matrix denoted ICP 14444, ICP 8817 and ICP 11890 to be potential early maturing, high yielding stable lines across the tested locations. Warangal and Kanpur noted to be the best environments with least and highest discriminating ability respectively. ICP 11543, ICP 16309, ICP 6992 were identified as best-fit cultivars for Patancheru, Warangal and Kanpur, respectively.

Interaction between non-alcoholic fatty liver disease and obesity on the risk of developing cardiovascular diseases

BackgroundsThis investigation seeks to explore the correlation between nonalcoholic fatty liver disease (NAFLD) and cardiovascular diseases (CVDs), and to to provide evidence for the prevention and treatment of CVDs.MethodsThis study utilized data from the Jinchang cohort platform, including 19,399 participants without pre-existing major CVDs. Based on the general population and gender stratification, Cox models were used to analyze the risk of NAFLD for CVDs. The combined effect of NAFLD and different obesity indicators on CVDs was analyzed by additive and multiplicative interaction models and subgroups.ResultsThere were 3129 NAFLD patients out of 19399 subjects, and 723 (23.11%) of them had the CVD. After adjusting for multiple confounding factors, the Cox model revealed a 1.17-fold increase in the risk of CVDs among patients with NAFLD compared to those without NAFLD. Moreover, there was no notable disparity in CVDs risk among most NAFLD patients at the same level of obesity. The results indicated no additive interaction between NAFLD and obesity concerning CVDs risk, but rather a positive multiplicative interaction. Using the normal population as a reference, it was found that people with both obesity and NAFLD significantly increased the risk of developing CVDs, with HRs and 95% CIs of 1.790 (1.508, 2.126), 1.356 (1.213, 1.517), and 1.807 (1.503, 2.174), respectively, for BMI, WC, and the combination of BMI and WC.ConclusionsNAFLD and obesity are independent risk factors for CVDs. The synergy of obesity and NAFLD implies that NAFLD patients should control weight gain. Larger BMI and WC values may increase the CVDs risk for NAFLD patients, especially women.

Genotype by environment interaction across water regimes in relation to cropping season response of quinoa (Chenopodium quinoa).

Genotype × environment (GxE) interaction effects are one of the major challenges in identifying cultivars with stable performance across agri-environments. In this study we analysed GE interactions to identify quinoa (Chenopodium quinoa) cultivars with high and stable yields under different soil moisture regimes, representing control conditions, waterlogging and drought. Waterlogging and drought treatments were artificially induced using normoxia, a combination of hypoxia-normoxia, and 10% PEG (Polyethylene glycol) under hydroponic growth conditions, respectively. Both waterlogging and drought conditions significantly reduced the plant height (PH), number of leaves (NoL) and number of branches (NoB), stem diameter (SD), leaf area (LA) and dry weight (DW) of quinoa genotypes. The genotype, water regime, and genotype by water regime effects all significantly affected the measured quinoa traits. Based on the additive main effects and multiplicative interaction (AMMI) model for DW, the genotypes G18, Puno, Q4, 2-Want, Puno, Real1 x Ruy937 and Titicaca were found to exhibit tolerance and were stable across water regimes. A second-stage evaluation was conducted to test genotype × environment interaction effects in crop production field trials, selecting two contrasting seasons based on soil moisture conditions involving a diverse set of genotypes (58 varieties in total). Our results demonstrate significant variations in both growth and yield among the quinoa genotypes across the cropping seasons. The GGE analysis for grain yield indicate that field conditions matched to G × E under hydroponic experimental conditions and the cultivars G18, Q1, Q4, NL-3, G28, 42-Test, Atlas and 59-ALC were classified within a range of high productivity. Our findings provide a basis for understanding the mechanisms of wide adaptation, while identifying germplasm that enhances the water stress tolerance of quinoa cultivars at early growth stages.

Screening and Site Adaptability Evaluation of Qi-Nan Clones (Aquilaria sinensis) in Southern China

In recent years, plantations of Aquilaria sinensis in China have been dominated by Qi-nan, yet there remains limited research on the growth evaluation and breeding of these clones. In this study, a multi-point joint variance analysis, an additive main effect and multiplicative interaction (AMMI) model, a weighted average of absolute scores (WAASB) stability index, and a genotype main effect plus a genotype-by-environment interaction (GGE) biplot were used to comprehensively analyze the yield, stability, and suitable environment of 25 3-year-old Qi-Nan clones from five sites in southern China. The results showed that all the growth traits exhibited significant differences in the clones, test sites, and interactions between the clones and test sites. The phenotypic variation coefficient (PCV) and genetic variation coefficient (GCV) of the clones’ growth traits at the different sites ranged from 16.56% to 32.09% and 5.24% to 27.06%, respectively, showing moderate variation. The medium–high repeatability (R) of tree height and ground diameter ranged from 0.50 to 0.96 and 0.69 to 0.98, respectively. Among the clones, Clones G04, G05, G10, G11 and G13 showed good growth performance and could be good candidates for breeding. Environmental effects were found to be the primary source of variation, with temperature and light primarily affecting growth, while rainfall influenced survival and preservation rates. Yangjiang (YJ) was found to be the most suitable experimental site for screening high-yield and stable clones across the different sites, whereas the tree height and ground diameter at the Chengmai (CM) site were significantly higher than at the other sites, and the Pingxiang (PX) and Zhangzhou (ZZ) sites showed poor growth performance. The findings suggest that Qi-nan clones are suitable for planting in southern China. There were also abundant genetic variations in germplasm resources for the Qi-nan clones. The five selected clones could be suitable for extensive planting. Therefore, large-scale testing is necessary for determining genetic improvements in Qi-nan clones, which will be conducive to the precise localization of their promotion areas.

Do social cash transfers bridge the wellbeing gender gap in developing countries? The case of Ghana’s LEAP programme

ABSTRACT Gender inequality remains a significant problem in Ghana with many women socially and economically disadvantaged. Social protection interventions, in particular cash transfer programmes, have the potential to increase wellbeing and reduce gender gaps in poor households. This article analyses the wellbeing differences between men and women beneficiaries of Ghana's Livelihood Empowerment Against Poverty (LEAP) programme. Using a multiple regression moderation interaction model, we analyse six wellbeing dimensions (financial, health, education, food, housing and social) of 322 LEAP cash transfer beneficiaries in Ghana's KEEA district. We found that female-headed households achieve better wellbeing than male-headed households when LEAP income is paid at regular intervals. Regular LEAP payments also reduce gender gaps in wellbeing. These findings contribute to the design of policies seeking to bridge the wellbeing gap between men and women.

PRKCB methylation: a potential biomarker of MDD with childhood chronic stress, a cross-sectional study in drug-naive, first-episode adolescent MDD

ABSTRACT The purpose of this study was to investigate the relationship between childhood chronic stress(CCS), Protein kinase C beta (PRKCB) methylation and adolescent major depressive disorder (MDD). After recruiting 100 adolescents with MDD and 50 healthy controls (HCs), we evaluated the severity of CCS. PRKCB methylation was assessed by pyrosequencing using whole blood-derived DNA. To explore the relationship between CCS, PRKCB and adolescent MDD, we conducted correlation analysis and regression analysis, and constructed multiplicative interaction models and generalized linear models. PRKCB methylation and CCS were both found to be associated with MDD, and CCS was associated with PRKCB methylation. No significant CCS-PRKCB methylation interactions were observed. However, we found the interaction of CCS and MDD on PRKCB methylation. Our results found that PRKCB methylation was influenced by CCS and the disease itself, and PRKCB methylation was significantly positively associated with MDD severity, suggesting that PRKCB methylation may be a potential biomarker for adolescent MDD. This study is a cross-sectional observational study, which cannot draw the conclusion of causality. Prospective cohort studies are needed to further examine the relationship between CCS, adolescent MDD, and PRKCB methylation.

Deciphering the stability indices using additive main effect and multiplicative interaction model for high yielding registered variety of Lemongrass (Jor Lab L-8)

Cymbopogon flexuosus, commonly known as Lemongrass (Poaceae) is an industrially significant plant due to its fragrant compounds and ability to produce a large amount of essential oil. However, variations in chemical components, agronomical characters and essential oil output can be observed due to grown in different climatic conditions. Pertaining to the economic importance of the crop, the present study was designed to evaluate the stability indices of the developed and registered variety of lemongrass ‘Jor Lab L-8’. In this experiment, cv. Jor Lab L-8 was assessed along with two check varieties at eight different locations of NE India for two years (2021-2022, 2022-2023). The analysis of variance (ANOVA) by regression method revealed that Jor Lab L-8 had a significantly higher herbage yield, essential oil yield, citral content and total essential oil yield than the other two check varieties. Furthermore, AMMI ANOVA was used to confirm the consistent performance of the recorded agro-morphological and quality characters and Jor Lab L-8 was shown to be stable in plant height, herbage yield, essential oil yield, citral content and total essential oil yield. The GC-MS study of the essential oil identified 18 constituents, with citral emerging as the predominant constituent. Additionally, majority of the locations were pinpointed as ideal environments for cultivating Jor Lab L-8, underscoring its resilience across multiple locations and highlighting its long-term industrial promise.

Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains

Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security, and promoting environmental sustainability. We analyzed data from nutrient omission plot trials (NOPTs) conducted in 324 farmers' fields across ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields. An additive main effect and multiplicative interaction (AMMI) model was used to explain maize yield variability with nutrient addition. Interpretable machine learning (ML) algorithms in automatic machine learning (AutoML) frameworks were subsequently used to predict attainable yield relative nutrient-limited yield (RY) and to rank variables that control RY. The stack-ensemble model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model's square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature importance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn. The predicted median RY of N, P, K, and Zn, representing average soil fertility, was 0.51, 0.84, 0.87, and 0.97, accounting for 44, 54, 54, and 48% upland dry season crop area of Bangladesh, respectively. Efforts are needed to update databases cataloging variability in land type inundation classes, soil characteristics, and INS and combine them with farmers' crop management information to develop more precise nutrient guidelines for maize in the EIGP.

The interaction between TMEM161B (rs768705) and paranoid personality traits in relation to the risk of major depressive disorder: Results form a longitudinal study of 7642 Chinese freshmen

BackgroundRs768705 (TMEM161B) is one of the identified single nucleotide polymorphisms related to major depressive disorder (MDD). Paranoid personality traits are independently associated with the risk of MDD. This study aimed to investigate the interaction effect between rs768705 (TMEM161B) and paranoid personality traits on the new-onset risk of MDD in Chinese freshmen. MethodsA longitudinal study was conducted among 7642 Chinese freshmen without lifetime MDD at baseline in 2018. 158 new-onset MDD cases were ascertained in 2019. DNA samples were extracted to detect the genotype of rs768705. The diagnostic and statistical manual of mental disorders-IV criteria were used to determine MDD and personality disorder traits. Multiplicative interaction was assessed by logistic regression models. Tomas Andersson's method for calculating biological interactions was used to estimate the additive interaction. ResultsRs768705(AG) (OR = 1.88, 95 % CI: 1.24–2.83) and paranoid personality traits (OR = 3.68, 95 % CI: 2.57–5.26) were significantly associated with the risk of MDD. The multiplicative interaction model with the product term of rs768705 and paranoid personality trait traits had a significant interaction effect (OR = 4.20, 95 % CI:1.62–10.91). There was also a significant additive interaction effect (RR = 7.08, 95 % CI:4.31–11.65) for the incidence of MDD. Seventy seven percent patients among new MDD cases were attributed to the additive interaction effect between rs768705 and paranoid personality traits. ConclusionsRs768705 (AG) may interact with paranoid personality traits to increase the incidence of MDD among Chinese college students. Schools and psychosocial health organizations should pay more attention to individuals with paranoid personality traits for MDD intervention and prevention.

Genotype-environment interaction for grain yield in maize (Zea mays L.) using the additive main effects and multiplicative interaction (AMMI) model.

Genotype-environment interaction consists of the different response of individual genotypes resulting from changing environmental conditions. Its significance is a phenomenon that makes the breeding process very difficult. On the one hand, the breeder expects stable genotypes, i.e., yielding similarly regardless of environmental conditions. On the other hand, selecting the best genotypes for each region is one of the key challenges for breeders and farmers. The aim of this study was to evaluate genotype-by-environment interaction for grain yield in new maize hybrids developed by Plant Breeding Smolice Co. Ltd., utilizing the additive main effects and multiplicative interaction (AMMI) model. The investigation involved 69 maize (Zea mays L.) hybrids, tested across five locations in a randomized complete block design with three replications. Grain yield varied from 8.76 t ha-1 (SMH_16417 in Smolice) to 16.89 t ha-1 (SMH_16043 in Płaczkowo), with a mean yield of 13.16 t ha-1. AMMI analysis identified significant effects of genotype, environment, and their interaction on grain yield. Analysis of variance indicated that 25.12% of the total variation in grain yield was due to environment factor, 35.20% to genotypic differences, and 21.18% to genotype by environmental interactions. Hybrids SMH_1706 and SMH_1707 are recommended for further breeding programs due to their high stability and superior average grain yield.

Evaluation of the stability of quantitative traits of winter oilseed rape (Brassica napus L.) by AMMI analysis

Agronomical traits of crop plants exhibit quantitative variation that is controlled by multiple genes and is dependent on environmental conditions. The main objective of this study was to decipher the genotype-by-environment interaction (GEI) for six yield-related traits of 25 winter oilseed rape (WOSR) genotypes using the additive main effects and multiplicative interaction (AMMI) model. The genotypes chosen included canola cultivars, our newly developed WOSR breeding lines, yellow-seeded, semi-resynthesized and mutant genotypes, together with ogu-INRA F1 hybrids and their parental lines. These were tested in field trials at two locations over three growing seasons. Field experiments were conducted in a randomized block design with four replicates. We recorded the beginning of flowering, seed yield (SY) and SY components, the number of siliques per plant, the length of siliques, the number of seeds per silique, and the weight of 1000 seeds. The average SY in six environments varied from 16.55 to 41.64 dt·ha−1. The AMMI analysis showed significant effects of both G and E, as well as GEI, for the above traits. In this study, we observed that the climate condition, especially precipitation in addition to the soil type were the most influential factors on the SY and SY-trait value. Seed yield was positively correlated with: the number of siliques per plant, the length of siliques, the number of seeds per silique and the weight of 1000 seeds. We also found that our new ogu-INRA F1 hybrids, as well as cultivars Monolit, Mendel, Starter and Sherlock, showed stability for the analyzed traits.

Evaluating effectiveness of clonal plant selection of alfalfa (Medicago sativa L.) and sainfoin (Onobrychis viciifolia Scop.) in mixtures: Mean performance and stability in a multi‐environment trial

AbstractAlfalfa (Medicago sativa L.) ‐ grass or alfalfa ‐ sainfoin (Onobrychis viciifolia Scop.) mixtures are commonly used for pastures in western Canada because of their high forage quality and their low risk of causing frothy bloat in grazing animals. However, the proportion of these two legumes declines in mixed forage stands over time. The objective of this study was to evaluate the effectiveness of selecting superior genotypes from clonally propagated alfalfa or sainfoin under plant competition in different growth environments. For this study, around 100 genotypes of each legume were cloned and transplanted into meadow bromegrass (Bromus riparius Relm.) or alfalfa swards at Saskatoon, SK and Lethbridge, AB, Canada in 2017. Genotype‐environment (G x E) interactions of alfalfa and sainfoin genotypes were analysed by an additive main‐effects and multiplicative interaction (AMMI) model. Significant variations in plant height, spring vigour and total dry matter yield (TDM) were observed for both species. Among the measured traits, plant height was a more highly heritable trait (H2 = .16 for alfalfa and H2 = .18 for sainfoin), while TDM was the least heritable (H2 = .08 for alfalfa and H2 = .04 for sainfoin). In the AMMI Analysis of variance for TDM, the genotype explained <12% of the variation for both species, suggesting a direct selection of yield would result in a low genetic gain. The biplot mean performance (Y) x weighted average of absolute scores from the singular value decomposition of the matrix of BLUPs stability index (WAASB) identified several promising genotypes with superior performance and stability across different environments that were selected for producing synthetic lines. However, the synthetic lines of both alfalfa and sainfoin did not consistently exhibit superior performance in mixtures compared to their respective check cultivars.

Genotype by environment interaction in mustard (Brassica juncea) under Terai Agro-Climatic zone using the AMMI model and GGE biplot

In the present experiment, 71 Indian mustard (Brassica juncea) genotypes were evaluated in six environments for two successive years (three environments in each year) to identify the influence of environment over the genotypes and to identify the stable, better-performing genotypes in respect to yield in Terai region of West Bengal. Statistical analysis was carried out to evaluate genotype × environment interaction using two different approaches, namely, additive main effect, and their multiplicative interaction model (AMMI) and genotype plus genotype by environment interaction (GGE) biplot for seed yield of mustard genotypes. AMMI ANOVA showed that genotype and environment accounted for 11.46% and 47.50% of the variation respectively, where GEI captured 36.57% of the variation for seed yield. The AMMI model and GGE biplots showed that genotype RGIN-73 was the highest yielding genotype to a specific environment but DRWR-15-9 was found to be well stable with good yielding in all environments.

Comparison of additive main effect–multiplicative interaction model and factor analytic model for genotypes ordination from multi‐environment trials

AbstractAn additive main effects and multiplicative interaction (AMMI) model is used to explore the genotype × environment interaction (GEI) in complete multi‐environmental trials. This model orders genotypes (G) according to their performance across environments (E) on a vectorial plane generated by the first two axes of a principal component analysis (AMMI‐biplot). Alternatively, interaction terms can be regarded as random effects, which can be predicted from linear mixed models using a factor analytic (FA) covariance structure for the GEI terms. Here, an FA‐biplot was obtained by plotting the G and E scores derived from the FA mixed model with complete and incomplete data. The aim of this work was to compare AMMI‐biplot with FA‐biplot for balanced data and then show the impact of the imbalance on the FA‐biplot. The G ordinations were assessed in four scenarios generated using datasets of 3 consecutive years obtained from comparative wheat trials conducted under a complete random block design in different environments across the Argentine network of cultivar assessment. For each scenario, G with the lowest performance in the third year were deleted, one by one, from all sites to generate a scenario with missing G. Although we used different statistical procedures to obtain AMMI‐biplot and FA‐biplot, they showed the same interaction pattern in the case of up to 50% of G dropped from all E in the last year of the multiyear trials. We conclude that the FA‐biplot yields a robust G ordination even when with incomplete datasets.

Genotype by year interaction for selected traits in sweet maize (Zea maize L.) hybrids using AMMI model

This study investigated genotype × environment interactions for the stability of expression of four productivity traits (cobs yield, cobs I class trade share, lend of cobs and fulfilment of cobs) of sweet maize hybrids (Zea mays L.). The additive main effects and multiplicative interaction (AMMI) model was employed to assess genotype × environment interaction. AMMI stability value was used to evaluate both stability and genotype. The genotype selection index was calculated for each hybrid, incorporating both the average trait value and the stability index. Ten sweet maize hybrids were evaluated: Golda, GSS 1453, GSS 3071, GSS 5829, GSS 8529, Overland, Noa, Shinerock, Sindon, and Tessa. Trials were ran conducted over four vegetative seasons at a single location in the Wielkopolska region using replicated field experiments. The AMMI model revealed significant genotypic and environmental effects for all analyzed traits. Based on their superior stability and favorable average trait values, both the Golda cultivar and the GSS 3071 hybrid are recommended for further breeding program inclusion.

Genotype x environment interaction of essential oil by Additive Main effects and Multiplicative Interaction (AMMI) model and metabolomics based profiling of volatile constituents in turmeric (Curcuma longa L.)

Genotype x environment interaction of essential oil by Additive Main effects and Multiplicative Interaction (AMMI) model and metabolomics based profiling of volatile constituents in turmeric (Curcuma longa L.)

Interacción genotipo por ambiente de Solanum quitoense (Lam.) usando el modelo AMMI

The basis for genetic improvement for the cultivation of lulo (Solanum quitoense Lam.) in Colombia is limited. Research related to genotype-by-environment interaction (GEI) and stability in agronomic traits is scarce. To identify outstanding genotypes, GEI and the stability of fruit weight (FW), ascorbic acid (AA), and yield of 10 half-sib families (HSF) were evaluated. At the locations of San Pedro de Cartago, Arboleda, Tangua and La Union of the department of Nariño (Colombia), four trials were established under the randomised complete block design with four repetitions. To analyse GEI, the additive main effects and multiplicative interaction (AMMI) model and some AMMI stability parameters were used. In yield, the HSF7 and the control presented specific adaptation for Tangua, with 12.82 and 13.41 t ha-1, respectively and FW greater than 100 g. In Arboleda, HSF29 obtained the highest yield (16.14 t ha-1) with an FW of 100.53 g. HSF4, HSF28 and HSF49 reached yields above 9.0 t ha-1 and a FW greater than 100 g; therefore, they are recommended for any of the environments evaluated given their stability. HSF25 in AA and HSF29 in yield presented specific adaptation in Arboleda. Stable families across environments and with good yield were HSF4, HSF28, and HSF49, HSF4 and HSF28 showed higher values in two of the three traits, and HSF49 was outstanding in all three variables. These families can be used in plant breeding programmes as parents or distributed to farmers as improved varieties.

Country-wide, multi-location trials of Green Super Rice lines for yield performance and stability analysis using genetic and stability parameters

Rice (Oryza sativa L.) is an important member of the family Poaceae and more than half of world population depend for their dietary nutrition on rice. Rice cultivars with higher yield, resilience to stress and wider adaptability are essential to ensure production stability and food security. The fundamental objective of this study was to identify higher-yielding rice genotypes with stable performance and wider adaptability in a rice growing areas of Pakistan. A triplicate RCBD design experiment with 20 Green Super Rice (GSR) advanced lines was conducted at 12 rice growing ecologies in four Provinces of Pakistan. Grain yield stability performance was assessed by using different univariate and multivariate statistics. Analysis of variance revealed significant differences among genotypes, locations, and G x E interaction for mean squares (p < 0.05) of major yield contributing traits. All the studied traits except for number of tillers per plant revealed higher genotypic variance than environmental variance. Broad sense heritability was estimated in the range of 44.36% to 98.60%. Based on ASV, ASI, bi, Wi2, σ2i and WAAS statistics, the genotypes G1, G4, G5, G8, G11 and G12 revealed lowest values for parametric statistics and considered more stable genotypes based on paddy yield. The additive main effects and multiplicative interaction (AMMI) model revealed significant variation (p < 0.05) for genotypes, non-signification for environment and highly significant for G × E interaction. The variation proportion of PC1 and PC2 from interaction revealed 67.2% variability for paddy yield. Based on ‘mean verses stability analysis of GGE biplot’, ‘Which-won-where’ GGE Biplot, ‘discriminativeness vs. representativeness’ pattern of stability, ‘IPCA and WAASB/GY’ ratio-based stability Heat-map, and ranking of genotypes, the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 were observed ideal genotypes with yield potential more than 8 tons ha−1. Discriminativeness vs. representativeness’ pattern of stability identifies two environments, E5 (D.I Khan, KPK) and E6 (Usta Muhammad, Baluchistan) were best suited for evaluating genotypic yield performance. Based on these findings we have concluded that the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 could be included in the commercial varietal development process and future breeding program.

Expanding the molecular language of protein liquid-liquid phase separation.

Understanding the relationship between a polypeptide sequence and its phase separation has important implications for analysing cellular function, treating disease and designing novel biomaterials. Several sequence features have been identified as drivers for protein liquid-liquid phase separation (LLPS), schematized as a 'molecular grammar' for LLPS. Here we further probe how sequence modulates phase separation and the material properties of the resulting condensates, targeting sequence features previously overlooked in the literature. We generate sequence variants of a repeat polypeptide with either no charged residues, high net charge, no glycine residues or devoid of aromatic or arginine residues. All but one of 12 variants exhibited LLPS, albeit to different extents, despite substantial differences in composition. Furthermore, we find that all the condensates formed behaved like viscous fluids, despite large differences in their viscosities. Our results support the model of multiple interactions between diverse residue pairs-not just a handful of residues-working in tandem to drive the phase separation and dynamics of condensates.

Reliable and consistent genotype selection for leaf biomass and natural sennosides yield across multi-environments in Senna (Senna alexandrina Mill)

The relationship between genotype and season significantly hinders the development of stable genotype selection and improved genotypes for senna breeding programs. This study aimed to assess stable, high-yielding senna genotypes under South Indian conditions. Twenty-five different senna genotypes were tested for yield stability using a randomized, completely-block design. The additive main effects and multiplicative interaction (AMMI) model was used to investigate the relationships between genotype (G), environment (E), and genotype × environment. Based on the findings, the three genotypes—GEN 1, GEN 9, and GEN 18—might be recommended for large-scale production. Stable and high-yielding genotypes should be the main genetic resources used in senna-based cropping season breeding programs.