Optogenetic modulation of Delta reveals the role of Notch signalling dynamics during tissue differentiation

Spatio-temporal regulation of signalling pathways plays an important role in generating diverse responses during the development of multicellular organisms. While increasing number studies are uncovering the importance of signalling dynamics in controlling tissue patterning and morphogenesis, the precise role of signal dynamics in transferring information in vivo is incompletely understood owing to the lack of methods to manipulate protein activity at the relevant spatio-temporal scales. In this PhD thesis, I employ genome engineering in Drosophila melanogaster to generate a functional optogenetic allele of the Notch ligand Delta (opto-Delta), at its endogenous locus. Light mediated activation of opto-Delta disrupts Notch signalling during different developmental stages. Using clonal analysis, I show that optogenetic activation blocks Notch activation through cis-inhibition in signal-receiving cells. To investigate how a Notch input is dynamically translated into a differentiation output, I focused on mesectoderm specification during early Drosophila embryogenesis. Signal perturbation in combination with quantitative analysis of a live transcriptional reporter of Notch pathway activity reveals different modes of regulation at the tissue and cellular level. While at the tissue-level the duration of Notch signalling determines the probability with which a cellular response will occur, in individual cells Notch activation needs to reach a minimum threshold to generate a response. Taken together these results provide novel insights into the dynamic input-output regulation of Notch signalling, supporting a model in which the Notch receptor is an integrator of (noisy) analog signals that generates a digital switch-like behaviour at the level of target gene expression during tissue differentiation. In order to further test this model, I attempted to develop an optogenetic system to activate Notch in vivo (opto-Notch). Despite showing light-responsive changes in localization, a certain level of Notch is activated even prior to photo-activation, thus necessitating further optimization. Finally, I describe efforts for further characterization of opto-Delta as a tool to spatially perturb signalling, to study Notch signalling during neuroblast delamination, and for adaptation to mammalian cell-culture systems

Desensitisation of Notch signalling through dynamic adaptation in the nucleus

During embryonic development, signalling pathways orchestrate organogenesis by controlling tissue‐specific gene expression programmes and differentiation. Although the molecular components of many common developmental signalling systems are known, our current understanding of how signalling inputs are translated into gene expression outputs in real‐time is limited. Here we employ optogenetics to control the activation of Notch signalling during Drosophila embryogenesis with minute accuracy and follow target gene expression by quantitative live imaging. Light‐induced nuclear translocation of the Notch Intracellular Domain (NICD) causes a rapid activation of target mRNA expression. However, target gene transcription gradually decays over time despite continuous photo‐activation and nuclear NICD accumulation, indicating dynamic adaptation to the signalling input. Using mathematical modelling and molecular perturbations, we show that this adaptive transcriptional response fits to known motifs capable of generating near‐perfect adaptation and can be best explained by state‐dependent inactivation at the target cis‐regulatory region. Taken together, our results reveal dynamic nuclear adaptation as a novel mechanism controlling Notch signalling output during tissue differentiation

Optogenetic inhibition of Delta reveals digital Notch signalling output during tissue differentiation

International audienceSpatio-temporal regulation of signalling pathways plays a key role in generating diverse responses during the development of multicellular organisms. The role of signal dynamics in transferring signalling information in vivo is incompletely understood. Here we employ genome engineering in Drosophila melanogaster to generate a functional optogenetic allele of the Notch ligand Delta (opto-Delta), which replaces both copies of the endogenous wild type locus. Using clonal analysis, we show that optogenetic activation blocks Notch activation through cis-inhibition in signal-receiving cells. Signal perturbation in combination with quantitative analysis of a live transcriptional reporter of Notch pathway activity reveals differential tissue- and cell-scale regulatory modes. While at the tissue-level the duration of Notch signalling determines the probability with which a cellular response will occur, in individual cells Notch activation acts through a switch-like mechanism. Thus, time confers regulatory properties to Notch signalling that exhibit integrative digital behaviours during tissue differentiation

Clinical profile of diabetes in the young seen between 1992 and 2009 at a specialist diabetes centre in South India

Aim: To describe the trends and clinical profile of young diabetic patients (YD) attending a tertiary diabetes centre in south India. Methods: We reviewed medical records of 2630 YD patients (age at onset ≤25 years) registered between 1992 and 2009. Patients were classified as type 1 diabetes (T1DM), type 2 diabetes (T2DM) gestational diabetes mellitus (GDM) and other types. Retinopathy was assessed initially by direct and indirect ophthalmoscopy and later by retinal photography, nephropathy if urine protein excretion was > 500mg/day, neuropathy if vibration perception threshold on biothesiometry was ≥20V. Results: The percentage of YD patients rose from 0.55% in 1992 to 2.5% in 2009 (trend chi square, 15.1, p<0.001). Of the 2630 YD subjects registered, 1135 (43.2%) had T1DM, 1262 (48.0%) had T2DM, 118 (4.5%) had GDM and 115 (4.4%) other types. T1DM patients were younger, had lower body mass index, waist circumference, systolic and diastolic blood pressures, and less family history of diabetes compared to T2DM (p<0.001 for each). Retinopathy was seen in 71.9% and 77.3% nephropathy in 22.1% and 12.1% and neuropathy in 34.5% and 21.4% of T2DM and T1DM respectively in those with ≥15 years duration of diabetes. Conclusions: The percentage of YD in south India is increasing, predominantly due to early onset T2DM

Molecular Breeding for Improving Productivity of Oryza sativa L. cv. Pusa 44 under Reproductive Stage Drought Stress through Introgression of a Major QTL, qDTY12.1

Increasing rice production is quintessential to the task of sustaining global food security, as a majority of the global population is dependent on rice as its staple dietary cereal. Among the various constraints affecting rice production, reproductive stage drought stress (RSDS) is a major challenge, due to its direct impact on grain yield. Several quantitative trait loci (QTLs) conferring RSDS tolerance have been identified in rice, and qDTY12.1 is one of the major QTLs reported. We report the successful introgression of qDTY12.1 into Pusa 44, a drought sensitive mega rice variety of the northwestern Indian plains. Marker-assisted backcross breeding (MABB) was adopted to transfer qDTY12.1 into Pusa 44 in three backcrosses followed by four generations of pedigree selection, leading to development of improved near isogenic lines (NILs). Having a recurrent parent genome (RPG) recovery ranging from 94.7–98.7%, the improved NILs performed 6.5 times better than Pusa 44 under RSDS, coupled with high yield under normal irrigated conditions. The MABB program has been modified so as to defer background selection until BC3F4 to accelerate generational advancements. Deploying phenotypic selection alone in the early backcross generations could help in the successful recovery of RPG. In addition, the grain quality could be recovered in the improved NILs, leading to superior selections. Owing to their improved adaptation to drought, the release of improved NILs for regions prone to intermittent drought can help enhance rice productivity and production

Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice

Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R2 = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice