Developmental normalization of phenomics data generated by high throughput plant phenotyping systems.

BACKGROUND: Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. RESULTS: The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. CONCLUSIONS: DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.

ANAC017 Coordinates Organellar Functions and Stress Responses by Reprogramming Retrograde Signaling.

Mitochondria adjust their activities in response to external and internal stimuli to optimize growth via the mitochondrial retrograde response signaling pathway. The Arabidopsis (Arabidopsis thaliana) NAC domain transcription factor ANAC017 has previously been identified as a regulator of the mitochondrial retrograde response. We show here that overexpression of ANAC017 in Arabidopsis leads to growth retardation, altered leaf development with decreased cell size and viability, and early leaf senescence. RNA sequencing analyses revealed that increased ANAC017 expression leads to higher expression of genes related to mitochondrial stress, cell death/autophagy, and leaf senescence under nonlimiting growth conditions as well as extensive repression of chloroplast function. Gene regulatory network analysis indicated that a complex hierarchy of transcription factors exists downstream of ANAC017. These involve a set of up-regulated ANAC and WRKY transcription factors associated with organellar signaling and senescence. The network also includes a number of ethylene- and gibberellic acid-related transcription factors with established functions in stress responses and growth regulation, which down-regulate their target genes. A number of BASIC LEUCINE-ZIPPER MOTIF transcription factors involved in the endoplasmic reticulum unfolded protein response or balancing of energy homeostasis via the SNF1-RELATED PROTEIN KINASE1 were also down-regulated by ANAC017 overexpression. Our results show that the endoplasmic reticulum membrane tethering of the constitutively expressed ANAC017, and its controlled release, are crucial to fine-tune a fast reactive but potentially harmful signaling cascade. Thus, ANAC017 is a master regulator of cellular responses with mitochondria acting as central sensors.

An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings.

Respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), are affecting a huge percentage of the world's population with mortality rates exceeding those of lung cancer and breast cancer combined. The major challenge is the number of patients who are incorrectly diagnosed. To address this, we developed an expert diagnostic system that can differentiate among patients with asthma, COPD or a normal lung function based on measurements of lung function and information about patient's symptoms. To develop accurate classification algorithms, data from 3657 patients were used and then independently verified using data from 1650 patients collected over a period of two years. Our results demonstrate that the expert diagnostic system can correctly identify patients with asthma and COPD with sensitivity of 96.45% and specificity of 98.71%. Additionally, 98.71% of the patients with a normal lung function were correctly classified, which contributed to a 49.23% decrease in demand for conducting additional tests, therefore decreasing financial cost.