Surprisal analysis of genome-wide transcript profiling identifies differentially expressed genes and pathways associated with four growth conditions in the microalga Chlamydomonas

Type II metacaspase mediates light-dependent programmed cell death in Chlamydomonas reinhardtii

Among the crucial processes that preside over the destiny of cells from any type of organism are those involving their self-destruction. This process is well characterized and conceptually logical to understand in multicellular organisms; however, the levels of knowledge and comprehension of its existence are still quite enigmatic in unicellular organisms. We use Chlamydomonas (Chlamydomonas reinhardtii) to lay the foundation for understanding the mechanisms of programmed cell death (PCD) in a unicellular photosynthetic organism. In this paper, we show that while PCD induces the death of a proportion of cells, it allows the survival of the remaining population. A quantitative proteomic analysis aiming at unveiling the proteome of PCD in Chlamydomonas allowed us to identify key proteins that led to the discovery of essential mechanisms. We show that in Chlamydomonas, PCD relies on the light dependence of a photosynthetic organism to generate reactive oxygen species and induce cell death. Finally, we obtained and characterized mutants for the 2 metacaspase genes in Chlamydomonas and showed that a type II metacaspase is essential for PCD execution.

A novel estimator of the interaction matrix in Graphical Gaussian Model of omics data using the entropy of non-equilibrium systems

MOTIVATION

Inferring the direct relationships between biomolecules from omics datasets is essential for the understanding of biological and disease mechanisms. Gaussian Graphical Model (GGM) provides a fairly simple and accurate representation of these interactions. However, estimation of the associated interaction matrix using data is challenging due to a high number of measured molecules and a low number of samples.

RESULTS

In this article, we use the thermodynamic entropy of the non-equilibrium system of molecules and the data-driven constraints among their expressions to derive an analytic formula for the interaction matrix of Gaussian models. Through a data simulation, we show that our method returns an improved estimation of the interaction matrix. Also, using the developed method, we estimate the interaction matrix associated with plasma proteome and construct the corresponding GGM and show that known NAFLD-related proteins like ADIPOQ, APOC, APOE, DPP4, CAT, GC, HP, CETP, SERPINA1, COLA1, PIGR, IGHD, SAA1 and FCGBP are among the top 15% most interacting proteins of the dataset.

AVAILABILITY AND IMPLEMENTATION

The supplementary materials can be found in the following URL: http://dynamic-proteome.utmb.edu/PrecisionMatrixEstimater/PrecisionMatrixEstimater.aspx.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TMEA: A Thermodynamically Motivated Framework for Functional Characterization of Biological Responses to System Acclimation

The objective of gene set enrichment analysis (GSEA) in modern biological studies is to identify functional profiles in huge sets of biomolecules generated by high-throughput measurements of genes, transcripts, metabolites, and proteins. GSEA is based on a two-stage process using classical statistical analysis to score the input data and subsequent testing for overrepresentation of the enrichment score within a given functional coherent set. However, enrichment scores computed by different methods are merely statistically motivated and often elusive to direct biological interpretation. Here, we propose a novel approach, called Thermodynamically Motivated Enrichment Analysis (TMEA), to account for the energy investment in biological relevant processes. Therefore, TMEA is based on surprisal analysis, which offers a thermodynamic-free energy-based representation of the biological steady state and of the biological change. The contribution of each biomolecule underlying the changes in free energy is used in a Monte Carlo resampling procedure resulting in a functional characterization directly coupled to the thermodynamic characterization of biological responses to system perturbations. To illustrate the utility of our method on real experimental data, we benchmark our approach on plant acclimation to high light and compare the performance of TMEA with the most frequently used method for GSEA.

Metabolic, Physiological, and Transcriptomics Analysis of Batch Cultures of the Green Microalga Chlamydomonas Grown on Different Acetate Concentrations

Acetate can be efficiently metabolized by the green microalga Chlamydomonas reinhardtii. The regular concentration is 17 mM, although higher concentrations are reported to increase starch and fatty acid content. To understand the responses to higher acetate concentrations, Chlamydomonas cells were cultivated in batch mode in the light at 17, 31, 44, and 57 mM acetate. Metabolic analyses show that cells grown at 57 mM acetate possess increased contents of all components analyzed (starch, chlorophylls, fatty acids, and proteins), with a three-fold increased volumetric biomass yield compared to cells cultivated at 17 mM acetate at the entry of stationary phase. Physiological analyses highlight the importance of photosynthesis for the low-acetate and exponential-phase samples. The stationary phase is reached when acetate is depleted, except for the cells grown at 57 mM acetate, which still divide until ammonium exhaustion. Surprisal analysis of the transcriptomics data supports the biological significance of our experiments. This allows the establishment of a model for acetate assimilation, its transcriptional regulation and the identification of candidates for genetic engineering of this metabolic pathway. Altogether, our analyses suggest that growing at high-acetate concentrations could increase biomass productivities in low-light and CO₂-limiting air-bubbled medium for biotechnology.