The detection and quantitation of protein-ligand binding interactions is critical in a number of different areas of biochemical research from fundamental studies of biological processes to drug discovery efforts. Described here is a protocol that can be used to identify the protein targets of biologically relevant ligands (e.g. drugs like tamoxifen or cyclosporin A) in complex protein mixtures such as cell lysates. The protocol utilizes quantitative, bottom-up, shotgun proteomics technologies (iTRAQ) with a covalent labeling technique, termed Stability of Proteins from Rates of Oxidation (SPROX). In SPROX, the thermodynamic properties of proteins and protein-ligand complexes are assessed using the hydrogen peroxide-mediated oxidation of methionine residues as a function of the chemical denaturant (e.g. guanidine Hydrochloride or urea) concentration. The proteome-wide SPROX experiments described here enable the ligand binding properties of hundreds of proteins to be simultaneously assayed in the context of complex biological samples. The proteomic capabilities of the protocol render it amenable to detection of both the on- and off-target effects of ligand binding.

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BackgroundDunaliella salina Teodoresco, a unicellular, halophilic green alga belonging to the Chlorophyceae, is among the most industrially important microalgae. This is because D. salina can produce massive amounts of β-carotene, which can be collected for commercial purposes, and because of its potential as a feedstock for biofuels production. Although the biochemistry and physiology of D. salina have been studied in great detail, virtually nothing is known about the genomes it carries, especially those within its mitochondrion and plastid. This study presents the complete mitochondrial and plastid genome sequences of D. salina and compares them with those of the model green algae Chlamydomonas reinhardtii and Volvox carteri.ResultsThe D. salina organelle genomes are large, circular-mapping molecules with ~60% noncoding DNA, placing them among the most inflated organelle DNAs sampled from the Chlorophyta. In fact, the D. salina plastid genome, at 269 kb, is the largest complete plastid DNA (ptDNA) sequence currently deposited in GenBank, and both the mitochondrial and plastid genomes have unprecedentedly high intron densities for organelle DNA: ~1.5 and ~0.4 introns per gene, respectively. Moreover, what appear to be the relics of genes, introns, and intronic open reading frames are found scattered throughout the intergenic ptDNA regions -- a trait without parallel in other characterized organelle genomes and one that gives insight into the mechanisms and modes of expansion of the D. salina ptDNA.ConclusionsThese findings confirm the notion that chlamydomonadalean algae have some of the most extreme organelle genomes of all eukaryotes. They also suggest that the events giving rise to the expanded ptDNA architecture of D. salina and other Chlamydomonadales may have occurred early in the evolution of this lineage. Although interesting from a genome evolution standpoint, the D. salina organelle DNA sequences will aid in the development of a viable plastid transformation system for this model alga, and they will complement the forthcoming D. salina nuclear genome sequence, placing D. salina in a group of a select few photosynthetic eukaryotes for which complete genome sequences from all three genetic compartments are available.

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Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods’ performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.

The halotolerant alga Dunaliella salina is a model for stress tolerance and is used commercially for production of beta-carotene (=pro-vitamin A). The presented draft genome of the genuine strain CCAP19/18 will allow investigations into metabolic processes involved in regulation of stress responses, including carotenogenesis and adaptations to life in high-salinity environments.

The β-cell protein synthetic machinery is dedicated to the production of mature insulin, which requires the proper folding and trafficking of its precursor, proinsulin. The complete network of proteins that mediate proinsulin folding and advancement through the secretory pathway, however, remains poorly defined. Here we used affinity purification and mass spectrometry to identify, for the first time, the proinsulin biosynthetic interaction network in human islets. Stringent analysis established a central node of proinsulin interactions with endoplasmic reticulum (ER) folding factors, including chaperones and oxidoreductases, that is remarkably conserved in both sexes and across three ethnicities. The ER-localized peroxiredoxin PRDX4 was identified as a prominent proinsulin-interacting protein. In β-cells, gene silencing of PRDX4 rendered proinsulin susceptible to misfolding, particularly in response to oxidative stress, while exogenous PRDX4 improved proinsulin folding. Moreover, proinsulin misfolding induced by oxidative stress or high glucose was accompanied by sulfonylation of PRDX4, a modification known to inactivate peroxiredoxins. Notably, islets from patients with type 2 diabetes (T2D) exhibited significantly higher levels of sulfonylated PRDX4 than islets from healthy individuals. In conclusion, we have generated the first reference map of the human proinsulin interactome to identify critical factors controlling insulin biosynthesis, β-cell function, and T2D.

A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical Autoencoder (scDHA), that reliably extracts representative information of each cell. The scDHA pipeline consists of two core modules. The first module is a non-negative kernel autoencoder able to remove genes or components that have insignificant contributions to the part-based representation of the data. The second module is a stacked Bayesian autoencoder that projects the data onto a low-dimensional space (compressed). To diminish the tendency to overfit of neural networks, we repeatedly perturb the compressed space to learn a more generalized representation of the data. In an extensive analysis, we demonstrate that scDHA outperforms state-of-the-art techniques in many research sub-fields of scRNA-seq analysis, including cell segregation through unsupervised learning, visualization of transcriptome landscape, cell classification, and pseudo-time inference.