Many neurodegenerative disorders such as Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS) and others often occur as a result of progressive loss of structure or function of neurons. Recently, many groups were able to generate neural cells, either differentiated from induced pluripotent stem cells (iPSCs) or converted from somatic cells. Advances in converted neural cells have opened a new era to ease applications for modeling diseases and screening drugs. In addition, the converted neural cells also hold the promise for cell replacement therapy (Kikuchi et al., 2011; Krencik et al., 2011; Kriks et al., 2011; Nori et al., 2011; Rhee et al., 2011; Schwartz et al., 2012). Here we will mainly discuss most recent progress on using converted functional neural cells to treat neurological diseases and highlight potential clinical challenges and future perspectives.

The inflammasome is an emerging new pathway in innate immune defense against microbial infection or endogenous danger signals. The inflammasome stimulates activation of inflammatory caspases, mainly caspase-1. Caspase-1 activation is responsible for processing and secretion of IL-1β and IL-18 as well as for inducing macrophage pyroptotic death. Assembly of the large cytoplasmic inflammasome complex is thought to be mediated by members of NOD-like receptor (NLR) family. While functions of most of the NLR proteins remain to be defined, several NLR proteins including NLRC4 have been shown to assemble distinct inflammasome complexes. These inflammasome pathways, particularly the NLRC4 inflammasome, play a critical role in sensing and restricting diverse types of bacterial infections. Here we review recent advances in defining the exact bacterial ligands and the ligand-binding receptors involved in NLRC4 inflammasome activation. Implications of the discovery of the NAIP family of inflammasome receptors for bacterial flagellin and type III secretion apparatus on future inflammasome and bacterial infection studies are also discussed.

Light is one of the key environmental signals regulating plant growth and development. Therefore, understanding the mechanisms by which light controls plant development has long been of great interest to plant biologists. Traditional genetic and molecular approaches have successfully identified key regulatory factors in light signaling, but recent genomic studies have revealed massive reprogramming of plant transcriptomes by light, identified binding sites across the entire genome of several pivotal transcription factors in light signaling, and discovered the involvement of epigenetic regulation in light-regulated gene expression. This review summarizes the key genomic work conducted in the last decade which provides new insights into light control of plant development.

Siva-1, as a p53-inducible gene, has been shown to induce extensive apoptosis in a number of different cell lines. Recent evidence suggests that Siva-1 functions as a part of the auto-regulatory feedback loop that restrains p53 through facilitating Mdm2-mediated p53 degradation. Also, Siva-1 plays an important role in suppressing tumor metastasis. Here we review the current understanding of Siva-1-mediated apoptotic signaling pathway. We also add comments on the p53-Siva-1 feedback loop, the novel function of Siva-1 in suppressing tumor metastasis, and their potential implications.

D-Psicose 3-epimerase (DPEase) is demonstrated to be useful in the bioproduction of D-psicose, a rare hexose sugar, from D-fructose, found plenty in nature. Clostridium cellulolyticum H10 has recently been identified as a DPEase that can epimerize D-fructose to yield D-psicose with a much higher conversion rate when compared with the conventionally used DTEase. In this study, the crystal structure of the C. cellulolyticum DPEase was determined. The enzyme assembles into a tetramer and each subunit shows a (β/α)₈ TIM barrel fold with a Mn²⁺ metal ion in the active site. Additional crystal structures of the enzyme in complex with substrates/ products (D-psicose, D-fructose, D-tagatose and D-sorbose) were also determined. From the complex structures of C. cellulolyticum DPEase with D-psicose and D-fructose, the enzyme has much more interactions with D-psicose than D-fructose by forming more hydrogen bonds between the substrate and the active site residues. Accordingly, based on these ketohexosebound complex structures, a C3-O3 proton-exchange mechanism for the conversion between D-psicose and D-fructose is proposed here. These results provide a clear idea for the deprotonation/protonation roles of E150 and E244 in catalysis.

Drugs sharing similar therapeutic function may not bind to the same group of targets. However, their targets may be involved in similar pathway profiles which are associated with certain pathological process. In this study, pathway fingerprint was introduced to indicate the profile of significant pathways being influenced by the targets of drugs. Then drug-drug network was further constructed based on significant similarity of pathway fingerprints. In this way, the functions of a drug may be hinted by the enriched therapeutic functions of its neighboring drugs. In the test of 911 FDA approved drugs with more than one known target, 471 drugs could be connected into networks. 760 significant associations of drug-therapeutic function were generated, among which around 60% of them were supported by scientific literatures or ATC codes of drug functional classification. Therefore, pathway fingerprints may be useful to further study on the potential function of known drugs, or the unknown function of new drugs.

Biologically important proteins related to membrane receptors, signal transduction, regulation, transcription, and translation are usually low in abundance and identified with low probability in mass spectroscopy (MS)-based analyses. Most valuable proteomics information on them were hitherto discarded due to the application of excessively strict data filtering for accurate identification. In this study, we present a stagedprobability strategy for assessing proteomic data for potential functionally important protein clues. MS-based protein identifications from the second (L2) and third (L3) layers of the cascade affinity fractionation using the Trans-Proteomic Pipeline software were classified into three probability stages as 1.00-0.95, 0.95-0.50, and 0.50-0.20 according to their distinctive identification correctness rates (i.e. 100%-95%, 95%-50%, and 50%-20%, respectively). We found large data volumes and more functionally important proteins located at the previously unacceptable lower probability stages of 0.95-0.50 and 0.50-0.20 with acceptable correctness rate. More importantly, low probability proteins in L2 were verified to exist in L3. Together with some MS spectrogram examples, comparisons of protein identifications of L2 and L3 demonstrated that the stagedprobability strategy could more adequately present both quantity and quality of proteomic information, especially for researches involving biomarker discovery and novel therapeutic target screening.

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data analysis. However, extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available software. Here, we present CloudLCA, a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data analysis. Results show that CloudLCA (1) has a running time nearly linear with the increase of dataset magnitude, (2) displays linear speedup as the number of processors grows, especially for large datasets, and (3) reaches a speed of nearly 215 million reads each minute on a cluster with ten thin nodes. In comparison with MEGAN, a well-known metagenome analyzer, the speed of CloudLCA is up to 5 more times faster, and its peak memory usage is approximately 18.5% that of MEGAN, running on a fat node. CloudLCA can be run on one multiprocessor node or a cluster. It is expected to be part of MEGAN to accelerate analyzing reads, with the same output generated as MEGAN, which can be import into MEGAN in a direct way to finish the following analysis. Moreover, CloudLCA is a universal solution for finding the lowest common ancestor, and it can be applied in other fields requiring an LCA algorithm.

The sigma-1 receptor is a molecular chaperone protein highly enriched in the brain. Recent studies linked it to many diseases, such as drug addition, Alzheimer’s disease, stroke, depression, and even cancer. Sigma-1 receptor is enriched in lipid rafts, which are membrane microdomains essential in signaling processes. One of those signaling processes is ADAM17- and ADAM10-dependent ectodomain shedding. By using an alkaline phosphatase tagged substrate reporter system, we have shown that ADAM10-dependent BTC shedding was very sensitive to both membrane lipid component change and sigma-1 receptor agonist DHEAS treatment while ADAM17-dependent HB-EGF shedding was not; and overexpression of sigma-1 receptor diminished ADAM17- and ADAM10-dependent shedding. Our results indicate that sigma-1 receptor plays an important role in modifying the function of transmembrane proteases.