The Surprising Complexity of Virus-Host Cell Interaction Revealed by the Powerful Systems Biology Approaches of Genomics and Proteomics

Understanding interaction between viruses and host cells during infection process is the first step in discovering appropriate drugs and vaccines against viral diseases. Advance technologies based on genomics and proteomics approaches provide great tools to disclose the complexity of virus-host interaction. In this essay, the application of RNAi screens method and proteomics-based approaches on influenza virus will be elucidated as an example. Using those methods, the primary factors controlling viral replication pathway were discovered. These findings are useful for the development of potential strategies to overcome viral diseases

appendix- – Supplemental material for Input frequency and construction interference interactions in L2 development

<p>Supplemental material, appendix- for Input frequency and construction interference interactions in L2 development by Xiaopeng Zhang and Xiaoli Dong in Second Language Research</p

Identification of SLIRP as a G Quadruplex-Binding Protein

The guanine quadruplex (G4) structure in DNA is a secondary structure motif that plays important roles in DNA replication, transcriptional regulation, and maintenance of genomic stability. Here, we employed a quantitative mass spectrometry-based approach to profile the interaction proteomes of three well-defined G4 structures derived from the human telomere and the promoters of <i>cMYC</i> and <i>cKIT</i> genes. We identified SLIRP as a novel G4-interacting protein. We also demonstrated that the protein could bind directly with G4 DNA with <i>K</i>_d values in the low nanomolar range and revealed that the robust binding of the protein toward G4 DNA requires its RRM domain. We further assessed, by using CRISPR-Cas9-introduced affinity tag and ChIP-Seq analysis, the genome-wide occupancy of SLIRP, and showed that the protein binds preferentially to G-rich DNA sequences that can fold into G4 structures. Together, our results uncovered a novel cellular protein that can interact directly with G4 DNA, which underscored the complex regulatory networks involved in G4 biology

Model performance (measured by total model efficiency coefficient) for perennial and non-perennial streams.

<p>Model performance (measured by total model efficiency coefficient) for perennial and non-perennial streams.</p

Map of Huachuca Mountains and San Pedro watershed, including streams labeled according to modeled hydrologic classifications (perennial, intermittent, and ephemeral), annual precipitation, invertebrate sampling points, USGS flow gauges (from north to south on the map: STAID 09471400, 09471380, 09471310, 09470800, 09470750, and 09470700), and electrical resistant sensors for recording water permanence.

<p>The main stem in the center of the map is the San Pedro River, and to the left are the Huachuca Mountains.</p

The model efficiency <i>E</i>_<i>k</i> of the model fitted with different parameter sets (<i>n</i> = 20, results from the same parameter set were connected with a line) within a range of values for (a) α diversity pattern, and (b) β diversity pattern across 8 sampling events in three years (2009, 2010, 2011).

<p>Season abbreviations are as follows: summer (‘<i>SM</i>’), fall (‘<i>FL</i>’), and winter (‘<i>WT</i>’).</p

The effect of hydrology on the model performance.

<p>(a) Instantaneous discharge from Garden Canyon, AZ (USGS 09470800; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176949#pone.0176949.g001" target="_blank">Fig 1</a>) between 2008 and 2011. Different gauges had different absolute discharge values, but showed similar hydrological patterns; the circle symbol indicates the time when sampling occurred; and (b) relationship of model fits (total model efficiency, <i>TE</i>, for α and β patterns combined) for those seasons <i>vs</i>. mean daily discharge during the sampling month, in the log scale, from gauges closest to the sampling sites. The shaded area denotes the high variability in model performance when mean daily discharge was very low.</p

Importance of neutral processes varies in time and space: Evidence from dryland stream ecosystems

<div><p>Many ecosystems experience strong temporal variability in environmental conditions; yet, a clear picture of how niche and neutral processes operate to determine community assembly in temporally variable systems remains elusive. In this study, we constructed neutral metacommunity models to assess the relative importance of neutral processes in a spatially and temporally variable ecosystem. We analyzed macroinvertebrate community data spanning multiple seasons and years from 20 sites in a Sonoran Desert river network in Arizona. The model goodness-of-fit was used to infer the importance of neutral processes. Averaging over eight stream flow conditions across three years, we found that neutral processes were more important in perennial streams than in non-perennial streams (intermittent and ephemeral streams). Averaging across perennial and non-perennial streams, we found that neutral processes were more important during very high flow and in low flow periods; whereas, at very low flows, the relative importance of neutral processes varied greatly. These findings were robust to the choice of model parameter values. Our study suggested that the net effect of disturbance on the relative importance of niche and neutral processes in community assembly varies non-monotonically with the severity of disturbance. In contrast to the prevailing view that disturbance promotes niche processes, we found that neutral processes could become more important when the severity of disturbance is beyond a certain threshold such that all organisms are adversely affected regardless of their biological traits and strategies.</p></div

Performance of the best-fit model (measured by model efficiency coefficient) for prediction of α diversity and β diversity (Chao similarity index) across the eight sampling seasons.

<p>Performance of the best-fit model (measured by model efficiency coefficient) for prediction of α diversity and β diversity (Chao similarity index) across the eight sampling seasons.</p