TFPIα Interacts with FVa and FXa to Inhibit Prothrombinase During the Initiation of Coagulation

Tissue factor pathway inhibitor α (TFPIα) inhibits prothrombinase, the thrombin-generating complex of factor Xa (FXa) and factor Va (FVa), during the initiation of coagulation. This inhibition requires binding of a conserved basic region within TFPIα to a conserved acidic region in FXa-activated and platelet-released FVa. In this study, the contribution of interactions between TFPIα and the FXa active site and FVa heavy chain to prothrombinase inhibition were examined to further define the inhibitory biochemistry. Removal of FXa active site binding by mutation or by deletion of the second Kunitz domain (K2) of TFPIα produced 17- or 34-fold weaker prothrombinase inhibition, respectively, establishing that K2 binding to the FXa active site is required for efficient inhibition. Substitution of the TFPIα basic region uncharged residues (Leu252, Ile253, Thr255) with Ala (TFPI-AAKA) produced 5.8-fold decreased inhibition. This finding was confirmed using a basic region peptide (Leu252-Lys261) and Ala substitution peptides, which established that the uncharged residues are required for prothrombinase inhibitory activity but not for binding the FVa acidic region. This suggests that the uncharged residues mediate a secondary interaction with FVa subsequent to acidic region binding. This secondary interaction seems to be with the FVa heavy chain, because the FV Leiden mutation weakened prothrombinase inhibition by TFPIα but did not alter TFPI-AAKA inhibitory activity. Thus, efficient inhibition of prothrombinase by TFPIα requires at least 3 intermolecular interactions: (1) the TFPIα basic region binds the FVa acidic region, (2) K2 binds the FXa active site, and (3) Leu252-Thr255 binds the FVa heavy chain

Structural and Energetic Mechanisms of Cooperative Autoinhibition and Activation of Vav1

SummaryVav proteins are guanine nucleotide exchange factors (GEFs) for Rho family GTPases. They control processes including T cell activation, phagocytosis, and migration of normal and transformed cells. We report the structure and biophysical and cellular analyses of the five-domain autoinhibitory element of Vav1. The catalytic Dbl homology (DH) domain of Vav1 is controlled by two energetically coupled processes. The DH active site is directly, but weakly, inhibited by a helix from the adjacent Acidic domain. This core interaction is strengthened 10-fold by contacts of the calponin homology (CH) domain with the Acidic, pleckstrin homology, and DH domains. This construction enables efficient, stepwise relief of autoinhibition: initial phosphorylation events disrupt the modulatory CH contacts, facilitating phosphorylation of the inhibitory helix and consequent GEF activation. Our findings illustrate how the opposing requirements of strong suppression of activity and rapid kinetics of activation can be achieved in multidomain systems

PVP-Assisted Hydrothermal Synthesis of Bi2O2Se Nanosheets for Self-Powered Photodetector

Abstract: Bi2O2Se nanosheets were successfully synthesized via a facile one-step PVP-assisted hydrothermal process for the first time. Corresponding characterizations, such as XRD, XPS, SEM and TEM, were carried out to investigate the formation of the products on the amount of PVP in the reaction system. Results revealed that the single-crystalline Bi2O2Se nanosheets with small mean lateral size of 176.3 nm were obtained when the amount of PVP is 0.75 g. Single-crystalline Bi2O2Se nanosheets self-powered photodetector exhibited excellent photodetection performance, superior to that of self-powered photodetectors based on the products synthesized without PVP and other nanomaterials. Under the illumination of 365 nm ultraviolet light, the rise time, responsivity and detectivity could approach up to 9 ms, 14.24 mA/W and 3.16×108 Jones, respectively. Bi2O2Se devices have high photoresponse even in the visible and near infrared bands due to its suitable band gap. The present work provides a novel preparation route of Bi2O2Se via hydrothermal method and PVP assisted synthesis of Bi2O2Se nanosheets is reported for the first time. Bi2O2Se nanosheets self-powered photodetector exhibited excellent photodetection performance and points out a direction for the evolution of self-powered photodetectors in the in the future

NetMiner-an ensemble pipeline for building genome-wide and high-quality gene co-expression network using massive-scale RNA-seq samples

<div><p>Accurately reconstructing gene co-expression network is of great importance for uncovering the genetic architecture underlying complex and various phenotypes. The recent availability of high-throughput RNA-seq sequencing has made genome-wide detecting and quantifying of the novel, rare and low-abundance transcripts practical. However, its potential merits in reconstructing gene co-expression network have still not been well explored. Using massive-scale RNA-seq samples, we have designed an ensemble pipeline, called NetMiner, for building genome-scale and high-quality Gene Co-expression Network (GCN) by integrating three frequently used inference algorithms. We constructed a RNA-seq-based GCN in one species of monocot rice. The quality of network obtained by our method was verified and evaluated by the curated gene functional association data sets, which obviously outperformed each single method. In addition, the powerful capability of network for associating genes with functions and agronomic traits was shown by enrichment analysis and case studies. In particular, we demonstrated the potential value of our proposed method to predict the biological roles of unknown protein-coding genes, long non-coding RNA (lncRNA) genes and circular RNA (circRNA) genes. Our results provided a valuable and highly reliable data source to select key candidate genes for subsequent experimental validation. To facilitate identification of novel genes regulating important biological processes and phenotypes in other plants or animals, we have published the source code of NetMiner, making it freely available at <a href="https://github.com/czllab/NetMiner" target="_blank">https://github.com/czllab/NetMiner</a>.</p></div

Flowchart of high-quality RNA-seq-based gene co-expression network inference.

<p>Flowchart of high-quality RNA-seq-based gene co-expression network inference.</p

A table summing up the main features of different algorithms.

<p>A table summing up the main features of different algorithms.</p

Co-expression subnetwork derived from guide-gene approach for <i>XLOC_057324</i> associated with panicle development and fertility.

<p>Within the subnetwork, red nodes represented the experimentally verified genes related to corresponding biological functions. Chrysoidine nodes represented transcription factors. Pink nodes indicated the genes whose <i>Arabidopsis thaliana</i> homologues were experimentally verified to be related to corresponding biological functions. Yellow nodes represented that the genes were potential function-related. Green nodes denoted the lncRNA genes and gray nodes indicated that the genes were function unknown or annotated with unrelated functions.</p

Enrichment folds of different algorithms for co-expression network inference.

<p>A) Comparing to GGM for standard positive links. B) Comparing to WGCNA for standard positive links. C) Comparing with BC3NET for standard positive links. D) Comparing with GGM for standard negative links. E) Comparing with WGCNA for standard negative links. F) Comparing with BC3NET for standard negative links. In the legends, the RAW, FPKM, UQ, TMM, RLE and VST represented the networks obtained by the single RNA-seq data set; INT indicated intra-method consensus networks established by integrating the predictions of different RNA-seq data sets, EBM denoted high-quality gene co-expression network obtained by integrating all intra-method consensus networks.</p