BID-F1 and BID-F2 Domains of Bartonella henselae Effector Protein BepF Trigger Together with BepC the Formation of Invasome Structures

The gram-negative, zoonotic pathogen Bartonella henselae (Bhe) translocates seven distinct Bartonella effector proteins (Beps) via the VirB/VirD4 type IV secretion system (T4SS) into human cells, thereby interfering with host cell signaling [1], [2]. In particular, the effector protein BepG alone or the combination of effector proteins BepC and BepF trigger massive F-actin rearrangements that lead to the establishment of invasome structures eventually resulting in the internalization of entire Bhe aggregates [2], [3]. In this report, we investigate the molecular function of the effector protein BepF in the eukaryotic host cell. We show that the N-terminal [E/T]PLYAT tyrosine phosphorylation motifs of BepF get phosphorylated upon translocation but do not contribute to invasome-mediated Bhe uptake. In contrast, we found that two of the three BID domains of BepF are capable to trigger invasome formation together with BepC, while a mutation of the WxxxE motif of the BID-F1 domain inhibited its ability to contribute to the formation of invasome structures. Next, we show that BepF function during invasome formation can be replaced by the over-expression of constitutive-active Rho GTPases Rac1 or Cdc42. Finally we demonstrate that BID-F1 and BID-F2 domains promote the formation of filopodia-like extensions in NIH 3T3 and HeLa cells as well as membrane protrusions in HeLa cells, suggesting a role for BepF in Rac1 and Cdc42 activation during the process of invasome formation

Integrating Functional and Diffusion Magnetic Resonance Imaging for Analysis of Structure-Function Relationship in the Human Language Network

The capabilities of magnetic resonance imaging (MRI) to measure structural and functional connectivity in the human brain have motivated growing interest in characterizing the relationship between these measures in the distributed neural networks of the brain. In this study, we attempted an integration of structural and functional analyses of the human language circuits, including Wernicke's (WA), Broca's (BA) and supplementary motor area (SMA), using a combination of blood oxygen level dependent (BOLD) and diffusion tensor MRI.Functional connectivity was measured by low frequency inter-regional correlations of BOLD MRI signals acquired in a resting steady-state, and structural connectivity was measured by using adaptive fiber tracking with diffusion tensor MRI data. The results showed that different language pathways exhibited different structural and functional connectivity, indicating varying levels of inter-dependence in processing across regions. Along the path between BA and SMA, the fibers tracked generally formed a single bundle and the mean radius of the bundle was positively correlated with functional connectivity. However, fractional anisotropy was found not to be correlated with functional connectivity along paths connecting either BA and SMA or BA and WA. for use in diagnosing and determining disease progression and recovery

An innovative platform for quick and flexible joining of assorted DNA fragments

Successful synthetic biology efforts rely on conceptual and experimental designs in combination with testing of multi-gene constructs. Despite recent progresses, several limitations still hinder the ability to flexibly assemble and collectively share different types of DNA segments. Here, we describe an advanced system for joining DNA fragments from a universal library that automatically maintains open reading frames (ORFs) and does not require linkers, adaptors, sequence homology, amplification or mutation (domestication) of fragments in order to work properly. This system, which is enhanced by a unique buffer formulation, provides unforeseen capabilities for testing, and sharing, complex multi-gene circuitry assembled from different DNA fragments

A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS)

Cell state-specific promoters constitute essential tools for basic research and biotechnology because they activate gene expression only under certain biological conditions. Synthetic Promoters with Enhanced Cell-State Specificity (SPECS) can be superior to native ones, but the design of such promoters is challenging and frequently requires gene regulation or transcriptome knowledge that is not readily available. Here, to overcome this challenge, we use a next-generation sequencing approach combined with machine learning to screen a synthetic promoter library with 6107 designs for high-performance SPECS for potentially any cell state. We demonstrate the identification of multiple SPECS that exhibit distinct spatiotemporal activity during the programmed differentiation of induced pluripotent stem cells (iPSCs), as well as SPECS for breast cancer and glioblastoma stem-like cells. We anticipate that this approach could be used to create SPECS for gene therapies that are activated in specific cell states, as well as to study natural transcriptional regulatory networks