Eukaryotic cells contain assemblies of RNAs and proteins termed RNA granules. Many proteins within these bodies contain KH or RRM RNA-binding domains as well as low complexity (LC) sequences of unknown function. We discovered that exposure of cell or tissue lysates to a biotinylated isoxazole (b-isox) chemical precipitated hundreds of RNA-binding proteins with significant overlap to the constituents of RNA granules. The LC sequences within these proteins are both necessary and sufficient for b-isox-mediated aggregation, and these domains can undergo a concentration-dependent phase transition to a hydrogel-like state in the absence of the chemical. X-ray diffraction and EM studies revealed the hydrogels to be composed of uniformly polymerized amyloid-like fibers. Unlike pathogenic fibers, the LC sequence-based polymers described here are dynamic and accommodate heterotypic polymerization. These observations offer a framework for understanding the function of LC sequences as well as an organizing principle for cellular structures that are not membrane bound.

The toxic proline:arginine (PR n ) poly-dipeptide encoded by the (GGGGCC) n repeat expansion in the C9orf72 form of heritable amyotrophic lateral sclerosis (ALS) binds to the central channel of the nuclear pore and inhibits the movement of macromolecules into and out of the nucleus. The PR n poly-dipeptide binds to polymeric forms of the phenylalanine:glycine (FG) repeat domain, which is shared by several proteins of the nuclear pore complex, including those in the central channel. A method of chemical footprinting was used to characterize labile, cross-β polymers formed from the FG domain of the Nup54 protein. Mutations within the footprinted region of Nup54 polymers blocked both polymerization and binding by the PR n poly-dipeptide. The aliphatic alcohol 1,6-hexanediol melted FG domain polymers in vitro and reversed PR n -mediated enhancement of the nuclear pore permeability barrier. These data suggest that toxicity of the PR n poly-dipeptide results in part from its ability to lock the FG repeats of nuclear pore proteins in the polymerized state. Our study offers a mechanistic interpretation of PR n poly-dipeptide toxicity in the context of a prominent form of ALS.C9orf72 repeat expansion | PR n poly-dipeptide | nuclear pore | FG domain | labile cross-β polymers

Although mechanical signals presented by the extracellular matrix are known to regulate many essential cell functions, the specific effects of these interactions, particularly in response to dynamic and heterogeneous cues, remain largely unknown. Here, a modular semisynthetic approach is introduced to create protein–polymer hydrogel biomaterials that undergo reversible stiffening in response to user‐specified inputs. Employing a novel dual‐chemoenzymatic modification strategy, fusion protein‐based gel crosslinkers are created that exhibit stimuli‐dependent intramolecular association. Linkers based on calmodulin yield calcium‐sensitive materials, while those containing the photosensitive light, oxygen, and voltage sensing domain 2 (LOV2) protein give phototunable constructs whose moduli can be cycled on demand with spatiotemporal control about living cells. These unique materials are exploited to demonstrate the significant role that cyclic mechanical loading plays on fibroblast‐to‐myofibroblast transdifferentiation in 3D space. The moduli‐switchable materials should prove useful for studies in mechanobiology, providing new avenues to probe and direct matrix‐driven changes in 4D cell physiology.

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Current biological and medical research is aimed at obtaining a detailed spatiotemporal map of a live cell's interior to describe and predict cell's physiological state. We present here an algorithm for complete 3-D modelling of cellular structures from a z-stack of images obtained using label-free wide-field bright-field light-transmitted microscopy. The method visualizes 3-D objects with a volume equivalent to the area of a camera pixel multiplied by the z-height.The computation is based on finding pixels of unchanged intensities between two consecutive images of an object spread function. These pixels represent strongly light-diffracting, light-absorbing, or light-emitting objects. To accomplish this, variables derived from Rényi entropy are used to suppress camera noise. Using this algorithm, the detection limit of objects is only limited by the technical specifications of the microscope setup-we achieve the detection of objects of the size of one camera pixel. This method allows us to obtain 3-D reconstructions of cells from bright-field microscopy images that are comparable in quality to those from electron microscopy images.

Biological RNA is a uniform polymer in three senses: it uses nucleotides of a single chirality; it uses only ribose sugars and four nucleobases rather than a mixture of other sugars and bases; and it uses only 3′-5′ bonds rather than a mixture of different bond types. We suppose that prebiotic chemistry would generate a diverse mixture of potential monomers, and that random polymerization would generate non-uniform strands of mixed chirality, monomer composition, and bond type. We ask what factors lead to the emergence of RNA from this mixture. We show that template-directed replication can lead to the emergence of all the uniform properties of RNA by the same mechanism. We study a computational model in which nucleotides react via polymerization, hydrolysis, and template-directed ligation. Uniform strands act as templates for ligation of shorter oligomers of the same type, whereas mixed strands do not act as templates. The three uniform properties emerge naturally when the ligation rate is high. If there is an exact symmetry, as with the chase of chirality, the uniform property arises via a symmetry-breaking phase transition. If there is no exact symmetry, as with monomer selection and backbone regioselectivity, the uniform property emerges gradually as the rate of template-directed ligation is increased.

APT, known as Advanced Persistent Threat, is a difficult challenge for cyber defence. These threats make many traditional defences ineffective as the vulnerabilities exploited by these threats are insiders who have access to and are within the network. This paper proposes DeepTaskAPT, a heterogeneous task-tree based deep learning method to construct a baseline model based on sequences of tasks using a Long Short-Term Memory (LSTM) neural network that can be applied across different users to identify anomalous behaviour. Rather than applying the model to sequential log entries directly, as most current approaches do, DeepTaskAPT applies a process tree based task generation method to generate sequential log entries for the deep learning model.To assess the performance of DeepTaskAPT, we use a recently released synthetic dataset, DARPA Operationally Transparent Computing (OpTC) dataset and a real-world dataset, Los Alamos National Laboratory (LANL) dataset. Both of them are composed of host-based data collected from sensors. Our results show that DeepTaskAPT outperforms similar approaches e.g. DeepLog and the DeepTaskAPT baseline model demonstrate its capability to detect malicious traces in various attack scenarios while having high accuracy and low false-positive rates. To the best of knowledge this is the very first attempt of using recently introduced OpTC dataset for cyber threat detection.