Effects of native human leukocyte interferon-alpha and recombinant human interferon-alpha on P3-X63-Ag8.653 mouse myeloma cell growth

Towards development of plasmacytoma cells-based expression systems utilizing alphavirus vectors: An NS0-VEE model

Plasmacytoma (myeloma) cells have a large protein expression capacity, although their industrial use is confined to stable expression systems. Vectors derived from genomes of viruses from the genus Alphavirus allow obtaining of high yields of target proteins but their use is limited to transient expression. Little information has been published to date on attempts to combine the myeloma cells as hosts with alphaviruses as expression vectors. A plasmid construct which allows rescue of a model alphavirus Venezuelan equine encephalitis virus (VEE) upon transfection of a cell culture was created. Mutations in the capsid and nsP2 genes allow for less cytopathogenic propagation of the virus. A cDNA-copy of the genome was placed in a plasmid under the control of the CMV promoter for virus rescue following DNA transfection. Parameters for the virus rescue by electroporating of the infectious clone in murine myeloma cells (NS0) were optimized. The highest FFU counts (1.2 × 10⁵ FFU per 10 ug DNA) were produced with 2 pulses (voltage 250 V, capacitance 960 u F) and the best electroporation buffer was selected from eight buffers. Self-sustained VEE infection was established in NS0 cultures with high titers (8 × 10⁸ FFU/ml) of the virus, despite a fraction of infected cells dying during 5-days observation. Further development of the NS0-VEE expression system may require addressing of apoptosis induced by VEE.

Discovering discovery patterns with Predication-based Semantic Indexing

In this paper we utilize methods of hyperdimensional computing to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery. Our approach, named Predication-based Semantic Indexing, is utilized to identify empirically sequences of relationships known as "discovery patterns", such as "drug x INHIBITS substance y, substance y CAUSES disease z" that link pharmaceutical substances to diseases they are known to treat. These sequences are derived from semantic predications extracted from the biomedical literature by the SemRep system, and subsequently utilized to direct the search for known treatments for a held out set of diseases. Rapid and efficient inference is accomplished through the application of geometric operators in PSI space, allowing for both the derivation of discovery patterns from a large set of known TREATS relationships, and the application of these discovered patterns to constrain search for therapeutic relationships at scale. Our results include the rediscovery of discovery patterns that have been constructed manually by other authors in previous research, as well as the discovery of a set of previously unrecognized patterns. The application of these patterns to direct search through PSI space results in better recovery of therapeutic relationships than is accomplished with models based on distributional statistics alone. These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts. In addition, the results provide strong support for the utility of the discovery pattern approach pioneered by Hristovski and his colleagues.