Mischa Reinhardt

Design of a genome-wide siRNA library using an artificial neural network

The largest gene knock-down experiments performed to date have used multiple short interfering/short hairpin (si/sh)RNAs per gene. To overcome this burden for design of a genome-wide siRNA library, we used the Stuttgart Neural Net Simulator to train algorithms on a data set of 2,182 randomly selected siRNAs targeted to 34 mRNA species, assayed through a high-throughput fluorescent reporter gene system. The algorithm, (BIOPREDsi), reliably predicted activity of 249 siRNAs of an independent test set (Pearson coefficient r = 0.66) and siRNAs targeting endogenous genes at mRNA and protein levels. Neural networks trained on a complementary 21-nucleotide (nt) guide sequence were superior to those trained on a 19-nt sequence. BIOPREDsi was used in the design of a genome-wide siRNA collection with two potent siRNAs per gene. When this collection of 50,000 siRNAs was used to identify genes involved in the cellular response to hypoxia, two of the most potent hits were the key hypoxia transcription factors HIF1A and ARNT.

Genome-wide functional analysis of human cell-cycle regulators

Human cells have evolved complex signaling networks to coordinate the cell cycle. A detailed understanding of the global regulation of this fundamental process requires comprehensive identification of the genes and pathways involved in the various stages of cell-cycle progression. To this end, we report a genome-wide analysis of the human cell cycle, cell size, and proliferation by targeting >95% of the protein-coding genes in the human genome using small interfering RNAs (siRNAs). Analysis of >2 million images, acquired by quantitative fluorescence microscopy, showed that depletion of 1,152 genes strongly affected cell-cycle progression. These genes clustered into eight distinct phenotypic categories based on phase of arrest, nuclear area, and nuclear morphology. Phase-specific networks were built by interrogating knowledge-based and physical interaction databases with identified genes. Genome-wide analysis of cell-cycle regulators revealed a number of kinase, phosphatase, and proteolytic proteins and also suggests that processes thought to regulate G1-S phase progression like receptor-mediated signaling, nutrient status, and translation also play important roles in the regulation of G2/M phase transition. Moreover, 15 genes that are integral to TNF/NF-κB signaling were found to regulate G2/M, a previously unanticipated role for this pathway. These analyses provide systems-level insight into both known and novel genes as well as pathways that regulate cell-cycle progression, a number of which may provide new therapeutic approaches for the treatment of cancer.

Whole genome functional analysis identifies novel components required for mitotic spindle integrity in human cells

BackgroundThe mitotic spindle is a complex mechanical apparatus required for accurate segregation of sister chromosomes during mitosis. We designed a genetic screen using automated microscopy to discover factors essential for mitotic progression. Using a RNA interference library of 49,164 double-stranded RNAs targeting 23,835 human genes, we performed a loss of function screen to look for small interfering RNAs that arrest cells in metaphase.ResultsHere we report the identification of genes that, when suppressed, result in structural defects in the mitotic spindle leading to bent, twisted, monopolar, or multipolar spindles, and cause cell cycle arrest. We further describe a novel analysis methodology for large-scale RNA interference datasets that relies on supervised clustering of these genes based on Gene Ontology, protein families, tissue expression, and protein-protein interactions.ConclusionThis approach was utilized to classify functionally the identified genes in discrete mitotic processes. We confirmed the identity for a subset of these genes and examined more closely their mechanical role in spindle architecture.

Corrigendum: Design of a genome-wide siRNA library using an artificial neural network

Nat. Biotechnol. 23, 995–1001 (2005), published online 17 July 2005; corrected after print 25 July 2006 In the Methods section, p. 1,000, col. 2, paragraph 2, the text beginning: “The whole plate was discarded if for any time point...” and ending, “The final data set contained 2,431 sequences.” inaccurately described the procedure.

AUTOMATED MICROSCOPY SCREEN TO IDENTIFY COMPONENTS REQUIRED FOR MITOTIC CELL CYCLE PROGRESSION IN HUMAN CELLS

We designed an image-based screen using automated microscopy to discover genetic factors essential for mitotic progression. Using a human genome-wide RNAi library, we performed a loss-of-function screen looking for siRNAs that arrest cells in metaphase. Supervised clustering of the multiparametric morphological data with gene ontology (GO) annotations, protein families, tissue expression and protein-protein interactions functionally classified these genes into discrete mitotic processes involved in spindle assembly. We used this approach to rapidly identify important novel genes based on their association with known genes in the clusters. We present here the automated methods used to identify these components.