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Clark, K.

Publications and source records attributed to Clark, K..

2 recordsLinked to original sources

Inducing controlled cell cycle arrest and re-entry during asexual proliferation of Plasmodium falciparum malaria parasites.

The life cycle of the malaria parasite Plasmodium falciparum is tightly regulated, oscillating between stages of intense proliferation and quiescence. Cyclic 48-hour asexual replication of Plasmodium is markedly different from cell division in higher eukaryotes, and mechanistically poorly understood. Here, we report tight synchronisation of malaria parasites during the early phases of the cell cycle by exposure to DL--difluoromethylornithine (DFMO), which results in the depletion of polyamines. This induces an inescapable cell cycle arrest in G1 (~15 hours post-invasion) by blocking G1/S transition. Cell cycle-arrested parasites enter a quiescent G0-like state but, upon addition of exogenous polyamines, re-initiate their cell cycle in a coordinated fashion. This ability to halt malaria parasites at a specific point in their cell cycle, and to subsequently trigger re-entry into the cell cycle, provides a valuable framework to investigate cell cycle regulation in these parasites. We therefore used gene expression analyses to show that re-entry into the cell cycle involves expression of Ca2+-sensitive (cdpk4 and pk2) and mitotic kinases (nima and ark2), with deregulation of the pre-replicative complex associated with expression of pk2. Changes in gene expression could be driven through transcription factors MYB1 and two ApiAP2 family members. This new approach to parasite synchronisation therefore expands our currently limited toolkit to investigate cell cycle regulation in malaria parasites.

cell biology

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics