Biology subjects

Patterson, M.

Publications and source records attributed to Patterson, M..

7 recordsLinked to original sources

bioRxiv2018

An Open-Source Plate Reader

Microplate readers are foundational instruments in experimental biology and bioengineering that enable multiplexed spectrophotometric measurements. To enhance their accessibility, we here report the design, construction, validation, and benchmarking of an open-source microplate reader. The system features full-spectrum absorbance and fluorescence emission detection, in situ optogenetic stimulation, and stand-alone touch screen programming of automated assay protocols. The total system costs <$3500, a fraction of the cost of commercial plate readers, and can detect the fluorescence of common dyes down to [~]10 nanomolar concentration. Functional capabilities were demonstrated in context of synthetic biology, optogenetics, and photosensory biology: by steady-state measurements of ligand-induced reporter gene expression in a model of bacterial quorum sensing, and by flavin photocycling kinetic measurements of a LOV (light-oxygen-voltage) domain photoreceptor used for optogenetic transcriptional activation. Fully detailed guides for assembling the device and automating it using the custom Python-based API (Application Program Interface) are provided. This work contributes a key technology to the growing community-wide infrastructure of open-source biology-focused hardware, whose creation is facilitated by rapid prototyping capabilities and low-cost electronics, optoelectronics, and microcomputers.\n\nTable of Contents Graphic\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC=\"FIGDIR/small/413781_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (30K):\norg.highwire.dtl.DTLVardef@c26eaborg.highwire.dtl.DTLVardef@efb695org.highwire.dtl.DTLVardef@1bc2f41org.highwire.dtl.DTLVardef@1c25c79_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2018

gpps: An ILP-based approach for inferringcancer progression with mutation losses fromsingle cell data

cancer biology↗

bioRxiv2018

High-risk human papillomaviruses down-regulate expression of the Ste20 family kinase MST1 to inhibit the Hippo pathway and promote transformation

Human papillomaviruses (HPV) are a major cause of malignancy worldwide They are the aetiological agent of almost all cervical cancers and an increasing number of head and neck carcinomas. Deregulation of the Hippo pathway component YAP1 has recently been demonstrated to play a role in HPV-mediated cervical cancer, but whether other components of this pathway are implicated in the pathogenesis of this disease remains poorly understood.\n\nThe expression level and activation status of critical Hippo pathway components were analysed across multiple cytology samples from patients with cervical disease, as well as HPV positive (HPV+) and HPV negative (HPV-) cervical cancer cell lines using real time qPCR, western blot and immunohistochemistry. In parallel, we assessed the effects of MST1 and MST2 overexpression upon cervical cancer cell proliferation, migration and invasion. Finally, we interrogated the consequences of interrupted MST1 and MST2 function using a targeted small molecule inhibitor in tandem with kinase inactive MST mutants. Our analysis found that expression of the Ste20 kinase MST1 was decreased within both HPV+ primary patient samples and cervical cancer cell lines. This effect was mediated by the virus-coded oncoproteins E6 and E7, which impair MST1 transcription. Reintroduction of MST1, or its paralogue MST2, into HPV positive cervical cancer cells re-activated the Hippo pathway, leading to a reduction in cell proliferation, migration and invasion. Finally, using a small molecule inhibitor of MST1/2 or kinase inactive mutants of either protein, we demonstrated that this effect required the kinase function of MST1/2. Our results reveal that HPV down regulates MST1 expression to inactivate the Hippo pathway and so drive cells towards transformation.

microbiology↗

bioRxiv2018

Inferring Cancer Progression from Single Cell Sequencing while allowing loss of mutations

MotivationIn recent years, the well-known Infinite Sites Assumption (ISA) has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions seen as an accumulation of mutations. However, recent studies (Kuipers et al., 2017) leveraging Single-cell Sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. Still, established methods that can infer phylogenies with mutation losses are however lacking.\n\nResultsWe present the SASC (Simulated Annealing Single-Cell inference) tool which is a new and robust approach based on simulated annealing for the inference of cancer progression from SCS data. More precisely, we introduce a simple extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of back mutations in the evolutionary history of the tumor: the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real data sets and in comparison with some other available methods.\n\nAvailabilityThe Simulated Annealing Single-cell inference (SASC) tool is open source and available at https://github.com/sciccolella/sasc.\n\nContacts.ciccolella@campus.unimib.it

bioinformatics↗

bioRxiv2017

HapCHAT: Adaptive haplotype assembly for efficiently leveraging high coverage in long reads

BackgroundHaplotype assembly is the process of assigning the different alleles of the variants covered by mapped sequencing reads to the two haplotypes of the genome of a human individual. Long reads, which are nowadays cheaper to produce and more widely available than ever before, have been used to reduce the fragmentation of the assembled haplotypes since their ability to span several variants along the genome. These long reads are also characterized by a high error rate, an issue which may be mitigated, however, with larger sets of reads, when this error rate is uniform across genome positions. Unfortunately, current state-of-the-art dynamic programming approaches designed for long reads deal only with limited coverages.\n\nResultsHere, we propose a new method for assembling haplotypes which combines and extends the features of previous approaches to deal with long reads and higher coverages. In particular, our algorithm is able to dynamically adapt the estimated number of errors at each variant site, while minimizing the total number of error corrections necessary for finding a feasible solution. This allows our method to significantly reduce the required computational resources, allowing to consider datasets composed of higher coverages. The algorithm has been implemented in a freely available tool, HapCHAT: Haplotype Assembly Coverage Handling by Adapting Thresholds. An experimental analysis on sequencing reads with up to 60x coverage reveals improvements in accuracy and recall achieved by considering a higher coverage with lower runtimes.\n\nConclusionsOur method leverages the long-range information of sequencing reads that allows to obtain assembled haplotypes fragmented in a lower number of unphased haplotype blocks. At the same time, our method is also able to deal with higher coverages to better correct the errors in the original reads and to obtain more accurate haplotypes as a result.\n\nAvailabilityHapCHAT is available at http://hapchat.algolab.eu under the GPL license.

bioinformatics↗

bioRxiv2016

Correlated Evolution of Metabolic Functions over the Tree of Life

We are interested in the structure and evolution of metabolism in order to better understand its complexity. We study metabolic functions in 1459 species within which several hundreds of thousands of families of homologous genes have been identified [17]. Given a protein sequence, PRIAM search [5] delivers probabilities of the presence of several thousand enzymes (ECs). This allows us to infer reaction sets and to construct a metabolic network for an organism, given its set of sequences.\n\nWe then propagate these ECs to the ancestral nodes of the species tree using maximimum likelihood methods. These evolutionary scenarios are systematically compared using pairwise mutual information. We identify co-evolving enzyme sets from the graph of these relationships using community detection algorithms [1,3]. This sheds light on the structure of the metabolic networks in terms of co-evolving metabolic modules. These modules are also interpreted from a functional perspective using stoichiometric models of metabolic networks.

evolutionary biology↗

bioRxiv2016

WhatsHap: fast and accurate read-based phasing

Read-based phasing allows to reconstruct the haplotypes of a sample purely from sequencing reads. While phasing is an important step for answering questions about population genetics, compound heterozygosity, and to aid in clinical decision making, there has been a lack of accurate, usable and standards-based software.\n\nWhatsHap is a production-ready tool for highly accurate read-based phasing. It was designed from the beginning to leverage third-generation sequencing technologies, whose long reads can span many variants and are therefore ideal for phasing. WhatsHap works also well with second-generation data, is easy to use and will phase not only SNVs, but also indels and other variants. It is unique in its ability to combine read-based with pedigree-based phasing, allowing to further improve accuracy if multiple related samples are provided.

bioinformatics↗