Biology subjects

Mitchell, T.

Publications and source records attributed to Mitchell, T..

30 records · Page 2Linked to original sources

bioRxiv2022

Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of S. cerevisiae by Gander, et al. Our experimental campaign used a novel high throughput experimentation framework developed under DARPAs Synergistic Discovery and Design (SD2) program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report, and make some remarks about lessons learned.

synthetic biology↗

bioRxiv2021

The coagulation factor IX (F9) loss of function prevents the cell cycle arrest induced by CDK4/6 inhibitors treatment

During this last decade the development of pro-senescence therapies has become an attractive strategy as cellular senescence acts as a barrier against tumour progression. In this context, CDK4/6 inhibitors induce senescence and have showed efficacy in reducing tumour growth in breast cancer patients. However, even though cancer cells are arrested after CDK4/6 inhibitor treatment, genes regulating senescence in this context are still unknown limiting their anti-tumour activity. Here, using a functional genome wide CRISPR/Cas9 genetic screen we found several genes that synergistically participate in the proliferation arrest induced by the CDK4/6 inhibitor, Palbociclib. We find that downregulation of the coagulation factor IX (F9) using sgRNA and shRNA prevents the cell cycle arrest and senescent-like phenotype induced in MCF7 breast tumour cells upon Palbociclib treatment. These results were confirmed using another breast cancer cell line and with an alternative CDK4/6 inhibitor, Abemaciclib, and further tested in a panel of 22 cancer cells. While F9 knockout reduces senescence, treatment with a recombinant F9 protein was sufficient to induce a cell cycle arrest and senescence-like state in MCF7 tumour cells. Besides, endogenous F9 is upregulated in different human primary cells cultures undergoing senescence. Importantly, bioinformatics analysis of cancer datasets suggest a role for F9 in human tumours. Altogether, these data collectively propose key genes involved in CDK4/6 inhibitors response that will be useful to design new therapeutic strategies in personalized medicine in order to increase their efficiency, stratify patients and avoid drug resistance.

cancer biology↗

bioRxiv2021

Data Representation in the DARPA SD2 Program

1Modern scientific enterprises are often highly complex and multidisciplinary, particularly in areas like synthetic biology where the subject at hand is itself inherently complex and multidisciplinary. Collaboration across many organizations is necessary to efficiently tackle such problems [6, 15], but remains difficult. The challenge is further amplified by automation that increases the pace at which new information can be produced, and particularly so for matters of fundamental research, where concepts and definitions are inherently fluid and may rapidly change as an investigation evolves [7]. The DARPA program Synergistic Discovery and Design (SD2) aimed to address these challenges by organizing the development of data-driven methods to accelerate discovery and improve design robustness, with one of the key domains under study being synthetic biology. The program was specifically organized such that teams provided complementary types of expertise and resources, and without any team being in a dominant organizational position, such that subject-matter investigations would necessarily require peer-level collaboration across multiple team boundaries. With more than 100 researchers across more than 20 organizations, several of which ran experimental facilities with high-throughput automation, participants were forced to confront challenges around effective data sharing. The default architecture for scientific collaboration is essentially one of anarchy, with ad-hoc bilateral relations between pairs of collaborators or experimental phases (Figure 1(a)). This was by necessity the case during early phases of the SD2 program as well, in which incorporating new tools into pipelines was ad-hoc and time-consuming, and data was generally disconnected from genetic designs and experimental plans. The other typical approach for collaboration is one of "command and control", in which a dominant organization determines the data sharing content and format for all participants (Figure 1(b)). This can be efficient, but tends to be limited in flexibility and extensibility, rendering it unsuitable for research collaboration, as indeed was found when we attempted this approach during the first year of the SD2 program. We addressed these problems with the application of distributed standards to create a "flexible rendezvous" model of collaboration (Figure 1(c)), enabling information flow to track evolving collaborative relationships, improving the sharing and utility of information across the community and supporting accelerated rates of experimentation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/460644v1_fig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@fc7371org.highwire.dtl.DTLVardef@1ff2733org.highwire.dtl.DTLVardef@66af53org.highwire.dtl.DTLVardef@1809640_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Architectures for data sharing: bilateral relations (a), command and control (b), and flexible rendezvous (c). C_FIG

synthetic biology↗

bioRxiv2021

A Platform for Oncogenomic Reporting and Interpretation

Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we created the Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework created to facilitate the interpretation and reporting of somatic variants in cancer. PORI is unique in its integration of reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents one of the first open-source platform alternatives to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 TCGA tumours to the graph knowledge base, revealing that 88.2% have at least one potentially targetable alteration, and making available reports describing select individual samples.

bioinformatics↗

bioRxiv2021

TGF-B signaling suppresses TCA cycle metabolism in renal cancer.

The Warburg effect is one of most-well studied metabolic phenomenon in cancer cells. For the most part, these studies have focused on enhanced rates of glycolysis observed in various models. The presumption has been that mitochondrial metabolism is suppressed. However, recent studies indicate that the extent of mitochondrial metabolism is far more heterogeneous in tumors than originally presumed. One tumor type with suppression of mitochondrial metabolism is renal cell carcinoma (RCC). Prior studies indicate that suppressed TCA cycle enzyme mRNA expression is associated with aggressive RCC. Yet, the mechanisms that regulate the TCA cycle in RCC remain uncharacterized. Here, we demonstrate that loss of TCA cycle enzyme expression is retained in RCC metastatic tissues. Moreover, proteomic analysis demonstrates that reduced TCA cycle enzyme expression is far more pronounced in RCC relative to other tumor types. Loss of TCA cycle enzyme expression is correlated with reduced expression of the transcription factor peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1) which is also lost in RCC tissues. PGC-1 re-expression in RCC cells restores the expression of TCA cycle enzymes in vitro and in vivo and leads to enhanced glucose carbon incorporation into TCA cycle intermediates. Mechanistically, TGF-{beta} signaling, in concert with histone deacetylase 7 (HDAC7), suppresses TCA cycle enzyme expression. In turn, pharmacologic inhibition of TGF-{beta} restores expression of TCA cycle enzyme expression and suppresses tumor growth in an orthotopic model of RCC. Taken together, our findings reveal a novel role for the TGF-{beta} /HDAC7 axis in global suppression of TCA cycle enzymes in RCC and provide novel insight into the molecular basis of altered mitochondrial metabolism in this malignancy.

cancer biology↗

bioRxiv2021

MVPA does not reveal neural representations of hierarchical linguistic structure in MEG

During comprehension, the meaning extracted from serial language input can be described by hierarchical phrase structure. Whether our brains explicitly encode hierarchical structure during processing is, however, debated. In this study we recorded Magnetoencephalography (MEG) during reading of structurally ambiguous sentences to probe neural activity for representations of underlying phrase structure. 10 human subjects were presented with simple sentences, each containing a prepositional phrase that was ambiguous with respect to its attachment site. Disambiguation was possible based on semantic information. We applied multivariate pattern analyses (MVPA) to the MEG data using linear classifiers as well as representational similarity analysis to probe various effects of phrase structure building on the neural signal. Using MVPA techniques we successfully decoded both syntactic (part-of-speech) as well as semantic information from the brain signal. Importantly, however, we did not find any patterns in the neural signal that differentiate between different hierarchical structures. Nor did we find neural traces of syntactic or semantic reactivation following disambiguating sentence material. These null findings suggest that subjects may not have processed the sentences with respect to their underlying phrase structure. We discuss methodological limits of our analysis as well as cognitive theories of "shallow processing", i.e. in how far rich semantic information can prevent thorough syntactic analysis during processing.

neuroscience↗

bioRxiv2020

Lipidome profiles of plasma microvesicles differ in experimental cerebral malaria, compared to malaria without neurological complications

Cerebral malaria (CM), a fatal complication of Plasmodium infection that affects children in sub-Saharan Africa and adults in South-East Asia, results from incompletely understood pathogenetic mechanisms, which include an excessive release of microvesicles (MV). Plasma MV levels have been found elevated in CM patients and in the experimental mouse model. We compared lipid profiles in circulating MV purified from CBA mice infected with P. berghei ANKA (PbA), which causes CM, to those from P. yoelii (Py), which does not. Here we show that plasma MV produced at the time of CM differed dramatically from those from non-CM mice, in spite of identical levels of parasitaemia. Using high-resolution LCMS, we identified over 300 lipid species within 12 lipid classes. Total lysophosphatidylethanolamine (LPE) levels were significantly lower in PbA infection compared to uninfected mice, while they were unchanged in Py MV, and lysophosphatidylcholine (LPC) was more significantly reduced in PbA mice compared to the other two groups. These results suggest, for the time, that experimental CM is characterised by specific changes in lipid composition of circulating MV, pointing towards triglycerides (TG) especially docosahexaenoic acid (DHA 22:6) containing species, phosphatidylethanolamine (PE), LPC, LPE, and diacylglycerol (DG) as potential important players in CM pathogenesis.

pathology↗

bioRxiv2020

Spatio-temporal Characteristics of Noun and Verb Processing during Sentence Comprehension in the Brain

The human brain is very effective at integrating new words one by one into the composed representation of a sentence as it is read left-to-right. This raises the important question of what happens to the neural representations of words present earlier in the sentence? For example, do the strength of word representations encountered earlier on in the sentence remain constant or do they evolve as additional words are processed? Representation of words by neural activity in the brain has been the subject of several previous studies. We perform the experiment with a naturalistic task in which the subjects read simple active and passive sentences. Naturalistic studies have tended to explore words in isolation or in a very limited context (e.g., adjective-noun phrases). Representation of previously encountered words during incremental sentence reading, and how such representation evolve as more parts of a sentence are read, is a fundamental but unexplored problem - we take a first step in this direction. In particular, we examine the spatio-temporal characteristics of neural activity encoding nouns and verbs encountered in a sentence as it is read word-by-word. We use Magnetoencephalography (MEG) to passively observe neural activity, providing 1 ms temporal resolution. Our experiments reveal that nouns and verbs read early in the sentence have a varying influence on neural activity while reading subsequent words, decreasing and increasing at particular word positions in active and passively voiced sentences, with particularly important contributions to activity in frontal and temporal cortical regions. We find the noun and verb information to be decodable from the neural activity for several seconds after sentence reading has completed. Our exploration is also the first to study the effect of question-answering task on the neural representation of the words post-sentence. We are releasing our 300 sentence MEG dataset to encourage further research in this important area.

neuroscience↗

bioRxiv2020

Verification of genetic engineering in yeasts with nanopore whole genome sequencing

Yeast genomes can be assembled from sequencing data, but genome integrations and episomal plasmids often fail to be resolved with accuracy, completeness, and contiguity. Resolution of these features is critical for many synthetic biology applications, including strain quality control and identifying engineering in unknown samples. Here, we report an integrated workflow, named Prymetime, that uses sequencing reads from inexpensive NGS platforms, assembly and error correction software, and a list of synthetic biology parts to achieve accurate whole genome sequences of yeasts with engineering annotated. To build the workflow, we first determined which sequencing methods and software packages returned an accurate, complete, and contiguous genome of an engineered S. cerevisiae strain with two similar plasmids and an integrated pathway. We then developed a sequence feature annotation step that labels synthetic biology parts from a standard list of yeast engineering sequences or from a custom sequence list. We validated the workflow by sequencing a collection of 15 engineered yeasts built from different parent S. cerevisiae and nonconventional yeast strains. We show that each integrated pathway and episomal plasmid can be correctly assembled and annotated, even in strains that have part repeats and multiple similar plasmids. Interestingly, Prymetime was able to identify deletions and unintended integrations that were subsequently confirmed by other methods. Furthermore, the whole genomes are accurate, complete, and contiguous. To illustrate this clearly, we used a publicly available S. cerevisiae CEN.PK113 reference genome and the accompanying reads to show that a Prymetime genome assembly is equivalent to the reference using several standard metrics. Finally, we used Prymetime to resequence the nonconventional yeasts Y. lipolytica Po1f and K. phaffii CBS 7435, producing an improved genome assembly for each strain. Thus, our workflow can achieve accurate, complete, and contiguous whole genome sequences of yeast strains before and after engineering. Therefore, Prymetime enables NGS-based strain quality control through assembly and identification of engineering features.

synthetic biology↗

bioRxiv2020

Highly Distinguished Amino Acid Sequences of 2019-nCoV (Wuhan Coronavirus)

Using a method for pathogen screening in DNA synthesis orders, we have identified a number of amino acid sequences that distinguish 2019-nCoV (Wuhan Coronavirus) from all other known viruses in Coronaviridae. We find three main regions of unique sequence: two in the 1ab polyprotein QHO60603.1, one in surface glycoprotein QHO60594.1.

genetics↗

bioRxiv2020

TNFα/Grnd mediate JNK/MMPs activation during glioma progression and neurodegeneration

Glioblastoma (GB) is the most aggressive and lethal tumour of the central nervous system (CNS). GB cells proliferate rapidly and display a network of ultra-long tumour microtubes (TMs) that mediate cell to cell communication. GB TMs infiltrate into the brain, enwrap neurons and facilitate the depletion of Wingless (Wg)/WNT from the neighbouring neurons. GB cells establish a positive feedback loop including Wg signalling upregulation that activates the JNK pathway and matrix metalloproteases (MMPs), in turn, these signals promote TMs infiltration, GB progression and neuronal synapse loss and degeneration. Thus, cellular and molecular signals other than primary mutations emerge as central players of GB. Here we describe the temporal organization of the events that occur in GB. We define the progressive activation of JNK pathway signalling mediated by Grindelwald (Grnd) receptor, is caused by the ligand Eiger (Egr)/TNF produced by the healthy tissue. We propose that cellular interactions of GB with the rest of the brain is an early event that precedes GB proliferation and expansion. We conclude that non-autonomous signals facilitate GB progression and contribute to the complexity and versatility of these incurable tumours.

cancer biology↗

bioRxiv2017

Gene flow between divergent cereal- and grass-specific lineages of the rice blast fungus Magnaporthe oryzae

Delineating species and epidemic lineages in fungal plant pathogens is critical to our understanding of disease emergence and the structure of fungal biodiversity, and also informs international regulatory decisions. Pyricularia oryzae (syn. Magnaporthe oryzae) is a multi-host pathogen that infects multiple grasses and cereals, is responsible for the most damaging rice disease (rice blast), and of growing concern due to the recent introduction of wheat blast to Bangladesh from South America. However, the genetic structure and evolutionary history of M. oryzae, including the possible existence of cryptic phylogenetic species, remain poorly defined. Here, we use whole-genome sequence information for 76 M. oryzae isolates sampled from 12 grass and cereal genera to infer the population structure of M. oryzae, and to reassess the species status of wheat-infecting populations of the fungus. Species recognition based on genealogical concordance, using published data or extracting previously-used loci from genome assemblies, failed to confirm a prior assignment of wheat blast isolates to a new species (Pyricularia graminis tritici). Inference of population subdivisions revealed multiple divergent lineages within M. oryzae, each preferentially associated with one host genus, suggesting incipient speciation following host shift or host range expansion. Analyses of gene flow, taking into account the possibility of incomplete lineage sorting, revealed that genetic exchanges have contributed to the makeup of multiple lineages within M. oryzae. These findings provide greater understanding of the eco-evolutionary factors that underlie the diversification of M. oryzae and highlight the practicality of genomic data for epidemiological surveillance in this important multi-host pathogen.\n\nImportanceInfection of novel hosts is a major route for disease emergence by pathogenic micro-organisms. Understanding the evolutionary history of multi-host pathogens is therefore important to better predict the likely spread and emergence of new diseases. Magnaporthe oryzae is a multi-host fungus that causes serious cereal diseases, including the devastating rice blast disease, and wheat blast, a cause of growing concern due to its recent spread from South America to Asia. Using whole genome analysis of 76 fungal strains from different hosts, we have documented the divergence of M. oryzae into numerous lineages, each infecting a limited number of host species. Our analyses provide evidence that inter-lineage gene flow has contributed to the genetic makeup of multiple M. oryzae lineages within the same species. Plant health surveillance is therefore warranted to safeguard against disease emergence in regions where multiple lineages of the fungus are in contact with one another.

microbiology↗