Biology subjects

Miller, G.

Publications and source records attributed to Miller, G..

3 recordsLinked to original sources

bioRxiv2018

Surface sediment samples from early age of seafloor exploration can provide a late 19th century baseline of the marine environment

Ocean-floor sediment samples collected up to 150 years ago represent an important historical archive to benchmark global changes in the seafloor environment, such as species range shifts and invasions and pollution trends. Such benchmarking requires that the historical sediment samples represent the state of the environment at or shortly before the time of collection. However, early oceanographic expeditions sampled the ocean floor using devices like the sounding tube or a dredge, which potentially disturb the sediment surface and recover a mix of Holocene (surface) and deeper, Pleistocene sediments. Here we use climate-sensitive microfossils as a fast biometric method to assess if historical seafloor samples contain a mixture of modern and glacial sediments. Our assessment is based on comparing the composition of planktonic foraminifera (PF) assemblages in historical samples with Holocene and Last Glacial Maximum (LGM) global reference datasets. We show that eight out of the nine historical samples contain PF assemblages more similar to the Holocene than to the LGM PF assemblages, but the comparisons are only significant when there is a high local species temporal turnover (from the LGM to the Holocene). When analysing temporal turnover globally, we show that upwelling and temperate regions had greatest species turnover, which are areas where our methodology would be most diagnostic. Our results suggest that sediment samples from historical collections can provide a baseline of the state of marine ecosystems in the late 19th century, and thus be used to assess ocean global change trends.

paleontology↗

bioRxiv2018

SHP2 Inhibition Abrogates MEK inhibitor Resistance in Multiple Cancer Models

Adaptive resistance to MEK inhibitors (MEK-Is) typically occurs via induction of genes for different receptor tyrosine kinases (RTKs) and/or their ligands, even in tumors of the same histotype, making combination strategies challenging. SHP2 (PTPN11) is required for RAS/ERK pathway activation by most RTKs, and might provide a common resistance node. We found that combining the SHP2 inhibitor SHP099 with a MEK-I inhibits proliferation of multiple cancer cells in vitro. PTPN11 knockdown/MEK-I had similar effects, while expressing SHP099-binding mutants conferred resistance, demonstrating that SHP099 was on-target. This combination was efficacious in xenograft and/or genetically engineered models of KRAS-mutant pancreas cancer and ovarian cancer and in wild-type RAS-expressing triple negative breast cancer. Biochemical studies show that SHP099 impedes SOS/RAS/MEK/ERK1/2 reactivation in response to MEK-Is and blocks ERK1/2-dependent transcriptional programs. SHP099 alone also inhibited RAS activation in some, but not all, KRAS-mutant lines. Hence, SHP099/MEK-I combinations could have therapeutic utility in multiple malignancies.\n\nSIGNIFICANCEMEK inhibitors have shown limited efficacy as single agents because of the rapid development of adaptive resistance. We find that combining SHP2 and MEK inhibition abrogates adaptive resistance in multiple cancer models, expressing mutant and wild-type KRAS.

cancer biology↗

bioRxiv2018

A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle

Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations. Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). DMI and IT were recorded with load cells. A total of 37 individual dynamic patterns of methane production were analysed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lins concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. Since IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.\n\nImplicationsReducing methane emissions from ruminants is a major target for sustainable and efficient livestock farming. For the animal, methane production represents a loss of feed energy. For the environment, methane exerts a potent greenhouse effect. Methane mitigation strategies require accurate, non-invasive and inexpensive techniques for estimating individual methane emissions on farm. In this study, we integrate measurements of feeding behaviour in cattle and a mathematical model to estimate individual methane production. Together, model and measurements form a software sensor that efficiently predicts methane output. Our software sensor is a promising approach for estimating methane emissions at large scale.

bioengineering↗