scholarly journals Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins

2022 ◽  
Vol 119 (1) ◽  
pp. e2113297119
Author(s):  
Helgi I. Ingólfsson ◽  
Chris Neale ◽  
Timothy S. Carpenter ◽  
Rebika Shrestha ◽  
Cesar A. López ◽  
...  

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

2020 ◽  
Author(s):  
Helgi Ingolfsson ◽  
Chris Neale ◽  
Timothy Carpenter ◽  
Rebika Shrestha ◽  
Cesar Lopez ◽  
...  

Abstract RAS is a signaling protein associated with the cell membrane that is mutated in 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.


2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.


1995 ◽  
Vol 305 (1) ◽  
pp. 133-137 ◽  
Author(s):  
M A Phelouzat ◽  
M Basselin ◽  
F Lawrence ◽  
M Robert-Gero

The involvement of a carrier for sinefungin (SF) uptake in Leishmania donovani promastigotes is indicated by saturation kinetics, competition studies and SF accumulation against a 270-fold concentration gradient across the cell membrane. Whether SF uptake occurs via nucleoside- or AdoMet-carrier systems was investigated by competition experiments and comparison of the uptake of various molecules in wild-type and SF-resistant cells. Results show that SF did not inhibit purine or pyrimidine uptake whereas it competitively inhibited AdoMet uptake. Furthermore, the uptake of nucleosides in SF-resistant cells is similar to that in wild-type cells, whereas uptake of SF and AdoMet is lower.


2021 ◽  
Vol 118 (21) ◽  
pp. e2016904118
Author(s):  
Derek K. Cheng ◽  
Tobiloba E. Oni ◽  
Jennifer S. Thalappillil ◽  
Youngkyu Park ◽  
Hsiu-Chi Ting ◽  
...  

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with limited treatment options. Although activating mutations of the KRAS GTPase are the predominant dependency present in >90% of PDAC patients, targeting KRAS mutants directly has been challenging in PDAC. Similarly, strategies targeting known KRAS downstream effectors have had limited clinical success due to feedback mechanisms, alternate pathways, and dose-limiting toxicities in normal tissues. Therefore, identifying additional functionally relevant KRAS interactions in PDAC may allow for a better understanding of feedback mechanisms and unveil potential therapeutic targets. Here, we used proximity labeling to identify protein interactors of active KRAS in PDAC cells. We expressed fusions of wild-type (WT) (BirA-KRAS4B), mutant (BirA-KRAS4BG12D), and nontransforming cytosolic double mutant (BirA-KRAS4BG12D/C185S) KRAS with the BirA biotin ligase in murine PDAC cells. Mass spectrometry analysis revealed that RSK1 selectively interacts with membrane-bound KRASG12D, and we demonstrate that this interaction requires NF1 and SPRED2. We find that membrane RSK1 mediates negative feedback on WT RAS signaling and impedes the proliferation of pancreatic cancer cells upon the ablation of mutant KRAS. Our findings link NF1 to the membrane-localized functions of RSK1 and highlight a role for WT RAS signaling in promoting adaptive resistance to mutant KRAS-specific inhibitors in PDAC.


2019 ◽  
Author(s):  
Sushant Kumar ◽  
Arif Harmanci ◽  
Jagath Vytheeswaran ◽  
Mark B. Gerstein

AbstractA rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a “pathogenicity score” to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.


2020 ◽  
Author(s):  
Laura Crocetti ◽  
Milan Fischer ◽  
Matthias Forkel ◽  
Aleš Grlj ◽  
Wai-Tim Ng ◽  
...  

<p>The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.</p><p>Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25°) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.</p><p>This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).</p>


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 888-888
Author(s):  
Iman Fares ◽  
Rahul S. Vedula ◽  
Shabbir M. Vahanvaty ◽  
Christopher S Waters ◽  
Marlise R. Luskin ◽  
...  

Abstract Somatic mutations can have highly stereotyped positions in the myeloid clonal hierarchy and distinct patterns of co-occurring mutations. Gene mutations that cause aberrant activation of RAS/MAPK signaling are typically late events in myeloid disease progression and are closely associated with leukemic transformation. We hypothesized that the phenotypic output of oncogenic RAS signaling is dynamically reprogrammed during leukemogenesis based on evolving genetic and epigenetic context. To identify genetic alterations that may modulate RAS-mediated transformation, we evaluated 1273 adults with myelodysplastic syndrome, including 150 with mutations in NRAS, KRAS, PTPN11, CBL, RIT1, NF1, or FLT3. Somatic mutations in ASXL1 (q<0.0001), RUNX1 (q<0.0001), EZH2 (q<0.0001), BCOR (q=0.0002), and STAG2 (q=0.001) were most significantly associated with co-occurring RAS pathway mutations, compared to those without RAS pathway mutations, while TP53 mutations were less frequent (q=0.059). We validated these observations in an independent cohort of 6343 unselected patients, including 1081 patients harboring either RAS pathway mutations (n=651),TP53 mutations (n=494), or both (n=57). To define the effects of sequential acquisition of driver mutations, we developed a mouse serial transplantation model of somatic myeloid transformation. First, we used in vivo pI:pC treatment to induce biallelic inactivation of Tet2 in adult Mx1-Cre/Tet2flox/floxmice. After 12 weeks, we purified Tet2-/-or control hematopoietic stem and progenitor cells (HSPCs) and used CRISPR/Cas9 to separately introduce inactivating mutations in Ezh2, Asxl1-exon12, Stag2, or Bcor, then evaluated their functional effects using ex vivo serial replating or in vivo competitive transplantation. Tet2-/-HSPCs with control sgRNA showed a modest enhancement of serial replating compared to Tet2-wild type HSPCs, while Tet2-/-HSPCs Asxl1, Stag2, and Bcor, but not Ezh2 sgRNA had markedly enhanced serial replating capacity (>6 platings in all replicates). In primary transplantation, secondary mutations caused in vivo clonal advantage after 16 weeks, but never resulted in histologic transformation to acute leukemia. We next evaluated the impact of tertiary NRASG12Dmutations in each pairwise Tet2-/-CRISPR combination (Asxl1, Bcor, Ezh2, Stag2, control). We purified HSPCs from recipient mice 16 weeks after primary transplantation, transduced with a lentiviral NRASG12Dexpression vector and transplanted into secondary recipients. Recipients of Tet2/Bcor/NRAS, Tet2/Asxl1/NRAS, or Tet2/Ezh2/NRAS cells succumbed to CD11b+myeloid disease with variable latency in Bcor (14 days), Ezh2 (50 days), and Asxl1 (120 days) cells, suggesting that combined Tet2 and PRC1/2 alterations may modify the effects of oncogenic RAS signaling. To determine whether pre-existing epigenetic mutations cooperate to alter the transcriptional response to acute oncogenic stress compared to wild type cells, weperformed RNA-seq 12 and 24 hours after induced expression of NRASG12D in isogenic immortalized mouse progenitor cells deficient for Tet2, Bcor, or both Tet2 and Bcor. We observed rapid activation of inflammatory and cellular senescence programs in all conditions, suggesting a genotype-independent immediate early response to oncogenic signaling. However, we also identified genotype-specific regulation of tumor suppressor and cell cycle checkpoint pathways. While Cdnk1a expression was strongly induced in all conditions, Cdnk2a expression (and p16Ink4a and p19ARF protein levels) was preferentially upregulated in the context of Bcor deficiency. Moreover, expression of the p53 negative regulator Mdm2 was increased 11-fold in Tet2/Bcor-deficient cells, but only 4 to 5-fold in wild type, Tet2-, or Bcor-deficient cells. Tet2/Bcor-deficient cells were significantly more sensitive to treatment with the Mdm2 antogonist, Nutlin, upon induction of NRAS expression than were wild-type cells, suggesting that Mdm2 overexpression directly mediates acquired tolerance of oncogene stress. These human genetic data and mouse models suggest that epigenetic alterations occurring during early myeloid leukemogenesis may enable evasion of oncogene protection mechanism. Bcor mutations can pair with initiating Tet2 mutations to facilitate RAS mediated transformation while incurring a dependency on Mdm2 overexpression. Disclosures No relevant conflicts of interest to declare.


2021 ◽  
Vol 37 (10) ◽  
pp. S65
Author(s):  
C Willis ◽  
K Kawamoto ◽  
A Watanabe ◽  
J Biskupiak ◽  
K Nolen ◽  
...  

2019 ◽  
Vol 8 (12) ◽  
pp. 580 ◽  
Author(s):  
Xuantong Wang ◽  
Paul C. Sutton ◽  
Bingxin Qi

Frequent and rapid spatially explicit assessment of socioeconomic development is critical for achieving the Sustainable Development Goals (SDGs) at both national and global levels. Over the past decades, scientists have proposed many methods for estimating human activity on the Earth’s surface at various spatiotemporal scales using Defense Meteorological Satellite Program Operational Line System (DMSP-OLS) nighttime light (NTL) data. However, the DMSP-OLS NTL data and the associated processing methods have limited their reliability and applicability for systematic measuring and mapping of socioeconomic development. This study utilized Visible Infrared Imaging Radiometer Suite (VIIRS) NTL and the Isolation Forest machine learning algorithm for more intelligent data processing to capture human activities. We used machine learning and NTL data to map gross domestic product (GDP) at 1 km2. We then used these data products to derive inequality indexes (e.g., Gini coefficients) at nationally aggregate levels. This flexible approach processes the data in an unsupervised manner at various spatial scales. Our assessments show that this method produces accurate subnational GDP data products for mapping and monitoring human development uniformly across the globe.


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