Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

The Protein Arginine Methyltransferase PRMT-5 Regulates SER-2 Tyramine Receptor-Mediated Behaviors in Caenorhabditis elegans

G protein-coupled receptors are 7-pass transmembrane receptors that couple to heterotrimeric G proteins to mediate cellular responses to a diverse array of stimuli. Understanding the mechanisms that regulate G protein-coupled receptors is crucial to manipulating their signaling for therapeutic benefit. One key regulatory mechanism that contributes to the functional diversity of many signaling proteins is post-translational modification. Whereas phosphorylation remains the best studied of such modifications, arginine methylation by protein arginine methyltransferases is emerging as a key regulator of protein function. We previously published the first functional evidence that arginine methylation of G protein-coupled receptors modulates their signaling. We report here a third receptor that is regulated by arginine methylation, the Caenorhabditis elegans SER-2 tyramine receptor. We show that arginines within a putative methylation motif in the third intracellular loop of SER-2 are methylated by PRMT5 in vitro. Our data also suggest that this modification enhances SER-2 signaling in vivo to modulate animal behavior. The identification of a third G protein-coupled receptor to be functionally regulated by arginine methylation suggests that this post-translational modification may be utilized to regulate signaling through a broad array of G protein-coupled receptors

Supplemental Material for Bowitch et al., 2018

<b>Figure S1</b><br><div><b><br></b></div><div>Supplemental Figure (sequence alignment) as described in Discussion</div

The <i>C. elegans</i> cGMP-Dependent Protein Kinase EGL-4 Regulates Nociceptive Behavioral Sensitivity

<div><p>Signaling levels within sensory neurons must be tightly regulated to allow cells to integrate information from multiple signaling inputs and to respond to new stimuli. Herein we report a new role for the cGMP-dependent protein kinase EGL-4 in the negative regulation of G protein-coupled nociceptive chemosensory signaling. <i>C. elegans</i> lacking EGL-4 function are hypersensitive in their behavioral response to low concentrations of the bitter tastant quinine and exhibit an elevated calcium flux in the ASH sensory neurons in response to quinine. We provide the first direct evidence for cGMP/PKG function in ASH and propose that ODR-1, GCY-27, GCY-33 and GCY-34 act in a non-cell-autonomous manner to provide cGMP for EGL-4 function in ASH. Our data suggest that activated EGL-4 dampens quinine sensitivity via phosphorylation and activation of the regulator of G protein signaling (RGS) proteins RGS-2 and RGS-3, which in turn downregulate Gα signaling and behavioral sensitivity.</p></div

RGS proteins are targets of EGL-4.

<p>(A) Animals lacking each of the 8 neuronally expressed RGS proteins were tested for response to 1 mM quinine. <i>rgs-2(lof)</i> and <i>rgs-3(lof)</i> animals respond better than wild-type animals to dilute (1 mM) quinine (p<0.001). (B) RNAi knock-down of <i>rgs-2</i> or <i>rgs-3</i> in the quinine-detecting ASH sensory neurons of otherwise wild-type animals, using the <i>osm-10</i> promoter <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Hart1" target="_blank">[3]</a>, resulted in behavioral hypersensitivity to dilute (1 mM) quinine, similar to <i>rgs-2(lof)</i> and <i>rgs-3(lof)</i> animals, respectively (p>0.05 for both transgenes when compared to the respective <i>rgs</i> loss-of-function animals). The <i>srb-6</i> promoter <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Troemel2" target="_blank">[61]</a> was also used for ASH knock-down of <i>rgs-3</i>, and similarly resulted in hypersensitivity to 1 mM quinine (data not shown). (C) Wild-type animals overexpressing ectopic <i>rgs-2</i> or <i>rgs-3</i> cDNA displayed diminished response to 10 mM quinine (p<0.0001 when compared to wild-type animals). (D) <i>rgs-2(lof);egl-4(lof)</i> and <i>rgs-3(lof);egl-4(lof)</i> double mutant animals responded to dilute (1 mM) quinine similarly to <i>egl-4(lof)</i> animals (p>0.1 for each). (E) <i>egl-4(gof)</i> animals lacking either RGS-2 or RGS-3 function responded to 10 mM quinine similarly to the <i>rgs-2(lof)</i> and <i>rgs-3(lof)</i> animals, respectively (p>0.5) and (F) were hypersensitive to 1 mM quinine (p<0.001 when compared to wild-type animals). (G) <i>rgs-2(lof)</i> and <i>rgs-3(lof)</i> animals are hypersensitive to dilute (1 mM) quinine. ASH expression of wild-type RGS-2 or RGS-3, using the <i>osm-10</i> promoter <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Hart1" target="_blank">[3]</a>, rescued quinine hypersensitivity in the respective loss-of-function animals. The predicted PKG phosphorylation target site in each was mutated (ΔP), making RGS-2(S126A) and RGS-3(S154A). <i>rgs-2(lof)</i> animals expressing RGS-2(ΔP) and <i>rgs-3(lof)</i> animals expressing RGS-3(ΔP) remained hypersensitive to dilute quinine (p>0.05 for each). The percentage of animals responding is shown. The combined data of ≥3 independent lines, n≥120 transgenic animals, is shown. Error bars represent the standard error of the mean (SEM). Alleles used: <i>egl-4(n479)</i>, <i>rgs-1(nr2017)</i>, <i>rgs-2(vs17)</i>, <i>rgs-3(vs19)</i>, <i>rgs-6(vs62)</i>, <i>rgs-10(ok1039)</i>, <i>rgs-10/11(vs109)</i>, <i>egl-10(md176)</i> and <i>eat-16(tm761)</i> loss-of-function and <i>egl-4(ad450)</i> gain-of-function. WT = the N2 wild-type strain. lof = loss-of-function. gof = gain-of-function.</p

<i>C. elegans</i> EGL-4 regulates quinine sensitivity in ASH.

<p>(A) <i>egl-4(lof)</i> animals respond better than wild-type animals to dilute (1 mM) quinine, while <i>egl-4(gof)</i> animals show a decreased sensitivity to 10 mM quinine, when compared to wild-type animals. p<0.0001 for each. (B) The ASH sensory neurons are the primary neurons used to detect quinine, but the ASK neurons also contribute <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Hilliard2" target="_blank">[7]</a>. The <i>osm-10 </i><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Hart1" target="_blank">[3]</a>, <i>srb-6 </i><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Troemel2" target="_blank">[61]</a> and <i>srbc-66 </i><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Kim1" target="_blank">[62]</a> promoters were used to drive expression of wild-type <i>egl-4</i> in <i>egl-4(lof)</i> animals. The <i>osm-10</i> promoter expresses in ASH, ASI, PHA and PHB, while the <i>srb-6</i> promoter drives expression in ASH, ADL, ADF, PHA and PHB. ASH is the only head sensory neuron common to both promoters. The <i>srbc-66</i> promoter expresses in ASK. While <i>egl-4(lof)</i> animals respond better than wild-type animals to 1 mM quinine, restoring EGL-4 function in ASH significantly diminished this hypersensitivity (p<0.0001 for both). EGL-4 expression in ASK had no effect (p>0.5). (C) RNAi knock-down of <i>egl-4</i> in the ASH sensory neurons of otherwise wild-type animals, using the <i>osm-10 </i><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Hart1" target="_blank">[3]</a> or <i>srb-6 </i><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Troemel2" target="_blank">[61]</a> promoter resulted in behavioral hypersensitivity to dilute (1 mM) quinine, similar to <i>egl-4(lof)</i> animals (p<0.0001 when compared to N2 animals for both transgenes). The percentage of animals responding is shown. The combined data of ≥3 independent lines, n≥120 transgenic animals, is shown. Error bars represent the standard error of the mean (SEM). Alleles used: <i>egl-4(n479)</i> loss-of-function and <i>egl-4(ad450)</i> gain-of-function. WT = the N2 wild-type strain. lof = loss-of-function. gof = gain-of-function.</p

EGL-4 does not regulate ASH sensitivity in general.

<p><i>C. elegans</i> respond to bitter stimuli in addition to quinine. (A) Animals lacking EGL-4 function are hypersensitive to dilute amodiaquine (p≤0.01 when compared to wild-type animals), but not dilute primaquine (B) (p≥0.05). The percentage of animals responding is shown. The ASH sensory neurons also detect the volatile odorant octanol, the heavy metal copper and the detergent SDS. (C) <i>egl-4(lof)</i> mutant animals are moderately hypersensitive to dilute octanol (p<0.02). Time to respond is shown. (D–E) <i>egl-4(lof)</i> animals respond similarly to wild-type animals to both copper and SDS, across a range of concentrations (p>0.1 for each concentration, except p = 0.03 for 1 mM copper). The percentage of animals responding is shown. n>40 for each. All tastants were dissolved in M13 buffer, pH 7.4. Error bars represent the standard error of the mean (SEM). Allele used: <i>egl-4(n479)</i> loss-of-function. WT = the N2 wild-type strain. lof = loss-of-function.</p

Model for EGL-4 regulation of nociceptive signaling.

<p>The cGMP-dependent protein kinase EGL-4 regulates <i>C. elegans</i> behavioral sensitivity to the bitter tastants quinine and amodiaquine and the volatile odorant octanol. Wild-type chemosensory signaling is initiated in the ASH sensory neurons when a ligand (such as quinine) binds to a GPCR to activate the associated heterotrimeric G proteins. The activated G proteins (Gα-GTP and Gβγ) interact with downstream effectors to generate second messengers that can activate channels in the plasma membrane, allowing Ca²⁺ influx. Through connections with downstream interneurons and motor neurons, ASH activation is ultimately translated into behavioral avoidance (backward locomotion). Signaling is terminated in part by regulator of G protein signaling (RGS) proteins, which promote the hydrolysis of GTP to GDP by the Gα subunit. EGL-4 phosphorylation of RGS-2 and RGS-3 stimulates their activity. The guanylyl cyclases ODR-1, GCY-27, GCY-33 and GCY-34 may function in alternate neurons to provide the cGMP that is required for EGL-4 function in ASH. In the absence of EGL-4 function, RGS-2 and RGS-3 do not efficiently downregulate Gα signaling, leading to increased Ca²⁺ levels in response to receptor activation. This increased signaling in the ASH sensory neurons leads behavioral hypersensitivity to weak stimuli. The molecular events believed to be happening within the ASHs themselves are included within the grayed area.</p

EGL-4 functions in the cytoplasm to regulate calcium signaling.

<p>(A) EGL-4 functions in the cytoplasm to regulate behavioral sensitivity to quinine. The <i>osm-10</i> promoter <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Hart1" target="_blank">[3]</a> was used to express wild-type EGL-4, EGL-4 lacking its endogenous nuclear localization sequence (NLS) or EGL-4 with an additional NLS in the ASH sensory neurons of <i>egl-4(lof)</i> animals. In each case, the EGL-4 was expressed as a fusion with the green fluorescent protein (GFP). Wild-type GFP−EGL-4 was localized throughout the cell and rescued the quinine hypersensitivity of <i>egl-4(lof)</i> animals. GFP−EGL-4(ΔNLS) was restricted to the cytoplasm and rescued the hypersensitivity of <i>egl-4(lof)</i> animals as well as wild-type GFP−EGL-4 (p>0.05). NLS−GFP−EGL-4 was sequestered to the nucleus and had only a small, but statistically significant, rescuing effect (p<0.001). (B) Stimulus-evoked calcium transients in the ASH neurons are enhanced in <i>egl-4(lof)</i> animals. The <i>sra-6</i> promoter <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Troemel2" target="_blank">[61]</a> was used to express the genetically encoded calcium indicator G-CaMP3 <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-Tian1" target="_blank">[69]</a> in the ASH sensory neurons, <i>kyEx2865</i> (<i>sra-6p::G-CaMP3;ofm-1p::gfp</i>) <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003619#pgen.1003619-McGrath1" target="_blank">[70]</a>. Using a microfluidic device, an adult animal was restrained while quinine was delivered to its nose for 10 seconds (black horizontal bar), and the change in fluorescence intensity was recorded. The average ratio change ± the standard error of the mean (SEM) is indicated on each trace. n = 10 animals for each condition. (C) The averaged maximum evoked calcium change for 10, 1 and 0 mM quinine is shown. <i>egl-4(lof)</i> animals showed an elevated ASH calcium flux upon exposure to 1 mM quinine when compared to wild-type animals (p<0.05) that is rescued by <i>osm-10p::egl-4</i> expression in ASH (p>0.1 when compared to wild-type animals). Error bars represent the SEM. Alleles used: <i>egl-4(n479)</i> loss-of-function and <i>egl-4(ad450)</i> gain-of-function. WT = the N2 wild-type strain. lof = loss-of-function, gof = gain-of-function. s = seconds.</p