Jung Y et al. (2021) Sci Adv "MON-2, a Golgi protein, mediates autophagy-dependent longevity in Caenorhabditis elegans."

Adams L, Ryu Y, Lee Y, Altintas O, Ju S, Park SY, Kim S, Hwang AB, Artan M, Nam HJ, Han SK, Kim EJE, Yeom J, Park SK, Jung Y, Lee C, Jeong DE, Sohn J, Kang C, Ha CM, Hwang W, Seo M, Moon DJ, Lee SV, Kim N, Lee D, Seo K, Yoo JY, Hansen M

[Sci Adv, 2021]

[Figure: see text].

Cao YY et al. (2013) Mol Divers "Salvianolic acid A, a polyphenolic derivative from Salvia miltiorrhiza bunge, as a multifunctional agent for the treatment of Alzheimer's disease."

Pei Z, Wang L, Gu Q, Xu J, Cao YY, Lu XL, Ge H

[Mol Divers, 2013]

The effects of Salvianolic acid A (Sal A) on the treatment of Alzheimer's disease (AD) were investigated. Sal A significantly inhibits amyloid beta [Formula: see text] self-aggregation and disaggregates pre-formed [Formula: see text] fibrils, reduces metal-induced [Formula: see text] aggregation through chelating metal ions, and blocks the formation of reactive oxygen species (ROS) in SH-SY5Y cells. Sal A protects cells against [Formula: see text]-induced toxicity. Furthermore, Sal A, possibly because of the effects of decreasing toxicity effects of [Formula: see text] species, alleviates [Formula: see text]-induced paralysis in transgenic Caenorhabditis elegans. Circular dichroism (CD) experiments and Molecular dynamic (MD) simulations demonstrate that Sal A inhibits [Formula: see text] self-aggregation through binding to the C-terminus of [Formula: see text], and therefore stabilizing the [Formula: see text]-helical conformations. Altogether, our data show that Sal A, as the multifunctional agent, is likely to be promising therapeutics for AD.

Defachelles L et al. (2020) Mol Biol Cell "PCH-2TRIP13 regulates spindle checkpoint strength."

Russo AE, Nelson CR, Bhalla N, Defachelles L

[Mol Biol Cell, 2020]

Spindle checkpoint strength is dictated by the number of unattached kinetochores, cell volume and cell fate. We show that the conserved AAA-ATPase, PCH-2/TRIP13, which remodels the checkpoint effector Mad2 from an active conformation to an inactive one, controls checkpoint strength in <i>C. elegans</i>. Having previously established that this function is required for spindle checkpoint activation, we demonstrate that in cells genetically manipulated to decrease in cell volume, PCH-2 is no longer required for the spindle checkpoint or recruitment of Mad2 at unattached kinetochores. This role is not limited to large cells: the stronger checkpoint in germline precursor cells also depends on PCH-2. PCH-2 is enriched in germline precursor cells and this enrichment relies on conserved factors that induce asymmetry in the early embryo. Finally, the stronger checkpoint in germline precursor cells is regulated by CMT-1, the ortholog of p31^comet, which is required for both PCH-2's localization to unattached kinetochores and its enrichment in germline precursor cells. Thus, PCH-2, likely by regulating the availability of inactive Mad2 at and near unattached kinetochores, governs checkpoint strength. This requirement may be particularly relevant in oocytes and early embryos enlarged for developmental competence, cells that divide in syncytial tissues and immortal germline cells. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].

Rodriguez-Garcia R et al. (2018) Mol Biol Cell "The polarity-induced force imbalance in Caenorhabditis elegans embryos is caused by asymmetric binding rates of dynein to the cortex."

Pastezeur S, Pecreaux J, Tramier M, Roul J, Chesneau L, Rodriguez-Garcia R

[Mol Biol Cell, 2018]

During asymmetric cell division, the molecular motor dynein generates cortical pulling forces which position the spindle to reflect polarity and adequately distribute cell fate determinants. In Caenorhabditis elegans embryos, despite a measured anteroposterior force imbalance, antibody staining failed to reveal dynein enrichment at the posterior cortex, suggesting a transient localization there. Dynein accumulates at the microtubule plus ends, indirectly binding to EBP-2^EB. This accumulation, although not transporting dynein, contributes modestly to cortical forces. Most dyneins may instead diffuse to the cortex. Tracking of cortical dynein revealed two motions: one directed, and the other diffusive-like, corresponding to force-generating events. Surprisingly, while dynein is not polarized at the plus ends or in the cytoplasm, diffusive-like tracks were more frequently found at the embryo posterior tip, where the forces are higher. This asymmetry depends on GPR-1/2^LGN and LIN-5^NuMA, which are enriched there. In csnk-1(RNAi) embryos, the inverse distribution of these proteins coincides with an increased frequency of diffusive-like tracks anteriorly. Importantly, dynein cortical residence time is always symmetric. We propose that the dynein binding rate at the posterior cortex is increased, causing the polarity-reflecting force imbalance. This mechanism of control supplements the regulation of mitotic progression through the non-polarized dynein detachment rate. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].

Choi K et al. (2022) Neuroinformatics "Polymer Physics-Based Classification of Neurons."

Hyeon C, Choi K, Kim WK

[Neuroinformatics, 2022]

Recognizing that diverse morphologies of neurons are reminiscent of structures of branched polymers, we put forward a principled and systematic way of classifying neurons that employs the ideas of polymer physics. In particular, we use 3D coordinates of individual neurons, which are accessible in recent neuron reconstruction datasets from electron microscope images. We numerically calculate the form factor, F(q), a Fourier transform of the distance distribution of particles comprising an object of interest, which is routinely measured in scattering experiments to quantitatively characterize the structure of materials. For a polymer-like object consisting of n monomers spanning over a length scale of r, F(q) scales with the wavenumber [Formula: see text] as [Formula: see text] at an intermediate range of q, where [Formula: see text] is the fractal dimension or the inverse scaling exponent ([Formula: see text]) characterizing the geometrical feature ([Formula: see text]) of the object. F(q) can be used to describe a neuron morphology in terms of its size ([Formula: see text]) and the extent of branching quantified by [Formula: see text]. By defining the distance between F(q)s as a measure of similarity between two neuronal morphologies, we tackle the neuron classification problem. In comparison with other existing classification methods for neuronal morphologies, our F(q)-based classification rests solely on 3D coordinates of neurons with no prior knowledge of morphological features. When applied to publicly available neuron datasets from three different organisms, our method not only complements other methods but also offers a physical picture of how the dendritic and axonal branches of an individual neuron fill the space of dense neural networks inside the brain.

Zabardast A et al. (2023) Sci Rep "An automated framework for evaluation of deep learning models for splice site predictions."

Tamer EG, Son YA, Zabardast A, Yilmaz A

[Sci Rep, 2023]

A novel framework for the automated evaluation of various deep learning-based splice site detectors is presented. The framework eliminates time-consuming development and experimenting activities for different codebases, architectures, and configurations to obtain the best models for a given RNA splice site dataset. RNA splicing is a cellular process in which pre-mRNAs are processed into mature mRNAs and used to produce multiple mRNA transcripts from a single gene sequence. Since the advancement of sequencing technologies, many splice site variants have been identified and associated with the diseases. So, RNA splice site prediction is essential for gene finding, genome annotation, disease-causing variants, and identification of potential biomarkers. Recently, deep learning models performed highly accurately for classifying genomic signals. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and its bidirectional version (BLSTM), Gated Recurrent Unit (GRU), and its bidirectional version (BGRU) are promising models. During genomic data analysis, CNN's locality feature helps where each nucleotide correlates with other bases in its vicinity. In contrast, BLSTM can be trained bidirectionally, allowing sequential data to be processed from forward and reverse directions. Therefore, it can process 1-D encoded genomic data effectively. Even though both methods have been used in the literature, a performance comparison was missing. To compare selected models under similar conditions, we have created a blueprint for a series of networks with five different levels. As a case study, we compared CNN and BLSTM models' learning capabilities as building blocks for RNA splice site prediction in two different datasets. Overall, CNN performed better with [Formula: see text] accuracy ([Formula: see text] improvement), [Formula: see text] F1 score ([Formula: see text] improvement), and [Formula: see text] AUC-PR ([Formula: see text] improvement) in human splice site prediction. Likewise, an outperforming performance with [Formula: see text] accuracy ([Formula: see text] improvement), [Formula: see text] F1 score ([Formula: see text] improvement), and [Formula: see text] AUC-PR ([Formula: see text] improvement) is achieved in C. elegans splice site prediction. Overall, our results showed that CNN learns faster than BLSTM and BGRU. Moreover, CNN performs better at extracting sequence patterns than BLSTM and BGRU. To our knowledge, no other framework is developed explicitly for evaluating splice detection models to decide the best possible model in an automated manner. So, the proposed framework and the blueprint would help selecting different deep learning models, such as CNN vs. BLSTM and BGRU, for splice site analysis or similar classification tasks and in different problems.

Galindo-Malagn XA et al. (2022) Zootaxa "New species, synonymies and records in the genus Rhagovelia Mayr, 1865 (Hemiptera: Heteroptera: Veliidae) from Colombia."

Moreira FFF, Morales I, Mondragn-F SP, Galindo-Malagn XA

[Zootaxa, 2022]

Rhagovelia medinae sp. nov., of the hambletoni group (angustipes complex), and R. utria sp. nov., of the hirtipes group (robusta complex), are described, illustrated, and compared with similar congeners. Based on the examination of type specimens, six new synonymies are proposed: R. elegans Uhler, 1894 = R. pediformis Padilla-Gil, 2010, syn. nov.; R. cauca Polhemus, 1997 = R. azulita Padilla-Gil, 2009, syn. nov., R. huila Padilla-Gil, 2009, syn. nov., R. oporapa Padilla-Gil, 2009, syn. nov, R. quilichaensis Padilla-Gil, 2011, syn. nov.; and R. gaigei, Drake Hussey, 1947 = R. victoria Padilla-Gil, 2012 syn. nov. The first record from Colombia is presented for R. trailii (White, 1879), and the distributions of the following species are extended in the country: R. cali Polhemus, 1997, R. castanea Gould, 1931, R. cauca Polhemus, 1997, R. gaigei Drake Hussey, 1957, R. elegans Uhler, 1894, R. femoralis Champion, 1898, R. malkini Polhemus, 1997, R. perija Polhemus, 1997, R. sinuata Gould, 1931, R. venezuelana Polhemus, 1997, R. williamsi Gould, 1931, and R. zeteki Drake, 1953.

Pearce KM et al. (2019) Mol Biol Cell "Gq-mediated calcium dynamics and membrane tension modulate neurite plasticity."

Bell M, Wen Q, Rangamani P, Pearce KM, Scarlata S, Srinivasan J, Linthicum WH

[Mol Biol Cell, 2019]

The formation and disruption of synaptic connections during development is a fundamental step in neural circuit formation. Subneuronal structures such as neurites are known to be sensitive to the level of spontaneous neuronal activity but the specifics of how neurotransmitter-induced calcium activity regulates neurite homeostasis are not yet fully understood. In response to stimulation by neurotransmitters such as acetylcholine, calcium responses in cells are mediated the Gq/phospholipase C (PLC)/ phosphatidylinositol 4,5 bisphosphate (PI(4,5)P₂) signaling pathway. Here, we show that prolonged Gq stimulation results in the retraction of neurites in PC12 cells and rupture of neuronal synapses by modulating membrane tension. To understand the underlying cause, we dissected the behavior of individual components of the Gq/PLC /PI(4,5)P₂ pathway during retraction, and correlated these to the retraction of the membrane and cytoskeletal elements impacted by calcium signaling. We developed a mathematical model that combines biochemical signaling with membrane tension and cytoskeletal mechanics, to show how signaling events are coupled to retraction velocity, membrane tension and actin dynamics. The coupling between calcium and neurite retraction is shown to be operative in the <i>C. elegans</i> nervous system. This study uncovers a novel mechanochemical connection between Gq/PLC /PI(4,5)P₂ that couples calcium responses with neural plasticity. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].

Kimura K et al. (2020) Mol Biol Cell "Cytoplasmic streaming drifts the polarity cue and enables posteriorization of the Caenorhabditis elegans zygote at the side opposite of sperm entry."

Kimura K, Kimura A

[Mol Biol Cell, 2020]

Cell polarization is required to define body axes during development. The position of spatial cues for polarization is critical to direct the body axes. In <i>Caenorhabditis elegans</i> zygotes, the sperm-derived pronucleus/centrosome complex (SPCC) serves as the spatial cue to specify the anterior-posterior axis. Approximately 30 minutes after fertilization, the contractility of the cell cortex is relaxed near the SPCC, which is the earliest sign of polarization and called symmetry breaking (SB). It is unclear how the position of SPCC at SB is determined after fertilization. Here, we show that SPCC drifts dynamically through the cell-wide flow of the cytoplasm, called meiotic cytoplasmic streaming. This flow occasionally brings SPCC to the opposite side of the sperm entry site before SB. Our results demonstrate that cytoplasmic flow determines stochastically the position of the spatial cue of the body axis, even in an organism like <i>C. elegans</i> for which development is stereotyped. [Media: see text] [Media: see text] [Media: see text] [Media: see text].

Hoehndorf R et al. (2013) PLoS One "Systematic analysis of experimental phenotype data reveals gene functions."

Osumi-Sutherland D, Gkoutos GV, Schofield PN, Tweedie S, Hardy NW, Hoehndorf R

[PLoS One, 2013]

High-throughput phenotyping projects in model organisms have the potential to improve our understanding of gene functions and their role in living organisms. We have developed a computational, knowledge-based approach to automatically infer gene functions from phenotypic manifestations and applied this approach to yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), zebrafish (Danio rerio), fruitfly (Drosophila melanogaster) and mouse (Mus musculus) phenotypes. Our approach is based on the assumption that, if a mutation in a gene [Formula: see text] leads to a phenotypic abnormality in a process [Formula: see text], then [Formula: see text] must have been involved in [Formula: see text], either directly or indirectly. We systematically analyze recorded phenotypes in animal models using the formal definitions created for phenotype ontologies. We evaluate the validity of the inferred functions manually and by demonstrating a significant improvement in predicting genetic interactions and protein-protein interactions based on functional similarity. Our knowledge-based approach is generally applicable to phenotypes recorded in model organism databases, including phenotypes from large-scale, high throughput community projects whose primary mode of dissemination is direct publication on-line rather than in the literature.