Recent Submissions

  • Set-aware Entity Synonym Discovery with Flexible Receptive Fields (Extended Abstract)

    Pei, Shichao; Yu, Lu; Zhang, Xiangliang (IEEE, 2022-08-02) [Conference Paper]
    Entity synonym discovery (ESD) from text corpus is an essential problem in many entity-leveraging applications. This paper aims to address three limitations that widely exist in the current ESD solutions: 1) the lack of effective utilization for synonym set information; 2) the feature extraction of entities from restricted receptive fields; and 3) the incapacity to capture higher-order contextual information. We propose a novel set-aware ESD model that enables a flexible receptive field for ESD by using entity synonym set information and constructing a two-level network. Extensive experimental results on public datasets show that our model consistently outperforms the state-of-the-art with significant improvement.
  • Unveiling the “Template-Dependent” Inhibition on the Viral Transcription of SARS-CoV-2

    Luo, Xueying; Wang, Xiaowei; Yao, Yuan; Gao, Xin; Zhang, Lu (The Journal of Physical Chemistry Letters, American Chemical Society (ACS), 2022-07-30) [Article]
    Remdesivir is one nucleotide analogue prodrug capable to terminate RNA synthesis in SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) by two distinct mechanisms. Although the “delayed chain termination” mechanism has been extensively investigated, the “template-dependent” inhibitory mechanism remains elusive. In this study, we have demonstrated that remdesivir embedded in the template strand seldom directly disrupted the complementary NTP incorporation at the active site. Instead, the translocation of remdesivir from the +2 to the +1 site was hindered due to the steric clash with V557. Moreover, we have elucidated the molecular mechanism characterizing the drug resistance upon V557L mutation. Overall, our studies have provided valuable insight into the “template-dependent” inhibitory mechanism exerted by remdesivir on SARS-CoV-2 RdRp and paved venues for an alternative antiviral strategy for the COVID-19 pandemic. As the “template-dependent” inhibition occurs across diverse viral RdRps, our findings may also shed light on a common acting mechanism of inhibitors.
  • Natural carbon fixation and advances in synthetic engineering for redesigning and creating new fixation pathways

    Santos Correa, Sulamita; Schultz, Junia; Lauersen, Kyle J.; Soares Rosado, Alexandre (Journal of advanced research, Elsevier BV, 2022-07-30) [Article]
    Background: Autotrophic carbon fixation is the primary route through which organic carbon enters the biosphere, and it is a key step in the biogeochemical carbon cycle. The Calvin–Benson–Bassham pathway, which is predominantly found in plants, algae, and some bacteria (mainly cyanobacteria), was previously considered to be the sole carbon-fixation pathway. However, the discovery of a new carbon-fixation pathway in sulfurous green bacteria almost two decades ago encouraged further research on previously overlooked ancient carbon-fixation pathways in taxonomically and phylogenetically distinct microorganisms. Aim of Review: In this review, we summarize the six known natural carbon-fixation pathways and outline the newly proposed additions to this list. We also discuss the recent achievements in synthetic carbon fixation and the importance of the metabolism of thermophilic microorganisms in this field. Key Scientific Concepts of Review: Currently, at least six carbon-fixation routes have been confirmed in Bacteria and Archaea. Other possible candidate routes have also been suggested on the basis of emerging “omics” data analyses, expanding our knowledge and stimulating discussions on the importance of these pathways in the way organisms acquire carbon. Notably, the currently known natural fixation routes cannot balance the excessive anthropogenic carbon emissions in a highly unbalanced global carbon cycle. Therefore, significant efforts have also been made to improve the existing carbon-fixation pathways and/or design new efficient in vitro and in vivo synthetic pathways.
  • Peptide nanogels as a scaffold for fabricating dermal grafts and 3D vascularized skin models

    Arab, Wafaa; Susapto, Hepi Hari; Alhattab, Dana Majed; Hauser, Charlotte (Journal of Tissue Engineering, SAGE Publications, 2022-07-29) [Article]
    Millions of people worldwide suffer from skin injuries, which create significant problems in their lives and are costly to cure. Tissue engineering is a promising approach that aims to fabricate functional organs using biocompatible scaffolds. We designed ultrashort tetrameric peptides with promising properties required for skin tissue engineering. Our work aimed to test the efficacy of these scaffolds for the fabrication of dermal grafts and 3D vascularized skin tissue models. We found that the direct contact of keratinocytes and fibroblasts enhanced the proliferation of the keratinocytes. Moreover, the expression levels of TGF-β1, b-FGF, IL-6, and IL-1α is correlated with the growth of the fibroblasts and keratinocytes in the co-culture. Furthermore, we successfully produced a 3D vascularized skin co-culture model using these peptide scaffolds. We believe that the described results represent an advancement in the fabrication of skin tissue equivalent, thereby providing the opportunity to rebuild missing, failing, or damaged parts.
  • DES-Amyloidoses “Amyloidoses through the looking-glass”: A knowledgebase developed for exploring and linking information related to human amyloid-related diseases

    Bajic, Vladan P.; Salhi, Adil; Lakota, Katja; Radovanovic, Aleksandar; Razali, Rozaimi; Zivkovic, Lada; Spremo-Potparevic, Biljana; Uludag, Mahmut; Tifratene, Faroug; Motwalli, Olaa; Marchand, Benoit; Bajic, Vladimir B.; Gojobori, Takashi; Isenovic, Esma R.; Essack, Magbubah (PLOS ONE, Public Library of Science (PLoS), 2022-07-25) [Article]
    More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer’s disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities’ influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.
  • MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data

    Albaradei, Somayah; Albaradei, Abdurhman; Alsaedi, Asim; Uludag, Mahmut; Thafar, Maha A.; Gojobori, Takashi; Essack, Magbubah; Gao, Xin (Frontiers in Molecular Biosciences, Frontiers Media SA, 2022-07-22) [Article]
    Deep learning has massive potential in predicting phenotype from different omics profiles. However, deep neural networks are viewed as black boxes, providing predictions without explanation. Therefore, the requirements for these models to become interpretable are increasing, especially in the medical field. Here we propose a computational framework that takes the gene expression profile of any primary cancer sample and predicts whether patients’ samples are primary (localized) or metastasized to the brain, bone, lung, or liver based on deep learning architecture. Specifically, we first constructed an AutoEncoder framework to learn the non-linear relationship between genes, and then DeepLIFT was applied to calculate genes’ importance scores. Next, to mine the top essential genes that can distinguish the primary and metastasized tumors, we iteratively added ten top-ranked genes based upon their importance score to train a DNN model. Then we trained a final multi-class DNN that uses the output from the previous part as an input and predicts whether samples are primary or metastasized to the brain, bone, lung, or liver. The prediction performances ranged from AUC of 0.93–0.82. We further designed the model’s workflow to provide a second functionality beyond metastasis site prediction, i.e., to identify the biological functions that the DL model uses to perform the prediction. To our knowledge, this is the first multi-class DNN model developed for the generic prediction of metastasis to various sites.
  • Harnessing the microbiome to prevent global biodiversity loss

    Peixoto, Raquel S; Voolstra, Christian R.; Sweet, Michael; Duarte, Carlos M.; Carvalho, Susana; Villela, Helena D. M.; Lunshof, Jeantine E.; Gram, Lone; Woodhams, Douglas C.; Walter, Jens; Roik, Anna Krystyna; Hentschel, Ute; Thurber, Rebecca Vega; Daisley, Brendan; Ushijima, Blake; Daffonchio, Daniele; Costa, Rodrigo; Keller-Costa, Tina; Bowman, Jeff S.; Rosado, Alexandre S.; Reid, Gregor; Mason, Christopher E.; Walke, Jenifer B.; Thomas, Torsten; Berg, Gabriele (Nature Microbiology, Springer Science and Business Media LLC, 2022-07-21) [Article]
    Global biodiversity loss and mass extinction of species are two of the most critical environmental issues the world is currently facing, resulting in the disruption of various ecosystems central to environmental functions and human health. Microbiome-targeted interventions, such as probiotics and microbiome transplants, are emerging as potential options to reverse deterioration of biodiversity and increase the resilience of wildlife and ecosystems. However, the implementation of these interventions is urgently needed. We summarize the current concepts, bottlenecks and ethical aspects encompassing the careful and responsible management of ecosystem resources using the microbiome (termed microbiome stewardship) to rehabilitate organisms and ecosystem functions. We propose a real-world application framework to guide environmental and wildlife probiotic applications. This framework details steps that must be taken in the upscaling process while weighing risks against the high toll of inaction. In doing so, we draw parallels with other aspects of contemporary science moving swiftly in the face of urgent global challenges.
  • Sinking seaweed in the deep ocean for carbon neutrality is ahead of science and beyond the ethics

    Ricart, Aurora M; Krause-Jensen, Dorte; Hancke, Kasper; Price, Nichole N; Masque, Pere; Duarte, Carlos M. (Environmental Research Letters, IOP Publishing, 2022-07-21) [Article]
    Sinking vast amounts of seaweed in the deep ocean is currently being proposed as a promising ocean carbon dioxide removal strategy as well as a natural-based solution to mitigate climate change. Still, marketable carbon offsets through large-scale seaweed sinking in the deep ocean lack documentation and could involve unintended environmental and social consequences. Managing the risks requires a number of urgent actions.
  • Alternative role of motif B in template dependent polymerase inhibition

    Luo, Xueying; Xu, Tiantian; Gao, Xin; Zhang, Lu (Chinese Journal of Chemical Physics, AIP Publishing, 2022-07-19) [Article]
    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relies on the central molecular machine RNA-dependent RNA polymerase (RdRp) for the viral replication and transcription. Remdesivir at the template strand has been shown to effectively inhibit the RNA synthesis in SARS-CoV-2 RdRp by deactivating not only the complementary UTP incorporation but also the next nucleotide addition. How-ever, the underlying molecular mechanism of the second inhibitory point remains unclear. In this work, we have performed molecular dynamics simulations and demonstrated that such inhibition has not directly acted on the nucleotide addition at the active site. Instead, the translocation of Remdesivir from +1 to −1 site is hindered thermodynamically as the post-translocation state is less stable than the pre-translocation state due to the motif B residue G683. Moreover, another conserved residue S682 on motif B further hinders the dynamic translocation of Remdesivir due to the steric clash with the 1′-cyano substitution. Overall, our study has unveiled an alternative role of motif B in mediating the translocation when Remdesivir is present in the template strand and complemented our understanding about the inhibitory mechanisms exerted by Remdesivir on the RNA synthesis in SARS-CoV-2 RdRp.
  • eDNA Reveals the Associated Metazoan Diversity of Mediterranean Seagrass Sediments

    Wesselmann, Marlene; Geraldi, Nathan; Marbà, Núria; Hendriks, Iris E; Diaz Rua, Ruben; Duarte, Carlos M. (Diversity, MDPI AG, 2022-07-08) [Article]
    Anthropogenic impacts on marine ecosystems have led to a decline of biodiversity across the oceans, threatening invaluable ecosystem services on which we depend. Ecological temporal data to track changes in diversity are relatively rare, and the few long-term datasets that exist often only date back a few decades or less. Here, we use eDNA taken from dated sediment cores to investigate changes over approximately the last 100 years of metazoan communities in native (Cymodocea nodosa and Posidonia oceanica) and exotic (Halophila stipulacea) seagrass meadows within the eastern Mediterranean Sea, at two locations in Greece and two in Cyprus. Overall, metazoan communities showed a high turnover of taxa during the past century, where losses of individual taxa in a seagrass meadow were compensated by the arrival of new taxa, probably due to the arrival of exotic species introduced in the Mediterranean Sea from the Suez Canal or the Gibraltar Strait. Specifically, bony fishes (Class Actinopteri) and soft corals (Class Anthozoa) presented significantly higher richness in the past (before the 1980s) than in the most recent time periods (from 1980–2017) and some Cnidarian orders were solely found in the past, whereas sponges and Calanoids (Class Hexanauplia), an order of copepods, showed an increase in richness since the 1980s. Moreover, the Phyla Porifera, Nematoda and the Classes Staurozoa, Hydrozoa and Ophiuroidea were detected in P. oceanica meadows but not in C. nodosa and H. stipulacea, which led to P. oceanica meadows having twice the richness of other seagrasses. The greater richness resulted from the more complex habitat provided by P. oceanica. The combination of eDNA and sediment cores allowed us to reconstruct temporal patterns of metazoan community diversity and provides a novel approach to follow natural communities back in time in the absence of time series and baseline data. The ongoing loss of P. oceanica meadows, likely to be compounded with future warming, might lead to a major loss of biodiversity and the replacement by other seagrass species, whether native or exotic, does not compensate for the loss.
  • Target-aware Abstractive Related Work Generation with Contrastive Learning

    Chen, Xiuying; Alamro, Hind; Li, Mingzhe; Gao, Shen; Yan, Rui; Gao, Xin; Zhang, Xiangliang (ACM, 2022-07-07) [Conference Paper]
    The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers. Authors can save their time and effort by using the automatically generated related work section as a draft to complete the final related work. Most of the existing related work section generation methods rely on extracting off-the-shelf sentences to make a comparative discussion about the target work and the reference papers. However, such sentences need to be written in advance and are hard to obtain in practice. Hence, in this paper, we propose an abstractive target-aware related work generator (TAG), which can generate related work sections consisting of new sentences. Concretely, we first propose a target-aware graph encoder, which models the relationships between reference papers and the target paper with target-centered attention mechanisms. In the decoding process, we propose a hierarchical decoder that attends to the nodes of different levels in the graph with keyphrases as semantic indicators. Finally, to generate a more informative related work, we propose multi-level contrastive optimization objectives, which aim to maximize the mutual information between the generated related work with the references and minimize that with non-references. Extensive experiments on two public scholar datasets show that the proposed model brings substantial improvements over several strong baselines in terms of automatic and tailored human evaluations.
  • A Parkinson's disease model composed of 3D bioprinted dopaminergic neurons within a biomimetic peptide scaffold

    Abdelrahman, Sherin; Alsanie, Walaa F; Khan, Zainab N; Albalawi, Hamed I; Felimban, Raed I; Moretti, Manola; Steiner, Nadia; Chaudhary, Adeel G; Hauser, Charlotte (Biofabrication, 2022-07-06) [Article]
    Parkinson's disease (PD) is a progressive neurological disorder that affects movement. It is associated with lost dopaminergic neurons in the substantia nigra, a process that is not yet fully understood. To understand this deleterious disorder, there is an immense need to develop efficient in vitro three-dimensional (3D) models that can recapitulate complex organs such as the brain. However, due to the complexity of neurons, selecting suitable biomaterials to accommodate them is challenging. Here, we report on the fabrication of functional dopaminergic neuronal 3D models using ultrashort self-assembling tetrapeptide scaffolds. Our peptide-based models demonstrate biocompatibility both for primary mouse embryonic dopaminergic neurons and for human dopaminergic neurons derived from human embryonic stem cells. Dopaminergic neurons encapsulated in these scaffolds responded to 6-hydroxydopamine, a neurotoxin that selectively induces loss of dopaminergic neurons. Using multi-electrode arrays, we recorded spontaneous activity in dopaminergic neurons encapsulated within these 3D peptide scaffolds for more than one month without decrease of signal intensity. Additionally, vascularization of our 3D models in a co-culture with endothelial cells greatly promoted neurite outgrowth, leading to denser network formation. This increase of neuronal networks through vascularization was observed for both primary mouse dopaminergic and cortical neurons. Furthermore, we present a 3D bioprinted model of dopaminergic neurons inspired by the mouse brain and created with an extrusion-based 3D robotic bioprinting system that was developed during previous studies and is optimized with time-dependent pulsing by microfluidic pumps. We employed a hybrid fabrication strategy that relies on an external mold of the mouse brain construct that complements the shape and size of the desired bioprinted model to offer better support during printing. We hope that our 3D model provides a platform for studies of the pathogenesis of Parkinson's disease and other neurodegenerative disorders that may lead to better understanding and more efficient treatment strategies.
  • NAC transcription factors ATAF1 and ANAC055 affect the heat stress response in Arabidopsis

    Alshareef, Nouf Owdah Hameed; Otterbach, Sophie L.; Allu, Annapurna Devi; Woo, Yong; de Werk, Tobias; Kamranfar, Iman; Mueller-Roeber, Bernd; Tester, Mark A.; Balazadeh, Salma; Schmoeckel, Sandra Manuela (Scientific Reports, Springer Science and Business Media LLC, 2022-07-04) [Article]
    Pre-exposing (priming) plants to mild, non-lethal elevated temperature improves their tolerance to a later higher-temperature stress (triggering stimulus), which is of great ecological importance. ‘Thermomemory’ is maintaining this tolerance for an extended period of time. NAM/ATAF1/2/CUC2 (NAC) proteins are plant-specific transcription factors (TFs) that modulate responses to abiotic stresses, including heat stress (HS). Here, we investigated the potential role of NACs for thermomemory. We determined the expression of 104 Arabidopsis NAC genes after priming and triggering heat stimuli, and found ATAF1 expression is strongly induced right after priming and declines below control levels thereafter during thermorecovery. Knockout mutants of ATAF1 show better thermomemory than wild type, revealing a negative regulatory role. Differential expression analyses of RNA-seq data from ATAF1 overexpressor, ataf1 mutant and wild-type plants after heat priming revealed five genes that might be priming-associated direct targets of ATAF1: AT2G31260 (ATG9), AT2G41640 (GT61), AT3G44990 (XTH31), AT4G27720 and AT3G23540. Based on co-expression analyses applied to the aforementioned RNA-seq profiles, we identified ANAC055 to be transcriptionally co-regulated with ATAF1. Like ataf1, anac055 mutants show improved thermomemory, revealing a potential co-control of both NAC TFs over thermomemory. Our data reveals a core importance of two NAC transcription factors, ATAF1 and ANAC055, for thermomemory.
  • Rough analysis of computation trees

    Moshkov, Mikhail (Discrete Applied Mathematics, Elsevier BV, 2022-07-02) [Article]
    This paper deals with computation trees over an arbitrary structure consisting of a set along with collections of functions and predicates that are defined on it. It is devoted to the comparative analysis of three parameters of problems with n input variables over this structure: the complexity of a problem description, the minimum complexity of a computation tree solving this problem deterministically, and the minimum complexity of a computation tree solving this problem nondeterministically. Rough classification of relationships among these parameters is considered and all possible seven types of these relations are enumerated. The changes of relation types with the growth of the number n of input variables are studied.
  • Convection Driven Ultra-Rapid Protein Detection Via Nanobody-Functionalized Organic Electrochemical Transistors

    Koklu, Anil; Wustoni, Shofarul; Guo, Keying; Silva, Raphaela; Salvigni, Luca; Hama, Adel; Diaz-Galicia, Escarlet; Moser, Maximilian; Marks, Adam; McCulloch, Iain; Grunberg, Raik; Arold, Stefan T.; Inal, Sahika (Advanced Materials, Wiley, 2022-06-30) [Article]
    Conventional biosensors rely on the diffusion-dominated transport of the target analyte to the sensor surface. Consequently, they require an incubation step that may take several hours to allow for the capture of analyte molecules by sensor biorecognition sites. This incubation step is a primary cause of long sample-to-result times. Here, we integrate alternating current electrothermal flow (ACET) in an organic electrochemical transistor (OECT)-based sensor to accelerate the device operation. The ACET is applied to the gate electrode functionalized with nanobody-SpyCatcher fusion proteins. Using the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein in human saliva as an example target, we show that ACET enables protein recognition within only 2 minutes of sample exposure, supporting its use in clinical practice. The ACET integrated sensor exhibits better selectivity, higher sensitivity, and lower limit of detection than the equivalent sensor with diffusion dominated operation. We compare the performance of ACET integrated sensors with two types of organic semiconductors in the channel and investigate grounds for device-to-device variations. Our results provide guidelines for the channel material choice in OECT based biochemical sensors, and demonstrate that ACET integration substantially decreases the detection speed while increasing the sensitivity and selectivity of transistor-based sensors.
  • Mangrove distribution and afforestation potential in the Red Sea

    Blanco Sacristan, Javier; Johansen, Kasper; Duarte, Carlos M.; Daffonchio, Daniele; Hoteit, Ibrahim; McCabe, Matthew (Science of The Total Environment, Elsevier BV, 2022-06-30) [Article]
    Mangrove ecosystems represent one of the most effective natural environments for fixing and storing carbon (C). Mangroves also offer significant co-benefits, serving as nurseries for marine species, providing nutrients and food to support marine ecosystems, and stabilizing coastlines from erosion and extreme events. Given these considerations, mangrove afforestation and associated C sequestration has gained considerable attention as a nature-based solution to climate adaptation (e.g., protect against more frequent storm surges) and mitigation (e.g. offsetting other C-producing activities). To advance our understanding and description of these important ecosystems, we leverage Landsat-8 and Sentinel-2 satellite data to provide a current assessment of mangrove extent within the Red Sea region and also explore the effect of spatial resolution on mapping accuracy. We establish that Sentinel-2 provides a more precise spatial record of extent and subsequently use these data together with a maximum entropy (MaxEnt) modeling approach to: i) map the distribution of Red Sea mangrove systems, and ii) identify potential areas for future afforestation. From these current and potential mangrove distribution maps, we then estimate the carbon sequestration rate for the Red Sea (as well as for each bordering country) using a meta-analysis of sequestration values surveyed from the available literature. For the mangrove classification, we obtained mapping accuracies of 98 %, with a total Red Sea mangrove extent estimated at approximately 175 km2. Based on the MaxEnt approach, which used soil physical and environmental variables to identify the key factors limiting mangrove growth and distribution, an area of nearly 410 km2 was identified for potential mangrove afforestation expansion. The factors constraining the potential distribution of mangroves were related to soil physical properties, likely reflecting the low sediment load and limited nutrient input of the Red Sea. The current rate of carbon sequestration was calculated as 1034.09 ± 180.53 Mg C yr-1, and the potential sequestration rate as 2424.49 ± 423.26 Mg C yr-1. While our results confirm the maintenance of a positive trend in mangrove growth over the last few decades, they also provide the upper bounds on above ground carbon sequestration potential for the Red Sea mangroves.
  • State of Play in Marine Soundscape Assessments

    Havlik, Michelle Nicole; Predragovic, Milica; Duarte, Carlos M. (Frontiers in Marine Science, Frontiers Media SA, 2022-06-29) [Article]
    A soundscape is the recording of all sounds present in an area, creating a holistic view of the acoustic profile in an ecosystem. Studying acoustic parameters of marine soundscapes as a whole has been shown to give an indication of the health status of the location, as well as correlate to which species may be present and using the area. With the rapid innovation of technology, especially data storage and declining cost of equipment, marine soundscape research is fast increasing, and these previous limitations have been switched for computing capacity for data analysis. Here, we perform a systematic assessment of literature of marine soundscape studies, from 1978, when the first soundscape study was reported, until 2021. We identified 200 primary research studies that recorded soundscapes and captured their geographical location, depth, habitat, duration of the study, and number of sites in each study. Using this data, we summarize the state of play in marine soundscapes studies, and identify knowledge gaps in the spatial coverage, depth profiles, habitat representation and study duration. Spatially, studies are biased towards the northern hemisphere. They are also more prevalent in more easily accessible ecosystems, in order from most to least studied, in coastal (38%), pelagic (20%), tropical coral reef (17%), rocky reef (7%), polar (5.5%), seagrass meadows, oyster reef and kelp/algal forest (4000 m. With anthropogenic noise and other pollution sources increasing globally, these gaps in research should be further addressed, especially as they pertain to vulnerable ecosystems, many of which are affected by global climate change and anthropogenic influences. It is crucial that marine soundscape studies continue to be developed and pursued, to establish baselines for healthy ecosystems and/or document recovery following management actions.
  • Predicting the antigenic evolution of SARS-COV-2 with deep learning

    Han, Wenkai; Chen, NingNing; Sun, Shiwei; Gao, Xin (Cold Spring Harbor Laboratory, 2022-06-29) [Preprint]
    The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) antigenic profile evolves in response to the vaccine and natural infection-derived immune pressure, resulting in immune escape and threatening public health. Exploring the possible antigenic evolutionary potentials improves public health preparedness, but it is limited by the lack of experimental assays as the sequence space is exponentially large. Here we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithm to model the viral fitness landscape and explore the antigenic evolution via in silico directed evolution. As demonstrated by existing SARS-COV-2 variants, MLEAP can infer the order of variants along antigenic evolutionary trajectories, which is also strongly correlated with their sampling time. The novel mutations predicted by MLEAP are also found in immunocompromised covid patients and newly emerging variants, like BA. 4/5. In sum, our approach enables profiling existing variants and forecasting prospective antigenic variants, thus may help guide the development of vaccines and increase preparedness against future variants.
  • Variants in Bedaquiline-Candidate-Resistance Genes: Prevalence in Bedaquiline-Naive Patients, Effect on MIC, and Association with Mycobacterium tuberculosis Lineage

    Rivière, Emmanuel; Verboven, Lennert; Dippenaar, Anzaan; Goossens, Sander; De Vos, Elise; Streicher, Elizabeth; Cuypers, Bart; Laukens, Kris; Ben Rached, Fathia; Rodwell, Timothy C.; Pain, Arnab; Warren, Robin M.; Heupink, Tim H.; Van Rie, Annelies (Antimicrobial Agents and Chemotherapy, American Society for Microbiology, 2022-06-27) [Article]
    Studies have shown that variants in bedaquiline-resistance genes can occur in isolates from bedaquiline-naive patients. We assessed the prevalence of variants in all bedaquiline-candidate-resistance genes in bedaquiline-naive patients, investigated the association between these variants and lineage, and the effect on phenotype. We used whole-genome sequencing to identify variants in bedaquiline-resistance genes in isolates from 509 bedaquiline treatment naive South African tuberculosis patients. A phylogenetic tree was constructed to investigate the association with the isolate lineage background. Bedaquiline MIC was determined using the UKMYC6 microtiter assay. Variants were identified in 502 of 509 isolates (98.6%), with the highest (85%) prevalence of variants in the Rv0676c (mmpL5) gene. We identified 36 unique variants, including 19 variants not reported previously. Only four isolates had a bedaquiline MIC equal to or above the epidemiological cut-off value of 0.25 μg/mL. Phylogenetic analysis showed that 14 of the 15 variants observed more than once occurred monophyletically in one Mycobacterium tuberculosis (sub)lineage. The bedaquiline MIC differed between isolates belonging to lineage 2 and 4 (Fisher’s exact test, P = 0.0004). The prevalence of variants in bedaquiline-resistance genes in isolates from bedaquiline-naive patients is high, but very few (<2%) isolates were phenotypically resistant. We found an association between variants in bedaquiline resistance genes and Mycobacterium tuberculosis (sub)lineage, resulting in a lineage-dependent difference in bedaquiline phenotype. Future studies should investigate the impact of the presence of variants on bedaquiline-resistance acquisition and treatment outcome.
  • How much do model organism phenotypes contribute to the computational identification of human disease genes?

    Alghamdi, Sarah M.; Schofield, Paul N.; Hoehndorf, Robert (Disease models & mechanisms, Cold Spring Harbor Laboratory, 2022-06-27) [Article]
    Computing phenotypic similarity has been shown to be useful in identification of new disease genes and for rare disease diagnostic support. Genotype-phenotype data from orthologous genes in model organisms can compensate for lack of human data to greatly increase genome coverage. Work over the past decade has demonstrated the power of cross-species phenotype comparisons, and several cross-species phenotype ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not yet fully explored. We use methods based on phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in different model organisms to disease-associated phenotypes in humans. Semantic machine learning methods are used to measure how much different model organisms contribute to the identification of known human gene-disease associations. We find that mouse genotype-phenotype data is the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Data from other model organisms does not improve identification over that obtained by using the mouse alone, and therefore does not contribute significantly to this task. Our work has implications for the future development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

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