Biology subjects

Martin, H.

Publications and source records attributed to Martin, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Maps of historical forests in France and their temporal continuity since the first half of the 19th century

AbstractIntegrating environmental history is essential for understanding present-day ecosystem dynamics and guiding modern conservation strategies, particularly under the EU Biodiversity Strategy for 2030. This data paper presents the nationwide digitisation and vectorisation of the French General Ordnance survey map (1818-1866), capturing the countrys forest cover at its historical minimum (a pivotal moment known as the "forest transition"). The original manuscript sheets surveyed at a 1:40,000 scale (976 sheets) were scanned, georeferenced and vectorised. They offer higher thematic accuracy than the older Cassini map and are far more feasible for nationwide vectorisation than the highly detailed Napoleonic cadastre. The area-weighted mean survey date is 1843, and the dataset covers 99.6% of modern mainland France. To correct for paper deformation, historical surveying errors and coordinate system transformations, a rigorous workflow was established. This shifted from a global 6-parameter affine transformation (root mean square error of 60 m) to a local elastic transformation based on thousands of control points, for 21% of the territory, which reduced the positioning error to 34 m. We assessed data quality by comparing the General Ordnance Survey maps with the Napoleonic cadastre--the standard reference for 19th-century land-use data--across more than 600 municipalities. The correlation between the two sources regarding forest cover percentages was exceptionally high (r>0.9). Because forests formed large, compact blocks of significant strategic interest to military engineers, they were mapped with high precision. Localised inaccuracies were primarily found in remote areas, most notably in the mountains. The resulting historical vector layer was intersected with contemporary forest data (BD Foret(R) v2, 2005-2019). Historical polygons smaller than 0.5 ha were filtered out to comply with modern FAO forest definitions. This spatial overlay generated a new dataset detailing four distinct land-use trajectories: . ancient forests (44.8% of present day forest): land classified as forest in both the 19th century and the present day (indicating maximum temporal continuity). . recent forests (55.2% of present day forest): land that was non-forested in the 19th century but has since undergone reforestation. . deforested areas (18.9% of 19th-century forest): land recorded as forest in the 19th century but subsequently converted to other land uses. . stable non-forest areas: land that has remained unforested across both periods. Our results suggest that the forest area of mainland France at its historical minimum should be revised upward to 9.9 million ha. A preliminary analysis further indicates that current spatial variation in forest cover is explained more by land-use dynamics occurring since the forest transition than by the initial extent of forest cover. These two open-access national datasets (the 19th-century forest layer and the land-use transition map) open the door to a better understanding of present-day forests : e.g. their biodiversity, soil quality, tree growth and belowground water quality. Furthermore, they provide decision-support tools for conservation planning. Key messageIn this data paper, we provide a map of 19th-century forests in France based on the digitisation of the military topographic map (1818-1866). By overlaying this historical source with the present-day forest map, we built a second map which allows the identification of ancient forests, recent forests, and deforestation. These maps offer avenues for historical ecology and the design of conservation strategies.

ecology↗

bioRxiv2026

Using iPALM to determine protein organisation in cardiac muscle Z-discs

Sarcomeres, the basic repeating unit of striated muscle, are joined together by crosslinked actin filaments found at the boundaries of muscle sarcomeres, termed Z-discs. Z-discs play a key role in cardiac signalling and disease, however, the arrangement and function of many of the proteins present in the Z-disc remain to be understood. Here, we determined the organisation of 3 key proteins, ZASP, [a]-Actinin-2 and the Z1Z2 epitope of titin, located within the Z-disc. We fluorescently labelled these proteins in cardiac myofibrils using Adhirons specific to each protein and used interferometric photoactivated localization microscopy (iPALM) to obtain the 3D position of these proteins to a high precision (<10nm in x,y,z). We then used PERPL (Pattern Extraction from Relative Positions of Localisations) to analyse patterns in the relative positions of the proteins and reveal their underlying organisation. This analysis revealed that ZASP and [a]-Actinin-2 have a similar repeating organisation, but that the organisation of Z1Z2 is different.

biophysics↗

bioRxiv2025

A Novel Glia-immune humanized mouse model for investigation of HIV CNS infection and neuroinflammation.

Dysregulation in neuroimmune interactions drives pathology in diverse neurological disorders. However, human cell-based in vivo models are limited for clinically relevant mechanistic studies and drug testing. To address this, we developed a novel humanized mouse model integrating all four major types of human glia - astrocytes, oligodendrocyte precursor cells (OPCs), oligodendrocytes, and microglia - in the brain, alongside donor-matched human immune system in peripheral blood and lymphoid tissues. We applied this model to study HIV-associated neurocognitive disorders, which can persist in tissue reservoirs including in the brain despite viral suppression by antiretroviral therapy. These glia-immune humanized mice supported robust HIV-1 replication in the peripheral blood, lymphoid tissues, and the brain, and recapitulated the synapse loss observed in patients. Furthermore, HIV-infected mice exhibited heightened inflammation in both peripheral and brain tissues. Notably, OPCs and oligodendrocytes in HIV-infected brains adopted an immune activated phenotype, upregulating interferon-stimulated genes, highlighting understudied roles of oligodendroglia in HIV infection. RNA-sequencing of human glia identified the upregulation of inflammasome-associated genes and the downregulation of genes involved in transcriptional, epigenetic, and metabolic regulation. Overall, this model provides a powerful in vivo platform to investigate human neuroimmune interactions relevant to diverse neurological disorders, offering critical insights into HIV CNS infection, pathology, and potential therapeutic strategies targeting CNS reservoirs and neuroinflammatory pathways.

immunology↗

bioRxiv2025

Genome-scale spatial mapping of the Hodgkin lymphoma microenvironment identifies tumor cell survival factors

A key challenge in cancer research is to identify the secreted factors that contribute to tumor cell survival. Nowhere is this more evident than in Hodgkin lymphoma, where malignant Hodgkin Reed Sternberg (HRS) cells comprise only 1-5% of the tumor mass, the remainder being infiltrating immune cells that presumably are required for the survival of the HRS cells. Until now, there has been no way to characterize the complex Hodgkin lymphoma tumor microenvironment at genome scale. Here, we performed genome-wide transcriptional profiling with spatial and single-cell resolution. We show that the neighborhood surrounding HRS cells forms a distinct niche involving 31 immune and stromal cell types and is enriched in CD4+ T cells, myeloid and follicular dendritic cells, while being depleted of plasma cells. Moreover, we used machine learning to nominate ligand-receptor pairs enriched in the HRS cell niche. Specifically, we identified IL13 as a candidate survival factor. In support of this hypothesis, recombinant IL13 augmented the proliferation of HRS cells in vitro. In addition, genome-wide CRISPR/Cas9 loss-of-function studies across more than 1,000 human cancer cell lines showed that IL4R and IL13RA1, the heterodimeric partners that constitute the IL13 receptor, were uniquely required for the survival of HRS cells. Moreover, monoclonal antibodies targeting either IL4R or IL13R phenocopied the genetic loss of function studies. IL13-targeting antibodies are already FDA-approved for atopic dermatitis, suggesting that clinical trials testing such agents should be explored in patients with Hodgkin lymphoma.

cancer biology↗

bioRxiv2024

Rapamycin Enhances CAR-T Control of HIV Replication and Reservoir Elimination in vivo.

Chimeric Antigen Receptor (CAR) T cell therapy has emerged as a powerful immune therapy for various diseases. Our studies in humanized mice and non-human primates (NHPs) demonstrate that hematopoietic stem cell (HSCs) modified with anti-HIV CAR leads to lifelong engraftment and supply of functional anti-viral CAR-T cells, leading to significantly reduced viral rebound after ART withdrawal. However, T cell exhaustion, driven by chronic immune activation, remains a major challenge for the continuous efficacy of CAR-T therapy, necessitating additional measures to achieve functional cure. We recently showed that in vivo treatment with low dose rapamycin reduced chronic inflammation and improved anti-HIV T cell function in HIV-infected humanized mice. Here, we report that rapamycin significantly improved CAR-T cell function both in vitro and in vivo. In vitro treatment with rapamycin improved CAR-T cell mitochondria respiration and cytotoxicity. In vivo treatment with low-dose rapamycin in HIV-infected, CAR-HSC treated mice reduced chronic inflammation, prevented exhaustion of CAR-T cells and improved CAR-T control of viral replication compared to CAR-HSCs treatment alone. RNAseq analysis of sorted CAR-T cells from humanized mice showed that rapamycin significantly modified the CAR-T cell transcriptome, including the downregulation of multiple check point inhibitors and the upregulation of key genes related to cell survival. We also observed significantly delayed viral rebound after ART withdrawal and diminished HIV reservoir in mice that were treated with rapamycin and CAR-HSCs as compared to CAR-HSCs treatment alone. Taken together, our data indicate that HSCs-based anti-HIV CAR-T combined with rapamycin treatment is a promising approach for treating persistent inflammation and improving immune control of HIV replication.

immunology↗

bioRxiv2024

Circumventing glioblastoma resistance to temozolomide through optimal drug combinations designed by quantitative systems pharmacology and machine learning

Glioblastoma is currently associated to a dismal prognosis despite intensive treatment involving maximal-safe surgery, radiotherapy and temozolomide (TMZ)-based chemotherapy. Disease progression or relapse is often due to initial or acquired resistance to temozolomide, which may be mediated by the over-expression of the repair enzyme MGMT. To design TMZ-based drug combinations circumventing the initial resistance of MGMT-overexpressing cells, a quantitative systems pharmacology (QSP) model representing TMZ cellular pharmacokinetics-pharmacodynamics and their connection to the most altered pathways in GBM was developed. This digital network representation of TMZ cellular pharmacology successfully integrates, in a mechanistic fashion, multi-type time- and dose-resolved datasets, available in control or MGMT-overexpressing cells. In silico target inhibition screening identified an optimal antitumor strategy consisting in priming cancer cells with inhibitors of the base excision repair and of the homologous recombination pathway prior to TMZ exposure. This drug combination was validated in dedicated experiments, thus allowing to re-sensitize cells which were initially resistant to TMZ. Using machine learning, functional signatures of response to such optimal multiagent therapy were derived to assist decision making about administering it to other cancer cell lines or patients. The developed framework can be extended to account for additional patientspecific altered pathways and may be translated towards the clinics by representing the tumor micro-environment and drug whole-body pharmacokinetics. Overall, we successfully demonstrated the relevance of combined QSP and machine learning to design multi-agent pharmacotherapies circumventing initial tumor resistance. One Sentence SummaryAn integrated in vitro-in silico approach allowed to design optimal drug combinations re-sensitizing temozolomide-resistant glioblastoma cells.

cancer biology↗

bioRxiv2024

Hybrid xyloglucan utilisation loci are prevalent among plant-associated Bacteroidota

The plant hemicellulose xyloglucan (XyG) is secreted from the roots of numerous plant species, including cereals, and contributes towards soil aggregate formation in terrestrial systems. Whether XyG represents a key nutrient for plant-associated bacteria is unclear. The phylum Bacteroidota are abundant in the plant microbiome and provide several beneficial functions for their host. However, the metabolic and genomic traits underpinning their success remain poorly understood. Here, using proteomics, bacterial genetics, and genomics, we revealed that plant-associated Flavobacterium, a genus within the Bacteroidota, can efficiently utilise XyG through the occurrence of a distinct and conserved gene cluster, referred to as the Xyloglucan Utilisation Loci (XyGUL). Flavobacterium XyGUL is a hybrid of the molecular machinery found in gut Bacteroides spp., Cellvibrio japonicus, and the plant pathogen Xanthomonas. Combining protein biochemistry, computational modelling and phylogenetics, we identified a mutation in the enzyme required for initiating hydrolysis of the XyG polysaccharide, an outer membrane endoxyloglucanase glycoside hydrolase family 5 subfamily 4 (GH5_4), which enhances activity towards XyG. A subclade of GH5_4 homologs carrying this mutation were the dominant form found in soil and plant metagenomes due to their occurrence in Bacteroidota and Proteobacteria. However, only in members of the Bacteroidota spp., particularly Flavobacterium spp. was such a remarkable degree of XyGUL conservation detected. We propose this mechanism enables plant-associated Flavobacterium to specialise in competitive acquisition of XyG exudates and that this hemicellulose may represent an important nutrient source, enabling them to thrive in the plant microbiome, which is typified by intense competition for low molecular weight carbon exudates.

microbiology↗

bioRxiv2022

Cytosolic pH controls fungal MAPK signaling and pathogenicity

In fungi, ambient pH acts as a key regulator of development and virulence. The vascular wilt pathogen Fusarium oxysporum uses host alkalinization to promote infection of plant hosts through activation of the invasive growth mitogen-activated protein kinase (MAPK) Fmk1. The molecular events underlying pH-driven MAPK regulation are unknown. Using the ratiometric GFP-based pH sensor pHluorin, we find that both F. oxysporum and Saccharomyces cerevisiae respond to extracellular alkalinization or acidification with a transitory shift in cytosolic pH (pHc) and rapid changes in phosphorylation levels of the three fungal MAPKs Fmk1, Mpk1/Slt2 (cell wall integrity) and Hog1 (hyperosmotic stress). Pharmacological inhibition of the essential plasma membrane H+-ATPase Pma1, which leads to pHc acidification, is sufficient to trigger reprogramming of MAPK phosphorylation even in the absence of an extracellular pH shift. Screening of a subset of S. cerevisiae mutants identified the sphingolipid-regulated AGC kinase Ypk1/2 as a key upstream component of pHc-modulated MAPK responses. We further show that acidification of pHc in F. oxysporum leads to an increase of the long chain base (LCB) sphingolipid dihydrosphingosine (dhSph) and that exogenous addition of dhSph activates Mpk1 phosphorylation. Our results reveal a pivotal role of pHc in the regulation of MAPK signaling and suggest new ways to control fungal growth and pathogenicity.

microbiology↗

bioRxiv2022

Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures

Machine learning protein structure prediction, such as RosettaFold and AlphaFold2, have impacted the structural biology field, raising a fair amount of discussion around its potential role in drug discovery. While we find some preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a target with low sequence identity. In order to address this, we have developed an AlphaFiold2 version where we exclude all structural templates with more than 30% sequence identity. In a previous study, we used those models in conjunction with state of the art free energy perturbation methods. In this work we focus on using them in rigid receptor ligand docking. Our results indicate that using out-of-the-box Alphafold2 models is not an ideal scenario; one might think in including some post processing modeling to drive the binding site into a more realistic holo target model.

biochemistry↗

bioRxiv2022

Deep Learning Structural Models Sufficiently Accurate for Free Energy Calculations? Application of FEP+ to AlphaFold2 Predicted Structures

The availability of AlphaFold2 has led to great excitement in the scientific community - particularly among drug hunters - due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with >30% identity to the target protein from the training set. We observed that in most cases, the {Delta}{Delta}G values for ligand transformations calculated with FEP, using these prospective AlphaFold2 structures, were comparable in accuracy to the corresponding calculations previously carried out using X-ray structures. We conclude that under the right circumstances, AlphaFold2 modeled structures are accurate enough to be used by physics-based methods such as FEP, in typical lead optimization stages of a drug discovery program.

biochemistry↗

bioRxiv2022

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latinx participants (32%). The multi-ancestry GWAS identified 190 significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic/Latinx group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with [≤]5 variants increased from 3 to 12. A transcriptome-wide association study identified 354 significantly associated genes, 205 of them novel. Mendelian Randomisation showed a bidirectional relationship with BMI exclusively in samples of European ancestry. This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

genetics↗

bioRxiv2022

Small Molecule Antiviral Compound Collection (SMACC): a database to support the discovery of broad-spectrum antiviral drug molecules.

Diseases caused by new viruses costs thousands if not millions of human lives and trillions of dollars in damage to the global economy. Despite the rapid development of vaccines for SARS-CoV-2, the lack of small molecule antiviral drugs that work against multiple viral families (broad-spectrum antivirals; BSAs) has left the entire worlds human population vulnerable to the infection between the beginning of the outbreak and the widespread availability of vaccines. Developing BSAs is an attractive, yet challenging, approach that could prevent the next, inevitable, viral outbreak from becoming a global catastrophe. To explore whether historical medicinal chemistry efforts suggest the possibility of discovering novel BSAs, we (i) identified, collected, curated, and integrated all chemical bioactivity data available in ChEMBL for molecules tested in respective assays for 13 emerging viruses that, based on published literature, hold the greatest potential threat to global human health; (ii) identified and solved the challenges related to data annotation accuracy including assay description ambiguity, missing cell or target information, and incorrect BioAssay Ontology (BAO) annotations; (iii) developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic (21,392 entries) and target-based (11,123 entries) assays for these viruses; and (iv) identified a subset of compounds showing BSA activity. For the latter task, we eliminated inconclusive and annotated duplicative entries by checking the concordance between multiple assay results and identified eight compounds active against 3-4 viruses from the phenotypic data, 16 compounds active against two viruses from the target-based data, and 35 compounds active in at least one phenotypic and one target-based assay. The pilot version of our SMACC (Small Molecule Antiviral Compound Collection) database contains over 32,500 entries for 13 viruses. Our analysis indicates that previous research yielded very small number of BSA compounds. We posit that focused and coordinated efforts strategically targeting the discovery of such agents must be established and maintained going forward. The SMACC database publicly available at https://smacc.mml.unc.edu may serve as a reference for virologists and medicinal chemists working on the development of novel BSA agents in preparation for future viral outbreaks.

bioinformatics↗

bioRxiv2021

Human Cytomegalovirus modifies placental small extracellular vesicle secretion and composition towards a proviral phenotype to enhance infection of fetal recipient cells

Although placental small extracellular vesicles (sEVs) are extensively studied in the context of pregnancy, little is known about their role during human cytomegalovirus (hCMV) congenital infection, especially at the beginning of pregnancy. In this study, we examined the consequences of hCMV infection on sEVs production, composition and function using an immortalized human cytotrophoblast cell line derived from first trimester placenta. By combining complementary approaches of biochemistry, electron microscopy and quantitative proteomic analysis, we showed that hCMV infection increases the yield of sEVs produced by cytotrophoblasts and modifies their protein content towards a proviral phenotype. We further demonstrate that sEVs secreted by hCMV-infected cytotrophoblasts potentiate infection in naive recipient cells of fetal origin, including human neural stem cells. Importantly, these functional consequences are also observed with sEVs prepared from either an ex vivo model of infected histocultures from early placenta or from the amniotic fluid of patients naturally infected by hCMV at the beginning of pregnancy. Based on these findings, we propose that placental sEVs could be key actors favoring viral dissemination to the fetal brain during hCMV congenital infection. Significance StatementHuman cytomegalovirus (hCMV) infection is a major issue during pregnancy, affecting 1% of births in western countries. Despite extensive research, the pathophysiology of this congenital infection remains unclear. Recently, increasing evidence point to the key role of placental small extracellular vesicles (sEVs) in materno-fetal communication during pregnancy. Here, we examined the impact of hCMV infection on the protein composition and function of placental sEVs. We observe that hCMV infection leads to major changes in placental sEV protein content. Functional studies show the ability of sEVs produced by placental infected cells to facilitate further infection of naive recipient fetal cells, notably human neural stem cells. Our study demonstrates that placental sEVs are key players of hCMV pathophysiology during congenital infection.

cell biology↗

bioRxiv2021

Telomere shortening causes distinct cell division regimes during replicative senescence in Saccharomyces cerevisiae

BackgroundTelomerase-negative cells have limited proliferation potential. In these cells, telomeres shorten until they reach a critical length and induce a permanently arrested state. This process called replicative senescence is associated with genomic instability and participates in tissue and organismal ageing. Experimental data using single-cell approaches in the budding yeast model organism show that telomerase-negative cells often experience abnormally long cell cycles, which can be followed by cell cycles of normal duration, before reaching the terminal senescent state. These series of non-terminal cell cycle arrests contribute to the heterogeneity of senescence and likely magnify its genomic instability. Due to their apparent stochastic nature, investigating the dynamics and the molecular origins of these arrests has been difficult. In particular, whether the non-terminal arrests series stem from a mechanism similar to the one that triggers terminal senescence is not known. ResultsHere, we provide a mathematical description of sequences of non-terminal arrests to understand how they appear. We take advantage of an experimental data set of cell cycle duration measurements performed in individual telomerase-negative yeast cells that keep track of the number of generations since telomerase inactivation. Using numerical simulations, we show that the occurrence of non-terminal arrests is a generation-dependent process that can be explained by the shortest telomere reaching a probabilistic threshold length. While the onset of senescence is also triggered by telomere shortening, we highlight differences in the laws that describe the number of consecutive arrests in non-terminal arrests compared to senescence arrests, suggesting distinct underlying mechanisms and cellular states. ConclusionsReplicative senescence is a complex process that affects cell divisions earlier than anticipated, as exemplified by the frequent occurrence of non-terminal arrests early after telomerase inactivation. The present work unravels two kinetically and mechanistically distinct generation-dependent processes underlying non-terminal and terminal senescence arrests. We suggest that these two processes are responsible for two consequences of senescence at the population level, the increase of genome instability on the one hand, and the limitation of proliferation capacity on the other hand.

bioinformatics↗

bioRxiv2020

Consequences of human cytomegalovirus infection on small extracellular vesicles isolated from first trimester placental histocultures

Currently, research on the use of non-invasive biomarkers as diagnosis and prognosis tools during pathological pregnancies is in full development. Among these, placenta-derived small extracellular vesicles (sEVs) are considered as serious candidates, since their composition is modified during many pregnancy pathologies. Moreover, sEVs are found in maternal serum and can thus be easily purified from a simple blood sample. In this study, we describe the isolation of sEVs from a histoculture model of first trimester placental explants. Using bead-based multiplex cytometry and electron microscopy combined with biochemical approaches, we characterized these sEVs and defined their associated markers and ultrastructure. We next examined the consequences of infection by human cytomegalovirus on sEVs secretion and characteristics. We observed that infection led to increased levels of expression of several surface markers, without any impact on the secretion and integrity of sEVs. Our findings open the prospect for the identification of new predictive biomarkers for the severity and outcome of this congenital infection early during pregnancy, which are still sorely lacking.

cell biology↗

bioRxiv2020

Olfactory transmucosal SARS-CoV-2 invasion as port of Central Nervous System entry in COVID-19 patients

The newly identified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a pandemic respiratory disease presenting with fever, cough, and often pneumonia. Moreover, thromboembolic events throughout the body including the central nervous system (CNS) have been described. Given first indication for viral RNA presence in the brain and cerebrospinal fluid and in light of neurological symptoms in a large majority of COVID-19 patients, SARS-CoV-2-penetrance of the CNS is likely. By precisely investigating and anatomically mapping oro- and pharyngeal regions and brains of 32 patients dying from COVID-19, we not only describe CNS infarction due to cerebral thromboembolism, but also demonstrate SARS-CoV-2 neurotropism. SARS-CoV-2 enters the nervous system via trespassing the neuro-mucosal interface in the olfactory mucosa by exploiting the close vicinity of olfactory mucosal and nervous tissue including delicate olfactory and sensitive nerve endings. Subsequently, SARS-CoV-2 follows defined neuroanatomical structures, penetrating defined neuroanatomical areas, including the primary respiratory and cardiovascular control center in the medulla oblongata.

neuroscience↗

bioRxiv2020

The parasite Schistocephalus solidus secretes proteins with putative host manipulation functions

BackgroundManipulative parasites are thought to liberate molecules in their external environment acting as manipulation factors with biological functions implicated in their hosts physiological and behavioural alterations. These manipulation factors are part of a complex mixture called the secretome. While the secretomes of various parasites have been described, there is very little data for a putative manipulative parasite. It is necessary to study the molecular interaction between a manipulative parasite and its host to better understand how such alterations evolve. MethodsHere, we used proteomics to characterize the secretome of a model cestode with a complex life cycle based on trophic transmission. We studied Schistocephalus solidus during the life stage in which behavioural changes take place in its obligatory intermediate fish host, the threespine stickleback (Gasterosteus aculeatus). We produced a novel genome sequence and assembly of S. solidus to improve protein coding gene prediction and annotation for this parasite. We then described the whole worms proteome and its secretome during fish host infection using LC-MS/MS. ResultsA total of 2 290 proteins were detected in the proteome of S. solidus, and 30 additional proteins were detected specifically in the secretome. We found that the secretome contains proteases, proteins with neural and immune functions, as well as proteins involved in cell communication. We detected Receptor-type tyrosine-protein phosphatases, which were reported in other parasitic systems to be manipulation factors. We also detected 12 S. solidus-specific proteins in the secretome that may play important roles in host-parasite interactions. ConclusionsOur results suggest that S. solidus liberates molecules with putative host manipulation functions in the host and that many of them are species specific.

genomics↗

bioRxiv2019

Integrating healthcare and research genetic data empowers the discovery of 49 novel developmental disorders

De novo mutations (DNMs) in protein-coding genes are a well-established cause of developmental disorders (DD). However, known DD-associated genes only account for a minority of the observed excess of such DNMs. To identify novel DD-associated genes, we integrated healthcare and research exome sequences on 31,058 DD parent-offspring trios, and developed a simulation-based statistical test to identify gene-specific enrichments of DNMs. We identified 285 significantly DD-associated genes, including 28 not previously robustly associated with DDs. Despite detecting more DD-associated genes than in any previous study, much of the excess of DNMs of protein-coding genes remains unaccounted for. Modelling suggests that over 1,000 novel DD-associated genes await discovery, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of dominant DDs.

genomics↗