Biology subjects

Clark, T.

Publications and source records attributed to Clark, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Single-Cell Study Designs Are Systematically Underpowered for Small-Effect Genes

Single cell differential expression analysis enables biologists to make statistical conclusions about which genes are up or downregulated in a particular cell type, between two conditions, such as those with or without a disease. However, due to biological and technical noise, these differences are hard to detect reliably. Determining if an experiment has sufficient power is often derived from simulations or small pilot samples, if done at all. Here, we use sex-biased differential expression on 1,494 donors in three brain cell types to derive empirically grounded power estimates for a variety of experimental setups. Our work reveals a substantial lack of power for reliably detecting the small effects in the range that many studies report, even in experiments containing 600 donors. When reducing the astrocyte data to the poor sequencing characteristics of microglia, over half the differentially expressed genes (DEGs) detected in the full set were lost, highlighting the damage caused by insufficient sequencing depth and cell counts. Despite standard thresholds of adjusted p-value with multiple testing correction, only the top quartile of significant genes were reproducible. Furthermore, from fitting a predictive model to the empirical outcomes, we find cell count and expression levels of genes to be a strong determinant of power. As such, we advocate for future studies to employ cell type enrichment and deeper sequencing, especially for rarer populations like microglia, emphasise the importance of powering experiments of this type when seeking robust findings, and suggest stricter significance thresholds for future discoveries.

genomics↗

bioRxiv2026

Study of microbe-microbe interactions between the sexually transmitted parasite Trichomonas vaginalis with the cervicovaginal bacteria Lactobacillus iners

Trichomonas vaginalis is the leading cause of non-viral sexually transmitted infections and it is associated with comorbidities that affect female health. Lactobacillus iners is one of the most predominant bacteria in the cervicovaginal microbiome. As so, both microbes are likely to encounter one another upon T. vaginalis infection. To our knowledge, the interaction of both microbes has not been previously investigated. Here, we report that T. vaginalis and L. iners bind to one another at early time points of co-incubation. Using imaging flow cytometry and scanning electron microscopy, we capture the dynamics of this microbe-microbe association. We observed active remodeling of the T. vaginalis cell surface leading to thin-membrane protrusions that make contact with L. iners. Larger T. vaginalis membrane extensions that surround and engulf L. iners were also visible. These T. vaginalis-L. iners interactions ultimately lead to a reduction of L. iners viability while T. vaginalis viability was unaffected by exposure to L. iners. Inhibition of actin polymerization blocked T. vaginalis antibacterial activity against L. iners. Together our findings reveal novel insight about T. vaginalis-L. iners interactions and highlight a new T. vaginalis pathogenic effect.

microbiology↗

bioRxiv2026

Non-invasive Bdnf mRNA therapy improves cognition in ageing and Alzheimers mouse models

Messenger RNA (mRNA) therapeutics have rapidly emerged as a transformative approach for treating a range of health challenges. Accelerated by the success of mRNA-lipid nanoparticle (LNP) vaccines during the COVID-19 pandemic, this platform holds promise beyond immunisation for the transient expression of therapeutic proteins in targeted tissues. Despite this promise, non-invasive delivery of mRNA to the brain, as with most therapeutics, remains a challenge due to the impermeability of the blood brain barrier. Here, we present a novel strategy to deliver neurotrophic factors to the brain via intranasal delivery of mRNA-LNP. As a proof of concept, we demonstrate that intranasal delivery of mRNA encoding the neurogenic factor BDNF (Brain Derived Neurotrophic Factor) enhances memory performance in both aged mice and a transgenic mouse model of Alzheimers disease. This approach offers a promising platform for delivering therapeutic proteins to the brain and opens new avenues for treating age-related and neurodegenerative disorders.

animal behavior and cognition↗

bioRxiv2025

The Dirofilaria immitis unc-49 gene encodes a pharmacologically unique cys-loop GABA receptor

Nematode cys-loop GABA receptors play important roles in movement and locomotion. GABA receptors encoded by unc-49 appear to be widespread in nematode genomes including parasitic nematodes but are poorly characterized in filarial parasites. Dirofilaria immitis is a filarial parasite responsible for heartworm disease in dogs. Macrocyclic lactones are widely used to prevent heartworm infection. However, like many anthelmintics, resistance has emerged and new solutions are needed including the discovery of new anthelmintic drug targets. In this study, we report the isolation of two unc-49 subunit mRNAs (unc-49b and unc-49c) from the canine parasitic nematode D. immitis. When expressed in Xenopus oocytes, Dim-UNC-49B formed a functional homomeric GABA-gated channel, whereas Dim-UNC-49C did not. We further demonstrated that Dim-UNC-49B and Dim-UNC-49C gave rise to a functional heteromeric channel. Pharmacological characterization of these receptors and cross-species co-expression with UNC-49 subunits from the parasitic nematode Haemonchus contortus showed some unique properties compared to the same receptors characterized from other nematodes. These results revealed new properties of UNC-49 receptors in filarial worms.

pharmacology and toxicology↗

bioRxiv2025

Histone methyltransferase PRDM9 promotes survival of drug-tolerant persister cells in glioblastoma

Chemotherapy often kills a large fraction of cancer cells but leaves behind a small population of drug- tolerant persister cells. These persister cells survive drug treatments through reversible, non-genetic mechanisms and cause tumour recurrence upon cessation of therapy. Here, we report a drug tolerance mechanism regulated by the germ-cell-specific H3K4 methyltransferase PRDM9. Through histone proteomic, transcriptomic, lipidomic, and ChIP-sequencing studies combined with CRISPR knockout and phenotypic drug screen, we identified that chemotherapy-induced PRDM9 upregulation promotes metabolic rewiring in glioblastoma stem cells, leading to chemotherapy tolerance. Mechanistically, PRDM9-dependent H3K4me3 at cholesterol biosynthesis genes enhances cholesterol biosynthesis, which persister cells rely on to maintain homeostasis under chemotherapy- induced oxidative stress and lipid peroxidation. PRDM9 inhibition, combined with chemotherapy, resulted in strong anti-cancer efficacy in preclinical glioblastoma models, significantly enhancing the magnitude and duration of the antitumor response by eliminating persisters. These findings demonstrate a previously unknown role of PRDM9 in promoting metabolic reprogramming that enables the survival of drug-tolerant persister cells.

cancer biology↗

bioRxiv2024

FAIRSCAPE: An Evolving AI-readiness Framework for Biomedical Research

ObjectiveBiomedical datasets intended for use in AI applications require packaging with rich pre-model metadata to support model development that is explainable, ethical, epistemically grounded and FAIR (Findable, Accessible, Interoperable, Reusable). MethodsWe developed FAIRSCAPE, a digital commons environment, using agile methods, in close alignment with the team developing the AI-readiness criteria and with the Bridge2AI data production teams. Work was initially based on an existing provenance-aware framework for clinical machine learning. We incrementally added RO-Crate data+metadata packaging and exchange methods, client-side packaging support, provenance visualization, and support metadata mapped to the AI-readiness criteria, with automated AI-readiness evaluation. LinkML semantic enrichment and Croissant ML-ecosystem translations were also incorporated. ResultsThe FAIRSCAPE framework generates, packages, evaluates, and manages critical pre-model AI-readiness and explainability information with descriptive metadata and deep provenance graphs for biomedical datasets. It provides ethical, schema, statistical, and semantic characterization of dataset releases, licensing and availability information, and an automated AI-readiness evaluation across all 28 AI-readiness criteria. We applied this framework to successive, large-scale releases of multimodal datasets, progressively increasing dataset AI-readiness to full compliance. ConclusionFAIRSCAPE enables AI-readiness in biomedical datasets using standard metadata components and has been used to establish this pattern across a major, multimodal NIH data generation program. It eliminates early-stage opacity apparent in many biomedical AI applications and provides a basis for establishing end-to-end AI explainability.

bioinformatics↗

bioRxiv2024

A PERTURBATION CELL ATLAS OF HUMAN INDUCED PLURIPOTENT STEM CELLS

Towards comprehensively investigating the genotype-phenotype relationships governing the human pluripotent stem cell state, we generated an expressed genome-scale CRISPRi Perturbation Cell Atlas in KOLF2.1J human induced pluripotent stem cells (hiPSCs) mapping transcriptional and fitness phenotypes associated with 11,739 targeted genes. Using the transcriptional phenotypes, we created a minimum distortion embedding map of the pluripotent state, demonstrating rich recapitulation of protein complexes, such as strong co-clustering of MRPL, BAF, SAGA, and Ragulator family members. Additionally, we uncovered transcriptional regulators that are uncoupled from cell fitness, discovering potential novel pluripotency (JOSD1, RNF7) and metabolic factors (ZBTB41). We validated these findings via phenotypic, protein-interaction, and metabolic tracing assays. Finally, we propose a contrastive human-cell engineering framework (CHEF), a machine learning architecture that learns from perturbation cell atlases to predict perturbation recipes that achieve desired transcriptional states. Taken together, our study presents a comprehensive resource for interrogating the regulatory networks governing pluripotency.

bioengineering↗

bioRxiv2024

AI-readiness for Biomedical Data: Bridge2AI Recommendations

Biomedical research is rapidly adopting artificial intelligence (AI). Yet the inherent complexity of biomedical data preparation requires implementing actionable, robust criteria for ethical and explainable AI (XAI) at the "pre-model" stage, encompassing data acquisition, detailed transformations, and ethical governance. Simple conformance to FAIR (Findable, Accessible, Interoperable, Reusable) Principles is insufficient. Here, we define criteria and practices for reliable AI-readiness of biomedical data, developed by the NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group across seven core dimensions of dataset AI-readiness: FAIRness, Provenance, Characterization, Ethics, Pre-model Explainability, Sustainability, and Computability. Conformance to these criteria provides a basis for pre-model scientific rigor and ethical integrity, mitigating downstream risks of bias and error prior to AI modeling. We apply and evaluate these standards across all four Bridge2AI flagship datasets, spanning functional genomics to clinical medicine, and encode them in machine-actionable metadata bound to the datasets. This framework sets a benchmark for preparing ethical, reusable datasets in biomedical AI and provides standardized methods for reliable pre-model data evaluation.

bioinformatics↗

bioRxiv2023

Machine Learning-Guided Antibody Engineering That Leverages Domain Knowledge To Overcome The Small Data Problem

The application of Machine Learning (ML) tools to engineer novel antibodies having predictable functional properties is gaining prominence. Herein, we present a platform that employs an ML-guided optimization of the complementarity-determining region (CDR) together with a CDR framework (FR) shuffling method to engineer affinity-enhanced and clinically developable monoclonal antibodies (mAbs) from a limited experimental screen space (order of 10^2 designs) using only two experimental iterations. Although high-complexity deep learning models like graph neural networks (GNNs) and large language models (LLMs) have shown success on protein folding with large dataset sizes, the small and biased nature of the publicly available antibody-antigen interaction datasets is not sufficient to capture the diversity of mutations virtually screened using these models in an affinity enhancement campaign. To address this key gap, we introduced inductive biases learned from extensive domain knowledge on protein-protein interactions through feature engineering and selected model hyper parameters to reduce overfitting of the limited interaction datasets. Notably we show that this platform performs better than GNNs and LLMs on an in-house validation dataset that is enriched in diverse CDR mutations that go beyond alanine-scanning. To illustrate the broad applicability of this platform, we successfully solved a challenging problem of redesigning two different anti-SARS-COV-2 mAbs to enhance affinity (up to 2 orders of magnitude) and neutralizing potency against the dynamically evolving SARS-COV-2 Omicron variants.

bioengineering↗

bioRxiv2022

Cost-efficient whole genome-sequencing using novel mostly natural sequencing-by-synthesis chemistry and open fluidics platform

We introduce a massively parallel novel sequencing platform that combines an open flow cell design on a circular wafer with a large surface area and mostly natural nucleotides that allow optical end-point detection without reversible terminators. This platform enables sequencing billions of reads with longer read length ([~]300bp) and fast runs times (<20hrs) with high base accuracy (Q30 > 85%), at a low cost of $1/Gb. We establish system performance by whole-genome sequencing of the Genome-In-A-Bottle reference samples HG001-7, demonstrating high accuracy for SNPs (99.6%) and Indels in homopolymers up to length 10 (96.4%) across the vast majority (>98%) of the defined high-confidence regions of these samples. We demonstrate scalability of the whole-genome sequencing workflow by sequencing an additional 224 selected samples from the 1000 Genomes project achieving high concordance with reference data.

genomics↗

bioRxiv2022

Ultra high-throughput whole-genome methylation sequencing reveals trajectories in precancerous polyps to early colorectal adenocarcinoma

Aberrant shifts in DNA methylation have long been regarded as an early marker for cancer onset and progression. To chart DNA methylation changes that occur during the transformation from normal healthy colon tissue to malignant colorectal cancer (CRC), we collected over 50 samples from 15 familial adenomatous polyposis (FAP) and non-FAP colorectal cancer patients, and generated 30-70x whole-genome methylation sequencing (WGMS) runs via the novel Ultima Genomics ultra high-throughput sequencing platform. We observed changes in DNA methylation that occur early in the malignant transformation process, in gene promoters and in distal regulatory elements. Among these changes are events of hyper-methylation which are associated with a bivalent "poised" chromatin state at promoters and are CRC-specific. Distal enhancers show nonlinear dynamics, lose methylation in the progression from normal mucosa to dysplastic polyps but regain methylation in the adenocarcinoma state. Enhancers that gain chromatin accessibility in the adenocarcinoma state and are enriched with HOX transcription factor binding sites, a marker of developmental genes. This work demonstrates the feasibility of generating large high quality WGMS data using the Ultima Genomics platform and provides the first detailed view of methylation dynamics during CRC formation and progression in a model case.

genomics↗

bioRxiv2022

A structural dynamic explanation for observed escape of SARS-CoV-2 BA.2 variant mutation S371L/F

The SARS-CoV-2 Omicron sub-variants BA.1 and BA.2 have become the dominant variants worldwide due to enhanced transmissibility and immune evasion. In response to the rise of BA.1 and BA.2, two recent studies by Liu et al. and Iketani et al. provide a detailed analysis of loss of therapeutic antibody potency through evaluation of escape by pseudotyped viruses harboring BA.1 and BA.2 receptor binding domain (RBD) point mutations. Surprisingly, Liu et al. and Iketani et al. observed a profoundly broad escape effect for the individual mutations S371L and S371F. This result cannot be explained by known escape mechanisms of the SARS-CoV-2 RBD, and conflicts with existing computational and experimental escape measurements for S371 mutations performed on monomeric RBD. Through an examination of these conflicting datasets and a structural analysis of the antibodies assayed by Liu et al. and Iketani et al., we propose a mechanism to explain S371L/F escape according to a perturbation of spike trimer conformational dynamics that has not yet been described for any SARS-CoV-2 escape mutation. The proposed mechanism is relevant to Omicron and future variant surveillance as well as therapeutic antibody design.

microbiology↗

bioRxiv2021

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant

The SARS-COV2 Omicron variant has sparked global concern due to the possibility of enhanced transmissibility and escape from vaccines and therapeutics. In this study, we describe the mutational landscape of the Omicron variant using amino acid interaction (AAI) networks. AAI network analysis is particularly well suited for interrogating the impact of constellations of mutations as occur on Omicron that may function in an epistatic manner. Our analyses suggest that as compared to previous variants of concern, the Omicron variant has increased antibody escape breadth due to mutations in class 3 and 4 antibody epitopes as well as increased escape depth due to accumulated mutations in class 1 antibody epitopes. We note certain RBD mutations that might further enhance Omicrons escape, and in particular advise careful surveillance of two subclades bearing R346S/K mutations with relevance for certain therapeutic antibodies. Further, AAI network analysis suggests that the function of certain therapeutic monoclonal antibodies may be disrupted by Omicron mutations as a result of the cumulative indirect perturbations to the epitope surface properties, despite point-mutation analyses suggesting these antibodies are tolerant of the set of Omicron mutations in isolation. Finally, for several Omicron mutations that do not appear to contribute meaningfully to antibody escape, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBD-RBD interface.

bioinformatics↗

bioRxiv2021

Pancreatic cancer ductal cell of origin drives CD73-dependent generation of immunosuppressive adenosine

The microenvironment that surrounds pancreatic ductal adenocarcinoma (PDAC) is profoundly desmoplastic and immunosuppressive. Understanding initial triggers of immunosuppression during the process of pancreatic tumorigenesis would aid in establishing novel targets for effective prevention and therapy. Here, we interrogate the differential molecular mechanisms dependent on cell of origin and pathology subtype that determine immunosuppression during PDAC initiation and in established tumors. Transcriptomic analysis of cell of origin dependent-epithelial gene signatures revealed that Nt5e/CD73, a cell surface enzyme that is the pacemaker for extracellular adenosine generation, is one of the top 10% of genes over-expressed in murine tumors arising from ductal pancreatic epithelium as opposed to those rising from acinar cells. These findings were confirmed by Imaging Mass Cytometry and High-Performance Liquid Chromatography. Our data indicate that ductal activation of oncogenic mutant Kras results in loss of PTEN and elevated AKT signaling which ultimately releases CD73 suppression. Delivery of CD73 small molecule inhibitors through various delivery routes reduced tumor development and growth in genetically engineered and syngeneic mouse models. Analysis in human PDAC subtypes indicates that high Nt5e in murine ductal PDAC models overlaps with high NT5E in human PDAC Squamous and Basal Subtypes, considered to have the highest immunosuppression and worst prognosis. These findings highlight a molecular trigger of the immunosuppressive PDAC microenvironment which is dependent on ductal cell of origin, linking biology with pathological subtype classification, critical components to personalized approaches for PDAC prevention and immunotherapeutic intervention. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=185 SRC="FIGDIR/small/470415v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@14625org.highwire.dtl.DTLVardef@1a43372org.highwire.dtl.DTLVardef@12dc0c8org.highwire.dtl.DTLVardef@cbb835_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Mapping Potential Antigenic Drift Sites (PADS) on SARS-CoV-2 Spike in Continuous Epitope-Paratope Space

SARS-CoV-2 mutations with antigenic effects pose a risk to immunity developed through vaccination and natural infection. While vaccine updates for current variants of concern (VOCs) are underway, it is likewise important to prepare for further antigenic mutations as the virus navigates the heterogeneous global landscape of host immunity. Toward this end, a wealth of data and tools exist that can augment existing genetic surveillance of VOC evolution. In this study, we integrate published datasets describing genetic, structural, and functional constraints on mutation along with computational analyses of antibody-spike co-crystal structures to identify a set of potential antigenic drift sites (PADS) within the receptor binding domain (RBD) and N-terminal domain (NTD) of SARS-CoV-2 spike protein. Further, we project the PADS set into a continuous epitope-paratope space to facilitate interpretation of the degree to which newly observed mutations might be antigenically synergistic with existing VOC mutations, and this representation suggests that functionally convergent and synergistic antigenic mutations are accruing across VOC NTDs. The PADS set and synergy visualization serve as a reference as new mutations are detected on VOCs, enable proactive investigation of potentially synergistic mutations, and offer guidance to antibody and vaccine design efforts. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/446560v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@13b884forg.highwire.dtl.DTLVardef@171fe3eorg.highwire.dtl.DTLVardef@eac445org.highwire.dtl.DTLVardef@fba613_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods and Results

IntroductionTransparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent. MethodsWe developed the Evidence Graph Ontology, EVI, in OWL 2, with a set of inference rules, to provide deep representations of supporting and challenging evidence for computations, services, software, data, and results, across arbitrarily deep networks of computations, in connected or fully distinct processes. EVI integrates FAIR practices on data and software, with important concepts from provenance models, and argumentation theory. It extends PROV for additional expressiveness, with support for defeasible reasoning. EVI treats any computational result or component of evidence as a defeasible assertion, supported by a DAG of the computations, software, data, and agents that produced it. ResultsWe have successfully deployed EVI for large-scale predictive analytics on clinical time-series data. Every result may reference its evidence graph as metadata, which can be extended when subsequent computations are executed. DiscussionEvidence graphs support transparency and defeasible reasoning on results. They are first-class computational objects and reference the datasets and software from which they are derived. They support fully transparent computation, with challenge and support propagation. The EVI approach may be extended to include instruments, animal models, and critical experimental reagents.

bioinformatics↗

bioRxiv2020

FAIRSCAPE: A Framework for FAIR and Reproducible Biomedical Analytics

Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the results metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

bioinformatics↗

bioRxiv2020

Diminutive, degraded but dissimilar: Wolbachia genomes from filarial nematodes do not conform to a single paradigm

Wolbachia are alpha-proteobacteria symbionts infecting a large range of arthropod species and two different families of nematodes. Interestingly, these endosymbionts are able to induce diverse phenotypes in their hosts: they are reproductive parasites within many arthropods, nutritional mutualists within some insects and obligate mutualists within their filarial nematode hosts. Defining Wolbachia "species" is controversial and so they are commonly classified into 16 different phylogenetic lineages, termed supergroups, named A to S. However, available genomic data remains limited and not representative of the full Wolbachia diversity; indeed, of the 24 complete genomes and 55 draft genomes of Wolbachia available to date, 84% belong to supergroups A and B, exclusively composed of Wolbachia from arthropods. For the current study, we took advantage of a recently developed DNA enrichment method to produce four complete genomes and two draft genomes of Wolbachia from filarial nematodes. Two complete genomes, wCtub and wDcau, are the smallest Wolbachia genomes sequenced to date (863,988bp and 863,427bp, respectively), as well as the first genomes representing supergroup J. These genomes confirm the validity of this supergroup, a controversial clade due to weaknesses of the multi-locus system typing (MLST) approach. We also produced the first draft Wolbachia genome from a supergroup F filarial nematode representative (wMhie), two genomes from supergroup D (wLsig and wLbra) and the complete genome of wDimm from supergroup C. Our new data confirm the paradigm of smaller Wolbachia genomes from filarial nematodes containing low levels of transposable elements and the absence of intact bacteriophage sequences, unlike many Wolbachia from arthropods, where both are more abundant. However, we observe differences among the Wolbachia genomes from filarial nematodes: no global co-evolutionary pattern, strong synteny between supergroup C and supergroup J Wolbachia, and more transposable elements observed in supergroup D Wolbachia compared to the other supergroups. Metabolic pathway analysis indicates several highly conserved pathways (haem and nucleotide biosynthesis for example) as opposed to more variable pathways, such as vitamin B biosynthesis, which might be specific to certain host-symbiont associations. Overall, there appears to be no single Wolbachia- filarial nematode pattern of co-evolution or symbiotic relationship. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY RepositoriesData generated are available in GenBank: BioProject PRJNA593581; BioSample SAMN13482485 for wLsig, Wolbachia endosymbiont of Litomosoides sigmodontis (genome: CP046577); Biosample SAMN15190311 for the nematode host Litomosoides sigmodontis (genome: JABVXW000000000); BioSample SAMN13482488 for wDimm, Wolbachia endosymbiont of Dirofilaria (D.) immitis (genome: CP046578); Biosample SAMN15190314 for the nematode host Dirofilaria (D.) immitis (genome: JABVXT000000000); BioSample SAMN13482046 for wCtub, Wolbachia endosymbiont of Cruorifilaria tuberocauda (genome: CP046579); Biosample SAMN15190313 for the nematode host Cruorifilaria tuberocauda (genome: JABVXU000000000); BioSample SAMN13482057 for wDcau, Wolbachia endosymbiont of Dipetalonema caudispina (genome: CP046580); Biosample SAMN15190312 for the nematode host Dipetalonema caudispina (genome: JABVXV000000000); BioSample SAMN13482459 for wLbra, Wolbachia endosymbiont of Litomosoides brasiliensis (genome: WQM000000000); Biosample SAMN15190311 for the nematode host Litomosoides brasiliensis (genome: JABVXW000000000); BioSample SAMN13482487 for wMhie, Wolbachia endosymbiont of Madathamugadia hiepei (genome: WQMP00000000); Biosample SAMN15190315 for the nematode host Madathamugadia hiepei (genome: JABVXS000000000). The raw data are available in GenBank as Sequence Read Archive (SRA): SRR10903008 to SRR10903010; SRR10902913 to SRR10902914; SRR10900508 to SRR10900511; SRR10898805 to SRR10898806. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Eleven Supplementary tables and two supplementary files are available with the online version of this article. Impact StatementWolbachia are endosymbiotic bacteria infecting a large range of arthropod species and two different families of nematodes, characterized by causing diverse phenotypes in their hosts, ranging from reproductive parasitism to mutualism. While available Wolbachia genomic data are increasing, they are not representative of the full Wolbachia diversity; indeed, 84% of Wolbachia genomes available on the NCBI database to date belong to the two main studied clades (supergroups A and B, exclusively composed of Wolbachia from arthropods). The present study presents the assembly and analysis of four complete genomes and two draft genomes of Wolbachia from filarial nematodes. Our genomics comparisons confirm the paradigm that smaller Wolbachia genomes from filarial nematodes contain low levels of transposable elements and the absence of intact bacteriophage sequences, unlike many Wolbachia from arthropods. However, data show disparities among the Wolbachia genomes from filarial nematodes: no single pattern of co-evolution, stronger synteny between some clades (supergroups C and supergroup J) and more transposable elements in another clade (supergroup D). Metabolic pathway analysis indicates both highly conserved and more variable pathways, such as vitamin B biosynthesis, which might be specific to certain host-symbiont associations. Overall, there appears to be no single Wolbachia-filarial nematode pattern of symbiotic relationship.

genomics↗