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Nematodes of the genus Trichinella are important pathogens of humans and animals. This study aimed to enhance the genomic and transcriptomic resources for T. pseudospiralis (non-encapsulated phenotype) and T. spiralis (encapsulated phenotype) and to explore transcriptional profiles. First, we improved the assemblies of the genomes of T. pseudospiralis (code ISS13) and T. spiralis (code ISS534), achieving genome sizes of 56.6 Mb (320 scaffolds, and an N50 of 1.02 Mb) and 63.5 Mb (568 scaffolds, and an N50 value of 0.44 Mb), respectively. Then, for each species, we produced RNA sequence data for three key developmental stages (first-stage muscle larvae [L1s], adults, and newborn larvae [NBLs]; three replicates for each stage), analysed differential transcription between stages, and explored enriched pathways and processes between species. Stage-specific upregulation was linked to cellular processes, metabolism, and host–parasite interactions, and pathway enrichment analysis showed distinctive biological processes and cellular localisations between species. Indeed, the secreted molecules calmodulin, calreticulin, and calsyntenin—with possible roles in modulating host immune responses and facilitating parasite survival—were unique to T. pseudospiralis and not detected in T. spiralis. These insights into the molecular mechanisms of Trichinella–host interactions might offer possible avenues for developing new interventions against trichinellosis. [ABSTRACT FROM AUTHOR]

The identification and characterization of essential genes are central to our understanding of the core biological functions in eukaryotic organisms, and has important implications for the treatment of diseases caused by, for example, cancers and pathogens. Given the major constraints in testing the functions of genes of many organisms in the laboratory, due to the absence of in vitro cultures and/or gene perturbation assays for most metazoan species, there has been a need to develop in silico tools for the accurate prediction or inference of essential genes to underpin systems biological investigations. Major advances in machine learning approaches provide unprecedented opportunities to overcome these limitations and accelerate the discovery of essential genes on a genome-wide scale. Here, we developed and evaluated a large language model- and graph neural network (LLM–GNN)-based approach, called 'Bingo', to predict essential protein-coding genes in the metazoan model organisms Caenorhabditis elegans and Drosophila melanogaster as well as in Mus musculus and Homo sapiens (a HepG2 cell line) by integrating LLM and GNNs with adversarial training. Bingo predicts essential genes under two 'zero-shot' scenarios with transfer learning, showing promise to compensate for a lack of high-quality genomic and proteomic data for non-model organisms. In addition, the attention mechanisms and GNNExplainer were employed to manifest the functional sites and structural domain with most contribution to essentiality. In conclusion, Bingo provides the prospect of being able to accurately infer the essential genes of little- or under-studied organisms of interest, and provides a biological explanation for gene essentiality. [ABSTRACT FROM AUTHOR]

Genomic data provide valuable insights into pest management issues such as resistance evolution, historical patterns of pest invasions and ongoing population dynamics. We assembled the first reference genome for the redlegged earth mite, Halotydeus destructor (Tucker, 1925), to investigate adaptation to pesticide pressures and demography in its invasive Australian range using whole‐genome pool‐seq data from regionally distributed populations. Our reference genome comprises 132 autosomal contigs, with a total length of 48.90 Mb. We observed a large complex of ace genes, which has presumably evolved from a long history of organophosphate selection in H. destructor and may contribute towards organophosphate resistance through copy number variation, target‐site mutations and structural variants. In the putative ancestral H. destructor ace gene, we identified three target‐site mutations (G119S, A201S and F331Y) segregating in organophosphate‐resistant populations. Additionally, we identified two new para sodium channel gene mutations (L925I and F1020Y) that may contribute to pyrethroid resistance. Regional structuring observed in population genomic analyses indicates that gene flow in H. destructor does not homogenize populations across large geographic distances. However, our demographic analyses were equivocal on the magnitude of gene flow; the short invasion history of H. destructor makes it difficult to distinguish scenarios of complete isolation vs. ongoing migration. Nonetheless, we identified clear signatures of reduced genetic diversity and smaller inferred effective population sizes in eastern vs. western populations, which is consistent with the stepping‐stone invasion pathway of this pest in Australia. These new insights will inform development of diagnostic genetic markers of resistance, further investigation into the multifaceted organophosphate resistance mechanism and predictive modelling of resistance evolution and spread. [ABSTRACT FROM AUTHOR]

The revolution in genomics has enabled large‐scale population genetic investigations of a wide range of organisms, but there has been a relatively limited focus on improving analytical pipelines. To efficiently analyse large data sets, highly integrated and automated software pipelines, which are easy to use, efficient, reliable, reproducible and run in multiple computational environments, are required. A number of software workflows have been developed to handle and process such data sets for population genetic analyses, but effective, specialized pipelines for genetic and statistical analyses of nonmodel organisms are lacking. For most species, resources for variomes (sets of genetic variations found in populations of species) are not available, and/or genome assemblies are often incomplete and fragmented, complicating the selection of the most suitable reference genome when multiple assemblies are available. Additionally, the biological samples used often contain extraneous DNA from sources other than the species under investigation (e.g., microbial contamination), which needs to be removed prior to genetic analyses. For these reasons, we established a new pipeline, called Escalibur, which includes: functionalities, such as data trimming and mapping; selection of a suitable reference genome; removal of contaminating read data; recalibration of base calls; and variant‐calling. Escalibur uses a proven gatk variant caller and workflow description language (WDL), and is, therefore, a highly efficient and scalable pipeline for the genome‐wide identification of nucleotide variation in eukaryotes. This pipeline is available at https://gitlab.unimelb.edu.au/bioscience/escalibur (version 0.3‐beta) and is essentially applicable to any prokaryote or eukaryote. [ABSTRACT FROM AUTHOR]

To describe the design of the CLARION post-approval safety study (EU PAS Register number, EUPAS24484) and provide a status update, including characteristics of patients included up to 1 May 2021. CLARION aims to further evaluate adverse events of special interest in patients who are newly initiating treatment with cladribine tablets for relapsing multiple sclerosis (MS). The study population consists of two cohorts: patients newly initiating cladribine tablets (cladribine cohort) and patients newly initiating oral fingolimod tablets (comparator fingolimod cohort), with an aim to include 8000 patients (4000 patients per cohort). The study relies on secondary use of data from pre-existing MS registries/data sources (except in Germany, where primary data collection is performed). The study is projected to last 15 years, with an anticipated 5-year inclusion period. Study outcomes are: malignancies; severe infections; tuberculosis; progressive multifocal leukoencephalopathy; other opportunistic infections; herpes zoster; severe lymphopenia (Grade ≥ 3); and treatment discontinuation. As of 1 May 2021, 2393 patients were included in CLARION from seven participating MS registries/data sources (cladribine cohort, n = 1266; fingolimod cohort, n = 1127). The majority of patients are female (cladribine cohort, 72.5%; fingolimod cohort, 68.0%), with mean age at onset of MS of 31.5 years for the cladribine cohort and 30.9 years for the fingolimod cohort. The majority of patients in both cohorts had relapsing MS (cladribine cohort, 92.1%; fingolimod cohort, 93.5%). By providing further information on adverse events of special interest during long-term follow-up, CLARION will assist neurologists and patients regarding treatment decision-making for management of relapsing MS. [ABSTRACT FROM AUTHOR]

Detailed explorations of the model organisms Caenorhabditis elegans (elegant worm) and Drosophila melanogaster (vinegar fly) have substantially improved our knowledge and understanding of biological processes and pathways in metazoan organisms. Extensive functional genomic and multi-omic data sets have enabled the discovery and characterisation of 'essential' genes that are critical for the survival of these organisms. Recently, we showed that a machine learning (ML)-based pipeline could be utilised to predict essential genes in both C. elegans and D. melanogaster using features from DNA, RNA, protein and/or cellular data or associated information. As these distantly-related species are within the Ecdysozoa, we hypothesised that this approach could be suited for non-model organisms within the same group (phylum) of protostome animals. In the present investigation, we cross-predicted essential genes within the phylum Nematoda - between C. elegans and the parasitic filarial nematodes Brugia malayi and Onchocerca volvulus , and then ranked and prioritised these genes. Highly ranked genes were linked to key biological pathways or processes, such as ribosome biogenesis, translation and RNA processing, and were expressed at relatively high levels in the germline, gonad, hypodermis and/or nerves. The present in silico workflow is hoped to expedite the identification of drug targets in parasitic organisms for subsequent experimental validation in the laboratory., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Over the years, comprehensive explorations of the model organisms Caenorhabditis elegans (elegant worm) and Drosophila melanogaster (vinegar fly) have contributed substantially to our understanding of complex biological processes and pathways in multicellular organisms generally. Extensive functional genomic-phenomic, genomic, transcriptomic, and proteomic data sets have enabled the discovery and characterisation of genes that are crucial for life, called 'essential genes'. Recently, we investigated the feasibility of inferring essential genes from such data sets using advanced bioinformatics and showed that a machine learning (ML)-based workflow could be used to extract or engineer features from DNA, RNA, protein, and/or cellular data/information to underpin the reliable prediction of essential genes both within and between C. elegans and D. melanogaster . As these are two distantly related species within the Ecdysozoa, we proposed that this ML approach would be particularly well suited for species that are within the same phylum or evolutionary clade. In the present study, we cross-predicted essential genes within the phylum Nematoda (evolutionary clade V)-between C. elegans and the pathogenic parasitic nematode H. contortus -and then ranked and prioritised H. contortus proteins encoded by these genes as intervention (e.g., drug) target candidates. Using strong, validated predictors, we inferred essential genes of H. contortus that are involved predominantly in crucial biological processes/pathways including ribosome biogenesis, translation, RNA binding/processing, and signalling and which are highly transcribed in the germline, somatic gonad precursors, sex myoblasts, vulva cell precursors, various nerve cells, glia, or hypodermis. The findings indicate that this in silico workflow provides a promising avenue to identify and prioritise panels/groups of drug target candidates in parasitic nematodes for experimental validation in vitro and/or in vivo.

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans -related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans , we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus . Two of these genes (i.e. Hc-eef-1A and Hc-eef1G ), coding for eukaryotic elongation factors (called Hc- eEF1A and Hc -eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc -eEF1A and/or Hc -eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus . Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus , and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Introduction: This study aimed to investigate if maternal pregnancy exposure to metformin is associated with increased risk of long-term and short-term adverse outcomes in the child. RESEARCH DESIGN AND METHODS : This register-based cohort study from Finland included singleton children born 2004-2016 with maternal pregnancy exposure to metformin or insulin (excluding maternal type 1 diabetes): metformin only (n=3967), insulin only (n=5273) and combination treatment (metformin and insulin; n=889). The primary outcomes were long-term offspring obesity, hypoglycemia, hyperglycemia, diabetes, hypertension, polycystic ovary syndrome, and challenges in motor-social development. In a sensitivity analysis, the primary outcomes were investigated only among children with maternal gestational diabetes. Secondary outcomes were adverse outcomes at birth. Analyses were conducted using inverse- probability of treatment weighting (IPTW), with insulin as reference. RESULTS  : Exposure to metformin or combination treatment versus insulin was not associated with increased risk of long-term outcomes in the main or sensitivity analyses. Among the secondary outcomes, increased risk of small for gestational age (SGA) was observed for metformin (IPTW-weighted OR 1.65, 95% CI 1.16 to 2.34); increased risk of large for gestational age, preterm birth and hypoglycemia was observed for combination treatment. No increased risk was observed for neonatal mortality, hyperglycemia, or major congenital anomalies. CONCLUSIONS : This study found no increased long-term risk associated with pregnancy exposure to metformin (alone or in combination with insulin), compared with insulin. The increased risk of SGA associated with metformin versus insulin suggests caution in pregnancies with at-risk fetal undernutrition. The increased risks of adverse outcomes at birth associated with combination treatment may reflect confounding by indication or severity., Competing Interests: Competing interests: KMGB, CF, EB, and JuS are employees of Merck Healthcare KGaA, Darmstadt, Germany, the metformin originator and sponsor of this study, and participated in the authoring/revision of the manuscript. IQVIA performs commissioned pharmacoepidemiological studies, including this one, for several pharmaceutical companies. LS, JaS, MV, RT and KMH are employees of IQVIA, which performs commissioned pharmacoepidemiological studies for several pharmaceutical companies. HS is an employee of IQVIA and has received consulting fees from Celgene, outside the submitted work. PK was at the time of conducting the study an employee of IQVIA., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)