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Each person’s genome sequence has thousands of missense variants. Practical interpretation of their functional significance must rely on computational inferences in the absence of exhaustive experimental measurements. Here we analyzed the efficacy of these inferences in 33 de novo missense mutations revealed by sequencing in first-generation progeny ofN-ethyl-N-nitrosourea–treated mice, involving 23 essential immune system genes. Poly-Phen2, SIFT, MutationAssessor, Panther, CADD, and Condel were used to predict each mutation’s functional importance, whereas the actual effect was measured by breeding and testing homozygotes for the expected in vivo loss-of-function phenotype. Only 20% of mutations predicted to be deleterious by PolyPhen2 (and 15% by CADD) showed a discernible phenotype in individual homozygotes. Half of all possible missense mutations in the same 23 immune genes were predicted to be deleterious, and most of these appear to become subject to purifying selection because few persist between separate mouse substrains, rodents, or primates. Because defects in immune genes could be phenotypically masked in vivo by compensation and environment, we compared inferences by the same tools with the in vitro phenotype of all 2,314 possible missense variants inTP53; 42% of mutations predicted by PolyPhen2 to be deleterious (and 45% by CADD) had little measurable consequence forTP53-promoted transcription. We conclude that for de novo or low-frequency missense mutations found by genome sequencing, half those inferred as deleterious correspond to nearly neutral mutations that have little impact on the clinical phenotype of individual cases but will nevertheless become subject to purifying selection.

Gastrointestinal (GI) parasites, hookworms in particular, have evolved to cause minimal harm to their hosts, allowing them to establish chronic infections. This is mediated by creating an immunoregulatory environment. Indeed, hookworms are such potent suppressors of inflammation that they have been used in clinical trials to treat inflammatory bowel diseases (IBD) and celiac disease. Since the recent description of helminths (worms) secreting extracellular vesicles (EVs), exosome-like EVs from different helminths have been characterized and their salient roles in parasite-host interactions have been highlighted. Here, we analyze EVs from the rodent parasite , which has been used as a model for human hookworm infection. EVs ( -EVs) are actively internalized by mouse gut organoids, indicating a role in driving parasitism. We used proteomics and RNA-Seq to profile the molecular composition of -EVs. We identified 81 proteins, including proteins frequently present in exosomes (like tetraspanin, enolase, 14-3-3 protein, and heat shock proteins), and 27 sperm-coating protein-like extracellular proteins. RNA-Seq analysis revealed 52 miRNA species, many of which putatively map to mouse genes involved in regulation of inflammation. To determine whether GI nematode EVs had immunomodulatory properties, we assessed their potential to suppress GI inflammation in a mouse model of inducible chemical colitis. EVs from but not those from the whipworm or control vesicles from grapes protected against colitic inflammation in the gut of mice that received a single intraperitoneal injection of EVs. Key cytokines associated with colitic pathology (IL-6, IL-1β, IFNγ, and IL-17a) were significantly suppressed in colon tissues from EV-treated mice. By contrast, high levels of the anti-inflammatory cytokine IL-10 were detected in -EV-treated mice. Proteins and miRNAs contained within helminth EVs hold great potential application in development of drugs to treat helminth infections as well as chronic non-infectious diseases resulting from a dysregulated immune system, such as IBD.

Whipworms are parasitic nematodes that live in the gut of more than 500 million people worldwide. Owing to the difficulty in obtaining parasite material, the mouse whipworm Trichuris muris has been extensively used as a model to study human whipworm infections. These nematodes secrete a multitude of compounds that interact with host tissues where they orchestrate a parasitic existence. Herein we provide the first comprehensive characterization of the excretory/secretory products of T. muris. We identify 148 proteins secreted by T. muris and show for the first time that the mouse whipworm secretes exosome-like extracellular vesicles (EVs) that can interact with host cells. We use an Optiprep® gradient to purify the EVs, highlighting the suitability of this method for purifying EVs secreted by a parasitic nematode. We also characterize the proteomic and genomic content of the EVs, identifying >350 proteins, 56 miRNAs (22 novel) and 475 full-length mRNA transcripts mapping to T. muris gene models. Many of the miRNAs putatively mapped to mouse genes are involved in regulation of inflammation, implying a role in parasite-driven immunomodulation. In addition, for the first time to our knowledge, colonic organoids have been used to demonstrate the internalization of parasite EVs by host cells. Understanding how parasites interact with their host is crucial to develop new control measures. This first characterization of the proteins and EVs secreted by T. muris provides important information on whipworm-host communication and forms the basis for future studies.

The reduced prevalence of insulin resistance and type 2 diabetes in countries with endemic parasitic worm infections suggests a protective role for worms against metabolic disorders, however clinical evidence has been non-existent. This 2-year randomised, double-blinded clinical trial in Australia of hookworm infection in 40 male and female adults at risk of type 2 diabetes assessed the safety and potential metabolic benefits of treatment with either 20 ( n  = 14) or 40 ( n  = 13) Necator americanus larvae (L3) or Placebo ( n  = 13) (Registration ACTRN12617000818336). Primary outcome was safety defined by adverse events and completion rate. Homoeostatic model assessment of insulin resistance, fasting blood glucose and body mass were key secondary outcomes. Adverse events were more frequent in hookworm-treated participants, where 44% experienced expected gastrointestinal symptoms, but completion rates were comparable to Placebo. Fasting glucose and insulin resistance were lowered in both hookworm-treated groups at 1 year, and body mass was reduced after L3-20 treatment at 2 years. This study suggests hookworm infection is safe in people at risk of type 2 diabetes and associated with improved insulin resistance, warranting further exploration of the benefits of hookworms on metabolic health. A beneficial effect of parasitic worms on metabolic health has been postulated based on epidemiological and animal studies. Here, the authors show in a phase I clinical trial that treatment of people at risk of type 2 diabetes with hookworms is safe and may improve key measures of metabolic health.

A diversity of tools is available for identification of variants from genome sequence data. Given the current complexity of incorporating external software into a genome analysis infrastructure, a tendency exists to rely on the results from a single tool alone. The quality of the output variant calls is highly variable however, depending on factors such as sequence library quality as well as the choice of short-read aligner, variant caller, and variant caller filtering strategy. Here we present a two-part study first using the high quality 'genome in a bottle' reference set to demonstrate the significant impact the choice of aligner, variant caller, and variant caller filtering strategy has on overall variant call quality and further how certain variant callers outperform others with increased sample contamination, an important consideration when analyzing sequenced cancer samples. This analysis confirms previous work showing that combining variant calls of multiple tools results in the best quality resultant variant set, for either specificity or sensitivity, depending on whether the intersection or union, of all variant calls is used respectively. Second, we analyze a melanoma cell line derived from a control lymphocyte sample to determine whether software choices affect the detection of clinically important melanoma risk-factor variants finding that only one of the three such variants is unanimously detected under all conditions. Finally, we describe a cogent strategy for implementing a clinical variant detection pipeline; a strategy that requires careful software selection, variant caller filtering optimizing, and combined variant calls in order to effectively minimize false negative variants. While implementing such features represents an increase in complexity and computation the results offer indisputable improvements in data quality.

Extensive evaluation of RNA-seq methods have demonstrated that no single algorithm consistently outperforms all others. Removal of unwanted variation (RUV) has also been proposed as a method for stabilizing differential expression (DE) results. Despite this, it remains a challenge to run multiple RNA-seq algorithms to identify significant differences common to multiple algorithms, whilst also integrating and assessing the impact of RUV into all algorithms. consensusDE was developed to automate the process of identifying significant DE by combining the results from multiple algorithms with minimal user input and with the option to automatically integrate RUV. consensusDE only requires a table describing the sample groups, a directory containing BAM files or preprocessed count tables and an optional transcript database for annotation. It supports merging of technical replicates, paired analyses and outputs a compendium of plots to guide the user in subsequent analyses. Herein, we assess the ability of RUV to improve DE stability when combined with multiple algorithms and between algorithms, through application to real and simulated data. We find that, although RUV increased fold change stability between algorithms, it demonstrated improved FDR in a setting of low replication for the intersect, the effect was algorithm specific and diminished with increased replication, reinforcing increased replication for recovery of true DE genes. We finish by offering some rules and considerations for the application of RUV in a consensus-based setting. consensusDE is freely available, implemented in R and available as a Bioconductor package, under the GPL-3 license, along with a comprehensive vignette describing functionality: http://bioconductor.org/packages/consensusDE/ .

Background Short-read resequencing of genomes produces abundant information of the genetic variation of individuals. Due to their numerous nature, these variants are rarely exhaustively validated. Furthermore, low levels of undetected variant miscalling will have a systematic and disproportionate impact on the interpretation of individual genome sequence information, especially should these also be carried through into in reference databases of genomic variation. Results We find that sequence variation from short-read sequence data is subject to recurrent-yet-intermittent miscalling that occurs in a sequence intrinsic manner and is very sensitive to sequence read length. The miscalls arise from difficulties aligning short reads to redundant genomic regions, where the rate of sequencing error approaches the sequence diversity between redundant regions. We find the resultant miscalled variants to be sensitive to small sequence variations between genomes, and thereby are often intrinsic to an individual, pedigree, strain or human ethnic group. In human exome sequences, we identify 2–300 recurrent false positive variants per individual, almost all of which are present in public databases of human genomic variation. From the exomes of non-reference strains of inbred mice, we identify 3–5000 recurrent false positive variants per mouse – the number of which increasing with greater distance between an individual mouse strain and the reference C57BL6 mouse genome. We show that recurrently miscalled variants may be reproduced for a given genome from repeated simulation rounds of read resampling, realignment and recalling. As such, it is possible to identify more than two-thirds of false positive variation from only ten rounds of simulation. Conclusion Identification and removal of recurrent false positive variants from specific individual variant sets will improve overall data quality. Variant miscalls arising are highly sequence intrinsic and are often specific to an individual, pedigree or ethnicity. Further, read length is a strong determinant of whether given false variants will be called for any given genome – which has profound significance for cohort studies that pool datasets collected and sequenced at different points in time.

Koalas (Phascolarctos cinereus) are threatened by habitat loss, fragmentation, and population isolation, increasing the risk of inbreeding and extinction. Genomic tools are valuable for guiding management decisions, and a standardised tool genomic is the most effective approach. In this study, an integrated genomic SNP assay was developed and validated as a comprehensive monitoring tool for koala conservation. The panel unifies SNP markers from previous approaches (DArTseq, exon-capture, whole-genome sequencing) into a standardised platform and incorporates novel fitness-related loci linked to immunity, thermoregulation, diet, and reproduction, alongside pathogen targets such as koala retrovirus (KoRV) and koala papillomavirus (KoAA). The assay was validated across key conservation applications, including population diversity and differentiation, parentage assignment, sex determination, provenance testing, and pathogen screening, using a variety of sample types (blood, tissue, swabs, scat), from previously tested populations across the distribution. A total of 3358 informative SNPs were identified, including 210 high-confidence outliers associated with immune and stress-response functions, indicating strong potential to capture adaptive variation. By integrating existing genomic resources with new adaptive and predominant pathogen loci, this cost-effective, standardised assay provides a unifying genomic framework for koala management, supporting applications from veterinary diagnostics to long-term monitoring under the National Koala Recovery Plan.

IgD and Igm are produced by alternative splicing of long primary RNA transcripts from the Ig heavy chain (Igh) locus and serve as the receptors for antigen on naïve mature B lymphocytes. IgM is made selectively in immature B cells, whereas IgD is coexpressed with IgM when the cells mature into follicular or marginal zone B cells, but the transacting factors responsible for this regulated change in splicing have remained elusive. Here, we use a genetic screen in mice to identify ZFP318, a nuclear protein with two U1-type zinc fingers found in RNA-binding proteins and no known role in the immune system, as a critical factor for IgD expression. A point mutation in an evolutionarily conserved lysine-rich domain encoded by the alternatively spliced Zfp318 exon 10 abolished IgD expression on marginal zone B cells, decreased IgD on follicular B cells, and increased IgM, but only slightly decreased the percentage of B cells and did not decrease expression of other maturation markers CD21, CD23, or CD62L. A targeted Zfp318 null allele extinguished IgD expression on mature B cells and increased IgM. Zfp318 mRNA is developmentally regulated in parallel with IgD, with little in pro-B cells, moderate amounts in immature B cells, and high levels selectively in mature follicular B cells. These findings identify ZFP318 as a crucial factor regulating the expression of the two major antibody isotypes on the surface of most mature B cells.

Background. Massively parallel sequencing technology is being used to sequence highly diverse populations of DNA such as that derived from heterogeneous cell mixtures containing both wild-type and disease-related states. At the core of such molecule tagging techniques is the tagging and identification of sequence reads derived from individual input DNA molecules, which must be first computationally disambiguated to generate read groups sharing common sequence tags, with each read group representing a single input DNA molecule. This disambiguation typically generates huge numbers of reads groups, each of which requires additional variant detection analysis steps to be run specific to each read group, thus representing a significant computational challenge. While sequencing technologies for producing these data are approaching maturity, the lack of available computational tools for analysing such heterogeneous sequence data represents an obstacle to the widespread adoption of this technology. Results. Using synthetic data we successfully detect unique variants at dilution levels of 1 in a 1,000,000 molecules, and find DeeepSNVMiner obtains significantly lower false positive and false negative rates compared to popular variant callers GATK, SAMTools, FreeBayes and LoFreq, particularly as the variant concentration levels decrease. In a dilution series with genomic DNA from two cells lines, we find DeepSNVMiner identifies a known somatic variant when present at concentrations of only 1 in 1,000 molecules in the input material, the lowest concentration amongst all variant callers tested. Conclusions. Here we present DeepSNVMiner; a tool to disambiguate tagged sequence groups and robustly identify sequence variants specific to subsets of starting DNA molecules that may indicate the presence of a disease. DeepSNVMiner is an automated workflow of custom sequence analysis utilities and open source tools able to differentiate somatic DNA variants from artefactual sequence variants that likely arose during DNA amplification. The workflow remains flexible such that it may be customised to variants of the data production protocol used, and supports reproducible analysis through detailed logging and reporting of results. DeepSNVMiner is available for academic non-commercial research purposes at https://github.com/mattmattmattmatt/DeepSNVMiner .