Implementation of artificial intelligence for the detection of cutaneous melanoma within a primary care setting: prevalence and types of skin cancer in outdoor enthusiasts.

Background: There is enthusiasm for implementing artificial intelligence (AI) to assist clinicians detect skin cancer. Performance metrics of AI from dermoscopic images have been promising, with studies documenting sensitivity and specificity values equal to or superior to specialists for the detection of malignant melanomas (MM). Early detection rates would particularly benefit Australia, which has the worlds highest incidence of MM per capita. The detection of skin cancer may be delayed due to late screening or the inherent difficulty in diagnosing early skin cancers which often have a paucity of clinical features and may blend into sun damaged skin. Individuals who participate in outdoor sports and recreation experience high levels of intermittent ultraviolet radiation (UVR), which is associated with the development of skin cancer, including MM. This research aimed to assess the prevalence of skin cancer in individuals who regularly participate in activities outdoors and to report the performance parameters of a commercially available AI-powered software to assess the predictive risk of MM development. Methods: Cross-sectional study design incorporating a survey, total body skin cancer screening and AI-embedded software capable of predictive scoring of queried MM. Results: A total of 423 participants consisting of surfers (n = 108), swimmers (n = 60) and walkers/runners (n = 255) participated. Point prevalence for MM was highest for surfers (6.48%), followed by walkers/runners (4.3%) and swimmers (3.33%) respectively. When compared to the general Australian population, surfers had the highest odds ratio (OR) for MM (OR 119.8), followed by walkers/runners (OR 79.74), and swimmers (OR 61.61) rounded out the populations. Surfers and swimmers reported comparatively lower lifetime hours of sun exposure (5,594 and 5,686, respectively) but more significant amounts of activity within peak ultraviolet index compared with walkers/runners (9,554 h). A total of 48 suspicious pigmented lesions made up of histopathology-confirmed MM (n = 15) and benign lesions (n = 33) were identified. The performance of the AI from this clinical population was found to have a sensitivity of 53.33%, specificity of 54.44% and accuracy of 54.17%. Conclusions: Rates of both keratinocyte carcinomas and MM were notably higher in aquatic and land-based enthusiasts compared to the general Australian population. These findings further highlight the clinical importance of sun-safe protection measures and regular skin screening in individuals who spend significant time outdoors. The use of AI in the early identification of MM is promising. However, the lower-than-expected performance metrics of the AI software used in this study indicated reservations should be held before recommending this particular version of this AI software as a reliable adjunct for clinicians in skin imaging diagnostics in patients with potentially sun damaged skin.

Genetic mapping of microbial and host traits reveals production of immunomodulatory lipids by Akkermansia muciniphila in the murine gut.

The molecular bases of how host genetic variation impacts the gut microbiome remain largely unknown. Here we used a genetically diverse mouse population and applied systems genetics strategies to identify interactions between host and microbe phenotypes including microbial functions, using faecal metagenomics, small intestinal transcripts and caecal lipids that influence microbe-host dynamics. Quantitative trait locus (QTL) mapping identified murine genomic regions associated with variations in bacterial taxa; bacterial functions including motility, sporulation and lipopolysaccharide production and levels of bacterial- and host-derived lipids. We found overlapping QTL for the abundance of Akkermansia muciniphila and caecal levels of ornithine lipids. Follow-up in vitro and in vivo studies revealed that A. muciniphila is a major source of these lipids in the gut, provided evidence that ornithine lipids have immunomodulatory effects and identified intestinal transcripts co-regulated with these traits including Atf3, which encodes for a transcription factor that plays vital roles in modulating metabolism and immunity. Collectively, these results suggest that ornithine lipids are potentially important for A. muciniphila-host interactions and support the role of host genetics as a determinant of responses to gut microbes.

Defining mitochondrial protein functions through deep multiomic profiling.

Mitochondria are epicentres of eukaryotic metabolism and bioenergetics. Pioneering efforts in recent decades have established the core protein componentry of these organelles1 and have linked their dysfunction to more than 150 distinct disorders2,3. Still, hundreds of mitochondrial proteins lack clear functions4, and the underlying genetic basis for approximately 40% of mitochondrial disorders remains unresolved5. Here, to establish a more complete functional compendium of human mitochondrial proteins, we profiled more than 200 CRISPR-mediated HAP1 cell knockout lines using mass spectrometry-based multiomics analyses. This effort generated approximately 8.3 million distinct biomolecule measurements, providing a deep survey of the cellular responses to mitochondrial perturbations and laying a foundation for mechanistic investigations into protein function. Guided by these data, we discovered that PIGY upstream open reading frame (PYURF) is an S-adenosylmethionine-dependent methyltransferase chaperone that supports both complex I assembly and coenzyme Q biosynthesis and is disrupted in a previously unresolved multisystemic mitochondrial disorder. We further linked the putative zinc transporter SLC30A9 to mitochondrial ribosomes and OxPhos integrity and established RAB5IF as the second gene harbouring pathogenic variants that cause cerebrofaciothoracic dysplasia. Our data, which can be explored through the interactive online MITOMICS.app resource, suggest biological roles for many other orphan mitochondrial proteins that still lack robust functional characterization and define a rich cell signature of mitochondrial dysfunction that can support the genetic diagnosis of mitochondrial diseases.

Large-Scale Multi-omic Analysis of COVID-19 Severity.

We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a machine learning approach for prediction of COVID-19 severity.

Argonaut: A Web Platform for Collaborative Multi-omic Data Visualization and Exploration.

Researchers now generate large multi-omic datasets using increasingly mature mass spectrometry techniques at an astounding pace, facing new challenges of "Big Data" dissemination, visualization, and exploration. Conveniently, web-based data portals accommodate the complexity of multi-omic experiments and the many experts involved. However, developing these tailored companion resources requires programming expertise and knowledge of web server architecture-a substantial burden for most. Here, we describe Argonaut, a simple, code-free, and user-friendly platform for creating customizable, interactive data-hosting websites. Argonaut carries out real-time statistical analyses of the data, which it organizes into easily sharable projects. Collaborating researchers worldwide can explore the results, visualized through popular plots, and modify them to streamline data interpretation. Increasing the pace and ease of access to multi-omic data, Argonaut aims to propel discovery of new biological insights. We showcase the capabilities of this tool using a published multi-omics dataset on the large mitochondrial protease deletion collection.

A large-scale genome-lipid association map guides lipid identification.

Despite the crucial roles of lipids in metabolism, we are still at the early stages of comprehensively annotating lipid species and their genetic basis. Mass spectrometry-based discovery lipidomics offers the potential to globally survey lipids and their relative abundances in various biological samples. To discover the genetics of lipid features obtained through high-resolution liquid chromatography-tandem mass spectrometry, we analysed liver and plasma from 384 diversity outbred mice, and quantified 3,283 molecular features. These features were mapped to 5,622 lipid quantitative trait loci and compiled into a public web resource termed LipidGenie. The data are cross-referenced to the human genome and offer a bridge between genetic associations in humans and mice. Harnessing this resource, we used genome-lipid association data as an additional aid to identify a number of lipids, for example gangliosides through their association with B4galnt1, and found evidence for a group of sex-specific phosphatidylcholines through their shared locus. Finally, LipidGenie's ability to query either mass or gene-centric terms suggests acyl-chain-specific functions for proteins of the ABHD family.

Large-scale Multi-omic Analysis of COVID-19 Severity.

We performed RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities. Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a comparative analysis with published data and a machine learning approach for prediction of COVID-19 severity.

Real-time health monitoring through urine metabolomics.

Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology of most diseases. Here we propose to integrate data from biohealth smartphone applications with continuous metabolic phenotypes derived from urine metabolites. This combination of molecular phenotypes with quantitative measurements of lifestyle reflect the biological consequences of human behavior in real time. We present data from an observational study involving two healthy subjects and discuss the challenges, opportunities, and implications of integrating this new layer of physiological information into digital medicine. Though our dataset is limited to two subjects, our analysis (also available through an interactive web-based visualization tool) provides an initial framework to monitor lifestyle factors, such as nutrition, drug metabolism, exercise, and sleep using urine metabolites.

Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests.

Empirical testing of chemicals for drug efficacy costs many billions of dollars every year. The ability to predict the action of molecules in silico would greatly increase the speed and decrease the cost of prioritizing drug leads. Here, we asked whether drug function, defined as MeSH "therapeutic use" classes, can be predicted from only a chemical structure. We evaluated two chemical-structure-derived drug classification methods, chemical images with convolutional neural networks and molecular fingerprints with random forests, both of which outperformed previous predictions that used drug-induced transcriptomic changes as chemical representations. This suggests that the structure of a chemical contains at least as much information about its therapeutic use as the transcriptional cellular response to that chemical. Furthermore, because training data based on chemical structure is not limited to a small set of molecules for which transcriptomic measurements are available, our strategy can leverage more training data to significantly improve predictive accuracy to 83-88%. Finally, we explore use of these models for prediction of side effects and drug-repurposing opportunities and demonstrate the effectiveness of this modeling strategy for multilabel classification.

Autometa: automated extraction of microbial genomes from individual shotgun metagenomes.

Shotgun metagenomics is a powerful, high-resolution technique enabling the study of microbial communities in situ. However, species-level resolution is only achieved after a process of 'binning' where contigs predicted to originate from the same genome are clustered. Such culture-independent sequencing frequently unearths novel microbes, and so various methods have been devised for reference-free binning. As novel microbiomes of increasing complexity are explored, sometimes associated with non-model hosts, robust automated binning methods are required. Existing methods struggle with eukaryotic contamination and cannot handle highly complex single metagenomes. We therefore developed an automated binning pipeline, termed 'Autometa', to address these issues. This command-line application integrates sequence homology, nucleotide composition, coverage and the presence of single-copy marker genes to separate microbial genomes from non-model host genomes and other eukaryotic contaminants, before deconvoluting individual genomes from single metagenomes. The method is able to effectively separate over 1000 genomes from a metagenome, allowing the study of previously intractably complex environments at the level of single species. Autometa is freely available at https://bitbucket.org/jason_c_kwan/autometa and as a docker image at https://hub.docker.com/r/jasonkwan/autometa under the GNU Affero General Public License 3 (AGPL 3).

An antifungal polyketide associated with horizontally acquired genes supports symbiont-mediated defense in Lagria villosa beetles.

Microbial symbionts are often a source of chemical novelty and can contribute to host defense against antagonists. However, the ecological relevance of chemical mediators remains unclear for most systems. Lagria beetles live in symbiosis with multiple strains of Burkholderia bacteria that protect their offspring against pathogens. Here, we describe the antifungal polyketide lagriamide, and provide evidence supporting that it is produced by an uncultured symbiont, Burkholderia gladioli Lv-StB, which is dominant in field-collected Lagria villosa. Interestingly, lagriamide is structurally similar to bistramides, defensive compounds found in marine tunicates. We identify a gene cluster that is probably involved in lagriamide biosynthesis, provide evidence for horizontal acquisition of these genes, and show that the naturally occurring symbiont strains on the egg are protective in the soil environment. Our findings highlight the potential of microbial symbionts and horizontal gene transfer as influential sources of ecological innovation.

Increased Biosynthetic Gene Dosage in a Genome-Reduced Defensive Bacterial Symbiont.

A symbiotic lifestyle frequently results in genome reduction in bacteria; the isolation of small populations promotes genetic drift and the fixation of deletions and deleterious mutations over time. Transitions in lifestyle, including host restriction or adaptation to an intracellular habitat, are thought to precipitate a wave of sequence degradation events and consequent proliferation of pseudogenes. We describe here a verrucomicrobial symbiont of the tunicate Lissoclinum sp. that appears to be undergoing such a transition, with low coding density and many identifiable pseudogenes. However, despite the overall drive toward genome reduction, this symbiont maintains seven copies of a large polyketide synthase (PKS) pathway for the mandelalides (mnd), cytotoxic compounds that likely constitute a chemical defense for the host. There is evidence of ongoing degradation in a small number of these repeats-including variable borders, internal deletions, and single nucleotide polymorphisms (SNPs). However, the gene dosage of most of the pathway is increased at least 5-fold. Correspondingly, this single pathway accounts for 19% of the genome by length and 25.8% of the coding capacity. This increased gene dosage in the face of generalized sequence degradation and genome reduction suggests that mnd genes are under strong purifying selection and are important to the symbiotic relationship. IMPORTANCE Secondary metabolites, which are small-molecule organic compounds produced by living organisms, provide or inspire drugs for many different diseases. These natural products have evolved over millions of years to provide a survival benefit to the producing organism and often display potent biological activity with important therapeutic applications. For instance, defensive compounds in the environment may be cytotoxic to eukaryotic cells, a property exploitable for cancer treatment. Here, we describe the genome of an uncultured symbiotic bacterium that makes such a cytotoxic metabolite. This symbiont is losing genes that do not endow a selective advantage in a hospitable host environment. Secondary metabolism genes, however, are repeated multiple times in the genome, directly demonstrating their selective advantage. This finding shows the strength of selective forces in symbiotic relationships and suggests that uncultured bacteria in such relationships should be targeted for drug discovery efforts.

Interpreting Microbial Biosynthesis in the Genomic Age: Biological and Practical Considerations.

Genome mining has become an increasingly powerful, scalable, and economically accessible tool for the study of natural product biosynthesis and drug discovery. However, there remain important biological and practical problems that can complicate or obscure biosynthetic analysis in genomic and metagenomic sequencing projects. Here, we focus on limitations of available technology as well as computational and experimental strategies to overcome them. We review the unique challenges and approaches in the study of symbiotic and uncultured systems, as well as those associated with biosynthetic gene cluster (BGC) assembly and product prediction. Finally, to explore sequencing parameters that affect the recovery and contiguity of large and repetitive BGCs assembled de novo, we simulate Illumina and PacBio sequencing of the Salinispora tropica genome focusing on assembly of the salinilactam (slm) BGC.

Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI): Instrument Design and Calibration.

The Michelson Interferometer for Global High-resolution imaging of the Thermosphere and Ionosphere (MIGHTI) instrument was built for launch and operation on the NASA Ionospheric Connection Explorer (ICON) mission. The instrument was designed to measure thermospheric horizontal wind velocity profiles and thermospheric temperature in altitude regions between 90km and 300km, during day and night. For the wind measurements it uses two perpendicular fields of view pointed at the Earth's limb, observing the Doppler shift of the atomic oxygen red and green lines at 630.0nm and 557.7nm wavelength. The wavelength shift is measured using field-widened, temperature compensated Doppler Asymmetric Spatial Heterodyne (DASH) spectrometers, employing low order échelle gratings operating at two different orders for the different atmospheric lines. The temperature measurement is accomplished by a multichannel photometric measurement of the spectral shape of the molecular oxygen A-band around 762nm wavelength. For each field of view, the signals of the two oxygen lines and the A-band are detected on different regions of a single, cooled, frame transfer charge coupled device (CCD) detector. On-board calibration sources are used to periodically quantify thermal drifts, simultaneously with observing the atmosphere. The MIGHTI requirements, the resulting instrument design and the calibration are described.

Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI): Monolithic Interferometer Design and Test.

The design and laboratory tests of the interferometers for the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) instrument which measures thermospheric wind and temperature for the NASA-sponsored Ionospheric Connection (ICON) Explorer mission are described. The monolithic interferometers use the Doppler Asymmetric Spatial Heterodyne (DASH) Spectroscopy technique for wind measurements and a multi-element photometer approach to measure thermospheric temperatures. The DASH technique and overall optical design of the MIGHTI instrument are described in an overview followed by details on the design, element fabrication, assembly, laboratory tests and thermal control of the interferometers that are the heart of MIGHTI.

Single sample resolution of rare microbial dark matter in a marine invertebrate metagenome.

Direct, untargeted sequencing of environmental samples (metagenomics) and de novo genome assembly enable the study of uncultured and phylogenetically divergent organisms. However, separating individual genomes from a mixed community has often relied on the differential-coverage analysis of multiple, deeply sequenced samples. In the metagenomic investigation of the marine bryozoan Bugula neritina, we uncovered seven bacterial genomes associated with a single B. neritina individual that appeared to be transient associates, two of which were unique to one individual and undetectable using certain "universal" 16S rRNA primers and probes. We recovered high quality genome assemblies for several rare instances of "microbial dark matter," or phylogenetically divergent bacteria lacking genomes in reference databases, from a single tissue sample that was not subjected to any physical or chemical pre-treatment. One of these rare, divergent organisms has a small (593 kbp), poorly annotated genome with low GC content (20.9%) and a 16S rRNA gene with just 65% sequence similarity to the closest reference sequence. Our findings illustrate the importance of sampling strategy and de novo assembly of metagenomic reads to understand the extent and function of bacterial biodiversity.

Lack of Overt Genome Reduction in the Bryostatin-Producing Bryozoan Symbiont "Candidatus Endobugula sertula".

The uncultured bacterial symbiont "Candidatus Endobugula sertula" is known to produce cytotoxic compounds called bryostatins, which protect the larvae of its host, Bugula neritina The symbiont has never been successfully cultured, and it was thought that its genome might be significantly reduced. Here, we took a shotgun metagenomics and metatranscriptomics approach to assemble and characterize the genome of "Ca Endobugula sertula." We found that it had specific metabolic deficiencies in the biosynthesis of certain amino acids but few other signs of genome degradation, such as small size, abundant pseudogenes, and low coding density. We also identified homologs to genes associated with insect pathogenesis in other gammaproteobacteria, and these genes may be involved in host-symbiont interactions and vertical transmission. Metatranscriptomics revealed that these genes were highly expressed in a reproductive host, along with bry genes for the biosynthesis of bryostatins. We identified two new putative bry genes fragmented from the main bry operon, accounting for previously missing enzymatic functions in the pathway. We also determined that a gene previously assigned to the pathway, bryS, is not expressed in reproductive tissue, suggesting that it is not involved in the production of bryostatins. Our findings suggest that "Ca Endobugula sertula" may be able to live outside the host if its metabolic deficiencies are alleviated by medium components, which is consistent with recent findings that it may be possible for "Ca Endobugula sertula" to be transmitted horizontally. IMPORTANCE: The bryostatins are potent protein kinase C activators that have been evaluated in clinical trials for a number of indications, including cancer and Alzheimer's disease. There is, therefore, considerable interest in securing a renewable supply of these compounds, which is currently only possible through aquaculture of Bugula neritina and total chemical synthesis. However, these approaches are labor-intensive and low-yielding and thus preclude the use of bryostatins as a viable therapeutic agent. Our genome assembly and transcriptome analysis for "Ca Endobugula sertula" shed light on the metabolism of this symbiont, potentially aiding isolation and culturing efforts. Our identification of additional bry genes may also facilitate efforts to express the complete pathway heterologously.

Microbial community response of nitrifying sequencing batch reactors to silver, zero-valent iron, titanium dioxide and cerium dioxide nanomaterials.

As nanomaterials in consumer products increasingly enter wastewater treatment plants, there is concern that they may have adverse effects on biological wastewater treatment. Effects of silver (nanoAg), zero-valent iron (NZVI), titanium dioxide (nanoTiO2) and cerium dioxide (nanoCeO2) nanomaterials on nitrification and microbial community structure were examined in duplicate lab-scale nitrifying sequencing batch reactors (SBRs) relative to control SBRs that received no nanomaterials or ionic/bulk analogs. Nitrification function was not measurably inhibited in the SBRs by any of the materials as dosing was initiated at 0.1 mg/L and sequentially increased every 14 days to 1, 10, and 20 mg/L. However, SBRs rapidly lost nitrification function when the Ag⁺ experiment was repeated at a continuous high load of 20 mg/L. Shifts in microbial community structure and decreased microbial diversity were associated with both sequential and high loading of nanoAg and Ag⁺, with more pronounced effects for Ag⁺. Bacteroidetes became more dominant in SBRs dosed with Ag⁺, while Proteobacteria became more dominant in SBRs dosed with nanoAg. The two forms of silver also had distinct effects on specific bacterial genera. A decrease in nitrification gene markers (amoA) was observed in SBRs dosed with nanoAg and Ag⁺. In contrast, impacts of NZVI, nanoTiO2, nanoCeO2 and their analogs on microbial community structure and nitrification gene markers were limited. TEM-EDS analysis indicated that a large portion of nanoAg remained dispersed in the activated sludge and formed Ag­S complexes, while NZVI, nanoTiO2 and nanoCeO2 were mostly aggregated and chemically unmodified. Overall, this study suggests a high threshold of the four nanomaterials in terms of exerting adverse effects on nitrification function. However, distinct microbial community responses to nanoAg indicate potential long-term effects.

The use of silicon-based tethers for the Pauson-Khand reaction.

A range of silicon-based tethers and promoters have been investigated for use in the development of a silyl-tethered Pauson-Khand reaction.