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ABSTRACT National standardization of ecological sampling protocols between different agencies in Aotearoa New Zealand has historically been difficult to attain, creating challenges for combining datasets for national scale analyses. The introduction of new methods for biological monitoring, such as environmental DNA (eDNA), presents an opportunity to standardize aquatic sampling protocols prior to widespread adoption. The objective of this study was to optimize eDNA sample replication for the consistent characterization of freshwater fish and macroinvertebrate communities in flowing waters, and ultimately, to inform the development of robust national monitoring standards. A comparison of field replication and extraction methods (pooling of preservation buffer) was also trialed as part of this high replication (n = 16) eDNA study to assess any potential benefits in measuring species richness and reducing processing costs alongside replication optimisation. This involved two ‘syringe’ sampling methods (‘standard’ and ‘boosted’, eight each) conducted across 54 riverine sites throughout the country. No significant difference was found for species richness between the standardized (eight replicates) or boosted (16 replicates composited to eight) eDNA methods for fish and macroinvertebrates. Results indicated that six replicates were needed to consistently detect 89.5% of fish species likely to be present using field‐based syringe eDNA sampling and preservation. However, an altitudinal species richness effect was observed for fish. For macroinvertebrates, six replicates were required to identify 86% of taxa identified to the NEMS (National Environmental Monitoring Standards) level used for the Macroinvertebrate Community Index (MCI: usually genera) while eight replicates were required to detect 89% of NEMS taxa. For fish and macroinvertebrate biodiversity, this study suggests that six replicates are a reasonable trade‐off between effective community characterization and cost in New Zealand lotic systems.

Abstract Wetlands are ecologically and culturally significant ecosystems that are experiencing biodiversity declines globally. Biomonitoring techniques that use environmental DNA (eDNA) to detect and monitor biodiversity are well established in lake, riverine, and marine ecosystems. However, their use in wetlands requires further development due to the presence of sediments that block eDNA filters to limit water filtration, alongside a lack of standardized methodology. In this study, we examined eDNA dynamics to understand spatiotemporal biodiversity patterns in an Aotearoa New Zealand wetland and to optimize their application to wetland‐specific challenges. We sampled four sites across Opuatia Wetland at three time points during an austral spring. We conducted conventional taxonomic surveys, tested three different filter sizes (1.2 μm, 5 μm, and semi‐quantitative dacron filters), and assessed our ability to detect foreign DNA (from kea; Nestor notabilis) at different time points and distances post‐release. We found significant differences in DNA sequence composition across time and space, and when using different sized filters. eDNA data generally complemented (versus replaced) conventional survey and identification methods, with certain species only detected by one method or the other. Taxonomic resolution of conventional sampling and identification methods often exceeded that of eDNA. Foreign DNA was detectable 10 m from its release point for up to 1 week post‐release. Our results provide new considerations for future eDNA research in wetland environments, where rapid biomonitoring techniques are needed to support conservation and preservation.

Abstract Background Chronic obstructive pulmonary disease (COPD) varies significantly in symptomatic and physiologic presentation. Identifying disease subtypes from molecular data, collected from easily accessible blood samples, can help stratify patients and guide disease management and treatment. Methods Blood gene expression measured by RNA-sequencing in the COPDGene Study was analyzed using a network perturbation analysis method. Each COPD sample was compared against a learned reference gene network to determine the part that is deregulated. Gene deregulation values were used to cluster the disease samples. Results The discovery set included 617 former smokers from COPDGene. Four distinct gene network subtypes are identified with significant differences in symptoms, exercise capacity and mortality. These clusters do not necessarily correspond with the levels of lung function impairment and are independently validated in two external cohorts: 769 former smokers from COPDGene and 431 former smokers in the Multi-Ethnic Study of Atherosclerosis (MESA). Additionally, we identify several genes that are significantly deregulated across these subtypes, including DSP and GSTM1, which have been previously associated with COPD through genome-wide association study (GWAS). Conclusions The identified subtypes differ in mortality and in their clinical and functional characteristics, underlining the need for multi-dimensional assessment potentially supplemented by selected markers of gene expression. The subtypes were consistent across cohorts and could be used for new patient stratification and disease prognosis.

Summary: Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype’s ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13–27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, “Watershed,” to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer’s disease.

Summary: Background: Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases. Methods: We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables. Findings: We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Cancers arise from successive rounds of mutation and selection, generating clonal populations that vary in size, mutational content and drug responsiveness. Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy. Mutation counts and frequencies resulting from next-generation sequencing (NGS) potentially reflect a tumor's clonal composition; however, deconvolving NGS data to infer a tumor's clonal structure presents a major challenge. We propose a generative model for NGS data derived from multiple subsections of a single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model. We demonstrate, via simulation, the validity of the approach, and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node. After dividing the tumor into subsections, we perform exome sequencing for each subsection to assess mutational content, followed by deep sequencing to precisely count normal and variant alleles within each subsection. By quantifying the frequencies of 17 somatic variants, we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time.

Discovery rates of new species are uneven across taxonomic groups and regions, with distinctive and widely distributed species being more readily described than species with secretive habits. The genus Rhynchocalamus includes five species of secretive snakes distributed from Egypt eastwards to Iran, including the Arabian Peninsula. A wide biogeographic gap exists within the genus, which separates R. dayanae found in south Israel from R. arabicus, which occurs in the coastal areas of south Yemen and Oman. We describe Rhynchocalamus hejazicus sp. nov., a small, secretive snake, with a distinctive colouration and a melanistic morph. The new species occurs in the northwestern Hejaz region of the Kingdom of Saudi Arabia (KSA) and fills a large part of the existing distribution gap of the genus in the Arabian Peninsula. Molecular analyses of mitochondrial (12S, 16S, cytb) and nuclear genes (cmos, MC1R, NT3, RAG1) indicate that R. hejazicus sp. nov. is closely related to R. dayanae and R. arabicus, but uncertainty on the deep relationship within the genus remains. The new species has a large distribution range which potentially includes other regions in Jordan and KSA, and is associated with mountainous areas with cold wet seasons. Furthermore, it inhabits sandy and stony soils with varying vegetation cover and can be found in anthropogenically disturbed habitats, suggesting that the species should not be categorised as threatened according to IUCN criteria. The discovery of such a distinctive species highlights the existing gap in the description of rare and secretive species, and the need to enhance sampling efforts and monitoring strategies to fully capture species diversity in unexplored areas.