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Metagenome-based vertical profiling of the Gulf of Mexico highlights its uniqueness and far-reaching effects of freshwater input.

Genomic and metagenomic explorations of the oceans have identified well-structured microbial assemblages showing endemic genomic adaptations with increasing depth. However, deep water column surveys have been limited, especially of the Gulf of Mexico (GoM) basin, despite its importance for human activities. To fill this gap, we report on 19 deeply sequenced (~5 Gbp/sample) shotgun metagenomes collected along a vertical gradient, from the surface to about 2,000 m deep, at three GoM stations. Beta diversity analysis revealed strong clustering by depth, and not by station. However, a community-level pangenome style gene content analysis revealed ~54% of predicted gene sequences to be station-specific within our GoM samples. Of the 154 medium-to-high-quality MAGs recovered, 145 represent novel species compared with the NCBI genomes and Tara Oceans MAGs databases. Two of these MAGs were relatively abundant at both surface and deep samples, revealing remarkable versatility across the water column. A few MAGs of freshwater origin (~6% of total detected) were relatively abundant at 600 m deep and 270 miles from the coast at one station, revealing that the effects of freshwater input in the GoM can sometimes be far-reaching and long-lasting. Notably, 1,447/16,068 of the total COGs detected were positively (Pearson's r ≥ 0.5) or negatively (Pearson's r ≤ -0.5) correlated with depth, including beta-lactamases, dehydrogenases, and CoA-associated oxidoreductases. Taken together, our results reveal substantial novel genome and gene diversity across the GoM's water column, and testable hypotheses for some of the diversity patterns observed.IMPORTANCETo what extent microbial communities are similar between different ocean basins at similar depths, and what the impact of freshwater input by major rivers may be on these communities, remain poorly understood issues with potentially important implications for modeling and managing marine biodiversity. In this study, we performed metagenomic sequencing and recovered 154 medium-to-high-quality metagenome-assembled genomes (MAGs) from three stations in the Gulf of Mexico (GoM) and from various depths up to about 2,000 m. Comparison to MAGs recovered from other ocean basins highlighted the unique diversity harbored by the GoM, which could be driven by more substantial input from the Mississippi River and by human activities, including offshore oil drilling. The data and results provided by this study should be useful for future comparative analysis of marine biodiversity and contribute to its more complete characterization.

Metagenome

Evolutionary patterns and repeated adaptive strategies of deep-sea anemones.

Sea anemones occupy the full depth range of the oceans, yet their evolutionary patterns and adaptive strategies to the enigmatic deep sea have remained contentious and poorly resolved. Here, we assemble genomes (n = 13) and transcriptomes for 15 species collected between 432 and 6,000 m and integrate them with publicly available actiniarian data. We find support for a shallow-water origin of Actiniaria through a framework that emphasizes genome-scale changes associated with habitat transitions. Most strikingly, these changes include repeated dismantling of the circadian toolkit across deep-sea lineages. In addition to convergent gene losses in photo- and temperature-regulatory genes, we find that some deep-sea lineages have experienced recurrent loss or pseudogenization of key meiotic genes (e.g., Meiosin, Ythdc2, Spo11, and Mlh3), suggesting reduced meiotic capacity in some lineages. Despite this extensive genomic erosion, deep-sea anemones exhibit molecular tuning: specific amino acid substitutions improve enzyme performance under low-temperature conditions relevant to the deep sea, while selective expansions of gene families related to neural excitability, membrane systems, and other functions may help maintain physiological performance in this environment. Functional assays in yeast indicate enhanced performance of the deep-sea variants at 4°C. These results define a "loss-optimization-innovation" triad that underlies bathymetric adaptations and may apply to other deep-sea fauna worldwide.

Actiniaria

Global Patterns of Net Ecosystem Exchange in peatlands: A Systematic Review and Meta-analysis of Drivers Across Land Use and Environmental Gradients.

Peatlands play an essential role in the global carbon cycle, storing approximately one-third of the world's soil carbon despite covering less than 3% of the land surface. Peatland degradation from anthropogenic activities and climate change can convert peatlands from net carbon sinks to sources by altering carbon cycling. Net Ecosystem Exchange (NEE), the balance between CO2 uptake and emission, is a critical indicator for assessing peatland condition and restoration efforts. We conducted a systematic quantitative literature review to investigate global patterns of NEE in peatlands and identify key environmental and anthropogenic drivers of CO2 flux variability. Annual NEE values from 120 globally distributed sites reported in peer-reviewed literature were analyzed in relation to climatic zone, land use, vegetation type, peatland condition, and water table depth. Our synthesis revealed significant geographic gaps, with peatland NEE studies substantially underrepresented in the Tropics, Africa, and Oceania. Agricultural peatlands emitted significantly more CO2 than sites under natural land uses or peat extraction, while degraded peatlands were significantly greater net CO2 sources than intact and restored systems. Restored peatlands remained net CO2 sources on average, emphasizing the importance of long-term monitoring and adaptive management following restoration interventions. Water table depth significantly affected NEE variability, with CO2 emissions increasing approximately 7.2 gCO2-C m-2yr-1 for every centimeter of water table drawdown. A substantial variability in measurement methods, data processing software, and protocols highlighted the critical need for methodological standardization. Our findings provide evidence-based targets for peatland conservation and restoration monitoring as nature-based climate solutions.

Ecosystem

Untargeted metabolomics reveals anion and organ-specific metabolic responses of salinity tolerance in willow.

Willows can alleviate soil salinisation while generating sustainable feedstock for biorefinery, yet the metabolomic adaptations underlying their tolerance remain poorly understood. Salix miyabeana was treated with two environmentally abundant salts, NaCl and Na2SO4, in a 12-week pot trial. Willows tolerated salts across all treatments (up to 9.1 dS m-1 soil ECe), maintaining biomass while selectively partitioning ions, confining Na+ to roots and accumulating Cl- andin the canopy and adapting to osmotic stress via reduced stomatal conductance. Untargeted metabolomics captured >5000 putative compounds, including 278 core willow metabolome compounds constitutively produced across organs. Across all treatments, salinity drove widespread metabolic reprogramming, altering 28% of the overall metabolome, with organ-tailored strategies. Comparing salt forms at equimolar sodium, shared differentially abundant metabolites were limited to 3% of the metabolome, representing the generalised salinity response, predominantly in roots. Anion-specific metabolomic responses were extensive. NaCl reduced carbohydrates and tricarboxylic acid cycle intermediates, suggesting potential carbon and energy resource pressure, and accumulated root structuring compounds, antioxidant flavonoids, and fatty acids. Na2SO4 salinity triggered accumulation of sulphur-containing larger peptides, suggesting excess sulphate incorporation leverages ion toxicity to produce specialised salt-tolerance-associated metabolites. This high-depth picture of the willow metabolome underscores the importance of capturing plant adaptations to salt stress at organ scale and considering ion-specific contributions to soil salinity.

Salix

Radiation Without Borders: Unraveling Bystander and Non-Targeted Effects in Oncology.

Radiotherapy (RT) remains a cornerstone of cancer treatment, offering spatially precise cytotoxicity against malignant cells. However, emerging evidence reveals that ionizing radiation (IR) exerts biological effects beyond the targeted tumor volume, manifesting as radiation bystander effects (BEs) and other non-targeted effects (NTEs). These phenomena challenge the traditional paradigm of RT as a localized intervention, highlighting systemic and long-term consequences in non-irradiated tissues. This comprehensive review synthesizes molecular, cellular, and clinical insights about BEs, elucidating the complex intercellular signaling networks gap junctions, cytokines, extracellular vesicles, and oxidative stress that propagate damage, genomic instability, and inflammation. We explore the role of mitochondrial dysfunction, epigenetic reprogramming, immune modulation, and stem cell niche disruption in shaping BEs outcomes. Clinically, BEs contribute to neurocognitive decline, cardiovascular disease, pulmonary fibrosis, gastrointestinal toxicity, and secondary malignancies, particularly in pediatric and long-term cancer survivors. The review also evaluates countermeasures including antioxidants, COX-2 inhibitors, exosome blockers, and FLASH RT, alongside emerging strategies targeting cfCh, inflammasomes, and senescence-associated secretory phenotypes. We discuss the dual nature of BEs: their potential to both harm and heal, underscoring adaptive responses and immune priming in specific contexts. By integrating mechanistic depth with translational relevance, this work posits that radiation BEs are a modifiable axis of RT biology. Recognizing and mitigating BEs is imperative for optimizing therapeutic efficacy, minimizing collateral damage, and enhancing survivorship outcomes. This review advocates for a paradigm shift in RT planning and post-treatment care, emphasizing precision, personalization, and systemic awareness in modern oncology.

Humans

Toward simple, rapid, and deep plant proteome analysis with an in-cell proteomics strategy.

While liquid chromatography-mass spectrometry (LCMS) has revolutionized plant proteomics over the past decade, plant sample preparation remains a major challenge due to rigid cell walls, abundant secondary metabolites, and wide dynamic range of protein abundance. These hurdles demand laborious tissue disruption, complex precipitation, and extensive cleanup prior to LCMS analysis, limiting the widespread adoption of proteomic technologies within the plant biology community. To overcome these barriers, we introduced an "in-cell proteomics" strategy that bypasses cell lysis and protein extraction by performing digestion directly inside methanol-fixed cells. We systematically benchmarked this strategy against conventional lysate-based workflows across 4 model plants (Arabidopsis thaliana, Nicotiana benthamiana, Zea mays, and Sorghum bicolor) and 3 tissue types (leaves, pollen, and seeds). Combined with minimal input material and single-shot LCMS, the in-cell approach consistently identified 9,000 to 12,000 proteins from leaves, 7,000 to 9,000 from pollen grains, and approximately 8,000 from seeds. Our comprehensive dataset demonstrates that this in-cell digestion approach substantially simplifies plant sample preparation while delivering proteomic performance equivalent to established workflows. Finally, to demonstrate the biological utility of this approach, we characterized the proteomes of N. benthamiana leaves infected with 2 fungal strains that exhibit different host specificities. Our in-depth proteomic data revealed distinct host response signatures differentiating the host-adapted Colletotrichum destructivum from the nonhost-adapted Colletotrichum sublineola strain. Overall, this study provides a simple, unbiased alternative for plant proteomic analysis that can be readily applied to tackle complex agricultural and physiological challenges in plant biology.

Proteomics

The chromosome-level genome assembly and annotation of the silver-lipped pearl oyster, Pinctada maxima.

The silver-lipped pearl oyster (Pinctada maxima) is a valuable tropical aquaculture species, playing a crucial economic role in the global pearl industry. However, the lack of genomic reference limits our in-depth understanding of this species in genome-based breeding, conservation, evolution and adaptation. Here, annotated chromosome-level reference genome for P. maxima was generated by integrating PacBio long-read sequencing, Illumina short-read sequencing, and Hi-C sequencing data. The total genome size is 1,264.93&#x2009;Mb, with contig N50 and scaffold N50 of 649&#x2009;kb and 89.19&#x2009;Mb, respectively. The majority (97.94%) of the assembled genome was anchored to the 14 chromosomes by Hi-C analysis. The relatively high genome completeness was observed, with 97.38% (metazoa_odb10 database) and 95.26% (mollusca_odb10 database) in BUSCO analysis. Genome annotation revealed approximately 65.46% of the repeat sequences and 26,315 protein-coding genes. Comparative genome analysis revealed 28 expanded and 48 contracted families (p&#x2009;<&#x2009;0.05) in P. maxima, with 3.2% of genes (894) being species-specific. This chromosome-level genome serves as an essential resource for research in evolutionary genomics, phylogenetics, and biomineralization.

Animals

UMI-nea: a fast, robust tool for reference-free UMI deduplication and accurate quantification.

MOTIVATION: One of the key applications of Unique Molecular Identifiers (UMIs) in high-throughput sequencing is to correct for PCR amplification bias and removal of PCR duplicates, thereby improving quantification in DNA-seq and RNA-seq applications. Accurately grouping error-bearing UMIs that originate from the same input molecule through a UMI deduplication method is a critical step in this process. However, many existing UMI deduplication tools rely on simple Hamming distance comparisons or suboptimal clustering algorithms, often resulting in erroneous UMI groupings, particularly in error-prone long-read sequencing or ultra-high-depth short-read sequencing. RESULTS: We introduce UMI-nea, a tool that utilizes Levenshtein distance comparisons and a novel clustering approach to optimize multithreading workflows. Compared against three other indel-aware UMI deduplication tools, UMI-nea achieves more accurate UMI groupings with efficient run time. It demonstrates robust performance across diverse sequencing platforms, depths, and UMI lengths. Additionally, UMI-nea incorporates a data-guided adaptive UMI filter, further enhancing quantification accuracy. AVAILABILITY AND IMPLEMENTATION: UMI-nea is available on github https://github.com/Qiaseq-research/UMI-nea.git or Zenodo https://doi.org/10.5281/zenodo.16745758. Sequencing data are stored at https://qiagenpublic.blob.core.windows.net/umi-nea-datasets/.

High-Throughput Nucleotide Sequencing

Minimizing decompression and warming during deep seawater collection increases abundance and activity of autochthonous bacteria and archaea.

The deep ocean hosts autochthonous pressure-adapted microorganisms that are unique to this environment, as well as allochthonous pressure-sensitive members transported from shallow depths by vertical advection and particle-sinking. However, conventional sampling instruments decompress and warm deep-sea samples during retrieval, potentially altering microbial properties when studied ex situ. Here, we assess this potential sampling bias by comparing seawater microbial communities collected with or without measures aimed at minimizing pressure and temperature effects. When compared to samples collected under pressurized conditions, conventional sampling (using Niskin bottles) was found to affect prokaryotic cells retrieved by reducing their total numbers, diminishing protein synthesis activity (>10%), and also causing overall shifts in the community composition. The most significant compositional change was a >20% decrease in metagenomic archaeal representation (TACK-group/Thaumarchaeota/Nitrososphaerota). Deep-sea bacterial groups had mixed responses to preserving pressure during retrieval, with some groups exhibiting higher representation when samples were maintained pressurized (e.g. members of the family Pelagibacteraceae, unclassified Thiotricales, Thioglobaceae, and Chitinophagaceae), whereas others increased their representation when decompressed (e.g. Burkholderiaceae, Comamonadaceae, and Oxalobacteraceae). This study reveals the existence of bias introduced by the complete decompression of samples retrieved with traditional instrumentation, as well as a decrease in overall bacterial activity when samples are completely decompressed during retrieval. Additionally, incubations lasting for >24&#xa0;h were shown to transform the original prokaryotic community composition. Precautions addressing these effects are necessary to enhance the reliability of ex situ measurements and improve our understanding of deep-sea microbial ecology and biogeochemistry.

Seawater

Comparative genomics reveals genotype-phenotype concordance and cryptic resistomes in clinical Pseudomonas aeruginosa.

BACKGROUND: Pseudomonas aeruginosa (P. aeruginosa) is a major pathogen because of its adaptability. It shows rapid evolution of multidrug resistance (MDR). Phenotype-based diagnostics often fail to detect silent resistance determinants and early adaptive changes. This study integrates phenotypic profiling with whole-genome sequencing (WGS) to examine resistance architecture in clinical isolates from eastern India. METHODS: From 1295 culture-positive P. aeruginosa specimens collected at a tertiary care hospital in eastern India. Using predefined criteria, representative MDR and non-MDR isolates were selected, including distinct resistance phenotypes, specimen-source diversity, and hospital and community-acquired settings; multivariate analysis of resistance profiles illustrated phenotypic diversity. Antimicrobial susceptibility assessed using VITEK-2 and Kirby-Bauer disk diffusion, species identity confirmed by 16&#xa0;S rRNA sequencing, and genomic analysis processed through a reference-guided workflow. Antimicrobial Resistance (AMR) determinants were identified through CARD, and phylogenetic tree constructed from 454 publicly available P. aeruginosa genomes. RESULTS: MDR exhibited greater sequence divergence relative to PA14 (~&#x2009;69,000 variants) than the non-MDR isolate (~&#x2009;58,700 variants), with >&#x2009;92% coverage at &#x2265;&#x2009;30X depth. Strong genotype-phenotype concordance observed in MDR isolates across five antibiotic classes, associated with &#x3b2;-lactamase variants (PDC-67, OXA-396) and regulatory adaptations (ArmR, cprS). The non-MDR isolate harboured gyrA (T83I) resistance-associated mutations, PDC-1, and OXA-847 without phenotypic expression, indicating silent resistome. Phylogenetically, MDR isolates clustered tightly within the phylogeny, while the non-MDR isolate formed a distinct lineage. CONCLUSION: Observed genomic differences align with adaptation under antimicrobial selection, though confirmation requires larger collections. The non-MDR isolate retained a silent resistome. Findings highlight limitations of phenotype-only diagnostics, support genomic data integration, and emphasize transcriptomics for hidden resistance expression and regulatory dynamics.

Pseudomonas aeruginosa

LYCEUM: learning to call copy number variants on low-coverage ancient genomes.

MOTIVATION: Copy number variants (CNVs) are pivotal in driving phenotypic variation that facilitates species adaptation. They are significant contributors to various disorders, making ancient genomes crucial for uncovering the genetic origins of disease susceptibility across populations. However, detecting CNVs in ancient DNA (aDNA) samples poses substantial challenges due to several factors: (i) aDNA is often highly degraded; (ii) contamination from microbial DNA and DNA from closely related species introduces additional noise into sequencing data; and finally, (iii) the typically low-coverage of aDNA renders accurate CNV detection particularly difficult. Conventional CNV calling algorithms, which are optimized for high-coverage read-depth signals, underperform under such conditions. RESULTS: To address these limitations, we introduce LYCEUM, the first machine learning-based CNV caller for aDNA. To overcome challenges related to data quality and scarcity, we employ a two-step training strategy. First, the model is pre-trained on whole genome sequencing data from the 1000 Genomes Project, teaching it CNV-calling capabilities similar to conventional methods. Next, the model is fine-tuned using high-confidence CNV calls derived from only a few existing high-coverage aDNA samples. During this stage, the model adapts to making CNV calls based on the downsampled read depth signals of the same aDNA samples. LYCEUM achieves accurate detection of CNVs even in typically low-coverage ancient genomes. We also observe that the segmental deletion calls made by LYCEUM show correlation with the demographic history of the samples and exhibit patterns of negative selection inline with natural selection. AVAILABILITY AND IMPLEMENTATION: LYCEUM is available at https://github.com/ciceklab/LYCEUM.

DNA Copy Number Variations

No receptor-binding domain adaptation detected in within-host H5N1 surveillance of 4,559 US dairy outbreak sequences.

BACKGROUND: The 2024-2026 US H5N1 clade 2.3.4.4b dairy cattle outbreak has been characterised primarily through consensus-level phylogenetics. Whether mammalian-adaptation variants are emerging at sub-consensus frequencies within infected hosts, particularly at the haemagglutinin receptor-binding domain (RBD), remains unknown because no systematic within-host variant analysis of the public sequencing corpus has been performed. METHODS: We conducted a pre-registered, corpus-wide intrahost single-nucleotide variant (iSNV) analysis of all publicly available H5N1 cattle, feline-spillover, and retail-milk sequences on the NCBI Sequence Read Archive (4559 samples across 7 BioProjects). A dual-caller concordance pipeline (iVar&#xa0;+&#xa0;LoFreq) with empirically determined allele frequency (AF) threshold (3%, set via four-criterion validation including synthetic spike-in controls) was applied to an 11-site Tier 1 mammalian-adaptation panel spanning the polymerase complex, haemagglutinin RBD, and accessory proteins. Within-host nucleotide diversity was compared across host categories. RESULTS: The HA RBD sites Q226L and G228S (H3 numbering) showed zero detections across >4300 adequately sequenced samples at all AF thresholds tested (1-5%), despite the pipeline detecting other non-synonymous variants at these exact codon positions (upper 95% CI for prevalence: 0.08%). Seven of eleven adaptation sites carried statistically significant iSNV signals after Bonferroni correction (corrected &#x3b1;&#x202f;=&#x202f;0.00417), though all at low prevalence (&#x2264;2.95%). Genotype stratification showed that most polymerase-site detections reflected genotype structure rather than within-host emergence: the apparent PB2 631&#x202f;L&#x2192;M "reversion" was largely the ancestral avian state of the D1.1 genotype (20 of 23 detections), which never acquired the 631L mammalian adaptation, with only two genuine sub-consensus events in the B3.13 background, while consensus-level PB2 701N was a fixed feature of the D1.1 genotype (10 of 14 detections) rather than independent sub-consensus emergence. Cattle exhibited significantly higher within-host nucleotide diversity than feline-spillover samples (&#x3c0;&#x202f;=&#x202f;1.59&#x202f;&#xd7;&#x202f;10-4 vs 6.11&#x202f;&#xd7;&#x202f;10-5; Kruskal-Wallis p&#x202f;=&#x202f;6.6&#x202f;&#xd7;&#x202f;10-15), a finding that persisted after depth-matching (p&#x202f;=&#x202f;4.6&#x202f;&#xd7;&#x202f;10-5); this may reflect prolonged mammary-gland infection, though sampling differences and host biology cannot be excluded. CONCLUSIONS: We did not detect HA receptor-switching adaptation (the acquisition of human-type &#x3b1;2,6 receptor binding via Q226L/G228S) at any tested allele frequency in the US dairy H5N1 outbreak. Sub-consensus mammalian-adaptation signals exist at polymerase-complex sites but at low prevalence, are genotype-structured rather than independently recurrent, and require functional characterisation before informing risk assessment.

Dairy cattle

Landscape genomics analysis reveals the genetic basis underlying cashmere goats and dairy goats adaptation to frigid environments.

Understanding the genetic mechanism of cold adaptation in cashmere goats and dairy goats is very important to improve their production performance. The purpose of this study was to comprehensively analyze the genetic basis of goat adaptation to cold environments, clarify the impact of environmental factors on genome diversity, and lay the foundation for breeding goat breeds to adapt to climate change. A total of 240 dairy goats were subjected to genome resequencing, and the whole genome sequencing data of 57 individuals from 6 published breeds were incorporated. By integrating multiple approaches such as phylogenetic analysis, population structure analysis, gene flow and population history exploration, selection signal analysis, and genome-environment association analysis, an in-depth investigation was carried out. Phylogenetic analysis unraveled the genetic relationships and differentiation patterns among dairy goats and other goat breeds. Through signal analysis (&#x3b8;&#x3c0;, FST, XP-CLR), we identified numerous candidate genes associated with cold adaptation in dairy goats (STRIP1, ALX3, HTR4, NTRK2, MRPL11, PELI3, DPP3, BBS1) and cashmere goats (MED12L, MARC2, MARC1, DSG3, C6H4orf22, CHD7, MYPN, KIAA0825, MITF). Genome-environment association (GEA) analysis confirmed the link between these genes and environmental factors. Moreover, a detailed analysis of the critical genes C6H4orf22 and STRIP1 demonstrated their significant roles in the geographical variations of cold adaptation and allele frequency differences among different breeds. This study contributes to understanding the genetic basis of cold adaptation, providing crucial theoretical support for precision breeding programs aimed at improving production performance in cold regions by leveraging adaptive alleles, thereby ensuring sustainable animal husbandry.

Environmental adaptation

Gloeotrichia echinulata genomes from the United States are nontoxigenic and likely geosmin producers.

Six Gloeotrichia echinulata genomes derived from planktonic harmful algal blooms (HABs) with similar colonial morphology have been sequenced from lakes in the west and northeast regions of USA, four of them to completion. The c. 7 Mbp genomes exhibit a high level of conservation, with 98-99% pairwise genome-wide average nucleotide identity and high levels of synteny, representing a single species cluster. We observed strong conservation of gene clusters responsible for the synthesis of the secondary metabolites and bioactive peptides that are characteristic of HAB-forming cyanobacteria. All six G. echinulata genomes lack genes for the synthesis of classic cyanotoxins, including microcystin, but possess genes responsible for the synthesis of the taste and odor compound geosmin. Interestingly, the geoA geosmin synthase gene in three genomes is homologous to other cyanobacterial geoA genes, while the other three geoA genes are related to actinomyces geoA. Phylogenomic analysis places the G. echinulata genomes within a clade of benthic Nostocales, reflecting an ecological niche featuring extensive growth on the sediment surface before colonies disperse into the epilimnion for planktonic growth. We identify genes conserved in all six genomes that could represent physiological adaptations supporting active growth on sediments and pelagic recruitment independent of wind-driven mixing: phycoerythrin light harvesting complexes for optimal photosynthesis at depth; gliding motility to access patchy nutrient distributions; and gas vesicles with relatively small GvpC proteins that predict resistance to higher hydrostatic pressure. The strong genomic similarity across geographically distant populations suggests that G. echinulata in the United States is a tightly related non-toxigenic species group with predictable properties relevant to public health and drinking water management.

Cyanobacteria

Temperature and Pressure Shaped the Evolution of Antifreeze Proteins in Polar and Deep Sea Zoarcoid Fishes.

Antifreeze proteins (AFPs) have enabled teleost fishes to repeatedly colonize polar seas. Four AFP types have convergently evolved in several fish lineages. AFPs inhibit ice crystal growth and lower tissue freezing point. In lineages with AFPs, species inhabiting colder environments may possess more AFP copies. Elucidating how differences in AFP copy number evolve is challenging due to the genes' tandem array structure and consequently poor resolution of these repetitive regions. Here, we explore the evolution of type III AFPs (AFP III) in the globally distributed suborder Zoarcoidei, leveraging six new long-read genome assemblies. Zoarcoidei has fewer genomic resources relative to other polar fish clades while it is one of the few groups of fishes adapted to both the Arctic and Southern Oceans. Combining these new assemblies with additional long-read genomes available for Zoarcoidei, we conducted a comprehensive phylogenetic test of AFP III evolution and modeled the effects of thermal habitat and depth on AFP III gene family evolution. We confirm a single origin of AFP III via neofunctionalization of the enzyme sialic acid synthase B. We also show that AFP copy number increased under low temperature but decreased with depth, potentially because pressure lowers freezing point. Associations between the environment and AFP III copy number were driven by duplications of paralogs that were translocated out of the ancestral locus at which AFP III arose. Our results reveal novel environmental effects on AFP evolution and demonstrate the value of high-quality genomic resources for studying how structural genomic variation shapes convergent adaptation.

Animals

Neurocorrelates of nocturnal enuresis in pre-adolescent children.

INTRODUCTION: Nocturnal enuresis (NE) is a common neurodevelopmental condition, yet its underlying neural mechanisms remain unclear. This study leverages the large-scale Adolescent Brain Cognitive Development (ABCD) dataset to identify structural and functional brain correlates associated with active symptoms and the resolution of bedwetting. METHODS: Using cross-sectional data from 3472 participants aged 9-10 years, children were categorized into three groups: active nocturnal enuresis (ANE, n = 225), history of nocturnal enuresis (HNE, n = 1171), and healthy control groups (CG, n = 2076). Multimodal neuroimaging protocol evaluated macrostructural properties via structural MRI (sMRI), microstructural white matter integrity via diffusion MRI (dMRI), and functional connectivity via resting-state fMRI (fMRI). Group differences were evaluated using linear models within an ANCOVA framework, adjusting for intracranial volume and handedness with False Discovery Rate (FDR) correction. RESULTS: Compared to controls, the ANE group exhibited a significant volume deficit in the right caudate, decreased sulcal depth in the left insula, and lower internal correlation within the Cingulo-Opercular Network (CON). Conversely, the dry HNE group demonstrated significant structural adaptations, including bilaterally larger putamen volumes and increased right caudate volume compared to the ANE group. The HNE group also showed increased microstructural density (decreased mean diffusivity) in the bilateral hippocampus and an increased cortical surface area in the left insula. Both NE groups demonstrated persistently reduced functional coupling within the CON. CONCLUSIONS: Nocturnal enuresis appears to be associated with a potential complex central signaling deficits. Reduced internal correlation within the CON across both active and former bedwetters indicates a potential for impairment in processing internal homeostatic bladder signals during sleep.

Humans

ZIPcnv: accurate and efficient inference of copy number variations from shallow whole-genome sequencing.

MOTIVATION: Shallow whole-genome sequencing (sWGS), a rapid and cost-effective sequencing technology, has gradually been widely adopted for CNV analyses. However, with genome&#x2011;wide coverage of only 0.1-5&#xd7;, sWGS data display a pronounced zero&#x2011;inflation phenomenon-a large fraction of loci has zero sequencing reads. Zero inflation causes read counts to fluctuate by several&#x2011;fold between adjacent windows. As a result, random upward blips in coverage can be misinterpreted as copy&#x2011;number gains (false positives), and true deletions often become indistinguishable from pervasive zero&#x2011;coverage noise. In addition, existing CNV detection tools developed for sWGS data often struggle to adapt across different CNV sizes. These combined effects severely constrain the accuracy of CNV inference. RESULTS: To address above challenges, we propose ZIPcnv, a novel CNV detection tool specifically designed for sWGS data. First, we apply a segment sliding window to smooth the raw read depth signal, which transforms the original zero-inflated statistical characteristics into approximately normal distribution characteristics. We then design a statistical process model that robustly detects persistent shifts under high background noise using a cumulative sum strategy, classifying genomic regions into candidate and non-candidate CNV regions. Finally, dynamic sliding windows are used for one-pass detection of CNVs of varying lengths, with window size adapting to the CNV region size. We evaluated the performance of ZIPcnv on simulated data and 190 real whole-genome sequencing samples. Experimental results show that ZIPcnv consistently outperforms currently popular CNV detection tools. AVAILABILITY AND IMPLEMENTATION: The ZIPcnv source code is freely available at https://github.com/Nevermore233/ZIPcnv.

DNA Copy Number Variations

Genome-wide variation analysis of two Salvia hispanica L. genotypes and implication for associations with metabolic and adaptive traits.

BACKGROUND: Advances in next-generation sequencing have accelerated genome-wide exploration of genetic diversity in underutilized oilseed crops. Salvia hispanica L. (chia), a high-nutrient pseudocereal rich in omega-3 fatty acids, is increasingly valued for its health benefits and commercial potential, yet it remains poorly characterized at the genomic level. Understanding the scale and nature of genomic variation is essential for improving complex traits such as oil yield, stress tolerance, and seed quality. METHODS: Two contrasting chia genotypes, Black-chia (CACH-B) and White- chia (CACH-W), were resequenced using the Bio-Resequencing Toolkit (BRT) pipeline. High-coverage sequencing, with a mapping rate exceeding 99% and an average depth of approximately 28&#xd7;, facilitated the detection and annotation of single-nucleotide polymorphisms (SNPs), insertions and deletions (InDels), copy-number variations (CNVs), and structural variants (SVs). The functional classification of variant impacts enabled the identification of genes potentially linked to metabolic and adaptive traits. RESULTS: A total of 1.97 million SNPs, 401,493 InDels, 836 CNVs, and 15,288 SVs were identified across the chia genome. Notably, approximately 53% of exonic SNPs were non-synonymous (dN/dS&#xa0;&#x2248;&#xa0;1.28), predominantly affecting lipid metabolism, transcriptional regulation, and stress response pathways, potentially altering key agronomic traits. In addition, CNV hotspots were concentrated in chromosomes 3 and 6, overlapping MYB, WRKY, and bZIP transcription factor loci, may potentially be involved in stress tolerance and yield. Furthermore, structural rearrangements, including inversions and duplications within the FAD2, FAD3, and CYP450 gene clusters, were potentially associated with seed pigmentation and omega-3 biosynthesis, pointing to their potential breeding relevance. Observed heterozygosity (H&#x2092;&#xa0;&#x2248;&#xa0;0.71) and nucleotide diversity (&#x3c0;&#xa0;&#x2248;&#xa0;7&#xa0;&#xd7;&#xa0;10-3) indicated moderate to high allelic richness. In addition, the low FST value (0.038) indicates substantial genomic similarity between the two genotypes. CONCLUSION: This study presents the first comprehensive map integrating SNPs, CNVs, and SVs in S. hispanica L. The results reveal a structurally dynamic genome characterized by substantial sequence and structural variation, providing valuable insights into genomic diversity and potential adaptive mechanisms in chia. The coexistence of high SNP diversity and abundant structural variation underpins chia's nutritional specialization and environmental resilience. These results deliver a foundational genomic resource for marker-assisted breeding, genome-wide association studies, and the development of climate-resilient chia cultivars.

Copy-number variation, structural variation