Study on the biodegradation characteristics and mechanism of tetracycline by Serratia entomophila TC-1.

Microbial degradation is an important solution for antibiotic pollution in livestock and poultry farming wastes. This study reports the isolation and identification of the novel bacterial strain Serratia entomophila TC-1, which can degrade 87.8 % of 200 mg/L tetracycline (TC) at 35 °C, pH 6.0, and an inoculation amount of 1 % (v/v). Based on the intermediate products, a possible biological transformation pathway was proposed, including dehydration, oxidation ring opening, decarbonylation, and deamination. Using Escherichia coli and Bacillus subtilis as biological indicators, TC degraded metabolites have shown low toxicity. Whole-genome sequencing showed that the TC-1 strain contained tet (d) and tet (34), which resist TC through multiple mechanisms. In addition, upon TC exposure, TC-1 participated in catalytic and energy supply activities by regulating gene expression, thereby playing a role in TC detoxification. We found that TC-1 showed less interference with changes in the bacterial community in swine wastewater. Thus, TC-1 provided new insights into the mechanisms responsible for TC biodegradation and can be used for TC pollution treatment.

Role of Veterinary Practitioners in the Genomic Era in Dairy: Economic Impact.

As veterinary practitioners serve modern dairies and ranches, genetic consultation is an area that many practitioners are implementing to bring value to their clients. As an unbiased professional with intimate knowledge of the herd's health and management practices and vision for the future, veterinarians are uniquely positioned to provide this consultative service.

Genome resequencing reveals population divergence and local adaptation of blacklegged ticks in the United States.

Tick vectors and tick-borne disease are increasingly impacting human populations globally. An important challenge is to understand tick movement patterns, as this information can be used to improve management and predictive modelling of tick population dynamics. Evolutionary analysis of genetic divergence, gene flow and local adaptation provides insight on movement patterns at large spatiotemporal scales. We develop low coverage, whole genome resequencing data for 92 blacklegged ticks, Ixodes scapularis, representing range-wide variation across the United States. Through analysis of population genomic data, we find that tick populations are structured geographically, with gradual isolation by distance separating three population clusters in the northern United States, southeastern United States and a unique cluster represented by a sample from Tennessee. Populations in the northern United States underwent population contractions during the last glacial period and diverged from southern populations at least 50 thousand years ago. Genome scans of selection provide strong evidence of local adaptation at genes responding to host defences, blood-feeding and environmental variation. In addition, we explore the potential of low coverage genome sequencing of whole-tick samples for documenting the diversity of microbial pathogens and recover important tick-borne pathogens such as Borrelia burgdorferi. The combination of isolation by distance and local adaptation in blacklegged ticks demonstrates that gene flow, including recent expansion, is limited to geographical scales of a few hundred kilometres.

Moving Genomics into the Clinic: Platforms for Implementing Clinical Genomic Data-Sharing in Ways That Address Ethical, Legal and Social Implications.

This section explores the challenges involved in translating genomic research into genomic medicine. A number of priorities have been identified in the Australian National Health Genomics Framework for addressing these challenges. Responsible collection, storage, use and management of genomic data is one of these priorities, and is the primary theme of this section. The recent release of Genomical, an Australian data-sharing platform, is used as a case study to illustrate the type of assistance that can be provided to the health care sector in addressing this priority. The section first describes the National Framework and other drivers involved in the move towards genomic medicine. The section then examines key ethical, legal and social factors at play in genomics, with particular focus on privacy and consent. Finally, the section examines how Genomical is being used to help ensure that the move towards genomic medicine is ethically, legally and socially sound and that it optimises advances in both genomic and information technology.

Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 h.

AIMS/HYPOTHESIS: Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycaemia, beta cell glucotoxicity and subsequently type 2 diabetes. In this study, we explored the effects of in vitro hyperglycaemic conditions on human pancreatic islet gene expression across 24 h in six pancreatic cell types: alpha; beta; gamma; delta; ductal; and acinar. We hypothesised that genes associated with hyperglycaemic conditions may be relevant to the onset and progression of diabetes. METHODS: We exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 h in vitro. To assess the transcriptome, we performed single-cell RNA-seq (scRNA-seq) at seven time points. We modelled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterised the effect on insulin production and secretion using CRISPR interference to knock down gene expression in EndoC-ßH1 cells, followed by a glucose-stimulated insulin secretion assay. RESULTS: In the discrete time models, we identified 1344 genes associated with time and 668 genes associated with glucose exposure across all cell types and time points. In the continuous time models, we identified 1311 genes associated with time, 345 genes associated with glucose exposure and 418 genes associated with interaction effects between time and glucose across all cell types. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2449 candidate effector genes for type 2 diabetes, HbA1c, random blood glucose and fasting blood glucose. Of these candidate effector genes, we showed that three (ERO1B, HNRNPA2B1 and RHOBTB3) exhibited an effect on glucose-stimulated insulin production and secretion in EndoC-ßH1 cells. CONCLUSIONS/INTERPRETATION: The findings of our study provide an in-depth characterisation of the 24 h transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production. DATA AVAILABILITY: The scRNA-seq data from the 24 h glucose exposure experiment performed in this study are available in the database of Genotypes and Phenotypes (dbGap; https://www.ncbi.nlm.nih.gov/gap/ ) with accession no. phs001188.v3.p1. Study metadata and summary statistics for the differential expression, gene set enrichment and candidate effector gene prediction analyses are available in the Zenodo data repository ( https://zenodo.org/ ) under accession number 11123248. The code used in this study is publicly available at https://github.com/CollinsLabBioComp/publication-islet_glucose_timecourse .

Saccharopolyspora mangrovi sp. nov., a novel mangrove soil actinobacterium with distinct metabolic potential revealed by comparative genomic analysis.

A novel mangrove soil-derived actinomycete, strain S2-29T, was found to be most closely related to Saccharopolyspora karakumensis 5K548T based on 16 S rRNA sequence (99.24% similarity) and genomic phylogenetic analyses. However, significant divergence in digital DNA-DNA hybridization, average nucleotide identity, and unique biosynthetic gene cluster possession distinguished S2-29T as a distinct Saccharopolyspora species. Pan genome evaluation revealed exceptional genomic flexibility in genus Saccharopolyspora, with > 95% accessory genome content. Strain S2-29T harbored 718 unique genes, largely implicated in energetic metabolisms, indicating different metabolic capacities from its close relatives. Several uncharacterized biosynthetic gene clusters in strain S2-29T highlighted the strain's untapped capacity to produce novel functional compounds with potential biotechnological applications. Designation as novel species Saccharopolyspora mangrovi sp. nov. (type strain S2-29T = JCM 34,548T = CGMCC 4.7716T) was warranted, expanding the known Saccharopolyspora diversity and ecology. The discovery of this mangrove-adapted strain advances understanding of the genus while highlighting an untapped source of chemical diversity.

Integrating genomics and biogeography to unravel the origin of a mountain biota: The case of a reptile endemicity hotspot in Arabia.

Advances in genomics have greatly enhanced our understanding of mountain biodiversity, providing new insights into the complex and dynamic mechanisms that drive the formation of mountain biotas. These span from broad biogeographic patterns to population dynamics and adaptations to these environments. However, significant challenges remain in integrating large-scale and fine-scale findings to develop a comprehensive understanding of mountain biodiversity. One significant challenge is the lack of genomic data, particularly in historically understudied arid regions where reptiles are a particularly diverse vertebrate group. In the present study, we assembled a de novo genome-wide SNP dataset for the complete endemic reptile fauna of a mountain range (19 described species with more than 600 specimens sequenced), and integrated state-of-the-art biogeographic analyses at the population, species, and community level. Thus, we provide a holistic integration of how a whole endemic reptile community has originated, diversified and dispersed through a mountain system. Our results show that reptiles independently colonized the Hajar Mountains of southeastern Arabia 11 times. After colonization, species delimitation methods suggest high levels of within-mountain diversification, supporting up to 49 deep lineages. This diversity is strongly structured following local topography, with the highest peaks acting as a broad barrier to gene flow among the entire community. Interestingly, orogenic events do not seem key drivers of the biogeographic history of reptiles in this system. Instead, past climatic events seem to have had a major role in this community assemblage. We observe an increase of vicariant events from Late Pliocene onwards, coinciding with an unstable climatic period of rapid shifts between hyper-arid and semiarid conditions that led to the ongoing desertification of Arabia. We conclude that paleoclimate, and particularly extreme aridification, acted as a main driver of diversification in arid mountain systems which is tangled with the generation of highly adapted endemicity. Overall, our study does not only provide a valuable contribution to understanding the evolution of mountain biodiversity, but also offers a flexible and scalable approach that can be reproduced into any taxonomic group and at any discrete environment.

The Primula edelbergii S-locus is an example of a jumping supergene.

Research on supergenes, non-recombining genomic regions housing tightly linked genes that control complex phenotypes, has recently gained prominence in genomics. Heterostyly, a floral heteromorphism promoting outcrossing in several angiosperm families, is controlled by the S-locus supergene. The S-locus has been studied primarily in closely related Primula species and, more recently, in other groups that independently evolved heterostyly. However, it remains unknown whether genetic architecture and composition of the S-locus are maintained among species that share a common origin of heterostyly and subsequently diverged across larger time scales. To address this research gap, we present a chromosome-scale genome assembly of Primula edelbergii, a species that shares the same origin of heterostyly with Primula veris (whose S-locus has been characterized) but diverged from it 18 million years ago. Comparative genomic analyses between these two species allowed us to show, for the first time, that the S-locus can 'jump' (i.e. translocate) between chromosomes maintaining its function in controlling heterostyly. Additionally, we found that four S-locus genes were conserved but reshuffled within the supergene, seemingly without affecting their expression, thus we could not detect changes explaining the lack of self-incompatibility in P. edelbergii. Furthermore, we confirmed that the S-locus is not undergoing genetic degeneration. Finally, we investigated P. edelbergii evolutionary history within Ericales in terms of whole genome duplications and transposable element accumulation. In summary, our work provides a valuable resource for comparative analyses aimed at investigating the genetics of heterostyly and the pivotal role of supergenes in shaping the evolution of complex phenotypes.

Diving into broad-scale and high-resolution population genomics to decipher drivers of structure and climatic vulnerability in a marine invertebrate.

Species with widespread distributions play a crucial role in our understanding of climate change impacts on population structure. In marine species, population structure is often governed by both high connectivity potential and selection across strong environmental gradients. Despite the complexity of factors influencing marine populations, studying species with broad distribution can provide valuable insights into the relative importance of these factors and the consequences of climate-induced alterations across environmental gradients. We used the northern shrimp Pandalus borealis and its wide latitudinal distribution to identify current drivers of population structure and predict the species' vulnerability to climate change. A total of 1514 individuals sampled across 24° latitude were genotyped at high geographic (54 stations) and genetic (14,331 SNPs) resolutions to assess genetic variation and environmental correlations. Four populations were identified in addition to finer substructure associated with local adaptation. Geographic patterns of neutral population structure reflected predominant oceanographic currents, while a significant proportion of the genetic variation was associated with gradients in salinity and temperature. Adaptive landscapes generated using climate projections suggest a larger genomic offset in the southern extent of the P. borealis range, where shrimp had the largest adaptive standing genetic variation. Our genomic results combined with recent observations point to further deterioration in southern regions and an impending vulnerable status in the regions at higher latitudes for P. borealis. They also provide rare insights into the drivers of population structure and climatic vulnerability of a widespread meroplanktonic species, which is crucial to understanding future challenges associated with invertebrates essential to ecosystem functioning.

Benchmarking computational variant effect predictors by their ability to infer human traits.

BACKGROUND: Computational variant effect predictors offer a scalable and increasingly reliable means of interpreting human genetic variation, but concerns of circularity and bias have limited previous methods for evaluating and comparing predictors. Population-level cohorts of genotyped and phenotyped participants that have not been used in predictor training can facilitate an unbiased benchmarking of available methods. Using a curated set of human gene-trait associations with a reported rare-variant burden association, we evaluate the correlations of 24 computational variant effect predictors with associated human traits in the UK Biobank and All of Us cohorts. RESULTS: AlphaMissense outperformed all other predictors in inferring human traits based on rare missense variants in UK Biobank and All of Us participants. The overall rankings of computational variant effect predictors in these two cohorts showed a significant positive correlation. CONCLUSION: We describe a method to assess computational variant effect predictors that sidesteps the limitations of previous evaluations. This approach is generalizable to future predictors and could continue to inform predictor choice for personal and clinical genetics.

Investigating food production-associated DNA methylation changes in paleogenomes: Lack of consistent signals beyond technical noise.

The Neolithic transition introduced major diet and lifestyle changes to human populations across continents. Beyond well-documented bioarcheological and genetic effects, whether these changes also had molecular-level epigenetic repercussions in past human populations has been an open question. In fact, methylation signatures can be inferred from UDG-treated ancient DNA through postmortem damage patterns, but with low signal-to-noise ratios; it is thus unclear whether published paleogenomes would provide the necessary resolution to discover systematic effects of lifestyle and diet shifts. To address this we compiled UDG-treated shotgun genomes of 13 pre-Neolithic hunter-gatherers (HGs) and 21 Neolithic farmers (NFs) individuals from West and North Eurasia, published by six different laboratories and with coverage c.1×-58× (median = 9×). We used epiPALEOMIX and a Monte Carlo normalization scheme to estimate methylation levels per genome. Our paleomethylome dataset showed expected genome-wide methylation patterns such as CpG island hypomethylation. However, analyzing the data using various approaches did not yield any systematic signals for subsistence type, genetic sex, or tissue effects. Comparing the HG-NF methylation differences in our dataset with methylation differences between hunter-gatherers versus farmers in modern-day Central Africa also did not yield consistent results. Meanwhile, paleomethylome profiles did cluster strongly by their laboratories of origin. Using larger data volumes, minimizing technical noise and/or using alternative protocols may be necessary for capturing subtle environment-related biological signals from paleomethylomes.

The genome-wide signature of short-term temporal selection.

Despite evolutionary biology's obsession with natural selection, few studies have evaluated multigenerational series of patterns of selection on a genome-wide scale in natural populations. Here, we report on a 10-y population-genomic survey of the microcrustacean Daphnia pulex. The genome sequences of [Formula: see text]800 isolates provide insights into patterns of selection that cannot be obtained from long-term molecular-evolution studies, including the following: the pervasiveness of near quasi-neutrality across the genome (mean net selection coefficients near zero, but with significant temporal variance about the mean, and little evidence of positive covariance of selection across time intervals); the preponderance of weak positive selection operating on minor alleles; and a genome-wide distribution of numerous small linkage islands of observable selection influencing levels of nucleotide diversity. These results suggest that interannual fluctuating selection is a major determinant of standing levels of variation in natural populations, challenge the conventional paradigm for interpreting patterns of nucleotide diversity and divergence, and motivate the need for the further development of theoretical expressions for the interpretation of population-genomic data.

Tracing the genealogy origin of geographic populations based on genomic variation and deep learning.

Assigning a query individual animal or plant to its derived population is a prime task in diverse applications related to organismal genealogy. Such endeavors have conventionally relied on short DNA sequences under a phylogenetic framework. These methods naturally show constraints when the inferred population sources are ambiguously phylogenetically structured, a scenario demanding substantially more informative genetic signals. Recent advances in cost-effective production of whole-genome sequences and artificial intelligence have created an unprecedented opportunity to trace the population origin for essentially any given individual, as long as the genome reference data are comprehensive and standardized. Here, we developed a convolutional neural network method to identify population origins using genomic SNPs. Three empirical datasets (an Asian honeybee, a red fire ant, and a chicken datasets) and two simulated populations are used for the proof of concepts. The performance tests indicate that our method can accurately identify the genealogy origin of query individuals, with success rates ranging from > 93 % to 100 %. We further showed that the accuracy of the model can be significantly increased by refining the informative sites through FST filtering. Our method is robust to configurations related to batch sizes and epochs, whereas model learning benefits from the setting of a proper preset learning rate. Moreover, we explained the importance score of key sites for algorithm interpretability and credibility, which has been largely ignored. We anticipate that by coupling genomics and deep learning, our method will see broad potential in conservation and management applications that involve natural resources, invasive pests and weeds, and illegal trades of wildlife products.

Evolutionary analysis of species-specific duplications in flatworm genomes.

Platyhelminthes, also known as flatworms, is a phylum of bilaterian invertebrates infamous for their parasitic representatives. The classes Cestoda, Monogenea, and Trematoda comprise parasitic helminths inhabiting multiple hosts, including fishes, humans, and livestock, and are responsible for considerable economic damage and burden on human health. As in other animals, the genomes of flatworms have a wide variety of paralogs, genes related via duplication, whose origins could be mapped throughout the evolution of the phylum. Through in-silico analysis, we studied inparalogs, i.e., species-specific duplications, focusing on their biological functions, expression changes, and evolutionary rate. These genes are thought to be key players in the adaptation process of species to each particular niche. Our results showed that genes related with specific functional terms, such as response to stress, transferase activity, oxidoreductase activity, and peptidases, are overrepresented among inparalogs. This trend is conserved among species from different classes, including free-living species. Available expression data from Schistosoma mansoni, a parasite from the trematode class, demonstrated high conservation of expression patterns between inparalogs, but with notable exceptions, which also display evidence of rapid evolution. We discuss how natural selection may operate to maintain these genes and the particular duplication models that fit better to the observations. Our work supports the critical role of gene duplication in the evolution of flatworms, representing the first study of inparalogs evolution at the genome-wide level in this group.

Geographic population structure and distinct intra-population dynamics of globally abundant freshwater bacteria.

Implications of geographic separation and temporal dynamics on the evolution of free-living bacterial species are widely unclear. However, the vast amount of metagenome sequencing data generated during the last decades from various habitats around the world provides an unprecedented opportunity for such investigations. Here we exploited publicly available and new freshwater metagenomes in combination with genomes of abundant freshwater bacteria to reveal geographic and temporal population structure. We focused on species that were detected across broad geographic ranges at high enough sequence coverage for meaningful population genomic analyses, associated to the predominant freshwater taxa acI, LD12, Polynucleobacter and Ca. Methylopumilus. Despite the broad geographic ranges, each species appeared as sequence-discrete cluster, in contrast to abundant marine taxa, for which continuous diversity structures were reported on global scale. Population differentiation increased significantly with spatial distance in all species, but notable dispersal barriers (e.g. oceanic) were not apparent. Yet, the different species showed contrasting rates of geographic divergence and strikingly different intra-population dynamics in time series within individual habitats. Change of an LD12 population over seven years was minor (FST = 0.04) compared to differentiation between lakes, whereas a Polynucleobacter population displayed strong changes within merely two months (FST up to 0.54), similar in scale to differentiation between populations separated by thousands of kilometers. The slowly and steadily evolving LD12 population showed high strain diversity, whereas the dynamic Polynucleobacter population exhibited alternating clonal expansions of mostly two strains only. Based on the contrasting population structures we propose distinct models of speciation.

Implications of noncoding regulatory functions in the development of insulinomas.

Insulinomas are rare neuroendocrine tumors arising from pancreatic ß cells, characterized by aberrant proliferation and altered insulin secretion, leading to glucose homeostasis failure. With the aim of uncovering the role of noncoding regulatory regions and their aberrations in the development of these tumors, we coupled epigenetic and transcriptome profiling with whole-genome sequencing. As a result, we unraveled somatic mutations associated with changes in regulatory functions. Critically, these regions impact insulin secretion, tumor development, and epigenetic modifying genes, including polycomb complex components. Chromatin remodeling is apparent in insulinoma-selective domains shared across patients, containing a specific set of regulatory sequences dominated by the SOX17 binding motif. Moreover, many of these regions are H3K27me3 repressed in ß cells, suggesting that tumoral transition involves derepression of polycomb-targeted domains. Our work provides a compendium of aberrant cis-regulatory elements affecting the function and fate of ß cells in their progression to insulinomas and a framework to identify coding and noncoding driver mutations.

Giants' cooperation: a draft genome of the giant ciliate Muniziella cunhai suggests its ecological role in the capybara's digestive metabolism.

Several hundred ciliate species live in animals' guts as a part of their microbiome. Among them, Muniziella cunhai (Trichostomatia, Pycnotrichidae), the largest described ciliate, is found exclusively associated with Hydrochoerus hydrochaeris (capybara), the largest known rodent reaching up to 90 kg. Here, we present the sequence, structural and functional annotation of this giant microeukaryote macronuclear genome and discuss its phylogenetic placement. The 85 Mb genome is highly AT rich (GC content 25.71 %) and encodes a total of 11 397 protein-coding genes, of which 2793 could have their functions predicted with automated functional assignments. Functional annotation showed that M. cunhai can digest recalcitrant structural carbohydrates, non-structural carbohydrates, and microbial cell walls, suggesting a role in diet metabolization and in microbial population control in the capybara's intestine. Moreover, the phylogenetic placement of M. cunhai provides insights on the origins of gigantism in the subclass Trichostomatia.

Draft Turicibacter sp. genome isolated from a spore-forming community in mice.

Turicibacter is a common mammalian gut commensal; however, very few genomes have been sequenced, and little is understood regarding its importance for host health. Here, we add a complete Turicibacter sp. genome isolated from a spore-forming community in mice.

Epigenetic determinants of fusion-driven sarcomas: paradigms and challenges.

We describe exciting recent advances in fusion-driven sarcoma etiology, from an epigenetics perspective. By exploring the current state of the field, we identify and describe the central mechanisms that determine sarcomagenesis. Further, we discuss seminal studies in translational genomics, which enabled epigenetic characterization of fusion-driven sarcomas. Important context for epigenetic mechanisms include, but are not limited to, cell cycle and metabolism, core regulatory circuitry, 3-dimensional chromatin architectural dysregulation, integration with ATP-dependent chromatin remodeling, and translational animal modeling. Paradoxically, while the genetic requirements for oncogenic transformation are highly specific for the fusion partners, the epigenetic mechanisms we as a community have uncovered are categorically very broad. This dichotomy prompts the question of whether the investigation of rare disease epigenomics should prioritize studying individual cell populations, thereby examining whether the mechanisms of chromatin dysregulation are specific to a particular tumor. We review recent advances focusing on rhabdomyosarcoma, synovial sarcoma, alveolar soft part sarcoma, clear cell sarcoma, undifferentiated round cell sarcoma, Ewing sarcoma, myxoid/round liposarcoma, epithelioid hemangioendothelioma and desmoplastic round cell tumor. The growing number of groundbreaking discoveries in the field, motivated us to anticipate further exciting advances in the area of mechanistic epigenomics and direct targeting of fusion transcription factors in the years ahead.