Structural color in the bacterial domain: The ecogenomics of a 2-dimensional optical phenotype.

Structural color is an optical phenomenon resulting from light interacting with nanostructured materials. Although structural color (SC) is widespread in the tree of life, the underlying genetics and genomics are not well understood. Here, we collected and sequenced a set of 87 structurally colored bacterial isolates and 30 related strains lacking SC. Optical analysis of colonies indicated that diverse bacteria from at least two different phyla (Bacteroidetes and Proteobacteria) can create two-dimensional packing of cells capable of producing SC. A pan-genome-wide association approach was used to identify genes associated with SC. The biosynthesis of uroporphyrin and pterins, as well as carbohydrate utilization and metabolism, was found to be involved. Using this information, we constructed a classifier to predict SC directly from bacterial genome sequences and validated it by cultivating and scoring 100 strains that were not part of the training set. We predicted that SCr is widely distributed within gram-negative bacteria. Analysis of over 13,000 assembled metagenomes suggested that SC is nearly absent from most habitats associated with multicellular organisms except macroalgae and is abundant in marine waters and surface/air interfaces. This work provides a large-scale ecogenomics view of SC in bacteria and identifies microbial pathways and evolutionary relationships that underlie this optical phenomenon.

Endometriosis: recent advances that could accelerate diagnosis and improve care.

Endometriosis is a common disorder associated with pain, gastrointestinal and urinary symptoms, infertility, and fatigue. It is defined by the presence of endometrial-like lesions found predominantly in the pelvis. Mechanisms that contribute to disease aetiology include changes in hormonal, inflammatory, and pain pathways. In this article, we focus on recent developments in imaging technologies, on our improved understanding of mechanisms contributing to infertility, on drug therapies that are in clinical trials, and on insights from studies on the gut that offer potential to support self-management strategies. We postulate that improvements in the quality of life of patients will be accelerated by reframing endometriosis as a multi-system disorder and learning from treatments targeting symptoms shared between endometriosis, neuroinflammatory, and gastrointestinal disorders.

An overview of key online resources for human genomics: a powerful and open toolbox for in silico research.

Recent advances in high-throughput molecular methods have led to an extraordinary volume of genomics data. Simultaneously, the progress in the computational implementation of novel algorithms has facilitated the creation of hundreds of freely available online tools for their advanced analyses. However, a general overview of the most commonly used tools for the in silico analysis of genomics data is still missing. In the current article, we present an overview of commonly used online resources for genomics research, including over 50 tools. This selection will be helpful for scientists with basic or intermediate skills in the in silico analyses of genomics data, such as researchers and students from wet labs seeking to strengthen their computational competencies. In addition, we discuss current needs and future perspectives within this field.

Microarray Images Contrast Enhancement and Gridding Using Genetic Algorithm.

Background: Microarray is a sophisticated tool that concurrently analyzes the expression levels of thousands of genes, giving scientists an overview of DNA and RNA study. This procedure is divided into three stages: contact with biological samples, data extraction, and data analysis. Because expression levels are disclosed by the interplay of light with fluorescent markers, the data extraction stage relies on image processing methods. To extract quantitative information from the microarray image (MAI), four steps of preprocessing, gridding, segmentation, and intensity quantification are required. During the generation of MAIs, a large number of error-prone processes occur, leading to structural problems and reduced quality in the resulting data, affecting the identification of expressed genes. Methods: In this article, the first stage has been examined. In the preprocessing stage, the contrast of the images is first enhanced using the genetic algorithm, then the source noises that appear as small artifacts are removed using morphology, and finally, to confirm the effect of the contrast enhancement (CE) on the main stages of microarray data processing, gridding is checked on complementary deoxyribonucleic acid MAIs. Results: The comparison of the obtained results with an adaptive histogram equalization (AHE) and multi-decomposition histogram equalization (M-DHE) methods shows the superiority and efficiency of the proposed method. For example, the image contrast of the Genomic Medicine Research Center Laboratory dataset is 3.24, which is 42.91 with the proposed method and 13.48 and 32.40 with the AHE and M-DHE methods, respectively. Conclusions: The performance of the proposed methods for CE is evaluated on 3 databases and a general conclusion is obtained as to which CE method is more suitable for each dataset.

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.

Genomics-based identification of a cold adapted clade in Deinococcus.

BACKGROUND: Microbes in the cold polar and alpine environments play a critical role in feedbacks that amplify the effects of climate change. Defining the cold adapted ecotype is one of the prerequisites for understanding the response of polar and alpine microbes to climate change. RESULTS: Here, we analysed 85 high-quality, de-duplicated genomes of Deinococcus, which can survive in a variety of harsh environments. By leveraging genomic and phenotypic traits with reverse ecology, we defined a cold adapted clade from eight Deinococcus strains isolated from Arctic, Antarctic and high alpine environments. Genome-wide optimization in amino acid composition and regulation and signalling enable the cold adapted clade to produce CO2 from organic matter and boost the bioavailability of mineral nitrogen. CONCLUSIONS: Based primarily on in silico genomic analysis, we defined a potential cold adapted clade in Deinococcus and provided an updated view of the genomic traits and metabolic potential of Deinococcus. Our study would facilitate the understanding of microbial processes in the cold polar and alpine environments.

Comparative genomics analysis to explore the biodiversity and mining novel target genes of Listeria monocytogenes strains from different regions.

As a common foodborne pathogen, infection with L. monocytogenes poses a significant threat to human life and health. The objective of this study was to employ comparative genomics to unveil the biodiversity and evolutionary characteristics of L. monocytogenes strains from different regions, screening for potential target genes and mining novel target genes, thus providing significant reference value for the specific molecular detection and therapeutic targets of L. monocytogenes strains. Pan-genomic analysis revealed that L. monocytogenes from different regions have open genomes, providing a solid genetic basis for adaptation to different environments. These strains contain numerous virulence genes that contribute to their high pathogenicity. They also exhibit relatively high resistance to phosphonic acid, glycopeptide, lincosamide, and peptide antibiotics. The results of mobile genetic elements indicate that, despite being located in different geographical locations, there is a certain degree of similarity in bacterial genome evolution and adaptation to specific environmental pressures. The potential target genes identified through pan-genomics are primarily associated with the fundamental life activities and infection invasion of L. monocytogenes, including known targets such as inlB, which can be utilized for molecular detection and therapeutic purposes. After screening a large number of potential target genes, we further screened them using hub gene selection methods to mining novel target genes. The present study employed eight different hub gene screening methods, ultimately identifying ten highly connected hub genes (bglF_1, davD, menE_1, tilS, dapX, iolC, gshAB, cysG, trpA, and hisC), which play crucial roles in the pathogenesis of L. monocytogenes. The results of pan-genomic analysis showed that L. monocytogenes from different regions exhibit high similarity in bacterial genome evolution. The PCR results demonstrated the excellent specificity of the bglF_1 and davD genes for L. monocytogenes. Therefore, the bglF_1 and davD genes hold promise as specific molecular detection and therapeutic targets for L. monocytogenes strains from different regions.

Comparative genomics of Deinococcus radiodurans: unveiling genetic discrepancies between ATCC 13939K and BAA-816 strains.

The Deinococcus genus is renowned for its remarkable resilience against environmental stresses, including ionizing radiation, desiccation, and oxidative damage. This resilience is attributed to its sophisticated DNA repair mechanisms and robust defense systems, enabling it to recover from extensive damage and thrive under extreme conditions. Central to Deinococcus research, the D. radiodurans strains ATCC BAA-816 and ATCC 13939 facilitate extensive studies into this remarkably resilient genus. This study focused on delineating genetic discrepancies between these strains by sequencing our laboratory's ATCC 13939 specimen (ATCC 13939K) and juxtaposing it with ATCC BAA-816. We uncovered 436 DNA sequence differences within ATCC 13939K, including 100 single nucleotide variations, 278 insertions, and 58 deletions, which could induce frameshifts altering protein-coding genes. Gene annotation revisions accounting for gene fusions and the reconciliation of gene lengths uncovered novel protein-coding genes and refined the functional categorizations of established ones. Additionally, the analysis pointed out genome structural variations due to insertion sequence (IS) elements, underscoring the D. radiodurans genome's plasticity. Notably, ATCC 13939K exhibited a loss of six ISDra2 elements relative to BAA-816, restoring genes fragmented by ISDra2, such as those encoding for α/ß hydrolase and serine protease, and revealing new open reading frames, including genes imperative for acetoin decomposition. This comparative genomic study offers vital insights into the metabolic capabilities and resilience strategies of D. radiodurans.

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.

Insights from different reproductive gene knockdowns via RNA interference in the lady beetle Eriopis connexa: Establishing a new model for molecular studies on natural enemies.

Insect pest control can be achieved by the application of RNA interference (RNAi), a key molecular tool in functional genomics. Whereas most RNAi research has focused on insect pests, few studies have been performed on natural enemies. Validating the efficacy of RNAi in natural enemies is crucial for assessing its safety and enabling molecular research on these organisms. Here, we assessed the efficacy of RNAi in the ladybird beetle Eriopis connexa Germar (Coleoptera: Coccinellidae), focusing on genes related to reproduction, such as vitellogenin (Vg) and its receptor (VgR). In the transcriptome of E. connexa, we found one VgR (EcVgR) and two Vg genes (EcVg1 and EcVg2). These genes have been validated by in silico analyses of functional domains and evolutionary relationships. Five-day-old females were injected with 500 ng/µL of a specific double-stranded RNA (dsRNA) (dsEcVg1, dsEcVg2, or dsEcVgR) for RNAi tests, while nonspecific dsRNA (dsGFP or dsAgCE8.1) was used as a control. Interestingly, dsEcVg2 was able to knockdown both Vg genes, while dsEcVg1 could silence only EcVg1. Additionally, the viability of the eggs was significantly reduced when both Vg genes were knocked down at the same time (after treatment with dsEcVg2 or "dsEcVg1+dsEcVg2"). Ultimately, malformed, nonviable eggs were produced when EcVgR was silenced. Interestingly, no dsRNA treatment had an impact on the quantity of eggs laid. Therefore, the feasibility of RNAi in E. connexa has been confirmed, suggesting that this coccinellid is an excellent Neotropical model for molecular research on natural enemies and for studying RNAi nontarget effects.

Neuropathogenesis of SARS-CoV-2 in human neuronal, microglial and glial cells.

Neurological complications, both acute and chronic, are reported commonly in COVID-19 affected individuals. In this context, the understanding of pathogenesis of SARS-CoV-2 in specific cells of central nervous system (CNS) origin is relevant. The present study explores infection biology of a clinical isolate of SARS-CoV-2 in human cell lines of neural origin such as the glioblastoma (U87-MG), neuroblastoma (SHSY5Y) and microglia (C20). Despite showing clear evidence of infection by immunofluorescence with an anti-spike protein antibody, all the three neural cell lines were observed to be highly restrictive to the replication of the infecting virus. While the U87-MG glioblastoma cells demonstrated no cytopathic effects and a low viral titre with no signs of replication, the SHSY5Y neuroblastoma cells exhibited cytopathic effects with bleb formation but no evidence of viable virus. The C20 microglial cells showed neither signs of cytopathic effects nor viable virus. Ultrastructural studies demonstrated intracellular virions in infected neural cells. The presence of lipid droplets in infected SHSY5Y cells suggested an impact on host cell metabolism. The decrease in viral RNA levels over time in all the neural cell lines suggested restricted viral replication. In conclusion, this study highlights the limited susceptibility of neural cells to SARS-CoV-2 infection. This reduced permissibility of neural cell lines to SARS-CoV-2 may point to their inherent lower expression of receptors that support viral entry in addition to the intracellular factors that potently inhibit viral replication. The study findings prompt further investigation into the mechanisms of SARS-CoV-2 infection of neural cells.

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.

Interface-guided phenotyping of coding variants in the transcription factor RUNX1.

Single-gene missense mutations remain challenging to interpret. Here, we deploy scalable functional screening by sequencing (SEUSS), a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups, wild-type (WT)-like, loss-of-function (LoF)-like, and hypomorphic, that we validate in orthogonal assays. LoF-like variants dominate the DNA-binding site and are recurrent in cancer; however, recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LoF-like but favor protein interactions, promoting gene expression indicative of nerve growth factor (NGF) response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contains RUNX1-binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations.

Forensic investigative genetic genealogy: expanding pedigree tracing and genetic inquiry in the genomic era.

Genetic genealogy provides crucial insights into the complex biological relationships within contemporary and ancient human populations by analyzing shared alleles and chromosomal segments that are identical by descent, to understand kinship, migration patterns, and population dynamics. Within forensic science, forensic investigative genetic genealogy (FIGG) has gained prominence by leveraging next-generation sequencing technologies and population-specific genomic resources, opening new investigative avenues. In this review, we synthesize current knowledge, underscore recent advancements, and discuss the growing role of FIGG in forensic genomics. FIGG has been pivotal in revitalizing dormant inquiries and offering new genetic leads in numerous cold cases. Its effectiveness relies on the extensive SNP profiles contributed by individuals from diverse populations to specialized genomic databases. Advances in computational genomics and the growth of human genomic databases have spurred a profound shift in the application of genetic genealogy across forensics, anthropology, and ancient DNA studies. As the field progresses, FIGG is evolving from a nascent practice into a more sophisticated and specialized discipline, shaping the future of forensic investigations.

Integrating very high resolution environmental proxies in genotype-environment association studies.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Admixture Mapping of Chronic Kidney Disease and Risk Factors in Hispanic/Latino Individuals From Central America Country of Origin.

BACKGROUND: Chronic kidney disease (CKD) is highly prevalent in Central America, and genetic factors may contribute to CKD risk. To understand the influences of genetic admixture on CKD susceptibility, we conducted an admixture mapping screening of CKD traits and risk factors in US Hispanic and Latino individuals from Central America country of origin. METHODS: We analyzed 1023 participants of HCHS/SOL (Hispanic Community Health Study/Study of Latinos) who reported 4 grandparents originating from the same Central America country. Ancestry admixture findings were validated on 8191 African Americans from WHI (Women's Health Initiative), 3141 American Indians from SHS (Strong Heart Study), and over 1.1 million European individuals from a multistudy meta-analysis. RESULTS: We identified 3 novel genomic regions for albuminuria (chromosome 14q24.2), CKD (chromosome 6q25.3), and type 2 diabetes (chromosome 3q22.2). The 14q24.2 locus driven by a Native American ancestry had a protective effect on albuminuria and consisted of 2 nearby regions spanning the RGS6 gene. Variants at this locus were validated in American Indians. The 6q25.3 African ancestry-derived locus, encompassing the ARID1B gene, was associated with increased risk for CKD and replicated in African Americans through admixture mapping. The European ancestry type 2 diabetes locus at 3q22.2, encompassing the EPHB1 and KY genes, was validated in European individuals through variant association. CONCLUSIONS: US Hispanic/Latino populations are culturally and genetically diverse. This study focusing on Central America grandparent country of origin provides new loci discovery and insights into the ancestry-of-origin influences on CKD and risk factors in US Hispanic and Latino individuals.

The Psychoneurologic Symptom Cluster and Its Association With Breast Cancer Genomic Instability.

OBJECTIVES: To phenotype the psychoneurologic (PN) symptom cluster in individuals with metastatic breast cancer and associate those phenotypes with individual characteristics and cancer genomic variables from circulating tumor DNA. SAMPLE & SETTING: This study included 201 individuals with metastatic breast cancer recruited in western Pennsylvania. METHODS & VARIABLES: A descriptive, cross-sectional design was used. Symptom data were collected via the MD Anderson Symptom Inventory, and cancer genomic data were collected via ultra-low-pass whole-genome sequencing of circulating tumor DNA from participant blood. RESULTS: Three distinct PN symptom phenotypes were described in a population with metastatic breast cancer: mild symptoms, moderate symptoms, and severe mood-related symptoms. Breast cancer TP53 deletion was significantly associated with membership in a moderate to severe symptoms phenotype (p = 0.013). IMPLICATIONS FOR NURSING: Specific cancer genomic changes associated with increased genomic instability may be predictive of PN symptoms. This finding may enable proactive treatment or reveal new therapeutic targets for symptom management.