TrieDedup: a fast trie-based deduplication algorithm to handle ambiguous bases in high-throughput sequencing

BACKGROUND

High-throughput sequencing is a powerful tool that is extensively applied in biological studies. However, sequencers may produce low-quality bases, leading to ambiguous bases, 'N's. PCR duplicates introduced in library preparation are conventionally removed in genomics studies, and several deduplication tools have been developed for this purpose. Two identical reads may appear different due to ambiguous bases and the existing tools cannot address 'N's correctly or efficiently.

RESULTS

Here we proposed and implemented TrieDedup, which uses the trie (prefix tree) data structure to compare and store sequences. TrieDedup can handle ambiguous base 'N's, and efficiently deduplicate at the level of raw sequences. We also reduced its memory usage by approximately 20% by implementing restrictedDict in Python. We benchmarked the performance of the algorithm and showed that TrieDedup can deduplicate reads up to 270-fold faster than pairwise comparison at a cost of 32-fold higher memory usage.

CONCLUSIONS

The TrieDedup algorithm may facilitate PCR deduplication, barcode or UMI assignment, and repertoire diversity analysis of large-scale high-throughput sequencing datasets with its ultra-fast algorithm that can account for ambiguous bases due to sequencing errors.

Deletion of a conserved genomic region associated with adolescent idiopathic scoliosis leads to vertebral rotation in mice

Adolescent idiopathic scoliosis (AIS) is the most common form of scoliosis, in which spinal curvature develops in adolescence, and 90% of patients are female. Scoliosis is a debilitating disease that often requires bracing or surgery in severe cases. AIS affects 2%-5.2% of the population; however, the biological origin of the disease remains poorly understood. In this study, we aimed to determine the function of a highly conserved genomic region previously linked to AIS using a mouse model generated by CRISPR-CAS9 gene editing to knockout this area of the genome to understand better its contribution to AIS, which we named AIS_CRMΔ. We also investigated the upstream factors that regulate the activity of this enhancer in vivo, whether the spatial expression of the LBX1 protein would change with the loss of AIS-CRM function, and whether any phenotype would arise after deletion of this region. We found a significant increase in mRNA expression in the developing neural tube at E10.5, and E12.5, for not only Lbx1 but also other neighboring genes. Adult knockout mice showed vertebral rotation and proprioceptive deficits, also observed in human AIS patients. In conclusion, our study sheds light on the elusive biological origins of AIS, by targeting and investigating a highly conserved genomic region linked to AIS in humans. These findings provide valuable insights into the function of the investigated region and contribute to our understanding of the underlying causes of this debilitating disease.

Mutational landscape of inflammatory breast cancer

BACKGROUND

Inflammatory breast cancer (IBC) is the most pro-metastatic form of BC. Better understanding of its enigmatic pathophysiology is crucial. We report here the largest whole-exome sequencing (WES) study of clinical IBC samples.

METHODS

We retrospectively applied WES to 54 untreated IBC primary tumor samples and matched normal DNA. The comparator samples were 102 stage-matched non-IBC samples from TCGA. We compared the somatic mutational profiles, spectra and signatures, copy number alterations (CNAs), HRD and heterogeneity scores, and frequencies of actionable genomic alterations (AGAs) between IBCs and non-IBCs. The comparisons were adjusted for the molecular subtypes.

RESULTS

The number of somatic mutations, TMB, and mutational spectra were not different between IBCs and non-IBCs, and no gene was differentially mutated or showed differential frequency of CNAs. Among the COSMIC signatures, only the age-related signature was more frequent in non-IBCs than in IBCs. We also identified in IBCs two new mutational signatures not associated with any environmental exposure, one of them having been previously related to HIF pathway activation. Overall, the HRD score was not different between both groups, but was higher in TN IBCs than TN non-IBCs. IBCs were less frequently classified as heterogeneous according to heterogeneity H-index than non-IBCs (21% vs 33%), and clonal mutations were more frequent and subclonal mutations less frequent in IBCs. More than 50% of patients with IBC harbored at least one high-level of evidence (LOE) AGA (OncoKB LOE 1-2, ESCAT LOE I-II), similarly to patients with non-IBC.

CONCLUSIONS

We provide the largest mutational landscape of IBC. Only a few subtle differences were identified with non-IBCs. The most clinically relevant one was the higher HRD score in TN IBCs than in TN non-IBCs, whereas the most intriguing one was the smaller intratumor heterogeneity of IBCs.

Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases.

SPADE: spatial deconvolution for domain specific cell-type estimation

Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE's capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.

Genome-wide identification and stress response analysis of BcaCPK gene family in amphidiploid Brassica carinata

BACKGROUND

Calcium-dependent protein kinases (CPKs) are crucial for recognizing and transmitting Ca2+ signals in plant cells, playing a vital role in growth, development, and stress response. This study aimed to identify and detect the potential roles of the CPK gene family in the amphidiploid Brassica carinata (BBCC, 2n = 34) using bioinformatics methods.

RESULTS

Based on the published genomic information of B. carinata, a total of 123 CPK genes were identified, comprising 70 CPK genes on the B subgenome and 53 on the C subgenome. To further investigate the homologous evolutionary relationship between B. carinata and other plants, the phylogenetic tree was constructed using CPKs in B. carinata and Arabidopsis thaliana. The phylogenetic analysis classified 123 family members into four subfamilies, where gene members within the same subfamily exhibited similar conserved motifs. Each BcaCPK member possesses a core protein kinase domain and four EF-hand domains. Most of the BcaCPK genes contain 5 to 8 introns, and these 123 BcaCPK genes are unevenly distributed across 17 chromosomes. Among these BcaCPK genes, 120 replicated gene pairs were found, whereas only 8 genes were tandem duplication, suggesting that dispersed duplication mainly drove the family amplification. The results of the Ka/Ks analysis indicated that the CPK gene family of B. carinata was primarily underwent purification selection in evolutionary selection. The promoter region of most BcaCPK genes contained various stress-related cis-acting elements. qRT-PCR analysis of 12 selected CPK genes conducted under cadmium and salt stress at various points revealed distinct expression patterns among different family members in response to different stresses. Specifically, the expression levels of BcaCPK2.B01a, BcaCPK16.B02b, and BcaCPK26.B02 were down-regulated under both stresses, whereas the expression levels of other members were significantly up-regulated under at least one stress.

CONCLUSION

This study systematically identified the BcaCPK gene family in B. carinata, which contributes to a better understanding the CPK genes in this species. The findings also serve as a reference for analyzing stress responses, particularly in relation to cadmium and salt stress in B. carinata.

Genetic barriers more than environmental associations explain Serratia marcescens population structure

Bacterial species often comprise well-separated lineages, likely emerged and maintained by genetic isolation and/or ecological divergence. How these two evolutionary actors interact in the shaping of bacterial population structure is currently not fully understood. In this study, we investigate the genetic and ecological drivers underlying the evolution of Serratia marcescens, an opportunistic pathogen with high genomic flexibility and able to colonise diverse environments. Comparative genomic analyses reveal a population structure composed of five deeply-demarcated genetic clusters with open pan-genome but limited inter-cluster gene flow, partially explained by Restriction-Modification (R-M) systems incompatibility. Furthermore, a large-scale research on hundred-thousands metagenomic datasets reveals only a partial habitat separation of the clusters. Globally, two clusters only show a separate gene composition coherent with ecological adaptations. These results suggest that genetic isolation has preceded ecological adaptations in the shaping of the species diversity, an evolutionary scenario coherent with the Evolutionary Extended Synthesis.

Analysis of REST binding sites with canonical and non-canonical motifs in human cell lines

BACKGROUND

Repressor element 1 (RE1) silencing transcription factor (REST) is a transcriptional repressor abundantly expressed in aging human brains. It is known to regulate genes associated with oxidative stress, inflammation, and neurological disorders by binding to a canonical form of sequence motif and its non-canonical variations. Although analysis of genomic sequence motifs is crucial to understand transcriptional regulation by transcription factors (TFs), a comprehensive characterization of various forms of RE1 motifs in human cell lines has not been performed.

RESULTS

Here, we analyzed 23 ENCODE REST ChIP-seq datasets from diverse human cell lines and identified a non-redundant set of 68,975 loci with ChIP-seq peaks. Our systematic characterization of these binding sites revealed that the canonical form of REST binding motif was found primarily in ChIP-seq peaks shared across multiple cell lines, while non-canonical forms of motifs were identified in both cell-line-specific binding sites and those shared across cell lines. Remarkably, we observed a notable prevalence of non-canonical motifs that corresponded to half segments of the canonical motif. Furthermore, our analysis unveiled the presence of cell-line-specific REST binding patterns, as evidenced by the clustering of ChIP-seq experiments according to their respective cell lines. This observation underscores the cell-line specificity of REST binding at certain genomic loci, implying intricate cell-line-specific regulatory mechanisms.

CONCLUSIONS

Overall, our study provides a comprehensive characterization of REST binding motifs in human cell lines and genome-wide RE1 motif profiles. These findings contribute to a deeper understanding of REST-mediated transcriptional regulation and highlight the importance of considering cell-line-specific effects in future investigations.

Comparison of the performance of multiple whole-genome sequence-based tools for the identification of Bacillus cereus sensu stricto biovar Thuringiensis

The Bacillus cereus sensu stricto (s.s.) species comprises strains of biovar Thuringiensis (Bt) known for their bioinsecticidal activity, as well as strains with foodborne pathogenic potential. Bt strains are identified (i) based on the production of insecticidal crystal proteins, also known as Bt toxins, or (ii) based on the presence of cry, cyt, and vip genes, which encode Bt toxins. Multiple bioinformatics tools have been developed for the detection of crystal protein-encoding genes based on whole-genome sequencing (WGS) data. However, the performance of these tools is yet to be evaluated using phenotypic data. Thus, the goal of this study was to assess the performance of four bioinformatics tools for the detection of crystal protein-encoding genes. The accuracy of sequence-based identification of Bt was determined in reference to phenotypic microscope-based screening for the production of crystal proteins. A total of 58 diverse B. cereus sensu lato strains isolated from clinical, food, environmental, and commercial biopesticide products underwent WGS. Isolates were examined for crystal protein production using phase contrast microscopy. Crystal protein-encoding genes were detected using BtToxin_Digger, BTyper3, IDOPS (identification of pesticidal sequences), and Cry_processor. Out of 58 isolates, the phenotypic production of crystal proteins was confirmed for 18 isolates. Specificity and sensitivity of Bt identification based on sequences were 0.85 and 0.94 for BtToxin_Digger, 0.97 and 0.89 for BTyper3, 0.95 and 0.94 for IDOPS, and 0.88 and 1.00 for Cry_processor, respectively. Cry_processor predicted crystal protein production with the highest specificity, and BtToxin_Digger and IDOPS predicted crystal protein production with the highest sensitivity. Three out of four tested bioinformatics tools performed well overall, with IDOPS achieving high sensitivity and specificity (>0.90).IMPORTANCEStrains of Bacillus cereus sensu stricto (s.s.) biovar Thuringiensis (Bt) are used as organic biopesticides. Bt is differentiated from the foodborne pathogen Bacillus cereus s.s. by the production of insecticidal crystal proteins. Thus, reliable genomic identification of biovar Thuringiensis is necessary to ensure food safety and facilitate risk assessment. This study assessed the accuracy of whole-genome sequencing (WGS)-based identification of Bt compared to phenotypic microscopy-based screening for crystal protein production. Multiple bioinformatics tools were compared to assess their performance in predicting crystal protein production. Among them, identification of pesticidal sequences performed best overall at WGS-based Bt identification.

Genomic Approach of Listeria monocytogenes Strains Isolated from Deli-Meats in Mexico

Listeria monocytogenes is a foodborne pathogen that causes listeriosis worldwide. In México, L. monocytogenes has been identified as a hazard of deli-meats. However, the genomic analysis that supports the transmission of L. monocytogenes strains via deli-meats and its role as a source for virulence and resistance genes is lacking. Here, we present four high-quality genome drafts of L. monocytogenes strains isolated from deli-meats in Mexico. In silico typing was used to determine the serotype, lineage, clonal complexes (CC), and multilocus sequence (ST). Also, comparative genomics were performed to explore the diversity, virulence, mobile elements, antimicrobial resistant and stress survival traits. The genome sequence size of these strains measured 3.05 ± 0.07 Mb with a mean value of 37.9%G+C. All strains belonged to linage I, which was divided into two groups: 4b, CC2, ST1 (n = 3) and 1/2b, CC5, ST5 (n = 1). The pangenome and core genome contained 3493 and 2625 genes, respectively. The strains harbor the L. monocytogenes pathogenicity island-1 (LIPI-1) and the same multidrug resistance pattern (fosX, norB, mprF, lin) via in silico analysis. Comparative analysis delineated the genomes as essentially syntenic, whose genomic differences were due to phage insertion. These results expand what is known about the biology of the L. monocytogenes strains isolated from deli-meats in Mexico and warns of the risk that these strains belong to epidemic linage and harbor virulence genes linked to human disease.

Comprehensive genomic analysis of the SARS-CoV-2 Omicron variant BA.2.76 in Jining City, China, 2022

OBJECTIVE

This study aims to analyze the molecular characteristics of the novel coronavirus (SARS-CoV-2) Omicron variant BA.2.76 in Jining City, China.

METHODS

Whole-genome sequencing was performed on 87 cases of SARS-CoV-2 infection. Evolutionary trees were constructed using bioinformatics software to analyze sequence homology, variant sites, N-glycosylation sites, and phosphorylation sites.

RESULTS

All 87 SARS-CoV-2 whole-genome sequences were classified under the evolutionary branch of the Omicron variant BA.2.76. Their similarity to the reference strain Wuhan-Hu-1 ranged from 99.72 to 99.74%. In comparison to the reference strain Wuhan-Hu-1, the 87 sequences exhibited 77-84 nucleotide differences and 27 nucleotide deletions. A total of 69 amino acid variant sites, 9 amino acid deletions, and 1 stop codon mutation were identified across 18 proteins. Among them, the spike (S) protein exhibited the highest number of variant sites, and the ORF8 protein showed a Q27 stop mutation. Multiple proteins displayed variations in glycosylation and phosphorylation sites.

CONCLUSION

SARS-CoV-2 continues to evolve, giving rise to new strains with enhanced transmission, stronger immune evasion capabilities, and reduced pathogenicity. The application of high-throughput sequencing technologies in the epidemic prevention and control of COVID-19 provides crucial insights into the evolutionary and variant characteristics of the virus at the genomic level, thereby holding significant implications for the prevention and control of the COVID-19 pandemic.

Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed)

BACKGROUND

Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred.

RESULTS

We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations.

CONCLUSIONS

Our algorithms provide a biologically assumption free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

TOPMed imputed genomics enhances genomic atlas of the human proteome in brain, cerebrospinal fluid, and plasma

Comprehensive expression quantitative trait loci studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein quantitative trait loci in brain, cerebrospinal fluid, and plasma. We now enhance this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in cerebrospinal fluid, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 loci in brain, 30 loci in cerebrospinal fluid, and 22 loci in plasma. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These resources are accessible through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer for use by the scientific community.

Proteasome Inhibition Reprograms Chromatin Landscape in Breast Cancer

The 26S proteasome is the major protein degradation machinery in cells. Cancer cells use the proteasome to modulate gene expression networks that promote tumor growth. Proteasome inhibitors have emerged as effective cancer therapeutics, but how they work mechanistically remains unclear. Here, using integrative genomic analysis, we discovered unexpected reprogramming of the chromatin landscape and RNA polymerase II (RNAPII) transcription initiation in breast cancer cells treated with the proteasome inhibitor MG132. The cells acquired dynamic changes in chromatin accessibility at specific genomic loci termed differentially open chromatin regions (DOCR). DOCRs with decreased accessibility were promoter proximal and exhibited unique chromatin architecture associated with divergent RNAPII transcription. Conversely, DOCRs with increased accessibility were primarily distal to transcription start sites and enriched in oncogenic superenhancers predominantly accessible in non-basal breast tumor subtypes. These findings describe the mechanisms by which the proteasome modulates the expression of gene networks intrinsic to breast cancer biology.

SIGNIFICANCE

Our study provides a strong basis for understanding the mechanisms by which proteasome inhibitors exert anticancer effects. We find open chromatin regions that change during proteasome inhibition, are typically accessible in non-basal breast cancers.

High-quality chromosome-level genome assembly of Nicotiana benthamiana

Nicotiana benthamiana is a fundamental model organism in plant research. Recent advancements in genomic sequencing have revealed significant intraspecific genetic variations. This study addresses the pressing need for a precise genome sequence specific to its geographic origin by presenting a comprehensive genome assembly of the N. benthamiana LAB strain from the Republic of Korea (NbKLAB). We compare this assembly with the widely used NbLAB360 strain, shedding light on essential genomic differences between them. The outcome is a high-quality, chromosome-level genome assembly comprising 19 chromosomes, spanning 2,762 Mb, with an N50 of 142.6 Mb. Comparative analyses revealed notable variations, including 46,215 protein-coding genes, with an impressive 99.5% BUSCO completeness score. Furthermore, the NbKLAB assembly substantially improved the QV from 33% for NbLAB360 to 49%. This refined chromosomal genome assembly for N. benthamiana, in conjunction with comparative insights, provides a valuable resource for genomics research and molecular biology. This accomplishment forms a strong foundation for in-depth exploration into the intricacies of plant genetics and genomics, improved precision, and a comparative framework.

National genomic epidemiology investigation revealed the spread of carbapenem-resistant Escherichia coli in healthy populations and the impact on public health

BACKGROUND

Carbapenem-resistant Escherichia coli (CREC) has been considered as WHO priority pathogens, causing a great public health concern globally. While CREC from patients has been thoroughly investigated, the prevalence and underlying risks of CREC in healthy populations have been overlooked. Systematic research on the prevalence of CREC in healthy individuals was conducted here. We aimed to characterize CREC collected from healthy populations in China between 2020 and 2022 and to compare the genomes of CREC isolates isolated from healthy individuals and clinical patients.

METHODS

We present a nationwide investigation of CREC isolates among healthy populations in China, employing robust molecular and genomic analyses. Antimicrobial susceptibility testing, whole-genome sequencing, and bioinformatics were utilized to analyze a cohort of CREC isolates (n = 113) obtained from fecal samples of 5 064 healthy individuals. Representative plasmids were extracted for third-generation nanopore sequencing. We previously collected 113 non-duplicate CREC isolates (59 in 2018, 54 in 2020) collected from ICU patients in 15 provinces and municipalities in China, and these clinical isolates were used to compare with the isolates in this study. Furthermore, we employ comparative genomics approaches to elucidate molecular variations and potential correlations between clinical and non-clinical CREC isolates.

RESULTS

A total of 147 CREC isolates were identified from 5 064 samples collected across 11 provinces in China. These isolates were classified into 64 known sequence types (STs), but no dominant STs were observed. In total, seven carbapenemase genes were detected with blaNDM-5 (n = 116) being the most prevalent one. Genetic environments and plasmid backbones of blaNDM were conserved in CREC isolated from healthy individuals. Furthermore, we compared clinical and healthy human-originated CRECs, revealing noteworthy distinctions in 23 resistance genes, including blaNDM-1, blaNDM-5, and blaKPC (χ2 test, p < 0.05). Clinical isolates contained more virulence factors associated with iron uptake, adhesion, and invasion than those obtained from healthy individuals. Notably, CREC isolates generally found healthy people are detected in hospitalized patients.

CONCLUSIONS

Our findings underscore the significance of healthy populations-derived CRECs as a crucial reservoir of antibiotic resistance genes (ARGs). This highlights the need for ongoing monitoring of CREC isolates in healthy populations to accurately assess the potential risks posed by clinical CREC isolates.

Genomic analysis reveals genes that encode the synthesis of volatile compounds by a Bacillus velezensis-based biofungicide used in the treatment of grapes to control Aspergillus carbonarius

Fungal control strategies based on the use of Bacillus have emerged in agriculture as eco-friendly alternatives to replace/reduce the use of synthetic pesticides. Bacillus sp. P1 was reported as a new promising strain for control of Aspergillus carbonarius, a known producer of ochratoxin A, categorized as possible human carcinogen with high nephrotoxic potential. Grape quality can be influenced by vineyard management practices, including the use of fungal control agents. The aim of this study was to evaluate, for the first time, the quality parameters of Chardonnay grapes exposed to an antifungal Bacillus-based strategy for control of A. carbonarius, supporting findings by genomic investigations. Furthermore, genomic tools were used to confirm that the strain P1 belongs to the non-pathogenic species Bacillus velezensis and also to certify its biosafety. The genome of B. velezensis P1 harbors genes that are putatively involved in the production of volatiles and hydrolytic enzymes, which are responsible for releasing the free form of aroma compounds. In addition to promote biocontrol of phytopathogenic fungi and ochratoxins, the treatment with B. velezensis P1 did not change the texture (hardness and firmness), color and pH of the grapes. Heat map and hierarchical clustering analysis (HCA) of volatiles evaluated by GC/MS revealed that Bacillus-treated grapes showed higher levels of compounds with a pleasant odor descriptions such as 3-hydroxy-2-butanone, 2,3-butanediol, 3-methyl-1-butanol, 3,4-dihydro-β-ionone, β-ionone, dihydroactinidiolide, linalool oxide, and β-terpineol. The results of this study indicate that B. velezensis P1 presents desirable properties to be used as a biocontrol agent.

Genetic diversity, disease resistance, and environmental adaptation of Arachis duranensis L.: New insights from landscape genomics

The genetic diversity that exists in natural populations of Arachis duranensis, the wild diploid donor of the A subgenome of cultivated tetraploid peanut, has the potential to improve crop adaptability, resilience to major pests and diseases, and drought tolerance. Despite its potential value for peanut improvement, limited research has been focused on the association between allelic variation, environmental factors, and response to early (ELS) and late leaf spot (LLS) diseases. The present study implemented a landscape genomics approach to gain a better understanding of the genetic variability of A. duranensis represented in the ex-situ peanut germplasm collection maintained at the U.S. Department of Agriculture, which spans the entire geographic range of the species in its center of origin in South America. A set of 2810 single nucleotide polymorphism (SNP) markers allowed a high-resolution genome-wide characterization of natural populations. The analysis of population structure showed a complex pattern of genetic diversity with five putative groups. The incorporation of bioclimatic variables for genotype-environment associations, using the latent factor mixed model (LFMM2) method, provided insights into the genomic signatures of environmental adaptation, and led to the identification of SNP loci whose allele frequencies were correlated with elevation, temperature, and precipitation-related variables (q < 0.05). The LFMM2 analysis for ELS and LLS detected candidate SNPs and genomic regions on chromosomes A02, A03, A04, A06, and A08. These findings highlight the importance of the application of landscape genomics in ex situ collections of peanut and other crop wild relatives to effectively identify favorable alleles and germplasm for incorporation into breeding programs. We report new sources of A. duranensis germplasm harboring adaptive allelic variation, which have the potential to be utilized in introgression breeding for a single or multiple environmental factors, as well as for resistance to leaf spot diseases.

Prevalence and genomic characterization of the Bacillus cereus group strains contamination in food products in Southern China

The Bacillus cereus group, as one of the important opportunistic foodborne pathogens, is considered a risk to public health due to foodborne diseases and an important cause of economic losses to food industries. This study aimed to gain essential information on the prevalence, phenotype, and genotype of B. cereus group strains isolated from various food products in China. A total of 890 strains of B. cereus group bacteria from 1181 food samples from 2020 to 2023 were identified using the standardized detection method. These strains were found to be prevalent in various food types, with the highest contamination rates observed in cereal flour (55.8 %) and wheat/rice noodles (45.7 %). The tested strains exhibited high resistance rates against penicillin (98.5 %) and ampicillin (98.9 %). Strains isolated from cereal flour had the highest rate of meropenem resistance (7.8 %), while strains from sausages were most resistant to vancomycin (16.8 %). A total of 234 out of the 891 B. cereus group strains were randomly selected for WGS analysis, 18.4 % of which displayed multidrug resistance. The species identification by WGS analysis revealed the presence of 10 distinct species within the B. cereus group, with B. cereus species being the most prevalent. The highest level of species diversity was observed in sausages. Notably, B. anthracis strains lacking the anthrax toxin genes were detected in flour-based food products and sausages. A total of 20 antibiotic resistance genes have been identified, with β-lactam resistance genes (bla1, bla2, BcI, BcII, and blaTEM-116) being the most common. The B. tropicus strains exhibit the highest average number of virulence genes (23.4). The diarrheal virulence genes nheABC, hblACD, and cytK were found in numerous strains. Only 4 of the 234 (1.7 %) sequenced strains contain the ces gene cluster linked to emetic symptoms. These data offer valuable insights for public health policymakers on addressing foodborne B. cereus group infections and ensuring food safety.

Molecular mechanisms of selenite reduction by Lactiplantibacillus plantarum BSe: An integrated genomic and transcriptomic analysis

The reduction of selenite [Se(Ⅳ)] by microorganisms is a green and efficient detoxification strategy. We found that Se(Ⅳ) inhibited exopolysaccharide and protein secretion by Lactiplantibacillus plantarum BSe and compromised cell integrity. In this study, L. plantarum BSe reduced Se(Ⅳ) by increasing related enzyme activity and electron transfer. Genomic analysis demonstrated that L. plantarum BSe should be able to reduce Se(Ⅳ). Further transcriptome analysis showed that L. plantarum BSe enhanced its tolerance to Se(Ⅳ) by upregulating the expression of surface proteins and transporters, thus reducing the extracellular Se(Ⅳ) concentration through related enzymatic reactions and siderophore-mediated pathways. Lactiplantibacillus plantarum BSe was able to regulate the expression of related genes involved in quorum sensing and a two-component system and then select appropriate strategies for Se(Ⅳ) transformation in response to varying environmental Se(Ⅳ) concentrations. In addition, azo reductase was linked to the reduction of Se(Ⅳ) for the first time. The present study established a multipath model for the reduction of Se(Ⅳ) by L. plantarum, providing new insights into the biological reduction of Se(Ⅳ) and the biogeochemical cycle of selenium.

StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes

KEY MESSAGE

We constructed a gene expression atlas and co-expression network for potatoes and identified several novel genes associated with various agronomic traits. This resource will accelerate potato genetics and genomics research. Potato (Solanum tuberosum L.) is the world's most crucial non-cereal food crop and ranks third in food production after wheat and rice. Despite the availability of several potato transcriptome datasets at public databases like NCBI SRA, an effort has yet to be put into developing a global transcriptome atlas and a co-expression network for potatoes. The objectives of our study were to construct a global expression atlas for potatoes using publicly available transcriptome datasets, identify housekeeping and tissue-specific genes, construct a global co-expression network and identify co-expression clusters, investigate the transcriptional complexity of genes involved in various essential biological processes related to agronomic traits, and provide a web server (StCoExpNet) to easily access the newly constructed expression atlas and co-expression network to investigate the expression and co-expression of genes of interest. In this study, we used data from 2299 publicly available potato transcriptome samples obtained from 15 different tissues to construct a global transcriptome atlas. We found that roughly 87% of the annotated genes exhibited detectable expression in at least one sample. Among these, we identified 281 genes with consistent and stable expression levels, indicating their role as housekeeping genes. Conversely, 308 genes exhibited marked tissue-specific expression patterns. We exemplarily linked some co-expression clusters to important agronomic traits of potatoes, such as self-incompatibility, anthocyanin biosynthesis, tuberization, and defense responses against multiple pathogens. The dataset compiled here constitutes a new resource (StCoExpNet), which can be accessed at https://stcoexpnet.julius-kuehn.de . This transcriptome atlas and the co-expression network will accelerate potato genetics and genomics research.

Access and barriers to genomic classifiers for breast cancer and prostate cancer in India

The incidence of cancer in general, including breast and prostate cancer specifically, is increasing in India. Breast and prostate cancers have genomic classifiers developed to guide therapy decisions. However, these genomic classifiers are often inaccessible in India due to high cost. These classifiers may also be less suitable to the Indian population, as data primarily from patients in wealthy Western countries were used in developing these genomic classifiers. In addition to the limitations in using these existing genomic classifiers, developing and validating new genomic classifiers for breast and prostate cancer in India is challenging due to the heterogeneity in the Indian population. However, there are steps that can be taken to address the various barriers that currently exist for accurate, accessible genomic classifiers for cancer in India.

Genomic and transcriptomic characterization of methylmercury detoxification in a deep ocean Alteromonas mediterranea ISS312

Methylmercury (MeHg) is one of the most worrisome pollutants in marine systems. MeHg detoxification is mediated by merB and merA genes, responsible for the demethylation of MeHg and the reduction of inorganic mercury, respectively. Little is known about the biological capacity to detoxify this compound in marine environments, and even less the bacterial transcriptional changes during MeHg detoxification. This study provides the genomic and transcriptomic characterization of the deep ocean bacteria Alteromonas mediterranea ISS312 with capacity for MeHg degradation. Its genome sequence revealed four mer operons containing three merA gene and two merB gene copies, that could be horizontally transferred among distant related genomes by mobile genetic elements. The transcriptomic profiling in the presence of 5 μM MeHg showed that merA and merB genes are within the most expressed genes, although not all mer genes were equally transcribed. Besides, we aimed to identify functional orthologous genes that displayed expression profiles highly similar or identical to those genes within the mer operons, which could indicate they are under the same regulatory controls. We found contrasting expression profiles for each mer operon that were positively correlated with a wide array of functions mostly related to amino acid metabolism, but also to flagellar assembly or two component systems. Also, this study highlights that all merAB genes of the four operons were globally distributed across oceans layers with higher transcriptional activity in the mesopelagic deeper waters. Our study provides new insights about the transcriptional patterns related to the capacity of marine bacteria to detoxify MeHg, with important implications for the understanding of this process in marine ecosystems.

Isolation and characterization of novel Staphylococcus aureus bacteriophage Hesat from dairy origin

A novel temperate phage, named Hesat, was isolated by the incubation of a dairy strain of Staphylococcus aureus belonging to spa-type t127 with either bovine or ovine milk. Hesat represents a new species of temperate phage within the Phietavirus genus of the Azeredovirinae subfamily. Its genome has a length of 43,129 bp and a GC content of 35.11% and contains 75 predicted ORFs, some of which linked to virulence. This includes (i) a pathogenicity island (SaPln2), homologous to the type II toxin-antitoxin system PemK/MazF family toxin; (ii) a DUF3113 protein (gp30) that is putatively involved in the derepression of the global repressor Stl; and (iii) a cluster coding for a PVL. Genomic analysis of the host strain indicates Hesat is a resident prophage. Interestingly, its induction was obtained by exposing the bacterium to milk, while the conventional mitomycin C-based approach failed. The host range of phage Hesat appears to be broad, as it was able to lyse 24 out of 30 tested S. aureus isolates. Furthermore, when tested at high titer (108 PFU/ml), Hesat phage was also able to lyse a Staphylococcus muscae isolate, a coagulase-negative staphylococcal strain. KEY POINTS: • A new phage species was isolated from a Staphylococcus aureus bovine strain. • Pathogenicity island and PVL genes are encoded within phage genome. • The phage is active against most of S. aureus strains from both animal and human origins.

The genomic basis of nitrogen utilization efficiency and trait plasticity to improve nutrient stress tolerance in cultivated sunflower

Maintaining crop productivity is challenging as population growth, climate change, and increasing fertilizer costs necessitate expanding crop production to poorer lands whilst reducing inputs. Enhancing crops' nutrient use efficiency is thus an important goal, but requires a better understanding of related traits and their genetic basis. We investigated variation in low nutrient stress tolerance in a diverse panel of cultivated sunflower genotypes grown under high and low nutrient conditions, assessing relative growth rate (RGR) as performance. We assessed variation in traits related to nitrogen utilization efficiency (NUtE), mass allocation, and leaf elemental content. Across genotypes, nutrient limitation generally reduced RGR. Moreover, there was a negative correlation between vigor (RGR in control) and decline in RGR in response to stress. Given this trade-off, we focused on nutrient stress tolerance independent of vigor. This tolerance metric correlated with the change in NUtE, plasticity for a suite of morphological traits, and leaf element content. Genome-wide associations revealed regions associated with variation and plasticity in multiple traits, including two regions with seemingly additive effects on NUtE change. Our results demonstrate potential avenues for improving sunflower nutrient stress tolerance independent of vigor, and highlight specific traits and genomic regions that could play a role in enhancing tolerance.

Genomic characterization and related functional genes of γ- poly glutamic acid producing Bacillus subtilis

γ- poly glutamic acid (γ-PGA), a high molecular weight polymer, is synthesized by microorganisms and secreted into the extracellular space. Due to its excellent performance, γ-PGA has been widely used in various fields, including food, biomedical and environmental fields. In this study, we screened natto samples for two strains of Bacillus subtilis N3378-2at and N3378-3At that produce γ-PGA. We then identified the γ-PGA synthetase gene cluster (PgsB, PgsC, PgsA, YwtC and PgdS), glutamate racemase RacE, phage-derived γ-PGA hydrolase (PghB and PghC) and exo-γ-glutamyl peptidase (GGT) from the genome of these strains. Based on these γ-PGA-related protein sequences from isolated Bacillus subtilis and 181 B. subtilis obtained from GenBank, we carried out genotyping analysis and classified them into types 1-5. Since we found B. amyloliquefaciens LL3 can produce γ-PGA, we obtained the B. velezensis and B. amyloliquefaciens strains from GenBank and classified them into types 6 and 7 based on LL3. Finally, we constructed evolutionary trees for these protein sequences. This study analyzed the distribution of γ-PGA-related protein sequences in the genomes of B. subtilis, B. velezensis and B. amyloliquefaciens strains, then the evolutionary diversity of these protein sequences was analyzed, which provided novel information for the development and utilization of γ-PGA-producing strains.

A joint Bayesian hierarchical model for estimating SARS-CoV-2 genomic and subgenomic RNA viral dynamics and seroconversion

Understanding the viral dynamics of and natural immunity to the severe acute respiratory syndrome coronavirus 2 is crucial for devising better therapeutic and prevention strategies for coronavirus disease 2019 (COVID-19). Here, we present a Bayesian hierarchical model that jointly estimates the genomic RNA viral load, the subgenomic RNA (sgRNA) viral load (correlated to active viral replication), and the rate and timing of seroconversion (correlated to presence of antibodies). Our proposed method accounts for the dynamical relationship and correlation structure between the two types of viral load, allows for borrowing of information between viral load and antibody data, and identifies potential correlates of viral load characteristics and propensity for seroconversion. We demonstrate the features of the joint model through application to the COVID-19 post-exposure prophylaxis study and conduct a cross-validation exercise to illustrate the model's ability to impute the sgRNA viral trajectories for people who only had genomic RNA viral load data.

Central conducting lymphatic anomaly: from bench to bedside

Central conducting lymphatic anomaly (CCLA) is a complex lymphatic anomaly characterized by abnormalities of the central lymphatics and may present with nonimmune fetal hydrops, chylothorax, chylous ascites, or lymphedema. CCLA has historically been difficult to diagnose and treat; however, recent advances in imaging, such as dynamic contrast magnetic resonance lymphangiography, and in genomics, such as deep sequencing and utilization of cell-free DNA, have improved diagnosis and refined both genotype and phenotype. Furthermore, in vitro and in vivo models have confirmed genetic causes of CCLA, defined the underlying pathogenesis, and facilitated personalized medicine to improve outcomes. Basic, translational, and clinical science are essential for a bedside-to-bench and back approach for CCLA.

Genomic insights into local adaptation and vulnerability of Quercus longinux to climate change

BACKGROUND

Climate change is expected to alter the factors that drive changes in adaptive variation. This is especially true for species with long life spans and limited dispersal capabilities. Rapid climate changes may disrupt the migration of beneficial genetic variations, making it challenging for them to keep up with changing environments. Understanding adaptive genetic variations in tree species is crucial for conservation and effective forest management. Our study used landscape genomic analyses and phenotypic traits from a thorough sampling across the entire range of Quercus longinux, an oak species native to Taiwan, to investigate the signals of adaptation within this species.

RESULTS

Using ecological data, phenotypic traits, and 1,933 single-nucleotide polymorphisms (SNPs) from 205 individuals, we classified three genetic groups, which were also phenotypically and ecologically divergent. Thirty-five genes related to drought and freeze resistance displayed signatures of natural selection. The adaptive variation was driven by diverse environmental pressures such as low spring precipitation, low annual temperature, and soil grid sizes. Using linear-regression-based methods, we identified isolation by environment (IBE) as the optimal model for adaptive SNPs. Redundancy analysis (RDA) further revealed a substantial joint influence of demography, geology, and environments, suggesting a covariation between environmental gradients and colonization history. Lastly, we utilized adaptive signals to estimate the genetic offset for each individual under diverse climate change scenarios. The required genetic changes and migration distance are larger in severe climates. Our prediction also reveals potential threats to edge populations in northern and southeastern Taiwan due to escalating temperatures and precipitation reallocation.

CONCLUSIONS

We demonstrate the intricate influence of ecological heterogeneity on genetic and phenotypic adaptation of an oak species. The adaptation is also driven by some rarely studied environmental factors, including wind speed and soil features. Furthermore, the genetic offset analysis predicted that the edge populations of Q. longinux in lower elevations might face higher risks of local extinctions under climate change.

CCDC58 is a potential biomarker for diagnosis, prognosis, immunity, and genomic heterogeneity in pan-cancer

Coiled-coil domain-containing 58 (CCDC58) is a member of the CCDC protein family. Similar to other members, CCDC58 exhibits potential tumorigenic roles in a variety of malignancies. However, there is no systematic and comprehensive pan-cancer analysis to investigate the diagnosis, prognosis, immune infiltration, and other related functions of CCDC58. We used several online websites and databases, such as TCGA, GTEx, UALCAN, HPA, CancerSEA, BioGRID, GEPIA 2.0, TIMER 2.0, and TISIDB, to extract CCDC58 expression data and clinical data of patients in pan-cancer. Then, the relationship between CCDC58 expression and diagnosis, prognosis, genetic alterations, DNA methylation, genomic heterogeneity, and immune infiltration level were determined. In addition, the biological function of CCDC58 in liver hepatocellular carcinoma (LIHC) was investigated. Pan-cancer analysis results showed that CCDC58 was differentially expressed in most tumors and showed excellent performance in diagnosis and prediction of prognosis. The expression of CCDC58 was highly correlated with genetic alterations, DNA methylation, and genomic heterogeneity in some tumors. In addition, the correlation analysis of CCDC58 with the level of immune infiltration and immune checkpoint marker genes indicated that CCDC58 might affect the composition of the tumor immune microenvironment. Enrichment analysis showed that CCDC58-related genes were mainly linked to mitosis, chromosome, and cell cycle. Finally, biological function experiments demonstrated that CCDC58 plays an important role in tumor cell proliferation and migration. CCDC58 was first identified as a pan-cancer biomarker. It may be used as a potential therapeutic target to improve the prognosis of patients in the future.

A cluster of putative resistance genes is associated with a dominant resistance to sunflower broomrape

KEY MESSAGE

The HaOr5 resistance gene is located in a large genomic insertion containing putative resistance genes and provides resistance to O. cumana, preventing successful connection to the sunflower root vascular system. Orobanche cumana (sunflower broomrape) is a parasitic plant that is part of the Orobanchaceae family and specifically infests sunflower crops. This weed is an obligate parasitic plant that does not carry out photosynthetic activity or develop roots and is fully dependent on its host for its development. It produces thousands of dust-like seeds per plant. It possesses a high spreading ability and has been shown to quickly overcome resistance genes successively introduced by selection in cultivated sunflower varieties. The first part of its life cycle occurs underground. The connection to the sunflower vascular system is essential for parasitic plant survival and development. The HaOr5 gene provides resistance to sunflower broomrape race E by preventing the connection of O. cumana to the root vascular system. We mapped a single position of the HaOr5 gene by quantitative trait locus mapping using two segregating populations. The same location of the HaOr5 gene was identified by genome-wide association. Using a large population of thousands of F2 plants, we restricted the location of the HaOr5 gene to a genomic region of 193 kb. By sequencing the whole genome of the resistant line harboring the major resistance gene HaOr5, we identified a large insertion of a complex genomic region containing a cluster of putative resistance genes.

Epidemiological, clinical, and genomic landscape of coccidioidomycosis in northeastern Brazil

Coccidioidomycosis, listed as a priority mycosis by the WHO, is endemic in the United States but often overlooked in Central and South America. Employing a multi-institutional approach, we investigate how disease characteristics, pathogen genetic variation, and environmental factors impact coccidioidomycosis epidemiology and outcomes in South America. We identified 292 cases (1978-2021) and 42 outbreaks in Piauí and Maranhão states, Brazil, the largest series outside the US/Mexico epidemic zone. The male-to-female ratio was 57.4:1 and the most common activity was armadillo hunting (91.1%) 4 to 30 days before symptom onset. Most patients (92.8%) exhibited typical acute pulmonary disease, with cough (93%), fever (90%), and chest pain (77%) as predominant symptoms. The case fatality rate was 8%. Our negative binomial regression model indicates that reduced precipitation levels in the current (p = 0.015) and preceding year (p = 0.001) predict heightened incidence. Unlike other hotspots, acidic soil characterizes this region. Brazilian strains differ genomically from other C. posadasii lineages. Northeastern Brazil presents a distinctive coccidioidomycosis profile, with armadillo hunters facing elevated risks. Low annual rainfall emerges as a key factor in increasing cases. A unique C. posadasii lineage in Brazil suggests potential differences in environmental, virulence, and/or pathogenesis traits compared to other Coccidioides genotypes.

The evolution of transposable elements in Brachypodium distachyon is governed by purifying selection, while neutral and adaptive processes play a minor role

Understanding how plants adapt to changing environments and the potential contribution of transposable elements (TEs) to this process is a key question in evolutionary genomics. While TEs have recently been put forward as active players in the context of adaptation, few studies have thoroughly investigated their precise role in plant evolution. Here, we used the wild Mediterranean grass Brachypodium distachyon as a model species to identify and quantify the forces acting on TEs during the adaptation of this species to various conditions, across its entire geographic range. Using sequencing data from more than 320 natural B. distachyon accessions and a suite of population genomics approaches, we reveal that putatively adaptive TE polymorphisms are rare in wild B. distachyon populations. After accounting for changes in past TE activity, we show that only a small proportion of TE polymorphisms evolved neutrally (<10%), while the vast majority of them are under moderate purifying selection regardless of their distance to genes. TE polymorphisms should not be ignored when conducting evolutionary studies, as they can be linked to adaptation. However, our study clearly shows that while they have a large potential to cause phenotypic variation in B. distachyon, they are not favored during evolution and adaptation over other types of mutations (such as point mutations) in this species.

Population structure and identification of genomic regions associated with productive traits in five Italian beef cattle breeds

Italy has a long history in beef production, with local breeds such as Marchigiana, Chianina, Romagnola, Maremmana, and Podolica which produce high-quality meat. Selection has improved meat production, precocity, growth ability and muscle development, but the genetic determinism of such traits is mostly unknown. Using 33K SNPs-data from young bulls (N = 4064) belonging to these five Italian breeds, we demonstrated that the Maremmana and Podolica rustic breeds are closely related, while the specialised Marchigiana, Chianina, and Romagnola breeds are more differentiated. A genome-wide association study for growth and muscle development traits (average daily gain during the performance test, weight at 1 year old, muscularity) was conducted in the five Italian breeds. Results indicated a region on chromosome 2, containing the myostatin gene (MSTN), which displayed significant genome-wide associations with muscularity in Marchigiana cattle, a breed in which the muscle hypertrophy phenotype is segregating. Moreover, a significant SNP on chromosome 14 was associated, in the Chianina breed, to muscularity. The identification of diverse genomic regions associated with conformation traits might increase our knowledge about the genomic basis of such traits in Italian beef cattle and, eventually, such information could be used to implement marker-assisted selection of young bulls tested in the performance test.

Genomic basis determining root system architecture in maize

KEY MESSAGE

A total of 389 and 344 QTLs were identified by GWAS and QTL mapping explaining accumulatively 32.2-65.0% and 23.7-63.4% of phenotypic variation for 14 shoot-borne root traits using more than 1300 individuals across multiple field trails. Efficient nutrient and water acquisition from soils depends on the root system architecture (RSA). However, the genetic determinants underlying RSA in maize remain largely unexplored. In this study, we conducted a comprehensive genetic analysis for 14 shoot-borne root traits using 513 inbred lines and 800 individuals from four recombinant inbred line (RIL) populations at the mature stage across multiple field trails. Our analysis revealed substantial phenotypic variation for these 14 root traits, with a total of 389 and 344 QTLs identified through genome-wide association analysis (GWAS) and linkage analysis, respectively. These QTLs collectively explained 32.2-65.0% and 23.7-63.4% of the trait variation within each population. Several a priori candidate genes involved in auxin and cytokinin signaling pathways, such as IAA26, ARF2, LBD37 and CKX3, were found to co-localize with these loci. In addition, a total of 69 transcription factors (TFs) from 27 TF families (MYB, NAC, bZIP, bHLH and WRKY) were found for shoot-borne root traits. A total of 19 genes including PIN3, LBD15, IAA32, IAA38 and ARR12 and 19 GWAS signals were overlapped with selective sweeps. Further, significant additive effects were found for root traits, and pyramiding the favorable alleles could enhance maize root development. These findings could contribute to understand the genetic basis of root development and evolution, and provided an important genetic resource for the genetic improvement of root traits in maize.

Genome-wide analysis to uncover how Pocillopora acuta survives the challenging intertidal environment

Characterisation of genomic variation among corals can help uncover variants underlying trait differences and contribute towards genotype prioritisation in coastal restoration projects. For example, there is growing interest in identifying resilient genotypes for transplantation, and to better understand the genetic processes that allow some individuals to survive in specific conditions better than others. The coral species Pocillopora acuta is known to survive in a wide range of habitats, from reefs artificial coastal defences, suggesting its potential use as a starter species for ecological engineering efforts involving coral transplantation onto intertidal seawalls. However, the intertidal section of coastal armour is a challenging environment for corals, with conditions during periods of emersion being particularly stressful. Here, we scanned the entire genome of P. acuta corals to identify the regions harbouring single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) that separate intertidal colonies (n = 18) from those found in subtidal areas (n = 21). Findings revealed 74,391 high quality SNPs distributed across 386 regions of the P. acuta genome. While the majority of the detected SNPs were in non-coding regions, 12% were identified in exons (i.e. coding regions). Functional SNPs that were significantly associated with intertidal colonies were found in overrepresented genomic regions linked to cellular homeostasis, metabolism, and signalling processes, which may represent local environmental adaptation in the intertidal. Interestingly, regions that exhibited CNVs were also associated with metabolic and signalling processes, suggesting P. acuta corals living in the intertidal have a high capacity to perform biological functions critical for survival in extreme environments.

Employing bimodal representations to predict DNA bendability within a self-supervised pre-trained framework

The bendability of genomic DNA, which measures the DNA looping rate, is crucial for numerous biological processes of DNA. Recently, an advanced high-throughput technique known as 'loop-seq' has made it possible to measure the inherent cyclizability of DNA fragments. However, quantifying the bendability of large-scale DNA is costly, laborious, and time-consuming. To close the gap between rapidly evolving large language models and expanding genomic sequence information, and to elucidate the DNA bendability's impact on critical regulatory sequence motifs such as super-enhancers in the human genome, we introduce an innovative computational model, named MIXBend, to forecast the DNA bendability utilizing both nucleotide sequences and physicochemical properties. In MIXBend, a pre-trained language model DNABERT and convolutional neural network with attention mechanism are utilized to construct both sequence- and physicochemical-based extractors for the sophisticated refinement of DNA sequence representations. These bimodal DNA representations are then fed to a k-mer sequence-physicochemistry matching module to minimize the semantic gap between each modality. Lastly, a self-attention fusion layer is employed for the prediction of DNA bendability. In conclusion, the experimental results validate MIXBend's superior performance relative to other state-of-the-art methods. Additionally, MIXBend reveals both novel and known motifs from the yeast. Moreover, MIXBend discovers significant bendability fluctuations within super-enhancer regions and transcription factors binding sites in the human genome.

Control of false discoveries in grouped hypothesis testing for eQTL data

BACKGROUND

Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches.

RESULTS

In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed.

CONCLUSION

Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40-60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

Multiple genes in the Pate5-13 genomic region contribute to ADAM3 processing†

Sperm proteins undergo post-translational modifications during sperm transit through the epididymis to acquire fertilizing ability. We previously reported that the genomic region coding Pate family genes is key to the proteolytic processing of the sperm membrane protein ADAM3 and male fertility. This region contains nine Pate family genes (Pate5-13), and two protein-coding genes (Gm27235 and Gm5916), with a domain structure similar to Pate family genes. Therefore, in this study, we aimed to identify key factors by narrowing the genomic region. We generated three knockout (KO) mouse lines using CRISPR/Cas9: single KO mice of Pate10 expressed in the caput epididymis; deletion KO mice of six caput epididymis-enriched genes (Pate5-7, 13, Gm27235, and Gm5916) (Pate7-Gm5916 KO); and deletion KO mice of four genes expressed in the placenta and epididymis (Pate8, 9, 11, and 12) (Pate8-12 KO). We observed that the fertility of only Pate7-Gm5916 KO males was reduced, whereas the rest remained unaffected. Furthermore, when the caput epididymis-enriched genes, Pate8 and Pate10 remained in Pate7-Gm5916 KO mice were independently deleted, both KO males displayed more severe subfertility due to a decrease in mature ADAM3 and a defect in sperm migration to the oviduct. Thus, our data showed that multiple caput epididymis-enriched genes within the region coding Pate5-13 cooperatively function to ensure male fertility in mice.

Phylogenomic analyses and comparative genomics of Pseudomonas syringae associated with almond (Prunus dulcis) in California

We sequenced and comprehensively analysed the genomic architecture of 98 fluorescent pseudomonads isolated from different symptomatic and asymptomatic tissues of almond and a few other Prunus spp. Phylogenomic analyses, genome mining, field pathogenicity tests, and in vitro ice nucleation and antibiotic sensitivity tests were integrated to improve knowledge of the biology and management of bacterial blast and bacterial canker of almond. We identified Pseudomonas syringae pv. syringae, P. cerasi, and P. viridiflava as almond canker pathogens. P. syringae pv. syringae caused both canker and foliar (blast) symptoms. In contrast, P. cerasi and P. viridiflava only caused cankers, and P. viridiflava appeared to be a weak pathogen of almond. Isolates belonging to P. syringae pv. syringae were the most frequently isolated among the pathogenic species/pathovars, composing 75% of all pathogenic isolates. P. cerasi and P. viridiflava isolates composed 8.3 and 16.7% of the pathogenic isolates, respectively. Laboratory leaf infiltration bioassays produced results distinct from experiments in the field with both P. cerasi and P. syringae pv. syringae, causing significant necrosis and browning of detached leaves, whereas P. viridiflava conferred moderate effects. Genome mining revealed the absence of key epiphytic fitness-related genes in P. cerasi and P. viridiflava genomic sequences, which could explain the contrasting field and laboratory bioassay results. P. syringae pv. syringae and P. cerasi isolates harboured the ice nucleation protein, which correlated with the ice nucleation phenotype. Results of sensitivity tests to copper and kasugamycin showed a strong linkage to putative resistance genes. Isolates harbouring the ctpV gene showed resistance to copper up to 600 μg/ml. In contrast, isolates without the ctpV gene could not grow on nutrient agar amended with 200 μg/ml copper, suggesting ctpV can be used to phenotype copper resistance. All isolates were sensitive to kasugamycin at the label-recommended rate of 100μg/ml.

Shared genomic segments analysis identifies MHC class I and class III molecules as genetic risk factors for juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) is a complex rheumatic disease encompassing several clinically defined subtypes of varying severity. The etiology of JIA remains largely unknown, but genome-wide association studies (GWASs) have identified up to 22 genes associated with JIA susceptibility, including a well-established association with HLA-DRB1. Continued investigation of heritable risk factors has been hindered by disease heterogeneity and low disease prevalence. In this study, we utilized shared genomic segments (SGS) analysis on whole-genome sequencing of 40 cases from 12 multi-generational pedigrees significantly enriched for JIA. Subsets of cases are connected by a common ancestor in large extended pedigrees, increasing the power to identify disease-associated loci. SGS analysis identifies genomic segments shared among disease cases that are likely identical by descent and anchored by a disease locus. This approach revealed statistically significant signals for major histocompatibility complex (MHC) class I and class III alleles, particularly HLA-A∗02:01, which was observed at a high frequency among cases. Furthermore, we identified an additional risk locus at 12q23.2-23.3, containing genes primarily expressed by naive B cells, natural killer cells, and monocytes. The recognition of additional risk beyond HLA-DRB1 provides a new perspective on immune cell dynamics in JIA. These findings contribute to our understanding of JIA and may guide future research and therapeutic strategies.

Transforming our understanding of species-specific gene regulation

Mechanisms underlying phenotypic divergence across species remain unresolved. In this issue of Cell Genomics, Hansen, Fong, et al.1 systematically dissect human and rhesus macaque gene expression divergence by screening tens of thousands of orthologous elements for enhancer activity in lymphoblastoid cell lines, revealing a much greater role for trans divergence at levels equal to those of cis effects, counter to the prevailing consensus in the field.

[Analysis of a patient with Retinitis pigmentosa due to a novel variant of IMPDH1 gene]

OBJECTIVE

To explore the genetic basis for a patient with autosomal dominant retinitis pigmentosa (RP).

METHODS

A male patient with RP treated at Gansu Provincial Maternal and Child Health Care Hospital in September 2019 was selected as the study subject. Clinical data was collected. Peripheral blood samples of the patient and his parents were subjected to whole exome sequencing (WES). Candidate variant was validated by Sanger sequencing and bioinformatic analysis.

RESULTS

The patient, a 29-year-old male, developed night blindness, amblyopia, visual field defects and optic disc abnormalities since childhood. Gene sequencing revealed that he has harbored a heterozygous c.942G>C (p.Lys314Asn) variant of the IMPDH1 gene, which was inherited from his mother, whilst his father was of the wild type. Based on the guidelines from the American College of Medical Genetics and Genomics, the c.942G>C variant was predicted as likely pathogenic (PM1+PM2_Supporting+PP3+PP1).

CONCLUSION

The c.942G>C (p.Lys314Asn) variant in the IMPDH1 gene probably underlay the RP in this patient.

Dual-space disentangled-multimodal network (DDM-net) for glioma diagnosis and prognosis with incomplete pathology and genomic data

Objective. Effective fusion of histology slides and molecular profiles from genomic data has shown great potential in the diagnosis and prognosis of gliomas. However, it remains challenging to explicitly utilize the consistent-complementary information among different modalities and create comprehensive representations of patients. Additionally, existing researches mainly focus on complete multi-modality data and usually fail to construct robust models for incomplete samples.Approach. In this paper, we propose adual-space disentangled-multimodal network (DDM-net)for glioma diagnosis and prognosis. DDM-net disentangles the latent features generated by two separate variational autoencoders (VAEs) into common and specific components through a dual-space disentangled approach, facilitating the construction of comprehensive representations of patients. More importantly, DDM-net imputes the unavailable modality in the latent feature space, making it robust to incomplete samples.Main results. We evaluated our approach on the TCGA-GBMLGG dataset for glioma grading and survival analysis tasks. Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods, with a competitive AUC of 0.952 and a C-index of 0.768.Significance. The proposed model may help the clinical understanding of gliomas and can serve as an effective fusion model with multimodal data. Additionally, it is capable of handling incomplete samples, making it less constrained by clinical limitations.

Genomic wide association study and selective sweep analysis identify genes associated with improved yield under drought in Turkish winter wheat germplasm

A panel comprising of 84 Turkish winter wheat landraces (LR) and 73 modern varieties (MV) was analyzed with genome wide association study (GWAS) to identify genes/genomic regions associated with increased yield under favorable and drought conditions. In addition, selective sweep analysis was conducted to detect signatures of selection in the winter wheat genome driving the differentiation between LR and MV, to gather an understanding of genomic regions linked to adaptation and yield improvement. The panel was genotyped with 25 K wheat SNP array and phenotyped for agronomic traits for two growing seasons (2018 and 2019) in Konya, Turkey. Year 2018 was treated as drought environment due to very low precipitation prior to heading whereas year 2019 was considered as a favorable season. GWAS conducted with SNPs and haplotype blocks using mixed linear model identified 18 genomic regions in the vicinities of known genes i.e., TaERF3-3A, TaERF3-3B, DEP1-5A, FRIZZY PANICLE-2D, TaSnRK23-1A, TaAGL6-A, TaARF12-2A, TaARF12-2B, WAPO1, TaSPL16-7D, TaTGW6-A1, KAT-2B, TaOGT1, TaSPL21-6B, TaSBEIb, trs1/WFZP-A, TaCwi-A1-2A and TaPIN1-7A associated with grain yield (GY) and yield related traits. Haplotype-based GWAS identified five haplotype blocks (H1A-42, H2A-71, H4A-48, H7B-123 and H7B-124), with the favorable haplotypes showing a yield increase of > 700 kg/ha in the drought season. SNP-based GWAS, detected only one larger effect genomic region on chromosome 7B, in common with haplotype-based GWAS. On an average, the percentage variation (PV) explained by haplotypes was 8.0% higher than PV explained by SNPs for all the investigated traits. Selective sweep analysis detected 39 signatures of selection between LR and MV of which 15 were within proximity of known functional genes controlling flowering (PRR-A1, PPR-D1, TaHd1-6B), GY and GY components (TaSus2-2B, TaGS2-B1, AG1-1A/WAG1-1A, DUO-A1, DUO-B1, AG2-3A/WAG2-3A, TaLAX1, TaSnRK210-4A, FBP, TaLAX1, TaPIL1 and AP3-1-7A/WPA3-7A) and 10 regions underlying various transcription factors and regulatory genes. The study outcomes contribute to utilization of LR in breeding winter wheat.

An integrated toolkit for human microglia functional genomics

BACKGROUND

Microglia, the brain's resident immune cells, play vital roles in brain development, and disorders like Alzheimer's disease (AD). Human iPSC-derived microglia (iMG) provide a promising model to study these processes. However, existing iMG generation protocols face challenges, such as prolonged differentiation time, lack of detailed characterization, and limited gene function investigation via CRISPR-Cas9.

METHODS

Our integrated toolkit for in-vitro microglia functional genomics optimizes iPSC differentiation into iMG through a streamlined two-step, 20-day process, producing iMG with a normal karyotype. We confirmed the iMG's authenticity and quality through single-cell RNA sequencing, chromatin accessibility profiles (ATAC-Seq), proteomics and functional tests. The toolkit also incorporates a drug-dependent CRISPR-ON/OFF system for temporally controlled gene expression. Further, we facilitate the use of multi-omic data by providing online searchable platform that compares new iMG profiles to human primary microglia: https://sherlab.shinyapps.io/IPSC-derived-Microglia/ .

RESULTS

Our method generates iMG that closely align with human primary microglia in terms of transcriptomic, proteomic, and chromatin accessibility profiles. Functionally, these iMG exhibit Ca2 + transients, cytokine driven migration, immune responses to inflammatory signals, and active phagocytosis of CNS related substrates including synaptosomes, amyloid beta and myelin. Significantly, the toolkit facilitates repeated iMG harvesting, essential for large-scale experiments like CRISPR-Cas9 screens. The standalone ATAC-Seq profiles of our iMG closely resemble primary microglia, positioning them as ideal tools to study AD-associated single nucleotide variants (SNV) especially in the genome regulatory regions.

CONCLUSIONS

Our advanced two-step protocol rapidly and efficiently produces authentic iMG. With features like the CRISPR-ON/OFF system and a comprehensive multi-omic data platform, our toolkit equips researchers for robust microglial functional genomic studies. By facilitating detailed SNV investigation and offering a sustainable cell harvest mechanism, the toolkit heralds significant progress in neurodegenerative disease drug research and therapeutic advancement.