BackgroundFlowering plays an important role in completing the reproductive cycle of plants and obtaining next generation of plants. In case of citrus, it may take more than a year to achieve progeny. Therefore, in order to fasten the breeding processes, the juvenility period needs to be reduced. The juvenility in plants is regulated by set of various flowering genes. The citrus fruit and leaves possess various medicinal properties and are subjected to intensive breeding programs to produce hybrids with improved quality traits. In order to break juvenility in Citrus, it is important to study the role of flowering genes. The present study involved identification of genes regulating flowering in Citrus sinensis L. Osbeck via homology based approach. The structural and functional characterization of these genes would help in targeting genome editing techniques to induce mutations in these genes for producing desirable results.ResultsA total of 43 genes were identified which were located on all the 9 chromosomes of citrus. The in-silico analysis was performed to determine the genetic structure, conserved motifs, cis-regulatory elements (CREs) and phylogenetic relationship of the genes. A total of 10 CREs responsible for flowering were detected in 33 genes and 8 conserved motifs were identified in all the genes. The protein structure, protein-protein interaction network and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed to study the functioning of these genes which revealed the involvement of flowering proteins in circadian rhythm pathways. The gene ontology (GO) and gene function analysis was performed to functionally annotate the genes. The structure of the genes and proteins were also compared among other Citrus species to study the evolutionary relationship among them. The expression study revealed the expression of flowering genes in floral buds and ovaries. The qRT-PCR analysis revealed that the flowering genes were highly expressed in bud stage, fully grown flower and early stage of fruit development.ConclusionsThe findings suggested that the flowering genes were highly conserved in citrus species. The qRT-PCR analysis revealed the tissue specific expression of flowering genes (CsFT, CsCO, CsSOC, CsAP, CsSEP and CsLFY) which would help in easy detection and targeting of genes through various forward and reverse genetic approaches.

Beef marbling is considered a desirable trait in the meat industry. Therefore, understanding the genetic factors that cause marbling is important. Previously, we performed a genome-wide association study to examine genetic factors associated with beef marbling in Japanese Black cattle and identified a candidate region between 10-30 Mbp on chromosome 7. We verified the effect of the SNPs in this region on beef marbling using linkage disequilibrium block analysis. We narrowed down the candidate region to a range of 15.8-16.1 Mbp. In this study, we comprehensively detected all of the SNPs in this region and verified their effects on beef marbling. Genome resequencing using four animals exhibiting high beef marbling standard (BMS) and four with low BMS revealed a total of 1,846 polymorphisms within the candidate region. Based on the annotation, we selected 13 SNPs exhibiting a moderate impact, as no high-impact SNPs were detected. All of the SNPs represented missense polymorphisms and were located in the following seven genes: RDH8, ANGPTL6, DNMT1, MRPL4, ICAM1, ICAM3, and ICAM5. Finally, we determined the effects of these SNPs on the BMS of a Japanese Black cattle population (n = 529). Analysis of variance revealed that the five SNPs were located in genes encoding the intercellular adhesion molecules (ICAM1, ICAM3, and ICAM5), and showed a highly significant association compared with the remainder (p < 0.01). The lowest p-value was observed for ICAM3_c.739G > A (p = 1.18E-04). Previous studies have suggested that intercellular adhesion molecules (ICAM) may be an upstream factor that regulates adipocyte differentiation. Therefore, considering the polymorphism and putative gene function, we suggest that ICAM1 is potentially responsible for beef marbling. c.470C > G and/or c.994G > A on ICAM1 may be responsible for this quantitative trait locus. Promising SNP candidates responsible for beef marbling were identified using extensive polymorphism verification in a previously reported QTL region. We aim to elucidate the mechanism of beef marbling in future studies by investigating how these polymorphisms alter protein structure and function.

ObjectivesA genome sequence of a threatened species can provide valuable genetic information that is important for improving the conservation strategies. The white-eared night heron (Gorsachius magnificus) is an endangered and poorly known ardeid bird. In order to support future studies on conservation genetics and evolutionary adaptation of this species, we have reported a de novo assembled and annotated whole-genome sequence of the G. magnificus.Data descriptionThe final draft genome assembly of the G. magnificus was 1.19 Gb in size, with a contig N50 of 187.69 kb and a scaffold N50 of 7,338.28 kb. According to BUSCO analysis, the genome assembly contained 97.49% of the 8,338 genes in the Aves (odb10) dataset. Approximately 10.52% of the genome assembly was composed of repetitive sequences. A total of 14,613 protein-coding genes were predicted in the genome assembly, with functional annotations available for 14,611 genes. The genome assembly exhibited a heterozygosity rate of 0.49 heterozygosity per kilobase pair. This draft genome of G. magnificus provides valuable genomic resources for future studies on conservation and evolution.

We tackle the problem of estimating species TMRCAs (Time to Most Recent Common Ancestor), given a genome sequence from each species and a large known phylogenetic tree with a known structure (typically from one of the species). The number of transitions at each site from the first sequence to the other is assumed to be Poisson distributed, and only the parity of the number of transitions is observed. The detailed phylogenetic tree contains information about the transition rates in each site. We use this formulation to develop and analyze multiple estimators of the species' TMRCA. To test our methods, we use mtDNA substitution statistics from the well-established Phylotree as a baseline for data simulation such that the substitution rate per site mimics the real-world observed rates. We evaluate our methods using simulated data and compare them to the Bayesian optimizing software BEAST2, showing that our proposed estimators are accurate for a wide range of TMRCAs and significantly outperform BEAST2. We then apply the proposed estimators on Neanderthal, Denisovan, and Chimpanzee mtDNA genomes to better estimate their TMRCA with modern humans and find that their TMRCA is substantially later, compared to values cited recently in the literature. Our methods utilize the transition statistics from the entire known human mtDNA phylogenetic tree (Phylotree), eliminating the requirement to reconstruct a tree encompassing the specific sequences of interest. Moreover, they demonstrate notable improvement in both running speed and accuracy compared to BEAST2, particularly for earlier TMRCAs like the human-Chimpanzee split. Our results date the human - Neanderthal TMRCA to be [Formula: see text] years ago, considerably later than values cited in other recent studies.

ObjectivesEscallonia (Escalloniaceae) belongs to the Escalloniales, a diverse clade of flowering plants with unclear placement in the tree of life. Escallonia species show impressive morphological and ecological diversity and are widely distributed across three hotspots of biodiversity in the Neotropics. To shed light on the genomic substrate of this radiation and the phylogenetic placement of Escalloniales as well as to generate useful data for comparative evolutionary genomics across flowering plants, we produced and annotated draft genomes for two species of Escallonia.Data descriptionGenomic DNA from E. rubra and E. herrerae was sequenced with Oxford Nanopore sequencing chemistry, generating 3.4 and 12 million sequence reads with an average read length of 9.4 and 9.1 Kb (approximately 31 and 111 Gb of sequence data), respectively. In addition, we generated Illumina 100-bp paired-end short read data for E. rubra (approximately 75 Gb of sequence data). The Escallonia rubra genome was 566 Mb, with 3,233 contigs and an N50 of 285 Kb. The assembled genome for E. herrerae was 994 Mp, with 5,760 contigs and an N50 of 317 Kb. The genome sequences were annotated with 31,038 (E. rubra) and 47,905 (E. herrerea) protein-coding gene models supported by transcriptome/protein evidence and/or Pfam domain content. BUSCO assessments indicated completeness levels of approximately 98% for the genome assemblies and 88% for the genome annotations.

BackgroundPeucedanum praeruptorum Dunn, a traditional Chinese herbal medicine, contains coumarin and volatile oil components that have clinical application value. However, early bolting often occurs in the medicinal materials of Apiaceae plants. The rhizomes of the medicinal parts are gradually lignified after bolting, resulting in a sharp decrease in the content of coumarins. At present, the link between coumarin biosynthesis and early bolting in P. praeruptorum has not been elucidated.ResultsCombining the genome sequencing and the previous transcriptome sequencing results, we reanalyzed the differential transcripts of P. praeruptorum before and after bolting. A total of 62,088 new transcripts were identified, of which 31,500 were unknown transcripts. Functional classification and annotation showed that many genes were involved in the regulation of transcription, defense response, and carbohydrate metabolic processes. The main domains are the pentatricopeptide repeat, protein kinase, RNA recognition motif, leucine-rich repeat, and ankyrin repeat domains, indicating their pivotal roles in protein modification and signal transduction. Gene structure analysis showed that skipped exon (SE) was the most dominant alternative splicing, followed by the alternative 3’ splice site (A3SS) and the alternative 5’ splice site (A5SS). Functional enrichment of differentially expressed genes showed that these differentially expressed genes mainly include transmembrane transporters, channel proteins, DNA-binding proteins, polysaccharide-binding proteins, etc. In addition, genes involved in peroxisome, hexose phosphate pathway, phosphatidylinositol signaling system, and inositol phosphate metabolism pathway were greatly enriched. A protein-protein interaction network analysis discoverd 1,457 pairs of proteins that interact with each other. The expression levels of six UbiA genes, three UGT genes, and four OMT genes were higher during the bolting stage. This observation suggests their potential involvement in the catalytic processes of prenylation, glycosylation, and methylation of coumarins, respectively. A total of 100 peroxidase (PRX) genes were identified being involved in lignin polymerization, but only nine PRX genes were highly expressed at the bolting stage. It is worth noting that 73 autophagy-related genes (ATGs) were first identified from the KEGG pathway-enriched genes. Some ATGs, such as BHQH00009837, BHQH00013830, and novel8944, had higher expression levels after bolting.ConclusionsComparative transcriptome analysis and large-scale genome screening provide guidance and new opinions for the identification of bolting-related genes in P. praeruptorum.

BackgroundThe N6-methyladenosine (m6A) RNA modification is the most prevalent and abundant type found in eukaryotic cells. It plays a crucial role in the initiation and progression of cancers. In this study, we aimed to comprehensively investigate the landscape of m6A regulators and their association with tumor microenvironment (TME), immunotherapeutic strategies in colon adenocarcinoma (COAD).ResultsThe differential expression, mutation, CNV frequency and prognostic value of 27 m6A regulators were systematically analyzed in COAD. Patients were classified into two clusters based on m6A regulators through consistent clustering analysis, with cluster A showing significant survival benefits. Most of the m6A regulators were negatively correlated with immune cells, except for WTAP, IGF2BP3, FTO, ALKBH5, which showed a positive correlation. We developed an m6A scoring system to calculate the m6Ascore for each patient. Patients with a high-m6Ascore had a better outcome, with the AUC of 0.775. An independent cohort of 416 COAD patients acquired from GSE38832 database was used to validate the prognosis prediction ability of m6Ascore. Moreover, the m6Ascore was negatively correlated with infiltration of anti-tumor immune cells. Additionally, patients with a high-m6Ascore responded better to anti-PD1 and anti-CTLA4 therapies, and those with MSI-H had a higher m6Ascore. Finally, we investigated the value of m6Ascore in predicting the response of patients to 15 commonly used drugs.ConclusionsWe comprehensively analyzed m6A regulators in COAD, including RNA expression, CNV changes, mutations and their correlation with TME. Our results showed that the m6A scoring system had significant predictive power for the prognosis of COAD patients, potentially leading to new personalized immunotherapy strategies.

BackgroundEssential thrombocythemia (ET) is one of the most common types of Ph-negative myeloproliferative neoplasms, an infrequent group of blood cancers that arise from a CD34 + hematopoietic stem cell (HSC) in the bone marrow (BM) primarily due to driver mutations in JAK2, CALR or MPL. These aberrations result in an overproduction of mature myeloid cells in peripheral blood (PB). To date, no targeted therapies have been approved for ET patients, so the study of the molecular mechanisms behind the disease and the identification of new therapeutic targets may be of interest. For this reason, in this study, we have compared the transcriptomic profile of undifferentiated CD34 + cells and mature myeloid cells from ET patients (CALR and JAK2-mutated) and healthy donors deposited in publicly available databases. The study of the similarities and differences between these samples might help to better understand the molecular mechanisms behind the disease according to the degree of maturation of the malignant clone and the type of mutation and ultimately help identify new therapeutic targets for these patients.ResultsThe results show that most of the altered hallmarks in neutrophils were also found in CD34 + cells. However, only a few genes showed a similar aberrant expression pattern in both types of cells. We have identified a signature of six genes common to patients with CALR and JAK2 mutations (BPI, CRISP3, LTF, MMP8, and PTGS1 upregulated, and PBXIP1 downregulated), a different signature of seven genes for patients with CALR mutations (BMP6, CEACAM8, ITK, LCN2, and PRG2 upregulated, and MAN1A1 and MME downregulated) and a signature of 13 genes for patients with JAK2 mutations (ARG1, CAST, CD177, CLEC5A, DAPP1, EPS15, IL18RAP, OLFM4, OLR1, RIOK3, SELP, and THBS1 upregulated, and IGHM downregulated).ConclusionsOur results highlight transcriptomic similarities and differences in ET patients according to the degree of maturation of the malignant clone and the type of mutation. The genes and processes altered in both CD34 + cells and mature neutrophils may reveal altered sustained processes that could be studied as future therapeutic targets for ET patients.

BackgroundEvidence shows that genetic factors play important roles in the severity of coronavirus disease 2019 (COVID-19). Sulfatase modifying factor 1 (SUMF1) gene is involved in alveolar damage and systemic inflammatory response. Therefore, we speculate that it may play a key role in COVID-19.ResultsWe found that rs794185 was significantly associated with COVID-19 severity in Chinese population, under the additive model after adjusting for gender and age (for C allele = 0.62, 95% CI = 0.44–0.88, P = 0.0073, logistic regression). And this association was consistent with this in European population Genetics Of Mortality In Critical Care (GenOMICC: OR for C allele = 0.94, 95% CI = 0.90–0.98, P = 0.0037). Additionally, we also revealed a remarkable association between rs794185 and the prothrombin activity (PTA) in subjects (P = 0.015, Generalized Linear Model).ConclusionsIn conclusion, our study for the first time identified that rs794185 in SUMF1 gene was associated with the severity of COVID-19.

BackgroundRecently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition.ResultsHere we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package ‘genomic-benchmarks’, and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks.ConclusionsDeep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.