Protein Alignment Research Articles

Two novel sites determine genetic relationships between CPV-2 and FPV: an epidemiological survey of canine and feline parvoviruses in Changchun, China (2020)

Canine parvovirus (CPV-2) and feline parvovirus (FPV) cause severe hemorrhagic diarrhea disease in dogs, cats, and fur-bearing and wildlife carnivores worldwide, continuing to pose significant threats. In this study, 140 rectal swabs were collected from 70 domestic dogs and 70 cats with clinical diarrhea in veterinary clinics in Changchun during 2020. A total of 64.3% (45/70) of dogs and 55.7% (39/70) of cats tested positive for CPV-2 or FPV using colloidal gold strips. Amino acid (aa) sequence alignment of the VP2 protein from 39 CPV-2 and 36 FPV samples revealed that 79.5% (31/39) were CPV-2c, 17.9% (7/39) were a new CPV-2a, and 2.6% (1/39) were mink enteritis virus (MEV). and 8.3% (3/36) FPV from the cats was infected by CPV-2, which suggested that CPV-2c was the dominant variant in dogs and FPV was the major pathogen in cats in Changchun city. Phylogenetic relationships of VP2 genes showed that 26 parvoviruses were closely related to domestic strains previously published in China; however, 8 FPVs and CPV-JL-15/China/2020 were clustered in the lineage of South Asiatic and European countries, and 7 out of 8 FPVs were close to Italy. In addition to Q247H, I248Y, F544Y, and E/V545V/K, two novel site mutations of N23D or L630P in NS1 protein, associated with viral cross-species transmissions, were first found as a reminder of genetic relationships of CPV-2 variants and FPVs in the same branch. Thus, regular and massive virus surveillance of parvovirus is necessary to cope with its ongoing infection, circulation, mutations, and evolutions to new subtypes with strong survival abilities.

A new species of alien land flatworm in the Southern United States.

Specimens of a flat and dark brown land planarian were found in a plant nursery in North Carolina, USA in 2020. On the basis of examination of photographs of the live specimens only, the specimens were considered as belonging to Obama nungara, a species originally from South America, which has now invaded a large part of Europe. Unexpectedly, a molecular analysis revealed that the specimens did not belong to this species, neither to the genus Obama. We then undertook its histological study, which finally confirmed that the species is a member of the genus Amaga: the species is herein described as a new species, Amaga pseudobama n. sp. The species has been found in three locations in North Carolina and some infested plants were from Georgia. We reinvestigated specimens collected in Florida in 2015 and found that they also belong to this species. Citizen science observations suggest its presence in other states. Therefore, it is likely that A. pseudobama has already invaded a part of south-east USA and that the invasion took place more than ten years ago. The complete 14,909 bp long mitochondrial genome was obtained. The mitogenome is colinear with those of other Geoplanidae and it was possible to find and annotate a tRNA-Thr, which has been reported missing in several geoplanids. Amaga pseudobama shares with other Geoplaninae the presence of alternative start codons in three protein-coding genes of its mitogenome. The availability of this new genome helped us to improve our annotations of the ND3 gene, for which an ATT start codon is now suggested. Also, the sequence of the ATP6 gene raised questions concerning the use of genetic code 9 to translate the protein-coding genes of Geoplanidae, as the whole translated protein would not contain a single methionine residue when using this code. Two maximum likelihood phylogenies were obtained from genomic data. The first one was based on concatenated alignments of the partial 28S, Elongation Factor 1-alpha (EF1) and cox1 genes. The second was obtained from a concatenated alignment of the mitochondrial proteins. Both strictly discriminate A. pseudobama from O. nungara and instead associate it with Amaga expatria. We note that the nine species currently accepted within Amaga can be separated into two groups, one with extrabulbar prostatic apparatus, including the type species A. amagensis, and one with intrabulbar prostatic apparatus, including the new species A. pseudobama. This suggests that species of the latter group should be separated from Amaga and constitute a new genus. This finding again illustrates the possible emergence of new invasive species in regions naturally devoid of large land planarians, such as North America. Amaga pseudobama thus deserves to be monitored in the USA, although its superficial resemblance to O. nungara and Geoplana arkalabamensis will complicate the use of photographs obtained from citizen science. Our molecular information provides tools for this monitoring.

Impact of phylogeny on the inference of functional sectors from protein sequence data.

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.

A study on the development of O2O operation service platform using augmented reality

An attempt of sequence selection is made for multiple alignments of transmembrane proteins in order to detect the structural similarity in the protein families. Treatment for the sequence selection, which is based on the pairwise sequence identities, is applied to ten sequence data sets of functional group of transmembrane proteins extracted from SWISS-PROT. The treatment excludes the sequences with low sequence identity from the divergent data sets effectively. Obtained multiple alignments are evaluated using two newly developed indices. The selected sequences, which are well aligned with few inserted gaps, seem to contain enough information for extracting the structural features of transmembrane functional groups.

In silico study of RKD4 gene function in Coffea arabica L. and various cultivated plants related to embryo formation initiation

Arabica coffee supplies 60% of world coffee production because has a unique taste as superior quality beverage. Arabica coffee micropropagation can be conducted by somatic embryogenesis technique which produce clonal, fast dan uniform plant. The somatic embryogenesis (SE) process describes the integration of endogenous signals and gene reprogramming, which releases signals to initiate embryogenic processes. The use of endogenous auxin, either alone or in combination with other PGRs or stress, induces differential gene expression, which modifies the genetic program of somatic cells and regulates the transition to each stage during SE development. The RKD4 gene (RWP-RK DOMAIN-4) is a gene that plays a role in early initiation embryo formation and development. The characterization of RKD4 genes in C. arabica is still limited and under explored. The objective of this research is to explore the characteristics of RKD4 gene by comparing the difference and similarity of RKD4 gene in C. arabica and other cultivated plants. The method was initiate by identifying nucleotide sequences from the National Center for Biotechnology Information (NCBI) database. Furthermore, consists of analysis of nucletide alignment, alignment of amino acid sequences, protein analysis, protein motif functions discovery, analysis of phylogenetic tree, protein 2D and 3D-modelling and physiochemical properties. According to the analysis, there were 100 polymorphism points with a total number of mutations of 211 points. The phylogenetic tree show C. arabica L. has a very close relationship with grapes (Vitis vinivera) based on the RKD4 protein, gene structures and protein motifs. There are nine highly conserved motifs found in the protein alignment. C. arabica L. had more methyl jasmonate element responses than A. thaliana. The findings are useful to understand the intitiation of embryo formation mechanisms of C. arabica L and other cultivate plants during propagation through somatic embryogenesis in the long run.

Evolutionary Analysis of the Mammalian IL-17 Cytokine Family Suggests Conserved Roles in Female Fertility.

The interleukin-17 (IL-17) family includes pro-inflammatory cytokines IL-17A-F with important roles in mucosal defence, barrier integrity and tissue regeneration. IL-17A can be dysregulated in fertility complications, including pre-eclampsia, endometriosis and miscarriage. Because mammalian subclasses (eutherian, metatherian, and prototherian) have different related reproductive strategies, IL-17 genes and proteins were investigated in the three mammalian classes to explore their involvement in female fertility. Gene and protein sequences for IL-17s are found in eutherian, metatherian and prototherian mammals. Through synteny and multiple sequence protein alignment, the relationships among mammalian IL-17s were inferred. Publicly available datasets of early pregnancy stages and female fertility in therian mammals were collected and analysed to retrieve information on IL-17 expression. Synteny mapping and phylogenetic analyses allowed the classification of mammalian IL-17 family orthologs of human IL-17. Despite differences in their primary amino acid sequence, metatherian and prototherian IL-17s share the same tertiary structure as human IL-17s, suggesting similar functions. The analysis of available datasets for female fertility in therian mammals shows up-regulation of IL-17A and IL-17D during placentation. IL-17B and IL-17D are also found to be over-expressed in human fertility complication datasets, such as endometriosis or recurrent implantation failure. The conservation of the IL-17 gene and protein across mammals suggests similar functions in all the analysed species. Despite significant differences, the upregulation of IL-17 expression is associated with the establishment of pregnancy in eutherian and metatherian mammals. The dysregulation of IL-17s in human reproductive disorders suggests them as a potential therapeutic target.

Improvements in viral gene annotation using large language models and soft alignments

BackgroundThe annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings.ResultsCentral to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect.ConclusionThe embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology.

Large-scale structure-informed multiple sequence alignment of proteins with SIMSApiper.

SIMSApiper is a Nextflow pipeline that creates reliable, structure-informed MSAs of thousands of protein sequences faster than standard structure-based alignment methods. Structural information can be provided by the user or collected by the pipeline from online resources. Parallelization with sequence identity-based subsets can be activated to significantly speed up the alignment process. Finally, the number of gaps in the final alignment can be reduced by leveraging the position of conserved secondary structure elements. The pipeline is implemented using Nextflow, Python3, and Bash. It is publicly available on github.com/Bio2Byte/simsapiper.

Differential nanoscale organization of excitatory synapses onto excitatory vs. inhibitory neurons

A key feature of excitatory synapses is the existence of subsynaptic protein nanoclusters (NCs) whose precise alignment across the cleft in a transsynaptic nanocolumn influences the strength of synaptic transmission. However, whether nanocolumn properties vary between excitatory synapses functioning in different cellular contexts is unknown. We used a combination of confocal and DNA-PAINT super-resolution microscopy to directly compare the organization of shared scaffold proteins at two important excitatory synapses-those forming onto excitatory principal neurons (Ex→Ex synapses) and those forming onto parvalbumin-expressing interneurons (Ex→PV synapses). As in Ex→Ex synapses, we find that in Ex→PV synapses, presynaptic Munc13-1 and postsynaptic PSD-95 both form NCs that demonstrate alignment, underscoring synaptic nanostructure and the transsynaptic nanocolumn as conserved organizational principles of excitatory synapses. Despite the general conservation of these features, we observed specific differences in the characteristics of pre- and postsynaptic Ex→PV nanostructure. Ex→PV synapses contained larger PSDs with fewer PSD-95 NCs when accounting for size than Ex→Ex synapses. Furthermore, the PSD-95 NCs were larger and denser. The identity of the postsynaptic cell was also represented in Munc13-1 organization, as Ex→PV synapses hosted larger Munc13-1 puncta that contained less dense but larger and more numerous Munc13-1 NCs. Moreover, we measured the spatial variability of transsynaptic alignment in these synapse types, revealing protein alignment in Ex→PV synapses over a distinct range of distances compared to Ex→Ex synapses. We conclude that while general principles of nanostructure and alignment are shared, cell-specific elements of nanodomain organization likely contribute to functional diversity of excitatory synapses.

Meta-Quantitative Trait Loci Analysis and Candidate Gene Mining for Drought Tolerance-Associated Traits in Maize (Zea mays L.).

Drought is one of the major abiotic stresses with a severe negative impact on maize production globally. Understanding the genetic architecture of drought tolerance in maize is a crucial step towards the breeding of drought-tolerant varieties and a targeted exploitation of genetic resources. In this study, 511 quantitative trait loci (QTL) related to grain yield components, flowering time, and plant morphology under drought conditions, as well as drought tolerance index were collected from 27 published studies and then projected on the IBM2 2008 Neighbors reference map for meta-analysis. In total, 83 meta-QTL (MQTL) associated with drought tolerance in maize were identified, of which 20 were determined as core MQTL. The average confidence interval of MQTL was strongly reduced compared to that of the previously published QTL. Nearly half of the MQTL were confirmed by co-localized marker-trait associations from genome-wide association studies. Based on the alignment of rice proteins related to drought tolerance, 63 orthologous genes were identified near the maize MQTL. Furthermore, 583 candidate genes were identified within the 20 core MQTL regions and maize-rice homologous genes. Based on KEGG analysis of candidate genes, plant hormone signaling pathways were found to be significantly enriched. The signaling pathways can have direct or indirect effects on drought tolerance and also interact with other pathways. In conclusion, this study provides novel insights into the genetic and molecular mechanisms of drought tolerance in maize towards a more targeted improvement of this important trait in breeding.

POCP-nf: an automatic Nextflow pipeline for calculating the percentage of conserved proteins in bacterial taxonomy.

Sequence technology advancements have led to an exponential increase in bacterial genomes, necessitating robust taxonomic classification methods. The Percentage Of Conserved Proteins (POCP), proposed initially by Qin et al. (2014), is a valuable metric for assessing prokaryote genus boundaries. Here, I introduce a computational pipeline for automated POCP calculation, aiming to enhance reproducibility and ease of use in taxonomic studies. The POCP-nf pipeline uses DIAMOND for faster protein alignments, achieving similar sensitivity to BLASTP. The pipeline is implemented in Nextflow with Conda and Docker support and is freely available on GitHub under https://github.com/hoelzer/pocp. The open-source code can be easily adapted for various prokaryotic genome and protein datasets. Detailed documentation and usage instructions are provided in the repository.

Accurate Detection of Convergent Mutations in Large Protein Alignments With ConDor.

Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and changes at these different levels tend to be correlated. Notably, convergent mutations can lead to convergent changes in phenotype, such as changes in metabolism, drug resistance, and other adaptations to changing environments. We propose a two-component approach to detect mutations subject to convergent evolution in protein alignments. The "Emergence" component selects mutations that emerge more often than expected, while the "Correlation" component selects mutations that correlate with the convergent phenotype under study. With regard to Emergence, a phylogeny deduced from the alignment is provided by the user and is used to simulate the evolution of each alignment position. These simulations allow us to estimate the expected number of mutations in a neutral model, which is compared to the observed number of mutations in the data studied. In Correlation, a comparative phylogenetic approach, is used to measure whether the presence of each of the observed mutations is correlated with the convergent phenotype. Each component can be used on its own, for example Emergence when no phenotype is available. Our method is implemented in a standalone workflow and a webserver, called ConDor. We evaluate the properties of ConDor using simulated data, and we apply it to three real datasets: sedge PEPC proteins, HIV reverse transcriptase, and fish rhodopsin. The results show that the two components of ConDor complement each other, with an overall accuracy that compares favorably to other available tools, especially on large datasets.

Lambda3: homology search for protein, nucleotide, and bisulfite-converted sequences.

Local alignments of query sequences in large databases represent a core part of metagenomic studies and facilitate homology search. Following the development of NCBI Blast, many applications aimed to provide faster and equally sensitive local alignment frameworks. Most applications focus on protein alignments, while only few also facilitate DNA-based searches. None of the established programs allow searching DNA sequences from bisulfite sequencing experiments commonly used for DNA methylation profiling, for which specific alignment strategies need to be implemented. Here, we introduce Lambda3, a new version of the local alignment application Lambda. Lambda3 is the first solution that enables the search of protein, nucleotide as well as bisulfite-converted nucleotide query sequences. Its protein mode achieves comparable performance to that of the highly optimized protein alignment application Diamond, while the nucleotide mode consistently outperforms established local nucleotide aligners. Combined, Lambda3 presents a universal local alignment framework that enables fast and sensitive homology searches for a wide range of use-cases. Lambda3 is free and open-source software publicly available at https://github.com/seqan/lambda/.

CNCA aligns small annotated genomes

BackgroundTo explore the evolutionary history of sequences, a sequence alignment is a first and necessary step, and its quality is crucial. In the context of the study of the proximal origins of SARS-CoV-2 coronavirus, we wanted to construct an alignment of genomes closely related to SARS-CoV-2 using both coding and non-coding sequences. To our knowledge, there is no tool that can be used to construct this type of alignment, which motivated the creation of CNCA.ResultsCNCA is a web tool that aligns annotated genomes from GenBank files. It generates a nucleotide alignment that is then updated based on the protein sequence alignment. The output final nucleotide alignment matches the protein alignment and guarantees no frameshift. CNCA was designed to align closely related small genome sequences up to 50 kb (typically viruses) for which the gene order is conserved.ConclusionsCNCA constructs multiple alignments of small genomes by integrating both coding and non-coding sequences. This preserves regions traditionally ignored in conventional back-translation methods, such as non-coding regions.

Protein embedding based alignment

PurposeDespite the many progresses with alignment algorithms, aligning divergent protein sequences with less than 20–35% pairwise identity (so called "twilight zone") remains a difficult problem. Many alignment algorithms have been using substitution matrices since their creation in the 1970’s to generate alignments, however, these matrices do not work well to score alignments within the twilight zone. We developed Protein Embedding based Alignments, or PEbA, to better align sequences with low pairwise identity. Similar to the traditional Smith-Waterman algorithm, PEbA uses a dynamic programming algorithm but the matching score of amino acids is based on the similarity of their embeddings from a protein language model.MethodsWe tested PEbA on over twelve thousand benchmark pairwise alignments from BAliBASE, each one extracted from one of their multiple sequence alignments. Five different BAliBASE references were used, each with different sequence identities, motifs, and lengths, allowing PEbA to showcase how well it aligns under different circumstances.ResultsPEbA greatly outperformed BLOSUM substitution matrix-based pairwise alignments, achieving different levels of improvements of the alignment quality for pairs of sequences with different levels of similarity (over four times as well for pairs of sequences with <10% identity). We also compared PEbA with embeddings generated by different protein language models (ProtT5 and ESM-2) and found that ProtT5-XL-U50 produced the most useful embeddings for aligning protein sequences. PEbA also outperformed DEDAL and vcMSA, two recently developed protein language model embedding-based alignment methods.ConclusionOur results suggested that general purpose protein language models provide useful contextual information for generating more accurate protein alignments than typically used methods.

China-origin G1 group isolate FPV072 exhibits higher infectivity and pathogenicity than G2 group isolate FPV027.

Feline parvovirus (FPV), a single-stranded DNA virus, is accountable for causing feline panleukopenia, a highly contagious and often lethal disease that primarily affects cats. The epidemiology prevalence and pathogenicity of FPV in certain regions of China, however, remains unclear. The aim of this research was to investigate the epidemiology of FPV in different regions of China in 2021 and compare its infectivity and pathogenicity. In this research, a total of 36 FPV strains were obtained from diverse regions across China. Phylogenetic analysis was performed based on the VP2 and NS1 sequences, and two representative strains, FPV027 and FPV072, which belonged to different branches, were selected for comparative assessment of infectivity and pathogenicity. The results revealed that all strains were phylogenetically classified into two groups, G1 and G2, with a higher prevalence of G1 strains in China. Both in vitro and in vivo experiments demonstrated that FPV072 (G1 group) exhibited enhanced infectivity and pathogenicity compared to FPV027 (G2 Group). The structural alignment of the VP2 protein between the two viruses revealed mutations in residues 91, 232, and 300 that may contribute to differences in infectivity and pathogenicity. The findings from these observations will contribute significantly to the overall understanding of the molecular epidemiology of FPV in China and facilitate the development of an effective FPV vaccine.

Epitope delimitation: A new method for defining epitopes of human IgG-reactive antigenic peptides based on rabbit-recognized epitope motifs.

The use of precise epitope peptides as antigens is essential for accurate serological diagnosis of viral-infected individuals, but now it remains an unsolvable problem for mapping precise B cell epitopes (BCEs) recognized by human serum. To address this challenge, we propose a novel epitope delimitation (ED) method to uncover BCEs in the delineated human IgG-reactive (HR) antigenic peptides (APs). Specifically, the method based on the rationale of similarities in humoral immune responses between mammalian species consists of a pair of elements: experimentally delineated HR-AP and rabbit-recognized (RR) BCE motif and corresponding pair of sequence alignment analysis. As a result of using the ED approach, after decoding four RR-epitomes of human papillomavirus types 16/18-E6 and E7 proteins utilizing rabbit serum against each recombinant protein and sequence alignment analysis of HR-APs and RR-BCEs, 19 fine BCEs in 17 of 22 known HR-APs were defined based on each corresponding RR-BCE motifs, including the type-specificity of each delimited BCE in homologous proteins. The test with 22 known 16/20mer HR-APs demonstrated that the ED method is effective and efficient, indicating that it can be used as an alternative method to the conventional identification of fine BCEs using overlapping 8mer peptides.

Bioinformatic Approaches for Comparative Analysis of Viruses.

The field of viral genomic studies has experienced an unprecedented increase in data volume. New strains of known viruses are constantly being added to the GenBank database and so are completely new species with little or no resemblance to our databases of sequences. In addition to this, metagenomic techniques have the potential to further increase the number and rate of sequenced genomes. Besides, it is important to consider that viruses have a set of unique features that often break down molecular biology dogmas, e.g., the flux of information from RNA to DNA in retroviruses and the use of RNA molecules as genomes. As a result, extracting meaningful information from viral genomes remains a challenge and standard methods for comparing the unknown and our databases of characterized sequences may need adaptations. Thus, several bioinformatic approaches and tools have been created to address the challenge of analyzing viral data. This chapter offers descriptions and protocols of some of the most important bioinformatic techniques for comparative analysis of viruses. The authors also provide comments and discussion on how viruses' unique features can affect standard analyses and how to overcome some of the major sources of problems. Protocols and topics emphasize online tools (which are more accessible to users) and give the real experience of what most bioinformaticians do in day-by-day work with command-line pipelines. The topics discussed include (1) clustering related genomes, (2) whole genome multiple sequence alignments for small RNA viruses, (3) protein alignment for marker genes and species affiliation, (4) variant calling and annotation, and (5) virome analyses and pathogen identification.

Застосування інформаційної технології BLAST для пошуку мікроорганізмів, потенційних продуцентів біогенних магнітних наночасток, які призводять до серцевих захворювань

With the help of BLAST information technology, the alignment of MTB proteins with the proteins of pathogenic and conditionally pathogenic microorganisms, causative agents of heart diseases in humans, was carried out using the methods of comparative genomics.

Genome-wide expression analysis of LBD genes in tomato (Solanum lycopersicum L.) under different light conditions

ABSTRACTLateral organ boundaries (LOB) domain (LBD) genes, a gene family that encodes the transcription factors (TFs) of plants, plays crucial functions in the development and growth of plants. Currently, genome-wide studies of the LBD family are still limited to tomato (Solanum lycopersicum L.), which is considered an important economic crop. In this study, we performed a genome-wide analysis of LBD in tomato. In total, 56 LBDs were found in the tomato genome. Protein alignment and phylogenetic classification showed that LBDs were conserved with other species. Since light emitting diodes (LEDs) light have promising applications for tomato growth. To better understand the potential function of LBDs in response to LED light in tomato, we conducted a genome-wide expression analysis of LBD genes under different light conditions. As expected, different LED lights affected the tomato growth (e.g. hypocotyl length). RNA-seq data showed that eight LBDs in tomato seedlings were differentially expressed under different light treatments, including white, blue, red, and far-red light, compared to the dark-grown condition. It indicates that these LBDs might regulate plant development in different LED light conditions. Interestingly, two LBD genes (SlLBD1 and SlLBD2) were found to be differentially expressed in four distinct lights, which might be involved in regulating the plant architecture via a complicated TF network, which can be taken into consideration in further investigation.