Physalis L. is one of the largest genera within the Solanaceae family consisting of approximately 75–90 species, which are mostly distributed in tropical and temperate regions worldwide (Whitson and Manos, 2005; Pretz and Deanna, 2020). The most notable characteristic of the species in this genus is the calyx, which surrounds the fruit and increases in size as the fruit grows larger. The interest in Physalis species is mostly motivated by the economic importance of a subset of species that have been used in traditional medicine (Zhang and Tong, 2016). Most Physalis species have potential medicinal properties, including antibacterial, antileukemic, antipyretic, anti-inflammatory, immunomodulatory, and anticancer actions, and often have been used to treat various illnesses such as dermatitis, malaria, asthma, hepatitis, and liver disorders. Moreover, some Physalis species are extensively cultivated for their edible fruit or ornamental value in various countries. Recently, there has been a growing focus on the genus Physalis in molecular research related to taxonomy, systematics and evolution, genetic diversity, and omics. In this review, we aim to provide a comprehensive analysis of the genetic diversity, molecular identification, phylogenetics, functional genes, and the application of omics in the genus Physalis. The relevant references for this review were obtained from the PubMed database of NCBI and the Web of Science, which are widely recognized as leading databases for published articles and citations. The searches were conducted within a single day in May 2024. The term “Physalis” was utilized to search for instances in the title, abstract, and keywords. Articles focusing on authentication, genetic diversity, phylogenetics, functional genes, and omics were chosen for inclusion.

The accurate identification of germplasm resources is a crucial foundation for the systematic classification, population genetics, omics research, and molecular genetic breeding of Physalis plants. In the past, morphological methods were the primary means of identifying Physalis plants (Sinha, 1951; Axelius, 1996; Gonzalez et al., 2008). However, the morphological characteristics of Physalis plants are very similar, and these morphological traits are extremely susceptible to the restrictions of growth period and growth environment, which bring great difficulties to morphological identification methods (Whitson and Manos, 2005; Feng et al., 2016). With the development of biotechnology, various molecular markers have emerged and been widely used in the identification of plant species, varieties, and genotypes. Compared with morphological methods, DNA molecular markers are not easily affected by the external environment and growth period, and their identification has good stability and high accuracy (Schindel and Miller, 2005; Sarwat et al., 2012; Li et al., 2015).

Several chloroplast DNA regions (such as rbcL, atpF-atpH, ycf1, matK, rpoB, and psbA-trnH) and some nuclear ribosomal DNA (nrDNA) regions [such as internal transcribed spacer, ITS, and internal transcribed spacer 2 (ITS2)] have been advocated by some experts and the Consortium for the Barcode of Life (CBOL) as potential standard DNA barcodes for plant species identification (Schindel and Miller, 2005; Cbol Plant Working Group, 2009; Chen et al., 2010; China Plant et al., 2011; Chen et al., 2024). Feng et al. (2016) demonstrated the efficacy of nrDNA ITS2 regions for molecular identification in 45 Physalis species, as detailed in Table 1 . The findings revealed a high rate of species authentication using the ITS2 sequence suggesting its potential as an efficient barcode for identifying Physalis species. Furthermore, the variability in secondary structures of ITS2 among most Physalis species, including differences in loop number, size, position, and degree of angles from the center of the spiral arm, presents a novel approach for identifying challenging-to-distinguish species based on their ITS2 sequence. Terrones et al. (2021) successfully used nrDNA ITS sequences as a DNA barcode to authenticate the two species of the genus Physalis [Physalis acutifolia (Miers) Sandwith and Physalis angulata L.] in the Iberian Peninsula of Spain (Terrones et al., 2021). Chloroplast psbA-trnH region, as one of the highly recommended candidate DNA barcodes, had also been successfully applied to the molecular identification of the species of the genus Physalis (Feng et al., 2018a).

Some DNA-based markers, such as simple sequence repeat (SSR), random amplified microsatellites (RAMS), start codon targeted (SCoT), and sequence-characterized amplified region (SCAR), have also shown excellent performance in plant molecular identification. Simbaqueba et al. (2011) identified 1,520 SSRs in the assembled leaf transcriptome of Physalis peruviana and developed 138 SSR primer pairs that successfully amplified in P. peruviana L. and Physalis floridana Rydb. genotypes, with a polymorphism rate of 22%. Delgado-Alvarado et al. (2018) applied RAMS markers to authenticate the varieties and landraces of Physalis ixocarpa Brot. ex Hornem. The results indicated that RAMS could be used as good specific markers not only to distinguish P. ixocarpa from its close relatives but also to provide specific fingerprints for the authentication of different varieties of P. ixocarpa. Feng et al. (2018b) developed four specific SCAR markers for P. angulata, Physalis minima L., Physalis pubescens L., and Physalis alkekengi var. franchetii (Mast.) Makino based on polymorphism analysis of SCoT molecular markers providing a new method for rapid and accurate molecular identification of the four Physalis species.

Research on genetic diversity is crucial for species management planning, as the preservation of diversity plays a vital role in conservation and the breeding of superior individuals. In recent years, there has been a focus on studying the genetic diversity of Physalis plants, with several related studies being reported. Various types of DNA molecular markers, including inter-simple sequence repeats (ISSR), random amplified polymorphic DNA (RAPD), SSR, insertion and deletion (InDel), and single nucleotide polymorphism (SNP) markers, have been utilized in numerous studies to assess the genetic diversity and population dynamics of Physalis plants. A comprehensive summary of these studies on the genetic diversity of Physalis plants can be found in Table 1 .

RAPD markers, a relatively early molecular marker technology, have been widely utilized in studying genetic diversity in various plants due to their simple operation, high versatility, and cost effectiveness. Hidayat et al. (2017) used RAPD markers to assess the genetic diversity of 23 P. angulata plants from different regions of Bandung. Similarly, Khan et al. (2019) employed eight RAPD markers to determine the genetic diversity of 17 accessions of P. ixocarpa, with the results aligning with the ecological distribution of accessions and highlighting two accessions (P1512005 and PI360740) from Mexico and Ecuador as exhibiting the highest genetic diversity among P. ixocarpa accessions.

Microsatellites, also known as simple sequence repeats (SSRs), are designed based on conserved nucleotide sequences found on both sides of simple repeat sequences, widely distributed in plant genomes (Tautz, 1989). SSRs are co-dominant, multi-allelic, highly polymorphic, and have been widely used in various fields, including genetic diversity, phylogenetic studies, molecular identification, and genetic mapping (Poczai et al., 2013). Several SSR markers have been developed and extensively utilized in the investigation of genetic diversity within Physalis species (Simbaqueba et al., 2011; Wei et al., 2012; Delgado-Bastidas et al., 2019; Feng et al., 2023). In a study by Delgado-Bastidas et al. (2019), six SSR markers were employed to evaluate the genetic diversity of 40 genotypes of P. peruviana revealing that these genotypes were categorized into three populations. However, it was observed that the level of genetic diversity among the genotypes was notably low, with no discernible population structure. In a more recent study, Feng et al. (2023) developed a set of SSR markers based on chloroplast genome and applied them to assess the genetic diversity and population structure of P. angulata. The SSR analysis revealed that 16 populations of P. angulata formed four clusters displaying significant geography-related population structure as well as extensive admixture.

ISSR markers are molecular markers that utilize microsatellite oligonucleotides as primers, with two to four randomly selected nucleotides added to the 5′ or 3′ end of the SSR to facilitate annealing at specific sites. The result in PCR amplification of DNA fragments located between relatively spaced repeats that are complementary to the anchor primers (Zietkiewicz et al., 1994). The ISSR, which integrates the advantages of RAPD and SSR, not only exhibits excellent stability and polymorphism but also offers simplicity, rapidity, and efficiency. It has been successfully employed in assessing genetic diversity, genetic relationship, and molecular identification in plants (Wang et al., 2009; Kumar et al., 2018; Tyagi et al., 2020). The ISSR marker has been proven to be valuable in the analysis of genetic diversity and genetic relationships within Physalis plants (Vargas-Ponce et al., 2011; Zamora-Tavares et al., 2015). Vargas-Ponce et al. (2011) showed that 12 samples from eight Physalis species could be grouped into two clusters with an interspecific genetic similarity ranging from 0.48 to 0.58 based on ISSR analysis. Meanwhile, Zamora-Tavares et al. (2015) utilized 88 ISSR markers to study the genetic diversity and structure of nine Physalis philadelphica Lam. populations in western Mexico, revealing high genetic diversity among the samples and grouping the populations into two clusters based on structure analysis.

Single nucleotide polymorphism (SNP) is a widely utilized DNA marker technology that has been developed in recent years. It represents a common genetic variation caused by the alteration of a single nucleotide (A, T, C, and G) in the DNA sequence (Uppu et al., 2018). SNP markers are prevalent in genomes and hold significant value for applications such as plant genetic diversity analysis, genotype identification, high-density genetic map construction, and molecular marker-assisted breeding (Lu et al., 2018; Arca et al., 2020; Guo et al., 2021; Park et al., 2022). Additionally, SNP is one of the most popular molecular marker techniques used to study genetic diversity in Physalis plants, as demonstrated by several studies (Cely et al., 2015; Labate and Robertson, 2015; Enciso-Rodriguez et al., 2020; Alcala-Gomez et al., 2022). For example, Enciso-Rodriguez et al. (2020) identified 7,425 SNPs based on Genotyping-By-Sequencing (GBS) and utilized them to assess the diversity of P. peruviana and related taxa. Their findings revealed significant gene flow (F_ST: 0.01–0.05) in different subpopulations of P. peruviana. Similarly, Alcala-Gomez et al. (2022) investigated the genetic diversity of P. philadelphica using 270 SNP markers based on their study of 40 samples.

The taxonomy of Physalis is considered to be a highly complex issue within the Solanaceae due to the significant intraspecific morphological variation and substantial interspecific similarity (Axelius, 1996; Sullivan, 2004; Whitson and Manos, 2005; Olmstead et al., 2008; Pretz and Deanna, 2020). In recent years, molecular analyses have yielded new insights into this problem (Whitson and Manos, 2005; Olmstead et al., 2008; Feng et al., 2016; Zamora-Tavares et al., 2016; Feng et al., 2020; Pere et al., 2023). ITS regions of nrDNA are widely used for studying phylogenic relationships among angiosperms, including the genus Physalis, at the interspecific and infrageneric level. This is due to their biparental inheritance, simplicity, universality, intra-genome consistency, inter-genome variability, and high copy number (Whitson and Manos, 2005; Xiang et al., 2013; Feng et al., 2016; Zamora-Tavares et al., 2016; Pere et al., 2023; Jalab and Al-Rufaye, 2024). Whitson and Manos (2005) conducted a study on the phylogenetic relationships among 35 species of Physalis and the relationships among the genera of the subtribe Physalinae utilizing the sequence analysis of the nrDNA ITS region and the nuclear gene waxy. The findings revealed that the morphologically typical Physalis species formed a strongly supported clade. However, the morphologically atypical species, such as P. alkekengi L., Physalis carpenter Riddell, and Physalis microphysa A.Gray were found to be distantly related to any other Physalis species resulting in paraphyly within the genus. Zamora-Tavares et al. (2016) utilized five plastids (matK, rbcL, ndhF, rpl32-trnL, and ycf1) and two nuclear regions (ITS and waxy) to examine the phylogenetic relationships of 50 species within the Physalinae, which included 33 Physalis species. The study assessed the phylogenetic relationships among recognized genera in Physalinae, with a focus on identifying monophyletic groups and resolving the physaloid grade. Additionally, the study analyzed potential causes for recent divergence within Physalinae. All the aforementioned studies utilized single or a few gene sequence fragments from the plastid genome or nuclear genome to investigate the phylogeny of genus Physalis. Due to the limited length of these DNA sequences and their restricted genetic information, there are significant limitations in studying phylogenetic evolution using these methods. We are confident that the ongoing advancements in chloroplast genome and mitochondrial genome-sequencing technology will lead to a more refined and precise reconstruction of the phylogenetic tree of genus Physalis.

Several omic datasets of Physalis plants have been made available online offering extensive genetic information for the screening of functional genes and the identification of active compounds. In the present review, all the omic datasets of Physalis plants are summarized in Tables 2 , 3 .

Researchers have performed many studies on the screening of germplasm collections and identifying SNPs with continuously updating genetic techniques. Although several molecular markers have been developed, outdated platforms provide a limited ability to estimate the extent of Physalis genetic variability. In the future, a number of convenient and phenotype-based molecular markers will definitely be the direction for efficient genetic diversity analysis and germplasm resource identification. Genetic identification is a prerequisite for species conservation and resource utilization. Furthermore, the advancement of high-throughput sequencing technology will lead to an increasing number of published chloroplast genomes and mitochondrial genomes, thus contributing to the further development of phylogenetic tree reconstruction for genus Physalis.

Single-cell RNA sequencing (scRNA-seq) is a novel technology used to investigate cell heterogeneity at a high resolution (Bawa et al., 2022). Mass spectrometry (MS) imaging is a recently developed MS-based metabolomics approach to reveal the distribution of metabolites at the spatial level (Xiang et al., 2022; Zhan et al., 2024). However, scRNA-seq and MS imaging have not yet been applied to the study of Physalis plants. ScRNA-seq will provide novel insights into the inflated calyx syndrome and fruit development of Physalis plants. High-resolution MS imaging can be utilized to identify and visualize metabolic heterogeneity, including inflated calyx syndrome. Future researches on Physalis plants will move toward higher resolutions at both the temporal and spatial levels.

The recently sequenced genome of P. floridana acts as a starting point for genome-enabled research (Lu et al., 2021). As more species’ genomes are fully sequenced, new knowledge regarding evolutionary relationships, processes, and patterns, as well as the ability to map biosynthetic pathways through comparative means will emerge.

YJ: Writing – original draft, Data curation, Investigation. YYJ: Writing – original draft, Investigation, Methodology. YS: Writing – original draft, Investigation, Methodology. QZ: Writing – original draft, Investigation, Data curation. HW: Writing – original draft, Funding acquisition, Resources. CS: Methodology, Writing – review & editing. SF: Writing – original draft, Writing – review & editing, Project administration, Funding acquisition.