Genome-wide characterization, evolution, structure, and expression analysis of the F-box genes in Caenorhabditis.

BACKGROUND: F-box proteins represent a diverse class of adaptor proteins of the ubiquitin-proteasome system (UPS) that play critical roles in the cell cycle, signal transduction, and immune response by removing or modifying cellular regulators. Among closely related organisms of the Caenorhabditis genus, remarkable divergence in F-box gene copy numbers was caused by sizeable species-specific expansion and contraction. Although F-box gene number expansion plays a vital role in shaping genomic diversity, little is known about molecular evolutionary mechanisms responsible for substantial differences in gene number of F-box genes and their functional diversification in Caenorhabditis. Here, we performed a comprehensive evolution and underlying mechanism analysis of F-box genes in five species of Caenorhabditis genus, including C. brenneri, C. briggsae, C. elegans, C. japonica, and C. remanei. RESULTS: Herein, we identified and characterized 594, 192, 377, 39, 1426 F-box homologs encoding putative F-box proteins in the genome of C. brenneri, C. briggsae, C. elegans, C. japonica, and C. remanei, respectively. Our work suggested that extensive species-specific tandem duplication followed by a small amount of gene loss was the primary mechanism responsible for F-box gene number divergence in Caenorhabditis genus. After F-box gene duplication events occurred, multiple mechanisms have contributed to gene structure divergence, including exon/intron gain/loss, exonization/pseudoexonization, exon/intron boundaries alteration, exon splits, and intron elongation by tandem repeats. Based on high-throughput RNA sequencing data analysis, we proposed that F-box gene functions have diversified by sub-functionalization through highly divergent stage-specific expression patterns in Caenorhabditis species. CONCLUSIONS: Massive species-specific tandem duplications and occasional gene loss drove the rapid evolution of the F-box gene family in Caenorhabditis, leading to complex gene structural variation and diversified functions affecting growth and development within and among Caenorhabditis species. In summary, our findings outline the evolution of F-box genes in the Caenorhabditis genome and lay the foundation for future functional studies.

Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences.

BACKGROUND: Genomic islands (GIs) play an important role in the chromosome diversity of Enterococcus. In the current study, we aimed to investigate the spread of GIs between Enterococcus strains and their correlation with antibiotic resistance genes (ARGs). Bitsliced Genomic Signature Indexes (BIGSI) were used to screen the NCBI Sequence Read Archive (SRA) for multiple resistant Enterococcus. A total of 37 pairs of raw reads were screened from 457,000 whole-genome sequences (WGS) in the SRA database, which come from 37 Enterococci distributed in eight countries. These raw reads were assembled for the prediction and analysis of GIs, ARGs, plasmids and prophages. RESULTS: The results showed that GIs were universal in Enterococcus, with an average of 3.2 GIs in each strain. Network analysis showed that frequent genetic information exchanges mediated by GIs occurred between Enterococcus strains. Seven antibiotic-resistant genomic islands (ARGIs) were found to carry one to three ARGs, mdtG, tetM, dfrG, lnuG, and fexA, in six strains. These ARGIs were involved in the spread of antibiotic resistance in 45.9% of the 37 strains, although there was no significant positive correlation between the frequency of GI exchanges and the number of ARGs each strain harboured (r = 0. 287, p = 0.085). After comprehensively analysing the genome data, we found that partial GIs were associated with multiple mobile genetic elements (transposons, integrons, prophages and plasmids) and had potential natural transformation characteristics. CONCLUSIONS: All of these results based on genomic sequencing suggest that GIs might mediate the acquisition of some ARGs and might be involved in the high genome plasticity of Enterococcus through transformation, transduction and conjugation, thus providing a fitness advantage for Enterococcus hosts under complex environmental factors.

Genome-wide identification and evolution of HECT genes in soybean.

Proteins containing domains homologous to the E6-associated protein (E6-AP) carboxyl terminus (HECT) are an important class of E3 ubiquitin ligases involved in the ubiquitin proteasome pathway. HECT-type E3s play crucial roles in plant growth and development. However, current understanding of plant HECT genes and their evolution is very limited. In this study, we performed a genome-wide analysis of the HECT domain-containing genes in soybean. Using high-quality genome sequences, we identified 19 soybean HECT genes. The predicted HECT genes were distributed unevenly across 15 of 20 chromosomes. Nineteen of these genes were inferred to be segmentally duplicated gene pairs, suggesting that in soybean, segmental duplications have made a significant contribution to the expansion of the HECT gene family. Phylogenetic analysis showed that these HECT genes can be divided into seven groups, among which gene structure and domain architecture was relatively well-conserved. The Ka/Ks ratios show that after the duplication events, duplicated HECT genes underwent purifying selection. Moreover, expression analysis reveals that 15 of the HECT genes in soybean are differentially expressed in 14 tissues, and are often highly expressed in the flowers and roots. In summary, this work provides useful information on which further functional studies of soybean HECT genes can be based.

Community differentiation of rhizosphere microorganisms and their responses to environmental factors at different development stages of medicinal plant Glehnia littoralis.

Rhizosphere microorganisms play a key role in affecting plant quality and productivity through its interaction with plant root system. To figure out the bottleneck of the decline of yield and quality in the traditional Chinese medicinal herbs Glehnia littoralis they now encounter, it is important to study the dynamics of rhizosphere microbiota during the cultivation of G. littoralis. In the present study, the composition, diversity and function of rhizosphere microbes at different development stages of G. littoralis, as well as the correlation between rhizosphere microbes and environmental factors were systematically studied by high-throughput sequencing. There were significant differences between the rhizosphere microbes at early and middle-late development stages. More beneficial bacteria, such as Proteobacteria, and more symbiotic and saprophytic fungi were observed at the middle-late development stage of G. littoralis, while beneficial bacteria such as Actinobacteria and polytrophic transitional fungi were abundant at all development stages. The results of redundancy analysis show that eight environmental factors drive the changes of microflora at different development stages. pH, soil organic matter (SOM) and available phosphorus (AP) had important positive effects on the bacterial and fungal communities at the early development stage; saccharase (SC) and nitrate nitrogen (NN) showed significant positive effects on the bacterial and fungal communities at the middle and late stages; while urease (UE), available potassium (AK), and alkaline phosphatase (AKP) have different effects on bacterial and fungal communities at different development stages. Random forest analysis identified 47 bacterial markers and 22 fungal markers that could be used to distinguish G. littoralis at different development stages. Network analysis showed that the rhizosphere microbes formed a complex mutualistic symbiosis network, which is beneficial to the growth and development of G. littoralis. These results suggest that host development stage and environmental factors have profound influence on the composition, diversity, community structure and function of plant rhizosphere microorganisms. This study provides a reference for optimizing the cultivation of G. littoralis.

Recent death early warning value of ECG changes in patients with NSCLC.

We aimed to explore the predictive value of abnormal electrocardiogram (ECG) changes in non-small-cell lung carcinoma non-small cell lung cancer (NSCLC) patients for near-term mortality. Seventy-eight NSCLC patients hospitalized in Chaohu Hospital Affiliated to Anhui Medical University from October 2017 to August 2021 were selected as the observation group. Baseline medical history and type of ECG abnormalities were the same. Seventy-eight patients were included in the control group. To compare the difference in mortality within 3 months between the observation group and the control group after the occurrence of the same type of electrocardiogram abnormalities, and to explore the value of electrocardiogram abnormalities in the early warning of NSCLC patients recent death. When the average ventricular rate of patients in the observation group was ≥100 beats/minute, ST-segment depression ≥0.05 mm, QRS voltage decrease ≥0.5 mm, new atrial fibrillation and other electrocardiogram changes, all patients died within 3 months. The control group with the same abnormal ECG had no death within 3 months (P < .05). In the resting 12-lead synchronous ECG in NSCLC patients, when the average ventricular rate ≥100 beats/minute, QRS wave voltage significantly decreased ≥0.5 mm, ST segment depression ≥0.05 mm, new atrial fibrillation appeared, it suggested that the patients' cardiac function decreased and the potential for near-term adverse outcomes is high.

Rapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning.

Although MALDI-TOF mass spectrometry (MS) is widely known as a rapid and cost-effective reference method for identifying microorganisms, its commercial databases face limitations in accurately distinguishing specific subspecies of Bifidobacterium. This study aimed to explore the potential of MALDI-TOF MS protein profiles, coupled with prediction methods, to differentiate between Bifidobacterium longum subsp. infantis (B. infantis) and Bifidobacterium longum subsp. longum (B. longum). The investigation involved the analysis of mass spectra of 59 B. longum strains and 41 B. infantis strains, leading to the identification of five distinct biomarker peaks, specifically at m/z 2,929, 4,408, 5,381, 5,394, and 8,817, using Recurrent Feature Elimination (RFE). To facilate classification between B. longum and B. infantis based on the mass spectra, machine learning models were developed, employing algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). The evaluation of the mass spectrometry data showed that the RF model exhibited the highest performace, boasting an impressive AUC of 0.984. This model outperformed other algorithms in terms of accuracy and sensitivity. Furthermore, when employing a voting mechanism on multi-mass spectrometry data for strain identificaton, the RF model achieved the highest accuracy of 96.67%. The outcomes of this research hold the significant potential for commercial applications, enabling the rapid and precise discrimination of B. longum and B. infantis using MALDI-TOF MS in conjunction with machine learning. Additionally, the approach proposed in this study carries substantial implications across various industries, such as probiotics and pharmaceuticals, where the precise differentiation of specific subspecies is essential for product development and quality control.

Rapid radiation of Rheum (Polygonaceae) and parallel evolution of morphological traits.

In this study, we examined diversification history of Rheum and tested the hypothesis that morphological traits related to plant 'body-plans' evolved in parallel in this genus. We sequenced eight chloroplast DNA fragments (representing more than 8000 bps of the sequence for each species) of 34 species from the genus and 13 species from closely related genera. Phylogenetic analyses indicate that all species of Rheum form a monophyletic lineage sister to the two-species genus Oxyria, indicating that radiative diversifications have occurred in its evolutionary history. Our dating analyses further suggest that these radiations largely coincided with the extensive uplifts of the Qinghai-Tibetan Plateau (QTP). Ancestral state reconstruction and likelihood sensitivity tests strongly indicate that both decumbent and 'glasshouse-like' body-plan traits evolved in parallel in different clades. Our findings highlight the importance of the uplift of the QTP in promoting species diversification and the parallel evolution of morphological traits that are putatively adaptive during such an evolutionary history.

Systematic Tracing of Susceptible Animals to SARS-CoV-2 by a Bioinformatics Framework.

Since the outbreak of SARS-CoV-2 in 2019, the Chinese horseshoe bats were considered as a potential original host of SARS-CoV-2. In addition, cats, tigers, lions, mints, and ferrets were naturally or experimentally infected with SARS-CoV-2. For the surveillance and control of this highly infectious disease, it is critical to trace susceptible animals and predict the consequence of potential mutations at the binding region of viral spike protein and host ACE2 protein. This study proposed a novel bioinformatics framework to systematically trace susceptible animals to SARS-CoV-2 and predict the binding affinity between susceptible animals' mutated/un-mutated ACE2 receptors. As a result, we identified a few animals posing a potential risk of infection with SARS-CoV-2 using the docking analysis of ACE2 protein and viral spike protein. The binding affinity of some of these species is weaker than that of humans but more potent than that of Chinese horseshoe bats. We also found that a few point mutations in human ACE2 protein or viral spike protein could significantly enhance their binding affinity, posing an enormous potential threat to public health. The ancestors of the Omicron may evolve rapidly through the accumulation of mutations in infecting the host and jumped into human beings. These findings indicate that if the epidemic expands, there may be a human-animal-human transmission route, which will increase the difficulty of disease prevention and control.

A Retrospective and Multicenter Study on COVID-19 in Inner Mongolia: Evaluating the Influence of Sampling Locations on Nucleic Acid Test and the Dynamics of Clinical and Prognostic Indexes.

COVID-19 is spreading widely, and the pandemic is seriously threatening public health throughout the world. A comprehensive study on the optimal sampling types and timing for an efficient SARS-CoV-2 test has not been reported. We collected clinical information and the values of 55 biochemical indices for 237 COVID-19 patients, with 37 matched non-COVID-19 pneumonia patients and 131 healthy people in Inner Mongolia as control. In addition, the results of dynamic detection of SARS-CoV-2 using oropharynx swab, pharynx swab, and feces were collected from 197 COVID-19 patients. SARS-CoV-2 RNA positive in feces specimen was present in approximately one-third of COVID-19 patients. The positive detection rate of SARS-CoV-2 RNA in feces was significantly higher than both in the oropharynx and nasopharynx swab (P < 0.05) in the late period of the disease, which is not the case in the early period of the disease. There were statistically significant differences in the levels of blood LDH, CRP, platelet count, neutrophilic granulocyte count, white blood cell number, and lymphocyte count between COVID-19 and non-COVID-19 pneumonia patients. Finally, we developed and compared five machine-learning models to predict the prognosis of COVID-19 patients based on biochemical indices at disease onset and demographic characteristics. The best model achieved an area under the curve of 0.853 in the 10-fold cross-validation.

On the origin of the woody buckwheat Fagopyrum tibeticum (=Parapteropyrum tibeticum) in the Qinghai-Tibetan Plateau.

Here we tested whether 'insular woodiness', a striking evolutionary pattern that commonly occurs on islands, has also appeared in QTP continental endemics. Parapteropyrum, a monotypic shrubby genus occurring in the central QTP, has been previously placed in the tribe Atraphaxideae of the family Polygonaceae, while all the other woody species of this tribe mainly occur in western and central Asia. We studied sequence variations of nuclear ITS (internal transcribed spacer) and cp (chloroplast) DNA (rbcL and accD) of this genus and the other ten genera. The constructed phylogenies based on ITS, cpDNA or a combination of both datasets, suggest that the woody Parapteropyrum is nested within and most likely evolved from the herbaceous Fagopyrum. We propose that the large-scale uplift of the QTP not only promoted continental species radiation, but also the secondary feature of woodiness in a few herbaceous lineages in response to strong selection pressures, similar to those acting on island flora. In addition, the confirmation of Parapteropyrum within Fagopyrum highlights its potential use as a new, perennial source of buckwheat.

A Novel Method to Identify the Differences Between Two Single Cell Groups at Single Gene, Gene Pair, and Gene Module Levels.

Single-cell sequencing technology can not only view the heterogeneity of cells from a molecular perspective, but also discover new cell types. Although there are many effective methods on dropout imputation, cell clustering, and lineage reconstruction based on single cell RNA sequencing (RNA-seq) data, there is no systemic pipeline on how to compare two single cell clusters at the molecular level. In the study, we present a novel pipeline on comparing two single cell clusters, including calling differential gene expression, coexpression network modules, and so on. The pipeline could reveal mechanisms behind the biological difference between cell clusters and cell types, and identify cell type specific molecular mechanisms. We applied the pipeline to two famous single-cell databases, Usoskin from mouse brain and Xin from human pancreas, which contained 622 and 1,600 cells, respectively, both of which were composed of four types of cells. As a result, we identified many significant differential genes, differential gene coexpression and network modules among the cell clusters, which confirmed that different cell clusters might perform different functions.

LncRNA SLC16A1-AS1 Suppresses Cell Proliferation in Cervical Squamous Cell Carcinoma (CSCC) Through the miR-194/SOCS2 Axis.

BACKGROUND: SLC16A1-AS1 has been characterized as an oncogenic long non-coding (lncRNA) in breast cancer and bladder cancer, while its role in cervical squamous cell carcinoma (CSCC) is unknown. METHODS: CSCC and non-tumor tissue samples were collected from 60 female patients, and qPCR was performed to detect the expression of SLC16A1-AS1, miR-194 and SOCS2. Luciferase reporter assay was performed to detect the interaction between SLC16A1-AS1 and miR-194. Colony formation assay was used to detect cell proliferation. RESULTS: SLC16A1-AS1 was down-regulated in CSCC and correlated with poor survival. Overexpression of SLC16A1-AS1 could inhibit the proliferation of cervical cancer cells. In addition, SLC16A1-AS1 could sponge miR-194 and increase the expression levels of SOCS2, ultimately inhibiting the proliferation of cervical cancer cells. CONCLUSION: SLC16A1-AS1 was downregulated in CSCC and suppressed cell proliferation in cervical squamous cell carcinoma (CSCC) through the miR-194/SOCS2 axis.

Artificial Intelligence Systems for Diagnosis and Clinical Classification of COVID-19.

Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people's lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients. Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman's correlation test was applied to study correlations of CT characteristics and laboratory findings. Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features. Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.

A Systematic Review of Lymphangioleiomyomatosis on Diagnosis and Molecular Mechanism.

OBJECTIVE: Lymphangioleiomyomatosis (LAM) is a rare low-grade metastatic tumor; however, LAM patients were always found in young age with difficulty for diagnosis. Our study is aimed at observing the clinical characteristics of patients with lymphangiomatosis, including the clinical manifestations, imaging findings, histopathological features, and immunophenotype. METHODS: We did a systematic review on LAM/PLAM cases, especially on male cases, and collected the clinical features and molecular mechanisms of PLAM based on previous findings. RESULTS: Diagnosis criteria were summarized by combining CT scans, MRI, immunohistochemistry results, and gene sequencing results for effectively distinguishing between PLAM and similar diseases. Moreover, our study illustrated the molecular mechanism of PLAM as well as the signaling pathway involved in the disease initials. In addition, a male case was reported with differential diagnosis on the clinical manifestations, microscopic features, immunophenotypes, and genotypes. CONCLUSION: Our review will definitely improve the understanding of diagnosis and treatment in PLAM cases.

Genomic variation, origin tracing, and vaccine development of SARS-CoV-2: A systematic review.

COVID-19 has spread globally to over 200 countries with more than 40 million confirmed cases and one million deaths as of November 1, 2020. The SARS-CoV-2 virus, leading to COVID-19, shows extremely high rates of infectivity and replication, and can result in pneumonia, acute respiratory distress, or even mortality. SARS-CoV-2 has been found to continue to rapidly evolve, with several genomic variants emerging in different regions throughout the world. In addition, despite intensive study of the spike protein, its origin, and molecular mechanisms in mediating host invasion are still only partially resolved. Finally, the repertoire of drugs for COVID-19 treatment is still limited, with several candidates still under clinical trial and no effective therapeutic yet reported. Although vaccines based on either DNA/mRNA or protein have been deployed, their efficacy against emerging variants requires ongoing study, with multivalent vaccines supplanting the first-generation vaccines due to their low efficacy against new strains. Here, we provide a systematic review of studies on the epidemiology, immunological pathogenesis, molecular mechanisms, and structural biology, as well as approaches for drug or vaccine development for SARS-CoV-2.

Revealing the Interactions Between Diabetes, Diabetes-Related Diseases, and Cancers Based on the Network Connectivity of Their Related Genes.

Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connecting diabetes and its related diseases/cancers. We have proposed and compared 10 protein-protein interaction (PPI)-based computational methods to study the connections between diabetes and 254 diseases, among which a method called DIconnectivity_eDMN performs the best in the sense that it infers a disease rank (according to its relation with diabetes) most consistent with that by literature mining. DIconnectivity_eDMN takes diabetes-related genes, other disease-related genes, a PPI network, and genes in BPs as input. It first maps genes in a BP into the PPI network to construct a BP-related subnetwork, which is expanded (in the whole PPI network) by a random walk with restart (RWR) process to generate a so-called expanded modularized network (eMN). Since the numbers of known disease genes are not high, an RWR process is also performed to generate an expanded disease-related gene list. For each eMN and disease, the expanded diabetes-related genes and disease-related genes are mapped onto the eMN. The association between diabetes and the disease is measured by the reachability of their genes on all eMNs, in which the reachability is estimated by a method similar to the Kolmogorov-Smirnov (KS) test. DIconnectivity_eDMN achieves an area under receiver operating characteristic curve (AUC) of 0.71 for predicting both Type 1 DRDs and Type 2 DRDs. In addition, DIconnectivity_eDMN reveals important BPs connecting diabetes and DRDs. For example, "respiratory system development" and "regulation of mRNA metabolic process" are critical in associating Type 1 diabetes (T1D) and many Type 1 DRDs. It is also found that the average proportion of diabetes-related genes interacting with DRDs is higher than that of non-DRDs.

Adaptation and selection in the Senecio (Asteraceae) hybrid zone on Mount Etna, Sicily.

Hybrid zone theory provides a powerful theoretical framework for measuring and testing gene flow and selection. The Senecio aethnensis and Senecio chrysanthemifolius hybrid zone on Mount Etna, Sicily, was investigated to identify phenotypic traits under divergent selection and to assess the contributions of intrinsic and extrinsic selection against hybrids to hybrid zone maintenance. Senecio samples from 14 sites across Mount Etna were analyzed for 24 quantitative traits classified into four groups (QTGs), six allozymes and seven simple sequence repeat (SSR) loci to describe patterns of variation throughout the hybrid zone. Narrower cline widths or shifts in cline centre position were observed for three QTGs relative to the molecular clines, indicating that these traits are likely to be under extrinsic environmental selection. Altitude was key to describing species distributions, but dispersal and intrinsic selection against hybrids explained patterns at smaller spatial scales. The hybrid zone was characterized by strong selection against hybrids, high dispersal rates, recent species contact and few loci differentiating QTGs based on indirect measures. These results support the hypothesis that extrinsic and intrinsic selection against hybrids maintains the hybrid zone and species distinctiveness despite gene flow between the two Senecio species on Mount Etna.

Evolutionary Analysis of the F-Box Gene Family in Saccharomycetaceae.

F-box proteins are a core component of Skp1-Cul1-F-box (SCF) ubiquitin/ligase complexes and are involved in a lot of cellular processes in yeasts. However, the current knowledge of the molecular evolution of the F-box gene family in yeasts remains unclear. In this study, 136 F-box genes were identified in 10 yeast species of the Saccharomycetaceae. In addition to the F-box domain, the other six domains were identified in these F-box proteins. The evolutionary history of F-box gene numbers in 10 Saccharomycetaceae yeasts was reconstructed. Whole-genome duplication, interspersed repeats, and gene loss events were inferred. These events contributed to F-box gene number variation in the 10 yeast species. Eighty-seven and 33 positively selected sites were detected in program Selecton and Datamonkey web-server, respectively. Three of them were considered the significant positively selected sites, and 23 of them had changed radically in amino acid properties by using TreeSAAP. We investigated F-box gene number variation and underlying mechanisms, and selection patterns, all of which were beneficial to deeply understand genome evolution and figure out the function of the F-box proteins.

Synthesis and biological characterization of novel rose bengal derivatives with improved amphiphilicity for sono-photodynamic therapy.

Sono-Photodynamic therapy (SPDT) utilizing ultrasound and light has been demonstrated that this novel approach can lower dosage resulting in reduction of the potential side effects caused by sensitizers. Recently, a new formulation of rose bengal (RB) as an intralesional injection has completed clinical trials phase II for PDT treatment of melanoma cancer. However, the inherent unfavorable pharmacological properties of RB hindered its extensive clinical development. With the aim to identify new RB derivatives (RBDs) with enhanced photodynamic and sonodynamic anticancer efficiency, a series of amphiphilic RBDs have been designed, synthesized and biological characterized. Among them, RBD4 significantly improved cellular uptake and enhanced intracellular ROS generation efficiency upon light and ultrasound irradiation, resulting in dramatically improved anticancer potency. Notably, RBD4 has a relative potency similar to sinoporphyrin sodium (DVDMS), indicating its further potential application for SPDT.