Objective Magnetoreception, the remarkable ability of diverse animals to sense and utilize the geomagnetic field for orientation and navigation, remains a molecularly unresolved mystery in sensory biology. The putative magnetoreceptor (MagR, previously known as IscA1) is a highly conserved iron-sulfur protein implicated in both magnetoreception and iron metabolism; however, the functional diversity among its cross-species homologs remains poorly understood. Cellular morphology is a key genetically determined trait that can be altered through genetic or environmental modifications—a process known as cell morphology engineering. Constructing engineered cells with specific morphological features and magnetic sensitivity to achieve remote, non-invasive magnetic modulation represents a crucial goal in this field with significant application potential. Therefore, this study aims to systematically investigate the effects of MagR heterologous expression on bacterial morphology and magnetic sensing capabilities, screen for MagR-based magnetically sensitive morphology engineering pathways, and reveal the underlying molecular mechanisms. Methods We systematically screened 28 MagR homologous genes from diverse prokaryotic and animal taxa to evaluate their expression and corresponding phenotypic effects in Escherichia coli (E. coli). To compare the differential magnetic responses among bacteria expressing various recombinant MagR proteins, we utilized high-throughput automated bright-field microscopic imaging and scanning electron microscopy (SEM). Furthermore, comprehensive biochemical and biophysical characterizations of iron and iron-sulfur cluster binding were performed using Ferrozine colorimetric assays, electron paramagnetic resonance (EPR) spectroscopy, ultraviolet-visible (UV-Vis) absorption, and circular dichroism (CD) spectroscopy. Additionally, 100 mT static magnetic field (SMF) exposure experiments were conducted to assess magnetically tunable phenotypes, while the intrinsic magnetic properties of purified MagR proteins were directly measured using a superconducting quantum interference device (SQUID) magnetometer. Results Our results demonstrated that the heterologous expression of MagR homologs induced varying degrees of bacterial filamentation. From this comprehensive screen, two distinct morphological patterns were identified: hydra (Hydra vulgaris) MagR (hyMagR) promoted uniform cell elongation and filamentation, exhibiting robust magnetic sensitivity manifested as significantly enhanced filamentation under the 100 mT SMF. In contrast, pigeon (Columba livia) MagR (clMagR) induced only low-frequency, extreme filamentation (sporadically exceeding 80 μm) with a relatively weaker magnetic morphological response. Mechanistically, our data unambiguously proved that these phenotypic differences are primarily driven by distinct iron redox preferences rather than total cellular iron accumulation. Specifically, hyMagR preferentially binds ferrous iron (Fe²⁺), whereas clMagR favors ferric iron (Fe³⁺) and forms more stable iron-sulfur clusters. Intriguingly, although SQUID magnetometry showed that purified clMagR exhibited approximately five-fold higher mass magnetic susceptibility than hyMagR, its cellular magnetic response was weaker. We hypothesize that the Fe²⁺-preferred intracellular environment associated with hyMagR overexpression primes the cell for enhanced generation of reactive oxygen species (ROS) via the Fenton reaction. Exposure to an SMF synergizes with this primed redox state, triggering the bacterial SOS response and upregulating cell division inhibitors to efficiently induce uniform filamentation. Conclusion Our findings identify the Fe²⁺/Fe³⁺ redox state as a critical determinant of MagR-mediated morphological remodeling and magnetic responsiveness. This discovery suggests a potential strategy for engineering magnetically responsive cellular systems for synthetic biology applications, and provides a plausible framework, which potentially combines intrinsic protein magnetism with redox-state modulation, for further investigating the evolutionary mechanisms of MagR-mediated magnetoreception.

Objective Magnetoreception, the remarkable ability of diverse animals to sense and utilize the geomagnetic field for orientation and navigation, remains a molecularly unresolved mystery in sensory biology. The putative magnetoreceptor (MagR, previously known as IscA1) is a highly conserved iron-sulfur protein implicated in both magnetoreception and iron metabolism; however, the functional diversity among its cross-species homologs remains poorly understood. Cellular morphology is a key genetically determined trait that can be altered through genetic or environmental modifications—a process known as cell morphology engineering. Constructing engineered cells with specific morphological features and magnetic sensitivity to achieve remote, non-invasive magnetic modulation represents a crucial goal in this field with significant application potential. Therefore, this study aims to systematically investigate the effects of MagR heterologous expression on bacterial morphology and magnetic sensing capabilities, screen for MagR-based magnetically sensitive morphology engineering pathways, and reveal the underlying molecular mechanisms. Methods We systematically screened 28 MagR homologous genes from diverse prokaryotic and animal taxa to evaluate their expression and corresponding phenotypic effects in Escherichia coli (E. coli). To compare the differential magnetic responses among bacteria expressing various recombinant MagR proteins, we utilized high-throughput automated bright-field microscopic imaging and scanning electron microscopy (SEM). Furthermore, comprehensive biochemical and biophysical characterizations of iron and iron-sulfur cluster binding were performed using Ferrozine colorimetric assays, electron paramagnetic resonance (EPR) spectroscopy, ultraviolet-visible (UV-Vis) absorption, and circular dichroism (CD) spectroscopy. Additionally, 100 mT static magnetic field (SMF) exposure experiments were conducted to assess magnetically tunable phenotypes, while the intrinsic magnetic properties of purified MagR proteins were directly measured using a superconducting quantum interference device (SQUID) magnetometer. Results Our results demonstrated that the heterologous expression of MagR homologs induced varying degrees of bacterial filamentation. From this comprehensive screen, two distinct morphological patterns were identified: hydra (Hydra vulgaris) MagR (hyMagR) promoted uniform cell elongation and filamentation, exhibiting robust magnetic sensitivity manifested as significantly enhanced filamentation under the 100 mT SMF. In contrast, pigeon (Columba livia) MagR (clMagR) induced only low-frequency, extreme filamentation (sporadically exceeding 80 μm) with a relatively weaker magnetic morphological response. Mechanistically, our data unambiguously proved that these phenotypic differences are primarily driven by distinct iron redox preferences rather than total cellular iron accumulation. Specifically, hyMagR preferentially binds ferrous iron (Fe²⁺), whereas clMagR favors ferric iron (Fe³⁺) and forms more stable iron-sulfur clusters. Intriguingly, although SQUID magnetometry showed that purified clMagR exhibited approximately five-fold higher mass magnetic susceptibility than hyMagR, its cellular magnetic response was weaker. We hypothesize that the Fe²⁺-preferred intracellular environment associated with hyMagR overexpression primes the cell for enhanced generation of reactive oxygen species (ROS) via the Fenton reaction. Exposure to an SMF synergizes with this primed redox state, triggering the bacterial SOS response and upregulating cell division inhibitors to efficiently induce uniform filamentation. Conclusion Our findings identify the Fe²⁺/Fe³⁺ redox state as a critical determinant of MagR-mediated morphological remodeling and magnetic responsiveness. This discovery suggests a potential strategy for engineering magnetically responsive cellular systems for synthetic biology applications, and provides a plausible framework, which potentially combines intrinsic protein magnetism with redox-state modulation, for further investigating the evolutionary mechanisms of MagR-mediated magnetoreception.

Lung cancer poses a serious threat to global public health security.Chemotherapy,as the main strategy for cancer treatment,faces challenges such as high toxicity and drug resistance.Anticancer peptides have the potential of being developed into new anticancer drugs due to their advantages of broad-spectrum anticancer activity,rapid action,and difficulty in generating drug resistance,but they also face shortcomings such as weak activity and strong toxic side effects.The weakly acidic microenvironment of tumors(pH 6.5-6.8)provides a good idea for the design of anticancer peptides of high-efficiency and low-toxicity.Previously,we designed the acid-sensitive antibacterial peptide pHly-1 using the wolf spider(Lycosa singoriensis)toxin Lycosin-Ⅰ as a template.In this study,we found that pHly-1 also had acid-sensitive anticancer activity.Further alanine scanning analysis of pHly-1 was carried out,and we ob-tained a mutant pHTP-2 with better acid sensitivity,whose IC50(half maximal inhibitory concentration)against A549 cells was 15.68 μmol/L at pH 6.6 and was greater than 100 μmol/L at pH 7.4.At pH 6.6,pHTP-2 could act on various lung cancer cell lines and induce the death of A549 cells by rapid ly-sis;at pH 7.4,500 μmol/L pHTP-2 had weak toxicity to red blood cells(the hemolysis rate was ap-proximately 38％)and primary myocardial cells(the inhibition rate was 49.7％,with P＜0.05).Analy-sis of its charge,particle size,morphology,and secondary structure showed that at pH 6.6,the histidine in the sequence of pHTP-2 was protonated,increasing the positive charge(P＜0.01),decreasing the hy-drated particle size(P＜0.05)and forming an α-helical structure to induce membrane lysis of A549 cells.At pH 7.4,it was deprotonated,the positive charge decreases,a β-sheet structure was formed and self-aggregation occurred,limiting its effect on the A549 cell membrane and showing weak activity.In summary,pHTP-2 could respond to the weakly acidic microenvironment of tumors to exert selective cyto-toxic activity,effectively overcoming the shortcomings of anticancer peptides such as low efficiency and high toxicity.Our findings suggest that it is a high-quality lead molecule for anticancer drugs.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Lung cancer poses a serious threat to global public health security.Chemotherapy,as the main strategy for cancer treatment,faces challenges such as high toxicity and drug resistance.Anticancer peptides have the potential of being developed into new anticancer drugs due to their advantages of broad-spectrum anticancer activity,rapid action,and difficulty in generating drug resistance,but they also face shortcomings such as weak activity and strong toxic side effects.The weakly acidic microenvironment of tumors(pH 6.5-6.8)provides a good idea for the design of anticancer peptides of high-efficiency and low-toxicity.Previously,we designed the acid-sensitive antibacterial peptide pHly-1 using the wolf spider(Lycosa singoriensis)toxin Lycosin-Ⅰ as a template.In this study,we found that pHly-1 also had acid-sensitive anticancer activity.Further alanine scanning analysis of pHly-1 was carried out,and we ob-tained a mutant pHTP-2 with better acid sensitivity,whose IC50(half maximal inhibitory concentration)against A549 cells was 15.68 μmol/L at pH 6.6 and was greater than 100 μmol/L at pH 7.4.At pH 6.6,pHTP-2 could act on various lung cancer cell lines and induce the death of A549 cells by rapid ly-sis;at pH 7.4,500 μmol/L pHTP-2 had weak toxicity to red blood cells(the hemolysis rate was ap-proximately 38％)and primary myocardial cells(the inhibition rate was 49.7％,with P＜0.05).Analy-sis of its charge,particle size,morphology,and secondary structure showed that at pH 6.6,the histidine in the sequence of pHTP-2 was protonated,increasing the positive charge(P＜0.01),decreasing the hy-drated particle size(P＜0.05)and forming an α-helical structure to induce membrane lysis of A549 cells.At pH 7.4,it was deprotonated,the positive charge decreases,a β-sheet structure was formed and self-aggregation occurred,limiting its effect on the A549 cell membrane and showing weak activity.In summary,pHTP-2 could respond to the weakly acidic microenvironment of tumors to exert selective cyto-toxic activity,effectively overcoming the shortcomings of anticancer peptides such as low efficiency and high toxicity.Our findings suggest that it is a high-quality lead molecule for anticancer drugs.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.