In vitro and in vivo evaluation of the biofilm-degrading Pseudomonas phage Motto, as a candidate for phage therapy.

Infections caused by Pseudomonas aeruginosa are becoming increasingly difficult to treat due to the emergence of strains that have acquired multidrug resistance. Therefore, phage therapy has gained attention as an alternative to the treatment of pseudomonal infections. Phages are not only bactericidal but occasionally show activity against biofilm as well. In this study, we describe the Pseudomonas phage Motto, a T1-like phage that can clear P. aeruginosa infections in an animal model and also exhibits biofilm-degrading properties. The phage has a substantial anti-biofilm activity against strong biofilm-producing isolates (n = 10), with at least a twofold reduction within 24 h. To demonstrate the safety of using phage Motto, cytotoxicity studies were conducted with human cell lines (HEK 293 and RAW 264.7 macrophages). Using a previously established in vivo model, we demonstrated the efficacy of Motto in Caenorhabditis elegans, with a 90% survival rate when treated with the phage at a multiplicity of infection of 10.

Computational screening of damaging nsSNPs in human SOD1 genes associated with amyotrophic lateral sclerosis identifies destabilising effects of G38R and G42D mutations through in silico evaluation.

Amyotrophic lateral sclerosis (ALS), a complicated neurodegenerative disorder affected by hereditary and environmental variables, is a condition. In this study, the genetic makeup of ALS is investigated, with a focus on the SOD1 gene's single-nucleotide polymorphisms (SNPs) and their ability to affect disease risk. Eleven high-risk missense variations that may impair the functionality of the SOD1 protein were discovered after a thorough examination of SNPs in the SOD1 gene. These mutations were chosen using a variety of prediction approaches, highlighting their importance in the aetiology of ALS. Notably, it was discovered that the stability of the SOD1 wild-type protein structure was compromised by the G38R and G42D SOD1 variants. Additionally, Edaravone, a possible ALS medication, showed a greater affinity for binding mutant SOD1 structures, pointing to potential personalised treatment possibilities. The high-risk SNPs discovered in this investigation seem to have functional effects, especially on the stability of proteins and their interactions with other molecules. This study clarifies the complex genetics of ALS and offers insights into how these genetic variations may affect the effectiveness of therapeutic interventions, particularly in the context of edaravone. In this study advances our knowledge of the genetic mechanisms causing ALS vulnerability and prospective therapeutic strategies. Future studies are necessary to confirm these results and close the gap between individualised clinical applications and improved ALS care.

In vitro combination effects of plant-derived quercetin with synthetic bicalutamide on prostate cancer and normal cell lines: in silico comparison.

Prostate cancer is the second most frequent and the fifth greatest cause of death in men. Although diet has been connected to the prevalence of cancer in addition to other factors, the relation between cancer and prevention is weak. Treatment options are at risk due to cell resistance. To identify new combinations, we tried plant-derived quercetin with bicalutamide on cell lines. To determine the cytotoxicity and apoptotic potential of plant-derived quercetin and its combination, MTT [3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide] and dual stain assays were performed. In silico protein-ligand interaction was performed to support the in vitro findings. A thin layer, column, and high-performance chromatography were used to purify quercetin along with an authentic sample. In the cytotoxic study, quercetin was minimized by 80% similar to bicalutamide and a combination of quercetin and bicalutamide by 50% when compared to controls by 2%. Quercetin and bicalutamide showed a similar binding affinity for androgen receptors (9.7 and 9.8), hub genes (10.8 and 10.0), and a few other PCa-related genes (9.4 and 9.1). We propose to conclude that the combination of quercetin plus bicalutamide can be used for chemotherapy if additional in vivo studies are conducted. The intake of foods high in polyphenolic compounds can help to prevent prostate cancer. Examination of quercetin on several cell lines will provide a definite conclusion to combat cancers.

Sequential model based on human cognitive processing to robot acceptance.

Robots have tremendous potential, and have recently been introduced not only for simple operations in factories, but also in workplaces where customer service communication is required. However, communication robots have not always been accepted. This study proposes a three-stage (first contact, interaction, and decision) model for robot acceptance based on the human cognitive process flow to design preferred robots and clarifies the elements of the robot and the processes that affect robot acceptance decision-making. Unlike previous robot acceptance models, the current model focuses on a sequential account of how people decide to accept, considering the interaction (or carry-over) effect between impressions established at each stage. According to the model, this study conducted a scenario-based experiment focusing on the impression of the first contact (a robot's appearance) and that formed during the interaction with robot (politeness of its conversation and behavior) on robot acceptance in both successful and slightly failed situations. The better the appearance of the robot and the more polite its behavior, the greater the acceptance rate. Importantly, there was no interaction between these two factors. The results indicating that the impressions of the first contact and interaction are additively processed suggest that we should accumulate findings that improving the appearance of the robot and making its communication behavior more human-like in politeness will lead to a more acceptable robot design.

Host proteins interact with viral elements and affect the life cycle of highly pathogenic avian influenza A virus H7N9.

Host-virus interactions can significantly impact the viral life cycle and pathogenesis; however, our understanding of the specific host factors involved in highly pathogenic avian influenza A virus H7N9 (HPAI H7N9) infection is currently restricted. Herein, we designed and synthesized 65 small interfering RNAs targeting host genes potentially associated with various aspects of RNA virus life cycles. Afterward, HPAI H7N9 viruses were isolated and RNA interference was used to screen for host factors likely to be involved in the life cycle of HPAI H7N9. Moreover, the research entailed assessing the associations between host proteins and HPAI H7N9 proteins. Twelve key host proteins were identified: Annexin A (ANXA)2, ANXA5, adaptor related protein complex 2 subunit sigma 1 (AP2S1), adaptor related protein complex 3 subunit sigma 1 (AP3S1), ATP synthase F1 subunit alpha (ATP5A1), COPI coat complex subunit alpha (COP)A, COPG1, heat shock protein family A (Hsp70) member 1A (HSPA)1A, HSPA8, heat shock protein 90 alpha family class A member 1 (HSP90AA1), RAB11B, and RAB18. Co-immunoprecipitation revealed intricate interactions between viral proteins (hemagglutinin, matrix 1 protein, neuraminidase, nucleoprotein, polymerase basic 1, and polymerase basic 2) and these host proteins, presumably playing a crucial role in modulating the life cycle of HPAI H7N9. Notably, ANXA5, AP2S1, AP3S1, ATP5A1, HSP90A1, and RAB18, were identified as novel interactors with HPAI H7N9 proteins rather than other influenza A viruses (IAVs). These findings underscore the significance of host-viral protein interactions in shaping the dynamics of HPAI H7N9 infection, while highlighting subtle variations compared with other IAVs. Deeper understanding of these interactions holds promise to advance disease treatment and prevention strategies.

The Impact of Baohuoside I on the Metabolism of Tofacitinib in Rats.

Purpose: To study the potential drug-drug interactions between tofacitinib and baohuoside I and to provide the scientific basis for rational use of them in clinical practice. Methods: A total of eighteen Sprague-Dawley rats were randomly divided into three groups: control group, single-dose group (receiving a single dose of 20 mg/kg of baohuoside I), and multi-dose group (receiving multiple doses of baohuoside I for 7 days). On the seventh day, each rat was orally administered with 10 mg/kg of tofacitinib 30 minutes after giving baohuoside I or vehicle. Blood samples were collected and determined using UPLC-MS/MS. In vitro effects of baohuoside I on tofacitinib was investigated in rat liver microsomes (RLMs), as well as the underlying mechanism of inhibition. The semi-inhibitory concentration value (IC50) of baohuoside I was subsequently determined and its inhibitory mechanism against tofacitinib was analyzed. Furthermore, the interactions between baohuoside I, tofacitinib and CYP3A4 were explored using Pymol molecular docking simulation. Results: The administration of baohuoside I orally has been observed to enhance the area under the concentration-time curve (AUC) of tofacitinib and decrease the clearance (CL). The observed disparity between the single-dose and multi-dose groups was statistically significant. Furthermore, our findings suggest that the impact of baohuoside I on tofacitinib metabolism may be a mixture of non-competitive and competitive inhibition. Baohuoside I exhibit an interaction with arginine (ARG) at position 106 of the CYP3A4 enzyme through hydrogen bonding, positioning itself closer to the site of action compared to tofacitinib. Conclusion: Our study has demonstrated the presence of drug-drug interactions between baohuoside I and tofacitinib, which may arise upon pre-administration of tofacitinib. Altogether, our data indicated that an interaction existed between tofacitinib and baohuoside I and additional cares might be taken when they were co-administrated in clinic.

Effects of hyperbaric oxygen therapy on autistic behaviors and GRIN2B gene expression in valproic acid-exposed rats.

Introduction: Autism is a complex neurodevelopmental condition characterized by deficits in social interaction, communication, and restricted repetitive behaviors. Hyperbaric oxygen therapy (HBOT) has emerged as a potential treatment for autism, although its effects on behavior and gene expression are not well understood. The GRIN2B gene, known for its involvement in encoding a glutamate receptor subunit crucial for neuron communication and associated with autism, was a focus of this study. Methods: Using a rat model induced by prenatal exposure to valproic acid, we examined the impact of HBOT on autism-like behaviors and GRIN2B gene expression. Male Wistar rats were categorized into four groups: control, VPA (valproic acid-exposed), VPA+HBOT [2 atmosphere absolute (ATA)], and VPA+HBOT (2.5 ATA). The rats underwent several behavioral tests to assess social behavior, anxiety, stereotype and exploratory behaviors, and learning. Following the behavioral tests, the HBOT groups received 15 sessions of HBOT at pressures of 2 and 2.5 (ATA), and their behaviors were re-evaluated. Subsequently, real-time PCR was employed to measure GRIN2B gene expression in the frontal lobe. Results: Our results indicated that HBOT significantly increased social interaction and exploratory behaviors in VPA-exposed rats, alongside elevated GRIN2B gene expression in their frontal lobe. Discussion: Our findings imply that HBOT might have a potential role in ameliorating autism-related behaviors in the VPA rat model of autism through potential modulation of GRIN2B gene expression. However, additional research is essential to fully comprehend the underlying mechanisms and refine the HBOT protocol for optimizing its effectiveness in improving autism-related symptoms.

Freedom comes at a cost?: An exploratory study on affordances' impact on users' perception of a social robot.

Along with the development of speech and language technologies, the market for speech-enabled human-robot interactions (HRI) has grown in recent years. However, it is found that people feel their conversational interactions with such robots are far from satisfactory. One of the reasons is the habitability gap, where the usability of a speech-enabled agent drops when its flexibility increases. For social robots, such flexibility is reflected in the diverse choice of robots' appearances, sounds and behaviours, which shape a robot's 'affordance'. Whilst designers or users have enjoyed the freedom of constructing a social robot by integrating off-the-shelf technologies, such freedom comes at a potential cost: the users' perceptions and satisfaction. Designing appropriate affordances is essential for the quality of HRI. It is hypothesised that a social robot with aligned affordances could create an appropriate perception of the robot and increase users' satisfaction when speaking with it. Given that previous studies of affordance alignment mainly focus on one interface's characteristics and face-voice match, we aim to deepen our understanding of affordance alignment with a robot's behaviours and use cases. In particular, we investigate how a robot's affordances affect users' perceptions in different types of use cases. For this purpose, we conducted an exploratory experiment that included three different affordance settings (adult-like, child-like, and robot-like) and three use cases (informative, emotional, and hybrid). Participants were invited to talk to social robots in person. A mixed-methods approach was employed for quantitative and qualitative analysis of 156 interaction samples. The results show that static affordance (face and voice) has a statistically significant effect on the perceived warmth of the first impression; use cases affect people's perceptions more on perceived competence and warmth before and after interactions. In addition, it shows the importance of aligning static affordance with behavioural affordance. General design principles of behavioural affordances are proposed. We anticipate that our empirical evidence will provide a clearer guideline for speech-enabled social robots' affordance design. It will be a starting point for more sophisticated design guidelines. For example, personalised affordance design for individual or group users in different contexts.

Human protein-protein interaction networks: A topological comparison review.

Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.

Arteriovenous Fistula Histology, Hemodynamics, and Wall Mechanics: A Case Report of Successful and Failed Access in a Single Patient.

The low 1-year patency rate of mature arteriovenous fistulas (AVFs) remains a significant clinical problem. Although vessel properties and biomechanics have been suggested to affect AVF function, understanding their roles in AVF patency failure is challenging owing to the heterogeneity within the patient population, including demographics and comorbid conditions. In this study, we present a case of a patient with 2 upper-arm AVFs with different 1-year patency outcomes and investigate whether they had different histologic features before the AVF creation surgery and biomechanics at 1 day and 6 weeks after the AVF creation surgery using magnetic resonance imaging-based fluid structure interaction simulations. Despite both AVFs being in the upper arm, created <1 year apart by the same surgeon, and having similar preoperative vessel diameters, the 1-year patent AVF had less preoperative intimal collagen and higher wall shear stress 1 day after AVF creation, when compared with the AVF that failed by 1 year. Thus, a low intimal collagen content before the AVF surgery and higher wall shear stress immediately after the AVF creation surgery may be important for long-term AVF patency and should be investigated with larger cohorts.

Bayesian estimation of the measurement of interactions in epidemiological studies.

Background: Interaction identification is important in epidemiological studies and can be detected by including a product term in the model. However, as Rothman noted, a product term in exponential models may be regarded as multiplicative rather than additive to better reflect biological interactions. Currently, the additive interaction is largely measured by the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (S), and confidence intervals are developed via frequentist approaches. However, few studies have focused on the same issue from a Bayesian perspective. The present study aims to provide a Bayesian view of the estimation and credible intervals of the additive interaction measures. Methods: Bayesian logistic regression was employed, and estimates and credible intervals were calculated from posterior samples of the RERI, AP and S. Since Bayesian inference depends only on posterior samples, it is very easy to apply this method to preventive factors. The validity of the proposed method was verified by comparing the Bayesian method with the delta and bootstrap approaches in simulation studies with example data. Results: In all the simulation studies, the Bayesian estimates were very close to the corresponding true values. Due to the skewness of the interaction measures, compared with the confidence intervals of the delta method, the credible intervals of the Bayesian approach were more balanced and matched the nominal 95% level. Compared with the bootstrap method, the Bayesian method appeared to be a competitive alternative and fared better when small sample sizes were used. Conclusions: The proposed Bayesian method is a competitive alternative to other methods. This approach can assist epidemiologists in detecting additive-scale interactions.

Noncontingent reinforcement in the treatment of pediatric feeding disorder: A concise review.

This concise review summarizes the literature on noncontingent reinforcement in the treatment of pediatric feeding disorder from 2018 to 2022. We reviewed 15 published behavior-analytic feeding studies to identify how often the term noncontingent reinforcement is used, what form of noncontingent reinforcement is delivered, and what the effects of noncontingent reinforcement are on behavior when it is included in treatment. Implications and directions for future research are discussed.

Fibroblast regulates angiogenesis in assembled oral cancer organoid: A possible role of NNMT.

OBJECTIVE: Tumour angiogenesis is affected by various cell types in the tumour microenvironment (TME), including cancer cells and cancer-associated fibroblasts (CAFs). Here, an assembled organoid model was generated to investigate the mechanism by which the TME regulates angiogenesis in oral squamous cell carcinoma (OSCC). MATERIALS AND METHODS: Secretion of vascular endothelial growth factor-A (VEGFA) was analysed to compare the proangiogenic properties of OSCC cells and corresponding CAFs. Cell aggregates consisting of endothelial cells (ECs), CAFs and cancer cells were generated to construct assembled organoids. Nicotinamide N-methyltransferase (NNMT) was pharmacologically or genetically inhibited to block the activation of CAFs. ATAC-seq was employed to test the transcriptional network of fibroblasts overexpressing NNMT. RESULTS: Compared with cancer cells, CAFs secreted more VEGFA. Coculture with CAFs more effectively promoted the sprouting of ECs. Blockade of CAF activation via inhibition of NNMT drastically reduced the expression of CD31 in the assembled organoids. Overexpression of NNMT enhanced the transcription of genes related to angiogenesis in fibroblasts. Specifically, NNMT orchestrated the enrichment of the transcription factor JUNB at the promoter of VEGFA. CONCLUSIONS: We clarify that stromal NNMT enables the steady reproduction of angiogenesis in assembled oral cancer organoids, providing a novel target for exploiting antiangiogenic therapy.

Synergistic Interaction Effect of Artemisia cina n-hexane Extract and Tagetes lucida Ethyl Acetate Extract on Haemonchus Contortus.

PURPOSE: We analysed the possible synergistic activity among active extracts from Artemisia cina and Tagetes lucida combinations on Haemonchus contortus, a nematode parasitising sheep. METHODS: The work was carried out in vitro on eggs and infective larvae (L3) of H. contortus. The results were analysed with SAS 9.1, applying the ANOVA and Tukey test, and the lethal concentration (LC) values LC50 and LC90 were determined with regression analysis, employing Proc Probit of SAS 9.1. Additionally, the lethal concentration (LC) was calculated with LC50 and LC90 to determine the synergistic effect. RESULTS: The results demonstrated a high efficacy of the two plants studied on both nematode eggs and L3 larvae as well as of their combinations. The highest egg hatching inhibition was obtained with a 50/50 combination, and the best larvae mortality was obtained with 25% A. cina and 75% T. lucida at 10 mg/mL. Additionally, this combination showed a synergistic effect. CONCLUSION: The two plant species studied here can be applied as natural anthelmintic alternatives due to their high bioactive effect and synergistic response.

Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine.

The gut microbiome affects the health status of the host through complex interactions with the host's intestinal wall. These host-microbiome interactions may spatially vary along the physical and chemical environment of the intestine, but these changes remain unknown. This study investigated these intricate relationships through a gene co-expression network analysis based on dual transcriptome profiling of different intestinal sites-cecum, transverse colon, and rectum-of the primate common marmoset. We proposed a gene module extraction algorithm based on the graph theory to find tightly interacting gene modules of the host and the microbiome from a vast co-expression network. The 27 gene modules identified by this method, which include both host and microbiome genes, not only produced results consistent with previous studies regarding the host-microbiome relationships, but also provided new insights into microbiome genes acting as potential mediators in host-microbiome interplays. Specifically, we discovered associations between the host gene FBP1, a cancer marker, and polysaccharide degradation-related genes (pfkA and fucI) coded by Bacteroides vulgatus, as well as relationships between host B cell-specific genes (CD19, CD22, CD79B, and PTPN6) and a tryptophan synthesis gene (trpB) coded by Parabacteroides distasonis. Furthermore, our proposed module extraction algorithm surpassed existing approaches by successfully defining more functionally related gene modules, providing insights for understanding the complex relationship between the host and the microbiome.IMPORTANCEWe unveiled the intricate dynamics of the host-microbiome interactions along the colon by identifying closely interacting gene modules from a vast gene co-expression network, constructed based on simultaneous profiling of both host and microbiome transcriptomes. Our proposed gene module extraction algorithm, designed to interpret inter-species interactions, enabled the identification of functionally related gene modules encompassing both host and microbiome genes, which was challenging with conventional modularity maximization algorithms. Through these identified gene modules, we discerned previously unrecognized bacterial genes that potentially mediate in known relationships between host genes and specific bacterial species. Our findings underscore the spatial variations in host-microbiome interactions along the colon, rather than displaying a uniform pattern throughout the colon.

Association of visceral adipose tissue with albuminuria and interaction between visceral adiposity and diabetes on albuminuria.

AIMS: To explore the correlation between visceral adipose tissue and albuminuria, and whether there is interaction between visceral adipose tissue and diabetes on albuminuria. METHODS: The study subjects were adult subjects (age ≥ 18 years) from the National Health and Nutrition Examination Surveys (NHANES) database of the USA in 2017-2018. Visceral fat area (VFA) was measured by dual-energy X-ray absorptiometry (DXA). Subjects were divided into three groups according to VFA: low (VFA 0-60cm2), medium (VFA 60-120 cm2) and high (VFA ≥ 120 cm2). Albuminuria was defined as urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g. The statistical analysis software used is STATA 17.0. RESULTS: Data pertaining to 2965 participants (2706 without albuminuria) were included in the analysis. High VFA is an independent risk factor for albuminuria (OR 1.367, 95% CI 1.023-1.827). In the low-VFA group, there is no significant association between diabetes and albuminuria (OR 1.415, 95% CI 0.145-13.849). In the medium-VFA group, diabetes is an independent risk factor for albuminuria (OR 2.217, 95% CI 1.095-4.488). In the high-VFA group, diabetes is also an independent risk factor for albuminuria (OR 5.150, 95% CI 3.150-8.421). There is an additive interaction between high VFA (VFA ≥ 120 cm2) and diabetes on the effect of albuminuria (RERI 3.757, 95% CI 0.927-6.587, p = 0.009), while no multiplication interaction (OR 1.881, 95% CI 0.997-1.023, p = 0.141). CONCLUSIONS: High VFA may represent an independent risk factor for albuminuria. The amount of visceral fat may affect the effect of diabetes on albuminuria. The higher the visceral fat, the stronger the correlation between diabetes and albuminuria should be present. We suppose an additive interaction between VFA and diabetes on the effect of albuminuria.

Creating a Bot-tleneck for malicious AI: Psychological methods for bot detection.

The standard approach for detecting and preventing bots from doing harm online involves CAPTCHAs. However, recent AI research, including our own in this manuscript, suggests that bots can complete many common CAPTCHAs with ease. The most effective methodology for identifying potential bots involves completing image-processing, causal-reasoning based, free-response questions that are hand coded by human analysts. However, this approach is labor intensive, slow, and inefficient. Moreover, with the advent of Generative AI such as GPT and Bard, it may soon be obsolete. Here, we develop and test various automated, bot-screening questions, grounded in psychological research, to serve as a proactive screen against bots. Utilizing hand coded free-response questions in the naturalistic domain of MTurkers recruited for a Qualtrics survey, we identify 18.9% of our sample to be potential bots, whereas Google's reCAPTCHA V3 identified only 1.7% to be potential bots. We then look at the performance of these potential bots on our novel bot-screeners, each of which has different strengths and weaknesses but all of which outperform CAPTCHAs.

Protein language model-embedded geometric graphs power inter-protein contact prediction.

Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.

Interaction between HTR2A rs3125 and negative life events in suicide attempts among patients with major depressive disorder: a cross-sectional study.

BACKGROUND: Both genetic and environmental factors play crucial roles in the development of major depressive disorder (MDD) and suicide attempts (SA). However, the interaction between both items remains unknown. This study aims to explore the interactions between the genetic variants of the serotonin 2 A receptor (HTR2A) and the nitric oxide synthase 1 (NOS1) and environmental factors in patients who experience MDD and SA. METHODS: A total of 334 patients with MDD and a history of SA (MDD-SA) were recruited alongside 518 patients with MDD with no history of SA (MDD-NSA), and 716 healthy controls (HC). The demographic data and clinical characteristics were collected. Sequenom mass spectrometry was used to detect eight tag-single nucleotide polymorphisms (tagSNPs) in HTR2A (rs1328683, rs17068986, and rs3125) and NOS1 (rs1123425, rs2682826, rs3741476, rs527590, and rs7959232). Generalized multifactor dimensionality reduction (GMDR) was used to analyze the gene-environment interactions. RESULTS: Four tagSNPs (rs17068986, rs3125, rs527590, and rs7959232) exhibited significant differences between the three groups. However, these differences were not significant between the MDD-SA and MDD-NSA groups after Bonferroni correction. A logistic regression analysis revealed that negative life events (OR = 1.495, 95%CI: 1.071-2.087, P = 0.018), self-guilt (OR = 2.263, 95%CI: 1.515-3.379, P < 0.001), and negative cognition (OR = 2.252, 95%CI: 1.264-4.013, P = 0.006) were all independently associated with SA in patients with MDD. Furthermore, GMDR analysis indicated a significant interaction between HTR2A rs3125 and negative life events. Negative life events in conjunction with the HTR2A rs3125 CG + GG genotype were associated with a higher SA risk in patients with MDD when compared to the absence of negative life events in conjunction with the CC genotype (OR = 2.547, 95% CI: 1.264-5.131, P = 0.009). CONCLUSION: Several risk factors and a potential interaction between HTR2A rs3125 and negative life events were identified in patients with SA and MDD. The observed interaction likely modulates the risk of MDD and SA, shedding light on the pathogenesis of SA in patients with MDD.

Enhanced protein-metabolite correlation analysis: To investigate the association between Staphylococcus aureus mastitis and metabolic immune pathways.

Mastitis is a disease characterized by congestion, swelling, and inflammation of the mammary gland and usually caused by infection with pathogenic microorganisms. Furthermore, the development of mastitis is closely linked to the exogenous pathway of the gastrointestinal tract. However, the regulatory mechanisms governing the gut-metabolism-mammary axis remain incompletely understood. The present study revealed alterations in the gut microbiota of mastitis rats characterized by an increased abundance of the Proteobacteria phylum. Plasma analysis revealed significantly higher levels of L-isoleucine and cholic acid along with 7-ketodeoxycholic acid. Mammary tissue showed elevated levels of arachidonic acid metabolites and norlithocholic acid. Proteomic analysis showed increased levels of IFIH1, Tnfaip8l2, IRGM, and IRF5 in mastitis rats, which suggests that mastitis triggers an inflammatory response and immune stress. Follistatin (Fst) and progesterone receptor (Pgr) were significantly downregulated, raising the risk of breast cancer. Extracellular matrix (ECM) receptors and focal adhesion signaling pathways were downregulated, while blood-milk barrier integrity was disrupted. Analysis of protein-metabolic network regulation revealed that necroptosis, protein digestion and absorption, and arachidonic acid metabolism were the principal regulatory pathways involved in the development of mastitis. In short, the onset of mastitis leads to changes in the microbiota and alterations in the metabolic profiles of various biological samples, including colonic contents, plasma, and mammary tissue. Key manifestations include disturbances in bile acid metabolism, amino acid metabolism, and arachidonic acid metabolism. At the same time, the integrity of the blood-milk barrier is compromised while inflammation is promoted, thereby reducing cell adhesion in the mammary glands. These findings contribute to a more comprehensive understanding of the metabolic status of mastitis and provide new insights into its impact on the immune system.