The Phase-Dependent Regulation of Lux-Type Genes on the Spoilage Characteristics of Hafnia alvei.

Hafnia alvei, a specific spoilage microorganism, has a strong capacity to destroy food protein and lead to spoilage. The aim of this study was to evaluate the phase-dependent regulation of lux-type genes on the spoilage characteristics of H. alvei H4. The auto-inducer synthase gene luxI and a regulatory gene luxR of the quorum sensing systems in H. alvei H4 were knocked out to construct the mutant phenotypes. On this basis, the research found that the luxI and luxR genes had a strong positive influence on not only flagella-dependent swimming ability and biofilm formation but also the production of putrescine and cadaverine. The luxR gene could downregulate putrescine production. The maximum accumulation of putrescine in wild type, ΔluxI, ΔluxR and ΔluxIR were detected at 24 h, reaching up to 695.23 mg/L, 683.02 mg/L, 776.30 mg/L and 724.12 mg/L, respectively. However, the luxI and luxR genes have a potential positive impact on the production of cadaverine. The maximum concentration of cadaverine produced by wild type, ΔluxI, ΔluxR and ΔluxIR were 252.7 mg/L, 194.5 mg/L, 175.1 mg/L and 154.2 mg/L at 72 h. Moreover, the self-organizing map analysis revealed the phase-dependent effects of two genes on spoilage properties. The luxI gene played a major role in the lag phase, while the luxR gene mainly acted in the exponential and stationary phases. Therefore, the paper provides valuable insights into the spoilage mechanisms of H. alvei H4.

Disrupted resting-state brain functional network properties in non-neuropsychiatric systemic lupus erythematosus patients.

INTRODUCTION: Previous fMRI studies revealed that the abnormal functional connectivity (FC) was related to cognitive impairment in patients with SLE. However, it remains unclear how the disease severity affects the functional topological organization of the whole-brain network in SLE patients without neuropsychiatric symptoms (non-NPSLE). OBJECTIVE: We aim to examine the impairment of the whole-brain functional network in SLE patients without neuropsychiatric symptoms (non-NPSLE), which may improve the understanding of neural mechanism in SLE. METHODS: We acquired resting-state fMRI data from 32 non-NPSLE patients and 32 healthy controls (HC), constructed their whole-brain functional network, and then estimated the topological properties including global and nodal parameters by using graph theory. Meanwhile, we also investigated the differences in intra- and inter-network FC between the non-NPSLE patients and the HC. RESULTS: The non-NPSLE patients showed significantly lower clustering coefficient, global and local efficiency, but higher characteristic path length than the HC. The non-NPSLE patients had significantly lower nodal strength in two regions, ventromedial prefrontal cortex (vmPFC) and anterior PFC (aPFC) than the HC. We found the non-NPSLE patients had significantly lower intra-network FC within frontal-parietal network (FPN) and within default mode network (DMN), and significantly lower inter-network FC between DMN and FPN than the HC. The intra-network FC within DMN was negatively correlated with systemic lupus erythematosus disease activity index (SLEDAI). CONCLUSION: Abnormal whole-brain functional network properties and abnormal intra- and inter-network FC may be related to cognitive impairment and disease degree in the non-NPSLE patients. Our findings provide a network perspective to understand the neural mechanisms of SLE.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. METHODS: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. RESULTS: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). CONCLUSIONS: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. KEY POINTS: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.