Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction.

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.

T2T-YAO: A Telomere-to-telomere Assembled Diploid Reference Genome for Han Chinese.

Since its initial release in 2001, the human reference genome has undergone continuous improvement in quality, and the recently released telomere-to-telomere (T2T) version - T2T-CHM13 - reaches its highest level of continuity and accuracy after 20 years of effort by working on a simplified, nearly homozygous genome of a hydatidiform mole cell line. Here, to provide an authentic complete diploid human genome reference for the Han Chinese, the largest population in the world, we assembled the genome of a male Han Chinese individual, T2T-YAO, which includes T2T assemblies of all the 22 + X + M and 22 + Y chromosomes in both haploid. The quality of T2T-YAO is much better than all currently available diploid assemblies, and its haploid version, T2T-YAO-hp, generated by selecting the better assembly for each autosome, reaches the top quality of fewer than one error per 29.5 Mb, even higher than that of T2T-CHM13. Derived from an individual living in the aboriginal region of the Han population, T2T-YAO shows clear ancestry and potential genetic continuity from the ancient ancestors. Each haplotype of T2T-YAO possesses ∼ 330-Mb exclusive sequences, ∼ 3100 unique genes, and tens of thousands of nucleotide and structural variations as compared with CHM13, highlighting the necessity of a population-stratified reference genome. The construction of T2T-YAO, a truly accurate and authentic representative of the Chinese population, would enable precise delineation of genomic variations and advance our understandings in the hereditability of diseases and phenotypes, especially within the context of the unique variations of the Chinese population.

Collateral sensitivity between tetracyclines and aminoglycosides constrains resistance evolution in carbapenem-resistant Klebsiella pneumoniae.

AIMS: The acquisition of resistance to one antibiotic may confer an increased sensitivity to another antibiotic in bacteria, which is an evolutionary trade-off between different resistance mechanisms, defined as collateral sensitivity (CS). Exploiting the role of CS in treatment design could be an effective method to suppress or even reverse resistance evolution. METHODS: Using experimental evolution, we systematically studied the CS between aminoglycosides and tetracyclines in carbapenem-resistant Klebsiella pneumoniae (CRKP) and explored the underlying mechanisms through genomic and transcriptome analyses. The application of CS-based therapies for resistance suppression, including combination therapy and alternating antibiotic therapy, was further evaluated in vitro and in vivo. RESULTS: Reciprocal CS existed between tetracyclines and aminoglycosides in CRKP. The increased sensitivity of aminoglycoside-resistant strains to tetracyclines was associated with the alteration of bacterial membrane potential, whereas the unbalanced oxidation-reduction process of tetracycline-resistant strains may lead to an increased bacterial sensitivity to aminoglycosides. CS-based combination therapy could efficiently constrain the evolution of CRKP resistance in vitro and in vivo. In addition, alternating antibiotic therapy can re-sensitize CRKP to previously resistant drugs, thereby maintaining the trade-off. CONCLUSIONS: These results provide new insights into constraining the evolution of CRKP resistance through CS-based therapies.

Metabolic landscape dysregulation in bronchoalveolar lavage fluid of checkpoint inhibitor pneumonitis.

BACKGROUND: Checkpoint inhibitor pneumonitis (CIP) is a potentially fatal adverse event resulting from immunotherapy in patients with malignant tumors. However, the pathogenesis of CIP remains poorly understood. METHODS: We collected bronchoalveolar lavage fluid (BALF) from cohorts of patients with CIP, new-onset lung cancer (LC), and idiopathic pulmonary fibrosis (IPF). Non-targeted metabolomics analysis was conducted to analyze metabolic signatures. Flow cytometry was used to evaluate immune cell subsets. RESULTS: Lymphocytes were predominant in the BALF of patients with CIP. A total of 903 metabolites were identified, among which lipid compounds were the most abundant. In a comparison between patients with CIP and LC, enrichment analysis of the altered metabolites showed suppressed amino sugar metabolism, and spermidine and spermine biosynthesis in the CIP group. Metabolism of alpha linolenic acid, linoleic acid, and their fatty acid derivatives was enriched in the CIP group relative to the IPF group. The twelve metabolites found to be enriched in the CIP group were positively correlated with the proportion of CD8+ T cells. One cluster of BALF metabolites, 57.14% of which were lipid molecules, was inversely correlated with the proportion of natural killer cells. CONCLUSIONS: In this study, the metabolomic landscape of BALF in patients with CIP was determined. We elucidated suppressed tumor metabolic signatures, enhanced pulmonary inflammatory signaling, and the characteristics of responsible immune cells, which helps to understand the pathogenesis of CIP.

Resistance to Cefiderocol Involved Expression of PER-1 ß-Lactamase and Downregulation of Iron Transporter System in Carbapenem-Resistant Acinetobacter baumannii.

Background: Cefiderocol (CFDC) is a promising antimicrobial agent against multidrug resistant Gram-negative bacteria. However, CFDC resistance has emerged in carbapenem-resistant Acinetobacter baumannii (CR-AB) but the underlying mechanisms remain unclear. Methods: Whole-genome sequencing and transcriptome sequencing were performed on CFDC-non-susceptible and CFDC-susceptible isolates. Two different recombinant plasmids was electro-transformed into the E. coli BL21 strain to determine the impact of blaPER and the combined impact of blaPER-1 and blaOXA-23 on CFDC resistance. Results: Fifty-five CR-AB isolates with minimum inhibitory concentrations (MICs) ranged from 0.06 mg/L to >256 mg/L were sequenced, including 47 CFDC-non-susceptible and eight CFDC-susceptible isolates. Two CFDC-non-susceptible isolates belonged to ST104 whereas the remaining isolates belonged to ST2, and blaPER-1 was present only in CFDC-non-susceptible isolates. Amino acid substitutions were noted in penicillin-binding proteins (PBPs) in four CFDC-susceptible isolates, with slightly elevated MICs. The MICs of recombinant E. coli BL21 carrying the blaPER-1 gene increased 64-fold and recombinant E. coli BL21 carrying both the blaPER-1 and blaOXA-23 genes increased 8-fold but both remained within the susceptibility range. Transcriptome sequencing of 17 CFDC-non-susceptible isolates and eight CFDC-susceptible isolates revealed that transcriptional levels of various iron transport proteins, such as fiu, feoA, and feoB, and the energy transduction system, TonB-ExbB-ExbD, were relatively downregulated in CFDC-non-susceptible isolates. GO enrichment analysis revealed that the upregulated genes in CFDC-non-susceptible isolates were mainly associated with redox homeostasis and stress response. Besides, the expression levels of the blaOXA-23 and exbD genes were negatively correlated with the MICs. Conclusion: PER-1 production, iron transport system downregulation, and mutations in PBPs may synergistically impart high-level resistance to CFDC in CR-AB.

Characteristics of lower respiratory tract microbiota in the patients with post-hematopoietic stem cell transplantation pneumonia.

Background: Pneumonia is a leading cause of non-relapse mortality after hematopoietic stem cell transplantation (HSCT), and the lower respiratory tract (LRT) microbiome has been proven to be associated with various respiratory diseases. However, little is known about the characteristics of the LRT microbiome in patients with post-HSCT compared to healthy controls (HC) and community-acquired pneumonia (CAP). Methods: Bronchoalveolar lavage samples from 55 patients with post-HSCT pneumonia, 44 patients with CAP, and 30 healthy volunteers were used to detect microbiota using 16S rRNA gene sequencing. Results: The diversity of the LRT microbiome significantly decreased in patients with post-HSCT pneumonia, and the overall community was different from the CAP and HC groups. At the phylum level, post-HSCT pneumonia samples had a high abundance of Actinobacteria and a relatively low abundance of Bacteroidetes. The same is true for non-survivors compared with survivors in patients with post-HSCT pneumonia. At the genus level, the abundances of Pseudomonas, Acinetobacter, Burkholderia, and Mycobacterium were prominent in the pneumonia group after HSCT. On the other hand, gut-associated bacteria, Enterococcus were more abundant in the non-survivors. Some pathways concerning amino acid and lipid metabolism were predicted to be altered in patients with post-HSCT pneumonia. Conclusions: Our results reveal that the LRT microbiome in patients with post-HSCT pneumonia differs from CAP patients and healthy controls, which could be associated with the outcome. The LRT microbiota could be a target for intervention during post-HSCT pneumonia.