A novel mitochondrial unfolded protein response-related risk signature to predict prognosis, immunotherapy and sorafenib sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common cause of cancer-associated death worldwide. The mitochondrial unfolded protein response (UPRmt) not only maintains mitochondrial integrity but also regulates cancer progression and drug resistance. However, no study has used the UPRmt to construct a prognostic signature for HCC. This work aimed to establish a novel signature for predicting patient prognosis, immune cell infiltration, immunotherapy, and chemotherapy response based on UPRmt-related genes (MRGs). Transcriptional profiles and clinical information were obtained from the TCGA and ICGC databases. Cox regression and LASSO regression analyses were applied to select prognostic genes and develop a risk model. The TIMER algorithm was used to investigate immunocytic infiltration in the high- and low-risk subgroups. Here, two distinct clusters were identified with different prognoses, immune cell infiltration statuses, drug sensitivities, and response to immunotherapy. A risk score consisting of seven MRGs (HSPD1, LONP1, SSBP1, MRPS5, YME1L1, HDAC1 and HDAC2) was developed to accurately and independently predict the prognosis of HCC patients. Additionally, the expression of core MRGs was confirmed by immunohistochemistry (IHC) staining, single-cell RNA sequencing, and spatial transcriptome analyses. Notably, the expression of prognostic MRGs was significantly correlated with sorafenib sensitivity in HCC and markedly downregulated in sorafenib-treated HepG2 and Huh7 cells. Furthermore, the knockdown of LONP1 decreased the proliferation, invasion, and migration of HepG2 cells, suggesting that upregulated LONP1 expression contributed to the malignant behaviors of HCC cells. To our knowledge, this is the first study to investigate the consensus clustering algorithm, prognostic potential, immune microenvironment infiltration and drug sensitivity based on the expression of MRGs in HCC. In summary, the UPRmt-related classification and prognostic signature could assist in determining the prognosis and personalized therapy of HCC patients from the perspectives of predictive, preventative and personalized medicine.

Pathogen Discovery in the Post-COVID Era

Pathogen discovery plays a crucial role in the fields of infectious diseases, clinical microbiology, and public health. During the past four years, the global response to the COVID-19 pandemic highlighted the importance of early and accurate identification of novel pathogens for effective management and prevention of outbreaks. The post-COVID era has ushered in a new phase of infectious disease research, marked by accelerated advancements in pathogen discovery. This review encapsulates the recent innovations and paradigm shifts that have reshaped the landscape of pathogen discovery in response to the COVID-19 pandemic. Primarily, we summarize the latest technology innovations, applications, and causation proving strategies that enable rapid and accurate pathogen discovery for both acute and historical infections. We also explored the significance and the latest trends and approaches being employed for effective implementation of pathogen discovery from various clinical and environmental samples. Furthermore, we emphasize the collaborative nature of the pandemic response, which has led to the establishment of global networks for pathogen discovery.

Transcription factors-related molecular subtypes and risk prognostic model: exploring the immunogenicity landscape and potential drug targets in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer, with a high mortality rate and poor prognosis. Mutated or dysregulated transcription factors (TFs) are significantly associated with carcinogenesis. The aim of this study was to develop a TF-related prognostic risk model to predict the prognosis and guide the treatment of HCC patients.

METHODS

RNA sequencing data were obtained from the TCGA database. The ICGC and GEO databases were used as validation datasets. The consensus clustering algorithm was used to classify the molecular subtypes of TFs. Kaplan‒Meier survival analysis and receiver operating characteristic (ROC) analysis were applied to evaluate the prognostic value of the model. The immunogenic landscape differences of molecular subtypes were evaluated by the TIMER and xCell algorithms. Autodock analysis was used to predict possible binding sites of trametinib to TFs. RT‒PCR was used to verify the effect of trametinib on the expression of core TFs.

RESULTS

According to the differential expression of TFs, HCC samples were divided into two clusters (C1 and C2). The survival time, signaling pathways, abundance of immune cell infiltration and responses to chemotherapy and immunotherapy were significantly different between C1 and C2. Nine TFs with potential prognostic value, including HMGB2, ESR1, HMGA1, MYBL2, TCF19, E2F1, FOXM1, CENPA and ZIC2, were identified by Cox regression analysis. HCC patients in the high-risk group had a poor prognosis compared with those in the low-risk group (p < 0.001). Moreover, the area under the ROC curve (AUC) values of the 1-year, 2-year and 3-year survival rates were 0.792, 0.71 and 0.695, respectively. The risk model was validated in the ICGC database. Notably, trametinib sensitivity was highly correlated with the expression of core TFs, and molecular docking predicted the possible binding sites of trametinib with these TFs. More importantly, the expression of core TFs was downregulated under trametinib treatment.

CONCLUSIONS

A prognostic signature with 9 TFs performed well in predicting the survival rate and chemotherapy/immunotherapy effect of HCC patients. Trimetinib has potential application value in HCC by targeting TFs.

A biosynthetic network for protoberberine production in Coptis chinensis

Protoberberine alkaloids are a group of tetracyclic isoquinoline compounds known for their well-established antimicrobial and anti-inflammatory properties. The richness and diversity of protoberberine alkaloids accumulated in the Coptis genus necessitate a comprehensive examination of the biosynthetic machinery to understand their ecological significance. Here, from Coptis chinensis we identified CcCYP719A1, which could install a methylenedioxy bridge on either ring A or ring D of the protoberberine backbone, thus diverging metabolite flux towards the biosynthesis of various protoberberine components. We also obtained CcCYP719A2 and CcCYP719A3, which underwent positive selection after diverging from CcCYP719A1 and maintained specific catalytic activity on ring D. Further, we resolved the biosynthetic pathway of jatrorrhizine by identifying two demethylases, which could also modulate protoberberine composition by removing the C-3 methyl group and methylenedioxy bridge of ring D, allowing demethylated metabolites to be redirected into different routes. Moreover, we characterized 2-O-methyltransferase CcOMT1 and flavin-dependent oxidase CcTHBO, respectively responsible for the commonly observed 2-O-methylation and aromatic ring-C assembly in protoberberine alkaloids. Overall, this study reveals an interconnected metabolite network from which diverse protoberberine alkaloids originate. It provides valuable insights into the existence of undiscovered protoberberine components, and paves the way for the targeted production of desired protoberberine components for potential therapeutic development.

Genetic and Haplotype Diversity of Manila Clam Ruditapes philippinarum in Different Regions of China Based on Three Molecular Markers

China has the largest production yield of Manila clam Ruditapes philippinarum in the world. Most of the clam seeds for aquaculture are mainly derived from artificial breeding in southern China, likely resulting in the loss of genetic variation and inbreeding depression. To understand the genetic and haplotype diversity of R. philippinarum, 14 clam populations sampled from different regions of China were analyzed by three molecular markers, including COI, 16SrRNA and ITS. Based on the results of the COI and ITS genes, the 14 populations showed a moderate to high level of genetic diversity, with an average haplotype diversity of 0.9242 and nucleotide diversity of 0.05248. AMOVA showed that there was significant genetic differentiation among all populations (mean FST of the total population was 0.4534). Pairwise FST analysis showed that genetic differentiation reached significant levels between Laizhou and other populations. Two Laizhou populations showed great divergence from other populations, forming an independent branch in the phylogenetic tree. The shared haplotypes Hap_2 and Hap_4 of COI appeared most frequently in most clam populations. In contrast, 16SrRNA analysis of the clam populations revealed the dominated haplotype Hap_2, accounting for 70% of the total number of individuals. The haplotype diversity of the Laizhou population (Laizhou shell-wide (KK) and Laizhou dock (LZMT)) was relatively higher than other populations, showing multiple unique haplotypes (e.g., Hap_40, Hap_41 and Hap_42). These findings of genetic and haplotype diversity of clam populations provide guiding information for genetic resource conservation and genetic improvement of the commercially important R. philippinarum.