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The challenges posed by energy and environmental issues have forced mankind to explore and utilize unconventional energy sources. It is imperative to convert the abundant coalbed gas (CBG) into high value-added products, i.e., selective and efficient conversion of methane from CBG. Methane activation, known as the “holy grail”, poses a challenge to the design and development of catalysts. The structural complexity of the active metal on the carrier is of particular concern. In this work, we have studied the nucleation growth of small Co clusters (up to Co 6 ) on the surface of CeO 2 (110) using density functional theory, from which a stable loaded Co/CeO 2 (110) structure was selected to investigate the methane activation mechanism. Despite the relatively small size of the selected Co clusters, the obtained Co x /CeO 2 (110) exhibits interesting properties. The optimized Co 5 /CeO 2 (110) structure was selected as the optimal structure to study the activation mechanism of methane due to its competitive electronic structure, adsorption energy and binding energy. The energy barriers for the stepwise dissociation of methane to form CH 3 *, CH 2 *, CH*, and C* radical fragments are 0.44, 0.55, 0.31, and 1.20 eV, respectively, indicating that CH* dissociative dehydrogenation is the rate-determining step for the system under investigation here. This fundamental study of metal-support interactions based on Co growth on the CeO 2 (110) surface contributes to the understanding of the essence of Co/CeO 2 catalysts with promising catalytic behavior. It provides theoretical guidance for better designing the optimal Co/CeO 2 catalyst for tailored catalytic reactions.

Recent advances in environmental genomics have provided unprecedented opportunities for the investigation of viruses in natural settings. Yet, our knowledge of viral biogeographic patterns and the corresponding drivers is still limited. Here, we perform metagenomic deep sequencing on 90 acid mine drainage (AMD) sediments sampled across Southern China and examine the biogeography of viruses in this extreme environment. The results demonstrate that prokaryotic communities dictate viral taxonomic and functional diversity, abundance and structure, whereas other factors especially latitude and mean annual temperature also impact viral populations and functions. In silico predictions highlight lineage-specific virus-host abundance ratios and richness-dependent virus-host interaction structure. Further functional analyses reveal important roles of environmental conditions and horizontal gene transfers in shaping viral auxiliary metabolic genes potentially involved in phosphorus assimilation. Our findings underscore the importance of both abiotic and biotic factors in predicting the taxonomic and functional biogeographic dynamics of viruses in the AMD sediments. The biogeography of viral communities in extreme environments remains understudied. Here, the authors use metagenomic sequencing on 90 acid mine drainage sediments sampled across Southern China, showing the predominant effects of prokaryotic communities and the influence of environmental variables on viral taxonomy and function.

Archaeal membrane lipids are markedly distinct from those in bacteria and eukaryotes, serving as biomarkers for unraveling their ecological and biogeochemical roles. Recent advancements in high-resolution mass spectrometry-based lipidomic research facilitate detailed cellular-level characterizations of lipid compounds. However, the lack of a comprehensive and dedicated database severely limits large-scale, high-throughput investigations of archaeal lipids. We present ArchLips, a comprehensive database containing 219,348 in silico molecular structures and tandem mass spectra of 199,248 corresponding archaeal lipid compounds. ArchLips enables the automatic and accurate annotation of archaeal lipid compounds characterized by high-resolution mass spectrometry from both pure cultures and environmental samples, serving as a transformative tool for enhancing our understanding of archaeal diversity and its ecological and evolutionary significance within global ecosystems. Archaeal membrane lipids have distinct structures but lack comprehensive databases for high-throughput identification. Here, authors present ArchLips, a database containing over 219,348 in silico structures and mass spectra, enabling automated, accurate annotation of archaeal lipids.

Long non-coding RNAs (lncRNAs) are broad-spectrum cellular transcripts that can directly act as RNA regulators and/or partly encode functional peptides (lncRNA-encoded peptides, LRPs) in cancer cells. Recently, cancer LRPs have been found to be involved in cancer cell variability and proliferation, thus gaining widespread attention for their potential in cancer diagnosis, prognosis and therapy. As structures determine functions, the structural diversities of LRPs are the sources of functional variations of LRPs in cancers. Since 6135 cancer LRPs are listed in SPENCER database and 24 SPENCER-unlisted cancer LRPs are reported in several previous studies, this article reviews recent advances of cancer LRPs, analyzes amino acid compositions of them, and undertakes in silico evaluations to assess their structural and functional attributes. These LRPs are dominated by the amino acids Glu, Leu, and Ser and are rarer in the amino acids Cys, His, and Trp, and that many of the LRPs are rich in secondary or tertiary structures. Like mRNA-encoded peptides, these structure-rich cancer LRPs have a wide range of functions, including anti-cancer, cell-penetrating, anti-inflammatory, and antibacterial activities. Relatively, two groups of anticancer values (predicted by AntiCP 2.0 and PreTP-Stack) of these LRPs commonly showed positive and negative correlations with their total charge content and metal-bind aa content, respectively. The increasing amount of data and analysis on cancer LRPs, as reported here, offers opportunities to enhance practical cancer diagnosis and treatment, and to overcome remaining research challenges for cancer LRPs. Graphical Abstract

The challenges posed by energy and environmental issues have forced mankind to explore and utilize unconventional energy sources. It is imperative to convert the abundant coalbed gas (CBG) into high value-added products, i.e., selective and efficient conversion of methane from CBG. Methane activation, known as the \"holy grail\", poses a challenge to the design and development of catalysts. The structural complexity of the active metal on the carrier is of particular concern. In this work, we have studied the nucleation growth of small Co clusters (up to Co.sub.6) on the surface of CeO.sub.2(110) using density functional theory, from which a stable loaded Co/CeO.sub.2(110) structure was selected to investigate the methane activation mechanism. Despite the relatively small size of the selected Co clusters, the obtained Co.sub.x/CeO.sub.2(110) exhibits interesting properties. The optimized Co.sub.5/CeO.sub.2(110) structure was selected as the optimal structure to study the activation mechanism of methane due to its competitive electronic structure, adsorption energy and binding energy. The energy barriers for the stepwise dissociation of methane to form CH.sub.3*, CH.sub.2*, CH*, and C* radical fragments are 0.44, 0.55, 0.31, and 1.20 eV, respectively, indicating that CH* dissociative dehydrogenation is the rate-determining step for the system under investigation here. This fundamental study of metal-support interactions based on Co growth on the CeO.sub.2(110) surface contributes to the understanding of the essence of Co/CeO.sub.2 catalysts with promising catalytic behavior. It provides theoretical guidance for better designing the optimal Co/CeO.sub.2 catalyst for tailored catalytic reactions.

Abstract The challenges posed by energy and environmental issues have forced mankind to explore and utilize unconventional energy sources. It is imperative to convert the abundant coalbed gas (CBG) into high value-added products, i.e., selective and efficient conversion of methane from CBG. Methane activation, known as the “holy grail”, poses a challenge to the design and development of catalysts. The structural complexity of the active metal on the carrier is of particular concern. In this work, we have studied the nucleation growth of small Co clusters (up to Co6) on the surface of CeO2(110) using density functional theory, from which a stable loaded Co/CeO2(110) structure was selected to investigate the methane activation mechanism. Despite the relatively small size of the selected Co clusters, the obtained Co x /CeO2(110) exhibits interesting properties. The optimized Co5/CeO2(110) structure was selected as the optimal structure to study the activation mechanism of methane due to its competitive electronic structure, adsorption energy and binding energy. The energy barriers for the stepwise dissociation of methane to form CH3*, CH2*, CH*, and C* radical fragments are 0.44, 0.55, 0.31, and 1.20 eV, respectively, indicating that CH* dissociative dehydrogenation is the rate-determining step for the system under investigation here. This fundamental study of metal-support interactions based on Co growth on the CeO2(110) surface contributes to the understanding of the essence of Co/CeO2 catalysts with promising catalytic behavior. It provides theoretical guidance for better designing the optimal Co/CeO2 catalyst for tailored catalytic reactions.

Background Various components of the immunological milieu surrounding tumors have become a key focus in cancer immunotherapy research. There are currently no reliable biomarkers for triple-negative breast cancer (TNBC), leading to limited clinical benefits. However, some studies have indicated that patients with TNBC may achieve better outcomes after immunotherapy. Therefore, this study aimed to identify molecular features potentially associated with conventional type 1 dendritic cell (cDC1) immunity to provide new insights into TNBC prognostication and immunotherapy decision-making. Methods Single-cell ribonucleic acid sequencing data from the Gene Expression Omnibus database were analyzed to determine which genes are differentially expressed genes (DEGs) in cDC1s. We then cross-referenced cDC1-related DEGs with gene sets linked to immunity from the ImmPort and InnateDB databases to screen for the genes linked to the immune response and cDC1s. We used univariate Cox and least absolute shrinkage and selection operator regression analyses to construct a risk assessment model based on four genes in patients with TNBC obtained from the Cancer Genome Atlas, which was validated in a testing group. This model was also used to assess immunotherapy responses among the IMvigor210 cohort. We subsequently utilized single sample Gene Set Enrichment Analysis, CIBERSORT, and ESTIMATE to analyze the immunological characteristics of the feature genes and their correlation with drug response. Results We identified 93 DEGs related to the immune response and cDC1s, of which four (IDO1, HLA-DOB, CTSD, and IL3RA) were substantially linked to the overall survival rate of TNBC patients. The risk assessment model based on these genes stratified patients into high- and low-risk groups. Low-risk patients exhibited enriched ‘‘hot tumor’’ phenotypes, including higher infiltration of memory-activated CD4 + T cells, CD8 + T cells, gamma delta T cells, and M1 macrophages, as well as elevated immune checkpoint expression and tumor mutational burden, suggesting potential responsiveness to immunotherapy. Conversely, high-risk patients displayed “cold tumor” characteristics, with higher infiltration of M0 and M2 macrophages and lower immune scores, which may be poorer in response to immunotherapy. However, experimental validation and larger clinical studies are necessary to confirm these findings and explore the underlying mechanisms of the identified genes. Conclusion This study developed a robust risk assessment model using four genes that effectively forecast the outcome of patients with TNBC and have the potential to guide immunotherapy. This model provided new theoretical insights for knowing the TNBC immune microenvironment and developing personalized treatment strategies.

Low immune infiltration is closely associated with poor clinical results and an unfavorable response to therapy in triple-negative breast cancer (TNBC). T-cell exhaustion (TEX) is a significant risk factor for tumor immunosuppression and invasion. Although improving TEX and enhancing effector function are promising strategies for strengthening immunotherapy, their role in the pathogenesis of TNBC remains unclear. This study’s objective was to develop a prognostic model for TNBC based on exhausted CD8+ T-cell (CD8+ Tex)-related differentially expressed genes (DEGs) and to investigate its clinical and immune relevance. Initially, 398 CD8+ Tex-related genes were screened utilizing single-cell RNA sequencing (scRNA-seq) data from TNBC patients. Pseudotime analysis confirmed that CD8+ Tex mainly clustered at the end of the differentiation pathways, making them a critical subset in TNBC progression. By analyzing the TCGA cohort, ten CD8+ Tex-related DEGs were identified as significantly correlated with overall survival (OS) in TNBC patients, and a prognostic model containing four biomarkers (GBP1, CTSD, ABHD14B, and HLA-A) was constructed. The model demonstrated robust predictive capability in both the TCGA cohort and an external cohort, with the low-risk group exhibiting elevated expression of immunological checkpoint molecules and immune cell infiltration, as well as better responses to immunotherapy and chemotherapy. Furthermore, these four biomarkers were found to be highly expressed on CD8+ Tex and were associated with cellular communication efficiency. Therefore, this model is expected to be a new method for forecasting TNBC patients’ prognosis and effectiveness of treatment, providing new insights for clinical decision-making.

Dissimilatory sulfite reduction (DSR) has been essential to microbial energy metabolism in the biogeochemical sulfur cycle since the Paleoarchean Era. However, due to the lack of an integrated assessment of geological record and genomic data, the evolutionary origin of DSR remains elusive in terms of time, habitat, and genetic basis. In this study, we reconstructed the evolutionary pathways and the ancestral sequences of Dsr proteins by mining metagenomes ranging from mesothermal to hyperthermal environments. A phylogenetic analysis of the key catalytic enzyme, DsrAB, and other Dsr proteins indicates that the earliest and most basic functional cascade, DsrABCNM, emerged prior to the latest common ancestor of several basal branching DsrAB clusters encoded by bacteria and archaea. Using a molecular dating strategy that calibrates the protein tree with a species tree, we predicted that the DSR originated 3.508 billion years ago (Ga). This finding strongly confirms the earliest geological evidence of DSR ( ~ 3.47 Ga). Further predictions from ancestral sequence reconstruction indicate that the optimal catalytic temperature of DsrA at the time of DSR origin was approximately 73°C, which is consistent with the petrographic and geochemical evidence in early Archean hydrothermal deposits. After its hot origin, DsrA diversified into subclades that adapted to various temperature levels following the Great Oxidation Event. This is exemplified by the evolution of the reductive archaeal‐type DsrA. Our results synchronize the molecular ages with the geological record, which advances our understanding of the earliest DSR systems and highlights the enzymatic adaptations of microbial life in the Archean biosphere. Sulfite reduction was an essential biogeochemical process on early Earth, but its evolutionary origin remains unclear. By developing a new molecular dating strategy, we estimate that the ubiquitous dissimilatory sulfite reductase DsrAB originated more than 3.508 billion years ago (Ga). This finding provides strong support for the oldest geological record of dissimilatory sulfite reduction (~3.47 Ga), which was discovered over two decades ago. Furthermore, ancestral protein reconstruction suggests that the first sulfite‐reducing microorganisms were likely thermophiles or moderate thermophiles, a conclusion consistent with geological evidence from Archean hydrothermal deposits.