CSI-Otter: isogeny-based (partially) blind signatures from the class group action with a twist.

In this paper, we construct the first provably-secure isogeny-based (partially) blind signature scheme. While at a high level the scheme resembles the Schnorr blind signature, our work does not directly follow from that construction, since isogenies do not offer as rich an algebraic structure. Specifically, our protocol does not fit into the linear identification protocol abstraction introduced by Hauck, Kiltz, and Loss (EUROCYRPT'19), which was used to generically construct Schnorr-like blind signatures based on modules such as classical groups and lattices. Consequently, our scheme is provably secure in the random oracle model (ROM) against poly-logarithmically-many concurrent sessions assuming the subexponential hardness of the group action inverse problem. In more detail, our blind signature exploits the quadratic twist of an elliptic curve in an essential way to endow isogenies with a strictly richer structure than abstract group actions (but still more restrictive than modules). The basic scheme has public key size 128 B and signature size 8 KB under the CSIDH-512 parameter sets-these are the smallest among all provably secure post-quantum secure blind signatures. Relying on a new ring variant of the group action inverse problem ( rGAIP ), we can halve the signature size to 4 KB while increasing the public key size to 512 B. We provide preliminary cryptanalysis of rGAIP and show that for certain parameter settings, it is essentially as secure as the standard GAIP . Finally, we show a novel way to turn our blind signature into a partially blind signature, where we deviate from prior methods since they require hashing into the set of public keys while hiding the corresponding secret key-constructing such a hash function in the isogeny setting remains an open problem.

Mucosal microbiota characterization in gastric cancer identifies immune-activated-related transcripts relevant gastric microbiome signatures.

Tumor microenvironment (TME) immune cells and gastric mucosal microbiome constitute two vital elements of tumor tissue. Increasing evidence has elucidated their clinicopathological significance in predicting outcomes and therapeutic efficacy. However, comprehensive characterization of immune cell-associated microbiome signatures in the TME is still in the early stages of development. Here, we characterized the gastric mucosa microbiome and its associations with immune-activated related transcripts (IATs) in 170 GC tumor tissues and matched non-tumor tissues using 16s rRNA gene sequencing and quantitative reverse transcription-PCR. Microbial diversity and richness were significantly higher in GC tumor tissues than in non-tumor tissues. Differences in microbial composition between the groups were evident, with Firmicutes, Proteobacteria, Bacteroidota, Campilobacterota, Actinobacteria, Fusobacteriota, Verrucomicrobiota, Acidobacteriota, and Cyanobacteria being the dominant phyla in the gastric mucosal microbiota. Microbial interaction network analysis revealed distinctive centralities of oral bacteria (such as Fusobacterium, Porphyromonas, Prevotella, etc.) in both tumor and normal mucosae networks, suggesting their significant influence on GC microbial ecology. Furthermore, we analyzed the expression of IATs (CXCL9, CXCL10, GZMA, GZMB, PRF1, CD8A, IFNG, TBX2, and TNF) and characterized IAT-relevant gastric microbiome signatures in GC patients. Our results showed that the expression of CXCL9, CXCL10, GZMA, GZMB, PRF1 and IFNG was significantly higher in tumor tissues than in adjacent normal tissues in GC patients. Notably, high expression of IATs in tumor tissues was associated with improved survival in GC patients and could serve as a powerful predictor for disease-free survival. Additionally, analysis of IAT levels and mucosal microbiota diversity revealed a correlation between higher IAT expression and increased microbiota richness and evenness in the IATs high group, suggesting potential interactions between mucosal microbiota and tumor immunopathology. Spearman correlation analysis showed positive associations between IAT expression and specific mucosal bacterial species. Notably, Akkermansia muciniphila demonstrated potential involvement in modulating GZMB expression in the GC mucosal microenvironment. These findings underscore the importance of mucosal microbiota alterations in GC and suggest potential therapeutic targets focusing on modulating the tumor microbiota for improved clinical outcomes. The detailed characterization of these elements has profound implications for both treatment and survival prediction in GC. We observed that microbial diversity and richness were significantly higher in GC tumor tissues compared to non-tumor tissues. These differences highlight the unique microbial landscape of GC tumors and suggest that the microbiome could influence tumor development and progression. Importantly, our study demonstrated that high expression levels of IATs in GC tumor tissues were associated with improved patient survival. This suggests that IATs not only reflect immune activation but also serve as valuable biomarkers for predicting disease-free survival. The potential of IATs as predictive markers underscores their utility in guiding therapeutic strategies and personalizing treatment approaches. Moreover, the correlation between higher IAT expression and increased microbiota richness and evenness suggests that a diverse and balanced microbiome may enhance immune responses and contribute to better clinical outcomes. These findings highlight the critical need to consider mucosal microbiota alterations in GC management. Targeting the tumor microbiota could emerge as a promising therapeutic strategy, potentially leading to more effective treatments and improved patient outcomes. Understanding and modulating the microbiome's role in GC opens new avenues for innovative therapeutic interventions and personalized medicine.

Myelination potential and injury susceptibility of grey versus white matter human oligodendrocytes.

Increasing evidence indicates heterogeneity in functional and molecular properties of oligodendrocyte lineage cells both during development and under pathologic conditions. In multiple sclerosis, remyelination of grey matter lesions exceeds that in white matter. Here we used cells derived from grey matter versus white matter regions of surgically resected human brain tissue samples, to compare the capacities of human A2B5-positive progenitor cells and mature oligodendrocytes to ensheath synthetic nanofibers, and relate differences to the molecular profiles of these cells. For both cell types, the percentage of ensheathing cells was greater for grey matter versus white matter cells. For both grey matter and white matter samples, the percentage of cells ensheathing nanofibers was greater for A2B5-positive cells versus mature oligodendrocytes. Grey matter A2B5-positive cells were more susceptible than white matter A2B5-positive cells to injury induced by metabolic insults. Bulk RNA sequencing indicated that separation by cell type (A2B5-positive vs mature oligodendrocytes) is more significant than by region but segregation for each cell type by region is apparent. Molecular features of grey matter versus white matter derived A2B5-positive and mature oligodendrocytes were lower expression of mature oligodendrocyte genes and increased expression of early oligodendrocyte lineage genes. Genes and pathways with increased expression in grey matter derived cells with relevance for myelination included those related to responses to external environment, cell-cell communication, cell migration, and cell adhesion. Immune and cell death related genes were up-regulated in grey matter derived cells. We observed a significant number of up-regulated genes shared between the stress/injury and myelination processes, providing a basis for these features. In contrast to oligodendrocyte lineage cells, no functional or molecular heterogeneity was detected in microglia maintained in vitro, likely reflecting the plasticity of these cells ex vivo. The combined functional and molecular data indicate that grey matter human oligodendrocytes have increased intrinsic capacity to myelinate but also increased injury susceptibility, in part reflecting their being at a stage earlier in the oligodendrocyte lineage.

Molecular Thumbprints: Biological Signatures That Measure Loss of Identity.

Each life is challenged to adapt to an ever-changing environment with integrity-simply put, to maintain identity. We hypothesize that this mission statement of adaptive homeostasis is particularly poignant in an adaptive response, like inflammation. A maladaptive response of unresolved inflammation can seed chronic disease over a lifetime. We propose the concept of a molecular thumbprint: a biological signature of loss of identity as a measure of incomplete return to homeostasis after an inflammatory response. Over time, personal molecular thumbprints can measure dynamic and precise trajectories to chronic inflammatory diseases and further loss of self to cancer. Why is this important? Because the phenotypes and molecular signatures of established complex inflammatory diseases are a far cry from the root of the complex problem, let alone the initial seed. Understanding the science behind key germinating seeds of disease helps to identify molecular factors of susceptibility, resilience, and early dietary or drug intervention. We pilot this hypothesis in a rat colitis model that is well-established for understanding molecular mechanisms of colonic health, disease, and transition of colitis to cancer.

The Influence of Microbiota on Breast Cancer: A Review.

Breast cancer remains one of the leading causes of death among women worldwide, and recent research highlights its growing connection to alterations in the microbiota. This review delves into the intricate relationship between microbiotas and breast cancer, exploring its presence in healthy breast tissue, its changes during cancer progression, and its considerable impact on both the tumor microenvironment (TME) and the tumor immune microenvironment (TIME). We extensively analyze how the microbiota influences cancer growth, invasion, metastasis, resistance to drugs, and the evasion of the immune system, with a special focus on its effects on the TIME. Furthermore, we investigate distinct microbial profiles associated with the four primary molecular subtypes of breast cancer, examining how the microbiota in tumor tissues compares with that in adjacent normal tissues. Emerging studies suggest that microbiotas could serve as valuable diagnostic and prognostic biomarkers, as well as targets for therapy. This review emphasizes the urgent need for further research to improve strategies for breast cancer prevention, diagnosis, and treatment. By offering a detailed examination of the microbiota's critical role in breast cancer, this review aims to foster the development of novel microbiota-based approaches for managing the disease.

Microglia Signatures: A Cause or Consequence of Microglia-Related Brain Disorders?

Microglia signatures refer to distinct gene expression profiles or patterns of gene activity that are characteristic of microglia. Advances in gene expression profiling techniques, such as single-cell RNA sequencing, have allowed us to study microglia at a more detailed level and identify unique gene expression patterns that are associated, but not always, with different functional states of these cells. Microglial signatures depend on the developmental stage, brain region, and specific pathological conditions. By studying these signatures, it has been possible to gain insights into the underlying mechanisms of microglial activation and begin to develop targeted therapies to modulate microglia-mediated immune responses in the CNS. Historically, the first two signatures coincide with M1 pro-inflammatory and M2 anti-inflammatory phenotypes. The first one includes upregulation of genes such as CD86, TNF-α, IL-1ß, and iNOS, while the second one may involve genes like CD206, Arg1, Chil3, and TGF-ß. However, it has long been known that many and more specific phenotypes exist between M1 and M2, likely with corresponding signatures. Here, we discuss specific microglial signatures and their association, if any, with neurodegenerative pathologies and other brain disorders.

Glucose impacts onto the reciprocal reprogramming between mammary adipocytes and cancer cells.

An established hallmark of cancer cells is metabolic reprogramming, largely consisting in the exacerbated glucose uptake. Adipocytes in the tumor microenvironment contribute toward breast cancer (BC) progression and are highly responsive to metabolic fluctuations. Metabolic conditions characterizing obesity and/or diabetes associate with increased BC incidence and mortality. To explore BC-adipocytes interaction and define the impact of glucose in such dialogue, Mammary Adipose-derived Mesenchymal Stem Cells (MAd-MSCs) were differentiated into adipocytes and co-cultured with ER+ BC cells while exposed to glucose concentration resembling hyperglycemia or normoglycemia in humans (25mM or 5.5mM). The transcriptome of both cell types in co-culture as in mono-culture was profiled by RNA-Seq to define the impact of adipocytes on BC cells and viceversa (i), the action of glucose on BC cells, adipocytes (ii) and their crosstalk (iii). Noteworthy, we provided evidence that co-culture with adipocytes in a glucose-rich environment determined a re-program of BC cell transcriptome driving lipid accumulation, a hallmark of BC aggressiveness, promoting stem-like properties and reducing Tamoxifen responsiveness. Moreover, our data point out to a transcriptional effect through which BC cells induce adipocytes de-lipidation, paralleled by pluripotency gain, as source of lipids when glucose lowering occurs. Thus, modulating plasticity of peri-tumoral adipocytes may represent a key point for halting BC progression in metabolically unbalanced patients.

RNA-seq validation of microRNA expression signatures for precision melanoma diagnosis and prognostic stratification.

BACKGROUND: New diagnostic tools are needed to improve the diagnosis and risk stratification of cutaneous melanoma. Disease-specific microRNA signatures have been previously described via NanoString profiling of solid biopsy tissue and plasma. This study validated these signatures via next-generation sequencing technology and compared their performance against clinical metrics and other published melanoma signatures. METHODS: RNA from 64 plasma and 60 FFPE biopsy samples from individuals with invasive melanoma or related benign/control phenotypes was extracted and enriched for microRNA. RNA sequencing was performed to compute MEL38/MEL12 signature scores. The results were evaluated with published NanoString and RNA sequencing datasets, comprising 548 solid tissue samples and 217 plasma samples, to predict disease status and patient outcome. RESULTS: The MEL38 diagnostic signature classifies patients into discrete diagnostic groups via RNA sequencing in either solid tissue or plasma (P < 0.001). In solid tissue, the prognostic MEL12 signature stratifies patients into low-, intermediate- and high-risk groups, independent of clinical covariates. The hazard ratios for 10-year overall survival, based on observed survival intervals, were 2.2 (MEL12 high-risk vs low-risk, P < 0.001) and 1.8 (intermediate-risk vs low-risk, P < 0.001), outperforming other published prognostic models. MEL12 also exhibited prognostic significance in the plasma of 42 patients with invasive disease. CONCLUSIONS: The MEL38 and MEL12 signatures can be assessed in either solid tissue or plasma using RNA-seq and are strong predictors of disease state and patient outcome. Compared with other genomic methods, MEL12 was shown to be the strongest predictor of poor prognosis. MicroRNA expression profiling offers objective, accurate genomic information about a patient's likelihood of invasive melanoma and prognosis.

Deciphering the Landscape of GATA-Mediated Transcriptional Regulation in Gastric Cancer.

Gastric cancer (GC) is an asymptomatic malignancy in early stages, with an invasive and cost-ineffective diagnostic toolbox that contributes to severe global mortality rates on an annual basis. Ectopic expression of the lineage survival transcription factors (LS-TFs) GATA4 and 6 promotes stomach oncogenesis. However, LS-TFs also govern important physiological roles, hindering their direct therapeutic targeting. Therefore, their downstream target genes are particularly interesting for developing cancer-specific molecular biomarkers or therapeutic agents. In this work, we couple inducible knockdown systems with chromatin immunoprecipitation and RNA-seq to thoroughly detect and characterize direct targets of GATA-mediated transcriptional regulation in gastric cancer cells. Our experimental and computational strategy provides evidence that both factors regulate the expression of several coding and non-coding RNAs that in turn mediate for their cancer-promoting phenotypes, including but not limited to cell cycle, apoptosis, ferroptosis, and oxidative stress response. Finally, the diagnostic and prognostic potential of four metagene signatures consisting of selected GATA4/6 target transcripts is evaluated in a multi-cancer panel of ~7000 biopsies from nineteen tumor types, revealing elevated specificity for gastrointestinal tumors. In conclusion, our integrated strategy uncovers the landscape of GATA-mediated coding and non-coding transcriptional regulation, providing insights regarding their molecular and clinical function in gastric cancer.

Identification of Cuproptosis-Associated Prognostic Gene Expression Signatures from 20 Tumor Types.

We investigated the mRNA expression of 124 cuproptosis-associated genes in 7489 biopsies from 20 different tumor types of The Cancer Genome Atlas (TCGA). The KM plotter algorithm has been used to calculate Kaplan-Meier statistics and false discovery rate (FDR) corrections. Interaction networks have been generated using Ingenuity Pathway Analysis (IPA). High mRNA expression of 63 out of 124 genes significantly correlated with shorter survival times of cancer patients across all 20 tumor types. IPA analyses revealed that their gene products were interconnected in canonical pathways (e.g., cancer, cell death, cell cycle, cell signaling). Four tumor entities showed a higher accumulation of genes than the other cancer types, i.e., renal clear cell carcinoma (n = 21), renal papillary carcinoma (n = 13), kidney hepatocellular carcinoma (n = 13), and lung adenocarcinoma (n = 9). These gene clusters may serve as prognostic signatures for patient survival. These signatures were also of prognostic value for tumors with high mutational rates and neoantigen loads. Cuproptosis is of prognostic significance for the survival of cancer patients. The identification of specific gene signatures deserves further exploration for their clinical utility in routine diagnostics.

Constructing polymorphonuclear cells: chromatin folding shapes nuclear morphology.

Immune cell fate decisions are regulated, at least in part, by nuclear architecture. Here, we outline how nuclear architecture instructs mammalian polymorphonuclear cell differentiation. We discuss how in neutrophils loop extrusion mechanisms regulate the expression of genes involved in phagocytosis and shape nuclear morphology. We propose that diminished loop extrusion programs also orchestrate eosinophil and basophil differentiation. We portray a new model in which competitive physical forces, loop extrusion, and phase separation, instruct mononuclear versus polymorphonuclear cell fate decisions. We posit that loop extrusion programs instruct the spatial organization of cytoplasmic organelles, including neutrophil granules, mitochondria, and endoplasmic reticulum. Finally, we suggest that changing loop extrusion programs might allow the engineering of new nuclear shapes and artificial cytoplasmic architectures.

Novel tumor gene expression signatures improve the overall survival prediction efficiency over tumor mutation burden and PD-L1 expression in bladder carcinoma with checkpoint blockade immunotherapy.

Although immune checkpoint blockade therapy (ICBT) has revolutionized cancer treatment with good therapeutic response in a number of human cancers, including bladder cancer, many cancers still do not respond to ICBT. Analyzing genetic signatures helps the understanding of underlying biological mechanisms. Here, based on two cohorts of bladder cancer patients receiving ICBT, we identified three novel ICBT-associated signatures in the bladder cancer microenvironment, involving genomic stability, angiogenesis and RNA regulatory, which affect PD-L1 expression and patient response to ICBT. The combinations of these signatures with TMB or PD-L1 expression improved the overall survival prediction efficiency over TMB and PD-L1 expression alone for patients receiving ICBT. Moreover, we utilized two methods to search potential drugs or small-molecules that have an impact on ICBT-associated signatures. This study provides new molecular insight into ICBT response of bladder cancer and has the potential to improve the prediction accuracy for patients to benefit from ICBT.

Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma.

High-Grade Serous Ovarian Cancer (HGSOC) originates from fallopian tube (FT) precursors. However, the molecular changes that occur as precancerous lesions progress to HGSOC are not well understood. To address this, we integrated high-plex imaging and spatial transcriptomics to analyze human tissue samples at different stages of HGSOC development, including p53 signatures, serous tubal intraepithelial carcinomas (STIC), and invasive HGSOC. Our findings reveal immune modulating mechanisms within precursor epithelium, characterized by chromosomal instability, persistent interferon (IFN) signaling, and dysregulated innate and adaptive immunity. FT precursors display elevated expression of MHC-class I, including HLA-E, and IFN-stimulated genes, typically linked to later-stage tumorigenesis. These molecular alterations coincide with progressive shifts in the tumor microenvironment, transitioning from immune surveillance in early STICs to immune suppression in advanced STICs and cancer. These insights identify potential biomarkers and therapeutic targets for HGSOC interception and clarify the molecular transitions from precancer to cancer.

Pharmacometabolomics of sulfonylureas in patients with type 2 diabetes: a cross-sectional study.

Background: Sulfonylureas have been a longstanding pharmacotherapy in the management of type 2 diabetes, with potential benefits beyond glycemic control. Although sulfonylureas are effective, interindividual variability exists in drug response. Pharmacometabolomics is a potent method for elucidating variations in individual drug response. Identifying unique metabolites associated with treatment response can improve our ability to predict outcomes and optimize treatment strategies for individual patients. Our objective is to identify metabolic signatures associated with good and poor response to sulfonylureas, which could enhance our capability to anticipate treatment outcome. Methods: In this cross-sectional study, clinical and metabolomics data for 137 patients with type 2 diabetes who are taking sulfonylurea as a monotherapy or a combination therapy were obtained from Qatar Biobank. Patients were empirically categorized according to their glycosylated hemoglobin levels into poor and good responders to sulfonylureas. To examine variations in metabolic signatures between the two distinct groups, we have employed orthogonal partial least squares discriminant analysis and linear models while correcting for demographic confounders and metformin usage. Results: Good responders showed increased levels of acylcholines, gamma glutamyl amino acids, sphingomyelins, methionine, and a novel metabolite 6-bromotryptophan. Conversely, poor responders showed increased levels of metabolites of glucose metabolism and branched chain amino acid metabolites. Conclusion: The results of this study have the potential to empower our knowledge of variability in patient response to sulfonylureas, and carry significant implications for advancing precision medicine in type 2 diabetes management.

Near-infrared microscopy reveals diabetic nephropathy in ob/ob mice.

Diabetic nephropathy (DN) is a major cause of global kidney failure. While histological kidney biopsy is the gold standard for diagnosis, it primarily reveals tissue morphology. In contrast, near-infrared (NIR) microscopy offers a label-free method for detailed molecular characterization of kidney tissue. Hematoxylin and eosin-stained kidney tissue samples from 17 ob/ob mice with DN and 14 healthy mice were examined using Fourier transform-NIR microscopy. Four different spectra were obtained from both the mesangium and tubulus. NIR spectral analysis unveiled distinct differences in wavenumbers between DN-affected and healthy kidneys, notably in the carbohydrate and protein-associated region (5500-4200 cm-1). In the mesangium, DN tissue samples exhibited higher median values at 4235 cm-1, 4659 cm-1, 4844 cm-1, 4906 cm-1, and 5222 cm-1 compared to controls (P < 0.05, P < 0.01, P < 0.05, P < 0.05 and P < 0.001, respectively). In tubular spectra, higher median values were found at 4258 cm-1, 4659 cm-1, 5222 cm-1, and 5346 cm-1 in the DN group (P < 0.01, P < 0.05, P < 0.05 and P < 0.01, respectively). These spectral differences strongly correlated with metabolic, histologic, and urinary parameters, providing valuable DN progression insights. The classification model achieved a visible clustering between the control and DN group for both the mesangial and tubular measurements. NIR microscopy demonstrated significant spectral differences between DN and healthy kidney tissues in mice, hinting at its potential for providing chemical insights, aiding in more accurate diagnoses, and offering a foundation for further clinical exploration and potential therapeutic advancements in DN.

Novel RNA biomarkers improve discrimination of children with tuberculosis disease from those with non-TB pneumonia after in vitro stimulation.

The diagnosis of pediatric tuberculosis (TB) poses a challenge for clinical teams worldwide. TB-mediated changes in the expression of host genes in the peripheral blood can serve as diagnostic biomarkers and can provide better insights into the host immune mechanisms of childhood TB. Peripheral blood mononuclear cells (PBMCs) from children (n=102) with microbiologically confirmed TB disease, TB infection (TBI), pneumonia, and healthy controls (HC) were stimulated with either the Purified Protein Derivative (PPD) or the Early Secretory Antigen 6kDa-Culture Filtrate Protein 10 (ESAT6-CFP10) complex of Mycobacterium tuberculosis (Mtb). RNA was extracted and quantified using gene expression microarrays. Differential expression analysis was performed comparing microbiologically confirmed TB to the other diagnostic groups for the stimulated and unstimulated samples. Using variable selection, we identified sparse diagnostic gene signatures; one gene (PID1) was able to distinguish TB from pneumonia after ESAT6-CFP10 stimulation with an AUC of 100% in the test set, while a combination of two genes (STAT1 and IFI44) achieved an AUC of 91.7% (CI95% 75.0%-100%) in the test set after PPD stimulation. The number of significantly differentially expressed (SDE) genes was higher when contrasting TB to pneumonia or HC in stimulated samples, compared to unstimulated ones, leading to a larger pool of candidate diagnostic biomarkers. Our approach provides enlightened aspects of peripheral TB-specific responses and can form the basis for a point of care test meeting the World Health Organization (WHO) Target Product Profile (TPP) for pediatric TB.

Effect of sequencing platforms on the sensitivity of chemical mutation detection using Hawk-Seq™.

BACKGROUND: Error-corrected next-generation sequencing (ecNGS) technologies have enabled the direct evaluation of genome-wide mutations after exposure to mutagens. Previously, we reported an ecNGS methodology, Hawk-Seq™, and demonstrated its utility in evaluating mutagenicity. The evaluation of technical transferability is essential to further evaluate the reliability of ecNGS-based assays. However, cutting-edge sequencing platforms are continually evolving, which can affect the sensitivity of ecNGS. Therefore, the effect of differences in sequencing instruments on mutation data quality should be evaluated. RESULTS: We assessed the performance of four sequencing platforms (HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400) with the Hawk-Seq™ protocol for mutagenicity evaluation using DNA samples from mouse bone marrow exposed to benzo[a]pyrene (BP). The overall mutation (OM) frequencies per 106 bp in vehicle-treated samples were 0.22, 0.36, 0.46, and 0.26 for HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400, respectively. The OM frequency of NextSeq2000 was significantly higher than that of HiSeq2500, suggesting the difference to be based on the platform. The relatively higher value in NextSeq2000 was a consequence of the G:C to C:G mutations in NextSeq2000 data (0.67 per 106 G:C bp), which was higher than the mean of the four platforms by a ca. of 0.25 per 106 G:C bp. A clear dose-dependent increase in G:C to T:A mutation frequencies was observed in all four sequencing platforms after BP exposure. The cosine similarity values of the 96-dimensional trinucleotide mutation patterns between HiSeq and the three other platforms were 0.93, 0.95, and 0.92 for NovaSeq, NextSeq, and DNBSeq, respectively. These results suggest that all platforms can provide equivalent data that reflect the characteristics of the mutagens. CONCLUSIONS: All platforms sensitively detected mutagen-induced mutations using the Hawk-Seq™ analysis. The substitution types and frequencies of the background errors differed depending on the platform. The effects of sequencing platforms on mutagenicity evaluation should be assessed before experimentation.

Interactions between nanobiochar and arsenic: Effects of biochar aging methods on arsenic binding capacity and mechanisms.

Nano-biochar (nanoBC), produced from biochar aging, exhibits significant molecular heterogeneity that may affect the fate and toxicity of co-occurring pollutants. However, the interaction between nanoBC and arsenic (As) remains unclear. Herein, we simulated biochar aging through water erosion, photoaging, and thermal chemical decomposition to generate three types of nanoBC (nUBC, nPBC, and nHBC). We then investigated their distinct binding affinities and interaction mechanisms with arsenite (AsIII) and arsenate (AsV). Complementary analysis using optical spectrophotometer and high-resolution mass spectrometry revealed significant differences in properties and chemical compositions among the three nanoBCs at a size of 100 nm. Specifically, nHBC had higher yield, nPBC had higher aromaticity, and nUBC had more intricate molecular compositions and larger molecular weights. Binding experiments showed that nHBC and nUBC exhibited the highest conditional distribution coefficient (KD) for AsIII and AsV, respectively. In nHBC, a higher proportion of humic-like fluorescent component C3 enhanced its affinity for AsIII, attributed to lignin-like molecules with CHONS formulas where thiol acted as active binding sites. In contrast, the robust AsV binding capacity of nUBC stemmed from its richness in humic-like fluorescent component C1 and tryptophan-like fluorescent component C2. This is facilitated by lipid-like molecules and CHO formulas in C1 and aliphatic/peptide-like molecules and CHON formulas in C2, which provided oxygenic and nitrogen-containing groups for binding. All nanoBC had a significantly higher binding affinity for As than bulk BC. These findings provide a deeper understanding of As-nanoBC binding mechanisms at the molecular level, facilitating more accurate prediction of As fate in biochar-amended soil and associated ecosystem risks.

Characterizing metabolomic signatures related to coffee and tea consumption and their association with incidence and dynamic progression of type 2 diabetes: A multi-state analysis.

Our study aimed to investigate the impact of tea and coffee consumption and related metabolomic signatures on dynamic transitions from diabetes-free status to incident type 2 diabetes (T2D), and subsequently to T2D-related complications and death. We included 438,970 participants in the UK Biobank who were free of diabetes and diabetes complications at baseline. Of these, 212,146 individuals had information on all metabolic biomarkers. We identified tea- and coffee-related metabolomic signatures using elastic net regression models. We examined associations of tea and coffee intake and related metabolomic signatures with the onset and progression of T2D using multi-state regression models. We observed that tea and coffee consumption and related metabolomic signatures were inversely associated with the risk of five T2D transitions. For example, HRs (95% CIs) per SD increase of the tea-related metabolomic signature were 0.87 (0.85, 0.89), 0.97 (0.95, 0.99), 0.91 (0.90, 0.92), 0.92 (0.91, 0.94), and 0.91 (0.90, 0.92) for transitions from diabetes-free state to incident T2D, from diabetes-free state to total death, from incident T2D to T2D complications, from incident T2D to death, and from T2D complications to death. These findings highlight the benefit of tea and coffee intake in reducing the risk of occurrence and progression of T2D.