Tumour-selective activity of RAS-GTP inhibition in pancreatic cancer.

Broad-spectrum RAS inhibition has the potential to benefit roughly a quarter of human patients with cancer whose tumours are driven by RAS mutations1,2. RMC-7977 is a highly selective inhibitor of the active GTP-bound forms of KRAS, HRAS and NRAS, with affinity for both mutant and wild-type variants3. More than 90% of cases of human pancreatic ductal adenocarcinoma (PDAC) are driven by activating mutations in KRAS4. Here we assessed the therapeutic potential of RMC-7977 in a comprehensive range of PDAC models. We observed broad and pronounced anti-tumour activity across models following direct RAS inhibition at exposures that were well-tolerated in vivo. Pharmacological analyses revealed divergent responses to RMC-7977 in tumour versus normal tissues. Treated tumours exhibited waves of apoptosis along with sustained proliferative arrest, whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. In the autochthonous KPC mouse model, RMC-7977 treatment resulted in a profound extension of survival followed by on-treatment relapse. Analysis of relapsed tumours identified Myc copy number gain as a prevalent candidate resistance mechanism, which could be overcome by combinatorial TEAD inhibition in vitro. Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS-GTP inhibition in the setting of PDAC and identify a promising candidate combination therapeutic regimen to overcome monotherapy resistance.

Ordered and deterministic cancer genome evolution after p53 loss.

Although p53 inactivation promotes genomic instability1 and presents a route to malignancy for more than half of all human cancers2,3, the patterns through which heterogenous TP53 (encoding human p53) mutant genomes emerge and influence tumorigenesis remain poorly understood. Here, in a mouse model of pancreatic ductal adenocarcinoma that reports sporadic p53 loss of heterozygosity before cancer onset, we find that malignant properties enabled by p53 inactivation are acquired through a predictable pattern of genome evolution. Single-cell sequencing and in situ genotyping of cells from the point of p53 inactivation through progression to frank cancer reveal that this deterministic behaviour involves four sequential phases-Trp53 (encoding mouse p53) loss of heterozygosity, accumulation of deletions, genome doubling, and the emergence of gains and amplifications-each associated with specific histological stages across the premalignant and malignant spectrum. Despite rampant heterogeneity, the deletion events that follow p53 inactivation target functionally relevant pathways that can shape genomic evolution and remain fixed as homogenous events in diverse malignant populations. Thus, loss of p53-the 'guardian of the genome'-is not merely a gateway to genetic chaos but, rather, can enable deterministic patterns of genome evolution that may point to new strategies for the treatment of TP53-mutant tumours.

Integrin-linked kinase control dental pulp stem cell senescence via the mTOR signaling pathway.

Human dental pulp stem cells (HDPSCs) showed an age-dependent decline in proliferation and differentiation capacity. Decline in proliferation and differentiation capacity affects the dental stromal tissue homeostasis and impairs the regenerative capability of HDPSCs. However, which age-correlated proteins regulate the senescence of HDPSCs remain unknown. Our study investigated the proteomic characteristics of HDPSCs isolated from subjects of different ages and explored the molecular mechanism of age-related changes in HDPSCs. Our study showed that the proliferation and osteogenic differentiation of HDPSCs were decreased, while the expression of aging-related genes (p21, p53) and proportion of senescence-associated ß-galactosidase (SA-ß-gal)-positive cells were increased with aging. The bioinformatic analysis identified that significant proteins positively correlated with age were enriched in response to the mammalian target of rapamycin (mTOR) signaling pathway (ILK, MAPK3, mTOR, STAT1, and STAT3). We demonstrated that OSU-T315, an inhibitor of integrin-linked kinase (ILK), rejuvenated aged HDPSCs, similar to rapamycin (an inhibitor of mTOR). Treatment with OSU-T315 decreased the expression of aging-related genes (p21, p53) and proportion of SA-ß-gal-positive cells in HDPSCs isolated from old (O-HDPSCs). Additionally, OSU-T315 promoted the osteoblastic differentiation capacity of O-HDPSCs in vitro and bone regeneration of O-HDPSCs in rat calvarial bone defects model. Our study indicated that the proliferation and osteoblastic differentiation of HDPSCs were impaired with aging. Notably, the ILK/AKT/mTOR/STAT1 signaling pathway may be a major factor in the regulation of HDPSC senescence, which help to provide interventions for HDPSC senescence.

SGAE-MDA: Exploring the MiRNA-disease associations in herbal medicines based on semi-supervised graph autoencoder.

Research indicates that miRNAs present in herbal medicines are crucial for identifying disease markers, advancing gene therapy, facilitating drug delivery, and so on. These miRNAs maintain stability in the extracellular environment, making them viable tools for disease diagnosis. They can withstand the digestive processes in the gastrointestinal tract, positioning them as potential carriers for specific oral drug delivery. By engineering plants to generate effective, non-toxic miRNA interference sequences, it's possible to broaden their applicability, including the treatment of diseases such as hepatitis C. Consequently, delving into the miRNA-disease associations (MDAs) within herbal medicines holds immense promise for diagnosing and addressing miRNA-related diseases. In our research, we propose the SGAE-MDA model, which harnesses the strengths of a graph autoencoder (GAE) combined with a semi-supervised approach to uncover potential MDAs in herbal medicines more effectively. Leveraging the GAE framework, the SGAE-MDA model exactly integrates the inherent feature vectors of miRNAs and disease nodes with the regulatory data in the miRNA-disease network. Additionally, the proposed semi-supervised learning approach randomly hides the partial structure of the miRNA-disease network, subsequently reconstructing them within the GAE framework. This technique effectively minimizes network noise interference. Through comparison against other leading deep learning models, the results consistently highlighted the superior performance of the proposed SGAE-MDA model. Our code and dataset can be available at: https://github.com/22n9n23/SGAE-MDA.

α-Ketoglutarate links p53 to cell fate during tumour suppression.

The tumour suppressor TP53 is mutated in the majority of human cancers, and in over 70% of pancreatic ductal adenocarcinoma (PDAC)1,2. Wild-type p53 accumulates in response to cellular stress, and regulates gene expression to alter cell fate and prevent tumour development2. Wild-type p53 is also known to modulate cellular metabolic pathways3, although p53-dependent metabolic alterations that constrain cancer progression remain poorly understood. Here we find that p53 remodels cancer-cell metabolism to enforce changes in chromatin and gene expression that favour a premalignant cell fate. Restoring p53 function in cancer cells derived from KRAS-mutant mouse models of PDAC leads to the accumulation of α-ketoglutarate (αKG, also known as 2-oxoglutarate), a metabolite that also serves as an obligate substrate for a subset of chromatin-modifying enzymes. p53 induces transcriptional programs that are characteristic of premalignant differentiation, and this effect can be partially recapitulated by the addition of cell-permeable αKG. Increased levels of the αKG-dependent chromatin modification 5-hydroxymethylcytosine (5hmC) accompany the tumour-cell differentiation that is triggered by p53, whereas decreased 5hmC characterizes the transition from premalignant to de-differentiated malignant lesions that is associated with mutations in Trp53. Enforcing the accumulation of αKG in p53-deficient PDAC cells through the inhibition of oxoglutarate dehydrogenase-an enzyme of the tricarboxylic acid cycle-specifically results in increased 5hmC, tumour-cell differentiation and decreased tumour-cell fitness. Conversely, increasing the intracellular levels of succinate (a competitive inhibitor of αKG-dependent dioxygenases) blunts p53-driven tumour suppression. These data suggest that αKG is an effector of p53-mediated tumour suppression, and that the accumulation of αKG in p53-deficient tumours can drive tumour-cell differentiation and antagonize malignant progression.

Senescence induction dictates response to chemo- and immunotherapy in preclinical models of ovarian cancer.

High-grade serous ovarian carcinoma (HGSOC) is a cancer with dismal prognosis due to the limited effectiveness of existing chemo- and immunotherapies. To elucidate mechanisms mediating sensitivity or resistance to these therapies, we developed a fast and flexible autochthonous mouse model based on somatic introduction of HGSOC-associated genetic alterations into the ovary of immunocompetent mice using tissue electroporation. Tumors arising in these mice recapitulate the metastatic patterns and histological, molecular, and treatment response features of the human disease. By leveraging these models, we show that the ability to undergo senescence underlies the clinically observed increase in sensitivity of homologous recombination (HR)-deficient HGSOC tumors to platinum-based chemotherapy. Further, cGas/STING-mediated activation of a restricted senescence-associated secretory phenotype (SASP) was sufficient to induce immune infiltration and sensitize HR-deficient tumors to immune checkpoint blockade. In sum, our study identifies senescence propensity as a predictor of therapy response and defines a limited SASP profile that appears sufficient to confer added vulnerability to concurrent immunotherapy and, more broadly, provides a blueprint for the implementation of electroporation-based mouse models to reveal mechanisms of oncogenesis and therapy response in HGSOC.

A preclinical platform for assessing antitumor effects and systemic toxicities of cancer drug targets.

Anticancer drug development campaigns often fail due to an incomplete understanding of the therapeutic index differentiating the efficacy of the agent against the cancer and its on-target toxicities to the host. To address this issue, we established a versatile preclinical platform in which genetically defined cancers are produced using somatic tissue engineering in transgenic mice harboring a doxycycline-inducible short hairpin RNA against the target of interest. In this system, target inhibition is achieved by the addition of doxycycline, enabling simultaneous assessment of efficacy and toxicity in the same animal. As proof of concept, we focused on CDK9­a cancer target whose clinical development has been hampered by compounds with poorly understood target specificity and unacceptable toxicities. We systematically compared phenotypes produced by genetic Cdk9 inhibition to those achieved using a recently developed highly specific small molecule CDK9 inhibitor and found that both perturbations led to robust antitumor responses. Remarkably, nontoxic levels of CDK9 inhibition could achieve significant treatment efficacy, and dose-dependent toxicities produced by prolonged CDK9 suppression were largely reversible upon Cdk9 restoration or drug withdrawal. Overall, these results establish a versatile in vivo target validation platform that can be employed for rapid triaging of therapeutic targets and lend support to efforts aimed at advancing CDK9 inhibitors for cancer therapy.

MDICC: novel method for multi-omics data integration and cancer subtype identification.

Each type of cancer usually has several subtypes with distinct clinical implications, and therefore the discovery of cancer subtypes is an important and urgent task in disease diagnosis and therapy. Using single-omics data to predict cancer subtypes is difficult because genomes are dysregulated and complicated by multiple molecular mechanisms, and therefore linking cancer genomes to cancer phenotypes is not an easy task. Using multi-omics data to effectively predict cancer subtypes is an area of much interest; however, integrating multi-omics data is challenging. Here, we propose a novel method of multi-omics data integration for clustering to identify cancer subtypes (MDICC) that integrates new affinity matrix and network fusion methods. Our experimental results show the effectiveness and generalization of the proposed MDICC model in identifying cancer subtypes, and its performance was better than those of currently available state-of-the-art clustering methods. Furthermore, the survival analysis demonstrates that MDICC delivered comparable or even better results than many typical integrative methods.

Depletion of regulatory T cells enhancing the anti-tumor effect of in situ vaccination in solid tumors.

The emergence of immune checkpoint inhibitors (ICIs) has revolutionized the clinical treatment for tumor. However, the low response rate of ICIs remains the major obstacle for curing patients and effective approaches for patients with primary or secondary resistance to ICIs remain lacking. In this study, immune stimulating agent unmethylated CG-enriched (CpG) oligodeoxynucleotide (ODN) was locally injected into the tumor to trigger a robust immune response to eradicate cancer cells, while anti-CD25 antibody was applied to remove immunosuppressive regulatory T cells, which further enhanced the host immune activity to attack tumor systematically. The combination of CpG and anti-CD25 antibody obtained notable regression in mouse melanoma model. Furthermore, rechallenge of tumor cells in the xenograft model has resulted in smaller tumor volume, which demonstrated that the combinational treatment enhanced the activity of memory T cells. Remarkably, this combinational therapy presented significant efficacy on multiple types of tumors as well and was able to prevent relapse of tumor partially. Taken together, our combinational immunotherapy provides a new avenue to enhance the clinical outcomes of patients who are insensitive or resistant to ICIs treatments.

Building a predictive model for depression risk in fracture patients: insights from cross-sectional NHANES 2005-2020 data and an external hospital-based dataset.

BACKGROUND: Depression represents a frequent mental health challenge in individuals with fractures, negatively impacting their recuperation and overall well-being. The purpose of this research was to formulate and corroborate a prognostic framework for pinpointing depression risk among fracture sufferers by utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2020 and a separate hospital-based group. METHODS: We analyzed records from 1,748 individuals with fractures documented in the NHANES database spanning 2005 to 2020, of which 362 were diagnosed with depression, as indicated by a Patient Health Questionnaire-9 (PHQ-9) score of 10 or higher. An additional validation group comprised 360 fracture patients sourced from a medical center. Considered variables for prediction encompassed demographic details, lifestyle habits, past medical conditions, and laboratory results. The method of least absolute shrinkage and selection operator (LASSO) regression facilitated the narrowing down of variables, while multivariate logistic regression was employed to pinpoint significant predictors. To assist in prediction, a nomogram was designed and subsequently validated. RESULTS: Five independent predictors were identified: drinking, insomnia, poverty-to-income ratio, education level, and white blood cell count. The nomogram showed good discrimination in the NHANES cohorts (training area under the curve (AUC) 0.734, validation AUC 0.740) and hospital-based external validation (AUC 0.711). Calibration curves and decision analysis supported its predictive accuracy and clinical value. CONCLUSION: The constructed nomogram offers a precise and clinically relevant instrument for forecasting depression risk in patients with fractures, facilitating the early detection of individuals at high risk and enabling prompt intervention.

High resolution copy number inference in cancer using short-molecule nanopore sequencing.

Genome copy number is an important source of genetic variation in health and disease. In cancer, Copy Number Alterations (CNAs) can be inferred from short-read sequencing data, enabling genomics-based precision oncology. Emerging Nanopore sequencing technologies offer the potential for broader clinical utility, for example in smaller hospitals, due to lower instrument cost, higher portability, and ease of use. Nonetheless, Nanopore sequencing devices are limited in the number of retrievable sequencing reads/molecules compared to short-read sequencing platforms, limiting CNA inference accuracy. To address this limitation, we targeted the sequencing of short-length DNA molecules loaded at optimized concentration in an effort to increase sequence read/molecule yield from a single nanopore run. We show that short-molecule nanopore sequencing reproducibly returns high read counts and allows high quality CNA inference. We demonstrate the clinical relevance of this approach by accurately inferring CNAs in acute myeloid leukemia samples. The data shows that, compared to traditional approaches such as chromosome analysis/cytogenetics, short molecule nanopore sequencing returns more sensitive, accurate copy number information in a cost effective and expeditious manner, including for multiplex samples. Our results provide a framework for short-molecule nanopore sequencing with applications in research and medicine, which includes but is not limited to, CNAs.

Comparative proteomic analysis of children FSGS FFPE tissues.

BACKGROUND: In children, focal segmental glomerulosclerosis (FSGS) is the main cause of steroid resistant nephrotic syndrome (SRNS). To identify specific candidates and the mechanism of steroid resistance, we examined the formalin-fixed paraffin embedded (FFPE) renal tissue protein profiles via liquid chromatography tandem mass spectrometry (LC-MS/MS). METHODS: Renal biopsies from seven steroid-sensitive (SS) and eleven steroid-resistant (SR) children FSGS patients were obtained. We examined the formalin-fixed paraffin embedded (FFPE) renal tissue protein profiles via liquid chromatography tandem mass spectrometry (LC-MS/MS). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and Gene Ontology (GO) analysis, as well as the construction of protein-protein interaction (PPI) network were performed. Two proteins were further valiadated by immunohistochemistry staining in FSGS patients and mice models. RESULTS: In total, we quantified more than 4000 proteins, of which 325 were found to be differentially expressed proteins (DEPs) between the SS and SR group (foldchange ≥2, P<0.05). The results of GO revealed that the most significant up-regulated proteins were primarily related to protein transportation, regulation of the complement activation process and cytolysis. Moreover, clustering analysis showed differences in the pathways (lysosome, terminal pathway of complement) between the two groups. Among these potential candidates, validation analyses for LAMP1 and ACSL4 were conducted. LAMP1 was observed to have a higher expression in glomerulus, while ACSL4 was expressed more in tubular epithelial cells. CONCLUSIONS: In this study, the potential mechanism and candidates related to steroid resistance in children FSGS patients were identified. It could be helpful in identifying potential therapeutic targets and predicting outcomes with these proteomic changes for children FSGS patients.

Logistic regression models of cytokines in differentiating vitreoretinal lymphoma from uveitis.

BACKGROUND: Vitreoretinal lymphoma (VRL) can commonly masquerade as chronic idiopathic uveitis due to its nonspecific clinical presentation. Thus, its early diagnosis is difficult. In this study, new logistic regression models were used to classify VRL and uveitis. Additionally, the diagnostic performance of interleukin (IL)-10, the IL-10/IL-6, and the Interleukin Score for IntraOcular Lymphoma Diagnosis (ISOLD) are evaluated. METHODS: Sixty-nine aqueous humors (AH) (46 VRL, 23 uveitis) and 65 vitreous humors (VH) (49 VRL, 16 uveitis) were collected from a single-center retrospective cohort. Logistic regression models were conducted based on IL-6 and IL-10. The cut-off values, area under the receiver operating characteristic curve (ROC) curve (AUC), sensitivity and specificity of IL-10, the IL-10/IL-6, the ISOLD, and the models were calculated from the ROC. Furthermore, Spearman's rank correlation analysis was performed to determine cytokine levels in VH and AH. RESULTS: We redefined the cut-off values of IL-10, the IL-10/IL-6, the ISOLD, and the logistic regression models. In AH, the AUC values of IL-10, ISOLD, IL10/IL6, and the model were 0.91, 0.953, 0.952, and 0.967. In VH, they were 0.93, 0.95, 0.954, and 0.954, respectively. IL-6 (r = 0.7844) and IL-10 (r = 0.8506) in AH and VH showed a strong correlation. CONCLUSIONS: IL-6 and IL-10 levels were introduced into new logistic regression models. The diagnostic efficacy of the models improved compared to the indicators mentioned above among Chinese patients. Additionally, the models could predict the probability of VRL more accurately. A strong correlation of cytokine levels showed the great potential of AH as prioritized auxiliary diagnostic for VRL.

[Study on effect of gypenosides on insulin sensitivity of rats with diabetes mellitus via regulating NF-κB signaling pathway].

This study focused on the ameliorative effects of gypenosides(GPS) on insulin sensitivity and inflammatory factors in rats with type 2 diabetes mellitus(T2 DM) and explored their possible molecular mechanisms. After the successful establishment of T2 DM model, diabetic rats were randomly divided into four groups, including model group, GPS groups(200, 100 mg·kg~(-1)) and metformin group(100 mg·kg~(-1)), with healthy rats serving as the control. After 6-week intragastric administration, fasting blood glucose(FBG) and oral glucose tolerance were examined. The levels of insulin, C-peptide, tumor necrosis factor-α(TNF-α), interleukin-1ß(IL-1ß), interleukin-6(IL-6) and C-reactive protein(CRP) in serum were examined. Then the homeostasis model assessment of insulin resistance(HOMA-IR) and insulin sensitivity index(ISI) were calculated. The protein expression levels of phosphorylated insulin receptor substrate-1(p-IRS-1) and phosphorylated protein kinase B(p-Akt) in skeletal muscle were measured by Western blot, as well as those of phosphorylated inhibitor of nuclear factor-κB(NF-κB) kinase ß(p-IKKß), phosphorylated alpha inhibitor of NF-κB(p-IκBα) and phosphorylated p65 subunit of NF-κB(p-p65) in adipose tissue. The relative expression levels of glucose transporter 4(GLUT4) mRNA in skeletal muscle and NF-κB mRNA in adipose tissue were measured by qRT-PCR, and the morphological changes of pancreatic tissue were observed. Compared with the model group, the GPS groups witnessed significant decrease in FBG, marked amelioration of impaired oral glucose tolerance and significant increase in ISI. Further, the high-dose GPS group saw significantly reduced HOMA-IR, TNF-α, IL-1ß and CRP, significantly increased expression levels of p-IRS-1(Tyr), p-Akt and GLUT4, and markedly inhibited p-IRS-1(Ser), p-IKKß, p-IκBα, p-p65 and NF-κB. The concentration of CRP and the expression levels of p-IRS-1(Ser), p-IKKß, p-IκBα and NF-κB were remarkably reduced in the low-dose GPS group. However, GPS was found less effective in the regulation of serum insulin, C-peptide and IL-6 levels and the alleviation of pancreatic islet injury. The results indicated that GPS can reduce FBG and improve insulin sensitivity in diabetic rats possibly by regulating the NF-κB signaling pathway, inhibiting inflammation, and thereby regulating the expression of key proteins in the insulin signaling pathway.

DNAJB1-PRKACA fusion kinase interacts with ß-catenin and the liver regenerative response to drive fibrolamellar hepatocellular carcinoma.

A segmental deletion resulting in DNAJB1-PRKACA gene fusion is now recognized as the signature genetic event of fibrolamellar hepatocellular carcinoma (FL-HCC), a rare but lethal liver cancer that primarily affects adolescents and young adults. Here we implement CRISPR-Cas9 genome editing and transposon-mediated somatic gene transfer to demonstrate that expression of either the endogenous fusion protein or a chimeric cDNA leads to the formation of indolent liver tumors in mice that closely resemble human FL-HCC. Notably, overexpression of the wild-type PRKACA was unable to fully recapitulate the oncogenic activity of DNAJB1-PRKACA, implying that FL-HCC does not simply result from enhanced PRKACA expression. Tumorigenesis was significantly enhanced by genetic activation of ß-catenin, an observation supported by evidence of recurrent Wnt pathway mutations in human FL-HCC, as well as treatment with the hepatotoxin 3,5-diethoxycarbonyl-1,4-dihydrocollidine, which causes tissue injury, inflammation, and fibrosis. Our study validates the DNAJB1-PRKACA fusion kinase as an oncogenic driver and candidate drug target for FL-HCC, and establishes a practical model for preclinical studies to identify strategies to treat this disease.

[Textual research on Zhuru Tang].

Zhuru Tang( from the Effective Prescriptions for Universal Relief) listed in the Catalogue of Ancient Classical Formulas( The First Batch) by the State Administration of Traditional Chinese Medicine,is usually used to treat stomach fever and vomiting. The first step of the research and development of the classic formula compound preparations is to follow the principle of the ancient method,comb through the literature of all dynasties,and then investigate the historical evolution of the prescription,the evolution of formula significance and the clinical application. Based on this principle,we searched the Chinese Medical Classics Database and relevant literature materials to conduct textual research on the history,evolution of formula significance,clinical application,decocting method,as well as the basis and processing of traditional Chinese medicine from the perspectives of " recipe" and " medicine",in order to provide reference for the development and research of Zhuru Tang.

Analysis of cysteine protease inhibitor gene (BmCPI) promoter activity in silkworms using bac-to-bac baculovirus systems.

A cysteine protease inhibitor of Bombyx mori (BmCPI) plays an important role in pupation, molting, and dissociation of tissues. The present study identified and analyzed the BmCPI promoter region to better understand its functional regulatory mechanisms. Eight promoter fragments of different lengths were analyzed using an improved Bac-to-Bac expression system. Luciferase activities were investigated both in BmE cells and larval organisms after infection with the Bac-to-Bac system, and similar changes in activity were observed in both models. Strong activity was detected in the longest promoter (2005 bp, -1969 to +36), and activity changed significantly with truncation of promoter length. An electrophoretic mobility shift assay showed that the promoter region from -32 to +6 bp played a critical role in activating the downstream gene promoter element, where some potential elements were also predicted by informatics tools. The findings offer a basic reference for the mechanism of transcriptional regulation of BmCPI.

Comparative proteomics analysis of silkworm hemolymph during the stages of metamorphosis via liquid chromatography and mass spectrometry.

The silkworm is a lepidopteran insect that has an open circulatory system with hemolymph consisting of blood and lymph fluid. Hemolymph is not only considered as a depository of nutrients and energy, but it also plays a key role in substance transportation, immunity response, and proteolysis. In this study, we used LC-MS/MS to analyze the hemolymph proteins of four developmental stages during metamorphosis. A total of 728 proteins were identified from the hemolymph of the second day of wandering stage, first day of pupation, ninth day of pupation, and first day as an adult moth. GO annotations and categories showed that silkworm hemolymph proteins were enriched in carbohydrate metabolism, proteolysis, protein binding, and antibacterial humoral response. The levels of nutrient, immunity-related, and structural proteins changed significantly during development and metamorphosis. Some, such as cuticle, odorant-binding, and chemosensory proteins, showed stage-specific expression in the hemolymph. In addition, the expression of several antimicrobial peptides exhibited their highest level of abundance in the hemolymph of the early pupal stage. These findings provide a comprehensive proteomic insight of the silkworm hemolymph and suggest additional molecular targets for studying insect metamorphosis.

Transcriptional Response of Silkworm (Bombyx mori) Eggs to O2 or HCl Treatment.

Diapause is a common biological phenomenon that occurs in many organisms, including fish, insects, and nematodes. In the silkworm (Bombyx mori), diapause generally occurs in the egg stage. Treatment with O2, HCl, or other compounds can prevent egg diapause. Here, we characterized the transcriptomic responses of newly laid eggs treated with O2 or HCl. Digital gene expression analysis showed that 610 genes in O2-treated eggs and 656 in HCl-treated eggs were differentially expressed. Of these, 343 genes were differentially expressed in both treatments. In addition to trehalases, sorbic acid dehydrogenases, and some enzymes involved in the carbohydrate metabolism, we also identified heat shock proteins, cytochrome P450, and GADD45, which are related to stress tolerance. Gene ontology enrichment analysis showed differentially expressed genes in O2-treated eggs were involved in oxidoreductase activity as well as in binding, catalytic, and metabolic processes. The Kyoto Encyclopedia of Genes and Genomes analysis showed that the pathways for ribosome biogenesis, spliceosome, and circadian rhythm were significantly enriched in HCl-treated eggs. The reliability of the data was confirmed by qRT-PCR analysis. Our results improved the understanding of the mechanism of diapause blocking in silkworm eggs treated with O2 or HCl and identified novel molecular targets for future studies.

SMCC: A Novel Clustering Method for Single- and Multi-Omics Data Based on Co-Regularized Network Fusion.

Clustering is a common technique for statistical data analysis and is essential for developing precision medicine. Numerous computational methods have been proposed for integrating multi-omics data to identify cancer subtypes. However, most existing clustering models based on network fusion fail to preserve the consistency of the distribution of the data before and after fusion. Motivated by this observation, we would like to measure and minimize the distribution difference between networks, which may not be in the same space, to improve the performance of data fusion. We were therefore motivated to develop a flexible clustering model, based on network fusion, that minimizes the distribution difference between the data before and after fusion by co-regularization; the model can be applied to both single- and multi-omics data. We propose a new network fusion model for single- and multi-omics data clustering for identifying cancer or cell subtypes based on co-regularized network fusion (SMCC). SMCC integrates low-rank subspace representation and entropy to fuse networks. In addition, it measures and minimizes the distribution difference between the similarity networks and the fusion network by co-regularization. The model can both reduce the noise interference in the source data and make the statistical characteristics of the fusion result closer to those of the source data. We evaluated the clustering performance of SMCC across 16 real single- and multi-omics dataset. The experimental results demonstrated that SMCC is superior to 17 state-of-the-art clustering methods. Moreover, it is effective for identifying cancer or cell subtypes, thereby promoting the development of precision medicine.