Cell-type-specific effects of autism-associated 15q duplication syndrome in the human brain.

Recurrent copy-number variation represents one of the most well-established genetic drivers in neurodevelopmental disorders, including autism spectrum disorder. Duplication of 15q11-q13 (dup15q) is a well-described neurodevelopmental syndrome that increases the risk of autism more than 40-fold. However, the effects of this duplication on gene expression and chromatin accessibility in specific cell types in the human brain remain unknown. To identify the cell-type-specific transcriptional and epigenetic effects of dup15q in the human frontal cortex, we conducted single-nucleus RNA sequencing and multi-omic sequencing on dup15q-affected individuals (n = 6) as well as individuals with non-dup15q autism (n = 7) and neurotypical control individuals (n = 7). Cell-type-specific differential expression analysis identified significantly regulated genes, critical biological pathways, and differentially accessible genomic regions. Although there was overall increased gene expression across the duplicated genomic region, cellular identity represented an important factor mediating gene-expression changes. As compared to other cell types, neuronal subtypes showed greater upregulation of gene expression across a critical region within the duplication. Genes that fell within the duplicated region and had high baseline expression in control individuals showed only modest changes in dup15q, regardless of cell type. Of note, dup15q and autism had largely distinct signatures of chromatin accessibility but shared the majority of transcriptional regulatory motifs, suggesting convergent biological pathways. However, the transcriptional binding-factor motifs implicated in each condition implicated distinct biological mechanisms: neuronal JUN and FOS networks in autism vs. an inflammatory transcriptional network in dup15q microglia. This work provides a cell-type-specific analysis of how dup15q changes gene expression and chromatin accessibility in the human brain, and it finds evidence of marked cell-type-specific effects of this genetic driver. These findings have implications for guiding therapeutic development in dup15q syndrome, as well as understanding the functional effects of copy-number variants more broadly in neurodevelopmental disorders.

An individualized Bayesian method for estimating genomic variants of hypertension.

BACKGROUND: Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual's genome that provide a strong explanation of the phenotype observed in this individual. RESULTS: We applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve. CONCLUSIONS: The results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.

Sirt1 Protects Subventricular Zone-Derived Neural Stem Cells from DNA Double-Strand Breaks and Contributes to Olfactory Function Maintenance in Aging Mice.

DNA damage is assumed to accumulate in stem cells over time and their ability to withstand this damage and maintain tissue homeostasis is the key determinant of aging. Nonetheless, relatively few studies have investigated whether DNA damage does indeed accumulate in stem cells and whether this contributes to stem cell aging and functional decline. Here, we found that, compared with young mice, DNA double-strand breaks (DSBs) are reduced in the subventricular zone (SVZ)-derived neural stem cells (NSCs) of aged mice, which was achieved partly through the adaptive upregulation of Sirt1 expression and non-homologous end joining (NHEJ)-mediated DNA repair. Sirt1 deficiency abolished this effect, leading to stem cell exhaustion, olfactory memory decline, and accelerated aging. The reduced DSBs and the upregulation of Sirt1 expression in SVZ-derived NSCs with age may represent a compensatory mechanism that evolved to protect stem cells from excessive DNA damage, as well as mitigate memory loss and other stresses during aging.

A dicyanoisophorone-based, near-infrared, lysosome-targeting pH sensor with an extremely large Stokes shift.

Intracellular pH plays an important role in various biological processes; abnormal pH changes in the intracellular compartment leads to the production of free radicals, the disruption of membrane contractility, inappropriate apoptosis, and necrosis, resulting in serious illness. Although fluorescent probes have widely been used to detect pH levels owing to their high sensitivity and specificity, there is still a demand for near-infrared (NIR) fluorescent probes with high Stokes shift. Here, a NIR fluorescent probe, PipDC, comprising N-ethyl piperazine (response unit) and naphthyl dicyanoisophorone (fluorophore), was designed for pH sensing. The probe has an extremely large Stokes shift (290 nm), and its fluorescence intensity at 730 nm sharply increases when the environment changes from basic to acidic owing to the protonation of piperazine, which results in the quenching of the photoinduced electron transfer effect. It exhibited a specific response to acidic microenvironments regardless of other interfering substances. In addition, PipDC operates well in the lysosome environment in living cells and displays an off-on fluorescence response with pH alterations. Together, these results suggest that PipDC is a promising fluorescent probe for intracellular pH sensing.

Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference.

Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors.

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

BACKGROUND: One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. RESULTS: Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to further investigate the disease mechanisms underlying each of these clusters. CONCLUSIONS: In summary, we show that a deep learning model can be trained to represent biologically and clinically meaningful abstractions of cancer gene expression data. Understanding what additional relationships these hidden layer abstractions have with the cancer cellular signaling system could have a significant impact on the understanding and treatment of cancer.

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model.

BACKGROUND: A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery. RESULTS: We have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn the encoding system of yeast transcriptomic machinery. After model selection, we evaluated whether the trained models captured biologically sensible information. We show that the latent variables in the first hidden layer correctly captured the signals of yeast transcription factors (TFs), obtaining a close to one-to-one mapping between latent variables and TFs. We further show that genes regulated by latent variables at higher hidden layers are often involved in a common biological process, and the hierarchical relationships between latent variables conform to existing knowledge. Finally, we show that information captured by the latent variables provide more abstract and concise representations of each microarray, enabling the identification of better separated clusters in comparison to gene-based representation. CONCLUSIONS: Contemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery.

Trans-species learning of cellular signaling systems with bimodal deep belief networks.

MOTIVATION: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. RESULTS: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. AVAILABILITY AND IMPLEMENTATION: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. CONTACT: xinghua@pitt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FAK loss reduces BRAFV600E-induced ERK phosphorylation to promote intestinal stemness and cecal tumor formation.

BRAF V600E mutation is a driver mutation in the serrated pathway to colorectal cancers. BRAFV600E drives tumorigenesis through constitutive downstream extracellular signal-regulated kinase (ERK) activation, but high-intensity ERK activation can also trigger tumor suppression. Whether and how oncogenic ERK signaling can be intrinsically adjusted to a "just-right" level optimal for tumorigenesis remains undetermined. In this study, we found that FAK (Focal adhesion kinase) expression was reduced in BRAFV600E-mutant adenomas/polyps in mice and patients. In Vill-Cre;BRAFV600E/+;Fakfl/fl mice, Fak deletion maximized BRAFV600E's oncogenic activity and increased cecal tumor incidence to 100%. Mechanistically, our results showed that Fak loss, without jeopardizing BRAFV600E-induced ERK pathway transcriptional output, reduced EGFR (epidermal growth factor receptor)-dependent ERK phosphorylation. Reduction in ERK phosphorylation increased the level of Lgr4, promoting intestinal stemness and cecal tumor formation. Our findings show that a "just-right" ERK signaling optimal for BRAFV600E-induced cecal tumor formation can be achieved via Fak loss-mediated downregulation of ERK phosphorylation.

FAK loss reduces BRAFV600E-induced ERK phosphorylation to promote intestinal stemness and cecal tumor formation.

BRAFV600E mutation is a driver mutation in the serrated pathway to colorectal cancers. BRAFV600E drives tumorigenesis through constitutive downstream extracellular signal-regulated kinase (ERK) activation, but high-intensity ERK activation can also trigger tumor suppression. Whether and how oncogenic ERK signaling can be intrinsically adjusted to a 'just-right' level optimal for tumorigenesis remains undetermined. In this study, we found that FAK (Focal adhesion kinase) expression was reduced in BRAFV600E-mutant adenomas/polyps in mice and patients. In Vil1-Cre;BRAFLSL-V600E/+;Ptk2fl/fl mice, Fak deletion maximized BRAFV600E's oncogenic activity and increased cecal tumor incidence to 100%. Mechanistically, our results showed that Fak loss, without jeopardizing BRAFV600E-induced ERK pathway transcriptional output, reduced EGFR (epidermal growth factor receptor)-dependent ERK phosphorylation. Reduction in ERK phosphorylation increased the level of Lgr4, promoting intestinal stemness and cecal tumor formation. Our findings show that a 'just-right' ERK signaling optimal for BRAFV600E-induced cecal tumor formation can be achieved via Fak loss-mediated downregulation of ERK phosphorylation.

A Reversible Neural Stem Cell Quiescence and Activation Culture System for Metabolic Study.

Stem cells in vivo can transit between quiescence and activation, two metabolically distinct states. It is increasingly appreciated that cell metabolism assumes profound roles in stem cell maintenance and tissue homeostasis. However, the lack of suitable models greatly hinders our understanding of the metabolic control of stem cell quiescence and activation. In the present study, we have utilized classical signaling pathways and developed a cell culture system to model reversible NSC quiescence and activation. Unlike activated ones, quiescent NSCs manifested distinct morphology characteristics, cell proliferation, and cell cycle properties but retained the same cell proliferation and differentiation potentials once reactivated. Further transcriptomic analysis revealed that extensive metabolic differences existed between quiescent and activated NSCs. Subsequent experimentations confirmed that NSC quiescence and activation transition was accompanied by a dramatic yet coordinated and dynamic shift in RNA metabolism, protein synthesis, and mitochondrial and autophagy activity. The present work not only showcases the broad utilities of this powerful in vitro NSC quiescence and activation culture system but also provides timely insights for the field and warrants further investigations.

Patient-derived ovarian cancer organoid carries immune microenvironment and blood vessel keeping high response to cisplatin.

Ovarian cancer is high recurrence and mortality malignant tumor. The most common ovarian cancer was High-Grade Serous Ovarian Cancer. However, High-Grade Serous Ovarian Cancer organoid is rare, which organoid with patient immune microenvironment and blood vessels even absence. Here, we report a novel High-Grade Serous Ovarian Cancer organoid system derived from patient ovarian cancer samples. These organoids recapitulate High-Grade Serous Ovarian Cancer organoids' histological and molecular heterogeneity while preserving the critical immune microenvironment and blood vessels, as evidenced by the presence of CD34 + endothelial cells. Whole exome sequencing identifies key mutations (CSMD3, TP53, GABRA6). Organoids show promise in testing cisplatin sensitivity for patients resistant to carboplatin and paclitaxel, with notable responses in cancer proteoglycans and p53 (TP53) signaling, like ACTG/ACTB1/AKT2 genes and BBC3/MDM2/PERP. Integration of immune microenvironment and blood vessels enhances potential for novel therapies like immunotherapies and angiogenesis inhibitors. Our work may provide a new detection system and theoretical basis for ovarian cancer research and individual therapy.

Cell-type-specific effects of autism-associated chromosome 15q11.2-13.1 duplications in human brain.

Recurrent copy number variation represents one of the most well-established genetic drivers in neurodevelopmental disorders, including autism spectrum disorder (ASD). Duplication of 15q11.2-13.1 (dup15q) is a well-described neurodevelopmental syndrome that increases the risk of ASD by over 40-fold. However, the effects of this duplication on gene expression and chromatin accessibility in specific cell types in the human brain remain unknown. To identify the cell-type-specific transcriptional and epigenetic effects of dup15q in the human frontal cortex we conducted single-nucleus RNA-sequencing and multi-omic sequencing on dup15q cases (n=6) as well as non-dup15q ASD (n=7) and neurotypical controls (n=7). Cell-type-specific differential expression analysis identified significantly regulated genes, critical biological pathways, and differentially accessible genomic regions. Although there was overall increased gene expression across the duplicated genomic region, cellular identity represented an important factor mediating gene expression changes. Neuronal subtypes, showed greater upregulation of gene expression across a critical region within the duplication as compared to other cell types. Genes within the duplicated region that had high baseline expression in control individuals showed only modest changes in dup15q, regardless of cell type. Of note, dup15q and ASD had largely distinct signatures of chromatin accessibility, but shared the majority of transcriptional regulatory motifs, suggesting convergent biological pathways. However, the transcriptional binding factor motifs implicated in each condition implicated distinct biological mechanisms; neuronal JUN/FOS networks in ASD vs. an inflammatory transcriptional network in dup15q microglia. This work provides a cell-type-specific analysis of how dup15q changes gene expression and chromatin accessibility in the human brain and finds evidence of marked cell-type-specific effects of this genetic driver. These findings have implications for guiding therapeutic development in dup15q syndrome, as well as understanding the functional effects CNVs more broadly in neurodevelopmental disorders.

Structural Evolution of Bacterial Polyphosphate Degradation Enzyme for Phosphorus Cycling.

Living organisms ranging from bacteria to animals have developed their own ways to accumulate and store phosphate during evolution, in particular as the polyphosphate (polyP) granules in bacteria. Degradation of polyP into phosphate is involved in phosphorus cycling, and exopolyphosphatase (PPX) is the key enzyme for polyP degradation in bacteria. Thus, understanding the structure basis of PPX is crucial to reveal the polyP degradation mechanism. Here, it is found that PPX structure varies in the length of ɑ-helical interdomain linker (ɑ-linker) across various bacteria, which is negatively correlated with their enzymatic activity and thermostability - those with shorter ɑ-linkers demonstrate higher polyP degradation ability. Moreover, the artificial DrPPX mutants with shorter ɑ-linker tend to have more compact pockets for polyP binding and stronger subunit interactions, as well as higher enzymatic efficiency (kcat/Km) than that of DrPPX wild type. In Deinococcus-Thermus, the PPXs from thermophilic species possess a shorter ɑ-linker and retain their catalytic ability at high temperatures (70 °C), which may facilitate the thermophilic species to utilize polyP in high-temperature environments. These findings provide insights into the interdomain linker length-dependent evolution of PPXs, which shed light on enzymatic adaption for phosphorus cycling during natural evolution and rational design of enzyme.

Landscape of human organoids: Ideal model in clinics and research.

In the last decade, organoid research has entered a golden era, signifying a pivotal shift in the biomedical landscape. The year 2023 marked a milestone with the publication of thousands of papers in this arena, reflecting exponential growth. However, amid this burgeoning expansion, a comprehensive and accurate overview of the field has been conspicuously absent. Our review is intended to bridge this gap, providing a panoramic view of the rapidly evolving organoid landscape. We meticulously analyze the organoid field from eight distinctive vantage points, harnessing our rich experience in academic research, industrial application, and clinical practice. We present a deep exploration of the advances in organoid technology, underpinned by our long-standing involvement in this arena. Our narrative traverses the historical genesis of organoids and their transformative impact across various biomedical sectors, including oncology, toxicology, and drug development. We delve into the synergy between organoids and avant-garde technologies such as synthetic biology and single-cell omics and discuss their pivotal role in tailoring personalized medicine, enhancing high-throughput drug screening, and constructing physiologically pertinent disease models. Our comprehensive analysis and reflective discourse provide a deep dive into the existing landscape and emerging trends in organoid technology. We spotlight technological innovations, methodological evolution, and the broadening spectrum of applications, emphasizing the revolutionary influence of organoids in personalized medicine, oncology, drug discovery, and other fields. Looking ahead, we cautiously anticipate future developments in the field of organoid research, especially its potential implications for personalized patient care, new avenues of drug discovery, and clinical research. We trust that our comprehensive review will be an asset for researchers, clinicians, and patients with keen interest in personalized medical strategies. We offer a broad view of the present and prospective capabilities of organoid technology, encompassing a wide range of current and future applications. In summary, in this review we attempt a comprehensive exploration of the organoid field. We offer reflections, summaries, and projections that might be useful for current researchers and clinicians, and we hope to contribute to shaping the evolving trajectory of this dynamic and rapidly advancing field.

Spatial Single-cell Analysis Decodes Cortical Layer and Area Specification.

The human cerebral cortex, pivotal for advanced cognitive functions, is composed of six distinct layers and dozens of functionally specialized areas1,2. The layers and areas are distinguished both molecularly, by diverse neuronal and glial cell subtypes, and structurally, through intricate spatial organization3,4. While single-cell transcriptomics studies have advanced molecular characterization of human cortical development, a critical gap exists due to the loss of spatial context during cell dissociation5,6,7,8. Here, we utilized multiplexed error-robust fluorescence in situ hybridization (MERFISH)9, augmented with deep-learning-based cell segmentation, to examine the molecular, cellular, and cytoarchitectural development of human fetal cortex with spatially resolved single-cell resolution. Our extensive spatial atlas, encompassing 16 million single cells, spans eight cortical areas across four time points in the second and third trimesters. We uncovered an early establishment of the six-layer structure, identifiable in the laminar distribution of excitatory neuronal subtypes by mid-gestation, long before the emergence of cytoarchitectural layers. Notably, while anterior-posterior gradients of neuronal subtypes were generally observed in most cortical areas, a striking exception was the sharp molecular border between primary (V1) and secondary visual cortices (V2) at gestational week 20. Here we discovered an abrupt binary shift in neuronal subtype specification at the earliest stages, challenging the notion that continuous morphogen gradients dictate mid-gestation cortical arealization6,10. Moreover, integrating single-nuclei RNA-sequencing and in situ whole transcriptomics revealed an early upregulation of synaptogenesis in V1-specific Layer 4 neurons, suggesting a role of synaptogenesis in this discrete border formation. Collectively, our findings underscore the crucial role of spatial relationships in determining the molecular specification of cortical layers and areas. This work not only provides a valuable resource for the field, but also establishes a spatially resolved single-cell analysis paradigm that paves the way for a comprehensive developmental atlas of the human brain.

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

PURPOSE: A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects. METHODS: We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect. RESULTS: Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS- patients did not (COLOXIS- HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65). CONCLUSION: The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.

PARPi Decreased Primary Ovarian Cancer Organoid Growth Through Early Apoptosis and Base Excision Repair Pathway.

Ovarian cancer (OC), particularly high-grade serous cancer (HGSC), is the leading cause of mortality among gynecological cancers owing to the treatment difficulty and high recurrence probability. As therapeutic drugs approved for OC, poly ADP-ribose polymerase inhibitors (PARPi) lead to synthetic lethality by inhibiting single-strand DNA repair, particularly in homologous recombination-deficient cancers. However, even PARPi have distinct efficacies and are prone to have drug resistance, the molecular mechanisms underlying the PARPi resistance in OC remain unclear. A patient-derived organoid platform was generated and treated with a PARPi to understand the factors associated with PARPi resistance. PARPi significantly inhibits organoid growth. After 72 h of treatment, both the size of organoids and the numbers of adherent cells decreased. Moreover, immunofluorescence results showed that the proportion of Ki67 positive cells significantly reduced. When the PARPi concentration reached 200 nM, the percentage of Ki67+/4',6-diamidino-2-phenylindole (DAPI) cells decreased approximately 50%. PARPi treatment also affected the expression of genes involved in base excision repair and cell cycle. Functional assays revealed that PARPi inhibits cell growth by upregulating early apoptosis. The expression levels of several key genes were validated. In addition to previously reported genes, some promising genes FEN1 and POLA2, were also be founded. The results demonstrate the complex effects of PARPi treatment on changes in potential genes relevant to PARPi resistance, and provide perspectives for further research on the PARPi resistance mechanisms.

An inorganic mineral-based protocell with prebiotic radiation fitness.

Protocell fitness under extreme prebiotic conditions is critical in understanding the origin of life. However, little is known about protocell's survival and fitness under prebiotic radiations. Here we present a radioresistant protocell model based on assembly of two types of coacervate droplets, which are formed through interactions of inorganic polyphosphate (polyP) with divalent metal cation and cationic tripeptide, respectively. Among the coacervate droplets, only the polyP-Mn droplet is radiotolerant and provides strong protection for recruited proteins. The radiosensitive polyP-tripeptide droplet sequestered with both proteins and DNA could be encapsulated inside the polyP-Mn droplet, and form into a compartmentalized protocell. The protocell protects the inner nucleoid-like condensate through efficient reactive oxygen species' scavenging capacity of intracellular nonenzymic antioxidants including Mn-phosphate and Mn-peptide. Our results demonstrate a radioresistant protocell model with redox reaction system in response to ionizing radiation, which might enable the protocell fitness to prebiotic radiation on the primitive Earth preceding the emergence of enzyme-based fitness. This protocell might also provide applications in synthetic biology as bioreactor or drug delivery system.

Wif1 Mediates Coordination of Bone Morphogenetic Protein and Wnt Signaling in Neural and Glioma Stem Cells.

Wnts, bone morphogenetic protein (BMP), and fibroblast growth factor (FGF) are paracrine signaling pathways implicated in the niche control of stem cell fate decisions. BMP-on and Wnt-off are the dominant quiescent niche signaling pathways in many cell types, including neural stem cells (NSCs). However, among the multiple inhibitory family members of the Wnt pathway, those with direct action after BMP4 stimulation in NSCs remain unclear. We examined 11 Wnt inhibitors in NSCs after BMP4 treatment. Wnt inhibitory factor 1 (Wif1) has been identified as the main factor reacting to BMP4 stimuli. RNA sequencing confirmed that Wif1 was markedly upregulated after BMP4 treatment in different gene expression analyses. Similar to the functional role of BMP4, Wif1 significantly decreased the cell cycle of NSCs and significantly inhibited cell proliferation (P < 0.05). Combined treatment with BMP4 and Wif1 significantly enhanced the inhibition of cell growth compared with the single treatment (P < 0.05). Wif1 expression was clearly lower in glioblastoma and low-grade glioma samples than in normal samples (P < 0.05). A functional analysis revealed that both BMP4 and Wif1 could decrease glioma cell growth. These effects were abrogated by the BMP inhibitor Noggin. The collective findings demonstrate that Wif1 plays a key role in quiescent NSC homeostasis and glioma cell growth downstream of BMP-on signaling. The functional roles of Wif1/BMP4 in glioma cells may provide a technical basis for regenerative medicine, drug discovery, and personal molecular therapy in future clinical treatments.