Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions.

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.

Language model enables end-to-end accurate detection of cancer from cell-free DNA.

We present a language model Affordable Cancer Interception and Diagnostics (ACID) that can achieve high classification performance in the diagnosis of cancer exclusively from using raw cfDNA sequencing reads. We formulate ACID as an autoregressive language model. ACID is pretrained with language sentences that are obtained from concatenation of raw sequencing reads and diagnostic labels. We benchmark ACID against three methods. On testing set subjected to whole-genome sequencing, ACID significantly outperforms the best benchmarked method in diagnosis of cancer [Area Under the Receiver Operating Curve (AUROC), 0.924 versus 0.853; P < 0.001] and detection of hepatocellular carcinoma (AUROC, 0.981 versus 0.917; P < 0.001). ACID can achieve high accuracy with just 10 000 reads per sample. Meanwhile, ACID achieves the best performance on testing sets that were subjected to bisulfite sequencing compared with benchmarked methods. In summary, we present an affordable, simple yet efficient end-to-end paradigm for cancer detection using raw cfDNA sequencing reads.

QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes.

Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.

Heterogeneity analysis of the immune microenvironment in laryngeal carcinoma revealed potential prognostic biomarkers.

Laryngeal squamous cell cancer (LSCC) is the second most prevalent malignancy occurring in the head and neck with a high incidence and mortality rate. Immunotherapy has recently become an emerging treatment for cancer. It is therefore essential to explore the role of tumour immunity in laryngeal cancer. Our study first delineated and evaluated the comprehensive immune infiltration landscapes of the tumour microenvironment in LSCC. A hierarchical clustering method was applied to classify the LSCC samples into two groups (high- and low-infiltration groups). We found that individuals with low immune infiltration characteristics had significantly better survival than those in the high-infiltration group, possibly because of the elevated infiltration of immune suppressive cells, such as regulatory T cells and myeloid-derived suppressor cells, in the high-infiltration group. Differentially expressed genes between two groups were involved in some immune-related terms, such as antigen processing and presentation. A univariate Cox analysis and least absolute shrinkage and selection operator analysis were performed to identify an immune gene-set-based prognostic signature (IBPS) to assess the risk of LSCC. The prognostic model comprising six IBPSs was successfully verified to be robust in different cohorts. The expression of the six IBPSs was detected by immunohistochemistry in 110 cases of LSCC. In addition, different inflammatory profiles and immune checkpoint landscape of LSCC were found between two groups. Hence, our model could serve as a candidate immunotherapeutic biomarker and potential therapeutic target for laryngeal cancer.

Lubricating Microneedles System with Multistage Sustained Drug Delivery for the Treatment of Osteoarthritis.

Osteoarthritis (OA) is a typical joint degenerative disease that is prevalent worldwide and significantly affects the normal activities of patients. Traditional treatments using diclofenac (DCF) as an anti-inflammatory drug by oral administration and transdermal delivery have many inherent deficiencies. In this study, a lubricating microneedles (MNs) system for the treatment of osteoarthritis with multistage sustained drug delivery and great reduction in skin damage during MNs penetration is developed. The bilayer dissolvable MNs system, namely HA-DCF@PDMPC, is prepared by designating the composite material of hyaluronic acid (HA) and covalently conjugated drug compound (HA-DCF) as the MNs tips and then modifying the surface of MNs tips with a self-adhesive lubricating copolymer (PDMPC). The MNs system is designed to achieve sustained drug release of DCF via ester bond hydrolysis, physical diffusion from MNs tips, and breakthrough of lubrication coating. Additionally, skin damage is reduced due to the presence of the lubrication coating on the superficial surface. Therefore, the lubricating MNs with multistage sustained drug delivery show good compliance as a transdermal patch for OA treatment, which is validated from anti-inflammatory cell tests and therapeutic animal experiments, down-regulating the expression levels of pro-inflammatory factors and alleviating articular cartilage destruction.

Scalable batch-correction approach for integrating large-scale single-cell transcriptomes.

Integration of accumulative large-scale single-cell transcriptomes requires scalable batch-correction approaches. Here we propose Fugue, a simple and efficient batch-correction method that is scalable for integrating super large-scale single-cell transcriptomes from diverse sources. The core idea of the method is to encode batch information as trainable parameters and add it to single-cell expression profile; subsequently, a contrastive learning approach is used to learn feature representation of the additive expression profile. We demonstrate the scalability of Fugue by integrating all single cells obtained from the Human Cell Atlas. We benchmark Fugue against current state-of-the-art methods and show that Fugue consistently achieves improved performance in terms of data alignment and clustering preservation. Our study will facilitate the integration of single-cell transcriptomes at increasingly large scale.

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings.

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.

Diffuse large B-cell lymphoma: the significance of CD8+ tumor-infiltrating lymphocytes exhaustion mediated by TIM3/Galectin-9 pathway.

BACKGROUND: Overexpression of T-cell immunoglobulin and mucin domain-containing protein 3 (TIM3) is related to the exhaustion of CD8+ tumor-infiltrating lymphocytes (TILs) in diffuse large B-cell lymphoma (DLBCL). However, the mechanism of TIM3-mediated CD8+TILs exhaustion in DLBCL remains poorly understood. Therefore, we aimed to clarify the potential pathway involved in TIM3-mediated CD8+TILs exhaustion and its significance in DLBCL. METHODS: The expression of TIM3 and its correlation with CD8+TILs exhaustion, the key ligand of TIM3, and the potential pathway of TIM3-mediated CD8+TILs exhaustion in DLBCL were analyzed using single-cell RNA sequencing and validated by RNA sequencing. The biological significance of TIM3-related pathway in DLBCL was investigated based on RNA sequencing, immunohistochemistry, and reverse transcription-quantitative polymerase chain reaction data. Finally, the possible regulatory mechanism of TIM3-related pathway in DLBCL was explored using single-cell RNA sequencing and RNA sequencing. RESULTS: Our results demonstrated that CD8+TILs, especially the terminally exhausted state, were the major clusters that expressed TIM3 in DLBCL. Galectin-9, mainly expressed in M2 macrophages, is the key ligand of TIM3 and can induce the exhaustion of CD8+TILs through TIM3/Galectin-9 pathway. Meanwhile, high TIM3/Galectin-9 enrichment is related to immunosuppressive tumor microenvironment, severe clinical manifestations, inferior prognosis, and poor response to CHOP-based chemotherapy, and can predict the clinical efficacy of immune checkpoint blockade therapy in DLBCL. Furthermore, the TIM3/Galectin-9 enrichment in DLBCL may be regulated by the IFN-γ signaling pathway. CONCLUSIONS: Our study highlights that TIM3/Galectin-9 pathway plays a crucial role in CD8+TILs exhaustion and the immune escape of DLBCL, which facilitates further functional studies and could provide a theoretical basis for the development of novel immunotherapy in DLBCL.

Exploring functional genes' correlation with (S)-equol concentration and new daidzein racemase identification.

With its estrogenic activity, (S)-equol plays an important role in maintaining host health and preventing estrogen-related diseases. Exclusive production occurs through the transformation of soy isoflavones by intestinal bacteria, but the reasons for variations in (S)-equol production among different individuals and species remain unclear. Here, fecal samples from humans, pigs, chickens, mice, and rats were used as research objects. The concentrations of (S)-equol, along with the genetic homology and evolutionary relationships of (S)-equol production-related genes [daidzein reductase (DZNR), daidzein racemase (DDRC), dihydrodaidzein reductase (DHDR), tetrahydrodaidzein reductase (THDR)], were analyzed. Additionally, in vitro functional verification of the newly identified DDRC gene was conducted. It was found that approximately 40% of human samples contained (S)-equol, whereas 100% of samples from other species contained (S)-equol. However, there were significant variations in (S)-equol content among the different species: rats > pigs > chickens > mice > humans. The distributions of the four genes displayed species-specific patterns. High detection rates across various species were exhibited by DHDR, THDR, and DDRC. In contrast, substantial variations in detection rates among different species and individuals were observed with respect to DZNR. It appears that various types of DZNR may be associated with different concentrations of (S)-equol, which potentially correspond to the regulatory role during (S)-equol synthesis. This enhances our understanding of individual variations in (S)-equol production and their connection with functional genes in vitro. Moreover, the newly identified DDRC exhibits higher potential for (S)-equol synthesis compared to the known DDRC, providing valuable resources for advancing in vitro (S)-equol production. IMPORTANCE: (S)-equol ((S)-EQ) plays a crucial role in maintaining human health, along with its known capacity to prevent and treat various diseases, including cardiovascular diseases, metabolic syndromes, osteoporosis, diabetes, brain-related diseases, high blood pressure, hyperlipidemia, obesity, and inflammation. However, factors affecting individual variations in (S)-EQ production and the underlying regulatory mechanisms remain elusive. This study examines the association between functional genes and (S)-EQ production, highlighting a potential correlation between the DZNR gene and (S)-EQ content. Various types of DZNR may be linked to the regulation of (S)-EQ synthesis. Furthermore, the identification of a new DDRC gene offers promising prospects for enhancing in vitro (S)-EQ production.

Lipidomics random forest algorithm of seminal plasma is a promising method for enhancing the diagnosis of necrozoospermia.

BACKGROUND: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia. METHODS: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography-mass spectrometry (LC-MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model. RESULTS: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16-18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0). CONCLUSIONS: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis.

A multiplex digital PCR assay for detection and quantitation of porcine circovirus type 2 and type 3.

Porcine circovirus (PCV) has become a major pathogen, causing major economic losses in the global pig industry, and PCV type 2 (PCV2) and 3 (PCV3) are distributed worldwide. We designed specific primer and probe sequences targeting PCV2 Cap and PCV3 Rap and developed a multiplex crystal digital PCR (cdPCR) method after optimizing the primer concentration, probe concentration, and annealing temperature. The multiplex cdPCR assay permits precise and differential detection of PCV2 and PCV3, with a limit of detection of 1.39 × 101 and 1.27 × 101 copies/reaction, respectively, and no cross-reaction with other porcine viruses was observed. The intra-assay and interassay coefficients of variation (CVs) were less than 8.75%, indicating good repeatability and reproducibility. To evaluate the practical value of this assay, 40 tissue samples and 70 feed samples were tested for both PCV2 and PCV3 by cdPCR and quantitative PCR (qPCR). Using multiplex cdPCR, the rates of PCV2 infection, PCV3 infection, and coinfection were 28.45%, 1.72%, and 12.93%, respectively, and using multiplex qPCR, they were 25.00%, 0.86%, and 4.31%, respectively This highly specific and sensitive multiplex cdPCR thus allows accurate simultaneous detection of PCV2 and PCV3, and it is particularly well suited for applications that require the detection of small amounts of input nucleic acid or samples with intensive processing and complex matrices.

Two laser-assisted hatching methods of embryos in ART: a systematic review and meta-analysis.

BACKGROUND: Laser-assisted hatching (LAH) stands as the predominant technique for removing the zona pellucida (ZP) in embryos, primarily consisting of two methods: drilling laser-assisted hatching (D-LAH) and thinning laser-assisted hatching (T-LAH). Presently, both methods have limitations, and their comparative efficacy for embryo implantation and clinical pregnancy remains uncertain. AIM: Evaluate the impact of D-LAH and T-LAH on clinical pregnancy rates within assisted reproductive technology (ART). METHODS: We systematically searched electronic databases including PubMed, Web of Science, and Cochrane Library until July 20, 2022. This study encompassed observational studies and randomized controlled trials (RCTs). A 95% confidence interval (CI) was utilized for assessing the risk ratio (RR) of pregnancy outcomes. The level of heterogeneity was measured using I2 statistics, considering a value exceeding 50% as indicative of substantial heterogeneity. RESULTS: The meta-analysis scrutinized 9 studies involving 2405 clinical pregnancies from D-LAH and 2239 from T-LAH. Findings suggested no considerable variation in the clinical pregnancy rates between the two techniques (RR = 0.93, 95% CI: 0.79-1.10, I2 = 71%, P = 0.41). Subgroup analyses also revealed no substantial differences. However, D-LAH exhibited a notably higher occurrence of singleton pregnancies compared to T-LAH (RR = 2.28, 95% CI: 1.08-4.82, I2 = 89%, P = 0.03). There were no noteworthy distinctions observed in other secondary outcomes encompassing implantation rate, multiple pregnancies, ongoing pregnancy, miscarriage, premature birth, and live birth. CONCLUSION: Both the primary findings and subgroup analyses showed no marked variance in clinical pregnancy rates between D-LAH and T-LAH. Therefore, patients with varying conditions should select their preferred LAH technique after assessing their individual situation. However, due to the restricted number of studies involved, accurately gauging the influence of these laser techniques on clinical outcomes is challenging, necessitating further RCTs and high-quality studies to enhance the success rate of ART. TRIAL REGISTRATION: PROSPERO: CRD42022347066.

Association between metabolic syndrome and kidney cancer risk: a prospective cohort study.

BACKGROUND: Kidney cancer has become known as a metabolic disease. However, there is limited evidence linking metabolic syndrome (MetS) with kidney cancer risk. This study aimed to investigate the association between MetS and its components and the risk of kidney cancer. METHODS: UK Biobank data was used in this study. MetS was defined as having three or more metabolic abnormalities, while pre-MetS was defined as the presence of one or two metabolic abnormalities. Hazard ratios (HRs) and 95% confidence intervals (CIs) for kidney cancer risk by MetS category were calculated using multivariable Cox proportional hazards models. Subgroup analyses were conducted for age, sex, BMI, smoking status and drinking status. The joint effects of MetS and genetic factors on kidney cancer risk were also analyzed. RESULTS: This study included 355,678 participants without cancer at recruitment. During a median follow-up of 11 years, 1203 participants developed kidney cancer. Compared to the metabolically healthy group, participants with pre-MetS (HR= 1.36, 95% CI: 1.06-1.74) or MetS (HR= 1. 70, 95% CI: 1.30-2.23) had a significantly greater risk of kidney cancer. This risk increased with the increasing number of MetS components (P for trend < 0.001). The combination of hypertension, dyslipidemia and central obesity contributed to the highest risk of kidney cancer (HR= 3.03, 95% CI: 1.91-4.80). Compared with participants with non-MetS and low genetic risk, those with MetS and high genetic risk had the highest risk of kidney cancer (HR= 1. 74, 95% CI: 1.41-2.14). CONCLUSIONS: Both pre-MetS and MetS status were positively associated with kidney cancer risk. The risk associated with kidney cancer varied by combinations of MetS components. These findings may offer novel perspectives on the aetiology of kidney cancer and assist in designing primary prevention strategies.

VannoPortal: multiscale functional annotation of human genetic variants for interrogating molecular mechanism of traits and diseases.

Interpreting the molecular mechanism of genomic variations and their causal relationship with diseases/traits are important and challenging problems in the human genetic study. To provide comprehensive and context-specific variant annotations for biologists and clinicians, here, by systematically integrating over 4TB genomic/epigenomic profiles and frequently-used annotation databases from various biological domains, we develop a variant annotation database, called VannoPortal. In general, the database has following major features: (i) systematically integrates 40 genome-wide variant annotations and prediction scores regarding allele frequency, linkage disequilibrium, evolutionary signature, disease/trait association, tissue/cell type-specific epigenome, base-wise functional prediction, allelic imbalance and pathogenicity; (ii) equips with our recent novel index system and parallel random-sweep searching algorithms for efficient management of backend databases and information extraction; (iii) greatly expands context-dependent variant annotation to incorporate large-scale epigenomic maps and regulatory profiles (such as EpiMap) across over 33 tissue/cell types; (iv) compiles many genome-scale base-wise prediction scores for regulatory/pathogenic variant classification beyond protein-coding region; (v) enables fast retrieval and direct comparison of functional evidence among linked variants using highly interactive web panel in addition to plain table; (vi) introduces many visualization functions for more efficient identification and interpretation of functional variants in single web page. VannoPortal is freely available at http://mulinlab.org/vportal.

Determinants of health poverty vulnerability in rural areas of Western China in the post-poverty relief era: an analysis based on the Anderson behavioral model.

BACKGROUND: Although China has eliminated absolute poverty, the effects of sickness still pose a threat to the prospect of returning to poverty in western rural areas. However, poverty governance extends beyond solving absolute poverty, and should enhance the family's ability to resist risks, proactively identify the existence of risks, and facilitate preventive measures to reduce the probability of falling into poverty again. This study aimed to assess the health poverty vulnerability of rural households in western China and decompose its determinants. METHODS: Based on survey data from 2022, the three-stage feasible generalized least squares method was used to calculate the health poverty vulnerability index. Then, Anderson's health behavior theory model was extended to analyse various influencing factors using binary logistic regression, and the contribution of each influencing factor was decomposed using the Shapley index. Finally, Tobit regression and the censored least absolute deviations estimation (clad) method were used to test the model's robustness. RESULTS: A total of 5455 families in the rural Ningxia region of western China were included in the study. The health poverty vulnerability index of the sample population in 2022 was 0.3000 ± 0.2223, and families with vulnerability ≥0.5 accounted for 16.9% of the sample population. From the Anderson behavioral model, the three models including propensity, enabling, and demand factors had the best fit, and the AIC and BIC values were the smallest. The Shapley decomposition showed that the dimensions of the propensity factor, number of residents, age and educational level of the household head, and dependency ratio were the most important factors influencing vulnerability to health poverty. Tobit regression and the clad method proved the reliability of the constructed model through a robustness test. CONCLUSION: Rural areas still face the risk of becoming poor or falling into poverty owing to residents' health problems. Health poverty alleviation should gradually change from a focus on treatment to prevention, and formulate a set of accurate and efficient intervention policies from a forward-looking perspective to consolidate the results of health poverty alleviation and prevent widescale poverty return.

Long-term fasting induced basal thermogenesis flexibility in female Japanese quails.

Male Japanese quails (Coturnix japonica) have been found to exhibit a three-phase metabolic change when subjected to prolonged fasting, during which basal thermogenesis is significantly reduced. A study had shown that there is a significant difference in the body temperature between male and female Japanese quails. However, whether female Japanese quails also show the same characteristic three-phase metabolic change during prolonged fasting and the underlying thermogenesis mechanisms associated with such changes are still unclear. In this study, female Japanese quails were subjected to prolonged starvation, and the body mass, basal metabolic rate (BMR), body temperature, mass of tissues and organs, body fat content, the state-4 respiration (S4R) and cytochrome c oxidase (CCO) activity in the muscle and liver of these birds were measured to determine the status of metabolic changes triggered by the starvation. In addition, the levels of glucose, triglyceride (TG) and uric acid, and thyroid hormones (T3 and T4) in the serum and the mRNA levels of myostatin (MSTN) and avian uncoupling protein (av-UCP) in the muscle were also measured. The results revealed the existence of a three-phase stage similar to that found in male Japanese quails undergoing prolonged starvation. Fasting resulted in significantly lower body mass, BMR, body temperature, tissues masses and most organs masses, as well as S4R and CCO activity in the muscle and liver. The mRNA level of av-UCP decreased during fasting, while that of MSTN increased but only during Phase I and II and decreased significantly during Phase III. Fasting also significantly lowered the T3 level and the ratio of T3/T4 in the serum. These results indicated that female Japanese quails showed an adaptive response in basal thermogenesis at multiple hierarchical levels, from organismal to biochemical, enzyme and cellular level, gene and endocrine levels and this integrated adjustment could be a part of the adaptation used by female quails to survive long-term fasting.

Copper-Catalyzed Regiodivergent Asymmetric Difunctionalization of Terminal Alkynes.

We herein describe the first example of ligand-controlled, copper-catalyzed regiodivergent asymmetric difunctionalization of terminal alkynes through a cascade hydroboration and hydroallylation process. The catalytic system, consisting of (R)-DTBM-Segphos and CuBr, could efficiently achieve asymmetric 1,1-difunctionalization of aryl terminal alkynes, while ligand switching to (S,S)-Ph-BPE could result in asymmetric 1,2-difunctionalization exclusively. In addition, alkyl substituted terminal alkynes, especially industrially relevant acetylene and propyne, were also valid feedstocks for asymmetric 1,1-difunctionalization. This protocol is characterized by good functional group tolerance, a broad scope of substrates (> 150 examples), and mild reaction conditions. We also showcase the value of this method in the late-stage functionalization of complicated bioactive molecules and simplifying the synthetic routes toward the key intermediacy of natural product (bruguierol A). Mechanistic studies combined with DFT calculations provide insight into the mechanism and origins of this ligand-controlled regio- and stereoselectivity.

Cortical Motion Perception Emerges from Dimensionality Reduction with Evolved Spike-Timing-Dependent Plasticity Rules.

The nervous system is under tight energy constraints and must represent information efficiently. This is particularly relevant in the dorsal part of the medial superior temporal area (MSTd) in primates where neurons encode complex motion patterns to support a variety of behaviors. A sparse decomposition model based on a dimensionality reduction principle known as non-negative matrix factorization (NMF) was previously shown to account for a wide range of monkey MSTd visual response properties. This model resulted in sparse, parts-based representations that could be regarded as basis flow fields, a linear superposition of which accurately reconstructed the input stimuli. This model provided evidence that the seemingly complex response properties of MSTd may be a by-product of MSTd neurons performing dimensionality reduction on their input. However, an open question is how a neural circuit could carry out this function. In the current study, we propose a spiking neural network (SNN) model of MSTd based on evolved spike-timing-dependent plasticity and homeostatic synaptic scaling (STDP-H) learning rules. We demonstrate that the SNN model learns compressed and efficient representations of the input patterns similar to the patterns that emerge from NMF, resulting in MSTd-like receptive fields observed in monkeys. This SNN model suggests that STDP-H observed in the nervous system may be performing a similar function as NMF with sparsity constraints, which provides a test bed for mechanistic theories of how MSTd may efficiently encode complex patterns of visual motion to support robust self-motion perception.SIGNIFICANCE STATEMENT The brain may use dimensionality reduction and sparse coding to efficiently represent stimuli under metabolic constraints. Neurons in monkey area MSTd respond to complex optic flow patterns resulting from self-motion. We developed a spiking neural network model that showed MSTd-like response properties can emerge from evolving spike-timing-dependent plasticity with STDP-H parameters of the connections between then middle temporal area and MSTd. Simulated MSTd neurons formed a sparse, reduced population code capable of encoding perceptual variables important for self-motion perception. This model demonstrates that complex neuronal responses observed in MSTd may emerge from efficient coding and suggests that neurobiological plasticity, like STDP-H, may contribute to reducing the dimensions of input stimuli and allowing spiking neurons to learn sparse representations.

Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additionally, the genetic interaction coefficient (GIC) is proposed to measure the correlation of two signature genes quantitatively and explain their joint influence on OCa-CR. Gene pair that possesses high GIC is identified as signature pair. A total of 24 signature gene pairs are selected that include 10 individual signature genes and the influence of signature gene pairs on OCa-CR is explored. Finally, a signature gene pair-based prediction of OCa-CR is identified. The area under curve (AUC) is a widely used performance measure for machine learning prediction. The AUC of signature gene pair reaches 0.9658, whereas the AUC of individual signature gene-based prediction is 0.6823 only. The identified signature gene pairs not only build an efficient GI network of OCa-CR but also provide an interesting way for OCa-CR prediction. This improvement shows that our proposed method is a useful tool to investigate GI related to OCa-CR.

Phosphorylation status of CPK28 affects its ubiquitination and protein stability.

Plant innate immunity is tightly regulated. The Arabidopsis thaliana CALCIUM-DEPENDENT PROTEIN KINASE28 (CPK28) functions as a negative immune regulator. We recently demonstrate that CPK28 undergoes ubiquitination that is mediated by two ubiquitin ligases, ARABIDOPSIS TÓXICOS EN LEVADURA31 (ATL31) and ATL6, which results in its proteasomal degradation. CPK28 undergoes both intermolecular autophosphorylation and BIK1-mediated phosphorylation. However, whether the phosphorylation status of CPK28 dictates its ubiquitination and degradation is unknown yet. We used immune response analysis, transient degradation system, ubiquitination assays, co-immunoprecipitation, and other biochemical and genetic approaches to investigate the effect of the phosphorylation status of CPK28 on its degradation mediated by ATL31/6. We found the mutation of Ser318 (a site of both intermolecular autophosphorylation and BIK1-mediated phosphorylation) or a BIK1 phosphorylation site on CPK28 leads to its compromised association with ATL31 and reduced ubiquitination by ATL31. Moreover, we confirm the previous findings that two CPK28s can interact with each other, which likely promotes the intermolecular autophosphorylation. We also show that the phosphorylation status of CPK28 in turn affects its intermolecular association. We demonstrate that the phosphorylation status of CPK28 affects its degradation mediated by ATL31. Our findings reveal a link between phosphorylation of CPK28 and its ubiquitination and degradation.