Machine learning-based identification of biomarkers and drugs in immunologically cold and hot pancreatic adenocarcinomas.

BACKGROUND: Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor milieu, which is associated with resistance to immune checkpoint blockade therapy; however, the underlying mechanisms are incompletely understood. Here, we aimed to improve our understanding of the molecular mechanisms occurring in the tumor microenvironment and to identify biomarkers, therapeutic targets, and potential drugs to improve PAAD treatment. METHODS: Patients were categorized according to immunologically hot or cold PAAD subtypes with distinct disease outcomes. Cox regression and weighted correlation network analysis were performed to construct a novel gene signature, referred to as 'Downregulated in hot tumors, Prognostic, and Immune-Related Genes' (DPIRGs), which was used to develop prognostic models for PAAD via machine learning (ML). The role of DPIRGs in PAAD was comprehensively analyzed, and biomarker genes able to distinguish PAAD immune subtypes and predict prognosis were identified by ML. The expression of biomarkers was verified using public single-cell transcriptomic and proteomic resources. Drug candidates for turning cold tumors hot and corresponding target proteins were identified via molecular docking studies. RESULTS: Using the DPIRG signature as input data, a combination of survival random forest and partial least squares regression Cox was selected from 137 ML combinations to construct an optimized PAAD prognostic model. The effects and molecular mechanisms of DPIRGs were investigated by analysis of genetic/epigenetic alterations, immune infiltration, pathway enrichment, and miRNA regulation. Biomarkers and potential therapeutic targets, including PLEC, TRPV1, and ITGB4, among others, were identified, and the cell type-specific expression of the biomarkers was validated. Drug candidates, including thalidomide, SB-431542, and bleomycin A2, were identified based on their ability to modulate DPIRG expression favorably. CONCLUSIONS: By combining multiple ML algorithms, we developed a novel prognostic model with excellent performance in PAAD cohorts. ML also proved to be powerful for identifying biomarkers and potential targets for improved PAAD patient stratification and immunotherapy.

Defects of microtubule cytoskeletal organization in NOA human testes.

The importance of actin and microtubule (MT) cytoskeletons in testis function in rodents is known to some extent, but its role in the etiology of azoospermia in humans remains unexplored. Here, we examined if MT cytoskeleton was defective in NOA (non-obstructive azoospermia) testes versus normal human testes based on histopathological, immunofluorescence (IF), and scRNA-Seq transcriptome profiling. Testis biopsy samples from n = 6 normal men versus n = 3 Sertoli cell only (SCO) and n = 3 MA (meiotic arrest) of NOA patients were used for histopathological analysis. IF analysis was also used to examine MT organization across the seminiferous epithelium, investigating the likely involvement of microtubule-associated proteins (MAPs). scRNA-Seq transcriptome profiling datasets from testes of 3 SCO patients versus 3 normal men in public domain in Gene Expression Omnibus (GEO) Sample (GSM) with identifiers were analyzed to examine relevant genes that regulate MT dynamics. NOA testes of MA and SCO patients displayed notable defects in MT organization across the epithelium with extensive truncation, mis-alignments and appeared as collapsed structures near the base of the tubules. These changes are in contrast to MTs in testes of normal men. scRNA-Seq analyses revealed considerable loss of spermatogenesis capacity in SCO testes of NOA patients versus normal men. An array of genes that support MT dynamics displayed considerable changes in expression and in spatial distribution. In summary, defects in MT cytoskeleton were noted in testes of NOA (SCO) patients, possibly mediated by defective spatial expression and/or distribution of MAPs. These changes, in turn, may impede spermatogenesis in SCO testes of NOA patients.

Peptides from the croceine croaker (Larimichthys crocea) swim bladder attenuate busulfan-induced oligoasthenospermia in mice.

CONTEXT: The swim bladder of the croceine croaker is believed to have a therapeutic effect on various diseases. However, there is no research about its effect on mammalian spermatogenesis. OBJECTIVE: We investigated the swim bladder peptides (SBPs) effect on busulfan-induced oligoasthenospermia in mice. MATERIALS AND METHODS: We first extracted SBP from protein hydrolysate of the croceine croaker swim bladder, and then five groups of ICR male mice were randomly assigned: control, control + SBP 60 mg/kg, busulfan, busulfan + SBP 30 mg/kg and busulfan + SBP 60 mg/kg. Mice received bilateral intratesticular injections of busulfan to establish oligoasthenospermia model. After treatment with SBP for 4 weeks, testis and epididymis were collected from all mice for further analysis. RESULTS: After treatment with SBP 30-60 mg/kg for 4 weeks, epididymal sperm concentration and motility increased by 3.9-9.6- and 1.9-2.4-fold than those of oligoasthenospermia mice induced by busulfan. Meanwhile, histology showed that spermatogenic cells decreased, leading to increased lumen diameters and vacuolization in the busulfan group. These features were reversed by SBP treatment. RNA-sequencing analysis revealed that, compared with the busulfan group, Lin28b and Igf2bp1 expression related to germ cell proliferation, increased with a >1.5-fold change after SBP treatment. Additionally, PGK2 and Cfap69 mRNAs associated with sperm motility, also increased with a >1.5-fold change. Furthermore, these findings were validated by quantitative real-time PCR and Western blotting. DISCUSSION AND CONCLUSIONS: This is the first reported evidence for the therapeutic effect of SBP on oligoasthenospermia. SBP may be a promising drug for oligoasthenospermia in humans.

A dissection model for mapping complex traits.

Many quantitative traits are composites of other traits that contribute differentially to genetic variation. Quantitative trait locus (QTL) mapping of these composite traits can benefit by incorporating the mechanistic process of how their formation is mediated by the underlying components. We propose a dissection model by which to map these interconnected components traits under a joint likelihood setting. The model can test how a composite trait is determined by pleiotropic QTLs for its component traits or jointly by different sets of QTLs each responsible for a different component. The model can visualize the pattern of time-varying genetic effects for individual components and their impacts on composite traits. The dissection model was used to map two composite traits, stemwood volume growth decomposed into its stem height, stem diameter and stem form components for Euramerican poplar adult trees, and total lateral root length constituted by its average lateral root length and lateral root number components for Euphrates poplar seedlings. We found the pattern of how QTLs for different components contribute to phenotypic variation in composite traits. The detailed understanding of the genetic machineries of composite traits will not only help in the design of molecular breeding in plants and animals, but also shed light on the evolutionary processes of quantitative traits under natural selection.

HpQTL: a geometric morphometric platform to compute the genetic architecture of heterophylly.

Heterophylly, i.e. morphological changes in leaves along the axis of an individual plant, is regarded as a strategy used by plants to cope with environmental change. However, little is known of the extent to which heterophylly is controlled by genes and how each underlying gene exerts its effect on heterophyllous variation. We described a geometric morphometric model that can quantify heterophylly in plants and further constructed an R-based computing platform by integrating this model into a genetic mapping and association setting. The platform, named HpQTL, allows specific quantitative trait loci mediating heterophyllous variation to be mapped throughout the genome. The statistical properties of HpQTL were examined and validated via computer simulation. Its biological relevance was demonstrated by results from a real data analysis of heterophylly in a wood plant, mei (Prunus mume). HpQTL provides a powerful tool to analyze heterophylly and its underlying genetic architecture in a quantitative manner. It also contributes a new approach for genome-wide association studies aimed to dissect the programmed regulation of plant development and evolution.

The genetic architecture of heterochrony as a quantitative trait: lessons from a computational model.

Heterochrony is known as a developmental change in the timing or rate of ontogenetic events across phylogenetic lineages. It is a key concept synthesizing development into ecology and evolution to explore the mechanisms of how developmental processes impact on phenotypic novelties. A number of molecular experiments using contrasting organisms in developmental timing have identified specific genes involved in heterochronic variation. Beyond these classic approaches that can only identify single genes or pathways, quantitative models derived from current next-generation sequencing data serve as a more powerful tool to precisely capture heterochronic variation and systematically map a complete set of genes that contribute to heterochronic processes. In this opinion note, we discuss a computational framework of genetic mapping that can characterize heterochronic quantitative trait loci that determine the pattern and process of development. We propose a unifying model that charts the genetic architecture of heterochrony that perceives and responds to environmental perturbations and evolves over geologic time. The new model may potentially enhance our understanding of the adaptive value of heterochrony and its evolutionary origins, providing a useful context for designing new organisms that can best use future resources.

Two resveratrol analogs, pinosylvin and 4,4'-dihydroxystilbene, improve oligoasthenospermia in a mouse model by attenuating oxidative stress via the Nrf2-ARE pathway.

Two synthesized resveratrol analogs from our laboratory, namely pinosylvin (3,5-dihydroxy-trans-stilbene, PIN) and 4,4'-dihydroxystilbene (DHS), have been carefully evaluated for treatment of oligoasthenospermia. Recent studies have demonstrated that PIN and DHS improved sperm quality in the mouse. However, the mechanism of action of PIN and DHS on oligoasthenospermia remains unknown. Herein, we investigated the mechanistic basis for improvements in sperm parameters by PIN and DHS in a mouse model of oligoasthenospermia induced by treatment with busulfan (BUS) at 6 mg/kg b.w.. Two weeks following busulfan treatment, mice were administered different concentrations of PIN or DHS daily for 2 consecutive weeks. Thereafter, epididymal sperm concentration and motility were determined, and histopathology of the testes was performed. Serum hormone levels including testosterone (T), luteinizing hormone (LH), and follicle stimulating hormone (FSH) were measured using corresponding specific enzyme-linked immunosorbent assay (ELISA) kits. Testicular mRNA expression profiles were determined by RNA sequencing analysis. These findings were validated by quantitative real-time PCR, western blotting and ELISA. Both PIN and DHS improved the epididymal sperm concentration and motility, enhanced testosterone levels, and promoted testicular morphological recovery following BUS treatment. PIN treatment was found to significantly reduce oxidative stress via the nuclear factor erythroid 2-related factor 2 (Nrf2)-antioxidant response element (ARE)-dependent antioxidant, glutathione peroxidase 3. DHS treatment significantly reduced oxidative stress via the Nrf2-ARE-dependent antioxidants glutathione S-transferase theta 2 and glutathione S-transferase omega 2. In summary, PIN and DHS ameliorated oligoasthenospermia in this mouse model by attenuating oxidative stress via the Nrf2-ARE pathway.

Two-stage identification of SNP effects on dynamic poplar growth.

This project proposes an approach to identify significant single nucleotide polymorphism (SNP) effects, both additive and dominant, on the dynamic growth of poplar in diameter and height. The annual changes in yearly phenotypes based on regular observation periods are considered to represent multiple responses. In total 156,362 candidate SNPs are studied, and the phenotypes of 64 poplar trees are recorded. To address this ultrahigh dimensionality issue, this paper adopts a two-stage approach. First, the conventional genome-wide association studies (GWAS) and the distance correlation sure independence screening (DC-SIS) methods (Li et al., 2012) were combined to reduce the model dimensions at the sample size; second, a grouped penalized regression was applied to further refine the model and choose the final sparse SNPs. The multiple response issue was also carefully addressed. The SNP effects on the dynamic diameter and height growth patterns of poplar were systematically analyzed. In addition, a series of intensive simulation studies was performed to validate the proposed approach.

An ecophysiologically based mapping model identifies a major pleiotropic QTL for leaf growth trajectories of Phaseolus vulgaris.

Crop modeling, a widely used tool to predict plant growth and development in heterogeneous environments, has been increasingly integrated with genetic information to improve its predictability. This integration can also shed light on the mechanistic path that connects the genotype to a particular phenotype under specific environments. We implemented a bivariate statistical procedure to map and identify quantitative trait loci (QTLs) that can predict the form of plant growth by estimating cultivar-specific growth parameters and incorporating these parameters into a mapping framework. The procedure enables the characterization of how QTLs act differently in response to developmental and environmental cues. We used this procedure to map growth parameters of leaf area and mass in a mapping population of the common bean (Phaseolus vulgaris L.). Different sets of QTLs are responsible for various aspects of growth, including the initiation time of growth, growth rate, inflection point and asymptotic growth. A major QTL of a large effect was identified to pleiotropically affect trait expression in distinct environments and different traits expressed on the same organism. The integration of crop models and QTL mapping through our statistical procedure provides a powerful means of building a more precise predictive model of genotype-phenotype relationships for crops.

Evo-Devo-EpiR: a genome-wide search platform for epistatic control on the evolution of development.

Evo-devo is a theory proposed to study how phenotypes evolve by comparing the developmental processes of different organisms or the same organism experiencing changing environments. It has been recognized that nonallelic interactions at different genes or quantitative trait loci, known as epistasis, may play a pivotal role in the evolution of development, but it has proven difficult to quantify and elucidate this role into a coherent picture. We implement a high-dimensional genome-wide association study model into the evo-devo paradigm and pack it into the R-based Evo-Devo-EpiR, aimed at facilitating the genome-wide landscaping of epistasis for the diversification of phenotypic development. By analyzing a high-throughput assay of DNA markers and their pairs simultaneously, Evo-Devo-EpiR is equipped with a capacity to systematically characterize various epistatic interactions that impact on the pattern and timing of development and its evolution. Enabling a global search for all possible genetic interactions for developmental processes throughout the whole genome, Evo-Devo-EpiR provides a computational tool to illustrate a precise genotype-phenotype map at interface between epistasis, development and evolution.

AlloMap6: an R package for genetic linkage analysis in allohexaploids.

Allopolyploids are a group of polyploids with more than two sets of chromosomes derived from different species. Previous linkage analysis of allopolyploids is based on the assumption that different chromosomes pair randomly during meiosis. A more sophisticated model to relax this assumption has been developed for allotetraploids by incorporating the preferential pairing behavior of homologous over homoeologous chromosomes. Here, we show that the basic principle of this model can be extended to perform linkage analysis of higher-ploidy allohexaploids, where multiple preferential pairing factors are used to characterize chromosomal-pairing meiotic features between different constituent species. We implemented the extended model into an R package, called AlloMap6, allowing the recombination fractions and preferential pairing factors to be estimated simultaneously. Allomap6 has two major functionalities, computer simulation and real-data analysis. By analyzing a real data from a full-sib family of allohexaploid persimmon, we tested and validated the usefulness and utility of this package. AlloMap6 lays a foundation for allohexaploid genetic mapping and provides a new horizon to explore the chromosomal kinship of allohexaploids.

A high-dimensional linkage analysis model for characterizing crossover interference.

Linkage analysis has played an important role in understanding genome structure and evolution. However, two-point linkage analysis widely used for genetic map construction can rarely chart a detailed picture of genome organization because it fails to identify the dependence of crossovers distributed along the length of a chromosome, a phenomenon known as crossover interference. Multi-point analysis, proven to be more advantageous in gene ordering and genetic distance estimation for dominant markers than two-point analysis, is equipped with a capacity to discern and quantify crossover interference. Here, we review a statistical model for four-point analysis, which, beyond three-point analysis, can characterize crossover interference that takes place not only between two adjacent chromosomal intervals, but also over multiple successive intervals. This procedure provides an analytical tool to elucidate the detailed landscape of crossover interference over the genome and further infer the evolution of genome structure and organization.

A Regulatory Network for miR156-SPL Module in Arabidopsis thaliana.

Vegetative phase changes in plants describes the transition between juvenile and adult phases of vegetative growth before flowering. It is one of the most fundamental mechanisms for plants to sense developmental signals, presenting a complex process involving many still-unknown determinants. Several studies in annual and perennial plants have identified the conservative roles of miR156 and its targets, SBP/SPL genes, in guiding the switch of plant growth from juvenile to adult phases. Here, we review recent progress in understanding the regulation of miR156 expression and how miR156-SPLs mediated plant age affect other processes in Arabidopsis. Powerful high-throughput sequencing techniques have provided rich data to systematically study the regulatory mechanisms of miR156 regulation network. From this data, we draw an expanded miR156-regulated network that links plant developmental transition and other fundamental biological processes, gaining novel and broad insight into the molecular mechanisms of plant-age-related processes in Arabidopsis.

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning.

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs. Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML. Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.

A pleiotropic-epistatic entangelement model of drug response.

Because drug response is multifactorial, graph models are uniquely powerful for comprehending its genetic architecture. We deconstruct drug response into many different and interdependent sub-traits, with each sub-trait controlled by multiple genes that act and interact in a complicated manner. The outcome of drug response is the consequence of multileveled intertwined interactions between pleiotropic effects and epistatic effects. Here, we propose a general statistical physics framework to chart the 3D geometric network that codes how epistasis pleiotropically influences a complete set of sub-traits to shape body-drug interactions. This model can dissect the topological architecture of epistatically induced pleiotropic networks (EiPN) and pleiotropically influenced epistatic networks (PiEN). We analyze and interpret the practical implications of the pleiotropic-epistatic entanglement model for pharmacogenomic studies.

Knockdown of disheveled-associated activator of morphogenesis 2 disrupts cytoskeletal organization and phagocytosis in rat Sertoli cells.

Disheveled-associated activator of morphogenesis 2 (DAAM2) regulates actin polymerization and cell motility. In this study, we investigated the role of DAAM2 in the cytoskeleton and phagocytosis of rat Sertoli cells in vitro and in vivo through siRNA transfection and intratesticular injection. We found that knockdown of DAAM2 significantly attenuated cytoskeletal and tight junction marker expression and reduced the integrity of the Sertoli cell monolayer. In rats, loss of DAAM2 induced disarrangement and deformation of sperms and promoted accumulation of apoptotic sperms in the testis, accompanied by morphological abnormalities in the blood-testis barrier. DAAM2 silencing also reduced the ability of Sertoli cells to engulf apoptotic spermatogenic cells and green fluorescence-labeled beads. RNA sequencing and bioinformatics analysis revealed that phagocytosis and cytoskeleton-related genes and pathways were significantly associated with DAAM2. Our study suggests that DAAM2 may be involved in spermatogenesis possibly by regulating cytoskeleton organization and phagocytosis of Sertoli cells.

Predicting Prognosis and Immunotherapy Response in Multiple Cancers Based on the Association of PANoptosis-Related Genes with Tumor Heterogeneity.

PANoptosis is a newly recognized inflammatory pathway for programmed cell death (PCD). It participates in regulating the internal environment, homeostasis, and disease process in various complex ways and plays a crucial role in tumor development, but its mechanism of action is still unclear. In this study, we comprehensively analyzed the expression of 14 PANoptosis-related genes (PANRGs) in 28 types of tumors. Most PANRGs are upregulated in tumors, including Z-DNA binding protein 1 (ZBP1), nucleotide-binding oligomerization domain (NOD)-like receptor pyrin domain-containing 3 (NLRP3), caspase (CASP) 1, CASP6, CASP8, PYCARD, FADD, MAP3K7, RNF31, and RBCK1. PANRGs are highly expressed in GBM, LGG, and PAAD, while their levels in ACC are much lower than those in normal tissues. We found that both the CNV and SNV gene sets in BLCA are closely related to survival performance. Subsequently, we conducted clustering and LASSO analysis on each tumor and found that the inhibitory and the stimulating immune checkpoints positively correlate with ZBP1, NLRP3, CASP1, CASP8, and TNFAIP3. The immune infiltration results indicated that KIRC is associated with most infiltrating immune cells. According to the six tumor dryness indicators, PANRGs in LGG show the strongest tumor dryness but have a negative correlation with RNAss. In KIRC, LIHC, and TGCT, most PANRGs play an important role in tumor heterogeneity. Additionally, we analyzed the linear relationship between PANRGs and miRNA and found that MAP3K7 correlates to many miRNAs in most cancers. Finally, we predicted the possible drugs for targeted therapy of the cancers. These data greatly enhance our understanding of the components of cancer and may lead to the discovery of new biomarkers for predicting immunotherapy response and improving the prognosis of cancer patients.

Genetic Architecture of Heterophylly: Single and Multi-Leaf Genome-Wide Association Mapping in Populus euphratica.

Heterophylly is an adaptive strategy used by some plants in response to environmental changes. Due to the lack of representative plants with typical heteromorphic leaves, little is known about the genetic architecture of heterophylly in plants and the genes underlying its control. Here, we investigated the genetic characteristics underlying changes in leaf shape based on the model species, Populus euphratica, which exhibits typical heterophylly. A set of 401,571 single-nucleotide polymorphisms (SNPs) derived from whole-genome sequencing of 860 genotypes were associated with nine leaf traits, which were related to descriptive and shape data using single- and multi-leaf genome-wide association studies (GWAS). Multi-leaf GWAS allows for a more comprehensive understanding of the genetic architecture of heterophylly by considering multiple leaves simultaneously. The single-leaf GWAS detected 140 significant SNPs, whereas the multi-leaf GWAS detected 200 SNP-trait associations. Markers were found across 19 chromosomes, and 21 unique genes were implicated in traits and serve as potential targets for selection. Our results provide novel insights into the genomic architecture of heterophylly, and provide candidate genes for breeding or engineering P. euphratica. Our observations also improve understanding of the intrinsic mechanisms of plant growth, evolution, and adaptation in response to climate change.

A graph model of combination therapies.

The simultaneous use of multiple medications causes drug-drug interactions (DDI) that impact therapeutic efficacy. Here, we argue that graph theory, in conjunction with game theory and ecosystem theory, can address this issue. We treat the coexistence of multiple drugs as a system in which DDI is modeled by game theory. We develop an ordinary differential equation model to characterize how the concentration of a drug changes as a result of its independent capacity and the dependent influence of other drugs through the metabolic response of the host. We coalesce all drugs into personalized and context-specific networks, which can reveal key DDI determinants of therapeutical efficacy. Our model can quantify drug synergy and antagonism and test the translational success of combination therapies to the clinic.