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The accurate quantification of microbial growth dynamics for species without complete genome sequences is biologically important, but computationally challenging in metagenomics. Here we present dynamic estimator of microbial communities (DEMIC; https://sourceforge.net/projects/demic/), a multi-sample algorithm based on contigs and coverage values, to infer the relative distances of contigs from the replication origin and to accurately compare bacterial growth rates between samples. We demonstrate robust performances of DEMIC for various sample sizes and assembly qualities using multiple synthetic and real datasets.

High-dimensional compositional data arise naturally in many applications such as metagenomic data analysis. The observed data lie in a high-dimensional simplex, and conventional statistical methods often fail to produce sensible results due to the unit-sum constraint. In this article, we address the problem of covariance estimation for high-dimensional compositional data and introduce a composition-adjusted thresholding (COAT) method under the assumption that the basis covariance matrix is sparse. Our method is based on a decomposition relating the compositional covariance to the basis covariance, which is approximately identifiable as the dimensionality tends to infinity. The resulting procedure can be viewed as thresholding the sample centered log-ratio covariance matrix and hence is scalable for large covariance matrices. We rigorously characterize the identifiability of the covariance parameters, derive rates of convergence under the spectral norm, and provide theoretical guarantees on support recovery. Simulation studies demonstrate that the COAT estimator outperforms some existing optimization-based estimators. We apply the proposed method to the analysis of a microbiome dataset to understand the dependence structure among bacterial taxa in the human gut.

Epigenetic changes may fill a critical gap in our understanding of kidney disease development, as they not only reflect metabolic changes but are also preserved and transmitted during cell division. We conducted a genome-wide cytosine methylation analysis of 399 human kidney samples, along with single-nuclear open chromatin analysis on over 60,000 cells from 14 subjects, including controls, and diabetes and hypertension attributed chronic kidney disease (CKD) patients. We identified and validated differentially methylated positions associated with disease states, and discovered that nearly 30% of these alterations were influenced by underlying genetic variations, including variants known to be associated with kidney disease in genome-wide association studies. We also identified regions showing both methylation and open chromatin changes. These changes in methylation and open chromatin significantly associated gene expression changes, most notably those playing role in metabolism and expressed in proximal tubules. Our study further demonstrated that methylation risk scores (MRS) can improve disease state annotation and prediction of kidney disease development. Collectively, our results suggest a causal relationship between epigenetic changes and kidney disease pathogenesis, thereby providing potential pathways for the development of novel risk stratification methods. Kidney disease affects more than 850 million people worldwide, but the development of drug therapeutics has been limited by poor mechanistic understanding. Here, the authors perform epigenome-wide analyses to find methylation changes, and disease mechanisms associated with kidney disease.

One important problem in microbiome analysis is to identify the bacterial taxa that are associated with a response, where the microbiome data are summarized as the composition of the bacterial taxa at different taxonomic levels. This paper considers regression analysis with such compositional data as covariates. In order to satisfy the subcompositional coherence of the results, linear models with a set of linear constraints on the regression coefficients are introduced. Such models allow regression analysis for subcompositions and include the log-contrast model for compositional covariates as a special case. A penalized estimation procedure for estimating the regression coefficients and for selecting variables under the linear constraints is developed. A method is also proposed to obtain debiased estimates of the regression coefficients that are asymptotically unbiased and have a joint asymptotic multivariate normal distribution. This provides valid confidence intervals of the regression coefficients and can be used to obtain the p-values. Simulation results show the validity of the confidence intervals and smaller variances of the debiased estimates when the linear constraints are imposed. The proposed methods are applied to a gut microbiome data set and identify four bacterial genera that are associated with the body mass index after adjusting for the total fat and caloric intakes.

Diet influences health as a source of nutrients and toxins, and by shaping the composition of resident microbial populations. Previous studies have begun to map out associations between diet and the bacteria and viruses of the human gut microbiome. Here we investigate associations of diet with fungal and archaeal populations, taking advantage of samples from 98 well-characterized individuals. Diet was quantified using inventories scoring both long-term and recent diet, and archaea and fungi were characterized by deep sequencing of marker genes in DNA purified from stool. For fungi, we found 66 genera, with generally mutually exclusive presence of either the phyla Ascomycota or Basiodiomycota. For archaea, Methanobrevibacter was the most prevalent genus, present in 30% of samples. Several other archaeal genera were detected in lower abundance and frequency. Myriad associations were detected for fungi and archaea with diet, with each other, and with bacterial lineages. Methanobrevibacter and Candida were positively associated with diets high in carbohydrates, but negatively with diets high in amino acids, protein, and fatty acids. A previous study emphasized that bacterial population structure was associated primarily with long-term diet, but high Candida abundance was most strongly associated with the recent consumption of carbohydrates. Methobrevibacter abundance was associated with both long term and recent consumption of carbohydrates. These results confirm earlier targeted studies and provide a host of new associations to consider in modeling the effects of diet on the gut microbiome and human health.

Background Pancreatic cancer, one of the top two most fatal cancers, is characterized by a desmoplastic reaction that creates a dense microenvironment, promoting hypoxia and inducing the epithelial-to-mesenchymal transition (EMT) to facilitate invasion and metastasis. Recent evidence indicates that the long noncoding RNA NORAD may be a potential oncogenic gene and that this lncRNA is significantly upregulated during hypoxia. However, the overall biological role and clinical significance of NORAD remains largely unknown. Methods NORAD expression was measured in 33 paired cancerous and noncancerous tissue samples by real-time PCR. The effects of NORAD on pancreatic cancer cells were studied by overexpression and knockdown in vitro. Insights into the mechanism of competitive endogenous RNAs (ceRNAs) were gained from bioinformatics analyses and luciferase assays. In vivo, metastatic potential was identified using an orthotopic model of PDAC and quantified using bioluminescent signals. Alterations in RhoA expression and EMT levels were identified and verified by immunohistochemistry and Western blotting. Results NORAD is highly expressed in pancreatic cancer tissues and upregulated in hypoxic conditions. NORAD upregulation is correlated with shorter overall survival in pancreatic cancer patients. Furthermore, NORAD overexpression promoted the migration and invasion of pancreatic carcinoma cells, while NORAD depletion inhibited EMT and metastasis in vitro and in vivo. In particular, NORAD may function as a ceRNA to regulate the expression of the small GTP binding protein RhoA through competition for hsa-miR-125a-3p, thereby promoting EMT. Conclusions Elevated expression of NORAD in pancreatic cancer tissues is linked to poor prognosis and may confer a malignant phenotype upon tumor cells. NORAD may function as a ceRNA to regulate the expression of the small GTP binding protein RhoA through competition for hsa-miR-125a-3p. This finding may contribute to a better understanding of the role played by lncRNAs in hypoxia-induced EMT and provide a potential novel diagnostic and therapeutic target for pancreatic cancer.

Background Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy. Methods Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments. Results The assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8 + T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy. Conclusions We unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy.

This study presents a Two-Layer Deep Deterministic Policy Gradient (TL-DDPG) energy management strategy for Hydrogen fuel cell hybrid train, that aims to solve the problem that traditional reinforcement learning strategies require high initial values and are difficult to optimize global variables. Augmenting the optimization capabilities of the inner layer, a frequency decoupling algorithm integrates into the outer layer, furnishing a fitting initial value for strategy optimization. This addition aims to bolster the stability of fuel cell output, thereby enhancing the overall efficiency of the hybrid power system. In comparison with the traditional reinforcement learning algorithm, the proposed approach demonstrates notable improvements: a reduction in hydrogen consumption per 100 km by 16.3 kg, a 9.7% increase in the output power stability of the fuel cell, and a 1.8% enhancement in its efficiency.

Changes in human microbiome are associated with many human diseases. Next generation sequencing technologies make it possible to quantify the microbial composition without the need for laboratory cultivation. One important problem of microbiome data analysis is to identify the environmental/biological covariates that are associated with different bacterial taxa. Taxa count data in microbiome studies are often over‐dispersed and include many zeros. To account for such an over‐dispersion, we propose to use an additive logistic normal multinomial regression model to associate the covariates to bacterial composition. The model can naturally account for sampling variabilities and zero observations and also allow for a flexible covariance structure among the bacterial taxa. In order to select the relevant covariates and to estimate the corresponding regression coefficients, we propose a group ℓ1 penalized likelihood estimation method for variable selection and estimation. We develop a Monte Carlo expectation‐maximization algorithm to implement the penalized likelihood estimation. Our simulation results show that the proposed method outperforms the group ℓ1 penalized multinomial logistic regression and the Dirichlet multinomial regression models in variable selection. We demonstrate the methods using a data set that links human gut microbiome to micro‐nutrients in order to identify the nutrients that are associated with the human gut microbiome enterotype.