Integrative study of lung cancer adeno-to-squamous transition in EGFR TKI resistance identifies RAPGEF3 as a therapeutic target.

Although adeno-to-squamous transition (AST) has been observed in association with resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) in clinic, its causality, molecular mechanism and overcoming strategies remain largely unclear. We here demonstrate that squamous transition occurs concomitantly with TKI resistance in PC9-derived xenograft tumors. Perturbation of squamous transition via DNp63 overexpression or knockdown leads to significant changes in TKI responses, indicative of a direct causal link between squamous transition and TKI resistance.

scVIC: deep generative modeling of heterogeneity for scRNA-seq data.

Motivation: Single-cell RNA sequencing (scRNA-seq) has become a valuable tool for studying cellular heterogeneity. However, the analysis of scRNA-seq data is challenging because of inherent noise and technical variability. Existing methods often struggle to simultaneously explore heterogeneity across cells, handle dropout events, and account for batch effects.

Predicting RNA polymerase II transcriptional elongation pausing and associated histone code.

RNA Polymerase II (Pol II) transcriptional elongation pausing is an integral part of the dynamic regulation of gene transcription in the genome of metazoans. It plays a pivotal role in many vital biological processes and disease progression. However, experimentally measuring genome-wide Pol II pausing is technically challenging and the precise governing mechanism underlying this process is not fully understood.

Correction to: Identification of TAZ as the essential molecular switch in orchestrating SCLC phenotypic transition and metastasis.

[This corrects the article DOI: 10.1093/nsr/nwab232.].

Moxibustion improves hypothalamus polarization in APP/PS1 mice: Evidence from spatial transcriptomics.

Aquaporin-4 (AQP4) is highly polarized to perivascular astrocytic endfeet. Loss of AQP4 polarization is associated with many diseases. In Alzheimer's disease (AD), AQP4 loses its normal location and thus reduces the clearance of amyloid-β plaques and tau protein.

Spatial transcriptomics shows moxibustion promotes hippocampus astrocyte and neuron interaction.

Aims: Alzheimer's disease (AD) is a common and irreversible neurodegenerative disease accompanied by extensive synaptic loss. Previous studies found that moxibustion had good therapeutic effects on AD. We here investigated whether moxibustion could alleviate the cognitive impairment of AD by promoting the "astrocyte-neuron" interaction and enhancing synaptic plasticity.

Identification of TAZ as the essential molecular switch in orchestrating SCLC phenotypic transition and metastasis.

Small-cell lung cancer (SCLC) is a recalcitrant cancer characterized by high metastasis. However, the exact cell type contributing to metastasis remains elusive. Using a mouse model, we identify the NCAMCD44 subpopulation as the SCLC metastasizing cell (SMC), which is progressively transitioned from the non-metastasizing NCAMCD44 cell (non-SMC).

hReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context.

Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC).

Complexity of the simplest species tree problem.

The multispecies coalescent model provides a natural framework for species tree estimation accounting for gene-tree conflicts. Although a number of species tree methods under the multispecies coalescent have been suggested and evaluated using simulation, their statistical properties remain poorly understood. Here, we use mathematical analysis aided by computer simulation to examine the identifiability, consistency, and efficiency of different species tree methods in the case of three species and three sequences under the molecular clock.

The Asymptotic Behavior of Bootstrap Support Values in Molecular Phylogenetics.

The phylogenetic bootstrap is the most commonly used method for assessing statistical confidence in estimated phylogenies by non-Bayesian methods such as maximum parsimony and maximum likelihood (ML). It is observed that bootstrap support tends to be high in large genomic data sets whether or not the inferred trees and clades are correct. Here, we study the asymptotic behavior of bootstrap support for the ML tree in large data sets when the competing phylogenetic trees are equally right or equally wrong.

Improved Ion Transport in Hydrogel-Based Nanofluidics for Osmotic Energy Conversion.

In nature, ultrafast signal transfer based on ion transport, which is the foundation of biological processes, commonly works in a hydrogel-water mixed mechanism. Inspired by organisms' hydrogel-based system, we introduce hydrogel into nanofluidics to prepare a hydrogel hybrid membrane. The introduction of a space charged hydrogel improves the ion selectivity evidently.

NeuralEE: A GPU-Accelerated Elastic Embedding Dimensionality Reduction Method for Visualizing Large-Scale scRNA-Seq Data.

The dramatic increase in amount and size of single-cell RNA sequencing data calls for more efficient and scalable dimensional reduction and visualization tools. Here, we design a GPU-accelerated method, NeuralEE, which aggregates the advantages of elastic embedding and neural network. We show that NeuralEE is both scalable and generalizable in dimensional reduction and visualization of large-scale scRNA-seq data.

Molecular Sparse Representation by a 3D Ellipsoid Radial Basis Function Neural Network via L1 Regularization.

The three-dimensional structures and shapes of biomolecules provide essential information about their interactions and functions. Unfortunately, the computational cost of biomolecular shape representation is an active challenge which increases rapidly as the number of atoms increase. Recent developments in sparse representation and deep learning have shown significant improvements in terms of time and space.

ERG orchestrates chromatin interactions to drive prostate cell fate reprogramming.

Although cancer is commonly perceived as a disease of dedifferentiation, the hallmark of early-stage prostate cancer is paradoxically the loss of more plastic basal cells and the abnormal proliferation of more differentiated secretory luminal cells. However, the mechanism of prostate cancer proluminal differentiation is largely unknown. Through integrating analysis of the transcription factors (TFs) from 806 human prostate cancers, we found that ERG was highly correlated with prostate cancer luminal subtyping.

Improved Ion Transport and High Energy Conversion through Hydrogel Membrane with 3D Interconnected Nanopores.

To mimic and use the functions of the ion transport system that are central to biological processes, bioinspired ion-selective membranes are developed and show great potential in a variety of fields. However, the practical applications of them are now limited due to low pore density, low conductivity, or scale-up difficulty. Herein, we demonstrate a 2-hydroxyethyl methacrylate phosphate (HEMAP) hydrogel membrane with 3D interconnected nanopores and space charged through simple photopolymerization.

Towards understanding residual and dilated dense neural networks via convolutional sparse coding.

Convolutional neural network (CNN) and its variants have led to many state-of-the-art results in various fields. However, a clear theoretical understanding of such networks is still lacking. Recently, a multilayer convolutional sparse coding (ML-CSC) model has been proposed and proved to equal such simply stacked networks (plain networks).

Unambiguous Phosphosite Localization through the Combination of Trypsin and LysargiNase Mirror Spectra in a Large-Scale Phosphoproteome Study.

Understanding of the kinase-guided signaling pathways requires the identification and analysis of phosphosites. Mass spectrometry (MS)-based phosphoproteomics is a rapid and highly sensitive approach for high-throughput identification of phosphosites. However, phosphosite determination from MS data with a single protease is more likely to be ambiguous, regardless of the strategy used for phosphopeptide detection.

Transfer posterior error probability estimation for peptide identification.

Background: In shotgun proteomics, database searching of tandem mass spectra results in a great number of peptide-spectrum matches (PSMs), many of which are false positives. Quality control of PSMs is a multiple hypothesis testing problem, and the false discovery rate (FDR) or the posterior error probability (PEP) is the commonly used statistical confidence measure. PEP, also called local FDR, can evaluate the confidence of individual PSMs and thus is more desirable than FDR, which evaluates the global confidence of a collection of PSMs.

Large-scale Identification of N-linked Intact Glycopeptides in Human Serum using HILIC Enrichment and Spectral Library Search.

Large-scale identification of -linked intact glycopeptides by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) in human serum is challenging because of the wide dynamic range of serum protein abundances, the lack of a complete serum N-glycan database and the existence of proteoforms. In this regard, a spectral library search method was presented for the identification of -linked intact glycopeptides from -linked glycoproteins in human serum with target-decoy and motif-specific false discovery rate (FDR) control. Serum proteins were firstly separated into low-abundance and high-abundance proteins by acetonitrile (ACN) precipitation.

Framework and algorithms for identifying honest blocks in blockchain.

Blockchain technology gains more and more attention in the past decades and has been applied in many areas. The main bottleneck for the development and application of blockchain is its limited scalability. Blockchain with directed acyclic graph structure (BlockDAG) is proposed in order to alleviate the scalability problem.

Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network.

Motivation: Interactions among cis-regulatory elements such as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epigenomic information, most of them neglect long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, HiChIP, a novel high-throughput experimental approach, has generated comprehensive data on high-resolution interactions between promoters and distal enhancers.

AP3: An Advanced Proteotypic Peptide Predictor for Targeted Proteomics by Incorporating Peptide Digestibility.

The selection of proteotypic peptides, that is, detectable unique representatives of proteins of interest, is a key step in targeted proteomics. To date, much effort has been made to understand the mechanisms underlying peptide detection in liquid chromatography-tandem mass spectrometry (LC-MS/MS) based shotgun proteomics and to predict proteotypic peptides in the absence of experimental LC-MS/MS data. However, the prediction accuracy of existing tools is still unsatisfactory.

Toward a Model for Activation of Orai Channel.

Store-operated calcium release-activated calcium (CRAC) channels mediate a variety of cellular signaling functions. The CRAC channel pore-forming protein, Orai1, is a hexamer arranged with 3-fold symmetry. Despite its importance in moving Ca ions into cells, a detailed mechanistic understanding of Orai1 activation is lacking.