scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning.

Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses.

In vitro evolution and whole genome analysis to study chemotherapy drug resistance in haploid human cells.

In vitro evolution and whole genome analysis has proven to be a powerful method for studying the mechanism of action of small molecules in many haploid microbes but has generally not been applied to human cell lines in part because their diploid state complicates the identification of variants that confer drug resistance. To determine if haploid human cells could be used in MOA studies, we evolved resistance to five different anticancer drugs (doxorubicin, gemcitabine, etoposide, topotecan, and paclitaxel) using a near-haploid cell line (HAP1) and then analyzed the genomes of the drug resistant clones, developing a bioinformatic pipeline that involved filtering for high frequency alleles predicted to change protein sequence, or alleles which appeared in the same gene for multiple independent selections with the same compound. Applying the filter to sequences from 28 drug resistant clones identified a set of 21 genes which was strongly enriched for known resistance genes or known drug targets (TOP1, TOP2A, DCK, WDR33, SLCO3A1).

Induction of a memory-like CD4 T-cell phenotype by airway smooth muscle cells.

In asthma, CD4 T-cell interaction with airway smooth muscle (ASM) may enhance its contractile properties and promote its proliferation. However, less is known about the effects of this interaction on T cells. To explore the consequences of interaction of CD4 T cells with ASM we placed the cells in co-culture and analyzed the phenotypic and functional changes in the T cells.

Regulation of long non-coding RNA expression by aryl hydrocarbon receptor activation.

The aryl hydrocarbon receptor (AhR) is a cytosolic transcription factor that can be activated by endogenous or xenobiotic ligands. Upon activation, the AhR translocates to the nucleus, dimerizes with the AhR nuclear translator (ARNT), and binds to specific DNA sequences called xenobiotic response elements (XRE) to promote target gene transcription, including cytochrome P450 (e.g.

Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope.

As the sophistication of machine learning force fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze and assess the practical performance of MLFFs. To go beyond average error metrics and into a complete picture of a model's applicability and limitations, we developed FFAST (force field analysis software and tools): a cross-platform software package designed to gain detailed insights into a model's performance and limitations, complete with an easy-to-use graphical user interface. The software allows the user to gauge the performance of any molecular force field,─such as popular state-of-the-art MLFF models, ─ on various popular data set types, providing general prediction error overviews, outlier detection mechanisms, atom-projected errors, and more.

Differences in the Tumor Microenvironment of EBV-Associated Gastric Cancers Revealed Using Single-Cell Transcriptome Analysis.

Epstein-Barr virus (EBV) is a gamma-herpesvirus associated with nearly 10% of gastric cancers (GCs). These EBV-associated GCs (EBVaGCs) are molecularly, histopathologically, and clinically distinct from EBV-negative GCs (EBVnGCs). While viral genes in EBVaGCs contribute to the carcinogenesis process, viral proteins also represent foreign antigens that could trigger enhanced immune responses compared to EBVnGCs.

Receptor-mediated effects of Δ -tetrahydrocannabinol and cannabidiol on the inflammatory response of alveolar macrophages.

Δ -Tetrahydrocannabinol (Δ -THC) and cannabidiol (CBD) are cannabinoids found in Cannabis sativa. While research supports cannabinoids reduce inflammation, the consensus surrounding receptor(s)-mediated effects has yet to be established. Here, we investigated the receptor-mediated properties of Δ -THC and CBD on alveolar macrophages, an important pulmonary immune cell in direct contact with cannabinoids inhaled by cannabis smokers.

DOT1L regulates chamber-specific transcriptional networks during cardiogenesis and mediates postnatal cell cycle withdrawal.

Mechanisms by which specific histone modifications regulate distinct gene networks remain little understood. We investigated how H3K79me2, a modification catalyzed by DOT1L and previously considered a general transcriptional activation mark, regulates gene expression during cardiogenesis. Embryonic cardiomyocyte ablation of Dot1l revealed that H3K79me2 does not act as a general transcriptional activator, but rather regulates highly specific transcriptional networks at two critical cardiogenic junctures: embryonic cardiogenesis, where it was particularly important for left ventricle-specific genes, and postnatal cardiomyocyte cell cycle withdrawal, with Dot1L mutants having more mononuclear cardiomyocytes and prolonged cardiomyocyte cell cycle activity.

Helminth-induced reprogramming of the stem cell compartment inhibits type 2 immunity.

Enteric helminths form intimate physical connections with the intestinal epithelium, yet their ability to directly alter epithelial stem cell fate has not been resolved. Here we demonstrate that infection of mice with the parasite Heligmosomoides polygyrus bakeri (Hpb) reprograms the intestinal epithelium into a fetal-like state marked by the emergence of Clusterin-expressing revival stem cells (revSCs). Organoid-based studies using parasite-derived excretory-secretory products reveal that Hpb-mediated revSC generation occurs independently of host-derived immune signals and inhibits type 2 cytokine-driven differentiation of secretory epithelial lineages that promote their expulsion.

Systematic analysis of naturally occurring insertions and deletions that alter transcription factor spacing identifies tolerant and sensitive transcription factor pairs.

Regulation of gene expression requires the combinatorial binding of sequence-specific transcription factors (TFs) at promoters and enhancers. Prior studies showed that alterations in the spacing between TF binding sites can influence promoter and enhancer activity. However, the relative importance of TF spacing alterations resulting from naturally occurring insertions and deletions (InDels) has not been systematically analyzed.

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning.

The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability of the MLFF model for predictive molecular simulations. However, most atomistic reference datasets are inhomogeneously distributed across configurational space (CS), and thus, choosing the training set randomly or according to the probability distribution of the data leads to models whose accuracy is mainly defined by the most common close-to-equilibrium configurations in the reference data. In this work, we combine unsupervised and supervised ML methods to bypass the inherent bias of the data for common configurations, effectively widening the applicability range of the MLFF to the fullest capabilities of the dataset.

Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules.

Dynamics of flexible molecules are often determined by an interplay between local chemical bond fluctuations and conformational changes driven by long-range electrostatics and van der Waals interactions. This interplay between interactions yields complex potential-energy surfaces (PESs) with multiple minima and transition paths between them. In this work, we assess the performance of the state-of-the-art Machine Learning (ML) models, namely, sGDML, SchNet, Gaussian Approximation Potentials/Smooth Overlap of Atomic Positions (GAPs/SOAPs), and Behler-Parrinello neural networks, for reproducing such PESs, while using limited amounts of reference data.

A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19.

Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated with hyperinflammation. The presence of HLH was described in severely ill patients during the H1N1 influenza epidemic, presenting with pulmonary vascular thrombosis. We tested the hypothesis that genes causing primary HLH regulate pathways linking pulmonary thromboembolism to the presence of SARS-CoV-2 using novel network-informed computational algorithms.

Angiotensin-converting enzyme 2 expression in COPD and IPF fibroblasts: the forgotten cell in COVID-19.

The COVID-19 pandemic is associated with severe pneumonia and acute respiratory distress syndrome leading to death in susceptible individuals. For those who recover, post-COVID-19 complications may include development of pulmonary fibrosis. Factors contributing to disease severity or development of complications are not known.

Infiltrating Hematogenous Macrophages Aggregate Around β-Amyloid Plaques in an Age- and Sex-Dependent Manner in a Mouse Model of Alzheimer Disease.

β-Amyloid (Aβ) plaques can trigger chronic inflammation in the cellular environment that recruits infiltrating macrophages during the course of Alzheimer disease (AD). Activated macrophages release pro-inflammatory cytokines that increase neurotoxicity associated with AD. A major impediment to investigating neuroinflammation involving macrophage activity is the inability to discriminate resident microglial macrophages (mMϕ) from hematogenous macrophages (hMϕ), as they are morphologically and phenotypically similar when activated.

Differential Effects of Human Adenovirus E1A Protein Isoforms on Aerobic Glycolysis in A549 Human Lung Epithelial Cells.

Viruses alter a multitude of host-cell processes to create a more optimal environment for viral replication. This includes altering metabolism to provide adequate substrates and energy required for replication. Typically, viral infections induce a metabolic phenotype resembling the Warburg effect, with an upregulation of glycolysis and a concurrent decrease in cellular respiration.

Transcriptomic and epigenetic mechanisms underlying myeloid diversity in the lung.

The lung is inhabited by resident alveolar and interstitial macrophages as well as monocytic cells that survey lung tissues. Each cell type plays distinct functional roles under homeostatic and inflammatory conditions, but mechanisms establishing their molecular identities and functional potential remain poorly understood. In the present study, systematic evaluation of transcriptomes and open chromatin of alveolar macrophages (AMs), interstitial macrophages (IMs) and lung monocytes from two mouse strains enabled inference of common and cell-specific transcriptional regulators.

Diverse motif ensembles specify non-redundant DNA binding activities of AP-1 family members in macrophages.

Mechanisms by which members of the AP-1 family of transcription factors play non-redundant biological roles despite recognizing the same DNA sequence remain poorly understood. To address this question, here we investigate the molecular functions and genome-wide DNA binding patterns of AP-1 family members in primary and immortalized mouse macrophages. ChIP-sequencing shows overlapping and distinct binding profiles for each factor that were remodeled following TLR4 ligation.

Analysis of Genetically Diverse Macrophages Reveals Local and Domain-wide Mechanisms that Control Transcription Factor Binding and Function.

Non-coding genetic variation is a major driver of phenotypic diversity and allows the investigation of mechanisms that control gene expression. Here, we systematically investigated the effects of >50 million variations from five strains of mice on mRNA, nascent transcription, transcription start sites, and transcription factor binding in resting and activated macrophages. We observed substantial differences associated with distinct molecular pathways.

Thrombospondin1 (TSP1) replacement prevents cerebral cavernous malformations.

mutations are the most common cause of cerebral cavernous malformation (CCM). Acute gene inactivation in mouse brain microvascular endothelial cells (BMECs) changes expression of multiple genes involved in vascular development. These changes include suppression of , which encodes thrombospondin1 (TSP1) and has been ascribed to KLF2- and KLF4-mediated repression of In vitro reconstitution of TSP1 with either full-length TSP1 or 3TSR, an anti-angiogenic TSP1 fragment, suppresses heightened vascular endothelial growth factor signaling and preserves BMEC tight junctions.

Genome-Wide Approaches to Defining Macrophage Identity and Function.

Macrophages play essential roles in the response to injury and infection and contribute to the development and/or homeostasis of the various tissues they reside in. Conversely, macrophages also influence the pathogenesis of metabolic, neurodegenerative, and neoplastic diseases. Mechanisms that contribute to the phenotypic diversity of macrophages in health and disease remain poorly understood.