Radially patterned morphogenesis of murine hair follicle placodes ensures robust epithelial budding.

The bending of simple cellular sheets into complex three-dimensional (3D) forms requires developmental patterning cues to specify where deformations occur, but how positional information directs morphological change is poorly understood. Here, we investigate how morphogen signaling and cell fate diversification contribute to the morphogenesis of murine hair placodes, in which collective cell movements transform radially symmetric primordia into bilaterally symmetric tubes. Through live imaging and 3D volumetric reconstructions, we demonstrate that Wnt and Shh establish radial patterns of cell fate, cell morphology, and movement within developing placodes.

SPIDER: constructing cell-type-specific protein-protein interaction networks.

Motivation: Protein-protein interactions (PPIs) play essential roles in the buildup of cellular machinery and provide the skeleton for cellular signaling. However, these biochemical roles are context dependent and interactions may change across cell type, time, and space. In contrast, PPI detection assays are run in a single condition that may not even be an endogenous condition of the organism, resulting in static networks that do not reflect full cellular complexity.

Concurrent TB and HIV therapies effectively control clinical reactivation of TB during co-infection but fail to eliminate chronic immune activation.

The majority of Human Immunodeficiency Virus (HIV) negative individuals exposed to () control the bacillary infection as latent TB infection (LTBI). Co-infection with HIV, however, drastically increases the risk to progression to tuberculosis (TB) disease. TB is therefore the leading cause of death in people living with HIV (PLWH) globally.

Multi-modal contrastive learning of subcellular organization using DICE.

The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information.

Chronic Lymphocytic Leukemia (CLL)-Derived Extracellular Vesicles Educate Endothelial Cells to Become IL-6-Producing, CLL-Supportive Cells.

We hypothesized that via extracellular vesicles (EVs), chronic lymphocytic leukemia (CLL) cells turn endothelial cells into CLL-supportive cells. To test this, we treated vein-derived (HUVECs) and artery-derived (HAOECs) endothelial cells with EVs isolated from the peripheral blood of 45 treatment-naïve patients. Endothelial cells took up CLL-EVs in a dose- and time-dependent manner.

Evaluating Planar Cell Polarity in the Developing Mouse Epidermis.

Epidermal tissues are among the most striking examples of planar polarity. Insect bristles, fish scales, and mammalian fur are all uniformly oriented along an animal's body axis. The collective alignment of epidermal structures provides a valuable system to interrogate the signaling mechanisms that coordinate cellular behaviors at both local and tissue-levels.

D'or: deep orienter of protein-protein interaction networks.

Motivation: Protein-protein interactions (PPIs) provide the skeleton for signal transduction in the cell. Current PPI measurement techniques do not provide information on their directionality which is critical for elucidating signaling pathways. To date, there are hundreds of thousands of known PPIs in public databases, yet only a small fraction of them have an assigned direction.

interFLOW: maximum flow framework for the identification of factors mediating the signaling convergence of multiple receptors.

Cell-cell crosstalk involves simultaneous interactions of multiple receptors and ligands, followed by downstream signaling cascades working through receptors converging at dominant transcription factors, which then integrate and propagate multiple signals into a cellular response. Single-cell RNAseq of multiple cell subsets isolated from a defined microenvironment provides us with a unique opportunity to learn about such interactions reflected in their gene expression levels. We developed the interFLOW framework to map the potential ligand-receptor interactions between different cell subsets based on a maximum flow computation in a network of protein-protein interactions (PPIs).

Vangl-dependent mesenchymal thinning shapes the distal lung during murine sacculation.

The planar cell polarity (PCP) complex is speculated to function in murine lung development, where branching morphogenesis generates an epithelial tree whose distal tips expand dramatically during sacculation. Here, we show that PCP is dispensable in the airway epithelium for sacculation. Rather, we find a Celsr1-independent role for the PCP component Vangl in the pulmonary mesenchyme: loss of Vangl1/2 inhibits mesenchymal thinning and expansion of the saccular epithelium.

A network-based method for associating genes with autism spectrum disorder.

Autism spectrum disorder (ASD) is a highly heritable complex disease that affects 1% of the population, yet its underlying molecular mechanisms are largely unknown. Here we study the problem of predicting causal genes for ASD by combining genome-scale data with a network propagation approach. We construct a predictor that integrates multiple omic data sets that assess genomic, transcriptomic, proteomic, and phosphoproteomic associations with ASD.

Batch correction of single-cell sequencing data via an autoencoder architecture.

Motivation: Technical differences between gene expression sequencing experiments can cause variations in the data in the form of batch effect biases. These do not represent true biological variations between samples and can lead to false conclusions or hinder the ability to integrate multiple datasets. Since there is a growing need for the joint analysis of singlecell sequencing datasets from different sources, there is also a need to correct the resulting batch effects while maintaining the true biological variations in the data.

Detecting Childhood Pneumonia Using Handcrafted and Deep Learning Cough Sound Features and Multilayer Perceptron.

Pneumonia is one of the leading causes of morbidity and mortality in children. This is especially true in resource poor regions lacking diagnostic facilities, bringing about the need for rapid diagnostic tests for pneumonia. Cough is a common symptom of acute respiratory diseases, including pneumonia, and the sound of cough can be indicative of the pathological variations caused by respiratory infections.

Denoising Cough Sound Recordings Using Neural Networks.

Objective cough sound evaluation is useful in the diagnosis and management of respiratory diseases. However, the performance of cough sound analysis models can degrade in the presence of background noises common in everyday environments. This brings forward the need for cough sound denoising.

Automated Cough Sound Analysis for Detecting Childhood Pneumonia.

Pneumonia is one of the leading causes of death in children. Prompt diagnosis and treatment can help prevent these deaths, particularly in resource poor regions where deaths due to pneumonia are highest. Clinical symptom-based screening of childhood pneumonia yields excessive false positives, highlighting the necessity for additional rapid diagnostic tests.

Apigenin promotes cell death in NCI-H23 cells by upregulation of PTEN: potential involvement of the binding of apigenin with WWP2 protein.

The tumour suppressor protein PTEN is often down-regulated in non-small cell lung cancer. A major protein promoting the lowering of the PTEN protein is WWP2. Polyphenols have been shown to promote the expression of tumour suppressor genes like PTEN.

Single-Cell Transcriptomics of /HIV Co-Infection.

Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) co-infection continues to pose a significant healthcare burden. HIV co-infection during TB predisposes the host to the reactivation of latent TB infection (LTBI), worsening disease conditions and mortality. There is a lack of biomarkers of LTBI reactivation and/or immune-related transcriptional signatures to distinguish active TB from LTBI and predict TB reactivation upon HIV co-infection.

SARS-CoV-2 variants evolve convergent strategies to remodel the host response.

SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host-selective forces driving VOC evolution. We discovered that VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions.

Region-specific reversal of epidermal planar polarity in the rosette fancy mouse.

The planar cell polarity (PCP) pathway collectively orients cells with respect to a body axis. Hair follicles of the murine epidermis provide a striking readout of PCP activity in their uniform alignment across the skin. Here, we characterize, from the molecular to tissue-scale, PCP establishment in the rosette fancy mouse, a natural variant with posterior-specific whorls in its fur, to understand how epidermal polarity is coordinated across the tissue.

Region-specific reversal of epidermal planar polarity in the fancy mouse.

Unlabelled: The planar cell polarity (PCP) pathway collectively orients thousands of cells with respect to a body axis to direct cellular behaviors that are essential for embryonic morphogenesis. Hair follicles of the murine epidermis provide a striking readout of PCP activity in their uniform alignment along the entire skin surface. Here, we characterize, from the molecular to tissue-scale, PCP establishment in the fancy mouse, a natural variant with posterior-specific whorls in its fur, to understand how epidermal polarity is coordinated across the tissue.

BERTwalk for integrating gene networks to predict gene- to pathway-level properties.

Motivation: Graph representation learning is a fundamental problem in the field of data science with applications to integrative analysis of biological networks. Previous work in this domain was mostly limited to shallow representation techniques. A recent deep representation technique, BIONIC, has achieved state-of-the-art results in a variety of tasks but used arbitrarily defined components.

From Leiden to Tel-Aviv University (TAU): exploring clustering solutions via a genetic algorithm.

Graph clustering is a fundamental problem in machine learning with numerous applications in data science. State-of-the-art approaches to the problem, Louvain and Leiden, aim at optimizing the modularity function. However, their greedy nature leads to fast convergence to sub-optimal solutions.

The Association of Homelessness With Rates of Diabetes Complications: A Population-Based Cohort Study.

Objective: To estimate the rates of diabetes complications and revascularization procedures among people with diabetes who have experienced homelessness compared with a matched cohort of nonhomeless control subjects.

Research Design And Methods: A propensity-matched cohort study was conducted using administrative health data from Ontario, Canada. Inclusion criteria included a diagnosis of diabetes and at least one hospital encounter between April 2006 and March 2019.

A mutation-level covariate model for mutational signatures.

Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes.