Notch signaling during human T cell development

Notch signaling is critical during multiple stages of T cell development in both mouse and human. Evidence has emerged in recent years that this pathway might regulate T-lineage differentiation differently between both species. Here, we review our current understanding of how Notch signaling is activated and used during human T cell development. First, we set the stage by describing the developmental steps that make up human T cell development before describing the expression profiles of Notch receptors, ligands, and target genes during this process. To delineate stage-specific roles for Notch signaling during human T cell development, we subsequently try to interpret the functional Notch studies that have been performed in light of these expression profiles and compare this to its suggested role in the mouse

Machine Learning Methods for Prediction of CDK-Inhibitors

Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://bioinfo.icgeb.res.in/cdkipred

Variants in the <em>DDX6-CXCR5</em> autoimmune disease risk locus influence the regulatory network in immune cells and salivary gland

\ua9 2025 The Author(s). Objectives: Sj\uf6gren\u27s disease (SjD) and systemic lupus erythematosus (SLE) share genetic risk at the DDX6-CXCR5 locus (11q23.3). Identifying and functionally characterising shared SNPs spanning this locus can provide new insights into common genetic mechanisms of autoimmunity. Methods: Transdisease meta-analyses, fine-mapping, and bioinformatic analyses prioritised shared likely functional single nucleotide polymorphisms (SNPs) for allele-specific and cell type–specific functional interrogation using electromobility shift, luciferase reporter, and quantitative chromatin conformation capture assays and clustered regularly interspaced short palindromic repeat (CRISPR) gene regulation. Results: Five shared SNPs were identified as likely functional in primary human immune cells, salivary gland and kidney tissues: rs57494551, rs4936443, rs4938572, rs7117261, and rs4938573. All 5 SNPs exhibited cell type-specific and allele-specific effects on nuclear protein binding affinity and enhancer/promoter regulatory activity in immune, salivary gland epithelial, and kidney epithelial cell models. Mapping of chromatin–chromatin interactions revealed a chromatin regulatory network that expanded beyond DDX6 and CXCR5 to include PHLDB1, lnc-PHLDB1-1, BCL9L, TRAPPC4, among others. Coalescence of functional assays and multiomic data analyses indicated that these SNPs likely modulate the activity of 3 regulatory regions: intronic rs57494551 regulatory region, intergenic SNP haplotype (rs4938572, rs4936443, and rs7117261) regulatory region, and rs4938573 regulatory region upstream of the CXCR5 promoter. Conclusions: Shared genetic susceptibly at the DDX6-CXCR5 locus in SjD and SLE likely alters common mechanisms of autoimmunity, including interferon signalling (DDX6), autophagy (TRAPPC4), and lymphocytic infiltration of disease-target tissues (CXCR5). Further, using multiomic data from patients with SjD, combined with bioinformatic and in vitro functional studies, can provide mechanistic insights into how genetic risk influences the biological pathways that drive complex autoimmunity

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS