Expression of the heparin-binding growth factors Midkine and pleiotrophin during ocular development

Integrative transcriptomic profiling of ncRNAs and mRNAs in developing mouse lens

In recent years, burgeoning research has underscored the pivotal role of non-coding RNA in orchestrating the growth, development, and pathogenesis of various diseases across organisms. However, despite these advances, our understanding of the specific contributions of long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) to lens development remains notably limited. Clarifying the intricate gene regulatory networks is imperative for unraveling the molecular underpinnings of lens-related disorders. In this study, we aimed to address this gap by conducting a comprehensive analysis of the expression profiles of messenger RNAs (mRNAs), lncRNAs, and circRNAs at critical developmental time points of the mouse lens, encompassing both embryonic (E10.5, E12.5, and E16.5) and postnatal stages (P0.5, P10.5, and P60). Leveraging RNA-sequencing technology, we identified key transcripts pivotal to lens development. Our analysis revealed differentially expressed (DE) mRNAs, lncRNAs, and circRNAs across various developmental stages. Particularly noteworthy, there were 1831 co-differentially expressed (CO-DE) mRNAs, 150 CO-DE lncRNAs, and 13 CO-DE circRNAs identified during embryonic stages. Gene Ontology (GO) enrichment analysis unveiled associations primarily related to lens development, DNA conformational changes, and angiogenesis among DE mRNAs and lncRNAs. Furthermore, employing protein-protein interaction networks, mRNA-lncRNA co-expression networks, and circRNA-microRNA-mRNA networks, we predicted candidate key molecules implicated in lens development. Our findings underscore the pivotal roles of lncRNAs and circRNAs in this process, offering fresh insights into the pathogenesis of lens-related disorders and paving the way for future exploration in this field.

Clustering and machine learning-based integration identify cancer associated fibroblasts genes’ signature in head and neck squamous cell carcinoma

Background: A hallmark signature of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) is abundantly infiltration of cancer-associated fibroblasts (CAFs), which facilitate HNSCC progression. However, some clinical trials showed targeted CAFs ended in failure, even accelerated cancer progression. Therefore, comprehensive exploration of CAFs should solve the shortcoming and facilitate the CAFs targeted therapies for HNSCC.

Methods: In this study, we identified two CAFs gene expression patterns and performed the single‐sample gene set enrichment analysis (ssGSEA) to quantify the expression and construct score system. We used multi-methods to reveal the potential mechanisms of CAFs carcinogenesis progression. Finally, we integrated 10 machine learning algorithms and 107 algorithm combinations to construct most accurate and stable risk model. The machine learning algorithms contained random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalised boosted regression modelling (GBM), and survival support vector machine (survival-SVM).

Results: There are two clusters present with distinct CAFs genes pattern. Compared to the low CafS group, the high CafS group was associated with significant immunosuppression, poor prognosis, and increased prospect of HPV negative. Patients with high CafS also underwent the abundant enrichment of carcinogenic signaling pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation. The MDK and NAMPT ligand–receptor cellular crosstalk between the cancer associated fibroblasts and other cell clusters may mechanistically cause immune escape. Moreover, the random survival forest prognostic model that was developed from 107 machine learning algorithm combinations could most accurately classify HNSCC patients.

Conclusion: We revealed that CAFs would cause the activation of some carcinogenesis pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation and revealed unique possibilities to target glycolysis pathways to enhance CAFs targeted therapy. We developed an unprecedentedly stable and powerful risk score for assessing the prognosis. Our study contributes to the understanding of the CAFs microenvironment complexity in patients with head and neck squamous cell carcinoma and serves as a basis for future in-depth CAFs gene clinical exploration.

scEvoNet: a gradient boosting-based method for prediction of cell state evolution

BACKGROUND

Exploring the function or the developmental history of cells in various organisms provides insights into a given cell type's core molecular characteristics and putative evolutionary mechanisms. Numerous computational methods now exist for analyzing single-cell data and identifying cell states. These methods mostly rely on the expression of genes considered as markers for a given cell state. Yet, there is a lack of scRNA-seq computational tools to study the evolution of cell states, particularly how cell states change their molecular profiles. This can include novel gene activation or the novel deployment of programs already existing in other cell types, known as co-option.

RESULTS

Here we present scEvoNet, a Python tool for predicting cell type evolution in cross-species or cancer-related scRNA-seq datasets. ScEvoNet builds the confusion matrix of cell states and a bipartite network connecting genes and cell states. It allows a user to obtain a set of genes shared by the characteristic signature of two cell states even between distantly-related datasets. These genes can be used as indicators of either evolutionary divergence or co-option occurring during organism or tumor evolution. Our results on cancer and developmental datasets indicate that scEvoNet is a helpful tool for the initial screening of such genes as well as for measuring cell state similarities.

CONCLUSION

The scEvoNet package is implemented in Python and is freely available from https://github.com/monsoro/scEvoNet . Utilizing this framework and exploring the continuum of transcriptome states between developmental stages and species will help explain cell state dynamics.

Single Cell Sequencing of Induced Pluripotent Stem Cell Derived Retinal Ganglion Cells (iPSC-RGC) Reveals Distinct Molecular Signatures and RGC Subtypes

We intend to identify marker genes with differential gene expression (DEG) and RGC subtypes in cultures of human-induced pluripotent stem cell (iPSC)-derived retinal ganglion cells. Single-cell sequencing was performed on mature and functional iPSC-RGCs at day 40 using Chromium Single Cell 3' V3 protocols (10X Genomics). Sequencing libraries were run on Illumina Novaseq to generate 150 PE reads. Demultiplexed FASTQ files were mapped to the hg38 reference genome using the STAR package, and cluster analyses were performed using a cell ranger and BBrowser2 software. QC analysis was performed by removing the reads corresponding to ribosomal and mitochondrial genes, as well as cells that had less than 1X mean absolute deviation (MAD), resulting in 4705 cells that were used for further analyses. Cells were separated into clusters based on the gene expression normalization via PCA and TSNE analyses using the Seurat tool and/or Louvain clustering when using BBrowser2 software. DEG analysis identified subsets of RGCs with markers like MAP2, RBPMS, TUJ1, BRN3A, SOX4, TUBB3, SNCG, PAX6 and NRN1 in iPSC-RGCs. Differential expression analysis between separate clusters identified significant DEG transcripts associated with cell cycle, neuron regulatory networks, protein kinases, calcium signaling, growth factor hormones, and homeobox transcription factors. Further cluster refinement identified RGC diversity and subtype specification within iPSC-RGCs. DEGs can be used as biomarkers for RGC subtype classification, which will allow screening model systems that represent a spectrum of diseases with RGC pathology.

The role of decidual natural killer cell derived soluble factors in early pregnancy

Decidual natural killer cell (dNK), the predominant decidual lymphocytes in early pregnancy, are primarily identified based on their CD56^bright CD16^- phenotype and play an important role in maintaining immune tolerance at the maternal-fetal interface. dNK dysfunction reportedly leads to pathological pregnancy. Indeed, various dNK-derived soluble factors are involved in a series of key processes related to pregnancy outcomes. In this review, we summarize the roles of these dNK-derived factors in immune tolerance and embryonic development to improve the current understanding regarding the physiological and pathological mechanisms that occur during pregnancy, while potentially informing the development of effective therapeutics.

PTN-PTPRZ signalling is involved in deer antler stem cell regulation during tissue regeneration

A growing deer antler contains a stem cell niche that can drive endochondral bone regeneration at up to 2 cm/day. Pleiotrophin (PTN), as a multifunctional growth factor, is found highly expressed at the messenger RNA level within the active antler stem cell tissues. This study aims to map the expression patterns of PTN protein and its receptors in a growing antler and investigate the effects of PTN on antler stem cells in vitro. Immunohistochemistry was employed to localise PTN/midkine (MDK) and their functional receptors, protein tyrosine phosphatase receptor type Z (PTPRZ), anaplastic lymphoma kinase (ALK), NOTCH2, and integrin α_V β_3, on serial slides of the antler growth centre. PTN was found to be the dominantly expressed growth factor in the PTN/MDK family. High expression of PTPRZ and ALK co-localised with PTN was found suggesting a potential interaction. The high levels of PTN and PTPRZ reflected the antler stem cell activation status during the regenerative process. When antler stem cells were cultured in vitro under the normoxic condition, no PTN protein was detected and exogenous PTN did not induce differentiation or proliferation but rather stem cell maintenance. Collectively, the antler stem cell niche appears to upregulate PTN and PTPRZ in vivo, and PTN-PTPRZ signalling may be involved in regulating antler stem cell behaviour during rapid antler regeneration.

Making NSCLC Crystal Clear: How Kinase Structures Revolutionized Lung Cancer Treatment

The parallel advances of different scientific fields provide a contemporary scenario where collaboration is not a differential, but actually a requirement. In this context, crystallography has had a major contribution on the medical sciences, providing a “face” for targets of diseases that previously were known solely by name or sequence. Worldwide, cancer still leads the number of annual deaths, with 9.6 million associated deaths, with a major contribution from lung cancer and its 1.7 million deaths. Since the relationship between cancer and kinases was unraveled, these proteins have been extensively explored and became associated with drugs that later attained blockbuster status. Crystallographic structures of kinases related to lung cancer and their developed and marketed drugs provided insight on their conformation in the absence or presence of small molecules. Notwithstanding, these structures were also of service once the initially highly successful drugs started to lose their effectiveness in the emergence of mutations. This review focuses on a subclassification of lung cancer, non-small cell lung cancer (NSCLC), and major oncogenic driver mutations in kinases, and how crystallographic structures can be used, not only to provide awareness of the function and inhibition of these mutations, but also how these structures can be used in further computational studies aiming at addressing these novel mutations in the field of personalized medicine.