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Background Given the differences in embryonic origin, vascular and nervous supplies, microbiotic burden, and main physiological functions of left and right colons, tumor location is increasingly suggested to dictate tumor behavior affecting pathology, progression and prognosis. Right-sided colon cancers arise in the cecum, ascending colon, hepatic flexure and/or transverse colon, while left-sided colon cancers arise in the splenic flexure, descending, and/or sigmoid colon. In contrast to prior reports, we attempt to delineate programs of tumorigenesis independently for each side. Methods Four hundred and eleven samples were extracted from The Cancer Genome Atlas-COAD cohort, based on a conservative sample inclusion criterion. Each side was independently analyzed with respect to their respective normal tissue, at the level of transcription, post-transcription, miRNA control and methylation in both a stage specific and stage-agnostic manner. Results Our results indicate a suppression of enzymes involved in various stages of carcinogen breakdown including CYP2C8 , CYP4F12 , GSTA1 , and UGT1A within right colon tumors. This implies its reduced capacity to detoxify carcinogens, contributing to a genotoxic tumor environment, and subsequently a more aggressive phenotype. Additionally, we highlight a crucial nexus between calcium homeostasis (sensing, mobilization and absorption) and immune/GPCR signaling within left-sided tumors, possibly contributing to its reduced proliferative and metastatic potential. Interestingly, two genes SLC6A4 and HOXB13 show opposing regulatory trends within right and left tumors. Post-transcriptional regulation mediated by both RNA-binding proteins (e.g. NKRF (in left) and MSI2 (in right)) and miRNAs (e.g. miR-29a (in left); miR-155, miR181-d, miR-576 and miR23a (in right)) appear to exhibit side-specificity in control of their target transcripts and is pronounced in right colon tumors. Additionally, methylation results depict location-specific differences, with increased hypomethylation in open seas within left tumors, and increased hypermethylation of CpG islands within right tumors. Conclusions Differences in molecular mechanisms captured here highlight distinctions in tumorigenesis and progression between left and right colon tumors, which will serve as the basis for future studies, influencing the efficacies of existing and future diagnostic, prognostic and therapeutic interventions.

Background Community detection algorithms are fundamental tools to uncover important features in networks. There are several studies focused on social networks but only a few deal with biological networks. Directly or indirectly, most of the methods maximize modularity, a measure of the density of links within communities as compared to links between communities. Results Here we analyze six different community detection algorithms, namely, Combo, Conclude, Fast Greedy, Leading Eigen, Louvain and Spinglass, on two important biological networks to find their communities and evaluate the results in terms of topological and functional features through Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology term enrichment analysis. At a high level, the main assessment criteria are 1) appropriate community size (neither too small nor too large), 2) representation within the community of only one or two broad biological functions, 3) most genes from the network belonging to a pathway should also belong to only one or two communities, and 4) performance speed. The first network in this study is a network of Protein-Protein Interactions (PPI) in Saccharomyces cerevisiae (Yeast) with 6532 nodes and 229,696 edges and the second is a network of PPI in Homo sapiens (Human) with 20,644 nodes and 241,008 edges. All six methods perform well, i.e., find reasonably sized and biologically interpretable communities, for the Yeast PPI network but the Conclude method does not find reasonably sized communities for the Human PPI network. Louvain method maximizes modularity by using an agglomerative approach, and is the fastest method for community detection. For the Yeast PPI network, the results of Spinglass method are most similar to the results of Louvain method with regard to the size of communities and core pathways they identify, whereas for the Human PPI network, Combo and Spinglass methods yield the most similar results, with Louvain being the next closest. Conclusions For Yeast and Human PPI networks, Louvain method is likely the best method to find communities in terms of detecting known core pathways in a reasonable time.

Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient’s breast tissue, which has the potential for identifying and characterizing cancer subtypes. However, the large dimensionality of this transcriptomic data and the heterogeneity between the molecular profiles of breast cancers poses a barrier to identifying minimal markers and mechanistic consequences. In this study, we develop an autoencoder to identify a reduced set of gene markers that characterize the four major breast cancer subtypes with the accuracy of 82.38%. The reduced feature space created by our model captures the functional characteristics of each breast cancer subtype highlighting mechanisms that are unique to each subtype as well as those that are shared. Our high prediction accuracy shows that our markers can be valuable for breast cancer subtype detection and have the potential to provide insights into mechanisms associated with each subtype.

The curse of dimensionality has long been a hurdle in the analysis of complex data in areas such as computational biology, ecology and econometrics. In this work, we present a forecasting algorithm that exploits the dimensionality of data in a nonparametric autoregressive framework. The main idea is that the dynamics of a chaotic dynamical system consisting of multiple time-series can be reconstructed using a combination of different variables. This nonlinear autoregressive algorithm uses multivariate attractors reconstructed as the inputs of a neural network to predict the future. We show that our approach, attractor ranked radial basis function network (AR-RBFN) provides a better forecast than that obtained using other model-free approaches as well as univariate and multivariate autoregressive models using radial basis function networks. We demonstrate this for simulated ecosystem models and a mesocosm experiment. By taking advantage of dimensionality, we show that AR-RBFN overcomes the shortcomings of noisy and short time-series data .

The two main blood flow patterns, namely, pulsatile shear (PS) prevalent in straight segments of arteries and oscillatory shear (OS) observed at branch points, are associated with atheroprotective (healthy) and atheroprone (unhealthy) vascular phenotypes, respectively. The effects of blood flow-induced shear stress on endothelial cells (ECs) and vascular health have generally been studied using human umbilical vein endothelial cells (HUVECs). While there are a few studies comparing the differential roles of PS and OS across different types of ECs at a single time point, there is a paucity of studies comparing the temporal responses between different EC types. In the current study, we measured OS and PS transcriptomic responses in human aortic endothelial cells (HAECs) over 24 h and compared these temporal responses of HAECs with our previous findings on HUVECs. The measurements were made at 1, 4, and 24 h in order to capture the responses at early, mid, and late time points after shearing. The results indicate that the responses of HAECs and HUVECs are qualitatively similar for endothelial function-relevant genes and several important pathways with a few exceptions, thus demonstrating that HUVECs can be used as a model to investigate the effects of shear on arterial ECs, with consideration of the differences. Our findings show that HAECs exhibit an earlier response or faster kinetics as compared to HUVECs. The comparative analysis of HAECs and HUVECs presented here offers insights into the mechanisms of common and disparate shear stress responses across these two major endothelial cell types.

Recent efforts toward an HIV vaccine focus on inducing broadly neutralizing antibodies, but eliciting both neutralizing antibodies (nAbs) and cellular responses may be superior. Here, we immunized macaques with an HIV envelope trimer, either alone to induce nAbs, or together with a heterologous viral vector regimen to elicit nAbs and cellular immunity, including CD8 + tissue-resident memory T cells. After ten vaginal challenges with autologous virus, protection was observed in both vaccine groups at 53.3% and 66.7%, respectively. A nAb titer >300 was generally associated with protection but in the heterologous viral vector + nAb group, titers <300 were sufficient. In this group, protection was durable as the animals resisted six more challenges 5 months later. Antigen stimulation of T cells in ex vivo vaginal tissue cultures triggered antiviral responses in myeloid and CD4 + T cells. We propose that cellular immune responses reduce the threshold of nAbs required to confer superior and durable protection. An HIV vaccine that elicits both antibodies and cellular immune responses confers long-lasting protection against viral challenge in nonhuman primates.

The optimal expression of endothelial nitric oxide synthase (eNOS), the hallmark of endothelial homeostasis, is vital to vascular function. Dynamically regulated by various stimuli, eNOS expression is modulated at transcriptional, post-transcriptional, and post-translational levels. However, epigenetic modulations of eNOS, particularly through long non-coding RNAs (lncRNAs) and chromatin remodeling, remain to be explored. Here we identify an enhancer-associated lncRNA that enhances eNOS expression (LEENE). Combining RNA-sequencing and chromatin conformation capture methods, we demonstrate that LEENE is co-regulated with eNOS and that its enhancer resides in proximity to eNOS promoter in endothelial cells (ECs). Gain- and Loss-of-function of LEENE differentially regulate eNOS expression and EC function. Mechanistically, LEENE facilitates the recruitment of RNA Pol II to the eNOS promoter to enhance eNOS nascent RNA transcription. Our findings unravel a new layer in eNOS regulation and provide novel insights into cardiovascular regulation involving endothelial function. eNOS expression is dynamically regulated both transcriptionally and post-transcriptionally by various stimuli. Here the authors identify an enhancer-associated lncRNA (LEENE) that is co-regulated with, and enhances eNOS expression.

B cells play an integral role in the immune response to both dengue fever and COVID-19. Prior scRNAseq analyses of peripheral plasmablasts in COVID-19 have revealed a heterogeneous population with distinct cell subsets associated with proliferation; prior studies in patients with dengue fever have likewise shown the presence of proliferative pre-plasmablasts in the circulation. These findings may have implications for disease severity. In this study, we sought to gain a mechanistic understanding of the intracellular processes in naive and memory B cells that are associated with and may lead to an expanded proliferative plasmablast population in the circulation. We analyzed age-controlled (pediatric and adult), peripheral blood mononuclear cell scRNAseq datasets from patients infected with either dengue (primary or secondary) or COVID-19 (non-severe or severe) from previously published studies. Our preliminary analysis showed that pediatric patients with dengue and adults with COVID-19 had an expanded proliferative plasmablast (p-PB) population. By contrast, neither the adults with dengue nor the children with COVID-19 in our dataset had p-PBs. We used this distinctive preliminary signature to guide our analyses design and expanded our analyses to naive and memory B cells. In age/disease conditions with and without p-PBs, we found differences in cell sensing and activation, including via the B cell receptor and downstream signal transduction. Likewise, inflammation was mediated differently: relative to groups without p-PBs, those with p-PBs had increased expression of interferon response and S100 genes (particularly severe COVID-19). Furthermore, several transcription factors at the nexus of activation, inflammation, and cell fate decisions were expressed differently in groups with and without p-PBs. We used dengue and COVID-19 infections in adult and pediatric patients (focusing on naive B, memory B, and plasmablast cells) as a model to better understand the mechanisms that may give rise to p-PB populations in the circulation. Our results indicate that a more pro-inflammatory state in naive and memory B cells correlated with - and could influence the generation of- proliferating plasmablasts. Further exploration of these mechanisms will have implications for immune memory, vaccine development, and post-viral autoimmune syndromes.