Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers

Background: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. Method: To discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers. Results: Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers. Conclusion: The proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers

Potential Autoimmunity Resulting from Molecular Mimicry between SARS-CoV-2 Spike and Human Proteins

Molecular mimicry between viral antigens and host proteins can produce cross-reacting antibodies leading to autoimmunity. The coronavirus SARS-CoV-2 causes COVID-19, a disease curiously resulting in varied symptoms and outcomes, ranging from asymptomatic to fatal. Autoimmunity due to cross-reacting antibodies resulting from molecular mimicry between viral antigens and host proteins may provide an explanation. Thus, we computationally investigated molecular mimicry between SARS-CoV-2 Spike and known epitopes. We discovered molecular mimicry hotspots in Spike and highlight two examples with tentative high autoimmune potential and implications for understanding COVID-19 complications. We show that a TQLPP motif in Spike and thrombopoietin shares similar antibody binding properties. Antibodies cross-reacting with thrombopoietin may induce thrombocytopenia, a condition observed in COVID-19 patients. Another motif, ELDKY, is shared in multiple human proteins, such as PRKG1 involved in platelet activation and calcium regulation, and tropomyosin, which is linked to cardiac disease. Antibodies cross-reacting with PRKG1 and tropomyosin may cause known COVID-19 complications such as blood-clotting disorders and cardiac disease, respectively. Our findings illuminate COVID-19 pathogenesis and highlight the importance of considering autoimmune potential when developing therapeutic interventions to reduce adverse reactions

Evidence for structural discordance in the inverted metamorphic sequence of Sikkim himalaya: Towards resolving the main central thrust controversy

Inverted, metamorphism in the Himalayas is closely associated with the Main Central Thrust (MCT). In the western Himalayas, the Main Central Thrust conventionally separates high grade metamorphic rocks of the Higher Himalayan Crystalline Sequence (HHCS) from unmetamorphosed rocks of the Inner sedimentary Belt. In the eastern Himalayas, the Inner sedimentary Belt is absent, and the HHCS and meta-sedimentary Lesser Himalayan Sequence (LHS) apparently form a continuous Barrovian metamorphic sequence, leading to confusion about the precise location of the MCT. In this study, it is demonstrated that migmatitic gneisses of the sillimanite zone in the higher structural levels of the HHCS are multiply deformed, with two phases of penetrative fabric formation (S 1HHCS and S2HHCS) followed by third folding event associated with a spaced, NW-SE trending, north-east dipping foliation (S 3HHCS). The underlying LHS schists (kyanite zone and lower) are also multiply deformed, with the bedding S0 being isoclinally folded (F1LHS), and subsequently refolded (F2LHS and F 3LHS). The contact zone between, the HHCS and LHS is characterized by ductile, top-to-the southwest shearing and stabilization of a pervasive foliation that is consistently oriented NW-SE and dips northeast. This foliation is parallel to the S3HHCS foliation in the HHCS, and the S 2LHS in the LHS. Early lineations in the HHCS and LHS also show different dispersions across the contact shear zone, implying that pre-thrusting orientations of the two units were distinct. The contact shear zone is therefore interpreted to be a plane of structural discordance, shows a shear sense consistent with thrust movement and is associated with mineral growth during Barrovian metamorphism. It may well be considered to represent the MCT in this region