Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

TEAD Inhibitors Sensitize KRASG12C Inhibitors via Dual Cell Cycle Arrest in KRASG12C-Mutant NSCLC.

KRASG12C is one of the most common mutations detected in non-small cell lung cancer (NSCLC) patients, and it is a marker of poor prognosis. The first FDA-approved KRASG12C inhibitors, sotorasib and adagrasib, have been an enormous breakthrough for patients with KRASG12C mutant NSCLC; however, resistance to therapy is emerging. The transcriptional coactivators YAP1/TAZ and the family of transcription factors TEAD1-4 are the downstream effectors of the Hippo pathway and regulate essential cellular processes such as cell proliferation and cell survival. YAP1/TAZ-TEAD activity has further been implicated as a mechanism of resistance to targeted therapies. Here, we investigate the effect of combining TEAD inhibitors with KRASG12C inhibitors in KRASG12C mutant NSCLC tumor models. We show that TEAD inhibitors, while being inactive as single agents in KRASG12C-driven NSCLC cells, enhance KRASG12C inhibitor-mediated anti-tumor efficacy in vitro and in vivo. Mechanistically, the dual inhibition of KRASG12C and TEAD results in the downregulation of MYC and E2F signatures and in the alteration of the G2/M checkpoint, converging in an increase in G1 and a decrease in G2/M cell cycle phases. Our data suggest that the co-inhibition of KRASG12C and TEAD leads to a specific dual cell cycle arrest in KRASG12C NSCLC cells.

Single-cell RNA-sequencing of PBMCs from SAVI patients reveals disease-associated monocytes with elevated integrated stress response.

Gain-of-function mutations in stimulator of interferon gene 1 (STING1) result in STING-associated vasculopathy with onset in infancy (SAVI), a severe autoinflammatory disease. Although elevated type I interferon (IFN) production is thought to be the leading cause of the symptoms observed in patients, STING can induce a set of pathways, which have roles in the onset and severity of SAVI and remain to be elucidated. To this end, we performed a multi-omics comparative analysis of peripheral blood mononuclear cells (PBMCs) and plasma from SAVI patients and healthy controls, combined with a dataset of healthy PBMCs treated with IFN-ß. Our data reveal a subset of disease-associated monocyte, expressing elevated CCL3, CCL4, and IL-6, as well as a strong integrated stress response, which we suggest is the result of direct PERK activation by STING. Cell-to-cell communication inference indicates that these monocytes lead to T cell early activation, resulting in their senescence and apoptosis. Last, we propose a transcriptomic signature of STING activation, independent of type I IFN response.

Non-intertwined binary patterns of hydrophobic/nonhydrophobic amino acids are considerably better markers of regular secondary structures than nonconstrained patterns.

Patterns of hydrophobic and hydrophilic residues (binary patterns) play an important role in protein architecture and can be roughly categorized into two classes regarding their preferential participation in alpha-helices or beta-strands. However, a single binary pattern can be embedded into different longer patterns carrying opposite structural information and thus cannot be as much informative as expected. Here, we consider conditional binary patterns, or hydrophobic clusters, whose existence is conditioned by the presence of a minimum number of nonhydrophobic residues, called the connectivity distance, that separate two hydrophobic amino acids assumed to belong to two distinct patterns. Conditional binary patterns are distinct from simple ones in that they are not intertwined, i.e., they can not include or be included in other conditional patterns and therefore carry a much more differentiated information, in particular being dramatically better correlated with regular secondary structures (especially beta ones). The distribution of these nonintertwined binary patterns in natural proteins was assessed relative to randomness, evidencing the structural bricks that are favored and disfavored by evolutionary selection. Several connectivity distances as well as several hydrophobic alphabets were tested, evidencing the clear superiority of a connectivity distance of 4, which mimics the minimum current length of loops in globular domains, and of the VILFMYW alphabet, selected from structural data (secondary structure propension and Voronoï tesselation), in highlighting fundamental properties of protein folds.