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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-β. 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., Competing Interests: Declaration of interests C.C., F.R.L., and M.M.M. are listed as inventors on a patent application related to this article (European Patent Application no. PCT/FR2023/050433, entitled “A gene signature for diagnosing stimulator of interferon genes (STING)-associated vasculopathy with onset in infancy (SAVI)”). F.R.L. and M.M.M. received grants from Sanofi (iAward Europe and research collaboration contract). C.C., L.D., M.D., F.A., G.B., J.C.G., and A.R. are or were employees of Sanofi and may hold shares and/or stock options in the company., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

KRAS G12C 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 KRAS G12C inhibitors, sotorasib and adagrasib, have been an enormous breakthrough for patients with KRAS G12C 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 KRAS G12C inhibitors in KRAS G12C mutant NSCLC tumor models. We show that TEAD inhibitors, while being inactive as single agents in KRAS G12C -driven NSCLC cells, enhance KRAS G12C inhibitor-mediated anti-tumor efficacy in vitro and in vivo. Mechanistically, the dual inhibition of KRAS G12C 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 KRAS G12C and TEAD leads to a specific dual cell cycle arrest in KRAS G12C NSCLC cells.

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).

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., (Copyright 2003 Wiley-Liss, Inc.)

Purpose: To determine the value of known computed tomographic (CT) criteria to differentiate non-complicated from complicated (strangulation, necrosis) small bowel obstruction., Materials and Methods: 43 patients with a definitive diagnosis of small bowel obstruction based on clinical, sonographic, CT, surgical and pathological findings were included. All patients had small bowel obstruction caused by adhesions confirmed at surgery. The obstruction was non-complicated in 28 patients and complicated in 15 patients. The CT examinations from all patients were retrospectively reviewed by three experienced radiologists using a set of pre-defined criteria. Attention was focused on the following signs: reduced enhancement of the small bowel wall, mural thickening, congestion of small mesenteric veins, and ascites. Results were correlated with surgical and/or pathological data., Results: For the diagnosis of complicated obstruction, reduced bowel wall enhancement had a sensitivity of 57% and a specificity of 100%, a bowel wall thickness greater than 3 mm had a sensitivity of 35% and a specificity of 100% and a bowel wall thickness less than 1 mm had a sensitivity of 35% and a specificity of 93%. Ascites and congestion of small mesenteric veins were not significant. The multivariate analysis showed that the association of bowel-wall thickening and reduced enhancement of the small bowel wall was significant (sensitivity of 71%, specificity 100%, and accuracy 90%)., Conclusion: Among the CT criteria used to diagnose complications from small-bowel obstruction that were evaluated in this study, only three were significant with a high specificity but low sensitivity.

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using comparative procedures. Although a loop can sometimes be modelled from a homologue, very often it must be selected from outside the family. The loop prediction procedure, SLoop, attempts to identify the conformational class of the loop rather than to select a specific loop from a set of fragments extracted from known structures or generated ab initio. Templates are constructed for each of the 161 loop conformational classes that have been identified from the clustering of the structures of some 2024 loops of one to eight residues in length. A class template describes both sequence preferences and relative disposition of bounding secondary structures. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible. The procedure is tested on an unrelated non-redundant set of 1785 loops under stringent and lax evaluation schemes. Optimal sequence score cut-offs are identified such that the prediction rate is equal to the percentage of loops assigned to acceptable classes. Under the stringent evaluation, at the optimal sequence score cut-off, a conformation is predicted for 50% of loops of which 47% are correct, while under the lax evaluation a conformation is predicted for 63% of loops of which 54% are correct. Sequence score is shown to be a good indicator of the probability of a prediction being correct. Loop length also has a strong affect on prediction outcomes. Considering only loops of two to five residues in length, under the stringent evaluation 62% of loops are predicted with 52% of these predictions being correct while under the lax evaluation predictions are provided for 75% of loops of which 57% are correct.

Loops are regions of nonrepetitive conformation connecting regular secondary structures. We identified 2,024 loops of one to eight residues in length, with acceptable main-chain bond lengths and peptide bond angles, from a database of 223 protein and protein-domain structures. Each loop is characterized by its sequence, main-chain conformation, and relative disposition of its bounding secondary structures as described by the separation between the tips of their axes and the angle between them. Loops, grouped according to their length and type of their bounding secondary structures, were superposed and clustered into 161 conformational classes, corresponding to 63% of all loops. Of these, 109 (51% of the loops) were populated by at least four nonhomologous loops or four loops sharing a low sequence identity. Another 52 classes, including 12% of the loops, were populated by at least three loops of low sequence similarity from three or fewer nonhomologous groups. Loop class suprafamilies resulting from variations in the termini of secondary structures are discussed in this article. Most previously described loop conformations were found among the classes. New classes included a 2:4 type IV hairpin, a helix-capping loop, and a loop that mediates dinucleotide-binding. The relative disposition of bounding secondary structures varies among loop classes, with some classes such as beta-hairpins being very restrictive. For each class, sequence preferences as key residues were identified; those most frequently at these conserved positions than in proteins were Gly, Asp, Pro, Phe, and Cys. Most of these residues are involved in stabilizing loop conformation, often through a positive phi conformation or secondary structure capping. Identification of helix-capping residues and beta-breakers among the highly conserved positions supported our decision to group loops according to their bounding secondary structures. Several of the identified loop classes were associated with specific functions, and all of the member loops had the same function; key residues were conserved for this purpose, as is the case for the parvalbumin-like calcium-binding loops. A significant number, but not all, of the member loops of other loop classes had the same function, as is the case for the helix-turn-helix DNA-binding loops. This article provides a systematic and coherent conformational classification of loops, covering a broad range of lengths and all four combinations of bounding secondary structure types, and supplies a useful basis for modelling of loop conformations where the bounding secondary structures are known or reliably predicted.

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using knowledge-based procedures. While the core of a protein can be straight forwardly modelled from the structurally conserved regions of homologues of known structure, loops must be modelled from a selected homologue or from a loop chosen from outside the family. Here we present a loop prediction procedure that attempts to identify the conformational class of the loop rather than to select a specific loop from a database of fragments. The structures of some 2083 loops of one to eight residues in length were extracted from a database of 225 protein and protein domain structures. For each loop, the relative disposition of its bounding secondary structures is described by the separation between the tips of their axes, the angle and dihedral angle between their axes. From the clustering of the loops according to the root mean square deviation of their spatial fit, a total of 162 loop conformational classes, including 79% of loops, were identified. One-hundred and eight of these, involving 66% of the loops, were populated by at least four non-homologous loops or four loops sharing a low sequence identity. Another 54 classes, including 13% of the loops, were populated by at least three loops of low sequence similarity from three or fewer non-homologous groups. Most of the previously described loop conformations were found among the populated classes. For each class a template was constructed containing both sequence preferences and the relative disposition of bounding secondary structures among member loops. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible.