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Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies 1 . Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8 + alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8 + cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention. Analyses of single epithelial cells from early-stage lung adenocarcinoma and normal lung identifies a population of intermediate cells that may have an increased likelihood of transforming to tumour cells after injury such as tobacco exposure.

In addition to their role as a source of reduced carbon, sugars may directly or indirectly control a wide range of activities in plant cells, through transcriptional and post-translational regulation. This control has been studied in detail using Arabidopsis thaliana, where genetic analysis offers many possibilities. Much less is known about perennial woody species. For several years, various aspects of sugar sensing and signalling have been investigated in the grape (Vitis vinifera L.) berry, an organ that accumulates high concentrations of hexoses in the vacuoles of flesh cells. Here we review various aspects of this topic: the molecular basis of sugar transport and its regulation by sugars in grapevine; the functional analysis of several sugar-induced genes; the effects of some biotic and abiotic stresses on the sugar content of the berry; and finally the effects of exogenous sugar supply on the ripening process in field conditions. A picture of complex feedback and multiprocess regulation emerges from these data.

Background: COVID-19 pathophysiology and the predictive factors involved are not fully understood, but lymphocytes dysregulation appears to play a role. This paper aims to evaluate lymphocyte subsets in the pathophysiology of COVID-19 and as predictive factors for severe disease. Patient and methods: A prospective cohort study of patients with SARS-CoV-2 bilateral pneumonia recruited at hospital admission. Demographics, medical history, and data regarding SARS-CoV-2 infection were recorded. Patients systematically underwent complete laboratory tests, including parameters related to COVID-19 as well as lymphocyte subsets study at the time of admission. Severe disease criteria were established at admission, and patients were classified on remote follow-up according to disease evolution. Linear regression models were used to assess associations with disease evolution, and Receiver Operating Characteristic (ROC) and the corresponding Area Under the Curve (AUC) were used to evaluate predictive values. Results: Patients with critical COVID-19 showed a decrease in CD3+CD4+ T cells count compared to non-critical (278 (485 IQR) vs. 545 (322 IQR)), a decrease in median CD4+/CD8+ ratio (1.7, (1.7 IQR) vs. 3.1 (2.4 IQR)), and a decrease in median CD4+MFI (21,820 (4491 IQR) vs. 26,259 (3256 IQR)), which persisted after adjustment. CD3+CD8+ T cells count had a high correlation with time to hospital discharge (PC = −0.700 (−0.931, −0.066)). ROC curves for predictive value showed lymphocyte subsets achieving the best performances, specifically CD3+CD4+ T cells (AUC = 0.756), CD4+/CD8+ ratio (AUC = 0.767), and CD4+MFI (AUC = 0.848). Conclusions: A predictive value and treatment considerations for lymphocyte subsets are suggested, especially for CD3CD4+ T cells. Lymphocyte subsets determination at hospital admission is recommended.

Background Despite existing extensive literature, a comprehensive and clinically relevant classification system for osteoarthritis (OA) has yet to be established. In this study, we aimed to further characterize four knee OA (KOA) inflammatory phenotypes (KOIP) recently proposed by our group, by identifying the inflammatory factors associated with KOA severity and progression in a phenotype-specific manner. Methods We performed an analysis within each of the previously defined four KOIP groups, to assess the association between KOA severity and progression and a panel of 13 cytokines evaluated in the plasma and synovial fluid of our cohort’s patients. The cohort included 168 symptomatic female KOA patients with persistent joint effusion. Results Overall, our analyses showed that associations with KOA outcomes were of higher magnitude within the KOIP groups than for the overall patient series (all p -values < 1.30e−16) and that several of the cytokines showed a KOIP-specific behaviour regarding their associations with KOA outcomes. Conclusion Our study adds further evidence supporting KOA as a multifaceted syndrome composed of multiple phenotypes with differing pathophysiological pathways, providing an explanation for inconsistencies between previous studies focussed on the role of cytokines in OA and the lack of translational results to date. Our findings also highlight the potential clinical benefits of accurately phenotyping KOA patients, including improved patient stratification, tailored therapies, and the discovery of novel treatments.

Delta-like Ligand 3 (DLL3) targeting therapies are promising in small cell lung cancer (SCLC) treatment. However, DLL3 expression in SCLC and other neuroendocrine neoplasms (NEN) is heterogeneous and not well characterized. We describe the landscape of DLL3 at the mRNA and protein levels across SCLC, large cell neuroendocrine carcinoma (LCNEC), and non-small cell lung cancer. Additionally, we explore its expression in extra-pulmonary NEN (EP-NEN) using a standardized DLL3 IHC assay. DLL3 expression is enriched in SCLC, LCNEC along with combined histology lung cancers. Moreover, we find a wide range of DLL3 expression in high-grade EP-NEN. We describe heterogenous DLL3 expression not only in SCLC but also in different NEN types. This comprehensive characterization of DLL3 can help guide future clinical trial design targeting DLL3 in NEN including LCNEC and EP-NEN that are lacking standard of care treatment options.

COVID-19 pathophysiology is currently not fully understood, reliable prognostic factors remain elusive, and few specific therapeutic strategies have been proposed. In this scenario, availability of biomarkers is a priority. MS-based Proteomics techniques were used to profile the proteome of 81 plasma samples extracted in four consecutive days from 23 hospitalized COVID-19 associated pneumonia patients. Samples from 10 subjects that reached a critical condition during their hospital stay and 10 matched non-severe controls were drawn before the administration of any COVID-19 specific treatment and used to identify potential biomarkers of COVID-19 prognosis. Additionally, we compared the proteome of five patients before and after glucocorticoids and tocilizumab treatment, to assess the changes induced by the therapy on our selected candidates. Forty-two proteins were differentially expressed between patients' evolution groups at 10% FDR. Twelve proteins showed lower levels in critical patients (fold-changes 1.20–3.58), of which OAS3 and COG5 found their expression increased after COVID-19 specific therapy. Most of the 30 proteins over-expressed in critical patients (fold-changes 1.17–4.43) were linked to inflammation, coagulation, lipids metabolism, complement or immunoglobulins, and a third of them decreased their expression after treatment. We propose a set of candidate proteins for biomarkers of COVID-19 prognosis at the time of hospital admission. The study design employed is distinctive from previous works and aimed to optimize the chances of the candidates to be validated in confirmatory studies and, eventually, to play a useful role in the clinical practice.

Auxin is a hormone that delays ripening in part by reducing anthocyanin content and impairing color development. Auxin content declines during the ripening process, whereas sugars accumulate from pre-veraison onwards. The spatio-temporal distribution of this hormone depends in part on polar auxin transport. On the other hand, sugar, acting as a signal molecule, modulates auxin distribution in model organisms; however, its effect on polar auxin transport during the coloring process in grapevine berries has not been investigated. To address this issue, we characterized auxin transport and sugar variations during the ripening process and performed treatments intended to alter auxin homeostasis in grape fruits. We found that polar auxin transport declines in concert with increasing sugar content prior to and at veraison. Moreover, N-1-napthylphthalamic acid (NPA; a polar auxin transport inhibitor) and glucose treatment increased berry coloration, reduced polar auxin transport and VvPIN1 transcript abundance at pre-veraison, and combined NPA and glucose treatment further increased berry color compared to glucose and NPA alone. Indole-3-acetic acid (IAA) treatment prevented the negative effect of glucose on auxin transport, suggesting that auxin homeostasis might be relevant for glucose modulation of berry ripening in grapevine. Impaired auxin transport is associated with increased ethylene sensitivity in several plant processes, including fruit abscission. For exploring a potential involvement of the ethylene pathway during the coloring process, we analyzed the transcript abundance of the putative ethylene receptor, VvETR2 , a possible negative regulator of the ethylene pathway. We found that glucose plus NPA treatment reduced VvETR2 transcript abundance, whose expression is reported to decline from veraison onwards. Our results suggest a possible mechanism in which a rise in glucose contributes to auxin transport inhibition in coloring berries. As glucose has been reported to promote ripening and IAA inhibits berry coloring, our results further support an antagonistic switch between IAA and glucose, that could also involve changes in the expression of the VvETR2 gene.

Grapevine fruit development is a dynamic process that can be divided into three stages: formation (I), lag (II), and ripening (III), in which physiological and biochemical changes occur, leading to cell differentiation and accumulation of different solutes. These stages can be positively or negatively affected by multiple environmental factors. During the last decade, efforts have been made to understand berry development from a global perspective. Special attention has been paid to transcriptional and metabolic networks associated with the control of grape berry development, and how external factors affect the ripening process. In this review, we focus on the integration of global approaches, including proteomics, metabolomics, and especially transcriptomics, to understand grape berry development. Several aspects will be considered, including seed development and the production of seedless fruits; veraison, at which anthocyanin accumulation begins in the berry skin of colored varieties; and hormonal regulation of berry development and signaling throughout ripening, focusing on the transcriptional regulation of hormone receptors, protein kinases, and genes related to secondary messenger sensing. Finally, berry responses to different environmental factors, including abiotic (temperature, water-related stress and UV-B radiation) and biotic (fungi and viruses) stresses, and how they can significantly modify both, development and composition of vine fruit, will be discussed. Until now, advances have been made due to the application of Omics tools at different molecular levels. However, the potential of these technologies should not be limited to the study of single-level questions; instead, data obtained by these platforms should be integrated to unravel the molecular aspects of grapevine development. Therefore, the current challenge is the generation of new tools that integrate large-scale data to assess new questions in this field, and to support agronomical practices.

Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed. It is overwhelming the amount of biological data whose high-dimensional structure exceeds mostly current computational architectures. The computational time and memory consumption optimization actually become decisive factors in choosing clustering algorithms. We propose a clustering algorithm based on Non-negative Matrix Factorization and K-means to reduce data dimensionality but whilst preserving the biological context and prioritizing gene selection, and it is implemented within parallel GPU-based environments through the CUDA library. A well-known dataset is used in our tests and the quality of the results is measured through the Rand and Accuracy Index. The results show an increase in the acceleration of 6.22× compared to the sequential version. The algorithm is competitive in the biological datasets analysis and it is invariant with respect to the classes number and the size of the gene expression matrix.