Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.

The present study aimed to identify a long non-coding (lnc) RNAs-based signature for prognosis assessment in gastric cancer (GC) patients. By integrating gene expression data of GC and normal samples from the National Center for Biotechnology Information Gene Expression Omnibus, the EBI ArrayExpress and The Cancer Genome Atlas (TCGA) repositories, the common RNAs in Genomic Spatial Event (GSE) 65801, GSE29998, E-MTAB-1338, and TCGA set were screened and used to construct a weighted correlation network analysis (WGCNA) network for mining GC-related modules. Consensus differentially expressed RNAs (DERs) between GC and normal samples in the four datasets were screened using the MetaDE method. From the overlapped lncRNAs shared by preserved WGCNA modules and the consensus DERs, an lncRNAs signature was obtained using L1-penalized (lasso) Cox-proportional hazard (PH) model. LncRNA-mRNA networks were constructed for these signature lncRNAs, followed by functional annotation. A total of 14,824 common mRNAs and 2,869 common lncRNAs were identified in the 4 sets and 5 GC-associated WGCNA modules were preserved across all sets. MetaDE method identified 1,121 consensus DERs. A total of 50 lncRNAs were shared by preserved WGCNA modules and the consensus DERs. Subsequently, an 11-lncRNA signature was identified by LASSO-based Cox-PH model. The lncRNAs signature-based risk score could divide patients into 2 risk groups with significantly different overall survival and recurrence-free survival times. The predictive capability of this signature was verified in an independent set. These signature lncRNAs were implicated in several biological processes and pathways associated with the immune response, the inflammatory response and cell cycle control. The present study identified an 11-lncRNA signature that could predict the survival rate for GC.

Control of the GABAA receptor activity in monocytes/macrophages can potently modulate post-infarction inflammation. Topiramate emerges as a promising drug, which may be feasible to translate for MI therapy in the future.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus driving the ongoing coronavirus disease 2019 (COVID-19) pandemic, continues to rapidly evolve. Due to the limited efficacy of vaccination in prevention of SARS-CoV-2 transmission and continuous emergence of variants of concern (VOC), orally bioavailable and broadly efficacious antiviral drugs are urgently needed. Previously we showed that the parent nucleoside of remdesivir, GS-441524, possesses potent anti-SARS-CoV-2 activity. Herein, we report that esterification of the 5′-hydroxyl moieties of GS-441524 markedly improved antiviral potency. This 5′-hydroxyl-isobutyryl prodrug, ATV006, demonstrated excellent oral bioavailability in rats and cynomolgus monkeys and exhibited potent antiviral efficacy against different SARS-CoV-2 VOCs in vitro and in three mouse models. Oral administration of ATV006 reduced viral loads and alleviated lung damage when administered prophylactically and therapeutically to K18-hACE2 mice challenged with the Delta variant of SARS-CoV-2. These data indicate that ATV006 represents a promising oral antiviral drug candidate for SARS-CoV-2.

Background1q43-q44 deletion syndrome is a well-defined chromosomal disorder which is characterized by moderate to severe mental retardation, and variable but characteristic facial features determined by the size of the segment and the number of genes involved. However, patients with 1q43-q44 duplication with a clinical phenotype comparable to that of 1q43-q44 deletion are rarely reported. Moreover, pure 1q43-q44 deletions and duplications derived from balanced insertional translocation within the same family with precisely identified breakpoints have not been reported.Case presentationThe proband is a 6-year-old girl with profound developmental delay, mental retardation, microcephaly, epilepsy, agenesis of the corpus callosum and hearing impairment. Her younger brother is a 3-month-old boy with macrocephaly and mild developmental delay in gross motor functions. G-banding analysis of the subjects at the 400-band level did not reveal any subtle structural changes in their karyotypes. However, single-nucleotide polymorphism (SNP) array analysis showed a deletion and a duplication of approximately 6.0 Mb at 1q43-q44 in the proband and her younger brother, respectively. The Levicare analysis pipeline of whole-genome sequencing (WGS) further demonstrated that a segment of 1q43-q44 was inserted at 14q23.1 in the unaffected mother, which indicated that the mother was a carrier of a 46,XX,ins(14;1)(q23.1;q43q44) insertional translocation. Moreover, Sanger sequencing was used to assist the mapping of the breakpoints and the final validation of those breakpoints. The breakpoint on chromosome 1 disrupted the EFCAB2 gene in the first intron, and the breakpoint on chromosome 14 disrupted the PRKCH gene within the 12th intron. In addition, fluorescence in situ hybridization (FISH) further confirmed that the unaffected older sister of the proband carried the same karyotype as the mother.ConclusionHere, we describe a rare family exhibiting pure 1q43-q44 deletion and duplication in two siblings caused by a maternal balanced insertional translocation. Our study demonstrates that WGS with a carefully designed analysis pipeline is a powerful tool for identifying cryptic genomic balanced translocations and mapping the breakpoints at the nucleotide level and could be an effective method for explaining the relationship between karyotype and phenotype.Electronic supplementary materialThe online version of this article (10.1186/s13039-018-0371-7) contains supplementary material, which is available to authorized users.