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Dipeptidyl peptidase-4 (DPP4) modulates inflammation by enzymatic cleavage of immunoregulatory peptides and through its soluble form (sDPP4) that directly engages immune cells. Here we examine whether reduction of DPP4 activity alters inflammation. Prolonged DPP4 inhibition increases plasma levels of sDPP4, and induces sDPP4 expression in lymphocyte-enriched organs in mice. Bone marrow transplantation experiments identify hematopoietic cells as the predominant source of plasma sDPP4 following catalytic DPP4 inhibition. Surprisingly, systemic DPP4 inhibition increases plasma levels of inflammatory markers in regular chow-fed but not in high fat-fed mice. Plasma levels of sDPP4 and biomarkers of inflammation are lower in metformin-treated subjects with type 2 diabetes (T2D) and cardiovascular disease, yet exhibit considerable inter-individual variation. Sitagliptin therapy for 12 months reduces DPP4 activity yet does not increase markers of inflammation or levels of sDPP4. Collectively our findings dissociate levels of DPP4 enzyme activity, sDPP4 and biomarkers of inflammation in mice and humans. DPP4 inhibitors are used for the treatment of diabetes, but the impact of DPP4 activity and soluble DPP4 on development of diabetes-associated inflammation remains uncertain. Here the authors study whether DPP4 inhibition controls sDPP4 and inflammatory biomarkers, and demonstrate that DPP4 inhibition is dissociated from changes in inflammation in mice and humans.

Cytotoxic lymphocyte-derived granzyme A (GZMA) cleaves GSDMB, a gasdermin-family pore-forming protein 1 , 2 , to trigger target cell pyroptosis 3 . GSDMB and the charter gasdermin family member GSDMD 4 , 5 have been inconsistently reported to be degraded by the Shigella flexneri ubiquitin-ligase virulence factor IpaH7.8 (refs. 6 , 7 ). Whether and how IpaH7.8 targets both gasdermins is undefined, and the pyroptosis function of GSDMB has even been questioned recently 6 , 8 . Here we report the crystal structure of the IpaH7.8–GSDMB complex, which shows how IpaH7.8 recognizes the GSDMB pore-forming domain. We clarify that IpaH7.8 targets human (but not mouse) GSDMD through a similar mechanism. The structure of full-length GSDMB suggests stronger autoinhibition than in other gasdermins 9 , 10 . GSDMB has multiple splicing isoforms that are equally targeted by IpaH7.8 but exhibit contrasting pyroptotic activities. Presence of exon 6 in the isoforms dictates the pore-forming, pyroptotic activity in GSDMB. We determine the cryo-electron microscopy structure of the 27-fold-symmetric GSDMB pore and depict conformational changes that drive pore formation. The structure uncovers an essential role for exon-6-derived elements in pore assembly, explaining pyroptosis deficiency in the non-canonical splicing isoform used in recent studies 6 , 8 . Different cancer cell lines have markedly different isoform compositions, correlating with the onset and extent of pyroptosis following GZMA stimulation. Our study illustrates fine regulation of GSDMB pore-forming activity by pathogenic bacteria and mRNA splicing and defines the underlying structural mechanisms. The cryo-EM structure of the GSDMB pore reveals mechanisms by which GSDMB pore-forming activity is regulated by pathogenic bacteria and mRNA splicing.

Splicing varies across brain regions, but the single-cell resolution of regional variation is unclear. We present a single-cell investigation of differential isoform expression (DIE) between brain regions using single-cell long-read sequencing in mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 ( www.isoformAtlas.com ). Isoform tests for DIE show better performance than exon tests. We detect hundreds of DIE events traceable to cell types, often corresponding to functionally distinct protein isoforms. Mostly, one cell type is responsible for brain-region specific DIE. However, for fewer genes, multiple cell types influence DIE. Thus, regional identity can, although rarely, override cell-type specificity. Cell types indigenous to one anatomic structure display distinctive DIE, e.g. the choroid plexus epithelium manifests distinct transcription-start-site usage. Spatial transcriptomics and long-read sequencing yield a spatially resolved splicing map. Our methods quantify isoform expression with cell-type and spatial resolution and it contributes to further our understanding of how the brain integrates molecular and cellular complexity. Alternative RNA splicing varies across the brain. Its mapping at single cell resolution is unclear. Here, the authors provide a spatial and single-cell splicing atlas reporting brain region- and cell type-specific expression of different isoforms in the postnatal mouse brain.

In a previous Phase 1/2b malaria vaccine trial testing the 3D7 isoform of the malaria vaccine candidate Merozoite surface protein 2 (MSP2), parasite densities in children were reduced by 62%. However, breakthrough parasitemias were disproportionately of the alternate dimorphic form of MSP2, the FC27 genotype. We therefore undertook a dose-escalating, double-blinded, placebo-controlled Phase 1 trial in healthy, malaria-naïve adults of MSP2-C1, a vaccine containing recombinant forms of the two families of msp2 alleles, 3D7 and FC27 (EcMSP2-3D7 and EcMSP2-FC27), formulated in equal amounts with Montanide® ISA 720 as a water-in-oil emulsion. The trial was designed to include three dose cohorts (10, 40, and 80 µg), each with twelve subjects receiving the vaccine and three control subjects receiving Montanide® ISA 720 adjuvant emulsion alone, in a schedule of three doses at 12-week intervals. Due to unexpected local reactogenicity and concern regarding vaccine stability, the trial was terminated after the second immunisation of the cohort receiving the 40 µg dose; no subjects received the 80 µg dose. Immunization induced significant IgG responses to both isoforms of MSP2 in the 10 µg and 40 µg dose cohorts, with antibody levels by ELISA higher in the 40 µg cohort. Vaccine-induced antibodies recognised native protein by Western blots of parasite protein extracts and by immunofluorescence microscopy. Although the induced anti-MSP2 antibodies did not directly inhibit parasite growth in vitro, IgG from the majority of individuals tested caused significant antibody-dependent cellular inhibition (ADCI) of parasite growth. As the majority of subjects vaccinated with MSP2-C1 developed an antibody responses to both forms of MSP2, and that these antibodies mediated ADCI provide further support for MSP2 as a malaria vaccine candidate. However, in view of the reactogenicity of this formulation, further clinical development of MSP2-C1 will require formulation of MSP2 in an alternative adjuvant. Australian New Zealand Clinical Trials Registry 12607000552482.

One of the hallmarks of Alzheimer’s disease and several other neurodegenerative disorders is the aggregation of tau protein into fibrillar structures. Building on recent reports that tau readily undergoes liquid–liquid phase separation (LLPS), here we explored the relationship between disease-related mutations, LLPS, and tau fibrillation. Our data demonstrate that, in contrast to previous suggestions, pathogenic mutations within the pseudorepeat region do not affect tau441’s propensity to form liquid droplets. LLPS does, however, greatly accelerate formation of fibrillar aggregates, and this effect is especially dramatic for tau441 variants with disease-related mutations. Most important, this study also reveals a previously unrecognized mechanism by which LLPS can regulate the rate of fibrillation in mixtures containing tau isoforms with different aggregation propensities. This regulation results from unique properties of proteins under LLPS conditions, where total concentration of all tau variants in the condensed phase is constant. Therefore, the presence of increasing proportions of the slowly aggregating tau isoform gradually lowers the concentration of the isoform with high aggregation propensity, reducing the rate of its fibrillation. This regulatory mechanism may be of direct relevance to phenotypic variability of tauopathies, as the ratios of fast and slowly aggregating tau isoforms in brain varies substantially in different diseases.

Background/Objectives: Blood flows through the body and reaches all tissues, contributing to homeostasis and physiological functions. Providing information and understanding on how the transcriptome of whole blood behaves in response to physiological or pathological stimuli is critical. Methods: We collected blood from four healthy individuals and performed long-read RNA sequencing (lrRNA-seq) for the precise identification and expression quantification of RNA variants. Moreover, we compared two genome references: the Genome Reference Consortium Human Build 38 (GRCh38) and the Telomere-to-Telomere (T2T) assembly of the CHM13 cell line (T2T-CHM13). Results: With GRCh38, we could identify an average of about 46,000 genes, 1.3-fold more genes than T2T-CHM13. Similarly, we identified about 185,000 isoforms with GRCh38 and 140,000 with T2T-CHM13, finding similar differences for full splice match (FSM) and incomplete splice match (ISM) transcript isoforms. There were about 90,000 novel isoforms for GRCh38 and 70,000 for T2T-CHM13, 47% and 50% of the total number of identified isoforms, respectively. Differences in isoform numbers between GRCh38 and T2T-CHM13 were identified for the subcategories “Genic Genomic”, “Intergenic”, and “Genic Intron”. Using GRCh38, we generally identified a higher number of non-coding isoforms, as well as a higher number of isoforms aligning within intron and intergenic regions. Nonetheless, GRCh38 might incur false positive results, and T2T-CHM13 is likely more accurate for genome sequences in the repetitive regions. Conclusions: LrRNA-seq is a valid method for the identification of novel isoforms in blood, and this study is a first step toward the creation of a comprehensive database of the structure and expression of transcript isoforms for optimized predictive medicine.

The ordered assembly of tau protein into abnormal filamentous inclusions underlies many human neurodegenerative diseases 1 . Tau assemblies seem to spread through specific neural networks in each disease 2 , with short filaments having the greatest seeding activity 3 . The abundance of tau inclusions strongly correlates with disease symptoms 4 . Six tau isoforms are expressed in the normal adult human brain—three isoforms with four microtubule-binding repeats each (4R tau) and three isoforms that lack the second repeat (3R tau) 1 . In various diseases, tau filaments can be composed of either 3R or 4R tau, or of both. Tau filaments have distinct cellular and neuroanatomical distributions 5 , with morphological and biochemical differences suggesting that they may be able to adopt disease-specific molecular conformations 6 , 7 . Such conformers may give rise to different neuropathological phenotypes 8 , 9 , reminiscent of prion strains 10 . However, the underlying structures are not known. Using electron cryo-microscopy, we recently reported the structures of tau filaments from patients with Alzheimer’s disease, which contain both 3R and 4R tau 11 . Here we determine the structures of tau filaments from patients with Pick’s disease, a neurodegenerative disorder characterized by frontotemporal dementia. The filaments consist of residues Lys254–Phe378 of 3R tau, which are folded differently from the tau filaments in Alzheimer’s disease, establishing the existence of conformers of assembled tau. The observed tau fold in the filaments of patients with Pick’s disease explains the selective incorporation of 3R tau in Pick bodies, and the differences in phosphorylation relative to the tau filaments of Alzheimer’s disease. Our findings show how tau can adopt distinct folds in the human brain in different diseases, an essential step for understanding the formation and propagation of molecular conformers. The structures of tau filaments from patients with the neurodegenerative disorder Pick’s disease show that the filament fold is different from that of the tau filaments found in Alzheimer’s disease.

Signal transducer and activator of transcription 3 (STAT3) is one of seven STAT family members involved with the regulation of cellular growth, differentiation and survival. STAT proteins are conserved among eukaryotes and are important for biological functions of embryogenesis, immunity, haematopoiesis and cell migration. STAT3 is widely expressed and located in the cytoplasm in an inactive form. STAT3 is rapidly and transiently activated by tyrosine phosphorylation by a range of signalling pathways, including cytokines from the IL‐6 family and growth factors, such as EGF and PDGF. STAT3 activation and subsequent dimer formation initiates nuclear translocation of STAT3 for the regulation of target gene transcription. Four STAT3 isoforms have been identified, which have distinct biological functions. STAT3 is considered a proto‐oncogene and constitutive activation of STAT3 is implicated in the development of various cancers, including multiple myeloma, leukaemia and lymphomas. In this review, we focus on recent progress on STAT3 and osteosarcoma (OS). Notably, STAT3 is overexpressed and associated with the poor prognosis of OS. Constitutive activation of STAT3 in OS appears to upregulate the expression of target oncogenes, leading to OS cell transformation, proliferation, tumour formation, invasion, metastasis, immune evasion and drug resistance. Taken together, STAT3 is a target for cancer therapy, and STAT3 inhibitors represent potential therapeutic candidates for the treatment of OS. Signal transducer and activator of transcription 3 (STAT3) is a member of the STAT protein family, vitally important for eukaryotic cells. We review the molecular structure and function of STAT3 and its isoforms, highlighting signalling pathways for the regulation of gene transcription. A critical appraisal of STAT3 in cancers, such as osteosarcoma, is provided emphasizing potential therapeutic approaches targeting STAT3 and its inhibitors

Cellular plasticity lies at the core of cancer progression, metastasis, and resistance to treatment. Stemness and epithelial-mesenchymal plasticity in cancer are concepts that represent a cancer cell’s ability to coopt and adapt normal developmental programs to promote survival and expansion. The cancer stem cell model states that a small subset of cancer cells with stem cell-like properties are responsible for driving tumorigenesis and metastasis while remaining especially resistant to common chemotherapeutic drugs. Epithelial-mesenchymal plasticity describes a cancer cell’s ability to transition between epithelial and mesenchymal phenotypes which drives invasion and metastasis. Recent research supports the existence of stable epithelial/mesenchymal hybrid phenotypes which represent highly plastic states with cancer stem cell characteristics. The cell adhesion molecule CD44 is a widely accepted marker for cancer stem cells, and it lies at a functional intersection between signaling networks regulating both stemness and epithelial-mesenchymal plasticity. CD44 expression is complex, with alternative splicing producing many isoforms. Interestingly, not only does the pattern of isoform expression change during transitions between epithelial and mesenchymal phenotypes in cancer, but these isoforms have distinct effects on cell behavior including the promotion of metastasis and stemness. The role of CD44 both downstream and upstream of signaling pathways regulating epithelial-mesenchymal plasticity and stemness make this protein a valuable target for further research and therapeutic intervention.

Background Early single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses. Results In this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results. Conclusions To accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq.