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Identification of SRSF10 as a regulator of SMN2 ISS-N1.
No full textUnderstanding the splicing code can be challenging as several splicing factors bind to many splicing-regulatory elements. The SMN1 and SMN2 silencer element ISS-N1 is the target of the antisense oligonucleotide drug, Spinraza, which is the treatment against spinal muscular atrophy. However, limited knowledge about the nature of the splicing factors that bind to ISS-N1 and inhibit splicing exists. It is likely that the effect of Spinraza comes from blocking binding of these factors, but so far, an unbiased characterization has not been performed and only members of the hnRNP A1/A2 family have been identified by Western blot analysis and nuclear magnetic resonance to bind to this silencer. Employing an MS/MS-based approach and surface plasmon resonance imaging, we show for the first time that splicing factor SRSF10 binds to ISS-N1. Furthermore, using splice-switching oligonucleotides we modulated the splicing of the SRSF10 isoforms generating either the long or the short protein isoform of SRSF10 to regulate endogenous SMN2 exon 7 inclusion. We demonstrate that the isoforms of SRSF10 regulate SMN1 and SMN2 splicing with different strength correlating with the length of their RS domain. Our results suggest that the ratio between the SRSF10 isoforms is important for splicing regulation
Combining natural language processing and metabarcoding to reveal pathogen-environment associations.
Get PDFCryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year-with 180,000 resulting deaths-mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans. We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations
Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn.
Get PDFSweet corn is one of the most important vegetables in the United States and Canada. Here, we present a de novo assembly of a sweet corn inbred line Ia453 with the mutated shrunken2-reference allele (Ia453-sh2). This mutation accumulates more sugar and is present in most commercial hybrids developed for the processing and fresh markets. The ten pseudochromosomes cover 92% of the total assembly and 99% of the estimated genome size, with a scaffold N50 of 222.2 Mb. This reference genome completely assembles the large structural variation that created the mutant sh2-R allele. Furthermore, comparative genomics analysis with six field corn genomes highlights differences in single-nucleotide polymorphisms, structural variations, and transposon composition. Phylogenetic analysis of 5,381 diverse maize and teosinte accessions reveals genetic relationships between sweet corn and other types of maize. Our results show evidence for a common origin in northern Mexico for modern sweet corn in the U.S. Finally, population genomic analysis identifies regions of the genome under selection and candidate genes associated with sweet corn traits, such as early flowering, endosperm composition, plant and tassel architecture, and kernel row number. Our study provides a high-quality reference-genome sequence to facilitate comparative genomics, functional studies, and genomic-assisted breeding for sweet corn
Fitting elephants in modern machine learning by statistically consistent interpolation
No full textTextbook wisdom advocates for smooth function fits and implies that interpolation of noisy data should lead to poor generalization. A related heuristic is that fitting parameters should be fewer than measurements (Occam’s razor). Surprisingly, contemporary machine learning approaches, such as deep nets, generalize well, despite interpolating noisy data. This may be understood via statistically consistent interpolation (SCI), that is, data interpolation techniques that generalize optimally for big data. Here, we elucidate SCI using the weighted interpolating nearest neighbours algorithm, which adds singular weight functions to k nearest neighbours. This shows that data interpolation can be a valid machine learning strategy for big data. SCI clarifies the relation between two ways of modelling natural phenomena: the rationalist approach (strong priors) of theoretical physics with few parameters, and the empiricist (weak priors) approach of modern machine learning with more parameters than data. SCI shows that the purely empirical approach can successfully predict. However, data interpolation does not provide theoretical insights, and the training data requirements may be prohibitive. Complex animal brains are between these extremes, with many parameters, but modest training data, and with prior structure encoded in species-specific mesoscale circuitry. Thus, modern machine learning provides a distinct epistemological approach that is different both from physical theories and animal brains
Oligophrenin-1 moderates behavioral responses to stress by regulating parvalbumin interneuron activity in the medial prefrontal cortex.
No full textAmple evidence indicates that individuals with intellectual disability (ID) are at increased risk of developing stress-related behavioral problems and mood disorders, yet a mechanistic explanation for such a link remains largely elusive. Here, we focused on characterizing the syndromic ID gene oligophrenin-1 (OPHN1). We find that Ophn1 deficiency in mice markedly enhances helpless/depressive-like behavior in the face of repeated/uncontrollable stress. Strikingly, Ophn1 deletion exclusively in parvalbumin (PV) interneurons in the prelimbic medial prefrontal cortex (PL-mPFC) is sufficient to induce helplessness. This behavioral phenotype is mediated by a diminished excitatory drive onto Ophn1-deficient PL-mPFC PV interneurons, leading to hyperactivity in this region. Importantly, suppressing neuronal activity or RhoA/Rho-kinase signaling in the PL-mPFC reverses helpless behavior. Our results identify OPHN1 as a critical regulator of adaptive behavioral responses to stress and shed light onto the mechanistic links among OPHN1 genetic deficits, mPFC circuit dysfunction, and abnormalities in stress-related behaviors
Molecular mechanisms of axo-axonic innervation.
No full textOne of the most intriguing features of inhibitory synapses is the precision by which they innervate their target, not only at the cellular level but also at the subcellular level (i.e. axo-dendritic, axo-somatic, or axo-axonic innervation). In particular, in the cerebellum, cortex, and spinal cord, distinct and highly specialized GABAergic interneurons, such as basket cells, chandelier cells, and GABApre interneurons, form precise axo-axonic synapses, allowing them to directly regulate neuronal output and circuit function. In this article, we summarize our latest knowledge of the cellular and molecular mechanisms that regulate the establishment and maintenance of axo-axonic synapses in these regions of the CNS. We also detail the key roles of the L1CAM family of cell adhesion molecules in such GABAergic subcellular target recognition
Stromal-driven and Amyloid β-dependent induction of neutrophil extracellular traps modulates tumor growth.
Get PDFTumors consist of cancer cells and a network of non-cancerous stroma. Cancer-associated fibroblasts (CAF) are known to support tumorigenesis, and are emerging as immune modulators. Neutrophils release histone-bound nuclear DNA and cytotoxic granules as extracellular traps (NET). Here we show that CAFs induce NET formation within the tumor and systemically in the blood and bone marrow. These tumor-induced NETs (t-NETs) are driven by a ROS-mediated pathway dependent on CAF-derived Amyloid β, a peptide implicated in both neurodegenerative and inflammatory disorders. Inhibition of NETosis in murine tumors skews neutrophils to an anti-tumor phenotype, preventing tumor growth; reciprocally, t-NETs enhance CAF activation. Mirroring observations in mice, CAFs are detected juxtaposed to NETs in human melanoma and pancreatic adenocarcinoma, and show elevated amyloid and β-Secretase expression which correlates with poor prognosis. In summary, we report that CAFs drive NETosis to support cancer progression, identifying Amyloid β as the protagonist and potential therapeutic target
Chromosomal instability and aneuploidy as causes of cancer drug resistance.
No full textHigh levels of aneuploidy and chromosomal instability (CIN) are correlated with poor patient outcomes, though the mechanism(s) underlying this relationship have not been established. Recent evidence has demonstrated that chromosome copy number changes can function as point mutation-independent sources of drug resistance in cancer, which may partially explain this clinical association. CIN generates intratumoral heterogeneity in the form of gene dosage alterations, upon which the selective pressures induced by drug treatments can act. Thus, although CIN and aneuploidy impair cell fitness under most conditions, CIN can augment cellular adaptability, establishing CIN as a bet-hedging mechanism in tumor evolution. CIN may also endow cancers with unique vulnerabilities, which could be exploited therapeutically to achieve better patient outcomes